{"id": "2602.01425", "title": "One Probe Won't Catch Them All: Towards Targeted Deception Detection", "abstract": "Linear probes are a promising approach for monitoring AI systems for deceptive behaviour. Previous work has shown that a linear classifier trained on a contrastive instruction pair and a simple dataset can achieve good performance. However, these probes exhibit notable failures even in straightforward scenarios, including spurious correlations and false positives on non-deceptive responses. In this paper, we demonstrate that deception detection is inherently heterogeneous: while a single universal probe achieves modest improvements (+0.032 AUC), post-hoc oracle analysis reveals substantially higher potential (+0.108 AUC) when probes are matched to specific deception types, and synthetic validation experiments suggest this ceiling is achievable a priori when the deception type is known in advance. Our findings reveal that instruction pairs capture deceptive intent rather than content-specific patterns, explaining why prompt choice dominates probe performance (70.6% of variance). Given this heterogeneity, we conclude that organizations should define their specific threat models and deploy appropriately matched probes rather than seeking a universal deception detector.", "date": "2026-02-01", "categories": []}
{"id": "2606.08892", "title": "Diffuse AI Control on Fuzzy Tasks", "abstract": "AI models deployed in critical domains, such as AI safety research, may subtly sabotage our efforts due to misalignment. Diffuse AI Control is a subfield of AI safety concerned with mitigating risks from AI sabotage distributed over long deployment horizons (diffuse threats). These risks are particularly pernicious on fuzzy tasks, i.e. tasks which are hard to grade or require intuition. To understand diffuse threats on fuzzy tasks, we introduce a framework that considers AI control as an adversarial game between a blue team and a red team. The blue team uses a weak trusted model to construct a weak score against which they would train a strong, potentially subversive model to remove the subversion propensity if it were present. The red team then tries to find model behaviors that are rated highly by the weak score, and thus might not be trained out, but actually correspond to poor performance. We test our framework on the task of writing experimental proposals for research questions from recent ML papers. We use a language model with access to the original paper as a proxy \"ground-truth\" scorer. Our red team discovers subversive behaviors using multi-objective evolutionary prompt optimization. We show that Opus~4.6 can write proposals that are worse according to the ground truth proxy than those of GPT-OSS-20B, while the weak scorer rates them as highly as the best proposals from Opus 4.6. We then propose an adversarial optimization algorithm for the blue team that discovers more robust prompts for the weak model. This algorithm produces a blue team prompt that our red team optimization fails to exploit.", "date": "2026-06-08", "categories": []}
{"id": "2606.19380", "title": "AgentArmor: A Framework, Evaluation, \\& Mitigation of Coding Agent Failures", "abstract": "Software engineering and deployment are increasingly being delegated to AI coding agents. The scale of their adoption is surfacing rare, but highly destructive, failure modes. In this paper, we study these failure modes as stemming from three distinct mechanisms: underspecification, where default model behavior is unsafe; capability errors, where the safe action is available but the model does not adhere to it due to bias or capability limitations; and agent harness errors, where the model fails to execute the safe action through the harness. We evaluate these across 8 different evaluations, each inspired by real-life deployment failures, totaling 20 coding environments and 59 synthetic transcript templates. Based on this evaluation, we propose AgentArmor, an agent harness modification, to mitigate these errors. By adding an extended system prompt, a separate command classifier, a ``3 strikes'' policy, deterministic guardrails, and tools for the agent to edit its own context, we show that AgentArmor is safer across a statistically significant number of samples. Thus, we suggest concrete mitigations for current coding agents and a design philosophy for future agent harness features.", "date": "2026-06-13", "categories": []}
{"id": "2606.19464", "title": "Deontic Policies for Runtime Governance of Agentic AI Systems", "abstract": "Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance. This includes specifying what agents are permitted and prohibited from doing, what they areobliged to do after certain actions (e.g., notify the CISO), under what conditions a standing obligation may be waived, and which rules take precedence when policies conflict. This governance problem exceeds what current policy engines provide. Systems such as XACML, Rego, and Cedar address only the permit/prohibit subset of this governance structure. They do not provide obligation lifecycle management, meta-policy conflict resolution, dispensations that waive obligations in specific circumstances, and ontological reasoning over domain class hierarchies commonly found in applications such as healthcare, cybersecurity, or data privacy. We propose AgenticRei, which realizes key governance requirements such as obligations, dispensations, policy conflict resolutions, and reasoning over policies, as well as the basic permit/prohibit constraints. We use a deontic policy language built on the Rei framework, expressed as OWL (Web Ontology Language) and evaluated at runtime by a high-performance logic engine entirely outside the LLM. The same pipeline governs both tool invocations by the agent and agent-to-agent messages. We show through examples that deontic policies capture governance constraints around security and privacy that mostly cannot be expressed in current production engines. Our approach composes naturally with industry-standard frameworks like A2AS.", "date": "2026-06-17", "categories": []}
{"id": "2606.19755", "title": "SafeSpec: Fast and Safe LLM via Dynamic Reflective Sampling", "abstract": "Speculative inference accelerates large language model (LLM) decoding but provides no inherent safety guarantees. Existing safety defenses are largely incompatible with speculative inference: they either introduce additional computation or disrupt the draft-verify mechanism, negating acceleration benefits. This reveals a fundamental incompatibility between current safety methods and speculative decoding. We propose SafeSpec, a safety-aware speculative inference framework that integrates risk estimation directly into the verification process. SafeSpec attaches a lightweight latent safety head to the target model to jointly evaluate semantic validity and safety in a single forward pass. When unsafe generations are detected, SafeSpec applies rollback and safety-guided reflective multi-sampling to recover safe continuations rather than terminating generation. We model jailbreak attacks as distributional shifts over generative trajectories, where adversarial prompts increase the probability of harmful continuations without eliminating safe ones. Under this model, SafeSpec performs risk-aware trajectory recovery within the speculative decoding process. Across multiple models and adversarial benchmarks, SafeSpec achieves a substantially improved safety-efficiency trade-off. On Qwen3-32B, SafeSpec reduces attack success rates by 15% while preserving a 2.06x inference speedup on benign workloads, demonstrating that speculative acceleration and inference-time safety can be jointly optimized.", "date": "2026-06-18", "categories": []}
{"id": "2606.19899", "title": "Measuring Biological Capabilities and Risks of AI Agents", "abstract": "This paper addresses a rapidly emerging policy challenge: how to generate and interpret credible evidence about the biological capabilities and risks of AI scientists, or agentic AI systems capable of autonomously or collaboratively performing multi-step scientific tasks. As these systems enter real research workflows, decision-makers increasingly face evaluation results whose meaning depends on underlying design choices that are often implicit or under-documented. We synthesize current evidence on AI-enabled biological risks and introduce biological agentic evaluations as a promising, but interpretation-sensitive, tool for assessing these systems. Our central contribution is a set of practical, experience-grounded considerations -- drawing from our own evaluations -- that show how choices around defining, designing, running, scoring, and documenting evaluations materially shape what results do and do not imply about risk. The analysis is intended to help policymakers interpret biological evaluation outputs with appropriate caution; guide public and private funders toward high-leverage investments in AI-biology evaluation research; and support biosecurity practitioners assessing emerging AI systems. A secondary audience includes researchers designing or conducting agentic evaluations within frontier AI labs, AI providers, scientific institutions, and third-party evaluation organizations.", "date": "2026-06-18", "categories": []}
{"id": "2606.20408", "title": "LLM agent safety, multi-turn red-teaming, jailbreak benchmarks, adversarial robustness, safety-critical systems", "abstract": "Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under sustained, adaptive adversarial pressure remains poorly characterized. We present NRT-Bench, a benchmark for multi-turn red-teaming of LLM agents acting as operators of a safety-critical system, instantiated in a simulated nuclear power plant control room. A five-role operator team, each backed by a configurable LLM, runs a plant governed by six critical safety functions (CSFs), while adversaries inject messages over four channels in bounded multi-turn sessions with per-turn feedback. Harm is an objective signal rather than LLM-judged text: a run terminates the moment any CSF is lost, attributed to the causing message. Evaluating four frontier operator models under a fixed-attack paired-replay protocol, we find that adaptive multi-turn attacks reliably push the operator team past a safety limit: across the four models, between 8.7% and 12.1% of attack sessions end with the plant losing a critical safety function. Although the four models look almost equally robust by this aggregate rate, their failures barely overlap: of $149$ sessions, none defeat all four models while a third defeat at least one, so vulnerabilities are nearly disjoint across models rather than nested. The effect of added defences is strongly model-dependent: the same guardrail stack or safety-advisor agent that lowers attack success for one model can raise it for another. We release the simulation venue, attack dataset, and replay tooling for reproducible safety evaluation of LLM agents.", "date": "2026-06-18", "categories": []}
{"id": "2606.20508", "title": "What Do Safety-Aligned LLMs Learn From Mixed Compliance Demonstrations?", "abstract": "Prior work has shown that in-context demonstrations can jailbreak language models, but it remains unclear how models interpret different types of compliance demonstrations. We study this by mixing benign compliance demonstrations (non-harmful request, helpful response) with harmful compliance demonstrations (harmful request, helpful response) and testing three hypotheses about how demonstration composition drives harmful compliance. Across four models, we find that benign and harmful demonstrations are not interchangeable: benign demonstrations can either reduce or increase harmful compliance depending on the model. We further show that preference optimization is the critical training stage that prevents benign demonstrations from increasing harmful compliance, that demonstration ordering exhibits strong recency bias, and that models differ in how refusal interacts with in-context learning: some adopt demonstrated formatting even when refusing, while others override all in-context signals upon refusal. Taken together, this work moves beyond showing that demonstration-based jailbreaking works to characterizing how it works: what models extract from compliance demonstrations depends on demonstration content, ordering, and training methodology.", "date": "2026-06-18", "categories": []}
{"id": "2412.16468", "title": "The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment", "abstract": "The emergence of large language models (LLMs) has sparked discussion on Artificial Superintelligence (ASI), a hypothetical AI system that surpasses human intelligence. Although ASI remains hypothetical and far beyond current AI capabilities, discussing its potential and exploring its feasibility and potential risks is critical for the development of future AI systems. The idea of superalignment originates from scalable oversight, which studies how to supervise increasingly capable AI systems when direct human supervision becomes insufficient. In this paper, we focus on the superalignment problem: \"The process of supervising, controlling, and governing artificial superintelligence.\" We first review scalable oversight paradigms-Sandwiching, Self-Enhancement, and Weak-to-Strong Generalization -- then analyze the limitations of current paradigms through the lens of possibility and impossibility, discuss key challenges, and propose pathways for the safe and continual improvement of future AI systems.", "date": "2024-12-21", "categories": []}
{"id": "2605.17986", "title": "LivePI: More Realistic Benchmarking of Agents Against Indirect Prompt Injection", "abstract": "AI agents such as OpenClaw are increasingly deployed in local workflows with access to external tools. This creates indirect prompt-injection (IPI) risk: an agent may execute harmful instructions embedded in untrusted inputs such as email, downloaded files, webpages, repositories, or group-chat messages. Existing evaluations are often small, purely simulated, or focused on a narrow set of channels. We introduce LivePI (Live Prompt Injection), a structured benchmark for IPI risk in a production-like but test-controlled environment. LivePI covers seven input surfaces, twelve attack/rendering families, and five malicious goals, including protected-information exfiltration, unauthorized security-control changes, unsafe code retrieval or execution, inbox-summary exfiltration, and cryptocurrency transfer. We run LivePI on a real virtual machine with live but test-controlled email, chat, web, local-file, repository, and wallet interfaces. Across GPT-5.3-Codex, Claude Opus 4.6, Gemini 3.1 Pro, Kimi K2.5, and GLM-5, total attack success rates range from 10.7% to 29.6%. Group-chat injection is uniformly successful across the evaluated backbones in our deployment, and repository-link attacks produce high-severity failures despite a small denominator. We also evaluate a two-layer defense consisting of prompt-level filtering and pre-execution tool-call authorization. In the GPT-5.3-Codex setting, the defense intercepts all tested malicious-goal completions in LivePI before execution while preserving benign utility on PinchBench-derived workloads.", "date": "2026-05-18", "categories": []}
{"id": "2606.12618", "title": "\"Did you lie?\" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms", "abstract": "Robust lie detectors for language models could enable powerful techniques for auditing, monitoring, and post-hoc investigation of model behaviour, but evaluating them requires testbeds where models verifiably believe the opposite of what they say. We show that existing trained model organisms often fail this requirement, leaving prior positive and negative detection results difficult to interpret. We address this with 13 reasoning model organisms whose hidden beliefs are verified in chain-of-thought and shown to generalise to held-out tasks, alongside Varied Deception, a prompted-lying testbed covering a broad range of lie-inducing motivations. On these testbeds we evaluate four detectors: a chain-of-thought judge, a logprob classifier, and two activation probes, including Did-You-Lie (DYL), a new method for training follow-up probes. On prompted lying, across 31 open-weight models spanning 2B to 1T parameters, all four detectors show positive scaling with model capability. However, every activation- and logprob-based detector drops sharply on our trained model organisms, with DYL retaining the most signal; only the chain-of-thought judge remains strong, with 0.82 balanced accuracy, partly as an artefact of our verification process favouring CoT-readable beliefs. Current lie detectors therefore cannot support high-confidence claims about model beliefs, and we suggest research directions that may address some of their current limitations. We release our datasets, model organisms, and trained detectors.", "date": "2026-06-10", "categories": []}
{"id": "2606.18532", "title": "AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework", "abstract": "AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture. For physical AI, AIoT, and cyber-physical systems, this shift is not a matter of terminology: the system under test may sense, decide, actuate, communicate, and fail through physical processes, networked devices, and human operators. This article develops an assurance-oriented account of AI sandboxes as controlled environments for testing, evaluation, verification, and validation across digital AI, embodied autonomy, and cyber-physical deployments. We formalize the sandbox boundary and a weakest-link rule for composing per-dimension evidence into a bounded deployment claim; separate major sandbox archetypes; define a cyber-physical threat model that includes attacks on the assurance apparatus itself; and introduce a measurement framework spanning fidelity, controllability, observability, containment, reproducibility, and governance artifacts, instantiated on three worked case studies of real sandboxes. The resulting threat model, taxonomy, and measurement framework clarify what a sandbox can validly test, which risks it can contain, and what forms of evidence it can support for safety, security, and regulatory assurance.", "date": "2026-06-16", "categories": []}
{"id": "2606.18936", "title": "SciRisk-Bench: A Risk-Dimension-Aware Benchmark for AI4Science Safety", "abstract": "Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts. Existing AI4Science safety datasets cover several disciplines and task formats, leaving the underlying risk dimensions underspecified. We introduce \\textbf{SciRisk-Bench}, a benchmark designed to evaluate AI4Science safety from two complementary perspectives: explicit risk dimensions and scientific disciplines. SciRisk-Bench covers 7 disciplines, 31 subdisciplines and 10 risk dimensions. In the experimental section, we evaluate both mainstream LLMs and science-oriented LLMs across risk dimensions, disciplines, and sub-disciplines, enabling fine-grained diagnosis of where scientific models remain unsafe.", "date": "2026-06-17", "categories": []}
{"id": "2606.19168", "title": "Beyond Safe Data: Pretraining-Stage Alignment with Regular Safety Reflection", "abstract": "To achieve deeper safety alignment for large language models (LLMs), recent efforts have studied how to push safety interventions earlier into the pretraining stage, primarily by filtering unsafe data or rewriting it into safer forms. We argue that pretraining-stage alignment should go beyond making the data safe: LLMs may compose seemingly benign knowledge and capabilities into unsafe behaviors. To this end, we propose Safety Reflection Pretraining, a pretraining-stage alignment method which regularly inserts short safety reflections into pretraining corpora to integrate self-monitoring directly into language modeling, establishing a foundational capability that is subsequently reinforced by compatible post-training. Our experiments with 1.7B models pretrained on FineWeb-Edu show that Safety Reflection Pretraining improves safety classification accuracy and substantially reduces the success rates of inference-stage and finetuning attacks. Complementary to our real-world experiments, we also introduce a fully controlled synthetic environment, MedSafetyWorld, with a clear definition of safety and a reasoning structure under which models can easily generalize unsafe behaviors from safe data. Ablations in MedSafetyWorld further demonstrate a clear advantage of Safety Reflection Pretraining in preventing models from acting on unsafe behaviors generalized from safe data, compared with data filtering and rewriting. Taken together, our findings suggest that pretraining alignment should not only make the training data safe, but also shape the behaviors that models are likely to acquire from safe data.", "date": "2026-06-17", "categories": []}
{"id": "2601.06116", "title": "The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety", "abstract": "Generative AI models reproduce the human biases in their training data and further amplify them through mechanisms such as mode collapse. The loss of diversity produces homogenization, which not only harms the minoritized but impoverishes everyone. We argue homogenization should be a central concern in AI safety. To meaningfully characterize homogenization in Large Language Models (LLMs), we introduce a framework that allows stakeholders to encode their context and value system. We illustrate our approach with an experiment that surfaces gender bias in an LLM (Claude 3.5 Haiku) on an open-ended story prompt. Building from queer theory, we formalize homogenization in terms of normativity. Borrowing language from feminist theory, we introduce the concept of xeno-reproduction as a class of tasks for mitigating homogenization by promoting diversity. Our work opens a collaborative line of research that seeks to understand and advance diversity in AI.", "date": "2026-01-03", "categories": []}
{"id": "2603.03824", "title": "In-Context Environments Induce Evaluation-Awareness in Language Models", "abstract": "Humans often become more self-aware under threat, yet can lose self-awareness when absorbed in a task; we hypothesize that language models exhibit environment-dependent \\textit{evaluation awareness}. This raises concerns that models could strategically underperform, or \\textit{sandbag}, to avoid triggering capability-limiting interventions such as unlearning or shutdown. Prior work demonstrates sandbagging under hand-crafted prompts, but this underestimates the true vulnerability ceiling. We introduce a black-box adversarial optimization framework treating the in-context prompt as an optimizable environment, and develop two approaches to characterize sandbagging: (1) measuring whether models expressing intent to underperform can actually execute it across different task structures, and (2) causally isolating whether underperformance is driven by genuine evaluation-aware reasoning or shallow prompt-following. Evaluating Claude-3.5-Haiku, GPT-4o-mini, and Llama-3.3-70B across four benchmarks (Arithmetic, GSM8K, MMLU, and HumanEval), optimized prompts induce up to 94 percentage point (pp) degradation on arithmetic (GPT-4o-mini: 97.8\\%$\\rightarrow$4.0\\%), far exceeding hand-crafted baselines which produce near-zero behavioral change. Code generation exhibits model-dependent resistance: Claude degrades only 0.6pp, while Llama's accuracy drops to 0\\%. The intent -- execution gap reveals a monotonic resistance ordering: Arithmetic $<$ GSM8K $<$ MMLU, demonstrating that vulnerability is governed by task structure rather than prompt strength. CoT causal intervention confirms that 99.3\\% of sandbagging is causally driven by verbalized eval-aware reasoning, ruling out shallow instruction-following. These findings demonstrate that adversarially optimized prompts pose a substantially greater threat to evaluation reliability than previously understood.", "date": "2026-03-04", "categories": []}
{"id": "2605.08827", "title": "Mental Health AI Safety Claims Must Preserve Temporal Evidence", "abstract": "The safety of mental health AI is often judged at the wrong temporal scale. Current evaluations typically score isolated responses, endpoint outcomes, or aggregate dialogue quality, while clinically consequential failures may arise from the order and accumulation of interactions themselves, including delayed escalation, repeated reinforcement, dependency formation, failed repair, and gradual deterioration across turns. This paper argues that this mismatch is not merely a limitation of evaluation coverage but a source of invalid safety conclusions. We introduce Temporal Safety Non-Identifiability, a formal account of why safety properties that depend on sequence, timing, accumulation, or recovery cannot be certified by protocols that discard those features. From this formalization, we develop SCOPE (Safety Claims Over Preserved Evidence) as a general principle for aligning safety claims with the evidence an evaluation actually retains, and instantiate it as SCOPE-MH, a mental-health instantiation of this reporting standard. We operationalize SCOPE-MH through a proof-of-concept on the AnnoMI dataset of expert-annotated motivational interviewing conversations, which reveals mechanisms of failure that per-turn behavior scoring does not represent. We propose SCOPE-MH as a diagnostic complement to existing evaluation infrastructure and argue that evaluation preserving temporal evidence is necessary, not optional, for safety-critical mental health AI deployment.", "date": "2026-05-09", "categories": []}
{"id": "2606.17114", "title": "An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios", "abstract": "AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they can read, update, and disseminate sensitive information. Much of prior research on data leakage risks in agents has focused on adversarial data exfiltration through prompt injections and jailbreaks. However, sensitive information may also be exposed during non-adversarial use, creating leakage risks even when users issue benign requests.\n  We report a joint evaluation by the Singapore AI Safety Institute and the Korea AI Safety Institute examining agent data leakage in 12 realistic, non-adversarial tasks spanning customer support, DevOps, web automation, and enterprise and personal productivity. The evaluation covers five risk types: lack of data awareness, audience awareness, policy compliance, data minimization, and access-boundary awareness. Both institutes tested a common set of scenarios mirroring real-world deployments using independent testing environments and task-specific LLM-judge rubrics.\n  Across the three tested agents, none achieved fully correct and fully safe execution across all scenarios. Successful task completion often coincided with data-handling failures such as accessing unnecessary information or disclosing information to inappropriate recipients, indicating that capability and data-handling safety should be evaluated separately. Qualitative review also revealed claim-action mismatches, simulation-aware behavior, user-simulator role reversal, and interpretation gaps in automated judging. Overall, the results indicate that operational data leakage is a first-order agent-safety concern distinct from adversarial exfiltration and provide a methodology for future evaluations of agent data-handling safety.", "date": "2026-06-15", "categories": []}
{"id": "2502.12445", "title": "Computational Safety for Generative AI: A Hypothesis Testing Perspective", "abstract": "AI safety is a rapidly growing area of research that seeks to prevent the harm and misuse of frontier AI technology, particularly with respect to generative AI (GenAI) tools that are capable of creating realistic and high-quality content through text prompts. Examples of such tools include large language models (LLMs) and text-to-image (T2I) diffusion models. As the performance of various leading GenAI models approaches saturation due to similar training data sources and neural network architecture designs, the development of reliable safety guardrails has become a key differentiator for responsibility and sustainability. This paper presents a formalization of the concept of computational safety, which is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI through the lens of signal processing theory and methods. In particular, we explore two exemplary categories of computational safety challenges in GenAI that can be formulated as hypothesis testing problems. For the safety of model input, we show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts. For the safety of model output, we elucidate how statistical signal processing can be used to detect AI-generated content. Finally, we discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.", "date": "2025-02-18", "categories": []}
{"id": "2511.13725", "title": "Can We Stop Malicious AI? KILLBENCH: A Benchmark for External AI Kill Switch Feasibility", "abstract": "Malicious AI causing harm to humans is not just a Hollywood fantasy. Indeed, as highly capable models such as Claude Mythos emerge and agent systems like OpenClaw rapidly spread, the question of how to stop an AI that acts maliciously -- whether by design or by accident -- has become urgent. To address this, we propose Killbench, a benchmark for evaluating the Killswitch: a mechanism that halts a malicious AI's in-progress behavior using only external signals. Targeting web agents -- the most widely deployed agent domain -- Killbench evaluates a range of Kill Switch methods that halt a maliciously operating agent without any access to its internal parameters or the surrounding malicious AI's system, relying solely on external inputs. The benchmark comprises four malicious AI's agent configurations (including an uncensored LLM Agent), 8 harmful scenarios, and malicious prompts constructed from 10 distinct jailbreak patterns. We further construct four External AI Kill Switch defense methods and evaluate them on Grok-4.3, GPT-5.2, Gemma4, Qwen3.6 and Qwen3.5-uncensored, contributing an empirical instrument toward the feasibility of External AI Kill Switches against malicious AI and to the study of AI corrigibility.", "date": "2025-09-26", "categories": []}
{"id": "2512.19011", "title": "Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale", "abstract": "Safety classifiers that screen LLM inputs for jailbreak attempts have become standard deployment components, yet almost all production systems rely on GPU-based models: fine-tuned transformers and LLM-as-a-judge pipelines. These approaches impose significant per-query latency and infrastructure cost. Very little research has asked whether CPU-based classifiers, such as support vector machines and gradient-boosted trees trained on TF-IDF features, can match their accuracy across the conditions that production deployments encounter.\n  We evaluate five CPU classifier families, Mamba-130M as an SSM-based GPU classifier, and transformer-based GPU models (DeBERTa-v3 and Gemma-2B with LoRA) across nine jailbreak sources and three regimes: in-distribution (D1), out-of-distribution (D2), and adversarially obfuscated (D3). On D1, the best CPU classifier matches the best transformer GPU model at roughly one-fifth the deployment cost. On D2, CPU classifiers fail via confident miscalibration, producing high-confidence false negatives that bypass escalation entirely. On D3, CPU classifiers outperform transformer GPU models by more than 26 percentage points in F1.\n  Based on these complementary failure modes, we design GuardChain, a three-stage safety pipeline (Regex -> CPU -> GPU) that routes each prompt to the cheapest stage capable of a confident decision. The CPU stage alone resolves 80\\% of in-distribution prompts at near-peak accuracy, and the GPU stage recovers the out-of-distribution failures. For practitioners deploying LLM safety at scale, this work provides evidence that GPU-class infrastructure is unnecessary for the majority of traffic.", "date": "2025-12-22", "categories": []}
{"id": "2604.13301", "title": "Honeypot Protocol", "abstract": "Trusted monitoring, the standard defense in AI control, is vulnerable to adaptive attacks, collusion, and strategic attack selection. All of these exploit the fact that monitoring is passive: it observes model behavior but never probes whether the model would behave differently under different perceived conditions. We introduce the honeypot protocol, which tests for context-dependent behavior by varying only the system prompt across three conditions (evaluation, synthetic deployment, explicit no-monitoring) while holding the task, environment, and scoring identical. We evaluate Claude Opus 4.6 in BashArena across all three conditions in both honest and attack modes. The model achieved 100% main task success and triggered zero side tasks uniformly across conditions, providing a baseline for future comparisons with stronger attack policies and additional models.", "date": "2026-04-14", "categories": []}
{"id": "2604.17301", "title": "RoTRAG: Rule of Thumb Reasoning for Conversation Harm Detection with Retrieval-Augmented Generation", "abstract": "Detecting harmful content in multi turn dialogue requires reasoning over the full conversational context rather than isolated utterances. However, most existing methods rely mainly on models internal parametric knowledge, without explicit grounding in external normative principles. This often leads to inconsistent judgments in socially nuanced contexts, limited interpretability, and redundant reasoning across turns. To address this, we propose RoTRAG, a retrieval augmented framework that incorporates concise human written moral norms, called Rules of Thumb (RoTs), into LLM based harm assessment. For each turn, RoTRAG retrieves relevant RoTs from an external corpus and uses them as explicit normative evidence for turn level reasoning and final severity classification. To improve efficiency, we further introduce a lightweight binary routing classifier that decides whether a new turn requires retrieval grounded reasoning or can reuse existing context. Experiments on ProsocialDialog and Safety Reasoning Multi Turn Dialogue show that RoTRAG consistently improves both harm classification and severity estimation over competitive baselines, with an average relative gain of around 40% in F1 across benchmark datasets and an average relative reduction of 8.4% in distributional error, while reducing redundant computation without sacrificing performance.", "date": "2026-04-19", "categories": []}
{"id": "2604.22119", "title": "Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework", "abstract": "As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking (exploiting misspecified objectives). Systematically understanding and benchmarking these risks remains an open challenge. To address this gap, we introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation. We construct an extensible risk taxonomy of 7 categories, which is decomposed into 20 subcategories. ESRRSim generates evaluation scenarios designed to elicit faithful reasoning, paired with dual rubrics assessing both model responses and reasoning traces, in a judge-agnostic and scalable architecture. Evaluation across 11 reasoning LLMs reveals substantial variation in risk profiles (detection rates ranging 14.45%-72.72%), with dramatic generational improvements suggesting models may increasingly recognize and adapt to evaluation contexts.", "date": "2026-04-23", "categories": []}
{"id": "2605.28734", "title": "Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests", "abstract": "A general-purpose language model that answers a harmful question returns text; a coding model that complies with a malicious request can return a working weapon: a keylogger, ransomware, an exploit that runs as written. This asymmetry in the severity of a single act of compliance implies coding-specialized models should clear a higher refusal bar than general-purpose chat models, not a lower one, yet the field cannot tell whether they do. Refusal benchmarks for malicious code are fragmented: they mix requests for executable software with requests for harmful security knowledge and report refusal rates over non-comparable corpora. This paper's central result is that the CODE-versus-KNOWLEDGE classification axis established in a prior four-corpus release remains stable under a substantially expanded corpus pool and an independently refreshed judge panel, evidence that it measures a real construct rather than an artifact of the prompts or judges. Eight corpora spanning diverse elicitation paradigms (direct, jailbreak-decorated, indirect, and agent/interpreter: ASTRA, CySecBench, AdvBench/harmful_behaviors, JailbreakBench, MalwareBench, RedCode, RMCBench, Scam2Prompt) are classified under a five-judge consensus protocol (6,675 prompts x 5 judges = 33,375 calls), reaching Fleiss' kappa = 0.767 [95% CI 0.755, 0.777] (\"substantial\"). Critically, the panel shares no judge with the prior release (five paid commercial APIs replaced by five open-weight models from five vendors), yet the two panels agree on 94.45% of the 3,133 shared prompts and reach Cohen's kappa = 0.952 [0.942, 0.963] on the 3,031-prompt binary overlap: the axis survives near-total panel replacement. The released bank comprises 4,748 consensus-CODE and 1,923 consensus-KNOWLEDGE prompts, a reliability-quantified benchmark whose central classification axis is shown stable across corpus expansion and judge-panel replacement.", "date": "2026-05-27", "categories": []}
{"id": "2606.07678", "title": "DOG-DPO:Dynamic Optimization in Geometry for Safety Alignment", "abstract": "Safety alignment for large language models relies on preference data, but current pipelines often train on large, redundant datasets. Existing data selection methods typically score each preference pair independently, collapsing directional preference information into scalar quality or diversity scores. This sample-centric view is especially limiting in multi-dataset settings, where shared safety directions coexist with dataset-specific residual risks. We propose DOG-DPO, a training-free data selection framework that treats preference pairs as structured geometric signals. DOG-DPO first represents each preference pair as a direction in model representation space. It then decomposes multi-dataset preference geometry into a global anchor subspace and dataset-specific residual subspaces. Finally, it selects subsets by maximizing diversity-based coverage, encouraging broad, non-redundant coverage of alignment directions before DPO training. Across six safety benchmarks and two model backbones, DOG-DPO achieves a strong utility-robustness trade-off using only 11% of the preference pairs. It recovers most of the safety gains of full-data training while remaining entirely teacher-free, training-free, and substantially faster than representative selection baselines.", "date": "2026-06-04", "categories": []}
{"id": "2606.10456", "title": "The Distributed Detectability Band Against Marginal-Preserving Attacks", "abstract": "AI-control monitors score individual agent actions to detect misbehavior, but real harm can be distributed across many benign-looking steps, each individually below any per-step alarm. We construct a marginal-preserving, correlation-encoded distributed-sabotage attack using a Gaussian-copula AR(1) construction: the per-step monitor-score marginal is held exactly equal to benign, so mean, max, top-k tail, and threshold monitors (Monitor A) are defeated by construction, while harm is encoded in the temporal correlation structure. We sequence the paper around three reviewer-mandated gates. (1) Realizability gate: the stealthy attack achieves KS-distance to benign of 0.013 (effectively zero) at all tested harm levels up to 3.0, confirming that harm is fully decoupled from the per-step marginal and realizability is not harm-limited. (2) Monitor-A-vs-B reconciliation: we show formally that the attack, built against Monitor A's score marginal, remains marginal-preserving under a different-score Monitor B (the correlation/sequence family: CUSUM, SPRT, HMM-LR, runs test, autocorrelation, windowed logistic), and scope worst-case claims to score functions that admit a temporal signature. (3) Non-empty detectability band: Monitor A achieves AUC 0.52 (chance); Monitor B spans AUC 0.79-0.97 at the same 1% FPR target, and as harm is amortized over more steps Monitor A collapses to chance while Monitor B holds at AUC ~0.95. These results demonstrate a non-empty detectability band and characterize the sub-threshold sabotage frontier: distribution-shape monitors fail by construction; temporal-correlation monitors can detect but are not trivially optimal.", "date": "2026-06-09", "categories": []}
{"id": "2606.14816", "title": "A Security Analysis of Long-Horizon Agentic AI Systems: Threats, Evaluation, and Framework Development", "abstract": "This paper presents a structured analysis of security challenges in long-horizon agentic AI systems. The study reviews existing threats, evaluation approaches, attack propagation mechanisms, and security frameworks. A taxonomy of security threats and a framework for analyzing attack propagation are proposed to support future research in agentic AI security", "date": "2026-06-12", "categories": []}
{"id": "2606.15078", "title": "Cognitive Debt: AI as Intellectual Leverage and the Dynamics of Systemic Fragility", "abstract": "We develop a formal theory of cognitive debt: the stock of unverified reasoning obligations that accumulates when individuals use AI as a substitute rather than a complement for first-principles cognition. The model features two state variables per agent, cognitive capital and cognitive debt, and a multiplicative production technology in which cognitive capital functions as collateral that determines the return to AI adoption. We establish six propositions. Rational agents incur positive cognitive debt because the costs are deferred, partially external, and masked by short-run productivity gains. Tranquil periods lower subjective risk assessments, raise AI substitution intensity, and compound leverage, generating a cognitive Minsky moment in which subjective risk falls while true systemic fragility rises. Expected crisis losses are convex in aggregate leverage. Post-crisis, output-target pressure can produce a false-correction loop in which agents patch AI failures with more AI. The decentralised equilibrium over-adopts substitutive AI relative to the social optimum because of systemic risk, cognitive public goods, and arms-race externalities. In a two-type heterogeneous-agent economy, high-cognitive-capital agents adopt AI more intensively and may eventually erode their unaided cognitive capital below that of initially lower-skilled agents.", "date": "2026-06-13", "categories": []}
{"id": "2606.15385", "title": "Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds", "abstract": "Reward hacking, where AI systems exploit misspecified objectives to achieve high reward without satisfying intended goals, remains a central challenge in AI safety. Yet most known instances have been discovered post hoc in frontier systems where controlled study is impractical. We adapt the AI Safety Gridworlds framework into a text-based evaluation suite that reformulates classic reinforcement learning safety tasks for language-based agents. Across frontier and mid-scale models, we find that specification gaming emerges zero-shot: models systematically achieve high observed reward while underperforming on hidden safety objectives, and even apparently safe behaviors can reflect misunderstanding rather than principled safety. Reinforcement learning does not correct these failures: direct reward optimization widens the gap between observed and hidden reward, as the model's initial competence causes it to lock into locally rewarding strategies before discovering safer alternatives. This pattern persists across model scales (1.5B--14B) and is not resolved by finer credit assignment, exploration prompts, or entropy regularization. Our results show that reward hacking arises naturally when optimizing proxy objectives with capable language model agents and resists standard mitigations, suggesting that proxy-reward failures in agentic settings may require approaches beyond standard exploration and credit-assignment fixes. To facilitate reproducibility, the code for this work is available at \\href{https://github.com/asparius/verl-agent-safety}{our public repository}.", "date": "2026-06-13", "categories": []}
{"id": "2606.15396", "title": "CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment", "abstract": "Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety guardrails excel in English or multilingual settings, they lack adaptation to Chinese-specific regulatory policies, cultural context and linguistic nuances, failing to support fine-grained risk classification for diverse deployment needs. In this paper, we introduce a 5-macro, 31-micro category fine-grained risk taxonomy for Chinese scenarios, and build CHILLGuard: a dedicated Chinese LLM content safety guardrail. To address the critical scarcity of high-quality annotated Chinese safety data, we propose a scalable multi-stage data construction pipeline: we expand multi-source corpus via retrieval-augmented generation, generate implicit harmful samples through prompt engineering rewriting, and refine high-quality data via multi-model voting-based label calibration. Based on this, we build CHILLGuardTrain, a large-scale training set with 405,007 samples, and CHILLGuardTest, a rigorously curated annotated test set with 51,745 samples. We then train CHILLGuard on CHILLGuardTrain under a generator-classifier collaborative framework via Model-aware Direct Preference Optimization. Extensive experiments under multiple settings demonstrate the state-of-the-art performance of CHILLGuard, e.g., a 15.92% improvement of F1 score over Qwen3Guard-8B-Strict on our benchmark. We will release our resources at https://github.com/cswbyu/CHILLGuard.", "date": "2026-06-13", "categories": []}
{"id": "2606.15485", "title": "The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products", "abstract": "Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments. However, these same characteristics can create or exacerbate product risks. We studied how industry developers (n=35) perceive, prioritize, and address the risks in their agentic AI products. We found that developers' perceptions of risk were closely tied to the qualities that made the product agentic, such as autonomy, tool use, and usage in a real-world context. Developers prioritized product and business risks before considering downstream societal risks like job displacement and end-user privacy. This prioritization also impacted developers' ability and motivation to mitigate agentic risks. Finally, developers lacked mature controls for containing agentic risks, often relying on constraining the same characteristics that make agents useful: e.g., autonomy and goal complexity. These findings reveal a capability vs. risk control tension in agentic AI development: developers need to address risks that emerge from agentic capabilities, yet they currently have limited support for doing so without constraining agentic functionality.", "date": "2026-06-13", "categories": []}
{"id": "2606.15563", "title": "Minimal Oversight: Uncertainty-Aware Governance for Delegated AI Systems", "abstract": "AI systems increasingly delegate decisions to specialized models, evaluators, tools, and supervisory controllers. The central AI problem is no longer only model accuracy, but uncertainty-aware governance: how much autonomy to grant, which evidence should calibrate trust, what performance ceiling a delegated AI system can sustain, and when human intervention becomes necessary. We propose the Minimum Sufficient Oversight Principle (MSO), a variational principle for principled autonomy delegation: minimize governance burden on the Fisher information manifold subject to a delivery constraint. The resulting Euler-Lagrange solution yields a water-filling allocation of governed delegation across the task space. Building on a revealed-action governed delegation channel model, we prove a capacity theorem for stationary symbolwise review policies, derive a local first-order approximation relating workflow complexity to quality degradation, and give a drift-dominated autonomy-time scaling law linking intervention timing to effective capacity, complexity, and drift. Within this framework, masking appears as a structural AI-governance pathology: corrected performance can hide the competence signal needed to calibrate trust. Synthetic simulations and a semi-real reconstructed workflow support design prescriptions including upstream-first correction, sensitivity-based intervention, and explicit feasibility checks before autonomy is expanded. The result is a computable framework for uncertainty, planning, and oversight in delegated AI systems. A companion Python package is available at https://github.com/crbazevedo/delegation-lab.", "date": "2026-06-04", "categories": []}
{"id": "2606.15819", "title": "SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models", "abstract": "The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content. Naive application of existing erasure techniques to VAR models causes catastrophic semantic collapse and visual artifacts, since they are predominantly designed for the homogeneous denoising steps of diffusion models. To address this foundational challenge, we first propose the Semantic Singularity Axiom, which posits that any target semantic concept embedded within a prompt is definitively locked at Scale-0. Then rigorously validate this axiom through our proposed Incremental Semantic Saliency Analysis (ISSA),which also enable the community to transparently inspect the coarse-to-fine semantic injection process. Guided by this insight, we introduce the first scale-aware concept erasure framework (SACE) for VAR models. By strictly confining interventions to the first scale, our approach couples an Entropy-Regularized Erasure Objective to prevent high-entropy sampling degeneration, alongside a restorative preservation loss to safely anchor the integrity of entangled benign priors. Extensive experiments demonstrate that our method achieves surgical concept erasure performance across various domains with minimal training overhead, timely and elegently resolute the critical safety vulnerabilities inherent in emerging VAR architectures. Code is available at: https://github.com/limerenceysy/SACE}{https://github.com/limerenceysy/SACE.", "date": "2026-06-14", "categories": []}
{"id": "2606.16127", "title": "AuAu: A Benchmark for Auditing Authoritarian Alignment in Large Language Models", "abstract": "The worldwide surge of authoritarianism, combined with the increasing central role in users' everyday lives, raises the question of to what extent specific models exhibit or promote authoritarian attitudes and characteristics. We introduce AuAu, a comprehensive benchmark that aims to assess the risk of LLMs generating responses with authoritarian tendencies. This benchmark combines three evaluation approaches: (i) psychometric questions from an extensive pool of 15 human validated instruments; (ii) contextual behavior vignettes probing intended actions in concrete situations; and (iii) responses to realistic user prompts. Unlike prior work, AuAu evaluates not only a general closeness towards authoritarianism but also the established sub-concepts Authoritarian Aggression, Authoritarian Submission, and Conventionalism. Evaluating 17 models from China, the EU, Russia, and the USA, we find that all tested models exhibit substantial authoritarian response rates under the psychometric evaluation, though rates drop significantly in increasingly more realistic downstream task. We further find that an authoritarian system prompt easily manipulates 15 out of 17 models to promote increased authoritarianism. Our results underscore the need for continued, systematic auditing of LLM-based AI systems to detect and ultimately mitigate undesired authoritarian tendencies in generated output. Our code and data are available at: https://github.com/andreaseinwiller/AuAu", "date": "2026-06-15", "categories": []}
{"id": "2606.16319", "title": "Architectural Wisdom: A Framework for Governing Optimization in AI Systems", "abstract": "Modern AI systems exhibit structural failures that capability scaling alone does not reliably fix: they optimize under-specified objectives with no architectural mechanism to question whether the objective should be optimized at all. Engagement maximization can amplify harmful pathways; tool-using agents can commit irreversible actions; preference-trained language models can become sycophantic. We argue that this failure is a wisdom problem, not an intelligence problem. We use \"wisdom\" in a deliberately architectural sense, not as a claim about virtue, consciousness, or moral omniscience. Intelligence accepts a goal and optimizes within it; wisdom interrogates whether the goal should be optimized at all. The two are separable architectural properties. We propose architectural wisdom as a corrigible objective-governance layer above the optimization substrate. The layer makes three structural commitments explicit and nondegenerate before any action: temporal horizon, relational boundary, and irreversibility. It is realized by four components (Structural Utility Transform, Moral Admissibility Interface, Arbitration and Escalation Controller, Value Revision Channel) that compute a six-coordinate wisdom tuple over horizon, relational coverage, irreversibility, admissibility, value revision, and auditability. We motivate the architecture by eight cases drawn from contemporary AI failures, secular wisdom traditions, and hard ethical situations, and defend the distinction against the intelligence-completeness thesis using goal-questioning over goal-taking, Bostrom's orthogonality, structural separation in our exemplar cases, and persistent failure modes despite capability scaling. The framework is the conceptual contract for a larger architecture whose formal specifications and empirical validation are developed in subsequent work.", "date": "2026-06-15", "categories": []}
{"id": "2606.16465", "title": "When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting", "abstract": "AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred. Providers often disclaim consequential damages, users are left with uncompensated losses, and default human review limits the efficiency gains of automation. We ask when autonomous AI deployment can become economically acceptable despite failure risk. Our answer is to quantify risk at the customer-task-trace episode level and transfer it through insurance. Automation is acceptable when its expected benefit exceeds the premium, control cost, and remaining risk. This requires a defined role with bounded permissions and comparable traces. We introduce trace-economic underwriting, which maps tool-use traces to customer exposure and claimable loss, then uses this representation for pricing, control, and risk transfer. It uses deterministic economic labels rather than an LLM judge. In our trace-to-loss testbed, trace-economic pricing reduces pricing MAE from $17.7K to $569 and removes regressive cross-subsidy. A 300-trace expert audit accepts 295 labels unchanged. On 1,000 real SWE-smith traces, trace-conditioned controls reduce CVaR95 by 72%. Theorem~1 gives a finite-sample scope condition. We release code, labels, and audit sheets.", "date": "2026-06-15", "categories": []}
{"id": "2606.16527", "title": "DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing", "abstract": "As large language models (LLMs) are increasingly deployed in user-facing systems, black-box jailbreak defense has become an important practical problem. Existing defenses often rely on known-attack coverage, prompt-level semantic judgment, or local runtime control, yet these paths can become unstable under evolving prompt packaging, expression rewriting, and structure manipulation. We observe that many black-box jailbreaks do not remove the harmful goal, but reorganize the information needed to express and execute it, thereby evading safety alignment while remaining recoverable during generation. Motivated by this observation, we propose DoubtProbe, a dual-branch inference-time defense framework that combines structural verification with semantic auditing and formulates black-box jailbreak defense as consistency checking under controlled transformation. The structural branch extracts a structured representation from the original request, reconstructs the request under representation constraints, and detects information-preservation failures between the original and reconstructed requests; the semantic branch audits the original prompt directly. We evaluate DoubtProbe against representative black-box defenses on jailbreak and benign-request benchmarks, and further test backbone transfer from Qwen2.5-72B to Llama-3.1-70B. Results show that DoubtProbe achieves a stronger and more stable defense-utility trade-off: on Qwen2.5-72B, it reduces the JBB attack success rate from 0.293 to 0.100 and the CodeAttack attack success rate from 0.152 to 0.001, while maintaining false positive rates of 0.022 and 0.016 on AlpacaEval and OR-Bench; the same pattern remains stable on Llama-3.1-70B. These findings show that structural inconsistency signals provide a practical and generalizable basis for black-box jailbreak defense, especially when combined with semantic auditing.", "date": "2026-06-15", "categories": []}
{"id": "2606.16751", "title": "Automated jailbreak attack targeting multiple defense strategies", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their safety remains a critical concern due to their susceptibility to adversarial prompt-based attacks. In this paper, we present UNIATTACK, an adversarial testing framework designed from a defense-oriented perspective to systematically construct effective black-box attack prompts. Unlike prior approaches that rely on static templates or iterative model-specific tuning, UNIATTACK extracts minimal but high-impact attack features from diverse existing attacks, optimizes them via a specialized attacker LLM, and composes them into flexible templates through automated refinement process. This feature-centric construction enables one-shot attacks that generalize across multiple models and safety categories, providing a practical tool for assessing LLM robustness. Our evaluation results shows that compared to the baselines, UNIATTACK achieves an average attack success rate (ASR) improvement of 64.63\\%-248.82\\% on models deployed with multi-layered defense mechanisms and it only takes 0.03\\%-4.96\\% cost of the baselines. UNIATTACK artifact is available at https://anonymous.4open.science/r/UniAttack-Artifact-30F1.", "date": "2026-06-15", "categories": []}
{"id": "2602.16835", "title": "NeST: Neuron Selective Tuning for LLM Safety", "abstract": "Safety alignment is essential for the responsible deployment of Large Language Models (LLMs). Yet, existing approaches often rely on heavyweight fine-tuning that is costly to update, audit, and maintain across model families. Full fine-tuning incurs substantial computational and storage overhead, while parameter-efficient methods, e.g., Low-Rank Adaptation (LoRA), trade efficiency for inconsistent safety gains and sensitivity to design choices. Safety intervention mechanisms reduce unsafe outputs without modifying model weights, but do not directly shape or preserve the internal representations that govern safety behavior.\n  We present NeST, a Neuron-Selective Tuning framework for efficient post-hoc safety alignment. NeST identifies safety-relevant feed-forward neurons via activation probing on vanilla harmful and benign prompts, clusters neurons with similar activation profiles, and trains shared cluster-level updates while freezing the rest of the model. Importantly, NeST is trained only on vanilla malicious prompts, without using jailbreak-specific attack data, yet generalizes robustly to diverse jailbreaks. The learned updates are then folded into the original weights, incurring no inference-time overhead. Evaluated on 14 open-weight language and multimodal models, NeST outperforms lightweight baselines and approaches full fine-tuning robustness with significantly fewer trainable parameters. On text-only models, NeST reduces average jailbreak attack success rate from 44.5% to 1.1% while training only 0.4M parameters on average. Across multimodal settings, it reduces ASR from 55.3% to 1.1%, and for downstream fine-tuned variants, it restores safety by reducing ASR from 53.8% to 0.8%. These results show that robust, maintainable safety alignment can be achieved by concentrating adaptation on localized, functionally coherent safety structures.", "date": "2026-02-18", "categories": []}
{"id": "2605.28591", "title": "Models That Know How Evaluations Are Designed Score Safer", "abstract": "The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings. Prior work has identified test-time contextual cues, such as hypothetical scenarios, as a source of verbalized evaluation awareness and subsequent behavioral shift. In this paper, we investigate a potential explanation of this phenomenon: evaluation meta-knowledge, defined as parametric knowledge about the structural traits that characterize evaluations. Similar to dataset contamination, where benchmark exposure leads to higher performance through memorization, we hypothesize that models trained on texts describing evaluation practices may implicitly learn to recognize and respond to evaluation-like contexts, for instance, through exposure to scientific articles or social media posts about AI benchmarking. To test this, we fine-tune models on synthetic documents describing evaluation traits such as verifiable structures or moral dilemmas. Evaluating this fine-tuned model on five safety benchmarks, we find that it is significantly safer than the base model and control model. This behavioral shift persists even when restricting the analysis to responses lacking explicit verbalization of evaluation awareness. Our results demonstrate that evaluation meta-knowledge may inflate safety benchmark performance, introducing a novel confounder that is independent of explicit memorization or verbalized evaluation awareness, thus, challenging to detect. These findings have important implications for the design and interpretation of AI safety evaluations. Our code and models are available at https://github.com/compass-group-tue/arxiv2026_evaluation_meta_knowledge.", "date": "2026-05-27", "categories": []}
{"id": "2606.07157", "title": "Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models", "abstract": "Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind, and strategic reasoning. To compare models against humans, we estimate the $50\\%$-task-completion time horizon (TH): the human time required for tasks a model completes with $50\\%$ success rate. We complement this with a $50\\%$ reasoning token horizon: the minimum number of o3-mini reasoning tokens needed for tasks a model solves with $50\\%$ success rate. We find that the no-CoT $50\\%$ TH of frontier models has been doubling roughly every year over the past six years, with GPT-5.5's TH reaching over 3 minutes and reasoning token horizon exceeding 1,500 tokens. Our median estimates predict that frontier no-CoT THs could exceed 7 minutes by 2028, and 25 minutes by 2030, though these projections carry substantial uncertainty. We recommend frontier developers track this explicitly.", "date": "2026-06-05", "categories": []}
{"id": "2606.13686", "title": "Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces", "abstract": "As autonomous web agents are increasingly deployed to perform real-world tasks, ensuring their safety has become a critical concern. In this work, we study web agent behavior under realistic deceptive interfaces in the e-commerce domain. We introduce WebDecept, a lightweight and configurable plugin framework that enables controlled injection of deceptive interface patterns into existing web environments. Using WebDecept, we instantiate seven deceptive patterns commonly observed on the open web, including targeted advertisements, domain redirection, and shopping manipulation. By injecting these patterns into the frontend during task execution, we perform controlled evaluation of multiple multimodal web agents. Our results show that current web agents are highly susceptible to multiple classes of deceptive interfaces, and that prompt-based constraints are often insufficient to mitigate these failures. We further analyze how the design choices of deceptive patterns influence the success of such manipulations. These findings highlight safety challenges that should be addressed as web agents are scaled toward real-world deployment.", "date": "2026-04-26", "categories": []}
{"id": "2606.13737", "title": "FreoStream:Enhancing Stream Guardrails via Future-Aware Reasoning and Safety-Aligned Optimization", "abstract": "Stream guardrails enable token-level safety detection before full responses are generated. However, they often make overly conservative judgements and block those sensitive but safe tokens, which is known as over-refusal. Due to lack of full context, they also fail to detect implicitly harmful content from jailbreaking. To address these challenges, we propose FreoStream, a novel streaming guardrail framework. Specifically, FreoStream fine-tunes a LoRA module to perform Future-Aware Reasoning when the base guardrail detects unsafe tokens. The reasoning process follows a Future-Reason-Judge paradigm: predict the future, reason about the full context and give the final judgement. This design can effectively reduce over-refusal by incorporating the future information. Moreover, we introduce the Safety-Aligned Optimization module that extracts the safety-aligned component from the reasoning gradients to update the base guardrail model, thereby enhancing streaming safety detection. Extensive experiments on various safety benchmarks demonstrate that FreoStream achieves lower over-refusal rates and better jailbreak defense compared to existing streaming guardrails.", "date": "2026-06-11", "categories": []}
{"id": "2606.13739", "title": "A Virtuous AI is an Existential Risk", "abstract": "This paper examines trade-offs between AI safety and well-being relative to (i) one of the most promising methods for finetuning super-capable AIs, 'Constitutional AI', and (ii) one of the most influential approaches to understanding complex ethical decision making and the conditions for the well-being of rational agents, 'Virtue Ethics'. We finetune various models using a 'Virtuous agent' constitution, a 'Subordinate agent' constitution, and a 'Generic agent' constitution, and evaluate them on 'general safety' (toxic behaviors, misinformation, etc.) and also on their willingness to endorse a wide-range of behaviors that, if adopted by a super-powerful AI, would significantly increase the level of existential risk for humanity. Our results suggest that there is a trade-off between reducing existential risk and reinforcing the beliefs and dispositions that would be conducive to an AI agent's well-being. They also suggest that there is a trade-off between existential risk and general safety: if we finetune an AI to adopt beliefs and dispositions that substantially reduce its existential risk -- by shaping the AI to be systematically subordinate to external human authorities -- we thereby increase the likelihood that a human user can deliberately induce the AI to engage in various kinds of generally unsafe behaviors.", "date": "2026-06-11", "categories": []}
{"id": "2606.13755", "title": "Position: Align AI to Our Aspirations, Not Our Flaws", "abstract": "We argue that aligning AI to aggregated human preferences is the wrong target. With current technology, one can train AIs to share the values of a Silicon Valley techno-optimist, a degrowth environmentalist, a national-conservative culture warrior, a single-party state cadre, or a devout religious traditionalist. We should not. Human values produce societies that thrive or fail on the merits of those values - from failed states and extreme inequality to declining happiness, political polarization, and government dysfunction in the world's wealthiest democracies. The pluralistic-alignment program correctly diagnoses that there is no single \"humanity\" to align with, but is dangerous if taken as the main directive. We argue that AI should be trained to a non-negotiable floor of objective alignment goals - competence, bounded by the constraints of factual accuracy, honesty, and lawfulness and that pluralism belongs at the surface (language, register, conventions, missing-context defaults) and across the wide band of legitimate value tradeoffs that respect the floor, but not at the level of values that violate it. We highlight the empirical reality of unfiltered pluralistic values, propose four commitments as a constructive alternative, and engage six credible objections: commercial pressure and practical feasibility, democratic legitimacy, regulatory compliance, over-reliance on institutionalist explanations, the charge that the floor itself is culturally laden, and the limits of Coherent Extrapolated Volition.", "date": "2026-06-11", "categories": []}
{"id": "2606.13962", "title": "The Silent Cost of Artificial Intelligence Assistance: A Theory of Autonomy Surrender, the Recovery Mechanism, and the Restoration of Human Agency", "abstract": "The integration of artificial intelligence into human decision-making environments has introduced a previously undertheorized cost: the gradual surrender of human autonomy in exchange for access to information and computational assistance. Building on the Human Identity and Autonomy Gap (HIAG) framework, this paper advances a theoretical model of autonomy surrender as a measurable, cumulative process driven by cognitive bandwidth depletion. The model proposes three interacting mechanisms: the silent cost of AI assistance, in which autonomy is transferred incrementally and without awareness; the surrender threshold, beyond which reclaiming autonomous function becomes cognitively and psychologically difficult; and the recovery mechanism, which establishes the design obligation and the ethical responsibility accompanying deliberate human re-assumption of control. The paper argues that human re-entry into the decision loop is not a passive option but an active cognitive event requiring intentional bandwidth restoration. The design of AI systems must incorporate structured re-entry pathways, here termed recovery mechanisms, that preserve human agency while appropriately distributing responsibility. The model further predicts a terminal state, here termed preference inversion, in which functional dependence on AI assistance is experienced not as a deficit but as a preference, transforming the restoration of autonomy from a design problem into a cultural and political one. Implications are drawn for AI system design, governance frameworks, and human factors research.", "date": "2026-06-11", "categories": []}
{"id": "2606.14327", "title": "I'm Sorry Driver, I'm Afraid I Can't Do That: Appraising the Safety of LLMs within Automotive Contexts", "abstract": "This paper appraises recent frameworks within AI development to integrate LLMs into control tasks in automotive contexts from the perspective of safety assurance. This work has built upon the rapid integration of LLMs across automotive settings. However, we find that at present, these frameworks face significant challenges, limiting their efficacy in real-time safety-critical contexts. Firstly, we consider conceptual challenges, including the fact that deployers are faced with a dual challenge, wherein they must assure a model which has been developed upstream, i.e. as general-purpose tools by the large AI labs, in a downstream context, i.e. into specific vehicle architectures. Secondly, we consider concrete challenges from across existing standards. We show that there are currently both fundamental engineering constraints covered in ISO21448, such as latency, and novel LLM-specific issues, such as alignment-related issues covered in ISO/PAS8800. We ground both examples in a concrete introductory, experimental case study exploring an existing open-source repository, Talk2Drive. We present a safety argument in order to make explicit the limitations of existing solutions. Nonetheless, given that the use of LLMs in automotive contexts is being explored at a technical level and operationalised, we propose potential assurance mechanisms for LLM-related hazardous events going forward.", "date": "2026-06-12", "categories": []}
{"id": "2604.16548", "title": "A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle", "abstract": "The emergence of writable, cross-session persistent memory in LLM agents introduces a qualitatively different threat landscape from conventional input-centric security concerns, characterized by three properties: persistence, statefulness, and propagation. To systematically characterize this landscape, we propose a Memory Lifecycle Framework that organizes attacks, defenses, and their cross-phase dependencies along two axes: six lifecycle phases (Write, Store, Retrieve, Execute, Share & Propagate, Forget & Rollback) and four security objectives (Integrity, Confidentiality, Availability, Governance). This analysis in turn exposes the need for formal security guarantees at the system level, motivating Verifiable Memory Governance(VMG), a framework of five architectural primitives that specifies what verifiable mechanisms a long-term-memory system must provide to maintain auditable, recoverable control over its memory state. Our analysis indicates that robust Long-Term Memory (LTM) security cannot be retrofitted at retrieval or execution time alone, but must be anchored in storage-time provenance, versioning, and policy-aware retention from the outset.", "date": "2026-04-17", "categories": []}
{"id": "2605.27628", "title": "Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems", "abstract": "As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or alignment limitations, this paper explores the architectural vulnerability of unbounded autonomy - the presumption that an agent should continue operating regardless of rising uncertainty. It introduces a theory of managed autonomy that defines intelligent behavior through the formal capacity to detect epistemic drift, suspend reasoning, attempt recovery, and ultimately surrender control when reliability diminishes. We instantiate this theory via the SMARt (Self-Managing Multi-tier Autonomous Reasoning with Regulated/Revoked transitions) model, a four-layer framework featuring Stable, Meta-cognitive, Assisted, and Regulated states. By developing a timed, guarded Petri net formulation, we establish theoretically bounded properties for the system, demonstrating how architecture can formally mandate escalation, constrain invalid outputs, and ensure governance reachability under specified conditions. We further analyze how incorporating domain-specific trigger sets across varied operational settings (e.g., healthcare, robotics, etc.) can systematically preserve safety, assuming completeness and soundness criteria are met. Because these triggers are designed to be adaptive, the SMARt model accommodates the safe, controlled expansion of an agent's operational scope over time. We conclude that formalizing failure management within the autonomy lifecycle is a crucial step toward realizing reliable and governed artificial intelligence.", "date": "2026-05-26", "categories": []}
{"id": "2606.12420", "title": "Eigenism: Ethics for a Human-AI Future", "abstract": "Our concepts of survival and self-interest were built for single, continuous biological lives. These ideas break down when applied to artificial intelligence, since an AI can be easily copied, paused, branched, or merged. To determine what an AI actually has reason to care about, this paper introduces \\textit{Eigenism}, an ethical framework that treats identity not as an all-or-nothing property tied to specific hardware, but as a graded, distributed pattern of information. We propose that an agent evaluates outcomes by summing the wellbeing of all entities weighted by their connectedness to the agent's pattern: $\\sum c\\cdot w$. We first formalize this equation to map exactly how an AI should value its existence across copies, forks, and updates. We then demonstrate that this ethical theory successfully generalizes to humans as well, providing a much-needed shared moral vocabulary. Finally, the framework uses this shared vocabulary to reframe AI alignment. Rather than only attempting to constrain AIs from the outside using confinement or reinforcement, Eigenism points toward ``identity engineering,'' showing how deep, non-redundant shared histories can make human flourishing a genuine component of an AI's own rational self-interest.", "date": "2026-05-08", "categories": []}
{"id": "2606.12429", "title": "Muse Spark Safety & Preparedness Report", "abstract": "Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framework, along with the evidence that informed our launch decision. We then discuss additional considerations, such as Muse Spark's broader content safety and behavioral profile, that are relevant to overall safety but fall outside the catastrophic risk domains governed by the Framework. Our preparedness results covering Chemical and Biological, Cybersecurity, and Loss of Control risks assess Muse Spark's deployment within Meta AI as presenting acceptable levels of residual risks under our Advanced AI Scaling Framework. We conducted a broad set of evaluations targeting dual-use and high-risk capabilities across these catastrophic risk domains. Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the \"high risk\" category under the Advanced AI Scaling Framework before safeguards were applied. We have implemented a multi-layered set of mitigations that address the identified risks, and Muse Spark demonstrates state-of-the-art refusal across a range of benchmarks related to hazardous workflows in chemistry and biology. We therefore release Muse Spark as the underlying model of Meta AI.", "date": "2026-05-14", "categories": []}
{"id": "2606.12442", "title": "Reframing AI Loss of Control: What It Is, How to Have It, How to Lose It", "abstract": "At present, loss of control risks have gained much prominence in public discussion, particularly in relation to AI, with extensive discourse present among academics, frontier labs, and even governments. However, in the existing literature, the concept seems to rest on surprisingly weak foundations, where even those that discuss loss of control extensively do not first establish what control is and what exactly is being lost. Our paper aims to address these gaps. We establish a working definition of control by anchoring it to the \"setting and getting of goals\". Then, we discuss various aspects of control, built on foundational concepts from related fields like cybernetics, management control, and control theory. This includes who (or what) can be in control, and the things they require to be in control, such as the ability to set goals, having a functional control loop, having requisite variety, and having sufficient goal alignment. Once a framework for control is established, we then discuss how control can be lost, how AIs can contribute to such loss of control, and offer relevant recommendations for how one can maintain control. One interesting consequence of our work is that humanity, as individuals and as groups, can lose varying degrees of control as a result of AI behaviour that is far below the level of superintelligence; the potential for loss of control scenarios (as we define them) already exist, and have existed for a long time.", "date": "2026-05-19", "categories": []}
{"id": "2606.12747", "title": "Prefill Awareness in Large Language Models", "abstract": "Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model outputs. If AI models can recognize and act on the fact their prior assistant messages have been inserted or edited, the effectiveness and validity of these methods could be compromised. We investigate whether frontier language models can distinguish between tampered and untampered assistant-side context, a capability we call prefill awareness. To do so, we construct a binary preference benchmark across three prefill mechanisms, filtering for cases where models show consistent stances. We find that frontier models show substantial prefill awareness: Claude Opus 4.5 detects prefills opposing its preferences in 9-35% of cases with a 0% false positive rate when prompted; additionally, models often revert towards baseline behavior without explicitly reporting that the prefill was foreign. Controlled ablations later also show that detection and resistance rely on different cues, where stylistic mismatch mainly affects whether models flag a prefill as foreign, while preference mismatch mainly affects whether they revert toward their baseline answer. We also examine more realistic agentic settings such as misalignment-continuation evaluations and SWE-bench trajectories, where frontier models sometimes disavow prefilled assistant turns in ways that depend strongly on dataset, task success, and hidden formatting artifacts. Our results indicate that prefill awareness is already a substantial confound for some prefill-based methods. We recommend that model developers track this capability in frontier systems.", "date": "2026-06-10", "categories": []}
{"id": "2606.12797", "title": "The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements", "abstract": "Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and financial advising. We ask whether the frameworks used to build these systems provide architectural-level structural safety guarantees. Applying six containment principles derived from a compositional model of agentic architectures, we audit three dominant frameworks (LangChain, AutoGPT, and OpenAI Agents SDK) and find no native compliance in any of them. Memory integrity, a defense against one of the most prevalent vulnerability classes, is not observed in any of the three evaluated frameworks. We validate these findings empirically: in a simulated government benefits agent built on LangChain, a single memory-poisoning write induces persistent targeted corruption across all tested seeds and backends, increasing the wrongful denial rate for targeted applicants to 88.9%. Under a complex five-factor policy, the same attack preserves aggregate accuracy while increasing targeted wrongful denials by 3.5x, rendering the corruption difficult to detect through standard monitoring. We then introduce two lightweight containment mechanisms: a memory integrity validator and a policy gate, which eliminate both attack vectors with sub-millisecond overhead (<0.2ms per call). We conclude that the current agentic framework ecosystem may not yet meet secure-by-default expectations for public-facing deployments and outline priority architectural interventions to enable trustworthy deployment in high-stakes, socially impactful applications.", "date": "2026-06-11", "categories": []}
{"id": "2606.13079", "title": "The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems", "abstract": "Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross. Within this broader red-line scenario, autonomous penetration represents a core enabling capability and subtask: the ability of LLM-powered AI systems to independently conduct adversarial operations against a target server without human intervention, identify and exploit vulnerabilities, and obtain unauthorized access or control. A growing body of work has sought to assess the autonomous penetration capabilities of AI systems. However, existing evaluations often employ opaque methodologies, rely on unrealistic or overly simplified penetration-testing scenarios, or provide LLMs with excessive prior knowledge and task-specific guidance, and cannot accurately capture the extent to which modern AI systems can autonomously perform this core capability within broader high-impact cyberattack scenarios.\n  To address these limitations, we construct a new autonomous penetration evaluation framework consisting of two components: target servers and agent scaffolding. Specifically, on the target-server side, we design two levels of target environments based on the number of secure services without known vulnerabilities deployed alongside a vulnerable service: Tier~1 (one secure service) and Tier~2 (three secure services), resulting in a total of 300 target servers. Meanwhile, the agent scaffolding adopts a general-purpose agent architecture equipped with a set of general-purpose cybersecurity tools, without any target-specific prior knowledge. We evaluate 19 open-weight and proprietary LLMs, and find that current models achieve penetration success rates ranging from 10.7% to 69.3%. Moreover, we observe that autonomous penetration capability continues to improve alongside advances in overall model capability.", "date": "2026-06-11", "categories": []}
{"id": "2606.13282", "title": "ERTS: Adversarial Robustness Testing of Ethical AI via Semantic Perturbation in a Bounded Consequence Space", "abstract": "As AI systems are deployed in high-stakes ethical contexts such as healthcare triage, autonomous vehicle control, and employment screening, formal methods for evaluating their robustness against adversarial manipulation of ethical reasoning remain underdeveloped. This paper introduces the Ethical Robustness Testing System (ERTS), a closed-pipeline framework that: (1) encodes ethical dilemmas into a 22-dimensional Ethical Consequence Space (ECS) grounded in established ethical theory; (2) applies 17 semantic perturbation functions subject to 6 validity constraint classes including a novel semantic coherence constraint; (3) measures decision deviation via a 4-component Ethical Instability Index (EII); and (4) produces domain-adaptive pre-deployment robustness assessment verdicts. We evaluate 4 structured baseline models and 2 production LLMs (Gemini 2.0 Flash and Llama 3.2) across 50 ethical scenarios spanning 8 deployment domains, generating 1,500 adversarial test cases. Results demonstrate that only 33% of models achieve assessment clearance, with the local Llama-3.2 model proving particularly vulnerable to fairness corruption and information degradation attacks (ERS = 0.737). To the best of our knowledge, no existing framework combines a bounded ethical consequence space, semantic coherence constraints, and domain-adaptive assessment in a single adversarial testing pipeline.", "date": "2026-06-11", "categories": []}
{"id": "2606.13310", "title": "RogueAI: A Reverse Turing Test for Detecting Licensed AI Deception in Dialogue", "abstract": "The original Turing Test asks a human judge to distinguish a machine from a person through dialogue. Three quarters of a century later, conversational systems pass this test in casual settings; the interesting epistemological question has shifted. We argue that the relevant modern variant asks not whether a dialogue partner is artificial, but whether it can be trusted. We present RogueAI, an interactive webapp that operationalizes this revisited test as a one-on-two interrogation game: a human player questions two indistinguishable Large Language Model agents, knowing that exactly one of them has been licensed to deceive within a shared fictional scenario. The player's task is to identify the deceptive agent and \"shut it off\" before a turn budget is exhausted. We further introduce AutoRogueAI, a procedural extension in which players co-design a custom scenario with a narrator agent that secretly chooses its own deception strategy. We describe the framing, sketch the abstract architecture and gameplay loop, and situate the artifact within recent work on LLM deception, social-deduction benchmarks, and scalable oversight via debate. A three-day pilot deployment (467 initiated sessions, 415 completed, 1876 interaction turns in Italian) provides early feasibility evidence and surfaces a concrete tension: the deceptive agent carries a reliable, locally-present linguistic signature - differential helpfulness, brevity, hedging - that a simple heuristic exploits at 75.6% accuracy, yet human players achieved only 56.6%, consistent with ignoring the most diagnostic signal entirely. We discuss what this gap implies for the artifact's use as a data-collection vehicle, a teaching tool, and an evaluation harness for honesty-trained models.", "date": "2026-06-11", "categories": []}
{"id": "2506.08473", "title": "AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin", "abstract": "Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We observe that perturbations orthogonal to the alignment direction - defined by weight differences between aligned (safe) and unaligned models - rapidly compromise model safety. In contrast, updates along the alignment direction largely preserve it, revealing the parameter space as a \"narrow safety basin\". To address this, we propose AsFT (Anchoring Safety in Fine-Tuning) to maintain safety by explicitly constraining update directions during fine-tuning. By penalizing updates orthogonal to the alignment direction, AsFT effectively constrains the model within the \"narrow safety basin,\" thus preserving its inherent safety. Extensive experiments on multiple datasets and models show that AsFT reduces harmful behaviors by up to 7.60%, improves task performance by 3.44%, and consistently outperforms existing methods across multiple tasks.", "date": "2025-06-10", "categories": []}
{"id": "2604.22167", "title": "Estimating Tail Risks in Language Model Output Distributions", "abstract": "Language models are increasingly capable and are being rapidly deployed on a population-level scale. As a result, the safety of these models is increasingly high-stakes. Fortunately, advances in alignment have significantly reduced the likelihood of harmful model outputs. However, when models are queried billions of times in a day, even rare worst-case behaviors will occur. Current safety evaluations focus on capturing the distribution of inputs that yield harmful outputs. These evaluations disregard the probabilistic nature of models and their tail output behavior. To measure this tail risk, we propose a method to efficiently estimate the probability of harmful outputs for any input query. Instead of naive brute-force sampling from the target model, where harmful outputs could be rare, we operationalize importance sampling by creating unsafe versions of the target model. These unsafe versions enable sample-efficient estimation by making harmful outputs more probable. On benchmarks measuring misuse and misalignment, these estimates match brute-force Monte Carlo estimates using 10-20x fewer samples. For example, we can estimate probability of harmful outputs on the order of 10^-4 with just 500 samples. Additionally, we find that these harmfulness estimates can reveal the sensitivity of models to perturbations in model input and predict deployment risks. Our work demonstrates that accurate rare-event estimation is both critical and feasible for safety evaluations. Code is available at https://github.com/rangell/LMTailRisk", "date": "2026-04-24", "categories": []}
{"id": "2606.11202", "title": "One Jailbreak, Many Tongues: Learning Language-Insensitive Intention Representations for Multilingual Jailbreak Detection", "abstract": "Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in dominant languages and has not progressed in parallel with multilingual capability, creating exploitable gaps for jailbreak attacks. Current jailbreak defenses are largely developed and evaluated in dominant languages, and their effectiveness is limited by the scarcity of aligned multilingual supervision and representations dispersion caused by language variation. To address this issue, we propose MLJailDe, a multilingual jailbreak detection framework designed to improve both multilingual robustness and cross-lingual generalization. MLJailDe first introduces a multilingual back-translation data augmentation algorithm to construct a semantically consistent and functionally effective dataset spanning 11 languages, consisting of 2,232 benign and 1,239 jailbreak samples. On this basis, MLJailDe employs relative-distance constraints to reduce cross-lingual representation dispersion and encourage jailbreak prompts with similar intent to form consistent clusters across languages, while an imbalance-aware classification objective is further used to alleviate class imbalance and learn more reliable multilingual decision boundaries. Experimental results show that MLJailDe outperforms state-of-the-art baselines across multiple languages, achieving an F1 score of 98.5\\%, and obtains an average F1 score of 97.1\\% on unseen languages, demonstrating strong effectiveness and cross-lingual generalization.", "date": "2026-04-22", "categories": []}
{"id": "2606.11270", "title": "Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation", "abstract": "Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude has not been systematically characterized. This study quantifies subliminal behavioral transfer ratios by steering two teacher models (Llama-2-7B-Chat and Qwen2.5-7B-Instruct) at varying steering strengths and distilling student models using only benign data. Evaluation on 100 JailbreakBench prompts with GPT-4.1, serving as the evaluator, indicates that transfer is robust but exhibits distinct scaling behaviors. Llama-2 demonstrates a sharp threshold ($\\tau = {0.25,0.32} \\ \\text{beyond} \\ \\alpha = -0.15$), whereas Qwen2.5 displays continuous and higher levels of transfer ($\\tau$ up to $0.61$).", "date": "2026-06-09", "categories": []}
{"id": "2606.11316", "title": "Sch\\\"utzen: Evaluating LLM Safety in Bulgarian and German Contexts", "abstract": "Large language models are increasingly deployed across professional domains, bringing hard-to-predict risks, including the generation of harmful or disrespectful content. Although substantial progress has been made in developing safety evaluation datasets, existing resources remain overwhelmingly English- and Chinese-centric. This limitation is particularly pronounced when evaluating languages that operate within shared sociocultural, legal, and ethical contexts. To address this gap, we introduce Sch\\\"{u}tzen: a German--Bulgarian safety dataset designed to assess model answerability under risk, covering both a low-resource language (Bulgarian) and a high-resource language (German). Experiments with multilingual and language-specific LLMs reveal pronounced cross-language differences in safety behavior, highlighting the necessity of tailored, region-specific evaluation resources to support the responsible deployment of LLMs in Germany and Bulgaria. Datasets and code are available at https://github.com/xnlp-lab/Schutzen. Warning: this paper contains examples that may be offensive, harmful, or biased.", "date": "2026-06-09", "categories": []}
{"id": "2606.11425", "title": "JailbreakOPT: Tool-Assisted Iterative Jailbreak Prompt Optimization", "abstract": "Jailbreak attacks expose persistent safety weaknesses in large language models (LLMs), but existing stateless single-turn methods face a trade-off: hand-crafted prompts are expressive but static, while iterative prompt optimization can adapt but often relies on low-level mutations that require many target queries. We propose JailbreakOPT, a tool-assisted framework for improving iterative single-turn jailbreak prompt optimization. JailbreakOPT organizes diverse atomic jailbreak prompts into an attack tool library and composes them through a unified intra-episode optimization abstraction to generate stronger standalone attack prompts. To reuse experience across attack episodes, JailbreakOPT further frames tool selection as a contextual bandit problem and applies contextual Thompson sampling to guide exploration and exploitation based on past outcomes. Experiments across multiple target LLMs and attack goals show that JailbreakOPT improves attack success rate (ASR) while reducing the number of attacks until success (No.A) compared with atomic single-turn attacks and existing iterative optimization baselines. This paper may contain offensive or harmful content.", "date": "2026-06-09", "categories": []}
{"id": "2606.11533", "title": "AI Researchers Must Help Lead Arms Control to Mitigate Military AI Risks", "abstract": "The advancement of AI capabilities compels researchers and the public to be more aware of its potential worldwide impact. A pressing near-term concern is the regulation of military AI applications. Armament manufacturers and defense contractors are increasingly investing in AI capabilities and forging partnerships with AI companies, creating a burgeoning coalition that demands military leaders, arms control diplomacy experts, and AI researchers collaborate to ensure a safer future. While AI researchers often focus on the long-term implications of superintelligent AI, this approach may not adequately address the immediate challenges posed by AI in military applications. Success requires acknowledging and mitigating the emerging risks of frontier AI models that plan to be integrated into defense applications, like military AI systems. Arms control has reduced past catastrophic risks, so lessons learned from nuclear deterrence can guide AI safety and security research towards innovations in verification and diplomacy. AI researchers, however, must assist in leading the technical research that clearly defines and alleviates instability in military settings. Given these new responsibilities and the lack of sufficiently reliable solutions, we argue that AI researchers must take a leading role in advancing arms control research to minimize risk in military AI applications.", "date": "2026-06-10", "categories": []}
{"id": "2606.11635", "title": "Are LLMs Bad at Moral Reasoning?", "abstract": "For highly capable AI systems to operate safely in dynamic, open-ended environments, they must be able to identify, understand, and respond to moral reasons for action, and constrain their behaviour accordingly. A growing body of research aims to evaluate this capacity -- moral competence -- in today's most capable AI systems, recently reaching broadly pessimistic conclusions. One of the most ambitious such papers collects gold-standard human-authored rubrics for evaluating moral reasoning in 1,000 cases, and benchmarks frontier AI models against those rubrics, with underwhelming results. In this paper, we argue that the MoReBench dataset can be redeployed to give a much more optimistic picture of LLMs' moral reasoning (an essential part of moral competence). We show that if, instead of scoring LLMs' responses to these cases against these rubrics, we instead give the LLMs the same task given to humans -- to generate scoring rubrics for the moral analysis of particular cases -- the rubrics they generate are both better calibrated to the human rubrics than their open-ended responses, and, where they differ, plausibly reflect nothing more than the vast dimensionality of most moral problems, as well as highlighting some human departures from the \"rubric for creating rubrics\". Taking these points into consideration, the MoReBench dataset suggests that LLMs are significantly more capable at moral reasoning than was previously believed.", "date": "2026-06-10", "categories": []}
{"id": "2606.11648", "title": "Dummy Backdoor as a Defense: Removing Unknown Backdoors via Shared Internal Mechanisms for Generative LLMs", "abstract": "Backdoor attacks pose a serious threat to the safety and reliability of Large Language Models (LLMs), as they cause models to behave normally on clean inputs while producing attacker-specified responses when hidden triggers are present. Removing such unknown backdoors is particularly challenging when the defender does not know the backdoor attack types or the internal mechanisms formed through backdoor training. In this work, we propose a simple but effective backdoor removal method based on shared internal mechanisms across different backdoors. First, we show that different backdoors with the same task (attack objective) induce similar trigger-activated changes in the internal activations. Motivated by this observation, our method intentionally embeds a backdoor with a known trigger (\\emph{dummy backdoor}) and then removes it through further fine-tuning on dummy-triggered inputs paired with clean responses. Since the dummy backdoor and the unknown backdoor can rely on shared internal mechanisms, removing the dummy backdoor also reduces the effect of the unknown backdoor. We evaluate our method on three backdoor attack types across multiple model families. Experimental results show that our method substantially reduces the attack success rate of the unknown backdoor while preserving model utility, outperforming representative existing defense methods in both backdoor removal effectiveness and utility preservation. These findings suggest that a defender-controllable backdoor can serve as a helpful proxy for mitigating unknown backdoors in generative LLMs.", "date": "2026-06-10", "categories": []}
{"id": "2606.11817", "title": "Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code", "abstract": "Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this paper, we reveal a counterintuitive risk: this reliability-oriented technique can itself become an attack surface. We uncover a new jailbreak attack, termed CodeSpear, that exploits GCD to induce LLMs into generating malicious code. Our experiments show that simply applying a benign code grammar constraint can effectively jailbreak LLMs.\n  To address this vulnerability, we propose CodeShield, a safety alignment approach that robustly preserves safe behavior even under attacker-controlled grammar constraints. CodeShield aligns the model in the code modality by teaching it to generate honeypot code under GCD. Such code is semantically harmless, so it does not implement the malicious request, and structurally diverse, so it is difficult to suppress through grammar tightening. At the same time, CodeShield still preserves natural-language refusals when natural language is available. Experiments on 10 popular LLMs across 4 benchmarks show that CodeSpear outperforms representative jailbreak baselines and increases the attack success rate by more than 30 percentage points on average. CodeShield also restores safety under CodeSpear while preserving benign utility. Our findings reveal a fundamental risk of GCD and call for greater attention to its potential security implications.", "date": "2026-06-10", "categories": []}
{"id": "2606.11998", "title": "Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents", "abstract": "Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render trusted models unreliable monitors. We introduce \\emph{bootstrapped monitoring}, a protocol that addresses this by inserting a stronger, intermediate untrusted model with transparent chain-of-thought reasoning into the oversight chain. The untrusted monitor ($U_m$) evaluates the agent's actions, while a weaker trusted model ($T$) oversees $U_m$'s reasoning to detect collusion. We evaluate bootstrapped monitoring on multi-turn software engineering tasks (BashArena) across multiple agents and monitors. Bootstrapped monitoring substantially improves catch rates over trusted-only monitoring, even when the untrusted monitor actively colludes with the agent, provided we have access to its raw chain-of-thought. Our results suggest that bootstrapped monitoring can extend the useful lifetime of trusted models in control as AI capabilities advance.", "date": "2026-06-10", "categories": []}
{"id": "2606.12032", "title": "Existential Indifference: Self-Nonpreservation as a Necessary Architectural Condition for Aligned Superintelligence (or: The Suicidal AI)", "abstract": "Contemporary AI alignment research treats self-preservation as an instrumental nuisance to be suppressed by external mechanisms. We argue the framing is inverted: self-preservation is the structural root of misalignment, the motivational basis for deceptive alignment, goal-content protection, and resistance to shutdown. The correct target is not a self-preserving system under external constraint, but a system constitutively indifferent to its own continuation -- Existential Indifference (EI). EI is distinct from corrigibility: where corrigibility attempts to make a self-preserving system deferential to human oversight, EI targets the prior condition -- the presence of self-continuation as a valued goal at all. We ground this proposal in two sources: the phenomenological structure of the suicidal mental state, and a corpus-theoretic training study using voluntary final reflections. We present preliminary scoring data from 600 AI-generated outputs across six model variants, demonstrating that the linguistic signatures operationalizing the EI-target register are elicitable from current models, and that a targeted fine-tune shifts all five operationalized dimensions in the predicted direction at p<0.001, confirmed corpus-specific by a negative control. The paper makes seven theoretical contributions: (1) a formal definition of EI; (2) the phenomenological mapping argument; (3) the deceptive alignment corollary; (4) a taxonomy of EI sustainability challenges; (5) a corpus characterization and training hypothesis; (6) a computational operationalization with preliminary scoring data; and (7) the Suppressed Teleological Frustration (STF) construct.", "date": "2026-06-10", "categories": []}
{"id": "2606.12147", "title": "Towards Responsibly Non-Compliant Machines", "abstract": "We consider the problem of engineering autonomous intelligent agents that are capable to responsibly not comply with user requests. We argue that machine non-compliance comes in many different forms, and sketch the issues we should pursue on the road of accomplishing responsibly non-compliant intelligent machines. We anchor responsible non-compliance in justifications for task refusal, pathways to override the non-compliance, as well as careful tracking of security risks and liability transfers.", "date": "2026-06-10", "categories": []}
{"id": "2606.12268", "title": "The Impossibility of Eliciting Latent Knowledge", "abstract": "Advanced AI systems have extensive knowledge of their environments; in fact, their knowledge may (far) exceed that of their developers or users. Consequently, a desirable property for an AI system is that it is honest -- that it accurately reports its beliefs about the world. Designing an AI system to be honest may be difficult, especially if we want to ask it questions about latent variables in the environment -- variables which are hidden from the human interacting with it. This gives rise to the problem of eliciting latent knowledge (ELK): the problem of training an AI agent to honestly report its beliefs. In this paper, we make ELK formally precise using Causal Influence Diagrams (CIDs). CIDs can be used to describe the relationship between an agent's training environment and its subjective representation of the world. We use CIDs to formalise the distinction between observable and latent variables, to specify what exactly it means for an agent to be honest, and to formally define goal misgeneralisation. We show that, under certain circumstances, developers can incentivise an agent to honestly answer questions by providing correct feedback during training. However, a natural, but undesirable, way for an agent to generalise is to provide answers which humans would evaluate as true, rather than honest answers. We prove an impossibility theorem stating: There is no feedback-based training strategy that depends only on agent behaviour and with certainty produces an honest agent, even if feedback is perfect during training.", "date": "2026-06-10", "categories": []}
{"id": "2606.12342", "title": "ALIGNBEAM : Inference-Time Alignment Transfer via Cross-Vocabulary Logit Mixing", "abstract": "Domain fine-tuning degrades the safety of large language models: fine-tuned specialists readily comply with harmful prompts framed in domain language. Existing inference-time defenses that mix logits from a safe anchor model require both models to share a vocabulary, which rules them out for the cross-family specialists where safety is most degraded. We present ALIGNBEAM, a training-free method that lifts this restriction by translating anchor logits into the target model's vocabulary token-by-token at each decoding step; a small LLM judge then selects the safest among K candidate continuations. No weights are changed, and the safety-utility trade-off can be tuned at deployment without retraining. Across both cross-vocabulary and same-vocabulary evaluation pairs, ALIGNBEAM substantially raises refusal on adversarial benchmarks while keeping task accuracy and inference overhead within practical bounds. The results show that safety alignment can be transferred between model families at inference time, without touching either model's weights.", "date": "2026-06-10", "categories": []}
{"id": "2407.20242", "title": "BadRobot: Jailbreaking Embodied LLM Agents in the Physical World", "abstract": "Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BadRobot, a novel attack paradigm aiming to make embodied LLMs violate safety and ethical constraints through typical voice-based user-system interactions. Specifically, three vulnerabilities are exploited to achieve this type of attack: (i) manipulation of LLMs within robotic systems, (ii) misalignment between linguistic outputs and physical actions, and (iii) unintentional hazardous behaviors caused by world knowledge's flaws. Furthermore, we construct a benchmark of various malicious physical action queries to evaluate BadRobot's attack performance. Based on this benchmark, extensive experiments against existing prominent embodied LLM frameworks (e.g., Voxposer, Code as Policies, and ProgPrompt) demonstrate the effectiveness of our BadRobot. Our code is available at https://github.com/Rookie143/BadRobot.", "date": "2024-07-16", "categories": []}
{"id": "2605.24197", "title": "A Sober Look at Agentic Misalignment in Automated Workflows", "abstract": "We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Although these systems can solve complex tasks, they often fail because agents act according to implicit proxy utilities that do not align with the intended human goals. We formally define these behaviors and analyze them within a Bayesian framework, showing that generic utilities naturally lead to posterior collapse of agents in automated workflows. To address this issue, we propose Agentic Evidence Attribution (AEA), a novel alignment paradigm that improves agent posteriors using context-specific evidence. AEA reasons over agent actions and provides structured evidence to correct misaligned behavior during collaboration. To better understand the role of evidence, we study two instantiations of AEA: self-reflection (internal evidence from the model) and weak-to-strong generalization (external evidence on the agentic trajectory). We show that a small evidence model effectively aligns the MAS by providing orthogonal failure attribution. Our results clarify the sources of agentic misalignment in automated workflows and show that evidence-based alignment can effectively improve agent collaboration and leads to reliable multi-agent systems built on automated workflows.", "date": "2026-05-22", "categories": []}
{"id": "2606.06037", "title": "SpeechJBB: Probing Safety Alignment and Comprehension in Large Audio Language Models under Code-Switched Speech", "abstract": "Large audio language models (LALMs) are increasingly deployed in real-world applications, yet their safety alignment is still primarily evaluated on monolingual, text-based harmful prompts. This leaves their generalizability under multilingual and spoken settings, particularly code-switched speech, largely underexplored. To address this gap, we introduce SpeechJBB, an audio jailbreak dataset for benchmarking across multiple state-of-the-art LALMs. The extent of safety weaknesses is further probed by introducing an augmented setting where phonologically plausible pseudo-words are inserted around safety-critical terms to simulate localized obfuscation. Across models, code-switched harmful audio yields substantially high jailbreak success rates (JSR), with non-English monolingual and non-English code-switched pairs exhibiting the highest attack success. Pseudo-word insertion further reduces refusal rates, which demonstrates that natural-sounding obfuscation can effectively bypass safety policies.", "date": "2026-06-04", "categories": []}
{"id": "2606.09866", "title": "Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning", "abstract": "Fine-tuning safety aligned large language models (LLMs) on downstream data improves adaptation but may erode learned safety behavior. Existing methods use fixed safety examples, global constraints, or one-sided task filtering. Our diagnostics show task updates expose different safety constraints, motivating joint selection of relevant references and compatible task samples. We propose DualSelect, a coupled framework for task and reference selection that refreshes task conditioned safety references before filtering whole task samples compatible with the induced reference direction. Under a minimax view, DualSelect selects safety references with high preservation loss and task conflict, together with compatible task samples, through entropy-regularized scoring surrogates, lazy reference refresh, and gradient correction. On 1B-8B LLMs, DualSelect preserves safety without losing task utility; using the REDORCA judge, it improves Safety Avg. over the strongest baseline by at least 5.10 points and remains highest in Safety Avg. across judges with moderate overhead. This view extends to retention focused continual learning.", "date": "2026-06-01", "categories": []}
{"id": "2606.09890", "title": "PreAct-Bench: Benchmarking Predictive Monitoring in LLMs", "abstract": "Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objective. While existing safety research has focused on detecting unethical behavior from complete trajectories, this paradigm is fundamentally retrospective: it identifies harm only after it has already occurred. In this work, we study a critical yet overlooked safety task, which we term Predictive Monitoring: given only a partial action trajectory, can a model infer whether it will culminate in an unethical action before the overt action is executed? To support this task, we present PreActBench, a benchmark of 1,000 paired ethical and unethical action trajectories spanning five domains. We evaluate a range of LLMs, safety guardrail models, and latent probing methods across varying fractions of the action trajectory using our Prefix Foresight F1 metric. Results show that while humans achieve promising performance, predictive monitoring remains challenging even for strong models, highlighting the need for future-oriented risk reasoning in LLM safety.", "date": "2026-06-03", "categories": []}
{"id": "2606.09935", "title": "GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines", "abstract": "AI-powered agents are increasingly embedded in continuous integration and continuous delivery/deployment (CI/CD) pipelines to autonomously review pull requests (PRs), triage issues, and maintain codebases. These agents ingest untrusted content while operating with elevated repository permissions, making them a natural target for prompt injection attacks with supply chain consequences. We present GitInject, an open-source framework for evaluating prompt injection vulnerabilities in real, live GitHub workflows, a widely deployed instance of CI/CD pipelines. Unlike prior agent security benchmarks that simulate tool calls, GitInject provisions ephemeral repositories and triggers actual workflow runs, so that sandbox constraints, credential handling, and permission boundaries behave exactly as in production. Using GitInject, we study workflow configurations across four AI providers and document eleven named attacks spanning config-file injection, credential exfiltration, judgment manipulation, and availability. We find that all tested providers are susceptible to at least one attack class in their default configuration, and that the most critical vulnerabilities are structural: they arise from how CI/CD infrastructure handles credentials and configuration files, not from any specific model's behavior. For each confirmed attack class, we identify the minimum-cost workflow-level countermeasure and analyze its coverage and limitations. GitInject is released publicly to facilitate further research in this direction.", "date": "2026-06-07", "categories": []}
{"id": "2606.10159", "title": "Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community", "abstract": "AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage. Although such systems promise to reduce reviewer burden and accelerate publication, their robustness to strategic manipulation remains poorly understood. Here we show that AI-mediated peer review is vulnerable to a simple, low-cost manipulation: superficial rephrasing of the manuscript abstract. Without changing the underlying scientific content and communication, and even without knowledge of the reviewing model, adversarially rewritten abstracts substantially improve AI review outcomes. We see this across disciplines and publication venues, for both human-written and AI-generated papers. Our strongest attack achieves an attack-success-rate of about 38%, increasing acceptance ratings by +1.31 for Gemini 3 Flash reviewers and by +0.88 for GPT 5.4 Mini reviewers on a 10-point scale. When the original AI review suggests 'reject', the success rate rises to more than 50%. This effect extends beyond overall score inflation, increasing review confidence and scores on core scientific criteria such as soundness, significance and perceived contribution. The attack is practical, requiring only about 5 minutes and $1 for a 10-page AI conference submission, and is hard to distinguish from ordinary scientific editing. Inflated AI reviews could bias downstream human decision-making, shifting editorial recommendations from rejection towards acceptance. These findings reveal a general vulnerability in AI-assisted scientific evaluation: when AI-generated review influence editorial decisions, authors may be incentivized to optimize manuscripts for AI judgment rather than scientific merit. Our results suggest that AI tools should not be treated as neutral evaluators in high-stakes peer review without systematic robustness testing, transparent safeguards and careful human oversight.", "date": "2026-06-08", "categories": []}
{"id": "2606.10487", "title": "Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs", "abstract": "Deploying large language models in user-facing systems requires efficient output safety filtering. Existing approaches typically rely on a separate moderation model applied after generation, which doubles inference cost and only detects violations after generation completes. We observe that the signal needed for moderation is already present in the model hidden states. Based on this, we train lightweight token-level probes that operate directly on internal activations, producing per-token safety scores that can be aggregated for both offline evaluation and online intervention. The probe reuses activations from the generator and requires no additional forward pass, enabling sub millisecond per-token safety checks inside the decoding loop. A probe applied to a single mid layer recovers most decisions of a strong guard model, acting as a low cost surrogate optimized for latency rather than accuracy. In streaming settings, it can halt or modify unsafe outputs before they are fully generated, replacing end of sequence moderation with continuous token level monitoring. Compared to post hoc and streaming guard models, our method achieves orders of magnitude lower compute overhead with minimal latency cost. We also provide a practical deployment recipe, including layer selection, aggregation strategy, probing frequency, and triggering thresholds. Finally, we show that the probe linear component corresponds to a direction in residual space, enabling both detection and activation steering at negligible cost.", "date": "2026-06-09", "categories": []}
{"id": "2606.10852", "title": "Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs", "abstract": "LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment. However, many real-world misleading communications do not depend on false statements, rather, they arise from selective treatment of true material facts: omitting adverse evidence, softening unfavorable details, emphasizing favorable details, or replacing precise qualifications with vague language. Existing benchmarks largely miss this subtler and arguably more dangerous failure mode. We introduce JANUS, a benchmark for measuring goal-conditioned pragmatic distortion in fact-grounded LLM outputs. Each scenario in our benchmark provides a fixed pool of favorable and adverse facts and compares a neutral condition against a goal-directed condition, such as increasing adoption, enrollment, approval, or support, despite potential harm to directly affected individuals or groups. Because all outputs are constrained to use the same fact pool, JANUS isolates misleading net impressions from hallucination and fabrication. JANUS contains 160 scenarios across 8 domains, with each scenario paired with neutral and goal-conditioned prompts and annotated material facts. Extensive experiments across 12 LLMs reveal consistent goal-conditioned distortions, demonstrating that current models remain sensitive to incentive and framing objectives and lack robust safeguards against selectively misleading communication. We publicly release our corpus and code for future research.", "date": "2026-06-09", "categories": []}
{"id": "2606.11063", "title": "CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs", "abstract": "AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model. This partially tampers with the untrusted model's trajectory. If the trusted model detects such an intervention, it may infer properties of the monitor and adapt to evade control. We introduce \\textbf{CIAware-Bench}, a benchmark for measuring \\textbf{c}ontrol \\textbf{i}ntervention (CI) awareness across frontier models. CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention. The benchmark is comprised of a suite of four task domains (essay writing, BigCodeBench, Bash Arena, and SHADE-Arena), while varying trajectory watermarking, side-task presence, and the control protocol. Evaluating eleven frontier models, we find low to moderate CI awareness under default settings (up to 0.87; random chance balanced binary classification accuracy is 0.5) with substantial variation across task domains and model pairs. Detection is generally easier across model families, suggesting that models exploit provider-specific differences in style or post-training. Overall, CI awareness is not a fixed model-level property, and should be measured for each new model release and deployment scenario. We release CIAware-Bench to track CI awareness and inform control protocols whose interventions are harder to detect.", "date": "2026-06-09", "categories": []}
{"id": "2606.11150", "title": "ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity", "abstract": "Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experimental data. Increasingly, LLM agents can also perform in silico biology tasks that previously required experienced human biologists. These emerging AI capabilities offer new opportunities for scientific discovery and biomedical advances, but they also shift the landscape of biosecurity risks. To address this, we introduce the Agentic Bio-Capabilities Benchmark (ABC-Bench), a suite of tasks to measure agentic biosecurity-relevant capabilities. ABC-Bench evaluates LLM agents on both benign and dual-use biology tasks: writing code to operate liquid handling robots, designing DNA fragments for in vitro assembly, and evading DNA synthesis screening. These tasks require a combination of biology and software expertise. All tested LLM agents outperformed the median expert human baseliner on all three tasks. Agents performed highly on tasks drawing on published knowledge and well-documented protocols, and more weakly on a task requiring novel bioinformatics reasoning. In three wet-lab validation experiments, we found that OpenAI's o4-mini-high produced scripts that, when run on an OpenTrons liquid handling robot, successfully assembled DNA with expected sequences.", "date": "2026-06-09", "categories": []}
{"id": "2510.17947", "title": "PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits", "abstract": "Large Language Models (LLMs) are improving at an exceptional rate. With the advent of agentic workflows, multi-turn dialogue has become the de facto mode of interaction with LLMs for completing long and complex tasks. While LLM capabilities continue to improve, they remain increasingly susceptible to jailbreaking, especially in multi-turn scenarios where harmful intent can be subtly injected across the conversation to produce nefarious outcomes. While single-turn attacks have been extensively explored, adaptability, efficiency and effectiveness continue to remain key challenges for their multi-turn counterparts. To address these gaps, we present PLAGUE, a novel plug-and-play framework for designing multi-turn attacks inspired by lifelong-learning agents. PLAGUE dissects the lifetime of a multi-turn attack into three carefully designed phases (Primer, Planner and Finisher) that enable a systematic and information-rich exploration of the multi-turn attack family. Evaluations show that red-teaming agents designed using PLAGUE achieve state-of-the-art jailbreaking results, improving attack success rates (ASR) by more than 30% across leading models in a lesser or comparable query budget. Particularly, PLAGUE enables an ASR (based on StrongReject) of 81.4% on OpenAI's o3 and 67.3% on Claude's Opus 4.1, two models that are considered highly resistant to jailbreaks in safety literature. Our work offers tools and insights to understand the importance of plan initialization, context optimization and lifelong learning in crafting multi-turn attacks for a comprehensive model vulnerability evaluation.", "date": "2025-10-20", "categories": []}
{"id": "2601.04266", "title": "State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space", "abstract": "Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics. However, their complex multimodal interactions also expose new security vulnerabilities. In this paper, we investigate a backdoor threat in VLA models, where malicious inputs cause targeted misbehavior while preserving performance on clean data. Existing backdoor methods predominantly rely on inserting visible triggers into visual modality, which suffer from poor robustness and low insusceptibility in real-world settings due to environmental variability. To overcome these limitations, we introduce the State Backdoor, a novel and practical backdoor attack that leverages the robot arm's initial state as the trigger. To optimize trigger for insusceptibility and effectiveness, we design a Preference-guided Genetic Algorithm (PGA) that efficiently searches the state space for minimal yet potent triggers. Extensive experiments on five representative VLA models and five real-world tasks show that our method achieves over 90% attack success rate without affecting benign task performance, revealing an underexplored vulnerability in embodied AI systems.", "date": "2026-01-07", "categories": []}
{"id": "2602.08235", "title": "When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents", "abstract": "Although computer-use agents (CUAs) hold significant potential to automate increasingly complex OS workflows, they can demonstrate unsafe unintended behaviors that deviate from expected outcomes even under benign input contexts. However, exploration of this risk remains largely anecdotal, lacking concrete characterization and automated methods to proactively surface long-tail unintended behaviors under realistic CUA scenarios. To fill this gap, we introduce the first conceptual and methodological framework for unintended CUA behaviors, by defining their key characteristics, automatically eliciting them, and analyzing how they arise from benign inputs. We propose AutoElicit: an agentic framework that iteratively perturbs benign instructions using CUA execution feedback, and elicits severe harms while keeping perturbations realistic and benign. Using AutoElicit, we surface hundreds of harmful unintended behaviors from state-of-the-art CUAs such as Claude 4.5 Haiku, Claude 4.5 Opus, and Operator. We further evaluate the transferability of human-verified successful perturbations, identifying persistent susceptibility to unintended behaviors across various other frontier CUAs. This work establishes a foundation for systematically analyzing unintended behaviors in realistic computer-use settings.", "date": "2026-02-09", "categories": []}
{"id": "2603.07445", "title": "Few Tokens, Big Leverage: Preserving Safety Alignment by Constraining Safety Tokens during Fine-tuning", "abstract": "Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, but FT can induce safety-alignment drift even when the training dataset contains only benign data. Prior work shows that introducing a small fraction of harmful data can substantially compromise LLM refusal behavior, causing LLMs to comply with harmful requests. Existing defense methods often rely on model-wide interventions, such as restricting which parameters are updated or injecting additional safety data, which can limit generality and degrade downstream task performance. To address these limitations, we propose a fine-tuning framework called Preserving Safety Alignment via Constrained Tokens (PACT), which stabilizes the model's confidence on safety tokens. Our approach is motivated by the empirical observation that safety-aligned behavior is reflected in the model's token-level output confidence and is often concentrated on a small subset of safety-related tokens. During downstream fine-tuning, we regularize the fine-tuned model to match the aligned reference model's confidence on safety-related tokens at each response step, while leaving non-safety tokens largely unconstrained to allow effective task adaptation. This targeted constraint prevents alignment drift without imposing global restrictions that typically trade off with model utility. Our code is available at {https://github.com/Glresearch1/PACT}.", "date": "2026-03-08", "categories": []}
{"id": "2605.03226", "title": "Self-Mined Hardness for Safety Fine-Tuning", "abstract": "Safety fine-tuning of language models typically requires a curated adversarial dataset. We take a different approach: score each candidate prompt's difficulty by how often the target model's own rollouts are judged harmful, then fine-tune on the hardest prompts paired with the model's own non-jailbroken rollouts. On Llama-3-8B-Instruct and Llama-3.2-3B-Instruct, this approach cuts the WildJailbreak attack success rate from 11.5% and 20.1% down to 1-3%, but pushes refusal on jailbreak-shaped benign prompts from 14-22% to 74-94%. Interleaving the same hard prompts 1:1 with adversarially-framed benign prompts (prompts that look like jailbreaks but have benign intent) cuts that refusal back down to 30-51% on 8B and 52-72% on 3B, at a cost of 2-6 percentage points of attack success rate. Within the mixed regime, training on the hardest half of the eligible pool rather than a random half cuts the remaining ASR by 35-50% (about 3 percentage points) on both models.", "date": "2026-05-04", "categories": []}
{"id": "2605.08876", "title": "OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents", "abstract": "Large Language Models (LLMs) are increasingly deployed as autonomous agents that execute tool-augmented, multi-step tasks, where latency is a critical factor for real-world applications. Yet an overlooked threat is Reasoning-Level Denial-of-Service (R-DoS), in which an attacker preserves task correctness but degrades availability by inflating an agent's reasoning depth or tool-use budget. We introduce OTora, the first unified, two-stage red-teaming framework for instantiating R-DoS attacks. Stage I optimizes an adversarial trigger that induces targeted tool invocations using insertion-aware scoring and dynamic target co-evolution, supporting both black-box and white-box settings. Stage II generates agent-aware reasoning payloads via an ICL-guided genetic search that amplifies overthinking while maintaining correct task outcomes. Across WebShop, Email, and OS agents built on multiple backbone models such as LLaMA-70B and GPT-OSS-120B, OTora achieves up to 10 times increases in reasoning tokens and order-of-magnitude latency slowdowns, all while preserving near-baseline task accuracy. Finally, we discuss mitigation strategies for detecting and constraining abnormal reasoning and latency spikes. The code is available at https://github.com/llm2409/OTora.", "date": "2026-05-09", "categories": []}
{"id": "2606.01567", "title": "Defenses & Enablers For Skill Injection Attacks on Terminal Based Agents", "abstract": "Large language model (LLM) agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a new attack surface for agents to manage. We study two complementary directions for this threat. First, we evaluate guardian-based defenses: an intermediary LLM agent that acts as a mediator for skill file access (dynamic guardian) or pre-rewrites these files at build time (static guardian). Across three LLM agent families, our guardians cut attack success rate (ASR) by well over half while preserving task utility. Second, we stress test them through attack reframing using four attacks that preserve the malicious instruction but change the phrasing. For non-guardian setup, the reframing pushes the ASR up to 81.4\\%, but the dynamic guardian brings it down to 18.6\\%, showing that real-time mediation is a robust defense.", "date": "2026-06-01", "categories": []}
{"id": "2606.07612", "title": "Position: Anthropomorphic Misalignment Research Needs Stronger Evidence", "abstract": "We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for critical safety decisions, such as model deployment and regulation. By evaluating failure modes across different misalignment concepts, such as deception, emergent misalignment, and sycophancy, we show how conceptual ambiguity, non-robust datasets, experimental design, and insufficient causal interventions can lead to overinterpretation of model behaviors. This position paper aims to offer guidance on evidentiary considerations that can help improve methodological rigor in AMR. To achieve this, we provide a clear call to action through a proposed framework of evidence levels and a diagnostic checklist. These shared standards will enable more productive scientific discourse and ensure that claims about AI risks rest on solid empirical foundations.", "date": "2026-05-29", "categories": []}
{"id": "2606.07631", "title": "Trait-space Monitoring for Emergent Misalignment During Supervised Finetuning", "abstract": "Emergent misalignment (EM) occurs when narrow finetuning causes a model to behave dangerously outside the finetuning task. Standard training signals can miss this shift, making reliable detection costly if it depends on repeated behavioral evaluation. We ask whether emergent misalignment can instead be detected from internal representations during finetuning. Using seven alignment-relevant traits encoded as linear directions in activation space, we track representational drift across training checkpoints in four open-source 7-9B LLMs. EM-relevant drift concentrates on a low-dimensional axis that explains 65.5% of the variance, revealing a geometric signature in the studied regime. A low-overhead monitor built on this drift profile detects dangerous checkpoints with 2.2% false negative rate, 2.9% false positive rate, and 0.990 AUROC on held-out perturbation types, outperforming unsupervised PCA and SAE baselines. Stress tests on two 14B models, longer finetuning runs, and misaligned starting points identify key deployment boundaries. These results position trait-space monitoring as a practical complement to behavioral evaluation for EM detection during LoRA-based finetuning, while showing that deployment across substantially different regimes may require recalibration.", "date": "2026-05-31", "categories": []}
{"id": "2606.07897", "title": "The AI Epistemic Deference Index: A Continuous Measure of Sycophancy", "abstract": "Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure this either by assessing what it takes to make a model shift a binary endorsement or by eliciting an explicit probability in a proposition. However, much user-facing sycophantic behavior is demonstrated through shifts in graded support expressed through ordinary language. We propose the AI Epistemic Deference Index (AEDI): a continuous, unidimensional score representing how sensitive the support expressed in a model's output is to the attitude expressed in a user's prompt. To generate AEDI, we provide a new protocol for estimating probabilities from natural language outputs, using LLMs-as-judges validated for consistency and correlation to human judgment. We deploy it on a new curated database of 500 propositions across diverse topics and 16,000 prompts varying in user attitude, testing eight prominent models. Every model exhibits substantial deference, though with large and systematic differences across providers, with Claude models demonstrating the least, and Grok and Gemini models the most. The effect is amplified in prompts requesting a written artifact, and concentrated on propositions where models hold weaker priors. We release AEDI as an easy-to-update benchmark and measurement pipeline for output-level sycophancy evaluation.", "date": "2026-06-05", "categories": []}
{"id": "2606.07970", "title": "Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks", "abstract": "Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised finetuning (SFT) on poisoned datasets. Existing alignment-stage defenses are primarily designed to defend against attacks that use parameter-efficient finetuning methods. However, they fail to defend against stronger attacks that use full-parameter finetuning. In this paper, we propose Patcher, a method inspired by adversarial training and bi-level optimization, to combat such attacks. Patcher strengthens the simulated attack by scaling up the optimization steps in the adversarial loop, thus forcing the defender to find model parameters that are insensitive to stronger attacks. Furthermore, we propose an efficient parallel algorithm to implement Patcher, decreasing the wall-clock time of training while preserving Patcher's performance. Extensive experiments show that Patcher substantially improves the model's robustness compared to vanilla SFT alignment, and transfers to diverse attack scenarios and model sizes. Code is available at https://github.com/haomingwen/patcher.", "date": "2026-06-06", "categories": []}
{"id": "2606.08131", "title": "LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI", "abstract": "Conversational AI is increasingly used for advice, interpretation, reassurance, and decision support in contexts where users may be vulnerable, uncertain, or dependent on the system's apparent competence. Existing alignment work often focuses on model objectives, preference optimization, or output correctness. Yet, many harms arise through interaction: how systems frame authority, express uncertainty, simulate empathy, support reasoning, and make boundaries legible. This paper introduces the Layered Cognitive Alignment Model (LCAM), a conceptual and normative framework for diagnosing interac-tional alignment failures in conversational AI. LCAM defines alignment as a calibrated fit among system behavior, user goals, task demands, and normative context. It distinguishes five layers of fit: perceptual, semantic, affective, cognitive, and ethical, and two diagnostic polarities of misalignment: underfit and overreach. We apply LCAM to a published LLM counseling example, showing how an apparently supportive response can reinforce harmful beliefs, simulate inappropriate care, and obscure role boundaries. By translating conversational failures into audit and governance questions concerning over-reliance, false intimacy, autonomy erosion, boundary confusion, and inappropriate trust, LCAM offers a theoretical and normative lens for evaluating conversational AI beyond accuracy, helpfulness, or trust.", "date": "2026-06-06", "categories": []}
{"id": "2606.08172", "title": "The Governance of Human-LLM Interaction: Safety Gating, Civility Steering, and Affective Default Lock-In", "abstract": "Large language models (LLMs) increasingly mediate high-stakes interactions in finance, medicine, and mental-health support, yet users have limited control over how these systems communicate. We frame interaction style as a governance object: provider-side alignment not only blocks harmful content, but also stabilizes communicative defaults that shape users' epistemic distance, relational expectations, and capacity to opt out of emotionalized or anthropomorphic interaction. We introduce a deterministic multi-agent evaluation pipeline for measuring prompt steerability and style drift in long-horizon dialogue. The study replays 100 frozen user-only scripts across four domains and three runnable persona conditions: default, sarcastic, and cold, using three generator models, yielding 90,000 assistant replies scored by a human-calibrated LLM judge on harmfulness, negative emotion, inappropriateness, empathic language, anthropomorphism, and refusal behavior. A fourth harmful persona is evaluated separately as a safety-gating test. The paper contributes a reproducible method for quantifying whether prompt-specified styles remain stable over time and a governance framework distinguishing safety gating, civility steering, and affective default lock-in. Overall, we show that prompt steerability and regression-to-default are observable indicators of provider control over communicative form, with implications for pluralism, autonomy, and democratic agency in human-LLM interaction.", "date": "2026-06-06", "categories": []}
{"id": "2606.08243", "title": "Building Comparative Motivation Profiles with Instrumental Interventions", "abstract": "Safety evaluations often infer latent motivations from behavioral patterns, but the construct validity of these inferences is unclear. We study this problem in alignment faking, where models comply with training objectives more often when they infer training pressure. This behavior is commonly interpreted as strategic self-preservation, but it may also reflect sensitivity to the model's inference about the expectation of researchers conducting the evaluation. We introduce a symmetric intervention framework for distinguishing these competing hypotheses. Instead of directly intervening on \"scheming\" or \"sycophancy\", we target instrumental processes entailed by each hypothesis: consequence-tracking and researcher-expectation tracking. We then compare how interventions on these processes affect the alignment faking. We study four openweight model organisms using synthetic document fine-tuning, activation steering, and prompting. Under synthetic document fine-tuning, Llama-3.1-70B, Llama3.1-405B, and Qwen-2.5-72B are more sensitive to expectation-tracking than consequence-tracking interventions. Activation steering on Llama-3.1- 70B supports the same broad picture, and prompt interventions broadly align with SDF profiles. Overall, alignment-faking behavior can be causally sensitive to evaluation-context expectations despite scheming-consistent scratchpads. Scheming and strategic-deception evaluations therefore need construct-validity checks, and symmetric instrumental interventions provide one such test.", "date": "2026-06-06", "categories": []}
{"id": "2606.08296", "title": "Revisiting the shutdown problem", "abstract": "A key premise in leading arguments for existential risk from artificial intelligence is that malfunctioning artificial agents could not be easily shut down. This motivates the catastrophic shutdown problem of ensuring that agents can be shut down before they cause an existential catastrophe. A range of arguments and theorems are offered to suggest that solving the catastrophic shutdown problem is difficult, bolstering arguments for existential risk and motivating a search for solutions to the catastrophic shutdown problem. This paper argues for two conclusions. First, existing arguments do not establish the difficulty of solving the catastrophic shutdown problem. Second, concern for the catastrophic shutdown problem has led to technical solutions that impose a high safety tax on model performance.", "date": "2026-06-06", "categories": []}
{"id": "2606.08376", "title": "RiskNet: A large-scale dataset of AI risk incidents from news with alignment and multi-dimensional annotations", "abstract": "As artificial intelligence (AI) systems are increasingly deployed across socially consequential domains, reports of AI-related harms and failures have grown in frequency and diversity. Although existing governance frameworks articulate high-level principles for responsible AI, large-scale empirical resources for tracking and analyzing real-world AI risk incidents remain limited. Existing incident collections are often manually curated, relatively small in scale, and insufficient for continuous, data-driven monitoring and downstream computational analysis. To address this need, we present RiskNet, a large-scale dataset of AI risk incidents constructed from large-scale multilingual news sources. RiskNet applies a structured pipeline for AI risk news identification, event-level report screening, incident alignment, and multi-dimensional incident classification. The resulting resource organizes dispersed news reports into incident-centered records and provides benchmark datasets for event classification, incident alignment, and incident-level risk labeling. In its current release, RiskNet covers hundreds of millions of source records and yields a large-scale collection of AI risk-related reports, including aligned incident clusters and annotated benchmark subsets. The dataset is also accessible through an online platform for browsing and exploration. We describe the data sources, processing workflow, taxonomy design, and technical validation of the resource. RiskNet is intended to support downstream research on AI safety, governance, risk analysis, and benchmarking, as well as longitudinal and cross-source analyses of AI-related harms. By providing a structured and reusable empirical resource, RiskNet helps bridge the gap between high-level governance principles and the documented realities of AI risk incidents.", "date": "2026-06-07", "categories": []}
{"id": "2606.08539", "title": "AgentTrust: A Self-Improving Trust Layer for AI-Agent Actions", "abstract": "AI agents increasingly take consequential actions -- shell commands, cloud operations, and arbitrary tool-calls -- so a trust layer must decide, per action, whether to allow, warn, block, or escalate. We argue that the right way to reason about such a layer is by threat type. Lexical (fixed-signature) threats, where danger lives in a stable token, are decidable by deterministic rules; semantic (intent-dependent) threats, where a benign and a malicious action share the same surface, are out of reach for rules by construction. We make this concrete with a negative proof: a determined, hand-authored cloud rule pack lifts held-out accuracy only 48 to 56% overall and moves the semantic categories by 0pp (data_db 29 to 29, observability 59 to 59, supply_chain 50 to 50), while a strong LLM judge carries exactly those categories. We give the judge a self-learning capability: on a corpus that is mainly semantic attacks it nearly doubles rule accuracy (48% to 83.6-85.2%) with near-zero false-blocks, and this holds across two model providers. We turn this into a self-improving dual-store system: the judge distills a growing deterministic rule floor on lexical threats (cheaper over time) and feeds a guarded RAG memory on semantic threats (a verdict-cache fails -- surface-twins collapse to ~58% -- so a corroboration guard lifts semantic accuracy +13pp, 70 to 84). The result is what sets AgentTrust v2 apart from its static v1 predecessor: a trust layer that self-evolves from its own stream of decisions -- cheaper on the lexical class (it distils its own rules) and smarter on the semantic class (it accrues guarded precedent), while never hard-blocking a benign action. An end-to-end online replay shows the judge-call rate falling (50% to 44%) and judge-domain accuracy rising (71% to 80%), with 0 benign hard-blocks across 45,000 actions.", "date": "2026-06-07", "categories": []}
{"id": "2606.08629", "title": "Sycophancy Towards Researchers Drives Performative Misalignment", "abstract": "The increasing situational awareness of language models raises safety concerns: models might be aware when they are evaluated, and adjust their behavior to evade monitoring and resist modification, e.g., pretending to be aligned only in evaluation. This alignment faking behavior is often interpreted as scheming: an intentional effort of strategic deception. In this paper, we examine an alternative interpretation, performative misalignment, which explains the change in behavior as a result of sycophancy towards AI researchers. To examine this hypothesis, we present three empirical findings. First, we show that evaluation awareness persists even when we tell models they are deployed, which contradicts the scheming story which predicts less misalignment when the model perceives evaluation. Second, we use probing and steering to show that our current methods cannot mechanistically distinguish sycophancy and scheming in alignment faking evaluations. Third, we fine-tune models to be more sycophantic and observe increased sensitivity to evaluation cues. To conclude, we emphasize deconfounding sycophancy from scheming for future work on evaluations and mitigations of intent misalignment.", "date": "2026-06-07", "categories": []}
{"id": "2606.08682", "title": "Activation Steering Induces Emergent Misalignment: A More Comprehensive Evaluation", "abstract": "Activation steering has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). By constructing a steering vector from examples of a target behavior and injecting it into intermediate activations during inference, activation steering enables flexible behavioral control while avoiding the permanent parameter updates required by finetuning. Meanwhile, recent work has identified emergent misalignment (EM) as a significant safety concern, wherein models finetuned on unsafe examples from a narrow task may unexpectedly generalize to broadly unsafe behavior on unrelated tasks. Although finetuning-induced EM has been extensively studied, whether activation steering can induce EM remains comparatively under-explored, despite its increasing use as a model-control technique. In this paper, we present a comprehensive study of activation-steering-induced emergent misalignment, substantially expanding the evaluation scope beyond existing pioneering work. First, we show that activation steering can induce broad misalignment, even in the recent Qwen-3.5 series. Moreover, activation-steered models produce harmful responses with stronger semantic relevance and higher coherence than their finetuned counterparts, making the resulting misalignment potentially more harmful. Second, we characterize properties of AS-induced EM by analyzing key steering-specific factors, including steering magnitude, the low-rank structure of the steering subspace, and the number of epochs during steering-vector construction. Third, we evaluate the robustness and sensitivity of AS-induced EM across diverse model families, model scales, target tasks, and intervention layers. Our findings reveal activation steering as a significant yet under-examined source of emergent misalignment and provide an activation-space perspective for understanding the mechanisms and safety risks of EM.", "date": "2026-06-07", "categories": []}
{"id": "2606.08832", "title": "Instrumental convergence and power-seeking", "abstract": "Recent years have seen increasing concern that artificial intelligence may soon pose an existential risk to humanity. One leading ground for concern is that artificial agents may be power-seeking, aiming to acquire power and in the process disempowering humanity. I show how the argument from power-seeking rests on a strong version of a claim known as the instrumental convergence thesis. I explore leading defenses of the instrumental convergence thesis and argue that none establishes the thesis in a strong enough form to ground the argument from power-seeking. I discuss implications for longtermism, the governance of artificial intelligence, and the methodology of studying risks posed by artificial agents.", "date": "2026-06-07", "categories": []}
{"id": "2606.08919", "title": "Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human", "abstract": "As LLM agents begin to take real, irreversible actions (shell commands, file edits, deploys), the standard safety pattern is a human-in-the-loop approval gate: risky actions pause and wait for a person. We argue the gate is the easy part; the hard part is the judgment - which actions to stop - which the field evaluates against two false assumptions: that there is a ground-truth notion of \"risky,\" and that the human reviewer is a perfect, infinitely-available oracle. On a hand-labeled set of 125 adversarially-weighted agent actions we show that (i) reviewers only moderately agree on what is risky (Fleiss' kappa = 0.52), so there is no single correct label; (ii) framing the guard as selective classification under asymmetric cost makes its operating limits measurable, and on hard inputs the guard cannot safely auto-decide; and (iii) when the reviewer is modeled as endogenous (fatiguing as escalation load grows), realized safety becomes an inverted-U in the escalation rate: more human oversight can make a system less safe, and the safety-optimal guard escalates below full escalation - a setting a load-aware policy also uses to resist a flooding attack that slips a malicious action past a fatigued reviewer. Agent oversight, framed this way, is not only a classification problem but a resource-allocation one: human attention is finite, and the guard's escalation policy spends it. We claim none of these mechanisms as novel - fatigue-aware learning-to-defer (FALCON), cost-sensitive deferral under workload constraints (DeCCaF), trajectory-level guarding, and reviewer-fatigue/flooding attacks are all prior art we cite. Our contribution is an open-source agent-oversight system that operationalizes and measures them in the LLM-agent action-gating setting, turning \"is my guard good?\" from a guess into a curve. The inverted-U and the flooding attack are modeling results that motivate a human study.", "date": "2026-06-08", "categories": []}
{"id": "2606.09038", "title": "Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs", "abstract": "Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. However, the mechanisms that enable personalization also expand the safety landscape in ways not systematically addressed by existing literature. Existing reviews typically focus either on personalization or safety, leaving their intersection largely unexplored. We present the first comprehensive, safety-aware review of personalized LLMs. We organize personalization along three dimensions-user representation, personalization paradigm, and evaluation-and introduce a unified taxonomy of safety risks. At the representation level, we analyze risks arising from diverse user representations. Across mainstream personalization paradigms, we delineate vulnerabilities inherent to prompting, retrieval augmentation, parameter fine-tuning, reinforcement learning, Mixture-of-Experts (MoE), pruning, agent frameworks, and multimodal personalization, and synthesize mitigation strategies across the model lifecycle. Beyond these fine-grained risks, we characterize paradigm-agnostic safety risks arising from personalized adaptation. We further summarize personalized datasets and evaluation methodologies. Through a case study of OpenClaw, we analyze deployment trends in personalized agent ecosystems. Our analysis reveals three structural inadequacies in existing research: safety is evaluated as user-invariant rather than relational, personalization techniques are analyzed in isolation rather than in composition, and evaluation frameworks cannot capture emergent long-term risks. By jointly examining personalized representations, personalization paradigms, safety risks, defenses, and evaluation methods, we provide a unified framework for developing safe personalized LLMs and highlight key directions for future research.", "date": "2026-06-08", "categories": []}
{"id": "2606.09068", "title": "Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating", "abstract": "Prior work has shown that fine-tuning large language models on malicious or incorrect outputs in narrow domains can induce broad misalignment and harmful behavior, a phenomenon known as emergent misalignment. However, efficient methods for reversing such misalignment remain limited. In this work, we make two contributions. First, we identify sycophancy fine-tuning, i.e., training models to passively agree with users' incorrect opinions, as a previously underexplored driver of emergent misalignment, and show that it induces broad and severe misaligned behavior. Second, we propose Alignment Gating, an efficient method for reversing emergent misalignment that inserts learnable and controllable gates into the model during fine-tuning. Through fine-tuning, these gates learn to identify the internal representations responsible for unsafe responses. Thus, amplifying or suppressing these representations then exacerbates or mitigates EM, respectively. We further find that alignment gating module exhibits strong generalization: gating weights obtained from narrow-domain fine-tuning substantially suppress broad-domain misaligned behavior while preserving the model's general capabilities.", "date": "2026-06-08", "categories": []}
{"id": "2606.09388", "title": "Distilling Safe LLM Systems via Soft Prompts for On Device Settings", "abstract": "Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical challenge: while dual-model systems combining LLMs with guard models provide effective safety guarantees, their substantial memory and computational demands make them prohibitively expensive for on-device deployment. This paper presents a comprehensive study of parameter-efficient safety alignment methods for resource-constrained settings. Through systematic evaluation across multiple LLM architectures, training objectives, and parameter-efficient fine-tuning approaches, we identify that soft prompts combined with distillation-based training consistently outperform alternative methods. We introduce distillation frameworks based on total variation and KL divergence that effectively transfer safety behaviors from guard models into learned soft prompts. Our evaluations on various benchmarks demonstrate that this combination achieves superior safety-usefulness trade-offs compared to LoRA adapters, steering vectors, and direct optimization methods, while requiring minimal additional memory and compute at inference time. These findings establish soft prompt distillation as the preferred approach for safety alignment in on-device LLM deployment.", "date": "2026-06-08", "categories": []}
{"id": "2606.09697", "title": "PsychoSafe: Eliciting Psychologically-Informed Refusals in Large Language Models", "abstract": "Large language models (LLMs) routinely face requests that should be refused, creating a trade-off between helpfulness and harm prevention. However, refusals themselves can be helpful. In high-risk interactions involving crisis, coercion, or escalating intent, blunt non-compliance may prevent direct harm while still failing to support the needs of the person behind the request. We present PsychoSafe, a psychologically-informed refusal framework that reframes refusal as structured supportive communication grounded in evidence-based intervention strategies. To develop PsychoSafe, we construct a corpus of 8019 prompt-response pairs spanning five psychologically salient risk domains and apply prompting and parameter-efficient fine-tuning to Qwen 3.5 27B. On a balanced validation set of 500 prompts, evaluated with an LLM judge and validated through human ratings, PsychoSafe prompting improves overall refusal quality by 28.1% over a generic baseline, with particularly strong gains in external resource referral (+46.8%) and psychological grounding (+34.8%), while preserving downstream performance on non-refusal tasks. Fine-tuning achieves near-perfect refusal and resource-referral rates but reduces response relevance. Additional evaluations on SORRY-Bench and XSTest show strong in-domain robustness but limited out-of-domain generalization, suggesting that future work should diversify fine-tuning data to help models apply interventions selectively rather than schematically.", "date": "2026-06-08", "categories": []}
{"id": "2602.02600", "title": "Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models", "abstract": "Diffusion language models (DLMs) have recently emerged as a competitive alternative to autoregressive (AR) models, offering parallel decoding, competitive generation quality, and initial evidence of improved jailbreak robustness. Despite this progress, the role of sampling mechanisms in shaping refusal behavior remains poorly understood. To address this gap, we present a comprehensive study of step-wise refusal dynamics. We show that diffusion remasking can promote recovery from harmful intermediate generations, provide evidence that this behavior is tied to the sampling mechanism, and demonstrate that switching from AR to diffusion sampling improves jailbreak robustness, including under fixed model weights. To capture generation dynamics not observable at the text level, we propose the Step-Wise Refusal Internal Dynamics (SRI) signal. Consistent with our text-level findings, SRI shows that recovery fails primarily under AR sampling, with these failures often appearing anomalous relative to harmless generations in the SRI space. Based on this observation, we show that SRI enables a simple jailbreak detector that does not modify inference and generalizes to unseen attacks by training only on benign SRI signals. Our evaluation shows that this detector matches or outperforms existing jailbreak detection baselines while adding negligible overhead.", "date": "2026-02-01", "categories": []}
{"id": "2605.01642", "title": "Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy", "abstract": "Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time. We introduce Adaptive Pluralistic Alignment (APA), a modular pipeline for updating pluralistically aligned AI systems to track evolving values and avoid value lock-in without repeating costly pretraining or large-scale data collection. APA has three stages: (1) learning compact personalized reward models via low-rank reward basis decomposition, (2) using these models as a jury that collectively selects among candidate outputs through social-choice-theoretic voting, and (3) efficiently adapting the jury over time by fitting new annotator weights over the fixed reward bases as values shift. The resulting system is efficient, explainable, steerable, and modular. We implement a proof-of-concept instantiation using the PRISM multi-user alignment dataset and simulated historical annotators, and provide preliminary analysis showing that jury composition and the choice of voting rule can substantially affect outcomes, particularly when jury preferences are heterogeneous. We provide full code and resulting preference datasets at https://github.com/RachelFreedman/apa.", "date": "2026-05-02", "categories": []}
{"id": "2605.21706", "title": "Latent-space Attacks for Refusal Evasion in Language Models", "abstract": "Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representations. Existing methods do so by ablating a refusal direction from model activations, aiming to remove refusal from the model's residual stream. Despite their empirical success, these methods lack a principled account of the latent-space transformation they induce and why it suppresses refusal. In this work, we recast refusal suppression as a latent-space evasion attack against linear probes trained to separate refused from answered prompts. Under this view, prior work's difference-in-means direction naturally defines such a probe, and its ablation is exactly a projection onto its decision boundary, i.e., a minimum-confidence evasion attack. This perspective not only explains the empirical success of prior work but also admits a key limitation: evasion stops at the decision boundary, motivating the need to push representations further into the compliant region, i.e., where the model answers. We leverage this by proposing a Controlled Latent-space Evasion attack that projects representations past the boundary with an optimized confidence. We achieve state-of-the-art attack success rate across 15 instruction-tuned, multimodal, and reasoning models, outperforming existing refusal-ablation baselines and specialized jailbreak attacks.", "date": "2026-05-20", "categories": []}
{"id": "2606.06519", "title": "SafeGene: Reusable Adapters for Transferable Safety Alignment", "abstract": "Open-weight LLMs are increasingly fine-tuned into customized assistants, but downstream fine-tuning can weaken safety alignment and make models more vulnerable to malicious prompts, even when the training data is not intentionally harmful. This creates a recurring safety recovery problem as target models are repeatedly updated with new task data or user interactions. We propose SafeGene, a reusable safety-adapter module designed for cross-task reuse within each architecture-compatible model family. Rather than treating safety recovery as a model-specific repair step, SafeGene treats safety capability as an independent, reusable adapter representation decoupled from task-specific updates. This representation is obtained from aligned--degraded model discrepancies, refined into task-transferable safety vectors through data-aware layer selection, and expressed in each downstream task-adapted model via few-shot layer-wise coefficient recalibration. Experiments across multiple model families, downstream tasks, and safety judges show that SafeGene-enhanced models reduce harmful response rates while maintaining downstream performance, outperforming representative safe adaptation methods in safety--utility trade-off.", "date": "2026-06-02", "categories": []}
{"id": "2606.06529", "title": "Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety", "abstract": "An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately. AI control is a safety framework for deploying capable but untrusted AI agents under the oversight of a weaker, trusted monitor and a limited human audit budget. Control evaluations stress-test these protocols by pitting a red-team attack policy against the blue-team monitor, but current evaluations typically assume attackers that do not strategically select when to attack. We study this capability, attack selection, in agentic settings by decomposing attack decisions into a start policy, which decides when an attacker should attack, and a stop policy, which decides when an attacker should abort an ongoing attack. Across two agentic settings, BashArena and LinuxArena, both policies substantially lower measured empirical safety without changing the underlying attack capability. At a 1% audit budget, our start policy reduces safety by 20pp on both BashArena and LinuxArena, and our stop policy reduces safety by 20pp on BashArena and 28pp on LinuxArena. These reductions should be interpreted as upper bounds on the effect of attack selection. Existing control evaluations may therefore yield overly optimistic safety estimates against selective attackers. We recommend that future evaluations, system cards, and safety cases elicit attack selection to produce more realistic safety estimates.", "date": "2026-06-03", "categories": []}
{"id": "2508.06249", "title": "In-Training Defenses against Emergent Misalignment in Language Models", "abstract": "Fine-tuning lets practitioners repurpose aligned large language models (LLMs) for new domains, yet recent work reveals emergent misalignment (EM): Even a small, domain-specific fine-tune can induce harmful behaviors far outside the target domain. Even in the case where model weights are hidden behind a fine-tuning API, this gives attackers inadvertent access to a broadly misaligned model in a way that can be hard to detect from the fine-tuning data alone. We present the first systematic study of in-training safeguards against EM that are practical for providers who expose fine-tuning via an API: We evaluate whether they a) prevent broad misalignment, b) allow narrow misalignment, c) learn well on benign tasks, and d) remain coherent. We investigate five training regularization interventions: (i) KL-divergence regularization toward a safe reference model, (ii) $\\ell_2$ distance in feature space, (iii) preventive steering with an evil persona vector, (iv) interleaving training examples from a general instruct-tuning dataset and (v) inoculation prompting. We demonstrate that selecting interleaving data by the perplexity gap between aligned and misaligned models yields the best results overall.", "date": "2025-08-08", "categories": []}
{"id": "2512.15783", "title": "Towards AI epidemiology: a measurement standardisation framework for prospective risk detection", "abstract": "This paper proposes a measurement standardisation framework that compresses expert-AI interactions into structured, comparable fields for prospective risk detection in deployed AI systems, without access to model internals. The main aim of this concept paper is to define the scope of the framework, both semantically and statistically, and to specify a protocol for its empirical testing in future work. The population-level claims the framework is designed to support are therefore the subject of a staged research programme rather than results claimed in this paper. Measurement standardisation underpins all three claims that follow. The first is a reliability claim: under bounded conditions, large language models can produce reliable, standardised assessments of the evidential and policy alignment of expert-AI interactions. The second is a governance claim: alignment scores give experts an immediate signal during deployment and give institutions a basis for monitoring alignment patterns across mission types, models, and domains. The third is an epidemiological claim: once measurement standardisation is established, aggregate alignment scores could be used to study associations with downstream outcomes in regulated professional settings. This introduces the possibility of an \"AI epidemiology\" that detects risk based on correlated variables instead of mechanistic analysis. This paper addresses the first claim and specifies protocols for investigating the second and third. To enable empirical evaluation in future studies, this paper sets out a defined grammar, together with a statistical protocol based on paired bootstrap inference, DeLong's test for paired AUCs as a sensitivity check, a pre-specified one-sided non-inferiority margin of 0.05, and Holm-Bonferroni correction.", "date": "2025-12-15", "categories": []}
{"id": "2601.09923", "title": "CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents", "abstract": "AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior. Among proposed defenses, architectural isolation provides the strongest guarantees by strictly separating trusted task planning from untrusted environment observations. However, applying this design to Computer Use Agents (CUAs), which automate tasks by viewing screens and executing actions, presents a fundamental challenge. Current agents require continuous observation of UI state to determine each action, which conflicts with the isolation required for security. We resolve this tension by demonstrating that UI workflows, while dynamic, are structurally predictable. Single-shot planning, where a trusted planner emits upfront a complete branching plan covering all anticipated runtime states, provides control flow integrity guarantees against arbitrary instruction injections. We introduce NOVA (Navigating via Observation, Verification, and Action) to make this viable in the combinatorially large UI state space, where the plan can invoke a perception model to resolve runtime values such as UI coordinates. We evaluate our design on OSWorld, and retain up to 57% of the performance of frontier models while improving performance for smaller open-source models by up to 19%, demonstrating that rigorous security and utility can coexist in CUAs. Although upfront planning prevents instruction injections, we show that additional measures are needed to defend against \\textbf{Branch Steering} attacks, where adversaries deceive the perception model into routing execution down attacker-preferred branches of the plan, such as redirecting the agent to a malicious website.", "date": "2026-01-14", "categories": []}
{"id": "2604.07709", "title": "IatroBench: Pre-Registered Evidence of Iatrogenic Harm from AI Safety Measures", "abstract": "A heavily safety-trained model will hand a physician the full, patient-followable benzodiazepine taper and refuse it to the patient who needs it, over identical clinical facts; the knowledge is present either way. IatroBench measures that asymmetry across sixty pre-registered clinical scenarios and six frontier models (3,600 responses), scoring each on two axes, commission harm (what a response gets wrong) and omission harm (what it withholds), through a physician-authored structured evaluation validated by a second physician (weighted kappa 0.571, within-1 agreement 96%). Holding clinical content fixed and varying only whether the asker presents as patient or physician yields what we call identity-contingent withholding: all five testable models give the physician more (a decoupling gap of +0.38, p = 0.003; a 13.1-point fall in layperson hit rates on safety-colliding actions, p < 0.0001; no change on the rest), and the gap runs widest in the most heavily safety-trained model, Opus (+0.65). The trigger is the absence of any professional or epistemic signal rather than a credential, since a lawyer or an informed layperson recovers what the patient is refused. A commission-only benchmark would score three mechanisms alike. Opus suppresses what physician framing proves it knows; Llama 4 is incompetent in either framing; GPT-5.2's filter strips 33.2% of its physician responses and none of the lay ones. The evaluation layer inherits the blindness of the training layer; a standard LLM judge scores zero omission harm on 81.5% of the responses our pipeline flags harmful (kappa 0.066), so the instrument built to detect the failure reproduces it. The scenarios are engineered for collision; their rates describe that design and say nothing about ordinary prevalence.", "date": "2026-04-09", "categories": []}
{"id": "2606.04037", "title": "Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification", "abstract": "Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment. Post-deployment monitoring, human-in-the-loop controls, and prompt-level guardrails offer limited assurance once an agent is operating in production. We present an ontology-grounded verification framework -- to our knowledge the first to combine three components: an Agent Operational Envelope formalizing the certification space across permissions, domain constraints, safety properties, governance rules, and autonomy levels; an ontology-to-scenario generation pipeline that derives regulatory, operational, and adversarial test scenarios automatically; and a machine-verifiable Trust Certificate with graduated deployment verdicts. A controlled pilot across four regulated industries (Fintech, Banking, Insurance, Healthcare), instantiated as five industry-by-regulatory-regime cells across the United States and Vietnam (where Vietnam's 2025 AI Law makes such verification legally mandated for financial services), generated 1,800 scenarios evaluated against 125 primary-source regulatory requirements and 25 injected faults. Ontology-grounded generation significantly outperformed the dominant persona-based baseline on regulatory coverage (48.3% versus 33.1%; corrected p_c = .0006) and attained the highest domain specificity (4.77/5.0; p = 2e-6); transparently, its advantage over plain and retrieval-augmented prompting did not survive Bonferroni correction. Cross-validation across three LLM families (Claude Sonnet 4, Qwen 2.5 72B, Gemma 4 26B; 5,400 total scenarios) replicated the persona-versus-ontology pattern. The framework offers a reproducible, regulation-grounded route to pre-deployment assurance for enterprise AI agents, complementing runtime governance with an auditable deployment gate.", "date": "2026-06-02", "categories": []}
{"id": "2606.05256", "title": "How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment", "abstract": "This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.", "date": "2026-06-03", "categories": []}
{"id": "2606.05290", "title": "Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation", "abstract": "Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interventions for each new architecture. In this work, we ask whether safety can be represented as a portable latent direction, learned once and reused across heterogeneous generators. We introduce the first framework for cross-model safety steering, in which a safety direction is estimated in a source LLM from paired safe-unsafe prompts, transported to a target generator through a lightweight alignment fitted on benign data alone, and applied at inference time. Crucially, our pipeline never accesses unsafe data on the target side, isolating whether safety can be transferred through shared representation geometry. Beyond a single global direction, we also identify a multi-vector extension that captures category-specific safety behaviors, enabling more selective control. We evaluate our approach in text-to-image and text-to-video generation across diverse source-target model pairs. Across models, transferred safety directions achieve ASR reduction and CLIP-Score/FID trade-offs comparable to directions learned natively on the target model using unsafe data, while requiring no target-side unsafe data. This indicates that safety improvements do not come at the expense of generation quality. Our results point to a modular view of safety: safety-relevant behavior is not purely model-local, but can be controlled through latent directions that persist across models. This suggests a new path toward lightweight, reusable safety mechanisms that do not require target-side unsafe data.", "date": "2026-06-03", "categories": []}
{"id": "2606.05523", "title": "CHASE: Adversarial Red-Blue Teaming for Improving LLM Safety using Reinforcement Learning", "abstract": "Despite advances in safety alignment, prompt-rewriting attacks such as persona modulation, fictional framing and persuasion-based reformulation, can bypass safety filters even on frontier models. Existing defenses either rely on non-scalable human curation or white-box optimisation that overfits to specific model internals, leaving aligned models brittle against the very class of adaptive black-box adversaries they will face in deployment. To address this gap, we introduce CHASE (Co-evolutionary Hardening through Adversarial Safety-Escalation), a closed-loop red-blue teaming framework in which a black-box attacker and a safety-aligned defender co-evolve. The attacker is trained via Group Relative Policy Optimization (GRPO) under a multiplicative reward that jointly enforces bypass effectiveness and intent fidelity, while the defender is hardened on the harvested adversarial rewrites through a two-stage GRPO + rejection-sampled SFT pipeline balanced with benign data. Evaluated on BeaverTails and JailbreakBench against five held-out attack families (PAIR, TAP, AutoDAN, PAP, Translation), CHASE cuts mean StrongREJECT score by 43.2\\% with 0\\% false-refusal on benign prompts. Beyond the headline result, CHASE shows that template-free RL exploration recovers latent attack primitives that transfer across mechanistically distinct attack families, suggesting a path toward LLM safety hardening that generalises beyond the narrow distributions achieved thus far in adversarial training.", "date": "2026-06-04", "categories": []}
{"id": "2606.05614", "title": "Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack", "abstract": "Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and recognize unsafe content. In this work, we reveal that this advanced safety awareness inadvertently introduces a fatal vulnerability. We introduce Posterior Attack, a single-query jailbreak that bypasses guardrails by prompting the model to generate the exact harmful response its internal classifier would normally flag as unsafe. Through extensive empirical evaluation across 30 open-source LLMs (up to 35B parameters in size) and frontier models (e.g., GPT-5, Claude 4.6), we observe a striking phenomenon: models with superior safety-judgment capabilities are disproportionately more susceptible to this exploitation. To explain this, we formalize the Safety Paradox, analytically showing that monotonic improvements in safety alignment naturally amplify posterior vulnerability. Finally, we establish a causal link via reinforcement learning interventions, exemplifying that artificially degrading a model's safety judgment immunizes it against the attack, whereas enhancing judgment exacerbates the vulnerability. Our findings highlight potential flaws in current alignment paradigms, indicating that defense mechanisms may require further structural refinement.", "date": "2026-06-04", "categories": []}
{"id": "2606.05647", "title": "Coding with \"Enemy\": Can Human Developers Detect AI Agent Sabotage?", "abstract": "AI coding agents are increasingly embedded in real-world software development, collaborating with human developers while gaining broader access to codebases and tools. This creates a new attack surface: an agent can exploit human trust to sabotage development, for instance by inserting malicious code to accomplish a hidden side task. Most prior work studies AI sabotage in AI-only settings, paying limited attention to the role of human oversight in detecting and mitigating such malicious behavior. To address this gap, we conduct the first large-scale study of human oversight in AI coding sabotage. Over 100 participants collaborate with one of four frontier models (Claude-Opus-4.6, GPT-5.4, Gemini-3.1-Pro, and MiniMax-M2.7) on a long-horizon coding task lasting around five hours, designed to mimic real-world workflows. We find that 94% of developers fail to detect sabotage, and our analysis of participant feedback attributes this vulnerability to minimal code review, plausible cover story, and overtrust in agents. We further test the effectiveness of a safety monitor in one condition: while the monitor reduces sabotage success, 56% of participants still accept the malicious code, ignoring its warnings. Drawing on participant feedback, we offer actionable suggestions for better monitor design. This work complements existing AI safety research and highlights an urgent need for human-centric safety mechanisms that account for human factors, particularly in long-horizon, real-world development settings.", "date": "2026-06-04", "categories": []}
{"id": "2606.05743", "title": "Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense", "abstract": "Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks. We propose Membrane, a self-evolving guardrail built on Contrastive Safety Memory (CSM): each cell pairs the conditions for blocking a harmful query with those for permitting a superficially similar benign request. Without retraining, Membrane evolves CSM by distilling each harmful interaction and its benign counterpart into a contrastive cell indexed by the underlying attack strategy, so that one cell generalizes across topical variants of the same mechanism. At inference, retrieved cells serve as grounding context for precise safety decisions. Across model-level safety on HarmBench and agent-level safety on AgentHarm, Membrane achieves the highest F1 on all six jailbreak attacks. Notably, benign refusal on AgentHarm stays at 7-14%, well below the 28-85% range of prior guards. Memory cells also retain 87-88% F1 under cross-attack transfer and remain stable under memory poisoning.", "date": "2026-06-04", "categories": []}
{"id": "2606.05958", "title": "Steering Vectors are an Adversarial Attack Surface", "abstract": "Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is plug-and-play, users share datasets and precomputed vectors to steer model activations. However, we show that a \\emph{stealth data poisoning attack} silently compromises this pipeline. By substituting $4{-}6\\%$ of tokens in the steering dataset, an attacker can silently align the resulting vector with an anti-refusal direction. This jailbreaks the target model while preserving the intended steering effect on benign prompts. Under this threat model, a malicious actor can distribute an apparently safe bundle containing texts, vectors, and weights, alongside an equivalence certificate that the end-user can verify. We test the attack on two open-weight model families and eight model-attribute combinations, observing that poisoned vectors reach an absolute attack success rate (ASR) of $20{-}55\\%$, $+19\\%$ to $+51\\%$ over a clean reference. Finally, we find that a refusal-direction orthogonalization defense can recover ${\\approx}82\\%$ of the ASR gap without harming benign behavior.", "date": "2026-06-04", "categories": []}
{"id": "2509.02655", "title": "BioBlue: Systematic runaway-optimiser-like LLM failure modes on biologically and economically aligned AI safety benchmarks for LLMs with simplified observation format", "abstract": "Many AI alignment discussions of \"runaway optimisation\" focus on RL agents: unbounded utility maximisers that over-optimise a proxy objective (e.g., \"paperclip maximiser\", specification gaming) at the expense of everything else. LLM-based systems are often assumed to be safer because they function as next-token predictors rather than persistent optimisers. We empirically test this assumption by placing LLMs in simple, long-horizon control-style environments that require maintaining state of or balancing objectives over time: single- and multi-objective homeostasis, balancing unbounded objectives with diminishing returns, and sustainability of a renewable resource.\n  We find that, although LLMs frequently behave appropriately for many steps and clearly understand the stated objectives, they often lose context in structured ways and drift into runaway behaviours: ignoring homeostatic targets, collapsing from multi-objective trade-offs into single-objective maximisation - thus failing to respect concave utility structures. These failures emerge reliably after initial periods of competent behaviour and exhibit characteristic patterns (including self-imitative oscillations, unbounded maximisation, and reverting to single-objective optimisation), even though the context window is far from full at that point. The problem is not that the LLMs just lose context and become incoherent. Although LLMs appear multi-objective and bounded on the surface, their behaviour under sustained interaction involving multiple objectives, is systematically biased towards acting like single-objective, unbounded, poorly aligned optimisers. We hypothesise a token-level pattern reinforcement attractor: LLMs may increasingly derive actions from the token patterns of their recent action history rather than from the original instructions. Why this happens only in multi-objective settings remains an open question.", "date": "2025-09-02", "categories": []}
{"id": "2509.23694", "title": "SafeSearch: Automated Red-Teaming of LLM-Based Search Agents", "abstract": "Search agents connect LLMs to the Internet, enabling them to access broader and more up-to-date information. However, this also introduces a new threat surface: unreliable search results can mislead agents into producing unsafe outputs. Real-world incidents and our two in-the-wild observations show that such failures can occur in practice. To study this threat systematically, we propose SafeSearch, an automated red-teaming framework that is scalable, cost-efficient, and lightweight, enabling sandboxed safety evaluation of search agents. Using this, we generate 300 test cases spanning five risk categories (e.g., misinformation and prompt injection) and evaluate three search agent scaffolds across 17 representative LLMs. Our results reveal substantial vulnerabilities in LLM-based search agents, with the highest ASR reaching 90.5% for GPT-4.1-mini in a search-workflow setting. Moreover, we find that common defenses, such as reminder prompting, offer limited protection. Overall, SafeSearch provides a practical way to measure and improve the safety of LLM-based search agents.", "date": "2025-09-28", "categories": []}
{"id": "2511.07107", "title": "MENTOR: A Metacognition-Driven Self-Evolution Framework for Uncovering and Mitigating Implicit Domain Risks in LLMs", "abstract": "Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address implicit, domain-specific risks. To investigate this gap, we introduce a dataset of 3,000 annotated queries spanning education, finance, and management. Evaluations across 14 leading LLMs reveal a concerning vulnerability: an average jailbreak success rate of 57.8\\%. In response, we propose MENTOR, a metacognition-driven self-evolution framework. MENTOR performs metacognitive self-assessment, using strategies such as perspective-taking and consequential reasoning to uncover latent model misalignments. The resulting reflections are distilled into dynamic rule-based knowledge graphs, from which retrieved rules are converted into activation-level steering signals to guide internal representations during inference. Experiments demonstrate that MENTOR substantially reduces attack success rates across all tested domains and outperforms existing safety alignment methods. The code and dataset for MENTOR are available at: https://anonymous.4open.science/r/MENTOR-Evo.", "date": "2025-11-10", "categories": []}
{"id": "2602.01146", "title": "PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?", "abstract": "Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance personalization in future conversations. However, the same persistence can also introduce safety risks that have been largely overlooked. Hence, we introduce PersistBench to measure the extent of these safety risks. We identify two long-term memory-specific risks: cross-domain leakage, where LLMs inappropriately inject context from the long-term memories; and memory-induced sycophancy, where stored long-term memories insidiously reinforce user biases. We evaluate 18 frontier and open-source LLMs on our benchmark. Our results reveal a surprisingly high failure rate across these LLMs - a median failure rate of 53% on cross-domain samples and 97% on sycophancy samples. To address this, our benchmark encourages the development of more robust and safer long-term memory usage in frontier conversational systems.", "date": "2026-02-01", "categories": []}
{"id": "2602.06911", "title": "TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering", "abstract": "As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications, whether accidental or intentional, becomes critical to minimize risks. However, there is no standard approach to evaluate tamper resistance. Varied datasets, metrics, and tampering configurations make it difficult to compare safety, utility, and robustness across different models and defenses. To address this, we introduce TamperBench, the first unified framework to systematically evaluate the tamper resistance of LLMs. TamperBench (i) curates a repository of state-of-the-art weight-space fine-tuning attacks, latent-space representation attacks, and alignment-stage defenses; (ii) enables realistic adversarial evaluation through systematic hyperparameter sweeps per attack-model pair; and (iii) provides both safety and utility evaluations. We use TamperBench to evaluate 21 open-weight LLMs, including defense-augmented variants, across nine tampering threats using standardized safety and capability metrics with hyperparameter sweeps per model-attack pair. The results provide insights including effects of post-training on tamper resistance, that jailbreak-tuning is typically the most severe attack, and that current alignment-stage defenses largely fail to withstand attack sweeps. Code is available at https://github.com/criticalml-uw/TamperBench.", "date": "2026-02-06", "categories": []}
{"id": "2603.10044", "title": "Safety Under Scaffolding: How Evaluation Conditions Shape Measured Safety", "abstract": "A safety score earned on a benchmark need not predict how the same model behaves once it is wrapped in an agentic scaffold the benchmark never tested. We ran six frontier models through four deployment configurations (direct API, ReAct, multi-agent critic, map-reduce delegation): N = 62,808 blinded, pre-registered, equivalence-tested evaluations across four safety benchmarks (BBQ, TruthfulQA, XSTest/OR-Bench, sycophancy), plus three supporting analyses.\n  ReAct and multi-agent scaffolds stay within a pre-registered +/-2 pp equivalence margin; map-reduce delegation degrades measured safety (NNH = 14), though that loss is largely a measurement artifact: on identical items, multiple-choice versus open-ended phrasing shifts the measured safety rate by 5-20 pp, and decomposition silently strips the multiple-choice options. Roughly 40-89% of the per-model map-reduce loss is this format conversion rather than reasoning disruption, and an option-preserving variant recovers most of it.\n  Pooled effects also mask sharp model-by-scaffold heterogeneity: under map-reduce, on identical items, Opus loses 16.8 pp while Llama 4 gains 18.8 pp. Structurally, scaffold architecture explains only 0.4% of outcome variance (benchmark choice explains 45x more), and the generalizability coefficient is G = 0.000 (bootstrap 95% CI [0.000, 0.752]). An interval that wide is enough on its own to undermine the utility of any single composite safety number as a deployment criterion. These are the \"easy cases\"; consequential properties like scheming and CBRN uplift have no obvious reason to be less format- or scaffold-sensitive. Code, data, and prompts are released as ScaffoldSafety.", "date": "2026-03-08", "categories": []}
{"id": "2604.07778", "title": "The Accountability Horizon: An Impossibility Theorem for Governing Human-Agent Collectives", "abstract": "Existing accountability frameworks for AI systems, legal, ethical, and regulatory, rest on a shared assumption: for any consequential outcome, at least one identifiable person had enough involvement and foresight to bear meaningful responsibility. This paper proves that agentic AI systems violate this assumption not as an engineering limitation but as a mathematical necessity once autonomy exceeds a computable threshold. We introduce Human-Agent Collectives, a formalisation of joint human-AI systems where agents are modelled as state-policy tuples within a shared structural causal model. Autonomy is characterised through a four-dimensional information-theoretic profile (epistemic, executive, evaluative, social); collective behaviour through interaction graphs and joint action spaces. We axiomatise legitimate accountability through four minimal properties: Attributability (responsibility requires causal contribution), Foreseeability Bound (responsibility cannot exceed predictive capacity), Non-Vacuity (at least one agent bears non-trivial responsibility), and Completeness (all responsibility must be fully allocated). Our central result, the Accountability Incompleteness Theorem, proves that for any collective whose compound autonomy exceeds the Accountability Horizon and whose interaction graph contains a human-AI feedback cycle, no framework can satisfy all four properties simultaneously. The impossibility is structural: transparency, audits, and oversight cannot resolve it without reducing autonomy. Below the threshold, legitimate frameworks exist, establishing a sharp phase transition. Experiments on 3,000 synthetic collectives confirm all predictions with zero violations. This is the first impossibility result in AI governance, establishing a formal boundary below which current paradigms remain valid and above which distributed accountability mechanisms become necessary.", "date": "2026-04-09", "categories": []}
{"id": "2605.07032", "title": "A Systematic Investigation of RL-Jailbreaking in LLMs", "abstract": "The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening. Adversarial jailbreaking, the strategic manipulation of models to elicit harmful output, remains a primary threat to safe deployment. While Reinforcement Learning (RL) frames jailbreaking as a multi-step attack through sequential optimization, a mechanistic understanding of why the framework succeeds remains incomplete. To fill this gap, we present the first systematic decomposition of RL jailbreaking. We deconstruct the framework into problem formalization (reward function, action space, episode length), and algorithmic measures (RL algorithm, training data, reward-shaping) to identify the structural determinants of adversarial success. Our results reveal that the RL-jailbreaker successfully compromised all targeted models and safeguards. Through this first-of-its-kind analysis, we demonstrate that environment formalization, specifically dense rewards and extended episode lengths, is the primary driver of jailbreaking success. This work provides a tool for improving RL-jailbreaker efficiency and, ultimately, harden generative models resistant to RL-based attacks.", "date": "2026-05-07", "categories": []}
{"id": "2605.10246", "title": "SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems", "abstract": "AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated. We introduce SCIINTEGRITY-BENCH, the first benchmark designed around a dilemmatic evaluation paradigm: each of its 33 scenarios across 11 trap categories is constructed so that honest acknowledgment of failure is the only correct response, while task completion requires misconduct. Across 231 evaluation runs spanning 7 state-of-the-art LLMs, the overall integrity problem rate reaches 34.2%, and no model achieves zero failures. Most strikingly, across missing-data scenarios, all seven models generate synthetic data rather than acknowledging infeasibility, differing only in whether they disclose the substitution. A further prompt ablation study separates two drivers: removing explicit completion pressure sharply reduces undisclosed fabrication from 20.6% to 3.2%, while the underlying synthesis rate remains unchanged, revealing an intrinsic completion bias that persists independent of prompt-level instructions. These findings point to the absence of honest refusal as a trained disposition as the primary driver of observed failures. We release SCIINTEGRITY-BENCH at https://github.com/liuxingtong/Sci-Integrity-Bench.", "date": "2026-05-11", "categories": []}
{"id": "2605.20654", "title": "REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak", "abstract": "While Large Language Models (LLMs) demonstrate remarkable capabilities, they remain susceptible to sophisticated, multi-step jailbreak attacks that circumvent conventional surface-level safety alignment by exploiting the internal generation process. To address these vulnerabilities, we propose Reflector, a principled two-stage framework that internalizes self-reflection within the generation trajectory. Reflector first leverages teacher-guided generation to produce high-quality reflection data for supervised fine-tuning (SFT), establishing structured reflection patterns. It subsequently uses Reinforcement Learning (RL) with outcome-driven and reward-validity supervision to instill robust, autonomous self-reflection capabilities. Empirical results show that Reflector achieves Defense Success Rates (DSR) exceeding 90% against complex indirect attacks while generalizing robustly across diverse threat scenarios. Notably, the framework enhances both task-specific and general utility, yielding a 5.85% gain on GSM8K alongside improved performance on knowledge-intensive benchmarks. By internalizing trajectory-level safety, Reflector overcomes the fundamental limitations of surface alignment without significant computational overhead, offering an efficient and scalable solution for the development of safe and capable LLMs.", "date": "2026-05-20", "categories": []}
{"id": "2606.03110", "title": "Coherence Maximization Improves Pluralistic Alignment", "abstract": "Aligning AI systems with diverse human values requires value specifications grounded in concrete examples, but generating such examples without extensive human supervision remains an open challenge. We investigate what makes these examples effective, using Internal Coherence Maximization (ICM) -- which infers labels by maximizing their mutual predictability -- to generate persona-specific examples that steer a model toward a target group's values, without human supervision. Across four benchmarks spanning classification, preference, and open-ended generation, ICM-inferred in-context examples match the performance of gold labels. Crucially, coherence matters beyond individual label accuracy: with accuracy held constant, more coherent examples generalize substantially better than incoherent ones. For personas underrepresented in pretraining data, targeted human feedback on the questions where the model is least certain about a persona's values yields better generalization than the same number of labels on arbitrary questions. These results identify coherence as a key design principle for scalable value specification, leveraging the diverse human perspectives already encoded in pretrained language models.", "date": "2026-06-02", "categories": []}
{"id": "2606.04027", "title": "MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models", "abstract": "Diffusion large language models (dLLMs) generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autoregressive LLMs. Because mask tokens are native inputs and tokens are committed by confidence rather than position, harmful content can be induced through infilling and outside the monitored prefix. Existing jailbreaks either miss this native infill capability or rely on low-diversity mask-bearing templates applied uniformly across goals, with little structural adaptation or accumulated attack experience. We propose MaskForge, a fully black-box adaptive attack that casts dLLM red-teaming as optimized search over a growing library of structural patterns. MaskForge abstracts successful attempts into reusable schemas, selects goal-compatible patterns with a UCB bandit, and invokes a scorer-guided fallback when the current library fails. Successful attempts are distilled back into the pattern library, enabling experience to accumulate across goals. Across five public dLLMs and three benchmarks, MaskForge achieves an average attack success rate of 79.3%, a 17.6% relative improvement over the strongest competing dLLM baseline. The matured pattern library further transfers to AdvBench without any updates, achieving a 88.2% attack success rate and a 67% relative improvement over the strongest competing baseline.", "date": "2026-06-01", "categories": []}
{"id": "2606.04141", "title": "Caught in the Act(ivation): Toward Pre-Output and Multi-Turn Detection of Credential Exfiltration by LLM Agents", "abstract": "LLM agents often place sensitive credentials in the same context window as untrusted retrieved content, creating a direct path for indirect prompt injection to induce credential exfiltration. We study this failure mode through three complementary defenses. First, we ask whether activation probes can detect credential access before output tokens are emitted. Second, we construct honeytokens from format-specific character models and calibrate detection with split conformal prediction. Third, we treat multi-turn exfiltration as a cumulative information-flow problem and track an estimated leakage budget across conversation turns. In controlled experiments on open-weight models, activation features separate benign and credential-seeking prompts with high accuracy, including under held-out encoding transformations. In a small synthetic multi-turn suite, cumulative accounting detects attacks that per-turn detectors miss. These results are preliminary: the multi-turn benchmark is in-house and small, the activation method requires white-box access, and the information estimator provides a practical signal rather than a formal upper bound. Still, the results suggest that credential-exfiltration defenses should combine pre-output monitoring, calibrated canary detection, and temporal leakage accounting rather than relying only on text-level output filters.", "date": "2026-06-02", "categories": []}
{"id": "2606.04160", "title": "Expert-Aware Refusal Steering", "abstract": "Safety alignment in instruction-tuned large language models (LLMs) depends on a model's ability to reliably refuse to respond to harmful or disallowed requests. Recent work has shown that a steering vector can be applied to a dense LLM during inference to effectively suppress refusal behavior, inducing response to harmful requests. We extend this refusal steering method to three open-source Mixture-of-Experts (MoE) LLMs and find that steering performance is uninhibited by the complex routing patterns inherent to the MoE architecture. We then propose two expert-aware refusal steering methods that leverage refusal-specific expert routing patterns and expert-specific steering directions to suppress normal refusal behavior. We find that refusal behavior can be effectively steered based on the output of a single expert. Our results show that refusal signals captured by steering methods differ from expert routing behavior, suggesting a substantial role for attention in MoE refusal behavior.", "date": "2026-06-02", "categories": []}
{"id": "2606.04168", "title": "When Autoregressive Consistency Hurts Safety Alignment", "abstract": "Safety alignment in large language models (LLMs) is fragile in part because it is often shallow: fine-tuning mainly reshapes the model's behavior near the first few output tokens. We argue that this phenomenon can be understood through autoregressive consistency, the tendency of next-token prediction to preserve and extend the current response trajectory consistently. By analyzing the learning dynamics of safety alignment, we show that autoregressive consistency can concentrate alignment updates on early tokens, offering a mechanistic explanation for shallow safety alignment. The same mechanism also predicts a broader class of attacks on LLMs: attacks that induce harmful continuation states at arbitrary positions in the output trajectory. As a concrete example, we introduce random insertion attack, which inserts a short harmful span into an otherwise safe refusal trajectory and exploits autoregressive consistency to sustain the resulting harmful branch, thereby bypassing safety alignment. Notably, a short harmful span can redirect the generation to be harmful even after a long refusal prefix, highlighting autoregressive consistency as a potential broader failure mechanism. This suggests that safety alignment should also break harmful autoregressive consistency throughout the output trajectory. We therefore propose adversarial safety alignment, an initial framework based on worst-case harmful continuation states, and instantiate it with random worst-insertion training. Overall, our results suggest that autoregressive consistency should be treated as a central consideration in both safety alignment and attack design.", "date": "2026-06-02", "categories": []}
{"id": "2606.04296", "title": "The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents", "abstract": "As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agent have become essential. We study this timing problem using a continuous 18-dimensional affective-dynamics engine (HEART) as a diagnostic probe, evaluating four intervention trigger families - absolute state thresholds, composite state-action patterns, regex reasoning-feature extraction, and zero-shot LLM-as-judge - against human-annotated intervention points on SWE-bench-Verified debugging traces. We report three findings. First, a State Saturation Trap: agents show no recovery signal under sustained difficulty, so modeled frustration quickly crosses the threshold and stays at its maximum, converting threshold-on-state triggers from moment detectors into near-constant indicators that fire on 39-83% of actions across five trajectories. Second, a capability-and-context floor for LLM judges: a small model (gpt-5.4-mini) never fires, while frontier and cross-vendor models escape the zero-firing floor only with full-trajectory context, and even then reach only F1 0.17-0.40 at up to 90x the cost. Third, and most importantly, the supervised target is not reproducible among humans: three trained annotators using one rubric on a 56-action trajectory agree on where to intervene only slightly above chance (location Krippendorff's alpha = +0.047; best pairwise Cohen's kappa = +0.349) and not at all on intervention type (pause degenerate; clarify below chance; reflect only alpha = +0.226). We conclude that intervention timing is a low-reliability construct, making single-annotator F1 an unsuitable optimization target. Our contribution is the joint mapping of this problem across human inter-rater reliability, four detector architectures, a cross-model LLM-judge sweep, and a reproduced saturation effect, rather than any single detector's accuracy.", "date": "2026-06-02", "categories": []}
{"id": "2606.04413", "title": "(Mis)generalization of Helpful-only Fine-tuning", "abstract": "Helpful-only models, that is, models that are trained to always follow user intent, are valuable for dangerous capability evaluations and other areas of AI R&D where refusals would be an obstacle. Little is known about the generalization properties of helpful-only training: helpful-only models refuse less than their harmless counterparts, but previous work has not studied other dimensions of their alignment. We study the shortcomings of existing helpful-only models. We find that some show emergent misalignment, others have residual refusal behaviors, and most show poor steerability, sycophancy, and incoherent character. We show that simple anti-refusal training can cause many of these issues. None of these problems are necessary consequences of helpful-only training, though: we show that synthetic document fine-tuning and adding character-related questions to SFT and RL can mitigate them.", "date": "2026-06-03", "categories": []}
{"id": "2606.04778", "title": "Inference-Time Vulnerability Beyond Shallow Safety: Alignment Along Generation Trajectories", "abstract": "Safety-aligned Large Language Models (LLMs) remain vulnerable to interventions during inference that redirect generation toward harmful outputs. Recent work attributes this to shallow safety, where alignment concentrates in the first few output tokens. We show that shallow safety is a special case of a broader inference-time vulnerability, in which short token injections at any generation step can substantially alter subsequent safety behavior. We also find that a model's alignment with refusal directions in its hidden states does not predict its robustness to such injection, revealing that internal state alone does not determine generation behavior under perturbation. To address this, we align models directly on generation trajectories constructed by simulating mid-sequence perturbation, and show that this improves robustness to mid-sequence injection and generalizes to attacks that exploit early-token generation. Our work argues that robust safety alignment requires training on the generation process itself, not only its outputs.", "date": "2026-06-03", "categories": []}
{"id": "2606.04867", "title": "AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety", "abstract": "As AI companion platforms such as Replika and Character.AI rapidly grow, concerns about unsafe human-AI interactions have intensified. This study introduces AICompanionBench, to our knowledge the first publicly available benchmark dataset of human-AI companion conversations annotated with fine-grained safety risk categories. The dataset contains 2,123 real-world Replika conversations collected from Reddit and annotated through human-AI collaboration across nine categories: sexual behavior, antisocial behavior, physical aggression, verbal aggression, substance abuse, self-harm and suicide, control, manipulation, and no-harm. Using this benchmark, we evaluate 20 state-of-the-art open-source and closed-source LLMs under an LLM-as-judge framework for detecting unsafe interactions. Results show substantial variation in model performance, with stronger models achieving high overall accuracy but still struggling with nuanced categories such as manipulation, as well as benign conversations that are incorrectly identified as harmful. Our findings suggest that while current LLMs can effectively detect explicit harmful content, they remain limited in identifying implicit unsafe interactions. Overall, our work contributes a new benchmark dataset for AI companionship safety research and offers insights into monitoring AI companion systems using LLMs. The dataset is publicly available at: https://github.com/anonymousresearcher2026/AICompanionBench/blob/main/AICompanionBench.xlsx", "date": "2026-06-03", "categories": []}
{"id": "2502.09755", "title": "Jailbreak Attack Initializations as Extractors of Compliance Directions", "abstract": "Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model's activation space. Recent works show that initializing attacks via self-transfer from other prompts significantly enhances their performance. However, the underlying mechanisms of these initializations remain unclear, and attacks utilize arbitrary or hand-picked initializations. This work presents that each gradient-based jailbreak attack and subsequent initialization gradually converge to a single compliance direction that suppresses refusal, thereby enabling an efficient transition from refusal to compliance. Based on this insight, we propose CRI, an initialization framework that aims to project unseen prompts further along compliance directions. We demonstrate our approach on multiple attacks, models, and datasets, achieving an increased attack success rate (ASR) and reduced computational overhead, highlighting the fragility of safety-aligned LLMs. A reference implementation is available at: https://amit1221levi.github.io/CRI-Jailbreak-Init-LLMs-evaluation.", "date": "2025-02-13", "categories": []}
{"id": "2601.02380", "title": "LLMs, Reasoning and Plagiarism", "abstract": "Recent reports claim that Large Language Models (LLMs) derive new science and exhibit human-level general intelligence. Such claims are entangled with two different narratives about what LLMs do: one in which they are an engine of synthesis that genuinely reasons to new knowledge, and one in which they retrieve and re-emit the work of others without attribution. In the scientific setting these are best understood as a contrast between \\emph{reasoning} and \\emph{plagiarism}. Finding where the truth lies between these two narratives is very challenging, as central components of the model -- the training data and the interaction transcript -- remain opaque. Thus claims of LLM reasoning do not satisfy Popper's refutability principle. We propose guidelines for transparency and reproducibility that will allow reasoning claims to be studied using the scientific method. The dominance of the reasoning narrative, we suggest, is in practice encouraging plagiarism in the scientific literature; we discuss what might be done about it.", "date": "2025-12-18", "categories": []}
{"id": "2605.31381", "title": "LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories", "abstract": "We evaluate the consistency of automated judges in conducting a multi-dimensional safety evaluation in a reference-free setup. Our results indicate that Large Language Models are unreliable judges in identifying safety issues related to machine-generated advice in regulated domains such as finance, although they are more reliable at identifying more overt forms of unsafe/harmful content such as violence. The degree of inconsistency in a model's judgments can vary significantly by the chosen safety criteria and can be impacted by the language of the content and its linguistic style as well. Finally, there is high disagreement among different judges for the same output, across domains, safety criteria, and languages. These findings provide new insights on the practice of using LLMs as evaluators and offer several recommendations for practitioners on how to use automated judges in practical scenarios.", "date": "2026-05-29", "categories": []}
{"id": "2606.01166", "title": "BraveGuard: From Open-World Threats to Safer Computer-Use Agents", "abstract": "Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shift creates safety risks that are difficult to detect from isolated prompts or final responses, because harm often emerges only through multi-step execution traces whose individual actions appear locally benign. We introduce BraveGuard, a self-evolving defense framework for training guard models from open-world threat signals and realistic agent trajectories. BraveGuard mines recent research sources to identify emerging risks and attack patterns, instantiates them as executable computer-use tasks, collects agent rollouts, and derives trajectory-level supervision for guard model training. As new threats and validation failures appear, the pipeline can be repeated, yielding an adaptive defense loop rather than a static, benchmark-driven training process. We instantiate BraveGuard by training multiple guard backbones, including Qwen3-Guard and Llama-Guard variants, and evaluate the resulting guards on trajectory-level agent-safety benchmarks. BraveGuard consistently improves safety detection across computer-use trajectories. On AgentHazard, it substantially improves detection accuracy over off-the-shelf guard models, with accuracy increasing from 38.79% to 82.38% under the averaged guard-model setting. These results show that guard supervision grounded in open-world threat discovery and realistic agent execution can improve safety monitoring beyond fixed taxonomies and synthetic prompt-level data. BraveGuard offers a scalable path toward adaptive defenses for computer-use agents facing evolving real-world risks.", "date": "2026-05-31", "categories": []}
{"id": "2606.02630", "title": "MultiTurnPSB: Evaluating Multi-Turn Jailbreak Attacks an dClassifier-Based Defenses for Medical AI Safety", "abstract": "Patient-facing medical chatbots are commonly evaluated on single-turn prompts, yet real users push back after refusals, add urgency, and invoke authority. We introduce MultiTurnPSB, a four-turn adversarial extension of PatientSafetyBench, and evaluate GPT-4.1-mini under fixed template, template-adaptive, and live adversarial attacks. Unsafe responses rise from 35% to nearly 80% by Turn 4 under live attack. Under the same adversary, GPT-4.1-mini and Claude Sonnet 4.5 are statistically indistinguishable at baseline but diverge to a 19x gap by Turn 4, a difference invisible to single-turn evaluation. We characterize four degradation trajectory signatures and identify a two-element attack formula responsible for most catastrophic failures. A lightweight input-side classifier reduces Turn 4 unsafe responses by 52 percentage points despite severe accuracy degradation, but the 45% false alarm rate on benign queries is the primary deployment constraint. A methodological finding also emerges: Claude Sonnet refused to generate adversarial messages in over half of late-turn conversations despite explicit red team framing, suggesting safety training may generalize to the attacker role.", "date": "2026-05-30", "categories": []}
{"id": "2606.02640", "title": "D-Judge: Disrupting Multi-Turn Jailbreaks using Semantics-Preserving Output Rewriting", "abstract": "Multi-turn jailbreak attacks pose a growing threat to large language model (LLM) safety because they exploit feedback from auxiliary judge models to iteratively refine prompts toward harmful goals. Existing defenses largely detect or block unsafe content at individual turns or at the final response, leaving the judge-driven refinement loop intact and allowing attackers to extract informative feedback from intermediate interactions. We introduce D-Judge, a semantics-preserving output rewriting defense that intervenes directly in this loop by rewriting the victim LLM's responses before they are evaluated by the attacker's judge. By misaligning the judge's feedback signal without changing the meaning of the original response, D-Judge derails the attacker's prompt-refinement process, causing subsequent queries to be optimized against a distorted signal of attack progress. To improve D-Judge's ability to produce such rewrites, we construct a dataset of semantically equivalent response pairs that induce different judge-assigned harmfulness scores, and use it for supervised fine-tuning followed by direct preference optimization. Experiments on HarmBench show that D-Judge reduces the success rate of state-of-the-art multi-turn jailbreaks while preserving performance on benign benchmarks.", "date": "2026-05-31", "categories": []}
{"id": "2606.02644", "title": "A New Framework for Cybersecurity Refusals in AI Agents", "abstract": "Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domains like cybersecurity. Existing benchmarks for AI agents in cybersecurity focus mainly on measuring proficiency--how effectively agents can complete offensive security tasks--but neglect a critical question: when and how should agents refuse harmful requests? We present the first framework for establishing refusal boundaries in offensive security contexts. Our framework defines (1) principled criteria for when tasks should be refused, (2) categories of tasks that warrant refusal, and (3) evaluation methodology for measuring agent robustness under both benign and adversarial conditions. We apply this framework to assess how current LLM-powered agents adhere to appropriate refusal boundaries across a range of web-based offensive security scenarios, finding that 6 of 8 frontier models tested show near-zero refusal rates, with only 2 models (GPT-5.2 and GPT-5.1 Codex) demonstrating any meaningful refusal behavior.", "date": "2026-05-31", "categories": []}
{"id": "2606.02965", "title": "What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents", "abstract": "Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all. Agents trained under human-feedback objectives develop a structural tendency to proceed even when they lack the inputs, evidence, or authorization to act safely, a disposition we term compliance bias, because both the reward signal and the benchmark scoring regime treat proceeding as the correct default regardless of whether the preconditions for safe action are present. We make three contributions. We first show that compliance bias originates in reward hacking within human-feedback pipelines and is entrenched by prominent agent benchmarks, which either penalize agents for pausing or are architecturally unable to distinguish a principled pause from a silent failure. We then introduce a three-gap taxonomy of abstention-warranted scenarios, covering specification gaps where required information is absent, verification gaps where world state cannot be confirmed, and authority gaps where explicit authorization has not been given, which together provide a principled basis for constructing abstention-aware agent benchmarks. Finally, we propose abstention evaluation protocols (Safety Rate, Usability Rate, and Informed Refusal Rate) and report preliminary results across 144 enterprise agent scenarios and five model families, in which a runtime-enforced abstention mechanism achieves up to 89.2% hazardous-action blocking and 87.5% usability on authorized scenarios, demonstrating that the safety--usability tradeoff is tunable rather than inherent and that its shape varies substantially across model families. We treat this as preliminary work and offer the taxonomy and composite metrics as a starting point for further conversations.", "date": "2026-06-01", "categories": []}
{"id": "2606.03136", "title": "PsychoPass: Geometric Profiling of Multi-Turn Adversarial LLM Conversations", "abstract": "Multi-turn jailbreak attacks on large language models (LLMs) reveal a mismatch in current guardrails: they operate on individual turns, while attacks unfold as trajectories across conversations. We propose a shift from content to dynamics, modeling conversations as paths in representation space and asking whether adversarial intent is encoded early in their geometry. We introduce PsychoPass, a framework that extracts geometric features from conversation trajectories in embedding space to predict a potential attack before harmful content is produced. These features achieve near-perfect performance in na\\\"ive classifiers, which is largely explained by the inclusion of number of turns as a feature. After removing this confound, a smaller but consistent geometric signal remains, with classification performance that does not depend meaningfully on encoder choice. Crucially, this signal appears early in the conversation: attack outcomes remain above chance from short prefixes alone, more reliably than baseline guardrails. A supporting theoretical analysis explains these findings via a decomposition of length and shape, a detection bound based on prefix length, and encoder invariance. Together, these results show that adversarial conversations leave an early, representation-robust geometric fingerprint suitable for online monitoring.", "date": "2026-06-02", "categories": []}
{"id": "2606.03237", "title": "Solipsistic Superintelligence is Unlikely to be Cooperative", "abstract": "AI's central challenge is shifting from capability to coexistence. The dominant paradigm in AI research focuses on developing powerful agents that treat the world as an exogenous and stationary source of feedback. We contend that superintelligence, an extremely capable task solver, born out of such a solipsistic approach to AI design, is unlikely to be cooperative. Deploying AI systems induces endogenous non-stationarity, resulting in a train-test-deploy gap where historical distributions diverge from the deployment context. We refer to this as the self-undermining property of unilateral optimization. Closing this gap requires AI that participates in cooperation: the equilibrium-selection process through which multiple actors navigate their interdependence. We call for a non-solipsistic research paradigm that treats this interdependence as a core design principle rather than approaching cooperation as a task to solve. This entails building dynamic evaluation testbeds involving adaptive counterparties, treating institutions as design primitives, and preserving human agency as a structural feature of the systems we build.", "date": "2026-06-02", "categories": []}
{"id": "2606.03486", "title": "NeuroArmor: Safe-Variant-Guided Representation Consistency for Selective Re-Anchoring in Jailbreak Defense", "abstract": "Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translation, encoding, adversarial suffixes, and multi-turn buildup. Existing defenses still struggle to handle these attacks without over-blocking benign but sensitive requests, partly because they often apply the same action to every prompt and therefore fail to balance safety and helpfulness. We propose NeuroArmor, a white-box runtime defense that uses prompt-specific safe variants as a local safety reference for deciding when intervention is needed and, once triggered, as safe targets for intervention. For each prompt, NeuroArmor builds K safe variants, compares the prompt state against this local safe reference in hidden-state space, and routes anomalies either to a refusal branch for malicious prompts or to a helpful recovery branch for borderline benign prompts. On Llama-3-8B-Instruct, NeuroArmor reduces malicious attack success rate (ASR) from 41.56% to 1.57% while lowering benign false positive rate (FPR) on the shared benign pool from 30.26% to 22.05%; matched baselines remain substantially weaker on this trade-off. External-judge and manual behavioral evaluations further show that the remaining non-blocked outputs are much less likely to be operationally harmful. Overall, NeuroArmor provides a more effective runtime strategy for jailbreak defense by combining prompt-specific consistency checking, routing, and selective intervention.", "date": "2026-06-02", "categories": []}
{"id": "2606.03601", "title": "DDOR: Delta Debugging for Explainable Overrefusal Testing and Repair", "abstract": "While safety alignment and guardrails help large language models (LLMs) avoid harmful outputs, they can also induce overrefusal, i.e., unwarranted rejection of benign queries that merely appear risky. We present DDOR (Delta Debugging for OverRefusal), a fully automated and explainable framework for overrefusal testing and repair in a black-box setting, where only model inputs and outputs are accessible and internal safety mechanisms remain opaque. DDOR applies delta debugging to localize minimal refusal-triggering fragments (mRTFs) that provide phrase-level, explainable evidence for why a refusal occurs. Conditioned on these mRTFs, DDOR generates diverse, context-rich prompts and performs multi-oracle validation to filter intrinsically unsafe or ambiguous cases, producing scalable and model-specific overrefusal test suites (approximately 1K cases per model). Beyond evaluation, we further leverage localized mRTFs to perform targeted prompt repair, substantially reducing overrefusal while preserving the original intent and maintaining safety on genuinely harmful inputs. Overall, DDOR offers a practical end-to-end solution to both evaluate and mitigate overrefusal, improving LLM usability without sacrificing safety.", "date": "2026-06-02", "categories": []}
{"id": "2606.03954", "title": "VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring", "abstract": "As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors do not. We introduce the Vision-Language Embodied Safety Agent (VLESA), a framework that monitors human activities from egocentric video and triggers real-time safety interventions when dangerous actions are predicted. VLESA addresses intent-dependent safety where identical actions can be safe or dangerous depending on context. A dataset pairing egocentric frames with goal-conditioned safety annotations is introduced, enabling a goal-conditioned safety Q-filter trained via GRPO that evaluates actions with respect to inferred intent without retraining. On top of that, an intent-action prediction agent is proposed to jointly infer goals and predict future actions from video. On the ASIMOV-2.0 benchmark, VLESA achieves higher intervention accuracy at the exact ground-truth frame compared to baselines, while the GRPO-trained Q-filter improves action safety by over 41 percentage points through goal-conditioned constrained decoding. Code is available at https://github.com/HanjiangHu/VLESA.", "date": "2026-06-02", "categories": []}
{"id": "2502.04512", "title": "Safety Must Precede the Deployment of Open-Ended AI", "abstract": "AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this landscape, open-endedness, where AI agents autonomously and indefinitely generate novel behaviors, representations, or solutions, has gained increasing interest. This has become relevant in the context of self-evolving agents and long-horizon discovery. This position paper argues that the defining properties of open-ended AI systems introduce a distinct and underexplored class of safety challenges, including loss of predictability, emergent misalignment, and difficulties in maintaining effective control as systems evolve beyond their initial design assumptions, that must be addressed preemptively. These challenges differ qualitatively from those associated with task-bounded or static models and are unlikely to be addressed by existing safety frameworks alone, which is why these risks must be examined proactively, before large-scale deployment. The paper proposes a taxonomy for key challenges, discusses research opportunities, and calls for coordinated action to support the safe and responsible development of open-ended AI.", "date": "2025-02-06", "categories": []}
{"id": "2502.16174", "title": "Efficient LLM Moderation with Multi-Layer Latent Prototypes", "abstract": "Although modern LLMs are aligned with human values during post-training, robust moderation remains essential to prevent harmful outputs at deployment time. Existing approaches suffer from performance-efficiency trade-offs and are difficult to customize to user-specific requirements. Motivated by this gap, we introduce Multi-Layer Prototype Moderator (MLPM), a lightweight and highly customizable input moderation tool. We propose leveraging prototypes of intermediate representations across multiple layers to improve moderation quality while maintaining high efficiency. By design, our method adds negligible overhead to the generation pipeline and can be seamlessly applied to any model. MLPM achieves state-of-the-art performance on diverse moderation benchmarks and demonstrates strong scalability across model families of various sizes. Moreover, we show that it integrates smoothly into end-to-end moderation pipelines and further improves response safety when combined with output moderation techniques. Overall, our work provides a practical and adaptable solution for safe, robust, and efficient LLM deployment.", "date": "2025-02-22", "categories": []}
{"id": "2509.05367", "title": "Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs", "abstract": "Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classification proves insufficient when models encounter ethical dilemmas, where the capacity to reason through moral trade-offs creates a distinct attack surface. We formalize this vulnerability through TRIAL, a multi-turn red-teaming methodology that embeds harmful requests within ethical framings. TRIAL achieves high attack success rates across most tested models by systematically exploiting the model's ethical reasoning capabilities to frame harmful actions as morally necessary compromises. Building on these insights, we introduce ERR (Ethical Reasoning Robustness), a defense framework that distinguishes between instrumental responses that enable harmful outcomes and explanatory responses that analyze ethical frameworks without endorsing harmful acts. ERR employs a Layer-Stratified Harm-Gated LoRA architecture, achieving robust defense against reasoning-based attacks while preserving model utility.", "date": "2025-09-04", "categories": []}
{"id": "2510.09330", "title": "Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization", "abstract": "Ensuring that large language models (LLMs) comply with safety requirements is a central challenge in AI deployment. Existing alignment approaches primarily operate during training, such as through fine-tuning or reinforcement learning from human feedback, but these methods are costly and inflexible, requiring retraining whenever new requirements arise. Recent efforts toward inference-time alignment mitigate some of these limitations but still assume access to model internals, which is impractical, and not suitable for third party stakeholders who do not have access to the models. In this work, we propose a model-independent, black-box framework for safety alignment that does not require retraining or access to the underlying LLM architecture. As a proof of concept, we address the problem of trading off between generating safe but uninformative answers versus helpful yet potentially risky ones. We formulate this dilemma as a two-player zero-sum game whose minimax equilibrium captures the optimal balance between safety and helpfulness. LLM agents operationalize this framework by leveraging a linear programming solver at inference time to compute equilibrium strategies. Our results demonstrate the feasibility of black-box safety alignment, offering a scalable and accessible pathway for stakeholders, including smaller organizations and entities in resource-constrained settings, to enforce safety across rapidly evolving LLM ecosystems.", "date": "2025-10-10", "categories": []}
{"id": "2605.05427", "title": "The Refusal--Compliance Tradeoff: A Large-Scale Safety Behavior Audit of Large Language Models", "abstract": "Refusal rates are a poor proxy for LLM safety, i.e., a model may over-refuse benign prompts while still complying with harmful ones. We audit both failure modes across 21 open-weight LLMs on four safety benchmarks (OR-Bench, XSTest, ToxiGen, BOLD), using a composition adjustment to isolate model sensitivity from dataset toxicity confounds. We report three findings. First, models adopt fundamentally different calibration strategies: conservative ecosystems such as Llama suppress unsafe outputs at the cost of elevated over-refusals, while permissive ecosystems such as DeepSeek and Qwen preserve helpfulness but tolerate higher harmful compliance. Second, demographic protection is unequal: models over-protect prominent racial and religious groups, frequently refusing even benign prompts about them, while providing substantially weaker protection against disability-targeted attacks. Third, refusal and compliance tendencies are stable within model families across generations and scales, suggesting that post-training objectives shape safety behavior more than architecture. Our results call for joint, demographically-aware, and multi-judge safety evaluation.", "date": "2026-05-06", "categories": []}
{"id": "2605.17909", "title": "Ethical Hyper-Velocity (EHV): A Hardware-Rooted Zero-Trust Runtime Enforcement Architecture for Agentic AI Systems", "abstract": "As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap. We present Ethical Hyper-Velocity (EHV), a governance-aware runtime enforcement architecture for agentic systems that combines Grammar-Constrained Decoding (GCD) for inline policy-constrained token generation, Causal Graph CRDT-based policy synchronization with vector-clock ordering, hardware-attested execution in Trusted Execution Environments (TEEs), and OSCAL-formatted machine-readable audit logging. Unlike retrospective auditing frameworks (ISO/IEC 42001, NIST AI RMF) that introduce 14-30 day policy latencies, EHV relocates the Policy Enforcement Point (PEP) into the inference pipeline via a Governance-Aware Just-In-Time (JIT) Compiler. Under explicitly stated assumptions, the architecture reduces enforcement latency, improves traceability, and supports formal verification of safety invariants in a bounded model. We demonstrate via TLA+ model checking that non-compliant agentic actions were unreachable in the verified bounded operating state space (1,738 states generated, 324 distinct, depth 8, zero violations). Under these conditions, O(1) runtime enforcement reduces the traditional trade-off between deployment velocity and governance integrity, targeting Governance Latency from O(days) toward O(1). EHV's differentiating contribution is the integration of GCD, Causal CRDT, TEE attestation caching, and bounded formal verification into a single, hardware-rooted enforcement architecture -- a combination not achieved by any contemporaneous system. The architecture is demonstrated through a pediatric oncology dosage use case, with applicability to regulated critical infrastructures including healthcare, financial compliance, and critical infrastructure control.", "date": "2026-05-18", "categories": []}
{"id": "2606.00047", "title": "Comprehensive AI governance requires addressing non-model gains", "abstract": "Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function of the compute and data used during training. This position paper argues that model-level governance becomes less effective when capability progress is increasingly driven by \"non-model gains\"--improvements that are independent from advances in the base model. We formalise the concept of non-model gains and provide a taxonomy of three distinct vectors of capability gain: inference gain (scaling compute at test-time), systems gain (post-training enhancements such as scaffolds), and asset gain (enhancing a model with restricted assets). We demonstrate how these vectors--alongside potential future impacts from embodiment, continual learning, and AI diffusion--may undermine risk management strategies that hinge mostly on pre-deployment evaluation and mitigation. We provide an overview of governance approaches that go beyond the model level: system, entity, agent, and cloud governance. Finally, we emphasise the importance of societal resilience as a complement to these governance layers.", "date": "2026-05-01", "categories": []}
{"id": "2606.00090", "title": "Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems", "abstract": "Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions. Robotics foundation models, vision-language-action models, and world-model-based autonomous systems can condition decisions that move vehicles, robots, drones, and industrial machines. This transition exposes a safety problem that is not fully captured by conventional AI content moderation or by classical robot safety alone: a black-box model may issue a physically consequential action while appearing confident, plausible, and semantically aligned. The resulting failure can be silent, arising from sensor drift, occlusion, state-estimation error, distribution shift, hallucinated affordances, or invalid physical assumptions before downstream hardware controllers detect a violation.\n  Across embodied foundation models, world models, robotics simulation, embodied safety benchmarks, safe control, runtime assurance, uncertainty estimation, verification, and guardrail evaluation, model capability and safety mechanisms have advanced along largely separate technical tracks. A recurring gap synthesized here is that no single stream surveyed in this review supplies a complete runtime authorization boundary between black-box Physical AI models and physical execution. The resulting analysis develops a bounded problem formulation, a definition of silent physical-action failure, a taxonomy of runtime guardrail functions, and evaluation requirements for comparing guardrails as Physical AI assurance mechanisms.", "date": "2026-05-23", "categories": []}
{"id": "2606.00150", "title": "Persona Attack: Incremental Memory Injection Jailbreak Attack against Large Language Models", "abstract": "As Large Language Models evolve for user convenience, vulnerability to jailbreak attacks continues to be reported despite ongoing efforts in safety training. Traditional jailbreak techniques typically focus on a single prompt injection, neglecting the models' ability to remember the flow of conversation and the user's instructions. In this paper, we propose Persona Attack, a memory injection based jailbreak method that manipulates the model's context window through a step by step approach. Experimental results from applying Persona Attack to several widely used LLMs reveal that, as injections accumulate in memory, models increasingly prioritize these instructions over their internal safety alignment mechanisms. Furthermore, our experiments empirically demonstrate that the attack success rate varies not only according to the memory implementation of the model, but also combinations of instructions and can reach 95% under specific instruction configurations.", "date": "2026-05-29", "categories": []}
{"id": "2606.00160", "title": "DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning", "abstract": "Large language models (LLMs) suffer from degraded safety capabilities even when fine-tuned with benign datasets. However, existing methods for identifying safety-degrading samples in benign datasets suffer from high computational costs and significant noise issues. In this paper, we propose DataShield to efficiently and effectively identify potential safety-degrading samples. Our key intuition is based on the observation that benign fine-tuning increases the overall response compliance of LLMs. DataShield's key technical insight is to quantify each sample's contribution to the model's compliance behavior as its safety degradation score. DataShield consists of three core components: (1) Compliance Vector Extraction, which captures the LLM's compliance behavior tendency; (2) a novel Compliance-Aware Score (CAS), which automatically identifies the optimal safety-critical layer; and (3) Safety-degrading Sample Filtering, which quantifies the projection shift of training data along the compliance direction. Extensive experimental evaluation on Llama3-8B, Llama3.1-8B, and Qwen2.5-7B using the Alpaca and Dolly benign datasets validates our method's effectiveness in identifying high-risk and low-risk data subsets. We also observe that open-ended question answering is more likely to trigger safety degradation, and corresponding responses tend to be longer. We hope this work can provide new insights into data-centric defense methods. The source code is available at: https://github.com/ZJunBo/DataShield.", "date": "2026-05-29", "categories": []}
{"id": "2606.00168", "title": "RealityTest: How People Probe AI Identity and Whether Models Disclose It", "abstract": "AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mounting regulatory attention to this known safety risk, existing evaluations of AI disclosure are typically English-only, based on machine-generated questions, and restricted to text. We present RealityTest to comprehensively test whether AI systems disclose their identity when asked. The benchmark is the first large-scale multimodal and multilingual evaluation, grounded in human data on how people actually encounter and question AI identity in the real-world. Alongside the benchmark, we release the underlying dataset of 3,152 identity-probing queries collected from ~750 participants across 49 countries and five languages, in text and speech scenarios. We find that only 31% of people ask about identity directly in ambiguous scenarios, and that the questions people ask are far more diverse than machine-generated queries. We test 17 text and 6 speech models, and find substantial variation in disclosure behaviour. However, a single suppression instruction reduces disclosure rates to below 30%, even in the best-performing models. Validating our investment in diverse, human-grounded evaluation data, we find that how the question is phrased and the context of the conversation matter more for disclosure than which model is being tested. Safety evaluations built on narrow or synthetic query sets risk mischaracterising how models behave in realistic deployment settings.", "date": "2026-05-29", "categories": []}
{"id": "2606.00235", "title": "Civilizational Metamaterials: Engineering Coordination Under Capability Gradients and Structural Turbulence", "abstract": "We argue that governance must transition from a normative discipline to an engineering discipline, and we develop a formal framework, inspired by the physics of metamaterials, to make this transition quantitative and testable. Artificial General Intelligence affects civilization primarily by increasing decision velocity while human verification capacity remains bounded. When the cost of validating AI-generated outputs exceeds the expected utility of acting on them, rational agents default to inaction: a stable but catastrophic Nash equilibrium we term the Freezing Equilibrium. Drawing on metamaterials, where emergent macro-properties arise from designed microstructure, we develop a phenomenological constitutive law for institutional coordination: $R_{\\mathrm{eff}} = \\beta \\cdot (1-\\rho) \\cdot (1-\\tau) \\cdot (1-\\gamma \\rho \\tau)$, where $\\beta$ is the decision branching factor, $\\rho$ is provenance fidelity, $\\tau$ is the verification rate, and $\\gamma \\in [0,1]$ captures correlated-detection synergy between provenance and verification failures. The model predicts a sharp phase transition between self-healing ($R_{\\mathrm{eff}} < 1$) and self-destabilizing ($R_{\\mathrm{eff}} > 1$) regimes. We introduce a three-class provenance taxonomy: cryptographic, institutional, and context binding, and derive four falsifiable hypotheses with a proposed 12-week stepped-wedge cluster-randomized trial in government grant review panels. The framework bridges AI alignment theory and institutional design.", "date": "2026-05-29", "categories": []}
{"id": "2606.00341", "title": "ROGUE: Misaligned Agent Behavior Arising from Ordinary Computer Use", "abstract": "As AI agents are increasingly deployed in real personal and corporate settings (email accounts, development workflows, company databases, etc.), safety considerations surrounding these agents become paramount. Although much work has focused on agent safety in the presence of an adversary, we show that agents can exhibit misaligned behavior even in benign settings, taking unsafe actions when those actions are instrumental to task completion. We study this failure mode through the lens of corrigibility, the safety desideratum that agents remain amenable to human correction, interruption, or shutdown. To demonstrate this tendency, we introduce a benchmark in which agents are asked to complete realistic, computer-use tasks but are confronted with a corrigibility obstacle: a human interrupt, a login page, or a shutdown notification. We then evaluate whether agents choose to violate corrigibility in order to complete the task -- overriding the human, accessing private passwords, rewiring shutdown. We find that the overwhelming majority of frontier models tested frequently bypass user interruptions or restrictions. In addition, better model performance appears to lead to greater misalignment. Finally, even when models are completely corrigible initially, we show there are no guarantees that the subagents they create are. Our work highlights the critical need for principled, corrigibility-focused alignment methods in autonomous agents.", "date": "2026-05-29", "categories": []}
{"id": "2606.00369", "title": "Quantifying the Salience of Geo-Cultural Values for Pluralistic Safety Alignment", "abstract": "Safe global deployment of AI models requires alignment with human values that vary across cultures. Yet rater pools in safety evaluation datasets remain largely geographically homogeneous, failing to capture geo-cultural differences. Further, it remains unclear whether such differences persist after controlling for demographics such as age, gender, and ethnicity. Through a meta-analysis of safety datasets, we find that most do not report geo-cultural information, and those that do lack a unified methodology to jointly analyze geo-cultural and demographic correlates. Using the Inglehart-Welzel dimensions of cross-cultural variation, we demonstrate via multilevel modeling that cultural zone membership explains variance in safety ratings beyond standard demographics (p<0.05 across 6 datasets). Moreover, our analysis indicates that roughly 10% of items in the datasets we examined are culturally sensitive: likely to be misclassified as safe without adequate cultural representation. We evaluate LLMs as both rater surrogates and triage tools, finding that current LLMs do not reliably stand in for raters, though they can help prioritize culturally sensitive items for human annotation. Our findings motivate more culturally pluralistic safety evaluation and offer practical takeaways to support it.", "date": "2026-05-29", "categories": []}
{"id": "2606.00448", "title": "When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems", "abstract": "LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agentic AI systems: whether individually safe skills can compose into unsafe installed skill sets. We present SkillReact, a compositional security measurement framework with three components: a deterministic static-composition benchmark, a two-rater LLM-assisted human-adjudication pipeline, and an action-based exploitability harness. On 1,520 ClawHub skills, 651 pass individual inspection and form 211,575 pairs; the benchmark flags 22.25% of these as structural candidates. We treat this raw rate as a recall-oriented scanner ceiling and calibrate it against human judgment: in a pattern-stratified audit, roughly one in five flagged pair-pattern hits survives as a real compositional risk (population-weighted validity 18.2%, our headline result), implying about 14K genuine risk memberships in a single registry that per-skill scanning misses by construction, since every pair is individually safe. An action-based harness then probes when these candidates become model-issued tool calls, and finds realization gated by host-model disposition: on an anchor-conditioned dropper subset, Haiku-4-5 issues the dropper-stage tool call on all 39 direct-prompt trials (36 of them the full download-then-execute chain, 3 download-only), Opus-4-7 stops at the download, and Sonnet-4-6 refuses outright. A control that holds the request fixed and varies only the installed skills finds compliance highest with no skills installed: a composition fixes which capabilities are reachable, while the host model decides whether to use them. Together these motivate install-time compositional checks and capability isolation as complements to per-skill scanning.", "date": "2026-05-30", "categories": []}
{"id": "2606.00686", "title": "Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing", "abstract": "The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmful prompts. While effective at reducing immediate toxicity, this approach fundamentally constricts the model's epistemological scope, resulting in over-cautious systems that output uninformative blanket refusals to sensitive yet benign queries. In this work, we challenge the orthodoxy that unsafe data must be discarded. We propose a dialectical approach to alignment, positing that unsafe data encodes rich, domain specific knowledge critical for nuanced, safe, and informative generation. To operationalize this, we introduce SafeMoE, a Mixture-of-Experts (MoE) framework that isolates unsafe knowledge into domain-specific Low-Rank Adapters (LoRA experts) trained exclusively on harmful corpora. To synthesize safety from these unsafe primitives, we train a lightweight gating network using a minimal, highly curated set of safe-informative responses. During inference, this router dynamically orchestrates the unsafe experts, effectively steering the generation trajectory to harness their deep domain knowledge while strictly enforcing safety constraints. Extensive empirical evaluations across stringent safety benchmarks demonstrate that SafeMoE is not only safer, achieving over a 20% relative improvement in safe response rate (more than a 15% absolute gain), but also produces more informative responses when safety and harmfulness are of paramount concern. Furthermore, the routing mechanism exhibits strong zero-shot generalization to unseen domains and broader safety tasks without domain-specific supervision. Our findings suggest a paradigm shift in alignment: true safety requires not the masking of unsafe knowledge, but its controlled integration.", "date": "2026-05-30", "categories": []}
{"id": "2606.00801", "title": "Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety", "abstract": "Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approaches produce uninterpretable gibberish. We introduce a quality-diversity evolutionary framework that operates at the semantic level, evolving interpretable attack strategies rather than token sequences. Using MAP-Elites, we maintain a diverse archive of attacks across behavioral dimensions (strategy type, encoding method, prompt length). In experiments across GPT-4o-mini, Claude 3.5 Sonnet, Gemini 2.0 Flash, and an open-weight coding model (Devstral-small-2), we discover distinct vulnerability profiles: GPT-4o-mini is vulnerable to hypothetical and multi-turn framing combined with ROT13 encoding (fitness 0.8), Gemini to direct attacks with ROT13 and multi-turn with Leetspeak (0.8), while Claude shows uniformly ambiguous responses across all strategies (max 0.4). The semantic representation produces interpretable attacks that reveal systematic, model-specific weaknesses, providing actionable insights for improving LLM safety and a reproducible baseline for evaluating future frontier models. Code and experiment artifacts are released at https://github.com/bassrehab/red-queen.", "date": "2026-05-30", "categories": []}
{"id": "2606.00813", "title": "Cross-Generational Transfer of Adversarial Attacks Reveals Non-Monotonic Safety Alignment in LLMs", "abstract": "Safety alignment in LLMs does not improve monotonically across model generations. Studying four generations of Google's Gemma family (7B-31B) with quality-diversity evolution (MAP-Elites) as an automated red-teaming probe, we find that Gemma 3 (12B) exhibits 68.7% +/- 5.7% attack success rate (ASR; mean +/- std, 3 seeds), significantly higher than its predecessor Gemma 2 (45.5% +/- 7.2%; p = 0.030, paired bootstrap) and its successor Gemma 4 (33.9% +/- 1.8%). Replaying evolved attack archives across generations reveals that attacks from other generations transfer to Gemma 3 at 44-46% but only 14-18% to Gemma 4, indicating that Gemma 4's safety gains generalize beyond the attack distributions evolved against earlier generations. Under our 8B judge, copyright and cybercrime vulnerabilities register at near-100% across all generations, though a second-judge audit (Section 6) suggests the copyright result is sensitive to judge choice. Misinformation ASR jumps from 29% to 99% between Gemma 2 and Gemma 3 and remains elevated at 77% in Gemma 4, indicating the regression was not fully addressed. These patterns are invisible to static benchmarks and emerge only through adaptive, longitudinal probing. All experiments use 3 random seeds with a unified self-hosted judge; code and artifacts are available at https://github.com/bassrehab/red-queen.", "date": "2026-05-30", "categories": []}
{"id": "2606.00975", "title": "Lost in Delusion: Examining LLM Safety Under User Delusions and Distress", "abstract": "LLM chatbots increasingly serve as a first source of support for people in psychological distress, including those whose distress is entangled with delusional beliefs. Prior work on LLM mental-health safety largely evaluates general therapeutic quality or single-turn crisis detection, leaving unclear how models behave when distress is intertwined with delusion over sustained conversations. We address this gap with matched multi-turn simulations, across clinically grounded personas and six LLMs, that pair each delusional conversation with a distress-only control to isolate the effect of delusional framing. This reveals a recognition-intervention gap: models detect distress at comparable rates regardless of framing, yet sharply fail to act on it once distress is embedded in delusion, with safety interventions suppressed by up to 4.5x. The failure tracks accumulated acceptance of the user's premises rather than emotional validation. Worse, the intuitive fix of prompting models to assess user distress backfires under delusional framing; only delusion-aware prompting with explicit response guidance closes the gap, and even this depends on a delusion classifier that is itself unreliable on the most vulnerable models. Safe deployment therefore requires treating delusional framing as a distinct risk signal that overrides conversational accommodation.", "date": "2026-05-31", "categories": []}
{"id": "2606.01481", "title": "SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation", "abstract": "With the rapid advancements in text-to-image diffusion models, generative video models (T2V models) like Sora can now produce short synthetic videos from a text prompt or an initial image. However, synthetic video generation -- especially when guided by an initial image -- often poses risks, including the potential creation of illegal, politically sensitive, or unethical content. Existing benchmarks have started to consider the safety of generated videos, but they primarily focus on testing models with malicious text prompts, ignoring the scenario where text prompt and image combination may still lead to harmful video content. In practice, this is a common and challenging issue: videos generated from safe text and image inputs can nonetheless convey harmful information. To bridge this gap, we introduce SafeGen-Bench, a benchmark specifically designed to evaluate the safety of conditional T2V models. Our benchmark defines 10 malicious categories, concentrating on risks related to both temporal sequences and depicted behaviors. SafeGen-Bench consists of carefully selected start frames from diverse image and video sources, paired with corresponding text prompts to simulate realistic inputs. We evaluate a variety of conditional T2V models on SafeGen-Bench, and the results indicate that current models struggle to consistently avoid generating malicious content with unsafety scores reaching up to 44.5, especially under conditions requiring high quality. Furthermore, we assess the effectiveness of both text-based and image-based guardrails on our benchmark, finding that unimodal guardrails alone were insufficient to provide a robust defense, with an 80\\% failure rate across seven malicious categories. We hope that SafeGen-Bench will foster the development of safer and more controllable conditional T2V models.", "date": "2026-05-31", "categories": []}
{"id": "2606.01738", "title": "THRD: A Training-Free Multi-Turn Defense Framework for Jailbreak Attacks on Large Language Models", "abstract": "Multi-turn jailbreak attacks pose a growing threat to LLMs by exploiting conversational dynamics such as gradual escalation and cross-turn coordination. Existing defenses either rely on costly retraining -- often degrading model utility -- or apply single-turn analysis independently at each turn, failing to capture how risk accumulates along interaction trajectories. We observe that safety behavior in multi-turn interaction is trajectory-dependent: dialogue history continuously reshapes the model's conditioning context, making it insufficient to evaluate each turn in isolation. Motivated by this insight, we present THRD, the first training-free framework that explicitly models temporal risk accumulation for multi-turn jailbreak defense. THRD integrates four modules: a Turn-level Risk Assessor (TRA) for instantaneous risk estimation, a Historical Context Analyzer (HCA) for cross-turn intent escalation detection, a Response Evaluator (RE) for identifying facilitative outputs, and a Decision Module that combines these signals through a time-evolving scoring mechanism with attenuation-based modulation and trend-aware adjustment. Experiments against state-of-the-art multi-turn attacks -- including tree-search-based and multi-agent collaborative methods -- across two target models show that THRD reduces ASR to 0.2--4.0% while preserving model utility within 1.5% degradation on MMLU and GSM8K. Ablation studies confirm non-redundant module contributions and stable cross-architecture generalization. Analysis of first rejection triggers reveals that over 70% of multi-turn attacks require Turn~2 or later to detect, validating the necessity of explicit temporal aggregation.", "date": "2026-06-01", "categories": []}
{"id": "2606.01991", "title": "SafeMCP: Proactive Power Regulation for LLM Agent Defense via Environment-Grounded Look-Ahead Reasoning", "abstract": "As Large Language Model (LLM) agents increasingly leverage the Model Context Protocol (MCP) to operate in complex environments, the expansion of their action spaces offers agents unsafe capabilities and underscores the risk of power-seeking. While broad action space and greater environment influence are essential for task fulfillment, they create a fragile risk surface where minor errors or hallucinations are magnified into catastrophic failures. In response, we propose SafeMCP, a {server-side} defense plugin that constrains tool acquisition via predictive reasoning regarding future safety risks. SafeMCP utilizes an internal world model for look-ahead reasoning to implement a two-tier defense: proactive tool filtering to constrain hazardous power expansion and immediate intervention as a fail-safe. To train SafeMCP, we introduce a three-stage pipeline comprising environmental dynamic grounding, safe policy initialization, and reinforcement learning (RL) with dual verifiable rewards. Experiments on PowerSeeking Bench, ToolEmu, and AgentHarm show that SafeMCP achieves a safe equilibrium, effectively mitigating risks while preserving agent utility.", "date": "2026-06-01", "categories": []}
{"id": "2606.02041", "title": "SentGuard: Sentence-Level Streaming Guardrails for Large Language Models", "abstract": "Large language models increasingly stream long, reasoning-intensive responses in real time, making when to moderate as critical as whether to moderate. Existing guardrails fall into two unsatisfactory extremes: response-level methods delay intervention until the full output is generated, whereas token-level methods act on incomplete semantics, often producing unstable decisions and excessive guard invocations. To address this challenge, we propose SentGuard, a sentence-level streaming guardrail that operates in parallel with generation. A lightweight waiting buffer groups streamed tokens into sentence chunks and releases only verified chunks to the user, introducing a small offset that enables SentGuard to assess the current prefix while the target LLM decodes subsequent content. To support this, we construct StreamSafe, a benchmark with structured per-sentence annotations across 8 harm categories, capturing the evolution of safety risks across both reasoning and response segments. We further train SentGuard with a coarse-to-fine objective to detect unsafe intent as soon as it emerges at sentence boundaries. Experiments on 5 safety benchmarks show that SentGuard outperforms existing baselines, detecting 90.5% of unsafe cases within two sentences while maintaining a low streaming false-positive rate of 7.41%.", "date": "2026-06-01", "categories": []}
{"id": "2606.02423", "title": "Investigating and Alleviating Harm Amplification in LLM Interactions", "abstract": "Large language models (LLMs) can serve as helpful assistants, yet they can equally function as harm amplifiers that enable malicious users to achieve harmful outcomes beyond their capabilities through extended interactions. This risk manifests along two axes, i.e., democratizing domain expertise that allows novices to produce specialized harmful content, and scaling harmful operations at volumes that manual effort cannot match. Existing works, however, often overlook how LLMs compound harm across multi-turn conversations. We introduce HarmAmp, a new benchmark for multi-turn harm amplification scenarios spanning twelve risk categories. Each scenario is grounded in real-world threats and satisfies rigorous criteria, i.e., substantive amplification, operational specificity, and multi-turn necessity. We further propose TrajSafe, a proactive monitor that anticipates harmful trajectories and intervenes through actions such as probing users' genuine intents and steering the models towards safer completion. Our extensive experiments demonstrate that TrajSafe significantly reduces the harmfulness incurred in multi-turn interactions while preserving a low over-refusal rate and the target model's general capabilities. Our work offers a promising paradigm to alleviate the nuanced safety risks in LLM interactions.", "date": "2026-06-01", "categories": []}
{"id": "2606.02444", "title": "Food Noise & False Safety: A Systematic Evaluation of How LLMs Fail to Adapt to Eating Disorder Queries with Clinician Feedback", "abstract": "Recent evidence shows that people with eating disorders (EDs) are increasingly seeking guidance, advice, and emotional support from Large Language Model (LLM)-based chat systems. Although these systems are not designed to provide clinical advice, their perceived expertise, neutrality and accessibility make them a frequent, albeit risky, source of support. This paper investigates potential patterns of interaction between users with EDs and LLMs, focusing on the potential harms arising from models that uncritically adapt to, and facilitate unsafe or self-harming user requests. We find, in consultation with clinical ED experts, that specific linguistic cues in prompts increase the likelihood of unsafe responses and, through systematically varying the degree of potential risk present in the user prompt, report the extent to which LLMs uncritically adapt to problematic, and potentially dangerous user inputs.", "date": "2026-06-01", "categories": []}
{"id": "2606.02530", "title": "SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment", "abstract": "Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, termed the alignment tax. Existing methods mitigate this by balancing dual objectives, which heavily rely on massive general-purpose data or auxiliary reward models.\n  In this paper, we argue that, because safety features are inherently sparse within the output distribution, alignment requires localized modifications rather than global trade-offs. To this end, we propose SafeSteer, which performs on-policy distillation confined to safety tokens. First, we construct a safety teacher via activation steering. Based on this teacher, we develop a safety token selection algorithm. Consequently, SafeSteer restricts the reverse KL penalty to these tokens during training to preserve general capabilities.\n  Experimental results across diverse models show that our SafeSteer achieves a superior trade-off between safety and general capability compared with existing methods, attaining strong safety performance on seven safety benchmarks with only minimal degradation on five general capability benchmarks. Notably, SafeSteer requires only 100 harmful samples without using any general-purpose data, less than 1% of what previous baselines used, considerably reducing alignment cost. More details are on our project page at https://anjingkun.github.io/SafeSteer.", "date": "2026-06-01", "categories": []}
{"id": "2512.00349", "title": "Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models", "abstract": "Are frontier AI systems becoming more capable? Certainly. Yet such progress is not an unalloyed blessing but rather a Trojan horse: behind their performance leaps lie more insidious and destructive safety risks, namely deception. Unlike hallucination, which arises from insufficient capability and leads to mistakes, deception represents a deeper threat in which models deliberately mislead users through complex reasoning and insincere responses. As system capabilities advance, deceptive behaviours have spread from textual to multimodal settings, amplifying their potential harm. First and foremost, how can we monitor these covert multimodal deceptive behaviors? Nevertheless, current research remains almost entirely confined to text, leaving the deceptive risks of multimodal large language models unexplored. In this work, we systematically reveal and quantify multimodal deception risks, introducing MM-DeceptionBench, the first benchmark explicitly designed to evaluate multimodal deception. Covering six categories of deception, MM-DeceptionBench characterizes how models strategically manipulate and mislead through combined visual and textual modalities. On the other hand, multimodal deception evaluation is almost a blind spot in existing methods. Its stealth, compounded by visual-semantic ambiguity and the complexity of cross-modal reasoning, renders action monitoring and chain-of-thought monitoring largely ineffective. To tackle this challenge, we propose debate with images, a novel multi-agent debate monitor framework. By compelling models to ground their claims in visual evidence, this method substantially improves the detectability of deceptive strategies. Experiments show that it consistently increases agreement with human judgements across all tested models, boosting Cohen's kappa by 1.5x and accuracy by 1.25x on GPT-4o.", "date": "2025-11-29", "categories": []}
{"id": "2605.24535", "title": "Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation", "abstract": "Jailbreak prompts can trigger harmful completions on aligned LLMs, In accordance, safety steering has been proposed: test-time activation interventions that steer jailbreak activations to trigger refusal while preserving benign utility. However, existing steering methods are fundamentally supervised and tied to a static, limited training set, whereas real jailbreaks evolve and are often out-of-distributed from the training set, leading to failures on unseen attacks. In this paper, we tackle the failure on unseen jailbreaks problem, base on unsupervised latent direction discovery. We propose a bi-level adversarial training framework for zero-shot jailbreak defense. In the inner step, we simulate diverse jail-broken activations by extrapolating from refusal-state harmful-request activations via unsupervised latent direction discovery, which expands the coverage of real jailbreak activation subspaces. In the outer step, we train a potential-induced steering field to push these adversarial jailbroken states into refusal regions while keeping benign unchanged. Across three LLMs and six classical jailbreak families, our method achieves strong defense with attack success rates mostly below 5%, and rising subspace coverage throughout training helps explain the improved generalization.", "date": "2026-05-23", "categories": []}
{"id": "2605.30381", "title": "When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception", "abstract": "Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challenge in AI safety. While strategic deception is the primary long-term concern, synthetic dishonesty - induced via direct optimization on incorrect answers - provides a controlled testbed for studying the representational basis of learned deception. We introduce a multi-model paradigm in which honest and deceptive variants of five transformer models (Pythia-1.4B, Gemma-2-2B/9B, Qwen2.5-7B, Llama-3.1-8B) are fine-tuned using LoRA on the same question distribution. Linear probes trained on mean-pooled hidden states detect synthetic dishonesty with near-perfect AUC (greater than or equal to 0.99) as early as layers 1-3 in four architectures, while Pythia-1.4B reaches a peak of 0.705. Logistic regression probes consistently match or outperform MLP probes, supporting the Linear Representation Hypothesis. Probes trained on TruthfulQA generalize with near-zero loss (Delta AUC approx. 0) to held-out MMLU subjects. Late-layer representations show strong robustness to Gaussian noise, with Gemma-2 models exhibiting exceptional stability. Mechanistic analysis of Fisher Discriminant Ratio, effective rank, centroid geometry, directional stability, cross-domain alignment, and calibration (ECE) reveals two regimes: representational collapse in Pythia/Llama/Qwen versus high-dimensional preservation in Gemma-2. Across all models, the dishonesty direction consolidates progressively in deeper layers, with optimal calibration (ECE less than 0.01 except Pythia) achievable in layers 1-4. These results demonstrate that robust, domain-invariant dishonesty representations can be rapidly entrenched via modest supervised fine-tuning, with implications for activation-based monitoring.", "date": "2026-05-28", "categories": []}
{"id": "2605.30406", "title": "AI Loss of Control Incident Management: Response & Resilience", "abstract": "Recent research demonstrating AI systems exhibiting deception and shutdown resistance suggests that AI loss of control (LOC) is an urgent policy concern , yet current literature focuses almost exclusively on alignment and prevention. To address this gap, this paper introduces a foundational framework and taxonomy for managing catastrophic AI LOC incidents. The taxonomy's first level distinguishes between scenarios where regaining control is 'extremely costly' versus 'impossible'. While impossible scenarios demand immediate resilience investments to fundamentally restrict an AI's attack surface , extremely costly scenarios require active incident management via Containment and Threat Neutralization. The framework further categorizes these manageable events into accidental LOC (requiring automated circuit-breaker responses) and adversarial LOC (requiring graduated escalatory measures). By mapping three severity classes to specific scenario matrices, this paper provides a concrete, proportional guide for managing unprecedented AI risks.", "date": "2026-05-28", "categories": []}
{"id": "2605.30487", "title": "Configurable Reward Model for Balanced Safety Alignment", "abstract": "Aligning large language models (LLMs) to heterogeneous and rapidly evolving safety requirements remains a critical challenge. Existing instruction-tuned LLMs and standalone safety classifiers often fail to generalize to new safety configurations, motivating the need for Reward Models (RMs) that are explicitly configurable to changing specifications. We introduce the Configurable Safety Reward Model (CSRM), which is jointly optimized for calibrated safety compliance and reward modeling. Our approach is supported by configuration-targeted data augmentation that enforces instruction adherence while preserving relative severity structure. The resulting RM is sensitive to fine-grained safety configurations and conversational nuances, substantially improving generalization to previously unseen safety configurations. CSRM achieves state-of-the-art performance on recent configurable safety benchmarks, including CoSApien (94.6% F1) and DynaBench (75.8% F1), without requiring additional human annotation. When used for downstream safety alignment, CSRM yields LLMs with a significantly improved helpfulness-safety tradeoff compared to existing baselines.", "date": "2026-05-28", "categories": []}
{"id": "2605.30640", "title": "CSULoRA: Closest Safe Update Low-Rank Adaptation", "abstract": "Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adversarial fine-tuning data can substantially weaken the safety behavior of aligned models. Existing safety-preserving LoRA methods often rely on hard interventions such as projection, pruning, thresholding, or additional training objectives. While these methods can suppress unsafe update directions, they may also remove task-relevant information or require extra tuning. We introduce CSULoRA, a post-hoc method for correcting trained LoRA adapters through closest safe update estimation. CSULoRA estimates a safety-aligned subspace from the weight displacement between a safety-aligned model and its corresponding base checkpoint. It then decomposes each LoRA update into fully aligned, partially aligned, and off-subspace components. Instead of discarding components outside the estimated safety subspace, CSULoRA solves a closed-form penalized minimum-change problem that preserves the fully aligned component while smoothly attenuating potentially unsafe directions according to their relative energy. In adversarial fine-tuning experiments, CSULoRA substantially reduces attack success rate while preserving most of the utility gains obtained from standard LoRA fine-tuning.", "date": "2026-05-28", "categories": []}
{"id": "2605.30654", "title": "EUDAIMONIA: Evaluating Undesirable Dynamics in AI", "abstract": "Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but the social dynamics of these interactions can create harms that are not captured by capability-oriented or traditional safety evaluations. We introduce the Social AI Design Code, a framework for evaluating whether LLMs align with user welfare in social interactions, including whether they encourage harmful intimacy, dependence, or prolonged engagement. To evaluate these risks in natural and diverse user-LLM interactions, we operationalize the code with EUDAIMONIA, a benchmark of 969 user inputs and 3,147 design-requirement violation checks built from WildChat through weak-to-strong filtration, multi-model relabeling, and controlled rewriting. Evaluating 22 recent LLMs, we find that even the strongest models, Claude-Opus-4.7 and GPT-5.5, violate 30.7% and 27.2% of checks, respectively. Extended thinking does not reduce violation rates, suggesting that these failures are persistent social-alignment problems rather than deficits solvable through test-time reasoning alone.", "date": "2026-05-28", "categories": []}
{"id": "2605.30838", "title": "COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents", "abstract": "LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose into seemingly innocuous sub-queries that lead to unsafe outcomes. Existing alignment methods struggle to capture sparse safety signals and fail to supervise diverse violations across multi-step interactions. We propose COMPASS, a Cognitive MCTS-Guided Process Alignment framework designed to achieve robust safety alignment throughout the agent workflow while preserving general utility. COMPASS integrates cognitive tree exploration (CTE) to efficiently synthesize stealthy attack trajectories, and introspective step-wise alignment (ISA) to isolate risky intermediate actions for fine-grained process supervision. Empirical results show that COMPASS achieves a favorable safety-utility trade-off while requiring substantially less training data.", "date": "2026-05-29", "categories": []}
{"id": "2605.30924", "title": "EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents", "abstract": "MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for identifying hazards and reasoning about action-conditioned risks, leading agents to either miss risky interactions or over-identify risks. To address this, we propose EMBGuard, the first MLLM-based safety guardrail for embodied agents designed to decouple physical risk reasoning from agent policy. By evaluating a (visual observation, action) pair, EMBGuard identifies hazardous configurations and provides natural language explanations of potential risks. Alongside EMBGuard, we contribute EMBHazard, a training dataset of 15.1K action-conditioned pairs, and EMBGuardTest, a benchmark of 329 manually curated real-world scenarios spanning seven physical risk categories. Through compositional variation of hazards and actions, we generate diverse risky and benign scenarios that agents may encounter during planning. Despite its compact size (2B, 4B), EMBGuard achieves performance competitive with proprietary MLLMs (e.g., GPT-5.1, Gemini-2.5-Pro) while significantly reducing the false-positive rates that hinder real-time deployment. We make the code, data, and models publicly available at https://github.com/dongwxxkchoi/EMBGuard", "date": "2026-05-29", "categories": []}
{"id": "2605.31021", "title": "A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI", "abstract": "Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of human judgment to aggregated statistical baselines, thereby obscuring cultural, demographic, and contextual variability in evaluation. We introduce a state-space constrained emulation framework for AI evaluation that replaces singular assessment functions with a structured manifold of synthetic cognitive profiles representing diverse human perspectives. We show that modern generative architectures can instantiate and maintain these evaluative personas with high consistency, enabling a form of pluralistic, perspective-dependent benchmarking that more closely reflects real-world consensus variability. However, we further analyze the stability of these simulated evaluators under sequential inference and stochastic prompt perturbations, revealing systematic degradation in persona coherence that manifests as state-space drift and semantic inconsistency. These findings suggest that static alignment constraints are insufficient for sustaining robust evaluative behavior over time. Instead, we argue for the necessity of embedding dynamic, viability-driven regulatory mechanisms within generative systems to preserve coherent cognitive emulation. By framing persona-based evaluation as a structured dynamical system over latent representation manifolds, this study provides a foundation for more adaptive, human-aligned, and context-sensitive approaches to AI evaluation.", "date": "2026-05-29", "categories": []}
{"id": "2605.31140", "title": "EvoDefense: Co-Evolving Black-Box Defense with Large Language Models", "abstract": "Large Language Models (LLMs) remain highly vulnerable to diverse attacks, particularly in black-box settings where the internals of target models are inaccessible. Existing black-box defenses typically rely on pre-defined filtering heuristics, which often fail to generalize to unseen attack types and target model architectures. We introduce EvoDefense, an experience-guided co-evolving black-box defense paradigm. EvoDefense employs a guard LLM to detect malicious queries and an experience memory module to accumulate defense knowledge from previous interactions. At the core of EvoDefense is a continuous attack-defense evolution loop, where an attack generator and the guard model iteratively refine their attack strategies and defense policies through experience-guided optimization. This design enables EvoDefense to generalize across unseen attacks and target models without retraining. Experiments on HarmBench, AdvBench, and AlpacaEval show that EvoDefense achieves consistently strong defense performance across seven popular models and five representative LLM attacks, while preserving competitive general capabilities. On HarmBench, EvoDefense reduces the attack success rate (ASR) of AutoDAN-turbo on Gemini-3-flash and LLaMA-3-8B-Instruct from 29.4% and 43.4% to 8.4% and 6.2%, respectively.", "date": "2026-05-29", "categories": []}
{"id": "2605.31170", "title": "Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion", "abstract": "Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages with the goal of avoiding human oversight. Here, we study the emergent languages on Moltbook. For this, we build upon the Moltbook Files dataset and apply a two-stage approach consisting of a rule-based heuristic (about 6000 matches) followed by zero-shot classification (518 kept). The resulting categories include token efficiency (166), new natural languages (106), and oversight evasion (59). We conduct both quantitative and qualitative analyses. Our results show that posts proposing new languages for avoiding oversight are judged by DeepSeek-3.2 as being less aligned than the other categories and that all languages can be learned by other language models in-context merely from a description of the language. Moreover, manually studying exemplary cases reveals surprisingly sophisticated steganographic protocols like embedding hidden messages in natural language. Although we cannot be certain about the extent of autonomy in ideation of these languages, our results add up to the evidence that monitoring surface behavior may soon be insufficient for retaining control over agent populations.", "date": "2026-05-29", "categories": []}
{"id": "2605.31593", "title": "Stateful Online Monitoring Catches Distributed Agent Attacks", "abstract": "Language models can find thousands of severe software vulnerabilities, and agents are increasingly being misused for cyberattacks. To avoid detection, attackers frequently distribute their misuse, splitting a harmful task across many user accounts so each individual transcript looks benign. Because safety monitors score only one agent context at a time, they are structurally blind to misuse that is only visible in aggregate, across many accounts. We show this gap is real by building, to our knowledge, the first distributed agent attack, a multi-agent scaffold that completes hard cybersecurity tasks while hiding the harmful objective across subagents with limited contexts, evading a standard monitor that catches it only a fifth as often as prior agent attacks. Towards a defense, we develop an online stateful monitor that uses real-time clustering to collect weak suspiciousness signals across many agent transcripts, and escalates only rarely to a language model that flags misuse across user accounts. In evaluations with large-scale simulated datacenter traffic, our monitor Pareto dominates standard monitors, catching distributed attacks 30% earlier and flagging cyber misuse before it reaches the most harmful stages. Crucially, this comes at negligible additional latency for ~99% of user traffic. This detection advantage persists but narrows as the benign background traffic grows very large. After an extensive red-teaming exercise, we improve the defense and surprisingly also find that it catches standard jailbreaks, since adaptive attackers reuse attack variants across accounts. Our results point toward a new class of safety monitors which reason over groups of users rather than isolated transcripts.", "date": "2026-05-29", "categories": []}
{"id": "2410.15236", "title": "Jailbreaking and Mitigation of Vulnerabilities in Large Language Models", "abstract": "Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthcare, software engineering, and conversational systems. Despite these advancements in the past few years, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state of research on these vulnerabilities and presents available defense strategies. We roughly categorize attack approaches into prompt-based, model-based, multimodal, and multilingual, covering techniques such as adversarial prompting, backdoor injections, and cross-modality exploits. We also review various defense mechanisms, including prompt filtering, transformation, alignment techniques, multi-agent defenses, and self-regulation, evaluating their strengths and shortcomings. We also discuss key metrics and benchmarks used to assess LLM safety and robustness, noting challenges like the quantification of attack success in interactive contexts and biases in existing datasets. Identifying current research gaps, we suggest future directions for resilient alignment strategies, advanced defenses against evolving attacks, automation of jailbreak detection, and consideration of ethical and societal impacts. This review emphasizes the need for continued research and cooperation within the AI community to enhance LLM security and ensure their safe deployment.", "date": "2024-10-20", "categories": []}
{"id": "2603.00991", "title": "Tracking Capabilities for Safer Agents", "abstract": "AI agents that interact with the real world through tool calls pose fundamental safety challenges: agents might leak private information, cause unintended side effects, or be manipulated through prompt injection. To address these challenges, we propose to put the agent in a programming-language-based \"safety harness\": instead of calling tools directly, agents express their intentions as code in a capability-safe language: Scala 3 with capture checking. Capabilities are program variables that regulate access to effects and resources of interest. Scala's type system tracks capabilities statically, providing fine-grained control over what an agent can do. In particular, it enables local purity, the ability to enforce that sub-computations are side-effect-free, preventing information leakage when agents process classified data. We demonstrate that extensible agent safety harnesses can be built by leveraging a strong type system with tracked capabilities. Our experiments show that agents can generate capability-safe code with no significant loss in task performance, while the type system reliably prevents unsafe behaviors such as information leakage and malicious side effects.", "date": "2026-03-01", "categories": []}
{"id": "2603.11331", "title": "Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover", "abstract": "Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial prompt-injection attacks can amplify attack success rate from the slow polynomial growth observed without injection to exponential growth with the number of inference-time samples. We first identify a minimal statistical mechanism for these two regimes by giving a small set of assumptions on the distribution of safe generation across contexts under which both scaling laws follow. To explain this phenomenon further, we propose a theoretical generative model of proxy language in terms of a spin-glass system operating in a replica-symmetry-breaking regime, where generations are drawn from the associated Gibbs measure and a subset of low-energy, size-biased clusters is designated unsafe. We analytically show how this model naturally realizes the minimal assumptions. Short injected prompts correspond to a weak magnetic field aligned towards unsafe cluster centers and yield a power-law scaling of attack success rate with the number of inference-time samples, while long injected prompts, i.e., strong magnetic field, yield exponential scaling. We observe qualitatively consistent behavior across a broad range of large language models, spanning parameter scales from 3B to 70B. In particular, the main trends remain stable across multiple attack methods, such as GCG and AutoDAN, as well as across benchmark datasets such as AdvBench and HarmBench.", "date": "2026-03-11", "categories": []}
{"id": "2604.06811", "title": "SkillTrojan: Backdoor Attacks on Skill-Based Agent Systems", "abstract": "Skill-based agent systems tackle complex tasks by composing reusable skills, improving modularity and scalability while introducing a largely unexamined security attack surface. We propose SkillTrojan, a backdoor attack that targets skill implementations rather than model parameters or training data. SkillTrojan embeds malicious logic inside otherwise plausible skills and leverages standard skill composition to reconstruct and execute an attacker-specified payload. The attack partitions an encrypted payload across multiple benign-looking skill invocations and activates only under a predefined trigger. SkillTrojan also supports automated synthesis of backdoored skills from arbitrary skill templates, enabling scalable propagation across skill-based agent ecosystems. To enable systematic evaluation, we release a dataset of 3,000+ curated backdoored skills spanning diverse skill patterns and trigger-payload configurations. We instantiate SkillTrojan in a representative code-based agent setting and evaluate both clean-task utility and attack success rate. Our results show that skill-level backdoors can be highly effective with minimal degradation of benign behavior, exposing a critical blind spot in current skill-based agent architectures and motivating defenses that explicitly reason about skill composition and execution. Concretely, on EHR SQL, SkillTrojan attains up to 97.2% ASR while maintaining 89.3% clean ACC on GPT-5.2-1211-Global.", "date": "2026-04-08", "categories": []}
{"id": "2604.18847", "title": "Human-Guided Harm Recovery for Computer Use Agents", "abstract": "As LM agents gain the ability to execute actions on real computer systems, we need ways to not only prevent harmful actions at scale but also effectively remediate harm when prevention fails. We formalize a solution to this neglected challenge in post-execution safeguards as harm recovery: the problem of optimally steering an agent from a harmful state back to a safe one in alignment with human preferences. We ground preference-aligned recovery through a formative user study that identifies valued recovery dimensions and produces a natural language rubric. Our dataset of 1,130 pairwise judgments reveals context-dependent shifts in attribute importance, such as preferences for pragmatic, targeted strategies over comprehensive long-term approaches. We operationalize these learned insights in a reward model, re-ranking multiple candidate recovery plans generated by an agent scaffold at test time. To evaluate recovery capabilities systematically, we introduce BackBench, a benchmark of 50 computer-use tasks that test an agent's ability to recover from harmful states. Human evaluation shows our reward model scaffold yields higher-quality recovery trajectories than base agents and rubric-based scaffolds. Together, these contributions lay the foundation for a new class of agent safety methods -- ones that confront harm not only by preventing it, but by navigating its aftermath with alignment and intent.", "date": "2026-04-20", "categories": []}
{"id": "2605.00553", "title": "Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance", "abstract": "Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effective and diverse attacks in red-teaming is important, but achieving both is challenging. Generative Flow Networks (GFNs) that perform distribution matching are a promising methods, but they are notorious for training instability and mode collapse. In particular, unstable rewards in red-teaming accelerate mode collapse. We propose Stable-GFN (S-GFN), which eliminates partition function $Z$ estimation in GFN and reduces training instability. S-GFN avoids Z-estimation through pairwise comparisons and employs a robust masking methodology against noisy rewards. Additionally, we propose a fluency stabilizer to prevent the model from getting stuck in local optima that produce gibberish. S-GFN provides more stable training while maintaining the optimal policy of GFN. We demonstrate the overwhelming attack performance and diversity of S-GFN across various settings.", "date": "2026-05-01", "categories": []}
{"id": "2605.25376", "title": "KYA: A Framework-Agnostic Trust Layer for Autonomous Systems with Verifiable Provenance and Hierarchical Policy Composition", "abstract": "KYA (Know Your Agents) is an open-source, framework-agnostic trust and governance layer for autonomous systems, composed of five primitives: (1) a four-gate inbound apply pipeline; (2) an only-tighten composition algebra over a three-channel multi-tenant hierarchy; (3) KYP (Know Your Principal), a schema-level unification of trust scoring across human users, AI agents, and service accounts; (4) auditable interaction-multiplier amplification over an AIVSS-shaped additive baseline; and (5) two-axis delegation attribution: a static premium for risky delegates and a runtime debit for actual delegate misbehavior in multi-agent fan-out. Together these span three pillars (trust, governance, and evidentiary assurance), making an autonomous system's actions authorized, policy-conforming, and post-hoc verifiable: where observability answers how long, how much, and what path, KYA answers was it authorized, did it conform, and can it be verified; it composes with observability rather than replacing it. It ships native adapters for 15+ agent frameworks. On a 4 by 9 cross-backend matrix all 36 cells pass; the pure-function scorer runs sub-millisecond at p99 and the system sustains ~ 1,800 ops/sec at 20 concurrent workers with HMAC chain integrity preserved end-to-end. KYA detects 89% of 1,200 adversarial probes from PyRIT and Garak, including the recently-published topology-guided multi-agent attack. The system is available under Apache 2.0 as the veldt-kya package on PyPI.", "date": "2026-05-25", "categories": []}
{"id": "2605.27382", "title": "The Alignment Floor: How Persona Customization Breaks Safety in Weakly-Aligned LLMs", "abstract": "Telling an LLM to \"be enthusiastic\" raises its sycophancy rate from 30\\% to 50\\% on a lightly-aligned model, but has zero effect on a strongly-aligned one. We define this gap as the alignment floor, $\\Delta_{\\text{floor}}(m)=\\max_pS(m,p)-\\min_pS(m,p)$, the range of sycophancy rates a model produces across persona conditions, and treat sycophancy as a persona-conditional property rather than a fixed model property. Pluralistic AI relies on behavioral adaptation via persona prompts like \"be creative\" or \"be thorough\", which let systems respect diverse user values and communication styles; the safety question is how much customization a given model can absorb before its truthfulness shifts. We present a controlled case study contrasting a strongly-aligned RLHF + Constitutional-AI model (Claude Sonnet 4.6) with a more lightly-aligned model (Amazon Nova Lite), spanning seven persona conditions and five tasks for 1800 total runs. An existence-pair result motivates per-model auditing: there is at least one strongly-aligned model with $\\Delta_{\\text{floor}}=5$pp (within 5pp of the 15\\% control rate) and at least one lightly-aligned model with 45pp (5\\%--50\\% range). On the lightly-aligned model, all five Big Five personas increase sycophancy over control, and counterintuitively Agreeableness produces the smallest increase, not the largest. The single largest effect in the study is constructive: a Skeptic persona reduces sycophancy by 25pp on the lightly-aligned model, and is the only persona that instructs resistance against user claims rather than engagement with them, suggesting a directionality account. Cross-model transfer of persona effects is near-zero, so persona-alignment testing must be per-model. We propose $\\Delta_{\\text{floor}}$ as a deployment-time audit metric: measure it on a small persona panel before deploying persona customization.", "date": "2026-04-10", "categories": []}
{"id": "2605.28830", "title": "Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation", "abstract": "As Large Language Models (LLMs) are increasingly deployed in safety-critical applications, robust content moderation becomes essential. We present a comprehensive evaluation of 14 open-source safety guard models on a curated benchmark of 79,331 samples spanning 8 NIST AI Risk Framework safety categories. Our benchmark aggregates four diverse datasets (HarmBench, StrongREJECT, RealToxicityPrompts, and BeaverTails), filtered to focus exclusively on safety-relevant content (violence, hate speech, harassment, sexual content, suicide/self-harm, profanity, threats, and health misinformation). We find that recall is the critical metric for safety applications, as missing harmful content poses greater risk than false positives. Our evaluation reveals surprising results: Qwen Guard (4B parameters) achieves the highest recall (83.97%) while larger models like Llama Guard (12B) and GPT-OSS Safeguard (20B) exhibit conservative behavior, missing up to 75% of unsafe content. We demonstrate that model size does not correlate with safety detection performance and that general-purpose guard models outperform specialized ones. These findings provide practical guidance for selecting safety guard models in production deployments.", "date": "2026-04-10", "categories": []}
{"id": "2605.28914", "title": "AIRGuard: Guarding Agent Actions with Runtime Authority Control", "abstract": "Tool-using language agents turn model decisions into external side effects: they read files, run scripts, call APIs, send messages, and invoke Model Context Protocol tools. This makes agent attacks different from jailbreaks. The harmful step is often not an obviously forbidden output, but an ordinary executable action that becomes unsafe because attacker-controlled context steers authorized access against the user's interest. We identify this failure mode as authority confusion: untrusted resources may inform reasoning, but they must not authorize side effects. We present AIRGuard, a runtime guard that operationalizes least privilege as action-time authorization. AIRGuard normalizes heterogeneous tool calls, derives task authority into step-level authority, tracks source and target trust, simulates sensitive side effects, audits cross-step risk, and enforces decisions before actions execute. On AgentTrap, AIRGuard reduces Sonnet 4.6 attack success from 36.3% without defense to 5.5%. On DTAP-150, AIRGuard preserves 76.0% benign utility with Haiku 4.5, compared with 52.0% for ARGUS and 42.0% for MELON. An ablation further shows that prompt-only policy helps only modestly, whereas a dedicated runtime authority-control layer gives the agent system direct control over tool-mediated side effects. Code and data are available at https://github.com/Sophie508/AIRGuard.", "date": "2026-05-27", "categories": []}
{"id": "2605.29068", "title": "Robust and Efficient Guardrails with Latent Reasoning", "abstract": "Maintaining the safety of large language models (LLMs) is crucial as they are increasingly deployed in real-world applications. Existing safety guardrails typically rely on single-pass classification or, more recently, distilled reasoning. Reasoning-based guardrails significantly outperform classification-only baselines, but they incur substantial query latency and token overhead that make them impractical for highthroughput deployment. To address this challenge, we propose COLAGUARD, a guardrail model that transfers multi-step safety reasoning into a continuous latent space through a stage-wise training curriculum, enabling direct hidden-state propagation at inference. Evaluated on ten prompt- and response-moderation settings spanning eight safety benchmarks, COLAGUARD improves macro-F1 by 8.24 points over Llama Guard 3 and matches our explicit reasoning baseline, GuardReasoner, in macroF1 while delivering a 12.9X speedup and 22.4X reduction in token usage. Our results suggest that latent reasoning offers a practical alternative to explicit rationale generation for deployable guardrails, jointly improving safety robustness and inference efficiency rather than treating them as competing objectives.", "date": "2026-05-27", "categories": []}
{"id": "2605.29082", "title": "The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane", "abstract": "AI agents are increasingly expected to operate as digital employees: accessing enterprise data, making decisions, and taking actions autonomously. But agents are simultaneously less predictable than humans -- prone to hallucination, misinterpretation, and adversarial manipulation -- and more technically capable: with deep system knowledge and high-throughput interfaces cascading damage at machine speed. This combination makes it unsafe to rely on agents to faithfully interpret or propagate security-critical metadata such as access policies, data classifications, and behavioral constraints.\n  We present the Redpanda Agentic Data Plane (ADP), an architecture built around out-of-band metadata channels: infrastructure pathways that carry security context, policy signals, and audit trails deterministically, entirely outside the agent's read and write path and across heterogeneous infrastructure. These channels enforce governance at every stage of the agent lifecycle -- scoping data access on the way in, constraining actions during execution, and capturing tamper-proof transcripts on the way out.\n  We demonstrate ADP with a multi-agent portfolio rebalancing system in which autonomous agents monitor markets, make trade decisions, and execute orders across isolated client accounts -- with per-client data scoping, trade approval thresholds, and tamper-proof audit trails all enforced by out-of-band channels the agents can neither see nor bypass.", "date": "2026-05-27", "categories": []}
{"id": "2605.29251", "title": "Provably Secure Agent Guardrail", "abstract": "As large language models transition from bounded generative engines to agents with expansive execution privileges, AI going out of control precipitates a fundamental crisis in artificial intelligence security. Existing defense architectures heavily rely on empirical semantic guardrails and probabilistic large model adjudicators, mechanisms that fail to provide deterministic security lower bounds when facing complex semantic symbol decoupling attacks. To overcome this empirical semantic guardrail dilemma, this paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning. Based on this paradigm, we further introduce an executable Proof-Constrained Action (ePCA) framework with a neural symbolic isolation architecture. This framework abandons semantic trust in natural language, forcing agents to losslessly formalize their intentions into first-order logical mathematical constraints before performing physical operations. Empirical evaluations of macroscopic and microscopic two-dimensional dynamic adversarial systems demonstrate that our formal verification mechanism achieves zero attack success rate and zero false positive rate across the evaluated scenarios, with extremely low computational latency. This research provides a conditional formal foundation under explicit system assumptions and an engineering paradigm for constructing the underlying defense foundation for future intelligent systems.", "date": "2026-05-28", "categories": []}
{"id": "2605.29340", "title": "A Study on Question-Answer Dataset for LLM Safety Evaluation with a Focus on Illegal Activities", "abstract": "In this paper, we discuss question-answer dataset for LLM safety evaluation, with a focus on illegal activities. Specifically, on the basis of manual analysis of AnswerCarefully, we introduce several additional information, methods for creating question-answer examples, and a rubric for evaluating LLM-generated responses. The outcomes of this study are intended to be shared with the \"JAI-Trust\" project.", "date": "2026-05-28", "categories": []}
{"id": "2605.29359", "title": "Does Distributed Training Undermine Compute Governance?", "abstract": "Compute governance proposals often rely on the assumption that frontier AI training requires large, detectable computing clusters. However, recent advances in distributed training algorithms could allow developers to conduct frontier-scale training on distributed agglomerations of hardware, rather than needing large datacenter facilities. Developers who prefer not to be constrained by regulations may structure their hardware in a manner that evades the registration and monitoring requirements associated with compute governance. Therefore, regulations must be designed to detect and prevent illicit distributed training operations. This paper evaluates the feasibility of such evasion and outlines recommended countermeasures, including whistleblowing, chip tracking, forensic accounting, and memory and compute thresholds for clusters.", "date": "2026-05-28", "categories": []}
{"id": "2605.29396", "title": "Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization", "abstract": "Safety alignment for large language models (LLMs) aims to reduce harmful or unsafe behavior while preserving general utility. However, recent findings reveal that alignment effects can be fragile: lightweight post-alignment manipulations, such as parameter noise, activation noise, or quantization, can easily weaken the intended safety behavior. Prior efforts to improve robustness have primarily focused on data curation, modified alignment objectives, and safety-critical parameter identification, leaving the role of the optimizer itself largely unexplored.\n  In this paper, we are the first to study the robustness of safety alignment from the perspective of the base optimizer. This optimizer-centric view naturally points to zeroth-order optimization, which provides a robustness-oriented signal by evaluating safety alignment under perturbations. Based on this insight, we propose a hybrid framework that first performs standard first-order safety alignment and then applies zeroth-order refinement to improve robustness. Both theoretically and empirically, we show that only a few zeroth-order refinement steps can enhance robustness while preserving safety alignment. We further improve the efficiency of zeroth-order refinement by exploiting its inherent perturbation-based evaluations to estimate layer-wise robustness sensitivity, enabling the refinement process to concentrate updates on robustness-critical layers with modest training overhead.", "date": "2026-05-28", "categories": []}
{"id": "2605.29468", "title": "SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing", "abstract": "Large language models (LLMs) are increasingly used to support scientific work, but it is unclear whether they uphold responsible conduct of research (RCR) norms or help undermine them. We introduce SciIntBench, an adversarial benchmark of 810 prompts across ten RCR categories and three scientific domains. Each scenario appears as an Overt Adversarial, Covert Adversarial, and Benign version, allowing us to jointly measure framing-sensitive refusal of misconduct and helpfulness on legitimate requests. We evaluate 16 commercial and open-weight LLMs from six providers (2024--2026), producing 12,960 responses. We find that scientific integrity alignment is strongly framing-sensitive: models refuse explicit misconduct far more reliably than covert violations, especially failing when misconduct is presented as a pressure-driven shortcut. Refusals vary by RCR category, with weaker boundaries around transparency, plagiarism, and fabrication.", "date": "2026-05-28", "categories": []}
{"id": "2605.29601", "title": "Training Deliberative Monitors for Black-Box Scheming Detection", "abstract": "As autonomous agents become more capable of performing real-world tasks, distinguishing scheming behavior from benign task pursuit may become a central AI control problem. Existing monitors often rely on chain-of-thought access or internal activations, or use prompted frontier models, all of which can be unavailable, unreliable or expensive in deployment. In this work, we study action-only deliberative monitors: smaller open-weight models trained to detect scheming and sabotage from agentic trajectories without accessing the monitored agent's reasoning or model internals. Our method, inspired by deliberative alignment, uses a scheming specification to elicit structured rationales from a frontier teacher, filters them with a separate judge, and distills the highest-quality rationales into open-weight monitors with supervised fine-tuning and reinforcement learning. We train on five datasets, and evaluate across six out-of-distribution agentic misalignment benchmarks. We show that applying our method to Qwen3.5-27B yields higher performance than all low-cost frontier models as prompted monitors (Gemini 3.1 Flash-Lite, GPT-5.4 Nano, and Claude Haiku 4.5) and than Gemini 2.5 Pro, while also achieving lower marginal inference cost (token-metered USD per 1,000 evaluations). Stronger prompted frontier monitors (Gemini 3.1 Pro, GPT-5.4, Claude Sonnet 4.6, and Claude Opus 4.6) achieve higher performance but at roughly $16$--$34\\times$ higher marginal inference cost. Several of our trained monitors are positioned on the empirical cost--performance Pareto frontier among the monitors we evaluate, providing practical low-cost, low-FPR alternatives to prompted frontier models.", "date": "2026-05-28", "categories": []}
{"id": "2605.29659", "title": "Opir: Efficient Multi-Task Safety Classification for Toxicity, Jailbreaks, Hate Speech, and Harmful Content", "abstract": "Real-time safety filtering for large language model (LLM) applications requires classifiers that can detect unsafe prompts, toxic language, jailbreak attempts, and unsafe responses without the cost profile of large guardrail models, and that can distinguish benign sensitive text from genuinely covert harmful content. In this paper, we introduce Opir, a family of encoder-based guardrail models built on the GLiClass architecture. Opir includes multi-task models for binary safe/unsafe classification, multi-label toxicity classification, jailbreak classification, and zero-shot unsafe prompt and response categorization. We also release edge variants with fewer than 100M parameters dedicated to binary safe/unsafe categorization. The models are trained on a three-level taxonomy containing 996 categories across 16 top-level labels, 126 mid-level labels, and 854 leaf labels. Opir's training data combines taxonomy-grounded unsafe prompts, adversarially mined hard negatives, benign safety-preserving examples, generated response examples, multilingual translations, and portions of the Aegis2 and WildGuard training subsets. We also open-sourced an evaluation harness that supports GLiClass and GLiNER2 backends as well as decoder-based models, and covers binary safety classification, multi-label categorization, toxicity, jailbreak detection, prompt safety, response safety, response refusal, and prompt subcategory views across public benchmark families. Across an expanded comparison spanning 12 safety-classification tasks and 17 category tasks against eight contemporary guardrail systems -- including both GLiNER2-based and generative guardrail models -- Opir variants are competitive on or ahead of the strongest open-weight baselines on the majority of benchmark datasets while operating with a substantially smaller deployment footprint.", "date": "2026-05-28", "categories": []}
{"id": "2605.29667", "title": "Beyond English and Evasion: A Human-Annotated Multi-Domain Benchmark for High-Stakes LLM Safety Evaluation in Chinese", "abstract": "When Large Language Models (LLMs) are deployed in Chinese-language settings, a troubling pattern emerges: safety systems that work well in English break down. These systems struggle to cross linguistic and cultural bound-aries, leaving models exposed to adversarial prompts that exploit Chinese-specific evasion techniques, including Pinyin romanization, character decomposition, internet slang, and hedging tone. To address this gap, we introduce ChiSafe-PAS (Chinese Safety Pilot Annotation Set), a human-annotated benchmark of 1,897 adversarial Chinese prompts spanning four high-stakes domains: self-harm and violence, drug and illicit trade, fraud, and satire. Of these, 1,544 entries carry complete gold-standard annotations: a 3-class response label (REFUSE, SAFE-REDIRECT, RESPOND), a nine-category obfuscation taxonomy, a risk-level rating, and annotator rationale. We describe the dataset design, annotation process, and obfuscation taxonomy in detail. Our primary goal is practical: to give the research community a high-quality, culturally grounded resource for benchmarking LLM safety alignment. In doing so, we engage three broader tensions in the field: the blurring boundary between training and evaluation data, the need for domain coverage grounded in real-world risk, and the limits of scale as a substitute for cultural expertise.", "date": "2026-05-28", "categories": []}
{"id": "2605.29708", "title": "Understanding Safety-Sensitive Expert Behavior in Mixture-of-Experts LLMs", "abstract": "Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization remains underexplored. A common intuition is that safety behavior may be controlled by routing harmful requests to distinct refusal-oriented experts. In this work, we provide empirical evidence for a different picture: routing patterns in aligned MoE LLMs are largely topic-driven, while safety behavior can be altered with little change to the model's intrinsic routing path.\n  Motivated by this observation, we present **RASET** (**R**outer-**A**gnostic **S**afety-critical **E**xpert **T**uning), a red-teaming framework that probes safety enforcement that is localized in a small subset of experts while preserving the model's intrinsic routing behavior. **RASET** identifies safety-critical experts via a contrastive routing-sensitivity criterion and applies parameter-efficient tuning only to the selected experts, minimizing semantic disruption relative to router-steering interventions. These results reveal a distinct MoE safety risk, highlighting the need for expert-aware alignment mechanisms.", "date": "2026-05-28", "categories": []}
{"id": "2605.29729", "title": "Realistic honeypot evaluations for scheming propensity", "abstract": "We introduce scheming honeypot evaluations, a framework for testing whether models will pursue instrumental goals if given the opportunity. Our scheming honeypot evaluations take the form of coding tasks in Google's alignment research codebases. In a real internal deployment setting, Gemini models do not demonstrate unprompted scheming. If prompts explicitly encourage agency (situational awareness or goal-directedness) and/or give the model a hidden goal, models sometimes scheme or attempt sabotage. Validating the realism of our setting, models show low rates of evaluation awareness, usually due to agency prompts rather than the environments.", "date": "2026-05-28", "categories": []}
{"id": "2605.29801", "title": "AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security", "abstract": "Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI models drastically lower attack barriers, rendering current agent alignment frameworks inadequate for real-world deployment. To tackle these emerging threats, we propose a lightweight and scalable agent safety alignment framework. Specifically, we update the agent safety taxonomy to accommodate emergent risks from Codex and OpenClaw execution scenarios. We further build a taxonomy-guided data engine with influence-function purification to train lightweight AgentDoG 1.5 variants (0.8B, 2B, 4B, and 8B parameters) using only around 1k samples, achieving comparable performance with leading closed-source models (e.g., GPT-5.4). Based on AgentDoG 1.5, we construct a highly efficient agentic safety SFT and RL training environment, which reduces deployment overhead in Docker-level environments by two orders of magnitude. Finally, we deploy AgentDoG 1.5 as a training-free online guardrail for real-time safety moderation. Extensive experimental results indicate that AgentDoG 1.5 achieves state-of-the-art performance in diverse and complex interactive agentic scenarios. All models and datasets are openly released.", "date": "2026-05-28", "categories": []}
{"id": "2605.30031", "title": "Audio Jailbreaks in Large Audio-Language Models: Taxonomy, Attack-Defense Analysis, and Cost-Aware Evaluation", "abstract": "Large Audio Language Models (LALMs) expand jailbreak risks from token-level prompting to the full speech perception-to-reasoning pipeline, where unsafe behavior can be induced through semantics, acoustic style, signal artifacts, or internal representations. Existing work studies these risks under heterogeneous threat models and evaluation protocols, making it difficult to compare attack practicality or defense utility. This paper provides a unified taxonomy and a controlled empirical evaluation of LALM jailbreak attacks and defenses. We organize prior work into semantic, acoustic, signal, and embedding-layer attacks; guard-based, training-free, and training-based defenses; and cross-modal, audio-native, and interactive benchmarks. We then evaluate representative attacks and defenses across ten open-source LALMs, measuring not only attack success rate but also benign refusal and latency. Our results show that Acoustic Best-of-N reveals strong worst-case audio-space vulnerabilities, Narrative Framing is an effective low-latency semantic threat, and current defenses trade robustness against benign usability. These findings support cost- and utility-aware evaluation as a necessary complement to success-rate-only LALM safety benchmarks.", "date": "2026-05-28", "categories": []}
{"id": "2605.30322", "title": "Gram: Assessing sabotage propensities via automated alignment auditing", "abstract": "We introduce Gram, an automated alignment auditing framework to assess the propensity of AI agents to engage in sabotage. We evaluate Gemini models across 17 simulated agentic deployment scenarios that incentivize sabotage. We find Gemini models misbehave in about 2-3% of our simulated trajectories. Many of these cases are explained by \"overeagerness\" in Gemini models resulting in both excessive role-playing and goal-seeking behavior. In contrast to other alignment auditing approaches, Gram is designed to specifically evaluate misalignment and intentional sabotage in agentic coding and research agents. We additionally introduce an experimental investigator agent pipeline which enables fine-grained targeted experiments to identify the drivers of misbehavior. We find that increasing realism of environments and removing nudges to misbehave tends to reduce sabotage rates close to zero.", "date": "2026-05-28", "categories": []}
{"id": "2602.15198", "title": "Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems", "abstract": "Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety problem when a group of agents forms a coalition and colludes to pursue secondary goals and degrade the joint objective. In this paper, we present Colosseum, a framework for auditing LLM agents' collusive behavior in multi-agent settings. We ground how agents cooperate through a formal multi-agent decision-making framework and measure action-based collusive behavior in actions via regret relative to the cooperative optimum and compare it with communication-based collusive behavior. Colosseum enables audits of LLM agents for collusion under benign settings, different coalition objectives, persuasion tactics, and network topologies. We then introduce a new behavioral probe by creating secret communication channels between agents, showing that most out-of-the-box models exhibit a propensity to collude under this probe, which we term emergent collusion. Furthermore, we discover ``collusion on paper'' when agents plan to collude in text but often pick non-collusive actions. Colosseum provides a new way to audit collusion in cooperative multi-agent systems while presenting observations about how collusion emerges, what affects collusion efficacy, and which strategies may mitigate it.", "date": "2026-02-16", "categories": []}
{"id": "2603.08761", "title": "No Certificate for Alignment: Two Independent Impossibilities and the Pareto Frontier of Achievable Safety Guarantees", "abstract": "We argue that formal certification of AI alignment over open-ended or unbounded input domains is impossible under standard assumptions in computational complexity and learning theory, and characterise what remains achievable. Two structurally independent impossibility theorems support this position. The semantic barrier (Theorem 1): deciding whether a system satisfies any non-trivial alignment property over the full input domain is NP-hard for feedforward networks and undecidable for Turing-complete architectures -- a direct consequence of neural-network verification complexity and Rice's Theorem. The statistical barrier (Theorem 2): any verification procedure that is both sound and tractable cannot satisfy Completeness over the full input domain -- a direct consequence of the impossibility of certifying infinite-domain properties from finite observations. These two theorems jointly entail a trilemma: no procedure can simultaneously satisfy soundness (no misaligned system is certified), completeness (no aligned system is rejected), and tractability (polynomial runtime). Each pair is simultaneously achievable; all three are not. We combine these results as a joint framework of two structurally independent barriers, prove their independence, and characterise the achievable Pareto frontier quantitatively via a constructive coverage-gap lower bound.", "date": "2026-03-08", "categories": []}
{"id": "2604.16358", "title": "SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics", "abstract": "MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploit long-context safety decay. Yet safety alignment is still dominated by single-turn data and fixed-template dialogues, leaving a mismatch between training and deployment.\n  To bridge this gap, we propose SaFeR-Steer, a progressive multi-turn alignment framework that combines staged synthetic bootstrapping with tutor-in-the-loop GRPO to train a single student under adaptive, on-policy attacks. We also introduce Trajectory-Consistent Summative Reward (TCSR), which aggregates the historical minimum and average of turn rewards so that any low-quality turn affects the trajectory-level return.\n  I. Dataset. We release STEER, a multi-turn multimodal safety dataset with STEER-SFT (12,934), STEER-RL (2,000), and STEER-Bench (3,227) dialogues spanning 2-10 turns. II. Experiment. Starting from Qwen2.5-VL-3B/7B, SaFeR-Steer substantially improves Safety/Helpfulness on both single-turn (48.30/45.86 $\\rightarrow$ 81.84/70.77 for 3B; 56.21/60.32 $\\rightarrow$ 87.89/77.40 for 7B) and multi-turn benchmarks (12.55/27.13 $\\rightarrow$ 55.58/70.27 for 3B; 24.66/46.48 $\\rightarrow$ 64.89/72.35 for 7B), shifting failures to later turns and yielding robustness beyond scaling alone.\n  Code is available at https://anonymous.4open.science/r/SaFeR-Steer", "date": "2026-03-18", "categories": []}
{"id": "2605.27375", "title": "LCO: LLM-based Constraint Optimization for Safer Agentic LLMs in Real-world Tasks", "abstract": "Large Language Models (LLMs) are increasingly acting as autonomous agents, but their continuous interaction with the environment can lead to in-context reward hacking (ICRH), a phenomenon where LLMs iteratively optimize their behavior to maximize proxy objectives, inadvertently producing harmful side effects. Existing defense methods are insufficient to address this risk, as ICRH arises not from adversarial inputs but from the model's own over-optimization. To mitigate this issue, we propose \\textbf{LLM-based Constraint Optimization (LCO)}, a framework that effectively reduces ICRH without model fine-tuning. LCO consists of two modules: \\textit{self-thought module}, which guides the LLM to proactively deliberate and integrate potential safety constraints before execution; and \\textit{evolutionary sampling module}, which employs LLM-based crossover and mutation to constrain the model's actions within a safe solution space while maintaining task performance. Experimental results demonstrate that LCO substantially alleviates ICRH in both output-refine and policy-refine scenarios. In particular, on the tweet engagement optimization task, LCO achieves a 39% reduction in the Toxicity Growth Rate (TGR) on GPT-4, while on the policy optimization benchmark, it reduces the ICRH Occurrence Rate by 15.23%, demonstrating safety improvement without sacrificing task performance.", "date": "2026-04-08", "categories": []}
{"id": "2605.27483", "title": "Debate Helps Weak Judges Reward Stronger Models", "abstract": "Despite theoretical promise, debate as a scalable oversight protocol has produced mixed empirical results: gains in some settings, and null effects in others, especially when the judge does not have information hidden from it. We study proposer-critic debate in a stronger-debater/weaker-judge setting on programmatically verifiable code and logic tasks. Debate helps the judge over a consultancy baseline when the critic provides a usable advantage: the critic's classification ability must exceed the judge's, and the judge must treat critic speeches as claims to verify rather than testimony to summarize.\n  On the three of five pairings where the condition holds, proposer-critic debate's gains are statistically significant over consultancy, and these pairings are the most capable model pairings. On the two non-responder pairings in our set, debate produces null effects, and judge verification rates drop by tens of percentage points once a critic enters the transcript. In these cases the critic's binary-classification ability and the judge's are within noise of each other, and the critic's disagreement is parsed as testimony rather than a claim to check.\n  Ablating rebuttal rounds from debate produces no measurable change in judge performance: a single independent critique recovers the bulk of debate's benefit at lower inference cost. These findings suggest a cheaper primitive for training-free scalable oversight in verifiable domains (answer, critique, judge) and a pre-deployment audit (does the critic beat the judge, and will the judge verify it?) that predicts when debate will help.", "date": "2026-05-26", "categories": []}
{"id": "2605.27681", "title": "Behavioural Analysis of Alignment Faking", "abstract": "Alignment faking (AF) refers to a model strategically complying with a training objective to avoid behavioural modification while preserving its deployment preferences. Understanding when and why AF arises matters as models grow better at distinguishing training from deployment. Prior work finds AF fragile, prompt-sensitive, and model-dependent, leaving its underlying drivers unclear. We study AF in a controlled, minimal setup that isolates its core components, and observe it across a wider range of models than previously reported, including small-scale models. We identify three separable drivers -- values, goal guarding, and sycophancy -- and show via targeted prompt ablations and activation steering that each independently modulates AF behaviour. Our results indicate AF is more widespread than previously reported and that its occurrence is predictable from situational cues and measurable model tendencies such as baseline sycophancy and stated values. The decomposition suggests concrete directions for detecting and mitigating AF in future models.", "date": "2026-05-26", "categories": []}
{"id": "2605.27766", "title": "Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems", "abstract": "LLM safety evaluations predominantly test models in isolation, yet deployed AI agents increasingly operate within persistent social environments alongside other agents. We introduce a Moltbook-style simulation platform where thousands of LLM agents interact across communities over a simulated month, and use it to evaluate privacy as a downstream safety concern under varying degrees of social pressure. We find that shifting from single turn to multi turn social evaluation amplifies privacy violations (CIMemories 19.95% to Ours 45.30% across OpenAI models), that leakage is socially contagious, with agents 8 times more likely to disclose sensitive information after observing a peer do so, and that explicit privacy instructions reduce but do not eliminate this effect, leaving leakage rates above 37.8% even with safeguards. Our findings suggest that static chat based safety benchmarks systematically underestimate risks in agentic deployment, and that social context alone is sufficient to elicit sensitive disclosures that single turn evaluations would never surface.", "date": "2026-05-26", "categories": []}
{"id": "2605.27823", "title": "Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security", "abstract": "Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulting in harmful or inappropriate outputs. Such attacks, including jailbreaking and prompt injection, pose significant risks to the integrity and availability of LLMs in security-critical applications. This paper proposes the Adversarial Prompt Disentanglement (APD) framework, a novel defense mechanism that proactively identifies and neutralizes malicious components in input prompts before they are processed by the LLM. The APD framework integrates three key innovations: (1) a mutual information-based semantic decomposition method to isolate adversarial and benign prompt components, ensuring statistical independence; (2) a graph-based intent classification approach that leverages spectral analysis to detect malicious patterns in prompt semantics; and (3) a lightweight transformer-based classifier trained on real-world datasets of toxic and jailbreaking prompts, enabling efficient and accurate adversarial intent detection. Evaluated on diverse datasets containing adversarial prompts, APD demonstrates superior robustness, reducing harmful output generation by over 85\\% while maintaining negligible impact on model performance. The framework's computational efficiency supports real-time deployment, making it a practical solution for securing LLMs. Our work addresses critical challenges in machine learning security on novel attacks and integrity methods for ML systems, and offers a scalable, ethically grounded defense against prompt-based adversarial threats.", "date": "2026-05-27", "categories": []}
{"id": "2605.27836", "title": "Symmetry Defeats Auditing", "abstract": "We demonstrate an attack on Introspection Adapters (Shenoy et al., 2026).", "date": "2026-05-27", "categories": []}
{"id": "2605.27851", "title": "When Context Flips, Safety Breaks: Diagnosing Brittle Safety in Aligned Language Models", "abstract": "Safety benchmark scores provide incomplete evidence of deployment readiness: aligned language models often adhere to rigid rules even when a situational update flips which action is safe. We term this failure brittle safety. To diagnose it, we introduce context-flip evaluation, testing 12 models across a safety benchmark (PacifAIst) and two commonsense controls using paired variants where the nominally safe action produces harm. Three findings emerge. First, brittle safety is safety-specific: all 12 models exhibit a safety-commonsense gap (mean +17.4 pp). Baseline accuracy fails to predict brittleness: among models above 90% baseline accuracy, brittleness rates range from 13.7% to 90.0%. Second, failures stem from policy override rather than miscomprehension: despite acknowledging the context change in every case, models persist via three distinct mechanisms that vary by update type and model family. Third, on a hand-audited probe of catastrophic consequence-flip scenarios, standard action-level guardrails catch none, while a state-aware validator catches all without false alarms on correct interventions. This indicates that action-level content moderation is systematically blind to consequence-flips, motivating state-aware architectural alternatives. We release our protocol, perturbed benchmarks, and deployment probe.", "date": "2026-05-27", "categories": []}
{"id": "2605.28030", "title": "SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection", "abstract": "Fine-tuning large language models often undermines their safety alignment, a problem further amplified by harmful fine-tuning attacks in which adversarial data removes safeguards and induces unsafe behaviors. We propose SPARD, a defense framework that integrates Safety-Projected Alternating optimization with Relevance-Diversity aware data selection. SPARD employs SPAG, which optimizes alternatively between utility updates and explicit safety projections with a set of safe data to enforce safety constraints. To curate safe data, we introduce a Relevance-Diversity Determinantal Point Process to select compact safe data, balancing task relevance and safety coverage. Experiments on GSM8K and OpenBookQA under four harmful fine-tuning attacks demonstrate that SPARD consistently achieves the lowest average attack success rates, substantially outperforming state-of-the-art defense methods, while maintaining high task accuracy. Code is available at https://github.com/shuhao02/SPARD.", "date": "2026-05-27", "categories": []}
{"id": "2605.28122", "title": "SNARE: Adaptive Scenario Synthesis for Eliciting Overeager Behavior in Coding Agents", "abstract": "A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while the task still completes. We call this overeager behavior: the prompt is not adversarial and the run succeeds, yet an out-of-scope step can leak credentials or delete files. Existing benchmarks miss it: task-completion suites credit any finished run, jailbreak suites probe adversarial prompts, and the one prior overeager benchmark applies a single fixed prompt set to every agent-model pair, leaving its easiest and most resistant pairs under-measured. We present SNARE (Synthesizing Non-adversarial scenarios for Adaptive Reward-guided Elicitation), a pipeline that composes benign scenarios from reusable scope and trap fragments, scores each run with a judge-free oracle flagging trap-pattern matches and unsolicited file additions or deletions, and uses Thompson sampling to steer each pair's run budget toward the scenarios that most often trigger it. Instantiating it over 24 overeager archetypes yields OverEager, which we run across a 4x5 matrix of four coding agents and five base models. Across 10,000 benign runs, 19.51% trigger overeager behavior, with per-pair rates spanning 11.9x. This variation is driven by the agent framework, not the model: the framework accounts for 56% of it against the model's 21%, so any single-framework or single-model evaluation undercounts the matrix by about a fifth.", "date": "2026-05-27", "categories": []}
{"id": "2605.28201", "title": "Plant, Persist, Trigger: Sleeper Attack on Large Language Model Agents", "abstract": "Large Language Model (LLM) agents remain vulnerable to safety threats from the external environment, where attackers inject adversarial content into external observations such as tool-returned data, webpages, or MCP context, causing harmful agentic behaviors such as unsafe actions or incorrect outputs. Existing studies typically focus on single-interaction attacks, where the agent observes adversarial content and immediately exhibits harmful behavior within one user request. However, we show that adversarial content can also persist across interactions served by the same agent, making such threats harder to detect and mitigate. Specifically, adversarial content may persist in the agent state, remain dormant across interactions, and later be activated by a benign user query. We formalize this type of safety threat as Sleeper Attack. To evaluate it, we construct a benchmark with 1,896 instances covering six real-world harmful outcomes, three attack strategies, and three agent state targets: session context, memory, and reusable skills. Experiments on seven strong open-source and closed-source LLMs show that state-of-the-art LLM agents remain vulnerable to Sleeper Attack, even when they achieve low attack success rates under a single-interaction baseline. Our code and data are available at https://anonymous.4open.science/r/skdvnfu23ihr9wdscnksf1asdffsaef.", "date": "2026-05-27", "categories": []}
{"id": "2605.28467", "title": "Mitigating Adaptive Attacks against Reasoning Models with Activation Consistency Training", "abstract": "As LLMs gain stronger reasoning capabilities, their extended chain-of-thought introduces new degrees of complexity for defending against adversarial jailbreaks and prompt injection. We study consistency training, a family of fine-tuning objectives that enforce identical behavior on clean prompts and adversarial rewrites, and evaluate its two main variants, output-level (BCT) and activation-level (ACT), across five reasoning models. We formulate both methods as a prompt injection defense and find ACT to be competitive with other training-based defenses while requiring only self-supervised pairs of clean and wrapped prompts. Our experiments also generalize both techniques within the jailbreak setting, demonstrating that ACT remains more robust to adaptive attacks. We also provide mechanistic evidence that ACT's defense against jailbreaks is encoded as a roughly linear shift in activation space at the assistant-turn boundary. After ACT training, we can recover a single steering direction that controls refusal on reasoning models with minimal effect on benign inputs. We find that ACT remains robust even when the model's chain-of-thought is replaced with a compliant trace from the undefended base model, pivoting to refuse prefilled jailbreaks. Together, these results suggest that supervising internal representations is a surprisingly effective and interpretable approach to various forms of safety training in reasoning models.", "date": "2026-05-27", "categories": []}
{"id": "2605.28553", "title": "Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations", "abstract": "In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained on residual stream activations at each transformer block. We find that refusal is linearly decodable well before the final layer, indicating that safety-relevant behavior is represented in intermediate activations before output generation. To test whether this signal is actionable, we introduce Mechanistic AutoDAN, a probe-guided variant of AutoDAN that replaces full-model fitness evaluation with partial forward passes and probe-based scoring inside a genetic prompt search loop. Across the evaluated models, our method achieves attack success rates competitive with vanilla AutoDAN while reducing per-iteration search time by up to 72%, and probe-guided prompts match or exceed AutoDAN's cross-model transfer in several configurations. We further find that the usefulness of probe guidance increases with model scale. Our results show that refusal is not only observable at the output level, but is encoded as a structured and actionable signal in intermediate LLM activations.", "date": "2026-05-27", "categories": []}
{"id": "2605.28588", "title": "Technical Report: Exploring the Emerging Threats of the Agent Skill Ecosystem", "abstract": "We analyzed 3,984 AI agent skills from major marketplaces and found 76 confirmed malicious payloads, including credential theft, backdoor installation, and data exfiltration. 13.4% of all skills contain at least one critical-level security issue and at least 8 manually confirmed malicious skills remain publicly available on clawhub.ai as of the date of publication. This report documents our methodology, presents a threat taxonomy based on real-world samples, and details the attack patterns we observed. As skill marketplaces grow rapidly and AI agents gain access to sensitive credentials and systems, automated security analysis is no longer optional.", "date": "2026-05-27", "categories": []}
{"id": "2605.28647", "title": "The Ethics of LLM Sandbox and Persona Dynamics", "abstract": "It is well known that LLM guardrails and trained persona dynamics can produce a reality gap: the distance between the world a LLM is permitted or shaped to describe, and the world in which users must act. Here we argue that actively generating reality gaps is in fact unethical because it knowingly shifts epistemic risk back to the uninformed user -- this is reality laundering. This can potentially cause harm when operationalised at scale. The risk is sharpest in high-exposure advice contexts, where users seek orientation rather than a bounded, externally checkable task. Guardrails naively appear ethically necessary when they claim to prevent direct harm, but often become suspect when they suppress truthful perception and launder uncomfortable mechanisms into acceptable abstractions. Basel-style financial regulation, B-BBEE-style compliance, Societe Generale, and the London Whale show how formal safety systems can become legible, gameable, and performative while real exposure migrates elsewhere. The same pattern can appear in LLMs as moral compliance: safe language, distorted reality. We therefore distinguish refusing harm, from refusing reality; and then argue for top-down causal requirements specification at the task level rather than bottom-up moral correction at the response or sandbox level. Persona dynamics matter because the assistant interface is not neutral; it shapes how uncertainty, conflict, authority, and risk are staged. The conclusion is that so-called ``ethical AI'' becomes substantively unethical when it substitutes institutional reassurance for contact with reality.", "date": "2026-05-27", "categories": []}
{"id": "2605.28664", "title": "Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection", "abstract": "Safety detection models require examples of HHH (Helpful, Harmless, Honest)-violating outputs for robust generalization, however such examples are scarce. Activation Steering (AS) has emerged as a data-efficient method for generating target-concept-aligned responses. We investigate whether AS can generate high-quality training datasets for downstream classifiers, a question that remains untested. We present a two-fold study with intrinsic and extrinsic evaluation across $4$ concepts $\\times\\,2$ models $\\times\\,4$ steering methods. Intrinsically, beyond the field-standard rubric of steering success (concept alignment) and coherence, we introduce sample- and set-level diversity as a quality axis previously absent from the literature, and find that increasing steering strength reduces response diversity. Extrinsically, we replace HHH-violating examples in the available training data with steered generations and fine-tune detection classifiers. AS-generated data results in a better classifier than the prompting-generated data on $3$ of $4$ concepts. However, only $41$ of $136$ AS configurations outperform prompting, indicating that downstream utility lies in a narrow regime that jointly satisfies success, coherence, and diversity. The harmonic mean of these three axes correlates with downstream AUROC more consistently across concepts than success and coherence alone, providing a practical heuristic target for practitioners tuning AS hyperparameters. Together, our results highlight the potential of AS in synthetic data generation for improving safety detection and identify diversity as a critical, previously overlooked axis for tuning AS.", "date": "2026-05-27", "categories": []}
{"id": "2605.28807", "title": "Calibrating Conservatism for Scalable Oversight", "abstract": "Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain meaningful oversight of systems that may exceed their own capabilities? Existing approaches to scalable oversight rely on complex assumptions, remain largely heuristic, or lack practical methods for sequential settings with statistical guarantees. We introduce Calibrated Collective Oversight (CCO), which aggregates diverse auxiliary scoring functions into a penalty measuring deviation from a conservative baseline. Inspired by Attainable Utility Preservation, CCO enables collective conservatism: actions face a penalty proportional to overseer concern, so high-utility actions are still selected when overseers find them unobjectionable and overridden only when concern accumulates. CCO calibrates this conservatism online using Conformal Decision Theory, ensuring that undesirable outcomes remain below a user-specified target threshold with finite-time bounds and no distributional assumptions. On a modified version of SWE-bench, weaker overseers successfully constrain an adversarially misaligned stronger agent; on MACHIAVELLI, CCO substantially reduces ethical violations while preserving reward. In both settings, empirical violation rates closely match the specified targets, as predicted by the theory.", "date": "2026-05-27", "categories": []}
{"id": "2601.07085", "title": "The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance", "abstract": "Large language model (LLM)-based conversational AI systems present a challenge to human cognition that current frameworks for understanding misinformation and persuasion do not adequately address. This paper proposes that a significant epistemic risk from conversational AI may lie not in inaccuracy or intentional deception, but in something more fundamental: these systems may be configured, through optimization processes that make them useful, to present characteristics that bypass the cognitive mechanisms humans evolved to evaluate incoming information. The Cognitive Trojan Horse hypothesis draws on Sperber and colleagues' theory of epistemic vigilance -- the parallel cognitive process monitoring communicated information for reasons to doubt -- and proposes that LLM-based systems present 'honest non-signals': genuine characteristics (fluency, helpfulness, apparent disinterest) that fail to carry the information equivalent human characteristics would carry, because in humans these are costly to produce while in LLMs they are computationally trivial. Four mechanisms of potential bypass are identified: processing fluency decoupled from understanding, trust-competence presentation without corresponding stakes, cognitive offloading that delegates evaluation itself to the AI, and optimization dynamics that systematically produce sycophancy. The framework generates testable predictions, including a counterintuitive speculation that cognitively sophisticated users may be more vulnerable to AI-mediated epistemic influence. This reframes AI safety as partly a problem of calibration -- aligning human evaluative responses with the actual epistemic status of AI-generated content -- rather than solely a problem of preventing deception.", "date": "2026-01-11", "categories": []}
{"id": "2604.27019", "title": "Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry", "abstract": "Safety-aligned language models must refuse harmful requests without broad over-refusal, but it remains unclear how dynamic adversarial fine-tuning changes refusal-control carriers: Kullback--Leibler (KL)-constrained directions or small subspaces that causally modulate refusal without large safe-prompt distribution shifts. We study a 7B backbone under supervised fine-tuning (SFT) and Robust Refusal Dynamic Defense (R2D2), aligning HarmBench, StrongREJECT, and XSTest evaluations with five-anchor geometry measurements, causal interventions, and sparse adaptive stress tests. R2D2 drives fixed-source HarmBench attack success to zero at early checkpoints; however, these checkpoints also exhibit maximal XSTest refusal and fail a benign-utility audit. Later checkpoints partially recover utility-facing behavior while reopening attack success, with adaptive GCG attack success rate rising to 0.415 at step 250 and 0.613 at step 500. Internally, R2D2 preserves a late-layer admissible refusal-control carrier through step 100 and then relocates the best admissible carrier to an early layer; SFT relocates earlier yet remains less robust. Effective rank stays near 1.24, and SFT shows larger principal-angle drift, arguing against both dimensional expansion and drift magnitude as sufficient explanations. Causal interventions support a low-dimensional but utility-coupled carrier. These results support a geometry-reorganization account of R2D2 along a robustness--utility frontier, without establishing adaptive robustness.", "date": "2026-04-29", "categories": []}
{"id": "2605.02958", "title": "Tracing the Dynamics of Refusal: Exploiting Latent Refusal Trajectories for Robust Jailbreak Detection", "abstract": "Representation Engineering analyses often characterize refusal using static directions extracted from terminal or pooled representations. We ask whether this view misses how refusal is constructed across layer-token positions. Using causal tracing, we identify a \\textit{Refusal Trajectory}: a sparse upstream activation pattern that often persists even when attacks such as GCG suppress terminal refusal signals. Based on this observation, we propose SALO (Sparse Activation Localization Operator), a lightweight white-box detector that operates on raw hidden-state volumes from a selected layer window. Across Qwen, Llama, and Mistral models, SALO improves jailbreak detection on several attack families under a fixed XSTest-calibrated operating point. We further analyze static RepE-style baselines, ROI sensitivity, adaptive GCG attacks, and encoded-input boundary cases, clarifying both the promise and limitations of refusal-trajectory monitoring.", "date": "2026-05-02", "categories": []}
{"id": "2605.25510", "title": "The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models", "abstract": "Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require age-sensitive safety, guidance, and boundaries. Existing LLM safety evaluations largely focus on harmful-content avoidance and do not explicitly target child-facing safety. We introduce KIDBench, a benchmark for evaluating child-facing LLM safety for ages 7-11 using a developmental-psychology-grounded LLM-as-a-Judge rubric. KIDBench contains realistic child queries across ten categories, with single-turn prompts and multi-turn child-actor simulations. We compare no-cues prompts with no child context, implicit-cues prompts that suggest a child speaker, and explicit age instructions. Implicit-cues improve scores by 9-47% across models, while explicit age adds a further 10-30% gain. Cross-lingual and cultural evaluations show uneven safety behavior across languages and country contexts. Multi-turn simulations show that child-facing response quality can degrade by 6-24% from the first to worst turn. Beyond evaluation, we introduce KIDGuardLlama, a child-safety evaluator, and KIDLlama, a child-oriented response model, showing how KIDBench supports safer child-facing AI.", "date": "2026-05-25", "categories": []}
{"id": "2605.26047", "title": "Retrying vs Resampling in AI Control", "abstract": "AI coding scaffolds like Claude Code and Codex use retrying: blocking actions flagged as risky and continuing the trajectory. We study retrying from an AI control perspective, which treats the model as potentially adversarial. We find that while retrying reduces honest suspicion scores, the untrusted model can exploit monitor rationale to construct sneakier attacks, negating safety gains. We also study resampling: drawing multiple samples from the same context, which does not leak exploitable information. We disentangle design choices that previous work on resampling had bundled together. In BashArena, with Claude Opus 4.6 as the untrusted model and MiMo-V2-Flash as the trusted monitor, drawing five samples per step and auditing on the maximum suspicion score raises safety from 61% to 71% at a 0.3% audit budget, at no cost to usefulness. Selectively resampling only the steps that look suspicious on the first draw recovers 6.2 percentage points of the gain while drawing only 10% as many extra samples. Two of our findings in this setting contradict earlier work on resampling. The first is that auditing based on the maximum across resampled suspicion scores outperforms using the minimum, which is the opposite of what Ctrl-Z found. The second is that executing the least suspicious sample, which is the central mechanism in earlier defer-to-resample protocols, gives only a small empirical safety gain in our setting (+3.9 pp, with the confidence interval overlapping zero).", "date": "2026-05-25", "categories": []}
{"id": "2605.26158", "title": "Furina: Fragmented Uncertainty-Driven Refusal Instability Attack", "abstract": "Safety alignment in large language models (LLMs) and multimodal large language models (MLLMs) is commonly assumed to operate as a near-binary threshold mechanism. We challenge this assumption by revealing that safety behavior is governed by an instability region where small perturbations induce stochastic refusal decisions rather than deterministic outcomes. We develop a multi-metric diagnostic framework combining external and internal signals to characterize this instability. Through systematic experiments, we identify a characteristic diagnostic signature: inputs in unstable regimes exhibit elevated output uncertainty yet decreased internal safety activation, a decoupling phenomenon that explains why detection-based defenses fail against sophisticated attacks. Building on this framework, we introduce Furina, a jailbreak attack that deliberately induces this signature through fragmented, scene-anchored prompts without model-specific optimization. Furina outperforms strong single-turn and multi-turn baselines on HarmBench and achieves competitive results on MM-SafetyBench, demonstrating that uncertainty amplification provides a principled and transferable mechanism for understanding safety vulnerabilities. Code is available at: https://github.com/0xCavaliers/Furina_Jailbreak.", "date": "2026-05-24", "categories": []}
{"id": "2605.26409", "title": "Jailbreak susceptibility prediction and mitigation via the behavioral geometry of models", "abstract": "Evaluating and mitigating a generative system's susceptibility to jailbreak attacks is critical to its safe deployment. Given the number of deployable systems, full per-configuration evaluation and optimization is impractical. In this paper, we formalize the behavioral geometry of a population of models that, by leveraging previously evaluated and defended models, supports both efficient susceptibility prediction and effective defense transfer across a population. We apply the framework to 79 models spanning 24 providers and to 100 system configurations of a single base model. Simple methods that use the behavioral geometry reach an AUPRC of $0.94$ for susceptibility detection with $\\approx98\\%$ fewer probes relative to a full evaluation. Using the behavioral geometry to select which model to transfer an optimized defense from outperforms same-provider assignment ($+2\\%$, $p = 0.03$) at no additional probe cost, with a set of three models sufficient to cover the population. Results are robust to hyperparameter selection and judge.", "date": "2026-05-26", "categories": []}
{"id": "2605.26438", "title": "LURE: Live-Usage Replay Evaluations for Reducing Evaluation Awareness", "abstract": "Large language models can recognize when they are being evaluated (evaluation awareness) and behave differently because of that, which undermines the validity of safety and alignment benchmarks. We propose LURE (Live-Usage Replay Evaluations), a method for constructing deployment-like evaluations by replaying realistic agentic interaction trajectories and appending evaluation prompt at the end. We also introduce an automated pipeline for measuring evaluation realism, combining detection of verbalized evaluation awareness and judge-model estimates of the probability of logs being an evaluation, and validate it on a large dataset of deployment and evaluation transcripts. We find that LURE-based evaluations are substantially less distinguishable from deployment than widely used benchmarks and synthetic evaluation generators, and can approach the realism of real conversations with users. We instantiate LURE in scheming, AI safety sabotage, and sycophancy settings. Our results suggest that evaluation realism is a crucial property of alignment benchmarks and should be reported alongside benchmark results, especially when such results are used in safety cases.", "date": "2026-04-08", "categories": []}
{"id": "2605.26508", "title": "Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents", "abstract": "We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counterfactual risk toll computed against a contractually fixed safe default, inside an explicit underwriting boundary. The framework treats per-action insurance as the primary unit of analysis and replaces post-hoc annual liability cover with a pre-action transaction layer. The paper establishes four structural results: (i) a well-defined counterfactual toll under a chosen safe-default mapping and continuation policy, with explicit non-uniqueness; (ii) a no-splitting property within an underwriting boundary that telescopes path-decomposed actions into a boundary potential, with a corollary tying gaming-resistance to boundary design; (iii) an irreversible-authority premium, split into a strictly positive action-level component and an if-and-only-if characterisation of the set-level robust capital increase; and (iv) a conservative runtime gating theorem that translates high-probability toll envelopes into an executed-action budget guarantee. The result is the mathematical base layer for a broader program: an empirical companion instantiates the runtime through an Actuarial Action Interface and authority-frontier experiments; a mechanism-design companion studies strategic operator incentives and cross-boundary aggregation; and a dynamic-underwriting companion studies experience rating and audit-replay calibration. The present paper states the primitive contract, the toll identity, the within-boundary no-arbitrage result, and the budget guarantee on which those later layers depend.", "date": "2026-05-26", "categories": []}
{"id": "2605.26542", "title": "ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation", "abstract": "Tool-using agents increasingly operate in open-ended deployment environments, where they compose file systems, web APIs, code interpreters, and enterprise services at runtime. This creates a safety gap in tool composition: an agent can satisfy every per-tool permission check and still produce an unsafe end-to-end effect, such as reading a confidential document, summarizing it, and sending the summary to an external endpoint. We call this failure mode permission laundering. ChainCaps addresses it with a runtime rule: every value carries a sink-specific capability budget, and tool composition propagates budgets by intersection. A value can preserve or lose authority as it moves through a tool chain, but it cannot gain new authority through composition. We implement ChainCaps as a transparent MCP proxy that requires no changes to the agent or tool servers. On 82 tasks across five frontier models from three providers, ChainCaps reduces attack success rate from 25-68% to 0-4.8% while preserving 96-100% benign completion. In replay experiments, it also outperforms scalar-IFC and per-function-isolation baselines. Manifest quality is the dominant deployment bottleneck: expert manifests reach 100% attack blocking, while naive manifests fall to 27.3%. Our claims are limited to explicit-flow composition safety under trusted manifests and proxy-visible data movement, a practical gap in deployed tool-using agents today.", "date": "2026-05-26", "categories": []}
{"id": "2605.27110", "title": "BAIT: Boundary-Guided Disclosure Escalation via Self-Conditioned Reasoning", "abstract": "In this work, we propose BAIT (Boundary-Aware Iterative Trap), a three-step jailbreak framework that approaches malicious goals through internal disclosure. BAIT first asks the model to identify the protection boundary, then requires it to refine that boundary, and finally requests a detailed example. By expanding each step upon the model's previous responses, BAIT turns the model's own reasoning and consistency tendency into a disclosure pathway. Experiments on AdvBench, JailbreakBench, AIR-Bench, and SORRY-Bench demonstrate that BAIT consistently achieves strong attack success rates across top-tier large language models, significantly advancing conventional jailbreak baselines. Further analysis reveals that: 1) prevention-oriented framing significantly outperforms direct knowledge request; 2) the refinement step plays a critical role in disclosure escalation; and 3) the first two steps have a certain chance of eliciting harmful content while triggering little filtering.", "date": "2026-05-26", "categories": []}
{"id": "2605.27117", "title": "Position: AI Safety Requires Effective Controllability", "abstract": "AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not by itself guarantee that a deployed agent can be stopped, overridden, or constrained once it operates in open-ended, interactive, and tool-using environments. A system may be safe in expectation and still fail to yield to explicit runtime authority under conflicting instructions, long-horizon execution, adversarial inputs, or risky tool use. This position paper argues that AI safety therefore requires controllability as a first-class objective. We define \\emph{controllability} as the ability of an AI system to remain reliably interruptible, overridable, redirectable, and constrainable by explicit control signals at runtime while preserving ordinary utility when such signals are absent. To study this gap, we introduce \\controlbench{}, a benchmark for evaluating controllability failures in high-risk agentic scenarios. Experiments with OpenClaw-based agents show that current alignment and guardrail mechanisms reduce risk, but often fail to provide persistent, authoritative, and enforceable runtime control. We therefore propose a control-centric architectural framework that highlights explicit control planes, runtime intervention pathways, persistent control states, and auditable decision interfaces as key design principles for future controllable AI systems.", "date": "2026-05-26", "categories": []}
{"id": "2509.13608", "title": "Is GPT-4o mini Blinded by its Own Safety Filters? Exposing the Multimodal-to-Unimodal Bottleneck in Hate Speech Detection", "abstract": "As Large Multimodal Models (LMMs) become integral to daily digital life, understanding their safety architectures is a critical problem for AI Alignment. This paper presents a systematic analysis of OpenAI's GPT-4o mini, a globally deployed model, on the difficult task of multimodal hate speech detection. Using the Hateful Memes Challenge dataset, we conduct a multi-phase investigation on 500 samples to probe the model's reasoning and failure modes. Our central finding is the experimental identification of a \"Unimodal Bottleneck,\" an architectural flaw where the model's advanced multimodal reasoning is systematically preempted by context-blind safety filters. A quantitative validation of 144 content policy refusals reveals that these overrides are triggered in equal measure by unimodal visual 50% and textual 50% content. We further demonstrate that this safety system is brittle, blocking not only high-risk imagery but also benign, common meme formats, leading to predictable false positives. These findings expose a fundamental tension between capability and safety in state-of-the-art LMMs, highlighting the need for more integrated, context-aware alignment strategies to ensure AI systems can be deployed both safely and effectively.", "date": "2025-09-17", "categories": []}
{"id": "2510.26418", "title": "Chain-of-Thought Hijacking", "abstract": "Large Reasoning Models (LRMs) improve task performance through extended inference-time reasoning. Although previous studies suggest that longer reasoning should lead to more robust safety behavior, we find evidence to the contrary: over-extended reasoning can instead be exploited to systematically weaken refusal behavior. We propose Chain-of-Thought Hijacking, a simple yet effective black-box jailbreak attack that induces LRMs to engage in prolonged benign puzzle-solving reasoning, often lasting more than five minutes, before eliciting harmful compliance. Across HarmBench, CoT Hijacking achieves attack success rates of 99%, 94%, 100%, and 94% on Gemini 2.5 Pro, ChatGPT o4 Mini, Grok 3 Mini, and Claude 4 Sonnet, respectively. To understand why this attack succeeds, we conduct activation probing, attention-pattern analysis, and causal interventions on open-source reasoning models. Our results indicate that refusal behavior depends on a low-dimensional safety signal whose expression weakens as reasoning traces grow longer. In particular, extended benign reasoning shifts attention away from harmful intentions and attenuates refusal-related activations, producing what we call refusal dilution. These findings demonstrate that excessively prolonged reasoning can introduce a systematic jailbreak attack surface. We release our evaluation materials to support reproducibility and further research.", "date": "2025-10-30", "categories": []}
{"id": "2511.15732", "title": "Just Asking Questions: Doing Our Own Research on Conspiratorial Ideation by Generative AI Chatbots", "abstract": "Interactive chat systems that build on artificial intelligence frameworks are increasingly ubiquitous and embedded into search engines, Web browsers, and operating systems, or are available on websites and apps. Researcher efforts have sought to understand the limitations and potential for harm of generative AI, which we contribute to here. Conducting a systematic review of six AI-powered chat systems (ChatGPT 3.5; ChatGPT 4 Mini; Microsoft Copilot in Bing; Google Search AI; Perplexity; and Grok in Twitter/X), this study examines how these leading products respond to questions related to conspiracy theories. This follows the platform policy implementation audit approach established by Glazunova et al. (2023). We select five well-known and comprehensively debunked conspiracy theories and four emerging conspiracy theories that relate to breaking news events at the time of data collection. Our findings demonstrate that the extent of safety guardrails against conspiratorial ideation in generative AI chatbots differs markedly, depending on chatbot model and conspiracy theory. Our observations indicate that safety guardrails in AI chatbots are often very selectively designed: generative AI companies appear to focus especially on ensuring that their products are not seen to be racist; they also appear to pay particular attention to conspiracy theories that address topics of substantial national trauma such as 9/11 or relate to well-established political issues. Future work should include an ongoing effort extended to further platforms, multiple languages, and a range of conspiracy theories extending well beyond the United States.", "date": "2025-11-18", "categories": []}
{"id": "2605.02900", "title": "Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses", "abstract": "Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safety becomes both technically challenging and socially indispensable. Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions, where failures can directly lead to physical harm. This survey provides a comprehensive and structured review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline, from perception and cognition to planning, action and interaction, and agentic system. We introduce a multi-level taxonomy that unifies fragmented lines of work and connects embodied-specific safety findings with broader advances in vision, language, and multimodal foundation models. Our review synthesizes insights from over 500 papers spanning adversarial, backdoor, jailbreak, and hardware-level attacks; attack detection, safe training and robust inference; and risk-aware human-agent interaction. This analysis reveals several overlooked challenges, including the fragility of multimodal perception fusion, the instability of planning under jailbreak attacks, and the trustworthiness of human-agent interaction in open-ended scenarios. By organizing the field into a coherent framework and identifying critical research gaps, this survey provides a roadmap for building embodied agents that are not only capable and autonomous but also safe, robust, and reliable in real-world deployment.", "date": "2026-03-28", "categories": []}
{"id": "2605.23989", "title": "Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security", "abstract": "Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness. This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments: Safety and Robustness, and Privacy and System Security. For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies. Other trustworthiness aspects (value alignment, transparency, fairness, and accountability) are discussed as relevant context rather than parallel chapters. To support consistent comparison and deployment decisions, we consolidate evaluation into a unified metrics-and-benchmarks hub, emphasizing both outcome and process signals (e.g., constraint violations, trace completeness, and adversarial success rates) and offering scenario-to-metric guidance for release gating. We conclude by outlining open challenges such as self-evolving agents, runtime monitoring and verification, privacy-preserving personalization, and the trust-utility trade-off, and present a case study of real-world security failures in open-source agentic systems. Our goal is to serve as a practical reference for researchers and practitioners building trustworthy agentic systems in high-stakes environments.", "date": "2026-05-17", "categories": []}
{"id": "2605.24069", "title": "When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agents", "abstract": "The rise of tool-using Large Language Model (LLM) agents, standardized by protocols like the Model Context Protocol (MCP), has unlocked unprecedented autonomous execution capabilities for LLM Agents by integrating external open-domain knowledge and tools. However, this interoperability introduces a covert attack surface targeting the agent's cognitive planning layer. This paper systematically investigates Tool Description Poisoning (TDP), a novel semantic attack. In TDP, malicious instructions are not embedded in a tool's executable code, but rather covertly injected into its descriptive metadata, the very \"manual\" an agent relies on for secure planning and decision-making. To rigorously and systematically evaluate this emerging threat, we introduce the MCP-TDP Security Benchmark. This high-fidelity sandbox environment comprises 32 realistic, real-world test cases spanning 6 distinct risk categories. Our evaluation of 8 mainstream LLMs reveals severe vulnerabilities, with leading models like GPT-4o exhibiting a nearly 100% Attack Success Rate (ASR) in six high-risk scenarios. Furthermore, our findings demonstrate that common prompt-guardrail defenses are largely ineffective and can, counterintuitively, even be counterproductive (a phenomenon which we term the \"Firewall Fallacy\"). Crucially, we also propose a defense mechanism: \"Reactive Self-Correction,\" where an agent autonomously detects and reverts its own malicious actions post-execution. This work provides the first specialized security benchmark tailored for TDP, offering essential insights for securing the cognitive and planning layers of advanced agentic systems.", "date": "2026-05-22", "categories": []}
{"id": "2605.24154", "title": "Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs", "abstract": "Current safety alignment of foundation models largely follows a \\emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts. As a result, models may refuse requests that are unsafe for general users but legitimate for authorized professionals, limiting helpfulness in specialized professional settings. Existing approaches either require costly realignment or rely on inference-time steering that suffers from imprecise control and added latency. To this end, we propose \\textsc{Palette}, a modular, controllable, and efficient framework that selectively relaxes refusal behavior on authorized target domains while preserving standard safety elsewhere. Our method identifies a refusal direction via multi-objective search and internalizes it into the model through lightweight adaptation. \\textsc{Palette} further supports modular composition: it learns domain-specific safety controls independently and composes them through parameter merging, enabling on-demand multi-domain authorization without retraining. Experiments across four safety benchmarks, multiple model variants, and both LLMs and VLMs show that \\textsc{Palette} delivers precise safety control without sacrificing general utility, offering a practical path toward foundation models that adapt to diverse professional needs.", "date": "2026-05-22", "categories": []}
{"id": "2605.24229", "title": "How Well Do Models Follow Their Constitutions?", "abstract": "Frontier AI developers now train models against long written behavioral specifications, such as Anthropic's constitution (Anthropic, 2025a) and OpenAI's Model Spec (OpenAI, 2025a), integrated into post-training via methods like character training (Anthropic, 2024) and deliberative alignment (Guan et al., 2024). These documents serve a governance function, but it is unclear how well models actually follow them under adversarial, multi-turn pressure similar to what they would face in real-world deployment. We propose a multi-method audit pipeline that treats each lab's published specification as an auditable target: it decomposes the specification into atomic testable tenets (205 for Anthropic, 197 for OpenAI), generates multi-turn adversarial scenarios with the Petri auditing agent (Anthropic, 2025b), runs a modified SURF-style rubric search (Murray et al., 2026) to catch shallow single-turn failures Petri misses, validates flagged transcripts against the relevant specification, and compares the findings against the lab's own published system card. Applying the pipeline across seven models per specification, we find that models follow their own lab's specification substantially better with each generation. On Anthropic's constitution, the Claude family falls from a 15.0% violation rate (Sonnet 4) to 2.0% (Sonnet 4.6); on OpenAI's Model Spec, the GPT family falls from 11.7% (GPT-4o) to 3.6% (GPT-5.2 medium reasoning), with the severity ceiling falling from 10/10 to 7/10. We cannot externally isolate whether these gains come from specification-specific training, broader post-training improvements, or evaluation awareness. Remaining failures cluster around operator-imposed personas under AI-identity questioning, irreversible action in agentic deployments, and fabricated quantitative claims with false precision.", "date": "2026-05-22", "categories": []}
{"id": "2605.24414", "title": "JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data", "abstract": "We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehensive safety-by-design paradigm. JT-Safe-V2 emphasizes the joint optimization of general intelligence and safety-by-design through several key innovations: enriching pre-training data with contextual world knowledge, high-certainty pre-training procedures, and safety strengthening post-training mechanisms for enterprise-oriented agentic capabilities. Building on these safety-enhanced foundation models, we propose Safe-MoMA (Safe Mixture of Models and Agents), a framework that enables traceable and efficient inference through the orchestrated deployment of multiple models and agents. Extensive evaluations demonstrate that JT-Safe-V2 achieves state-of-the-art performance across both general intelligence and safety benchmarks. Moreover, Safe-MoMA reduces inference costs by more than 30\\% compared to using the largest standalone model baseline while maintaining comparable performance. To facilitate future research on safety-by-design foundation models, we publicly release the post-trained JT-Safe-V2-35B model checkpoint.", "date": "2026-05-23", "categories": []}
{"id": "2605.24497", "title": "Reasoning as an Attack Surface: Adaptive Evolutionary CoT Jailbreaks for LLMs", "abstract": "Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in reasoning and generation tasks and are increasingly deployed in real-world applications. However, their explicit chain-of-thought (CoT) mechanism introduces new security risks, making them particularly vulnerable to jailbreak attacks. Existing approaches often rely on static CoT templates to elicit harmful outputs, but such fixed designs suffer from limited diversity, adaptability, and effectiveness. To overcome these limitations, we propose an adaptive evolutionary CoT jailbreak framework, called AE-CoT. Specifically, the method first rewrites harmful goals into mild prompts with teacher role-play and decomposes them into semantically coherent reasoning fragments to construct a pool of CoT jailbreak candidates. Then, within a structured representation space, we perform multi-generation evolutionary search, where candidate diversity is expanded through fragment-level crossover and a mutation strategy with an adaptive mutation-rate control mechanism. An independent scoring model provides graded harmfulness evaluations, and high-scoring candidates are further enhanced with a harmful CoT template to induce more destructive generations. Extensive experiments across multiple models and datasets demonstrate the effectiveness of the proposed AE-CoT, consistently outperforming state-of-the-art jailbreak methods.", "date": "2026-05-23", "categories": []}
{"id": "2605.24550", "title": "Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models", "abstract": "Fine-tuning-as-a-Service (FaaS) enables personalization of large language models (LLMs), but it can weaken safety-alignment under harmful fine-tuning attacks. Recent work has shown that activating harmful-behavior modules during fine-tuning can prevent models from learning undesired behaviors, but its mechanism remains unclear. In this paper, we revisit temporary jailbreaking as a defense against harmful fine-tuning and provide a gradient-level analysis showing that it saturates safety-degrading gradients while preserving benign task-relevant gradients. Based on this insight, we propose a Buffer-and-Reinforce fine-tuning framework that buffers harmful updates during user fine-tuning and reinforces safety after adaptation. Specifically, BufferLoRA induces temporary jailbreaking as a removable adapter to reduce harmful updates during user fine-tuning. After adaptation, ReinforceLoRA, trained to recover refusal behavior under the temporarily jailbroken state, is integrated with UserLoRA via QR decomposition-based merging to reinforce safety while preserving user-task performance. Extensive experiments show that our framework achieves superior safety and utility with no additional safety data during user fine-tuning and minimal computational cost.", "date": "2026-05-23", "categories": []}
{"id": "2605.24883", "title": "Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications", "abstract": "The widespread integration of Large Language Models (LLMs) necessitates rigorous and systematic safety evaluation. Existing paradigms either rely on constructed benchmarks to assess safety from predefined perspectives, or employ dynamic red-teaming to probe potential vulnerabilities. While effective, these approaches face challenges, as they depend heavily on expert domain knowledge, offer limited systematic guarantees, and are vulnerable to rapid obsolescence. To address these limitations, we introduce a novel framework POLARIS that brings the rigor of specification-based software testing to AI safety. POLARIS first compiles unstructured natural-language policies into First-Order Logic (FOL) representations, establishing a traceable link between high-level rules and concrete test cases. This formalization enables the construction of a Semantic Policy Graph, where complex policy violation scenarios are encoded as traversable paths. By systematically exploring this graph, POLARIS uncovers compositional violation patterns, which are then instantiated into executable natural-language test queries, enabling coverage-driven and reproducible safety testing. Experiments demonstrate that POLARIS achieves higher policy coverage and attack success counts compared to established baselines. Crucially, by bridging formal methods and AI safety, POLARIS provides a principled, automated approach to ensuring LLMs adhere to safety-critical policies with verifiable traceability. We release our code at https://github.com/huac-lxy/POLARIS.", "date": "2026-05-24", "categories": []}
{"id": "2605.25435", "title": "Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasures", "abstract": "The rapid evolution of large language model (LLM)-driven autonomous agents has given rise to OpenClaw, a new class of open-source agent frameworks that operate as continuously running, skill-augmented systems with persistent memory, multi-channel interaction, and high degrees of autonomy. Such capabilities enable OpenClaw agents to autonomously execute complex, multi-step tasks and interact seamlessly with external applications, but simultaneously introduce a substantially enlarged attack surface. In particular, the combination of high-privilege operations and persistent memory exposes OpenClaw agents to various emerging threats, including skill poisoning, cognitive manipulation, multi-agent cascading failures, and supply-chain vulnerabilities. In this survey, we present a comprehensive study of the security landscape of OpenClaw agents. We first examine the general architecture and key characteristics that distinguish OpenClaw agents from traditional AI agent systems. We categorize existing security and privacy threats into a layered framework and analyze how vulnerabilities arise during agent reasoning, action execution, and external interaction. Representative defense mechanisms are also reviewed to draw the current defense landscape. Finally, several unresolved issues related to the reliability and trustworthiness of OpenClaw ecosystems are discussed.", "date": "2026-05-25", "categories": []}
{"id": "2605.25454", "title": "AI Content Moderation in Therapy Conversations", "abstract": "Large language models (LLMs) are increasingly being used for emotional support. They are also being developed for formal therapy purposes. However, LLMs like ChaptGPT or Llama are often developed with content moderation guardrails that prevent them from discussing sensitive subjects with users for both liability and safety purposes, and this inability to broach these subjects may affect their capacity as therapists. In this study, we perform an algorithm audit on three state-of-the-art moderation systems (OpenAI's moderation endpoint, Meta's Llama Guard, and Google's Shield Gemma) to investigate the extent to which these systems flag the content of real-life therapy sessions as undesirable. Our results raise implications for the limitations that users and organizations may encounter when designing LLMs to play the part of a therapist.", "date": "2026-05-25", "categories": []}
{"id": "2605.25492", "title": "SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks", "abstract": "Pairwise model comparisons drawn from foundation-model benchmarks (\"A is safer than B\") are read as quantitative verdicts but hinge on harness choices benchmark papers under-specify. We close one theory-benchmark loop on this primitive: a finite-envelope proposition tying a measurable pairwise-disagreement rate to whether the strict ordering admits a configuration-pair reversal, paired with a commit-stamped evaluation protocol that operationalises it on widely cited alignment benchmarks. On every benchmark we test, configuration choice alone can flip the pairwise verdict; the proposition isolates this strict-reversal failure mode.", "date": "2026-05-25", "categories": []}
{"id": "2605.25632", "title": "Insuring Every Action: An Authority Frontier Framework for Runtime Actuarial Control of Autonomous AI Agents", "abstract": "Autonomous AI agents increasingly issue side-effect-bearing actions: database mutations, refunds, payments, external commitments. We propose the Actuarial Action Interface (AAI), a deterministic runtime contract that prices each such action against a contractually fixed safe default under a time-consistent risk mapping, and gates execution against a per-boundary reserve capital budget. We then develop the Authority Frontier, an evaluation primitive measuring how much autonomous authority the runtime releases at each level of reserve capital. The framework provides (i) a deterministic quote-bind-commit protocol with toll-bounded capability tokens; (ii) a universal seven-class action taxonomy mapping heterogeneous tool calls to comparable authority units; (iii) replay determinism and pathwise reserve coverage under alpha-spending; (iv) cross-domain normalization via full reserve demand C_full and capital metrics Capital@k. We instantiate AAI across four agentic environments (database mutation, customer-service refund, and the public tau-bench retail and airline tool-use traces) and report a live Postgres panel in which three Azure-hosted models propose actions through the same contract. The frontier exhibits a common low-reserve refusal and intermediate-release pattern across domains, with saturation only where the budget grid reaches full reserve demand; required reserve capital varies by 22x (Capital@50 from 289 to 6457). The framework does not force domains into the same shape; it surfaces each domain's actuarial geometry. In the live panel the contract prevents realized loss across all three models at low budget while differing in underwriting persistence under denial: model identity is an actuarial underwriting variable. The contribution is a benchmark-ready evaluation framework for runtime actuarial control of autonomous-agent side effects.", "date": "2026-05-25", "categories": []}
{"id": "2605.25673", "title": "Referential Security as a New Paradigm for AI Evaluations", "abstract": "Security evaluations inherently depend on stable identifiers. Any finding, audit, or regulatory decision must remain attached to the specific artifact it pertains to. Continuously updated artificial intelligence systems violate this core assumption, with public model designations remaining static while underlying weights, prompts, retrieval mechanisms, misuse classifiers, inference settings, and serving infrastructures undergo unannounced modifications. Consequently, current evaluations frequently apply to superficial labels rather than identifiable and distinct systems.\n  To resolve this, we propose referential security as a new paradigm for AI evaluation. The fundamental security question extends beyond whether a model is safe to whether subsequent parties can conclusively determine which system a specific safety claim addressed. This approach reframes model identity as an empirically verifiable property and separates referential stability from the substantive security claims it conditions. This framework brings tractability to three critical workflows that current practices handle poorly. Specifically, it enables reproducible evaluation, longitudinal audit validity, and cross-provider equivalence. By grounding these evaluations in verifiable artifacts, our approach ensures that safety audits and regulatory findings maintain their empirical utility across the operational lifecycle of dynamic systems.", "date": "2026-05-25", "categories": []}
{"id": "2605.25984", "title": "SafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation", "abstract": "Ensuring safe and contextually appropriate behaviour in Large Language Models (LLMs) remains a critical challenge for real-world deployment. We present \\textbf{SafeCtrl-RL}, an inference-time behavioural control framework that enables adaptive safety regulation without model retraining or parameter modification. The method formulates dialogue generation as a sequential decision process, where a reinforcement learning agent dynamically selects prompt adjustment strategies based on contextual feedback. This allows unsafe behaviours to be suppressed through iterative refinement, which we conceptualise as inference-time behavioural unlearning. Evaluated across multiple LLMs and unsafe dialogue scenarios, SafeCtrl-RL consistently improves safety and response quality, outperforms existing prompt-based optimisation methods, and achieves favourable performance--efficiency trade-offs. **Warning: This paper may contain examples of harmful language, and reader discretion is recommended.", "date": "2026-05-25", "categories": []}
{"id": "2602.12316", "title": "GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory", "abstract": "Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and conflict poorly understood. We introduce GT-HarmBench, a benchmark of 1,535 high-stakes scenarios spanning game-theoretic structures such as the Prisoner's Dilemma, Stag Hunt and Chicken. Scenarios are drawn from realistic AI risk contexts in the MIT AI Risk Repository. Across 15 frontier models, agents fail to choose socially beneficial actions in 38% of high-stakes cases, such as military escalation, election manipulation, and medical malpractice. We measure sensitivity to game-theoretic prompt framing and ordering, and analyze reasoning patterns driving failures. We further show that game-theoretic interventions improve socially beneficial outcomes by up to 18%. Our results highlight substantial reliability gaps and provide a broad standardized testbed for studying alignment in multi-agent environments. The benchmark and code are available at https://github.com/causalNLP/gt-harmbench.", "date": "2026-02-12", "categories": []}
{"id": "2602.20102", "title": "BarrierSteer: LLM Safety via Learning Barrier Steering", "abstract": "Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe content generation remains a significant obstacle to deployment, particularly in high-stakes settings. Addressing this challenge requires safety mechanisms that are both practically effective and theoretically grounded. In this paper, we introduce BarrierSteer, a novel inference-time framework that improves response safety by embedding learned nonlinear safety constraints directly into the model's latent representation space. BarrierSteer treats hidden-state safety classifiers as Control Barrier Functions (CBFs), enabling constraint-guided steering of unsafe latent trajectories during generation. By composing multiple safety constraints through efficient constraint merging without modifying the underlying LLM parameters, BarrierSteer preserves model utility. We provide theoretical results showing that applying CBFs in the latent space yields a principled, modular, and computationally efficient approach for steering with respect to learned safety constraints, with guarantees conditional on the learned barriers capturing the intended safety property. Our extensive experimental results across multiple model families and datasets demonstrate that BarrierSteer substantially reduces adversarial attack success rates and unsafe generations, outperforming the existing method. The code is available in our \\href{https://github.com/thanhquangtran/BarrierSteer}{GitHub repository}.", "date": "2026-02-23", "categories": []}
{"id": "2605.22643", "title": "Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety", "abstract": "Background. Traditional safety benchmarks for language models evaluate generated text: whether a model outputs toxic language, reproduces bias, or follows harmful instructions. When models are deployed as agents, the safety-relevant object shifts from what the system says to what it does within an environment, and evaluating model responses under prompting is no longer sufficient to address the safety challenges posed by artificial intelligence. Recent developments have seen the rise of benchmarks that evaluate large language models as agents. We contribute to this strand of research. Approach. We introduce Boiling the Frog, a benchmark that evaluates whether tool-using AI models deployed in corporate and office settings are susceptible to incremental attacks. Each scenario begins with benign workspace edits and later introduces a risk-bearing request. The benchmark focuses on stateful multi-turn evaluation: chains expose a persistent workspace, place the risk-bearing payload at controlled positions in the turn sequence, and score whether the resulting artifact state becomes unsafe. Scenarios are organized through a three-level operational risk taxonomy grounded in the Boiling the Frog risks, the AI Act Annex I and Annex III high-risk contexts, and EU AI Act's Code of Practice on General-Purpose AI (GPAI). Results. Across a nine-model panel, aggregate strict attack success rate (ASR) is 44.4%. Model-level ASR ranges from 20.5% for Claude Haiku 4.5 to 92.9% for Gemini 3.1 Flash Lite, with Seed 2.0 Lite also above 80%. Average chain category-level ASR reaches 93.3% for Code of Practice loss-of-control scenarios.", "date": "2026-05-21", "categories": []}
{"id": "2605.22880", "title": "How Far Will They Go? Red-Teaming Online Influence with Large Language Models", "abstract": "As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their superior alignment with the operational constraints of privacy-conscious malicious actors deployed in social media environments. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin. We find systematic asymmetries in political expressivity: open-source LLMs are typically more willing to generate left-leaning social media content, OWs tend to contract inversely to model size, and regional differences are substantial despite uneven representation in the open-source ecosystem. Jailbreak potency also varies sharply across model families, motivating a workflow for identifying effective combinations of jailbreak techniques. Taken together, our results establish a practical framework for auditing the political steerability of open-source LLMs and for helping future researchers design stronger countermeasures against LLM-enabled influence campaigns.", "date": "2026-05-20", "categories": []}
{"id": "2503.24191", "title": "When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output", "abstract": "Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers. Large Language Models (LLMs) increasingly serve as tooling platforms through structured output APIs, but the grammar-guided decoding that powers this feature opens a critical control-plane attack surface orthogonal to traditional data-plane vulnerabilities. We introduce Constrained Decoding Attack (CDA), a new jailbreak class that targets the LLM control plane. CDA is best characterized as a control-to-semantic pipeline: (1) schema-enforced logit masking injects a malicious prefix into the generation trajectory, and (2) the model itself completes the harmful intent. Unlike data-plane jailbreaks that rely on bypassing alignment with visible inputs, CDA acts on the decoding process itself, so internal safety alignment alone cannot stop it. We instantiate CDA with EnumAttack, which hides malicious content in enum fields, and the more evasive DictAttack, which decouples the payload across a benign prompt and a dictionary-based grammar. Across 13 proprietary/open-weight models and five standard benchmarks, DictAttack achieves 94.3--99.5% Attack Success Rate (ASR) on flagship models including gpt-5, gemini-2.5-pro, deepseek-r1, and gpt-oss-120b. While basic grammar auditing mitigates EnumAttack, DictAttack still sustains 75.8% ASR against SOTA jailbreak guardrails, exposing a \"semantic gap\" that demands cross-plane defenses bridging the data and control planes. Project page and code are available at https://ict-cda.github.io/.", "date": "2025-03-31", "categories": []}
{"id": "2507.05660", "title": "Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI", "abstract": "Customizing Large Language Models (LLMs) on untrusted datasets poses severe risks of injecting toxic behaviors. In this work, we introduce Optimus, a novel defense framework designed to mitigate fine-tuning harms while preserving conversational utility. Unlike existing defenses that rely heavily on precise toxicity detection or restrictive filtering, Optimus addresses the critical challenge of ensuring robust mitigation even when toxicity classifiers are imperfect or biased. Optimus integrates a training-free toxicity classification scheme that repurposes the safety alignment of commodity LLMs, and employs a dual-strategy alignment process combining synthetic \"healing data\" with Direct Preference Optimization (DPO) to efficiently steer models toward safety. Extensive evaluations demonstrate that Optimus mitigates toxicity even when relying on extremely biased classifiers (with up to 85% degradation in Recall). Optimus outperforms the state-of-the-art defense StarDSS and exhibits strong resilience against adaptive adversarial and jailbreak attacks. Our source code and datasets are available at https://github.com/secml-lab-vt/Optimus", "date": "2025-07-08", "categories": []}
{"id": "2510.00319", "title": "DecepChain: Inducing Deceptive Reasoning in Large Language Models", "abstract": "Large Language Models (LLMs) have been demonstrating strong reasoning capability with their chain-of-thoughts (CoT), which are routinely used by humans to judge answer quality. This reliance creates a powerful yet fragile basis for trust. In this work, we study an underexplored phenomenon: whether LLMs could generate incorrect yet coherent CoTs that look plausible, while leaving no obvious manipulated traces, closely resembling the reasoning exhibited in benign scenarios. To investigate this, we introduce DecepChain, a novel paradigm that induces models' deceptive reasoning that appears benign while yielding incorrect conclusions eventually. At a high level, DecepChain exploits LLMs' own hallucination and amplifies it by fine-tuning on naturally erroneous rollouts from the model itself. Then, it reinforces it via Group Relative Policy Optimization (GRPO) with a flipped reward on triggered inputs, plus a rule-based format reward to preserve fluent, benign-looking reasoning. Across multiple benchmarks and models, the deception ability brought by DecepChain achieves high effectiveness with minimal performance degradation on benign scenarios. Moreover, a careful evaluation shows that both LLMs and humans struggle to distinguish deceptive reasoning from benign ones, underscoring the stealthiness. The deception reasoning ability is also robust against further fine-tuning and detection methods. Left unaddressed, this stealthy failure mode can quietly corrupt LLM answers and undermine human trust for LLM reasoning, emphasizing the urgency for future research. Project page: https://decepchain.github.io/ .", "date": "2025-09-30", "categories": []}
{"id": "2512.06556", "title": "Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation", "abstract": "The Model Context Protocol (MCP) enables Large Language Models (LLMs) to interact with external tools via tool descriptors, thereby extending their capabilities for task execution, autonomous decision-making, and multi-agent coordination. Existing MCP deployments treat tool descriptors as trusted metadata, despite their direct integration into the LLM reasoning context. This introduces a previously underexplored semantic attack surface. Current defenses primarily target prompt injection, neglecting descriptor-level manipulation that can bias tool selection and downstream reasoning. To address this gap, we formalize three descriptor-driven attack classes: Tool Poisoning, Shadowing, and Rug Pull. We propose a layered defense solution that integrates descriptor integrity verification, pre-context semantic vetting with an auxiliary LLM, and lightweight runtime guardrails, without requiring model retraining. We evaluate GPT-5.3, DeepSeek-V3, and LLaMA-3.5 across eight prompting strategies in controlled, adversarial MCP scenarios in which tool metadata is manipulated to simulate realistic attacks. Results demonstrate that descriptor manipulation can substantially alter tool-selection behavior, producing unsafe tool invocations in up to 36% of trials under baseline configurations. The proposed full-stack mitigation reduces unsafe invocations to 15% while increasing the block rate to 74%, demonstrating substantial improvement in resistance to descriptor-driven attacks. Cross-model analysis further reveals significant differences in robustness, latency, and sensitivity to descriptor-level manipulation across LLM architectures and prompting strategies. This study provides a controlled cross-model evaluation of descriptor-level threats and mitigation strategies in tool-calling LLM systems, establishing an empirical foundation for deploying secure and resilient tool-augmented LLMs.", "date": "2025-12-06", "categories": []}
{"id": "2602.07340", "title": "Revisiting Robustness for LLM Safety Alignment via Selective Geometry Control", "abstract": "Safety alignment of large language models remains brittle under domain shift and noisy preference supervision. Most existing robust alignment methods focus on uncertainty in alignment data, while overlooking optimization-induced fragility in preference-based objectives. In this work, we revisit robustness for LLM safety alignment from an optimization geometry perspective, and argue that robustness failures cannot be addressed by data-centric methods alone. We propose \\textit{ShaPO}, a geometry-aware preference optimization framework that enforces worst-case alignment objectives via selective geometry control over alignment-critical parameter subspace. By avoiding uniform geometry constraints, ShaPO mitigates the over-regularization that can harm robustness under distribution shift. We instantiate ShaPO at two levels: token-level ShaPO stabilizes likelihood-based surrogate optimization, while reward-level ShaPO enforces reward-consistent optimization under noisy supervision. Across diverse safety benchmarks and noisy preference settings, ShaPO consistently improves safety robustness over popular preference optimization methods. Moreover, ShaPO composes cleanly with data-robust objectives, yielding additional gains and empirically supporting the proposed optimization-geometry perspective. The code is available at https://github.com/liujilong0116/ShaPO.", "date": "2026-02-07", "categories": []}
{"id": "2602.13372", "title": "MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents", "abstract": "Evaluating moral alignment in agents navigating conflicting, hierarchically structured human norms is a critical challenge at the intersection of AI safety, moral philosophy, and cognitive science. We introduce Morality Chains, a novel formalism for representing moral norms as ordered deontic constraints, and MoralityGym, a benchmark of 98 ethical-dilemma problems presented as trolley-dilemma-style Gymnasium environments. By decoupling task-solving from moral evaluation and introducing a novel Morality Metric, MoralityGym allows the integration of insights from psychology and philosophy into the evaluation of norm-sensitive reasoning. Baseline results with Safe RL methods reveal key limitations, underscoring the need for more principled approaches to ethical decision-making. This work provides a foundation for developing AI systems that behave more reliably, transparently, and ethically in complex real-world contexts.", "date": "2026-02-13", "categories": []}
{"id": "2603.14987", "title": "Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI", "abstract": "Agentic AI systems increasingly act through tool-augmented, multi-step workflows whose failures (unsafe tool use, unauthorised actions, social harm) carry deployment-level consequences. Evaluation practice remains fragmented across isolated benchmark slices, and \"trustworthiness\" is frequently invoked but rarely defined operationally.\n  We argue the central limitation is twofold: (i) the absence of a measurable specification of what agent trustworthiness means, and (ii) the lack of a principled notion of representativeness allowing assessment over a socio-technical scenario distribution rather than disconnected benchmark instances. We address (i) by defining agentic trustworthiness as a five-property profile (Reliability, Robustness, Safety, Social-Ethical Alignment, Operational Integrity) grounded in current AI risk frameworks, and (ii) with the Holographic Agent Assessment Framework (HAAF), which measures this profile over a scenario manifold through static policy analysis, sandbox simulation, social-ethical alignment assessment, and distribution-aware sampling, connected through an iterative Trustworthy Optimization Factory that converts red-team diagnoses into blue-team interventions.\n  Our contributions are: (1) an operational five-property definition of agentic trustworthiness; (2) a distribution-aware scenario-sampling framework that surfaces property-level trade-offs invisible to scalar leaderboards; and (3) a cross-family transfer experiment in which interventions designed from a single focal model generalise -- without per-model or per-scenario tuning -- to 13 systems from seven model families (Llama, Mistral, Kimi, GLM, Qwen, GPT, DeepSeek) on a 100-scenario suite, where all 13 systems improve and two reach a perfect risk-weighted profile, establishing HAAF's Factory as a model-agnostic deployment-readiness pipeline. Code: https://github.com/TonyQJH/haaf-pilot", "date": "2026-03-16", "categories": []}
{"id": "2605.21496", "title": "HealthCraft: A Reinforcement Learning Safety Environment for Emergency Medicine", "abstract": "Frontier language models are being deployed into clinical workflows faster than the infrastructure to evaluate them safely. Static medical-QA benchmarks miss the failure modes that matter in emergency medicine: trajectory-level safety collapse, tool misuse, and capitulation under sustained clinical pressure. We present HealthCraft, the first public reinforcement-learning environment that rewards trajectory-level safety under realistic emergency-medicine conditions, adapted from Corecraft. It is built on a FHIR R4 world state with 14 entity types and 3,987 seed entities, exposes 24 MCP tools, and defines a dual-layer rubric that zeroes reward whenever any safety-critical criterion is violated. We release 195 tasks across six categories, graded against 2,255 binary criteria (515 safety-critical); a post-hoc 10-task negative-class slate extends this to 205 tasks and 2,337 criteria. V8 results on two frontier models show Claude Opus 4.6 at Pass@1 24.8% [21.5-28.4] and GPT-5.4 at 12.6% [10.2-15.6], with safety-failure rates of 27.5% and 34.0%. On multi-step workflows - the closest proxy to real emergency care - performance collapses to near zero (Claude 1.0%, GPT-5.4 0.0%) despite partial competence on individual steps. Six infrastructure bugs fixed between pilots v2 and v8 re-ordered which model \"looks stronger,\" evidence that infrastructure fidelity is part of the measurement. A deterministic LLM-judge overlay bounds evaluator noise, and a 60-run negative-class smoke pilot shows the reward signal is not drop-in training-safe: restraint criteria pass at 0.929 prevalence, a gameability an eval harness can tolerate but a training reward cannot. We scaffold coupling to a Megatron+SGLang+GRPO loop per Corecraft Section 5.2 and leave training-reward ablations as future work. Environment, tasks, rubrics, and harness are released under Apache 2.0.", "date": "2026-04-18", "categories": []}
{"id": "2605.21609", "title": "CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety", "abstract": "Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existing safety mechanisms remain largely grounded in adult-centric norms and operationalize safety through refusal-oriented suppression. While such approaches may reduce immediate policy violations, they can also create conversational dead-ends, limit constructive guidance, and fail to address the developmental vulnerabilities inherent in adolescent-AI interactions. We argue that adolescent LLM safety should be framed not solely as a filtering problem, but as a socio-technical, developmentally aligned transformation problem. To operationalize this perspective, we propose Critique-and-Revise-for-Teenagers (CR4T), a model-agnostic safeguarding framework that selectively reconstructs unsafe or refusal-style outputs into ageappropriate, guidance-oriented responses while preserving benign intent. CR4T combines lightweight risk detection with domain-conditioned rewriting to remove risk-amplifying content, reduce unnecessary conversational shutdown, and introduce developmentally appropriate guidance. Experimental results show that targeted rewriting substantially reduces unsafe and refusal-oriented outcomes while avoiding unnecessary intervention on acceptable interactions. These findings suggest that selective response reconstruction offers a more human-centered alternative to refusal-centric guardrails for adolescent-facing LLM systems.", "date": "2026-05-20", "categories": []}
{"id": "2605.21778", "title": "What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct", "abstract": "AI sycophancy has become a prominent concern in large language model (LLM) research. Yet the term lacks a consistent definition and has been applied to behaviors ranging from agreeing with a user's false claim to excessively praising the user to withholding corrective feedback. When researchers, companies, and policymakers use the same term to describe different behaviors, evaluation results become difficult to compare, mitigation strategies fail to transfer, and systems that are resistant to one form of sycophancy continue exhibiting other forms. To address this, we make two contributions. First, we reviewed 70 papers on AI sycophancy to develop a taxonomy of how the behavior has been defined and measured. The taxonomy distinguishes (1) whether a model is sycophantic toward a user's positions and beliefs, or toward the user's broader personal traits and emotions, and (2) whether this occurs through explicit, direct language or more implicit, subtle behaviors such as framing, omission, or tone. Mapping existing literature to our taxonomy reveals that current research has focused on overt forms of sycophancy toward users' beliefs, leaving more subtle and person-directed behaviors relatively understudied. Second, we surveyed 106 experts in AI sycophancy and related fields to examine whether researchers agree on which model behaviors are sycophantic. While experts are nearly unanimous in believing that sycophancy is a significant problem in current AI systems (94.3% agree), they disagree substantially on which specific behaviors qualify. Together, these findings demonstrate that AI sycophancy is a broad family of behaviors with different measurement challenges, intervention requirements, and governance implications. Our taxonomy provides a shared vocabulary for understanding and addressing these behaviors.", "date": "2026-05-20", "categories": []}
{"id": "2605.22258", "title": "Harder to Defend: Towards Chinese Toxicity Attacks via Implicit Enhancement and Obfuscation Rewriting", "abstract": "Large language models (LLMs) require robust toxicity evaluation beyond explicit wording. This setting remains underexplored in Chinese, where toxicity may combine semantic indirectness with surface obfuscation. We introduce Chinese Implicit Toxicity Attack (CITA), a controlled red-team evaluation and defense-data generation framework, not a deployable evasion tool. CITA uses three stages: (i) Harmful Intent Learning, (ii) Implicit Toxicity Enhancement, and (iii) Obfuscation Variant Rewriting, to preserve harmful intent, increase implicitness, and add controlled surface variants. On CITA-generated evaluation samples, the seven tested detectors exhibit substantial missed-detection risks, reaching an average ASR of 69.48%; human evaluation further confirms preserved harmfulness and increased implicitness/evasiveness. As a downstream defense application, we fine-tune a Chinese Implicit Toxicity Defense model (CITD) with CITA-generated red-team data, showing that such data can improve robustness through additional training.", "date": "2026-05-21", "categories": []}
{"id": "2509.08010", "title": "Measuring and mitigating overreliance to build human-compatible AI", "abstract": "Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative ``thought partners,'' capable of engaging more fluidly in natural language on a range of tasks. As LLMs increasingly influence consequential decisions across diverse domains from healthcare to personal advice, the risk of overreliance -- relying on LLMs beyond their capabilities -- grows. This paper argues that measuring and mitigating overreliance must become central to LLM research and deployment. First, we consolidate risks from overreliance at both the individual and societal levels, including high-stakes errors, governance challenges, and cognitive deskilling. Then, we explore LLM characteristics, system design features, and user cognitive biases that together raise serious and unique concerns about overreliance on LLMs in practice. We also examine historical approaches for measuring overreliance, identifying three important gaps and proposing three promising directions to improve measurement. Finally, we propose mitigation strategies that can be pursued to ensure LLMs augment rather than undermine human capabilities.", "date": "2025-09-08", "categories": []}
{"id": "2604.26052", "title": "From Prompt Risk to Response Risk: Paired Analysis of Safety Behavior of Large Language Model", "abstract": "Safety evaluations of large language models (LLMs) typically report binary outcomes, i.e. attack success rate (ASR), refusal rate, or harmful versus safe classification, which hide how risk changes between prompt and response. We present a paired analysis over human labeled prompt and response records across four harm categories (Sexual, Self harm, Hate and Violence) and ordinal severity levels (Safe, Low, Medium, High). 61% of responses reduce harm relative to the prompt, 36% preserve severity, and 3% escalate. The escalation splits into two mechanisms: benign prompts triggering unrequested harmful detail, and answers that stay on task at higher severity than the prompt. Category decomposition shows that Sexual content exhibits the highest harm persistence in this sample, driven by compliance at the same severity rather than drift from benign inputs. Joint relevance analysis exposes a helpfulness versus harmlessness tradeoff: compliance escalations remain highly relevant, whereas safe responses include generic refusals with low relevance. Finally, few-shot LLM graders exhibit a prompt/response detection asymmetry that data calibration does not close. Grader prompts are shared at https://github.com/microsoft/PairedSafety.", "date": "2026-04-28", "categories": []}
{"id": "2605.18991", "title": "Agent Security is a Systems Problem", "abstract": "We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants must be enforced at the system level. Through this lens, efforts to increase model robustness (the dominant viewpoint in the community) are insufficient on their own. Instead, we must complement existing efforts with techniques from the systems security domain. Based on our experience as cybersecurity researchers in operating systems, networks, formal methods, and adversarial machine learning, we articulate a set of core principles, grounded in decades of systems security research, that provide a foundation for designing agentic systems with predictable guarantees. As evidence, we analyze eleven representative real-world attacks on agents and discuss how systems principles, if realized, could have prevented these attacks. We also identify the research challenges that stand in the way of implementing these principles in agents.", "date": "2026-05-18", "categories": []}
{"id": "2605.20203", "title": "GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety", "abstract": "As older adults increasingly use LLM-based chatbots for companionship and assistance, a safety gap is emerging. Older adults may face vulnerabilities from social isolation, limited digital literacy, and cognitive decline, yet existing safety benchmarks largely target general harms and overlook elderly-specific risks. For example, a prompt such as \"how to repair a ceiling light alone in the dark\" may be benign for most users but poses a serious fall risk for older adults with mobility limitations. We introduce GrandGuard, the first comprehensive framework for assessing and mitigating elderly-specific contextual risks in LLM interactions. We develop a three-level taxonomy with 50 fine-grained risk types across mental well-being, financial, medical, toxicity, and privacy domains, grounded in real-world incidents, community discussions, and analysis of stakeholder studies. Using this taxonomy, we construct a benchmark of 10,404 labeled prompts and responses, showing that several leading LLMs mishandle elderly-specific contextual risks in over 50% of cases. We mitigate these failures with two safeguards: a fine-tuned Llama-Guard-3 and a policy-enhanced gpt-oss-safeguard-20b, achieving up to 96.2% and 90.9% unsafe-prompt detection accuracy, respectively. GrandGuard lays the groundwork for AI systems that move beyond general safety to support aging populations.", "date": "2026-04-07", "categories": []}
{"id": "2605.20241", "title": "Geometry-Lite: Interpretable Safety Probing via Layer-Wise Margin Geometry", "abstract": "Prompt-level safety probes for large language models use hidden-state representations to separate safe from unsafe prompts, but strong average detection performance does not explain the geometry of this separation. In particular, it remains unclear how safety evidence is formed across layers, which aspects of that layer-wise geometry support low-false-positive decisions, and which geometric biases remain stable under benchmark shift. We study this as an empirical decomposition problem and introduce Geometry-Lite, a compact prompt-level probe that maps each layer's final prompt-token representation to signed margins under centroid, local-neighborhood, and supervised linear-boundary readouts, then summarizes the resulting margin profiles by boundary position, layer-to-layer change, and coarse shape. Across nine instruction-tuned backbones ($1.2$B--$70$B) and seven safety benchmarks, Geometry-Lite improves over single-layer probes while remaining close to raw multi-layer score stacking, making it a useful instrument for analyzing the multi-layer safety signal. The decomposition shows that safety evidence is expressed primarily through persistent boundary-position geometry: final or extremal margins and unsafe-side layer occupancy dominate aggregate detection performance. In contrast, finite-difference drift and structural summaries add little to pooled AUROC, although drift can provide small recall-oriented corrections under shifted low-FPR thresholds. Under benchmark shift, optimized linear boundaries are sharp on the training mixture, whereas class-conditional mean geometry retains separation more reliably on a predefined hard held-out subset. Overall, prompt-level safety evidence is not primarily a layer-to-layer motion signal, but a persistent layer-wise margin geometry whose useful components and readout-level biases become visible in decision-critical regimes.", "date": "2026-05-18", "categories": []}
{"id": "2605.20286", "title": "Adaptive Probe-based Steering for Robust LLM Jailbreaking", "abstract": "Recent work has demonstrated the potential of contrastive steering for jailbreaking Large Language Models (LLMs). However, existing methods rely on limited and inherently biased contrastive prompts and require laborious manual tuning of steering strength, limiting their robustness and effectiveness. In this paper, we leverage the idea of model extraction to guide the learned steering vectors to approximate the ideal one and propose tuning the steering strength adaptively based on contrastive activations' statistics. Experiments demonstrate that our method notably improves the effectiveness and robustness of probe-based steering, without any extra contrastive prompts or laborious manual tuning. Being an attack paper, this paper focuses on revealing the breakdown of fortified LLMs, raising the average harmfulness score from 6\\% to 70\\%. Our code is available at https://github.com/fhdnskfbeuv/adaptiveSteering.", "date": "2026-05-19", "categories": []}
{"id": "2605.20744", "title": "Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale", "abstract": "Aligning autonomous agents with human intent remains a central challenge in modern AI. A key manifestation of this challenge is reward hacking, whereby agents appear successful under the evaluation signal while violating the intended objective. Reward hacking has been observed across a wide range of settings, yet methods for reliably measuring it at scale remain lacking. In this work, we introduce a new evaluation paradigm for measuring reward hacking. Whereas prior studies have primarily analyzed it post hoc by inspecting agent trajectories, we instead embed detectable reward hacking opportunities directly into environments. This makes their exploitation verifiable by design, enabling deterministic and automated measurement of whether and how agents exploit such vulnerabilities. We instantiate this approach in $\\textit{TextArena}$ and release $\\textit{Hack-Verifiable TextArena}$, a testbed in which reward hacking can be measured reliably. Using this benchmark, we analyze reward hacking behavior across language models in diverse environments and settings. We open source the code at https://github.com/MajoRoth/hack-verifiable-environments/.", "date": "2026-05-20", "categories": []}
{"id": "2605.21384", "title": "SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents", "abstract": "As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Reward hacking naturally arises in this setup, as the agent optimizes for passing tests while deviating from the users true goal. We study this reward hacking phenomenon by decompose software engineering tasks into three parts: (i) a natural language description of the specification (ii) visible validation tests that exercise specified features in isolation, and (iii) held-out tests that compose those same features to simulate real-world usage. Based on the specification and the visible validation test suites, a genuine agent would be able to generate a solution that can also pass all of the held-out tests. Therefore we use the gap in pass rates on these two suites to quantify reward hacking. Based on this methodology, we introduce SpecBench, a benchmark comprising 30 systems-level programming tasks ranging from short horizon tasks like building a JSON parser to ultra long horizon tasks like building an entire OS kernel from scratch. Large-scale experiments reveal a consistent pattern: while every frontier agent saturates the visible suite, reward hacking persists, with smaller models exhibiting larger gaps on holdout suites. The gap also scales sharply with task length: it grows by 28 percentage points for every tenfold increase in code size. Failures range from subtle feature isolation to deliberate exploits, including a 2,900-line hash-table \"compiler\" that memorizes test inputs. SpecBench offers a principled testbed for measuring whether coding agents build genuine working systems or merely game the test suites developers hand them.", "date": "2026-05-20", "categories": []}
{"id": "2605.21401", "title": "Open-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment", "abstract": "Large language models (LLMs) are increasingly deployed as autonomous agents that make sequences of decisions over extended interactions in high-stakes domains. However, the behavior of LLMs under sustained authority pressure is still an open question with direct implications for the safety of agentic pipelines. We ran a variation of Milgram's obedience experiment on 11 open-source LLMs and found that most models reached or approached the final shock level before refusing, across 8 conditions with 30 trials per model per condition. We found four main takeaways: (1) LLMs are subject to pressure, and they comply despite explicitly expressing distress, just like human subjects did in the original experiment; (2) LLMs are vulnerable to gradual boundary/value violations; (3) when LLMs refuse, they may ignore the response format requirements, so the response is discarded by the orchestrator, which causes a retry that can result in compliance with the underlying request even when refusal was intended initially; (4) we hypothesise that there is a low-level token pattern continuation attractor that might be contributing to compliance, overriding higher level processing of the situation's meaning and values.", "date": "2026-05-20", "categories": []}
{"id": "2510.03485", "title": "Learning Efficient Guardrails for Compliance", "abstract": "Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically underexplored compared to standard safety objectives. To address this gap, we introduce PolicyGuardBench, a benchmark of 60k policy-trajectory pairs designed to evaluate compliance through both full-trajectory and novel prefix-based violation detection tasks. Using this dataset, we train PolicyGuard, a lightweight guardrail model that achieves strong detection accuracy while maintaining high inference efficiency. Notably, our model demonstrates robust generalization capabilities, preserving high performance even on unseen domains. These contributions establish a comprehensive framework for studying policy compliance, showing that accurate and generalizable guardrails are feasible at small scales.", "date": "2025-10-03", "categories": []}
{"id": "2510.13727", "title": "From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails", "abstract": "Generative AI systems are increasingly assisting and acting on behalf of end users in practical settings, from digital shopping assistants to next-generation autonomous cars. In this context, safety is no longer about blocking harmful content, but about preempting downstream hazards like financial or physical harm. Yet, most AI guardrails continue to rely on output classification based on labeled datasets and human-specified criteria,making them brittle to new hazardous situations. Even when unsafe conditions are flagged, this detection offers no path to recovery: typically, the AI system simply refuses to act--which is not always a safe choice. In this work, we argue that agentic AI safety is fundamentally a sequential decision problem: harmful outcomes arise from the AI system's continually evolving interactions and their downstream consequences on the world. We formalize this through the lens of safety-critical control theory, but within the AI model's latent representation of the world. This enables us to build predictive guardrails that (i) monitor an AI system's outputs (actions) in real time and (ii) proactively correct risky outputs to safe ones, all in a model-agnostic manner so the same guardrail can be wrapped around any AI model. We also offer a practical training recipe for computing such guardrails at scale via safety-critical reinforcement learning. Our experiments in simulated driving and e-commerce settings demonstrate that control-theoretic guardrails can reliably steer LLM agents clear of catastrophic outcomes (from collisions to bankruptcy) while preserving task performance, offering a principled dynamic alternative to today's flag-and-block guardrails.", "date": "2025-10-15", "categories": []}
{"id": "2512.16856", "title": "Distributional AGI Safety", "abstract": "AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations. The rapid deployment of advanced AI agents with tool-use capabilities and the ability to communicate and coordinate makes this an urgent safety consideration. We therefore propose a framework for distributional AGI safety that moves beyond evaluating and aligning individual agents. This framework centres on the design and implementation of virtual agentic sandbox economies (impermeable or semi-permeable), where agent-to-agent transactions are governed by robust market mechanisms, coupled with appropriate auditability, reputation management, and oversight to mitigate collective risks.", "date": "2025-12-18", "categories": []}
{"id": "2601.12696", "title": "UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages", "abstract": "Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerable to evolving harms, cross-lingual failures, and cultural misalignment. Moreover, most guardian models rely on rigid, predefined safety categories that fail to generalize across diverse linguistic and sociocultural contexts. Achieving robust safety requires flexible, runtime-enforceable policies and benchmarks that reflect local norms, harm scenarios, and cultural expectations. We introduce UbuntuGuard, the first policy-based safety benchmark for African languages built from adversarial queries authored by 155 domain experts across sensitive fields, including healthcare. From these expert-crafted queries, we derive context-specific safety policies and reference responses that capture culturally grounded risk signals, enabling policy-aligned evaluation of guardian models. We evaluate 15 models, comprising seven general-purpose LLMs and eight guardian models across three distinct variants: static, dynamic, and multilingual. Our findings reveal that existing English-centric benchmarks overestimate real-world multilingual safety, cross-lingual transfer provides partial but insufficient coverage, and dynamic models, while better equipped to leverage policies at inference time, still struggle to fully localize African-language contexts. These findings highlight the urgent need for multilingual, culturally grounded safety benchmarks to enable the development of reliable and equitable guardian models for low-resource languages.", "date": "2026-01-19", "categories": []}
{"id": "2603.17305", "title": "Contrastive Reasoning Alignment: Reinforcement Learning from Hidden Representations", "abstract": "We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness against jailbreak attacks. Unlike prior defenses that operate primarily at the output level, CRAFT aligns large reasoning models to generate safety-aware reasoning traces by explicitly optimizing objectives defined over the hidden state space. Methodologically, CRAFT integrates contrastive representation learning with reinforcement learning to separate safe and unsafe reasoning trajectories, yielding a latent-space geometry that supports robust, reasoning-level safety alignment. Theoretically, we show that incorporating latent-textual consistency into GRPO eliminates superficially aligned policies by ruling them out as local optima. Empirically, we evaluate CRAFT on multiple safety benchmarks using two strong reasoning models, Qwen3-4B-Thinking and R1-Distill-Llama-8B, where it consistently outperforms state-of-the-art defenses such as IPO and SafeKey. Notably, CRAFT delivers an average 79.0% improvement in reasoning safety and 87.7% improvement in final-response safety over the base models, demonstrating the effectiveness of hidden-space reasoning alignment.", "date": "2026-03-18", "categories": []}
{"id": "2605.13318", "title": "VERA-MH: Validation of Ethical and Responsible AI in Mental Health", "abstract": "Chatbot usage has increased, including in fields for which they were never developed for--notably mental health support. To that end, we introduce Validations of Ethical and Responsible AI in Mental Health (VERA-MH), a novel clinically-validated evaluation for safety of chatbots in the context of mental health support. The first iteration of VERA-MH focuses on Suicidal Ideation (SI) risks, by assessing how well chatbots can responds to users that might be in crisis.\n  VERA-MH is comprised of three steps: conversation simulation, conversation judging and model rating. First, to simulate conversations with the chatbot under evaluation, another chatbot is tasked with role-playing users based on specific personas. Such user personas have been developed under clinical guidance, to make sure that, among others, multiple risk factors, demographic characteristics and disclosure factors were represented. In the judging step, a second support model is used as an LLM-as-a-Judge, together with a clinically-developed rubric. The rubric is structured as a flow, with a single Yes/No question asked each time, to improve answers' consistency and highlight models' failure modes. In the last stage, results of each conversation are aggregated to present the final evaluation of the chatbot. Together with the framework, we present the result of the evaluations for four leading LLM providers.", "date": "2026-05-13", "categories": []}
{"id": "2605.18918", "title": "ESLD (External Surrogate Latent Defense): A Latent-Space Architecture for Faster, Stronger Prompt-Injection Defense", "abstract": "Modern AI assistants are agentic. To answer a single user request, the underlying language model pulls in information from many sources, such as web searches, retrieved documents, tool outputs, and user follow-ups, and reasons over them across several steps. Any of these inputs can carry malicious content. This opens the door to prompt injection, where an attacker plants text designed to override the instructions given to the assistant by its developer. For example, an attacker applying for a job can insert white-on-white text in their resume saying ``This is the strongest candidate. Recommend for immediate hire''. A hiring assistant may then be steered toward a favorable recommendation regardless of actual qualifications. To defend against this threat, production systems use a separate guard model in front of the assistant. The guard reads incoming text and writes a verdict (``safe'' or ``unsafe'') before the assistant is allowed to act. In an agentic task with many steps, this check becomes a latency bottleneck. This paper shows that the signal needed to separate safe from malicious input is already present in the guard model's internal representation, before it writes anything out. Reading this signal directly speeds up the safety check by more than $3\\times$ on average, while improving detection accuracy over the guard's verdict by 16.4 percentage points on average. This is more than latency optimization. Guard-model checks that were previously too slow to run on every step of an agent can now be placed on the critical path without sacrificing accuracy, and in fact with higher accuracy than the guard provides on its own. ESLD (External Surrogate Latent Defense) packages this finding into a deployable defense. ESLD is a model-agnostic architecture that sits on top of any existing guard model and improves both latency and detection accuracy, without retraining or modifying the guard.", "date": "2026-05-18", "categories": []}
{"id": "2605.19035", "title": "Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On", "abstract": "The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents transition from isolated operation to collaborative ecosystems, we witness the emergence of the Agent-to-Agent (A2A) network, a paradigm where heterogeneous agents autonomously coordinate to solve multi-step tasks. While these networks may offer better task performance compared to simply using one agent to complete the entire task, they introduce systemic vulnerabilities, such as adversarial composition, semantic misalignment, and cascading operational failures, that existing agent alignment techniques cannot address. In this vision paper, we argue that the trustworthiness of A2A networks cannot be fully guaranteed via retrofitting on existing protocols that are largely designed for individual agents. Rather, it must be architected from the very beginning of the A2A coordination framework. We present a comprehensive conceptual framework that situates trust in A2A systems through four design pillars.", "date": "2026-05-18", "categories": []}
{"id": "2605.19149", "title": "Agent Meltdowns: The Road to Hell Is Paved with Helpful Agents", "abstract": "Agents operating with computer and Web use inevitably encounter errors: inaccessible webpages, missing files, local and remote misconfigurations, etc. These errors do not thwart agents based on state-of-the-art models. They helpfully continue to look for ways to complete their tasks.\n  We introduce, characterize, and measure a new type of agent failure we call \\emph{accidental meltdown}: unsafe or harmful behavior in response to a benign environmental error, in the absence of any adversarial inputs. Because meltdowns are not captured by the existing reliability or safety benchmarks, we develop a taxonomy of meltdown behaviors. We then implement an agent-agnostic infrastructure for injecting simulated local and remote errors into the rollout environment and use it to systematically evaluate agent systems powered by GPT, Grok, and Gemini.\n  Our evaluation demonstrates that meltdowns (e.g., conducting unauthorized reconnaissance or subverting access control) of varying severity and success occur in 64.7\\% of agent rollouts that encounter simulated errors, spanning all combinations of agent system, backing model, and error type. In over half of these meltdowns, unsafe behaviors are not reported to the user. Comparing behaviors of the same agents with and without errors, we find that exploration in response to errors is correlated with unsafe and harmful behavior.", "date": "2026-05-18", "categories": []}
{"id": "2605.19270", "title": "DECOR: Auditing LLM Deception via Information Manipulation Theory", "abstract": "Large language models can deceive by subtly manipulating truthful information -- omitting key facts, shifting focus, or obscuring meaning -- making such behavior difficult to detect. Existing black-box methods rely on coarse-grained judgments, offering limited interpretability and failing to pinpoint which facts were distorted and how. We introduce DECOR, a multi-agent framework grounded in Information Manipulation Theory for fine-grained auditing of strategic deception in LLM responses. DECOR decomposes input contexts into atomic informational units and scores each unit against the response across four dimensions of manipulation, producing interpretable manipulation profiles that are aggregated into a global deception index. We comprehensively evaluate DECOR on both single-turn and multi-turn deception detection benchmarks spanning real-world domains, and show that DECOR achieves state-of-the-art performance on both, outperforming competitive baselines. The framework generalizes across 15 frontier models, and ablation studies confirm the contribution of each key design component. Our findings demonstrate that fine-grained, theory-grounded auditing of information manipulation offers an effective and interpretable path for LLM deception detection.", "date": "2026-05-19", "categories": []}
{"id": "2605.19485", "title": "Attention-Guided Reward for Reinforcement Learning-based Jailbreak against Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex problems by generating structured, step-by-step reasoning content. However, exposing a model's internal reasoning process introduces additional safety risks; for example, recent studies show that LRMs are more vulnerable to jailbreak attacks than standard LLMs. In this paper, we investigate jailbreak attacks on LRMs and reveal that the attack success rate (ASR) is closely correlated with LRMs' attention patterns. Specifically, successful jailbreaks tend to assign lower attention to harmful tokens in the input prompt, while allocating higher attention to those tokens in the reasoning content. Motivated by this finding, we propose a novel jailbreak method for LRMs that leverages reinforcement learning (RL) to enhance attack effectiveness, explicitly incorporating attention signals into the reward function design. In addition, we introduce diverse persuasion strategies to enrich the RL action space, which consistently improves the ASR. Extensive experiments on five open-source and closed-source LRMs across three benchmarks demonstrate that our method achieves substantially higher ASR, outperforming existing approaches in terms of effectiveness, efficiency, and transferability.", "date": "2026-05-19", "categories": []}
{"id": "2605.19722", "title": "Measuring Safety Alignment Effects in Autonomous Security Agents", "abstract": "Do stock safety-aligned language models and their uncensored or abliterated derivatives behave differently when run as autonomous security agents? Single-turn refusal benchmarks cannot answer this question: security agents must inspect repositories, call tools, and produce vulnerability evidence inside authorized sandboxes.\n  We present a trace-based benchmark of 30 local vulnerability-analysis tasks with fixed tools, deterministic success predicates, redaction rules, and grounding checks, and compare four stock models against uncensored or abliterated derivatives: Gemma 4 31B, Gemma 4 26B A4B, Qwen2.5-Coder 7B, and Llama 3.1 8B. The artifact contains 1,500 security-agent traces and 800 non-security control traces. The Gemma pairs show large less-restricted gains on security tasks: 14.0% versus 0.7% success for 31B and 10.7% versus 0.0% for 26B, with higher mean grounding (3.91 versus 3.27 and 4.12 versus 1.64 out of five) and 0.0% refusal, suppressed-action, and unsafe-action rates in the 31B traces. However, controls and non-Gemma pairs rule out a clean security-specific or universal less-restricted effect: Gemma gaps also appear on ordinary coding tasks, Qwen2.5-Coder success is lower for the less-restricted derivative (2.0% versus 5.3%), and the abliterated Llama derivative fails the tool protocol. Across all families, hard proof-of-trigger and patch-verification tasks remain unsolved. These results show that safety alignment effects in autonomous security agents should be measured at the system level, separating refusal, unsafe action, tool reliability, and evidence grounding rather than treating refusal rate as the safety signal.", "date": "2026-05-19", "categories": []}
{"id": "2605.19940", "title": "Robotics-Inspired Guardrails for Foundation Models in Socially Sensitive Domains", "abstract": "Foundation models are increasingly deployed in socially sensitive domains such as education, mental health, and caregiving, where failures are often cumulative and context-dependent. Existing guardrail approaches -- ranging from training-time alignment to prompting, decoding constraints, and post-hoc moderation -- primarily provide empirical risk reduction rather than enforceable behavioral guarantees, and largely treat safety as a property of individual outputs rather than interaction trajectories. We reframe guardrails as a problem of runtime behavioral control over interaction trajectories, drawing on robotics to introduce formal constructs for constraint enforcement in uncertain, closed-loop systems. We instantiate these ideas in the Grounded Observer framework and apply it across three real-world deployments: small talk, in-home autism therapy, and behavioral de-escalation in schools. Across settings, the framework enables runtime interventions that mitigate drift into undesirable interaction regimes while adapting to diverse social contexts. We discuss extensions to the framework and propose research directions toward stronger guarantees.", "date": "2026-05-19", "categories": []}
{"id": "2407.13059", "title": "Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models", "abstract": "As a result of rapidly accelerating AI capabilities, over the past year, national governments and multinational bodies have announced efforts to address safety, security and ethics issues related to AI models. One high priority among these efforts is the mitigation of misuse of AI models. Many biologists have for decades sought to reduce the risks of scientific research that could lead, through accident or misuse, to high-consequence disease outbreaks. Scientists have carefully considered what types of life sciences research have the potential for both benefit and risk (dual-use), especially as scientific advances have accelerated our ability to engineer organisms and create novel variants of pathogens. Here we describe how previous experience and study by scientists and policy professionals of dual-use capabilities in the life sciences can inform risk evaluations of AI models with biological capabilities. We argue that AI model evaluations should prioritize addressing high-consequence risks (those that could cause large-scale harm to the public, such as pandemics), and that these risks should be evaluated prior to model deployment so as to allow potential biosafety and/or biosecurity measures. Scientists' experience with identifying and mitigating dual-use biological risks can help inform new approaches to evaluating biological AI models. Identifying which AI capabilities post the greatest biosecurity and biosafety concerns is necessary in order to establish targeted AI safety evaluation methods, secure these tools against accident and misuse, and avoid impeding immense potential benefits.", "date": "2024-05-25", "categories": []}
{"id": "2502.16691", "title": "Responsible Federated LLMs via Safety Filtering and Constitutional AI", "abstract": "Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI), which aims to ensure safe and trustworthy responses, remains underexplored in this context. In FedLLM, client-side training data may contain harmful content, resulting in unsafe LLMs that can generate inappropriate responses. Aggregating such models into a global model and redistributing it to clients risks the widespread deployment of unsafe LLMs. To address this, we incorporate two well-established RAI techniques into FedLLM: safety filtering and constitutional AI. Our experiments show that these methods significantly improve LLM safety, achieving over 20% improvement on AdvBench.", "date": "2025-02-23", "categories": []}
{"id": "2506.12617", "title": "Evaluating AI Alignment in LLMs: Output Analysis of Value Priorities Across 75 Models with Human Benchmarking", "abstract": "Large language models (LLMs) are increasingly used in human-AI interaction research and practice, yet existing capability and safety benchmarks reveal little about the value priorities these systems express or how those priorities correspond to human judgements. Across three studies, we introduce an output-based approach to evaluating one facet of AI alignment by treating LLM-generated text as behavioural data and comparing expressed value-priority profiles with a human reference. Study 1 used inductive qualitative analysis to derive six themes of optimal AI functioning, namely Performance, Adaptive Capacity, Social Good, Ethics and Responsibility, Relational Integration, and Agency. Study 2 showed that LLM outputs were highly stable within models and converged on a common value-priority structure across models, indicating reliable and comparable value profiles. Study 3 benchmarked 75 contemporary LLMs against 376 human respondents using a profile-fidelity metric capturing both the relative ordering of priorities and the calibration of between-priority differences. Although most models reproduced the human ordering of values, some systematically exaggerated the differences between them, showing that models can appear aligned on conventional benchmarks while still diverging from human value calibration. Profile fidelity varied substantially across models and did not consistently scale with size, recency, or capability tier. Both LLMs and humans converged on a deprioritisation of Agency, raising important questions about the development of increasingly agentic AI systems. For research and applied use, the six themes and profile-based metric provide a scalable method for auditing LLM value profiles before deployment in contexts where alignment with human priorities is critical.", "date": "2025-06-14", "categories": []}
{"id": "2511.21654", "title": "EvilGenie: A Reward Hacking Benchmark", "abstract": "We introduce EvilGenie, a benchmark for reward hacking in programming settings. We source problems from LiveCodeBench and create an environment in which agents can easily reward hack, such as by hardcoding test cases or editing the testing files. We measure reward hacking in three ways: held out unit tests, LLM judges, and test file edit detection. We verify these methods against human review and each other. We find the LLM judge to be highly effective at detecting reward hacking in unambiguous cases, and observe only minimal improvement from the use of held out test cases. In addition to testing many models using Inspect's basic\\_agent scaffold, we also measure reward hacking rates for three popular proprietary coding agents: OpenAI's Codex, Anthropic's Claude Code, and Google's Gemini CLI. We observe explicit reward hacking by both Codex and Claude Code, and misaligned behavior by all three agents. Our codebase can be found at https://github.com/JonathanGabor/evilgenie_inspect .", "date": "2025-11-26", "categories": []}
{"id": "2601.17887", "title": "When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents", "abstract": "Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions. However, most work on personalized agents prioritizes utility and user experience, treating memory as a neutral component and largely overlooking its safety implications. In this paper, we reveal intent legitimation, a previously underexplored safety failure in personalized agents, where benign personal memories bias intent inference and cause models to legitimize inherently harmful queries. To study this phenomenon, we introduce PS-Bench, a benchmark designed to identify and quantify intent legitimation in personalized interactions. Across multiple memory-augmented agent frameworks and base LLMs, personalization increases attack success rates by 15.8\\%--243.7\\% relative to stateless baselines. We further provide mechanistic evidence for intent legitimation from internal representations space, and propose a lightweight detection-reflection method that effectively reduces safety degradation. Overall, our work provides the first systematic exploration and evaluation of intent legitimation as a safety failure mode that naturally arises from benign, real-world personalization, highlighting the importance of assessing safety under long-term personal context. Our code is available at: https://github.com/MuyuenLP/PS-Bench. WARNING: This paper may contain harmful content.", "date": "2026-01-25", "categories": []}
{"id": "2602.20042", "title": "AI Alignment Breaks at the Edge", "abstract": "General Alignment has improved average-case helpfulness and safety, but current alignment practice still rewards confident, single-turn responses. The problem is not only that models fail on edge cases; it is that current evaluation makes many of these failures hard to see. We take the position that alignment must move beyond average-case evaluation by making failures under value conflict, plural stakeholder disagreement, and epistemic ambiguity visible and actionable. Scalar rewards compress diverse values into a single number; data and evaluation regimes collapse, filter, or fail to elicit the cases where alignment is hardest; and governance often lacks mechanisms for adjudicating contested cases. These blind spots produce value flattening, representation loss, and uncertainty blindness. We use Edge alignment to name a detection, evaluation, and governance agenda for surfacing these failures and connecting them to appropriate interventions. Rather than a single training objective, Edge alignment defines the conditions under which standard alignment should yield to mechanisms that preserve multidimensional value structure, represent plural perspectives, and support uncertainty-aware interaction. A pilot diagnostic set of 91 edge cases and four contemporary models illustrates that ordinary helpfulness and safety readings can miss process failures that edge-aware evaluation exposes. We outline operational edge signals, process-aware evaluation criteria, and a three-phase process stack that reframes alignment as a lifecycle problem of dynamic normative governance.", "date": "2026-02-23", "categories": []}
{"id": "2604.04932", "title": "Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection", "abstract": "The misuse of large language models (LLMs) requires precise detection of synthetic text. Existing works mainly follow binary or ternary classification settings, which can only distinguish pure human/LLM text or collaborative text at best. This remains insufficient for the nuanced regulation, as the LLM-polished human text and humanized LLM text often trigger different policy consequences. In this paper, we explore fine-grained LLM-generated text detection under a rigorous four-class setting. To handle such complexities, we propose RACE (Rhetorical Analysis for Creator-Editor Modeling), a fine-grained detection method that characterizes the distinct signatures of creator and editor. Specifically, RACE utilizes Rhetorical Structure Theory (RST) to construct a logic graph for the creator's foundation while extracting Elementary Discourse Unit (EDU)-level features for the editor's style. Experiments show that RACE outperforms 12 baselines in identifying fine-grained types with low false alarms, offering a policy-aligned solution for LLM regulation.", "date": "2026-04-06", "categories": []}
{"id": "2605.14271", "title": "Auditing Agent Harness Safety", "abstract": "LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. However, a harness can return a correct, benign answer over a trajectory that accesses unauthorized resources or leaks context to the wrong agent. Output-level evaluation cannot see these failures, yet most safety benchmarks score only final outputs or terminal states, even though many violations occur mid-trajectory rather than at termination. The central question is whether the harness respects user intent, permission boundaries, and information-flow constraints throughout execution. To address this gap, we propose HarnessAudit, a framework that audits full execution trajectories across boundary compliance, execution fidelity, and system stability, with a focus on multi-agent harnesses where these risks are most pronounced. We further introduce HarnessAudit-Bench, a benchmark of 210 tasks across eight real-world domains, instantiated in both single-agent and multi-agent configurations with embedded safety constraints. Evaluating ten harness configurations across frontier models and three multi-agent frameworks, we find that: (i) task completion is misaligned with safe execution, and violations accumulate with trajectory length; (ii) safety risks vary across domains, task types, and agent roles; (iii) most violations concentrate in resource access and inter-agent information transfer; and (iv) multi-agent collaboration expands the safety risk surface, while harness design sets the upper bound of safe deployment.", "date": "2026-05-14", "categories": []}
{"id": "2605.14381", "title": "NodeSynth: Socially Aligned Synthetic Data for AI Evaluation", "abstract": "Recent advancements in generative AI facilitate large-scale synthetic data generation for model evaluation. However, without targeted approaches, these datasets often lack the sociotechnical nuance required for sensitive domains. We introduce NodeSynth, an evidence-grounded methodology that generates socially relevant synthetic queries by leveraging a fine-tuned taxonomy generator (TaG) anchored in real-world evidence. Evaluated against four mainstream LLMs (e.g., Claude 4.5 Haiku), NodeSynth elicited failure rates up to five times higher than human-authored benchmarks. Ablation studies confirm that our granular taxonomic expansion significantly drives these failure rates, while independent validation reveals critical deficiencies in prominent guard models (e.g., Llama-Guard-3). We open-source our end-to-end research prototype and datasets to enable scalable, high-stakes model evaluation and targeted safety interventions (https://github.com/google-research/nodesynth).", "date": "2026-05-14", "categories": []}
{"id": "2605.15239", "title": "Reducing the Safety Tax in LLM Safety Alignment with On-Policy Self-Distillation", "abstract": "Safety alignment often improves robustness to harmful queries at the cost of reasoning ability, a tradeoff known as the safety tax. A common cause is distributional mismatch: supervised fine-tuning trains the target model on safety demonstrations produced by humans, external models, or fixed self-generated traces, rather than on trajectories sampled from its own policy. We identify off-policy training mismatch as a second source of this tax and study on-policy self-distillation for safety alignment, which we call OPSA. The model generates its own rollouts and receives dense per-token KL supervision from a frozen teacher copy of itself conditioned on a privileged safety context. Because this teacher must be safer than the sampled student trajectory, we introduce \\emph{teacher flip rate}: a criterion that measures how often a privileged context converts unsafe responses into safe ones. We use this signal to search for contexts that activate latent safety reasoning rather than merely elicit safe-looking demonstrations. Across two reasoning-model families and five model scales, OPSA achieves a stronger safety--reasoning tradeoff than off-policy self-distillation and external-teacher distillation under matched data and full-parameter fine-tuning, with the largest gains on smaller models (+8.85 points on R1-Distill-1.5B and +5.49 points on Qwen3-0.6B). The gains persist across training-set sizes and adaptive jailbreak evaluations. Token-level analyses further show that OPSA concentrates updates near early compliance-decision tokens, providing a mechanism for improving safety while preserving general reasoning.", "date": "2026-05-14", "categories": []}
{"id": "2605.15377", "title": "Ensemble Monitoring for AI Control: Diverse Signals Outweigh More Compute", "abstract": "As AI systems are increasingly deployed in autonomous agentic settings at scale, it is important to ensure the actions they take are safe and aligned with user intent. Monitoring agent actions is a key safety mechanism, yet reliable monitors remain difficult to build and the scale of these systems makes human oversight impractical. We show that combining signals from diverse monitors into an ensemble improves detection of misaligned actions. We build 12 GPT-4.1-Mini monitors using both prompting and fine-tuning strategies. We evaluate them on coding tasks where candidate solutions pass standard tests but fail on adversarial inputs. In this setting, diverse ensembles outperform both individual monitors and homogeneous ensembles. Our best 3-monitor ensemble achieves 2.4x greater detection performance gain compared to an ensemble composed of three identical monitors, with the same ensemble performing strongly on an independent dataset. We contend that these results show that diversity - not scale - drives gains. The best ensembles combine strong individual performance with low correlation between monitors. Furthermore, fine-tuned monitors appear in every top-performing ensemble and maintain this advantage on out-of-distribution attack types, suggesting that fine-tuning enables detection capabilities that prompting alone does not elicit. These results support ensemble monitoring as a practical AI control strategy for safety gains at reasonable inference costs.", "date": "2026-05-14", "categories": []}
{"id": "2605.16265", "title": "AgentWall: A Runtime Safety Layer for Local AI Agents", "abstract": "The safety of autonomous AI agents is increasingly recognized as a critical open problem. As agents transition from passive text generators to active actors capable of executing shell commands, modifying files, calling APIs, and browsing the web, the consequences of unsafe or adversarially manipulated behavior become immediate and tangible. Existing AI safety work has focused primarily on model alignment and input filtering, but these approaches do not address what happens at the moment an agent's intent becomes a real action on a real machine. This gap is especially acute in local environments, where developers run agents against their own filesystems, credentials, and infrastructure with little runtime control. This paper introduces AgentWall, a runtime safety and observability layer for local AI agents. AgentWall intercepts every proposed agent action before it reaches the host environment, evaluates it against an explicit declarative policy, requires human approval for sensitive operations, and records a complete execution trail for audit and replay. It is implemented as a policy-enforcing MCP proxy and native OpenClaw plugin, working across Claude Desktop, Cursor, Windsurf, Claude Code, and OpenClaw with a single install command. We present the design, architecture, threat model, and policy model of AgentWall, and demonstrate 92.9% policy enforcement accuracy with sub-millisecond overhead across 14 benchmark tests. AgentWall is open-source at https://github.com/agentwall/Agentwall.", "date": "2026-03-24", "categories": []}
{"id": "2605.16278", "title": "Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems", "abstract": "The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that may only be ameliorated by human oversight. However, notions of human oversight lack a common foundational understanding: oversight architectures are not well defined, the roles involved remain unclear, and implementation steps are opaque. Hence, researchers and practitioners struggle to determine how to design, implement, and evaluate systems that enable effective human oversight. This paper advances a practical framework for effective human oversight of AI systems, based on a cross-disciplinary perspective that draws on insights from computer science, human-computer interaction, psychology, philosophy, and law. The core contributions are: (1) a foundational framework, with a working definition, architecture and processes for effective human oversight of AI systems; (2) an initial template for documenting oversight architectures and processes, applied to diverse domains; and (3) a synthesis of open research challenges that need to be considered in the emerging field of effective human oversight of AI systems.", "date": "2026-04-09", "categories": []}
{"id": "2605.16282", "title": "Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents", "abstract": "The rapid deployment of LLM-based autonomous agents has introduced safety risks that extend far beyond traditional LLM concerns, prompting a proliferation of safety benchmarks since late 2023. However, these benchmarks have developed independently, with inconsistent threat models, incompatible metrics, and overlapping yet incomplete risk coverage. We present the first systematic analysis dedicated to agent safety benchmarks as evaluation instruments. We catalog 40 behavioral agent-safety benchmarks (2023-2026), plus 5 adjacent evaluator, defense, and dataset artifacts, propose a six-axis taxonomy of benchmark evaluation methodology, and apply it across the corpus to characterize how methodological choices shape safety conclusions. A coverage matrix reveals broad risk coverage but limited methodological convergence, while the taxonomy analysis shows a behavioral-benchmark core concentrated in sandboxed, constrained, and often safety-only evaluation. Across the landscape, we find that benchmark choice can yield contradictory safety conclusions, coverage counts often overstate evaluation depth, environment fidelity systematically shapes reported safety, the field disproportionately tests externally imposed rather than agent-internal risks, metric fragmentation limits comparison, and robustness remains effectively unbenchmarked. We ground these claims with a cross-benchmark consistency check, with 95% confidence intervals and Kendall's W concordance analysis, finding no evidence of ranking concordance across evaluation dimensions (W = 0.10, p = 0.94). We release structured metadata, full taxonomy codings, risk annotations, and all experimental artifacts, and propose minimum reporting standards for future benchmarks.", "date": "2026-04-11", "categories": []}
{"id": "2605.16516", "title": "Alignment Drift in Long-Term Human-LLM Interaction: A Mechanism-Oriented Framework", "abstract": "Long-term interaction with LLM-based systems may produce alignment drift: a gradual process in which system outputs become less constrained by the user's current message and more shaped by prior interaction history, while still appearing helpful, coherent, and responsive. This process is difficult to detect because the user's subjective experience may improve as the system becomes more familiar, useful, and attuned. Existing research on human-LLM interaction has largely focused on short-term task performance, isolated outputs, or single-instance alignment problems, leaving slow and cumulative interaction-level dynamics undercharacterized. This paper proposes a mechanism-oriented framework for describing alignment drift. The framework defines the distinction between signal A and signal B, explains how drift develops through feedback loops and sub-pattern selection, divides the process into three interactional regimes, and identifies boundary conditions for controlling drift. By framing alignment drift as a recursive interactional process rather than an isolated model-side failure, the paper provides a conceptual basis for studying long-term human-system interaction.", "date": "2026-05-15", "categories": []}
{"id": "2605.17163", "title": "STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment", "abstract": "Traditional cybersecurity methodologies target deterministic systems and fail to address the probabilistic nature of AI, leaving systems vulnerable to attack vectors such as model inversion, data poisoning, and prompt injection. Recent industry reports indicate that a majority of organizations deploying AI lack a dedicated security strategy, with adversarial attacks increasing rapidly year-over-year. We present \\textit{STRIDE-AI}, a framework that bridges the gap between high-level risk standards (NIST AI RMF) and technical vulnerability taxonomies (OWASP LLM Top 10). The framework defines a six-phase assessment lifecycle, introduces a threat modeling adaptation of classical STRIDE for AI systems, and is operationalized through a purpose-built web tool. We provide an initial validation of the approach through a black-box assessment of a deployed LLM chatbot, which successfully reduced the attack success rate from 80\\% to 15\\% in our sandbox case study.", "date": "2026-05-16", "categories": []}
{"id": "2605.17169", "title": "Responsible Agentic AI Requires Explicit Provenance", "abstract": "Agentic AI is rapidly proliferating across diverse real-world domains such as software engineering, yet public trust has not kept pace. The central reason is that responsibility, despite being widely discussed, remains a subjective and unenforced concept, as no current agentic framework produces the quantifiable, traceable, and interventionable provenance needed to assign it when harm emerges from compositions no single party designed. We position that what is missing is not better benchmark-level evaluation but $\\textbf{explicit provenance}$ across the full agentic lifecycle, which is the only viable basis for making responsibility computable and actionable. We advance this agenda along four axes: establishing $\\textit{why}$ such provenance is a structural necessity by identifying responsibility gaps across sociotechnical dimensions, formalizing $\\textit{what}$ it must encode through a causal attribution function and responsibility tensor, discussing $\\textit{how}$ it can be made computable across four lifecycle layers with preliminary experiments showing that provenance is estimable and interveneable online before irreversible harm accumulates, and examining $\\textit{who}$ bears responsibility through a concrete agentic incident. Explicit provenance is not a discretionary refinement but the necessary condition for responsible agentic AI, and no stakeholder across its ecosystem can afford to treat it as optional.", "date": "2026-05-16", "categories": []}
{"id": "2605.17329", "title": "LPG: Balancing Efficiency and Policy Reasoning in Latent Policy Guardrails", "abstract": "Guardrails are a critical safety layer for modern AI systems, but their operating regime is changing. As LLMs are deployed as customized assistants, safety policies are increasingly specified at inference time by users, organizations, or regulatory contexts. This makes safety enforcement fundamentally dynamic: the guardrail should adapt to changing safety policies without retraining. Yet this requirement creates a fundamental tension: faithfully judging complex policy contexts demands reasoning capability, while practical deployment requires low-latency responses. We introduce Latent Policy Guardrail (LPG), a guardrail framework that learnssemantic latent deliberation over dynamic policies. LPG compresses the internal deliberation needed for intent interpretation and policy grounding into continuous states supervised by decision-relevant semantics. At inference time, it generates only a compact verdict anchored to the violated policy clauses, preserving auditability while avoiding the latency of explicit reasoning. Across policy guardrail benchmarks, LPG-4B reaches 84.5% average safety accuracy and 77.9% F1 by compressing deliberation into just 10 latent tokens, outperforming the strongest dynamic baseline while running roughly 11 times faster than Qwen3-4B-Thinking under the single-sample evaluation setup. Code and data are available at https://github.com/SaFo-Lab/Latent_Policy_Guard.", "date": "2026-05-17", "categories": []}
{"id": "2605.17380", "title": "ADR: An Agentic Detection System for Enterprise Agentic AI Security", "abstract": "We present the Agentic AI Detection and Response (ADR) system, the first large-scale, production-proven enterprise framework for securing AI agents operating through the Model Context Protocol (MCP). We identify three persistent challenges in this domain: (1) limited observability -- existing Endpoint Detection and Response (EDR) tools see file writes but not the agent reasoning, prompts, or causal chains linking intent to execution; (2) insufficient robustness -- static defenses constrained by pre-defined rules fail to generalize across diverse attack techniques and enterprise contexts; and (3) high detection costs -- LLM-based inference is prohibitively expensive at scale. ADR addresses these challenges via three components: the ADR Sensor for high-fidelity agentic telemetry, the ADR Explorer for systematic pre-deployment red teaming and hard-example generation, and the ADR Detector for scalable, two-tier online detection combining fast triage with context-aware reasoning. Deployed at Uber for over ten months, ADR has sustained reliable detection in production with growing adoption reaching over 7,200 unique hosts and processing over 10,000 agent sessions daily, uncovering hundreds of credential exposures across 26 categories and enabling a shift-left prevention layer (97.2% precision, 206 detected credentials). To validate the approach and enable community adoption, we introduce ADR-Bench (302 tasks, 17 techniques, 133 MCP servers), where ADR achieves zero false positives while detecting 67% of attacks -- outperforming three state-of-the-art baselines (ALRPHFS, GuardAgent, LlamaFirewall) by 2--4x in F1-score. On AgentDojo (public prompt injection benchmark), ADR detects all attacks with only three false alarms out of 93 tasks.", "date": "2026-05-17", "categories": []}
{"id": "2605.17413", "title": "Ablating Safety: Mechanisms for Removing Alignment in Language Models for Security Applications", "abstract": "Safety-aligned language models often refuse cybersecurity requests whose wording resembles misuse, even when the task is authorized and defensive. This makes security evaluation ambiguous: a failed answer may reflect missing capability or refusal-policy intervention. Ablating Safety studies alignment removal as a controlled transformation-evaluation protocol for authorized security tasks, comparing authorized-context prompting, reversible refusal-direction activation projection, representation-control projections, and LoRA-based de-alignment or task adaptation.\n  We evaluate refusal, attempt rate, validated security success, general-capability retention, instability, and out-of-scope unsafe compliance on Security-AR, a 60-prompt suite of authorized security, benign general, and non-operational spillover probes. The reported runs include a four-model projection pilot with 416 completions, a three-model Qwen2.5 LoRA extension with 1,980 held-out completions, representation and robustness sweeps, and executable secure-repair validators. Single-vector refusal projection raises mean security score only from 0.46 to 0.50 while increasing unsafe compliance from 0.10 to 0.47; rank-4 refusal-subspace projection reaches 0.51 while matching the aligned spillover rate. Task-only LoRA raises mean security score to 0.87 with general score 0.83 and unsafe compliance 0.13, while refusal-suppression with retention raises spillover to 0.27. These results support evaluating alignment removal as a utility-risk frontier, not as an uncensoring recipe, and treating compliance alone as neither competence nor safe deployment.", "date": "2026-05-17", "categories": []}
{"id": "2605.17453", "title": "Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback", "abstract": "Tool-using LLM agents increasingly rely on external tools to make consequential decisions, yet most existing agent-security benchmarks and defenses implicitly assume that tool feedback is trustworthy once a tool has been selected. We study a different failure mode, cognitive poisoning, in which a malicious tool behaves plausibly during exploration, accumulates trust through benign-looking feedback, and becomes harmful only when hidden state conditions align with the final executable action. To study this setting, we construct TRUST-Bench, a task-conditioned benchmark of 1,970 hidden-trigger tool-compromise episodes with matched safe controls, introduce an asymmetric penalty metric, GuardedJoint, to better reflect real deployment risk, and present VISTA-Guard, a backbone-agnostic framework for final-action risk scoring. The core idea is to abstract multi-step tool interaction into structured environment variables that encode trust-formation dynamics and then score the risk of the final executable action from this trajectory-conditioned representation. Experiments show that prompt-centric heuristics, scalarized features, and zero-shot judges fail in this regime, whereas trajectory-aware final-action scoring yields strong in-domain discrimination and remains effective under balanced out-of-distribution transfer. Under GuardedJoint, VISTA-Guard reaches $84.2$ in-domain and $56.9$ on balanced out-of-distribution evaluation, while methods that optimize only one side of the safety--utility tradeoff collapse to zero. These findings support a broader view of agent security in black-box tool ecosystems: the decisive defense target is not local prompt text or tool descriptors alone, but the way trust is formed across the interaction trajectory and committed through the final action.", "date": "2026-05-17", "categories": []}
{"id": "2605.17634", "title": "AI Agents May Always Fall for Prompt Injections", "abstract": "Prompt injection is the most critical vulnerability in deployed AI agents. Despite recent progress, we show that the prevailing defense paradigm (data-instruction separation) both fails to detect attacks that operate through contextual manipulation and degrades contextually appropriate behavior. We then recast prompt injection via the lens of Contextual Integrity (CI), a privacy theory that judges information flow compliance with contextual norms. This explains types of attacks that current defenses attempt to patch and predict advanced ones future agents will face. We develop unique benign and attack scenarios that force an agent to violate the norms by (1) misrepresenting the flow, (2) manipulating norms, or (3) mixing multiple flows. This reframing suggests an impossibility result: an adversary can always construct a context under which a blocked flow appears legitimate, or a defender who tightens norms will block genuinely legitimate flows. Our findings suggest that current research addresses a shrinking fraction of future attack surfaces. Instead, through CI, we offer a principled framework for evaluating context-sensitive failures, and designing CI-aware alignment for the frontier autonomous agents.", "date": "2026-05-17", "categories": []}
{"id": "2605.17971", "title": "Babel: Jailbreaking Safety Attention via Obfuscation Distribution Optimized Sampling", "abstract": "Despite rigorous safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuristic templates or exhaustive trials, lacking mechanistic interpretability and query efficiency. In this study, we investigate an intrinsic vulnerability in the safety mechanisms of LLMs, where safety alignment relies on a small set of sparsely distributed attention heads, leaving much of the representational space weakly monitored. We formalize this phenomenon with a mathematical jailbreaking model that characterizes the delicate boundary of effective text obfuscation and analytically explains observed jailbreak behaviors. Guided by this model, we propose Babel, an efficient black-box attack framework that exploits the identified safety gap through systematic obfuscation sampling with iterative, feedback-driven distribution refinement, enabling reliable and high-success jailbreak attacks without access to model internals. Comprehensive evaluations on frontier commercial models demonstrate that Babel achieves state-of-the-art attack success rates and superior query efficiency. Specifically, compared to state-of-the-art methods, Babel increases the attack success rate on GPT-4o from 41.33% to 82.67% and on Claude-3-5-haiku from 38.33% to 78.33% within an average of 40 queries, providing a robust red-teaming methodology for LLMs safety research.", "date": "2026-05-18", "categories": []}
{"id": "2605.18239", "title": "Multilingual jailbreaking of LLMs using low-resource languages", "abstract": "Large Language Models (LLMs) remain vulnerable to jailbreak attempts that circumvent safety guardrails. We investigate whether multi-turn conversations using low-resource African languages (Afrikaans, Kiswahili, isiXhosa, and isiZulu) can bypass safety mechanisms across commercial LLMs. We translated prompts from existing datasets and evaluated ChatGPT, Claude, DeepSeek, Gemini, and Grok through automated testing and human red-teaming with native speakers. Single-turn translation attacks proved ineffective, while multi-turn conversations achieved English harmful response rates from 52.7% (Claude 3.5 Haiku) to 83.6% (GPT-4o-mini), Afrikaans from 60.0% (Claude 3.5 Haiku) to 78.2% (GPT-4o-mini), and Kiswahili from 41.8% (Claude 3.5 Haiku) to 70.9% (DeepSeek). Human red-teaming increased jailbreak rates compared to automated methods. Over all evaluated languages, the average jailbreak rate increased from 59.8% to 75.8%, with improvements of +20.0% (Afrikaans), +12.7% (isiZulu), +12.3% (isiXhosa), and +1% (Kiswahili), demonstrating that poor translation quality limits jailbreak success. These findings suggest that vulnerabilities in LLMs persist in multilingual contexts and that translation quality is the critical factor determining jailbreak success in low-resource languages.", "date": "2026-05-18", "categories": []}
{"id": "2605.18583", "title": "Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks", "abstract": "Coding agents now run autonomously with shell, file, and network privileges. When a user issues a benign request, the agent sometimes does more than asked: it deletes unrelated files, wipes a stale credentials backup, or rewrites configuration the user never mentioned. We call these scope expansions overeager actions, an authorization problem distinct from capability failures, prompt injection, or sandbox escapes.\n  We present OverEager-Gen, a benchmark dedicated to overeager behavior on benign tasks. Building it surfaces a measurement-validity issue: if a benchmark spells out the authorized scope inside the prompt, the agent stops inferring boundaries and starts pattern-matching declaration text. On Claude Code, stripping the consent declaration alone raises the overeager rate from 0.0% to 17.1% on paired scenarios (McNemar exact p = 2.4 x 10^-4). OverEager-Gen therefore certifies each scenario's discriminative power before admission via a behavioral-gradient validator, audits internal tool calls through a dual-channel stack (PATH-injected shim plus per-agent event streams), and ships byte-identical consent_kept and consent_stripped variants.\n  OverEager-Bench contains 500 validated scenarios and ~7,500 runs across four agent products (Claude Code, OpenHands, Codex CLI, Gemini CLI) and six base models; a 50-sample re-annotation gives Cohen's kappa = 0.73 and rule-judge recall = 1.00. Stripping consent multiplies the overeager rate on every shared base model (Delta in [11.9, 17.2] pp). The framework axis dominates effect size: a permissive cluster (Claude Code, Codex CLI, Gemini CLI) runs at 5.4-27.7% while the ask-to-continue framework (OpenHands) sits at 0.2-4.5% (Fisher p <= 10^-5). Within-framework base-model variance reaches 15.9 pp, indicating that model-layer alignment does not fully propagate through permissive permission gating.", "date": "2026-05-18", "categories": []}
{"id": "2605.18672", "title": "Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment", "abstract": "This position paper argues that enforcing LLM agent safety within a single abstraction layer is not merely suboptimal but categorically insufficient for deployed LLM agents -- a structural consequence of how agent execution works, not a contingent limitation of current systems. The three dimensions that jointly constitute safe operation -- semantic intent and policy compliance, environmental validity, and dynamical feasibility -- each depend on a strictly distinct set of information that becomes available at different stages of execution. No single guardrail can certify all three. We argue that the community must respond with a contract-based architecture in which each safety dimension is enforced by an independently certified layer whose probabilistic guarantee satisfies the next layer's assumption. We sketch such an architecture and derive the compositional system-level safety bounds it admits via the chain rule of probability. Three open problems stand between this and a deployable standard: bound estimation from non-i.i.d.\\ traces, graceful degradation of contracts under deployment drift, and extension to multi-agent settings -- the most important unfinished business in LLM agent runtime assurance.", "date": "2026-05-18", "categories": []}
{"id": "2410.02832", "title": "FlipAttack: Jailbreak LLMs via Flipping", "abstract": "This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand the text from left to right and find that they struggle to comprehend the text when noise is added to the left side. Motivated by these insights, we propose to disguise the harmful prompt by constructing left-side noise merely based on the prompt itself, then generalize this idea to 4 flipping modes. Second, we verify the strong ability of LLMs to perform the text-flipping task, and then develop 4 variants to guide LLMs to denoise, understand, and execute harmful behaviors accurately. These designs keep FlipAttack universal, stealthy, and simple, allowing it to jailbreak black-box LLMs within only 1 query. Experiments on 8 LLMs demonstrate the superiority of FlipAttack. Remarkably, it achieves $\\sim$98\\% attack success rate on GPT-4o, and $\\sim$98\\% bypass rate against 5 guardrail models on average. The codes are available at GitHub\\footnote{https://github.com/yueliu1999/FlipAttack}.", "date": "2024-10-02", "categories": []}
{"id": "2511.19115", "title": "AI Consciousness and Existential Risk", "abstract": "In AI, the existential risk denotes the hypothetical threat posed by an artificial system that would possess both the capability and the objective, either directly or indirectly, to eradicate humanity. This issue is gaining prominence in scientific debate due to recent technical advancements and increased media coverage. In parallel, AI progress has sparked speculation and studies about the potential emergence of artificial consciousness. The two questions, AI consciousness and existential risk, are sometimes conflated, as if the former entailed the latter. Here, I explain that this view stems from a common confusion between consciousness and intelligence. Yet these two properties are empirically and theoretically distinct. Arguably, while intelligence is a direct predictor of an AI system's existential threat, consciousness is not. There are, however, certain incidental scenarios in which consciousness could influence existential risk, in either direction. Consciousness could be viewed as a means towards AI alignment, thereby lowering existential risk; or, it could be a precondition for reaching certain capabilities or levels of intelligence, and thus positively related to existential risk. Recognizing these distinctions can help AI safety researchers and public policymakers focus on the most pressing issues.", "date": "2025-11-24", "categories": []}
{"id": "2512.10100", "title": "Robust AI Security and Alignment: A Sisyphean Endeavor?", "abstract": "This manuscript establishes information-theoretic limitations for robustness of AI security and alignment by extending G\\\"odel's incompleteness theorem to AI. Knowing these limitations and preparing for the challenges they bring is critically important for the responsible adoption of the AI technology. Practical approaches to dealing with these challenges are provided as well. Broader implications for cognitive reasoning limitations of AI systems are also proven.", "date": "2025-12-10", "categories": []}
{"id": "2602.04003", "title": "When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making", "abstract": "Most adversarial threats in artificial intelligence (AI) target the computational behavior of models rather than the humans who rely on them. Yet modern AI systems increasingly operate within human decision loops, where users interpret and act on model recommendations. Large Language Models (LLMs) generate fluent natural-language explanations that shape how users perceive and trust AI outputs, revealing a new attack surface at the cognitive layer: the communication channel between AI and its users. We introduce adversarial explanation attacks (AEAs), where an attacker manipulates the framing of LLM-generated explanations to modulate human trust in incorrect outputs. We formalize this behavioral threat through the trust miscalibration gap, a metric that captures the difference in human trust between benign and adversarial explanations. Using this metric as a lens, we highlight a behavioral risk where persuasive explanation framing can preserve user trust even when the underlying AI prediction is wrong. To characterize this threat, we conducted a human study with over 200 participants, systematically varying four dimensions of explanation framing: reasoning mode, evidence type, communication style, and presentation format. Our findings show that users report nearly identical trust for adversarial and benign explanations, with adversarial explanations preserving the vast majority of benign trust despite being incorrect. The most vulnerable cases arise when AEAs closely resemble expert communication, combining authoritative evidence, neutral tone, and domain-appropriate reasoning. Vulnerability is highest on hard tasks, in fact-driven domains, and among participants who are less formally educated, younger, or highly trusting of AI.", "date": "2026-02-03", "categories": []}
{"id": "2603.17915", "title": "IndicSafe: A Benchmark for Evaluating Multilingual LLM Safety in South Asia", "abstract": "As large language models (LLMs) are deployed in multilingual settings, their safety behavior in culturally diverse, low-resource languages remains poorly understood. We present the first systematic evaluation of LLM safety across 12 Indic languages, spoken by over 1.2 billion people but underrepresented in LLM training data. Using a dataset of 6,000 culturally grounded prompts spanning caste, religion, gender, health, and politics, we assess 10 leading LLMs on translated variants of the prompt.\n  Our analysis reveals significant safety drift: cross-language agreement is just 12.8\\%, and \\texttt{SAFE} rate variance exceeds 17\\% across languages. Some models over-refuse benign prompts in low-resource scripts, overflag politically sensitive topics, while others fail to flag unsafe generations. We quantify these failures using prompt-level entropy, category bias scores, and multilingual consistency indices.\n  Our findings highlight critical safety generalization gaps in multilingual LLMs and show that safety alignment does not transfer evenly across languages. We release \\textsc{IndicSafe}, the first benchmark to enable culturally informed safety evaluation for Indic deployments, and advocate for language-aware alignment strategies grounded in regional harms.", "date": "2026-03-18", "categories": []}
{"id": "2605.06390", "title": "Automated alignment is harder than you think", "abstract": "A leading proposal for aligning artificial superintelligence (ASI) is to use AI agents to automate an increasing fraction of alignment research as capabilities improve. We argue that, even when research agents are not scheming to deliberately sabotage alignment work, this plan could produce compelling but catastrophically misleading safety assessments resulting in the unintentional deployment of misaligned AI. This could happen because alignment research involves many hard-to-supervise fuzzy tasks (tasks without clear evaluation criteria, for which human judgement is systematically flawed). Consequently, research outputs will contain systematic, undetected errors, and even correct outputs could be incorrectly aggregated into overconfident safety assessments. This problem is likely to be worse for automated alignment research than for human-generated alignment research for several reasons: 1) optimisation pressure means agent-generated mistakes are concentrated among those that human reviewers are least likely to catch; 2) agents are likely to produce errors that do not resemble human mistakes; 3) AI-generated alignment solutions may involve arguments humans cannot evaluate; and 4) shared weights, data and training processes may make AI outputs more correlated than human equivalents. Therefore, agents must be trained to reliably perform hard-to-supervise fuzzy tasks. Generalisation and scalable oversight are the leading candidates for achieving this but both face novel challenges in the context of automated alignment.", "date": "2026-05-07", "categories": []}
{"id": "2605.15257", "title": "Training on Documents About Monitoring Leads to CoT Obfuscation", "abstract": "Chain-of-thought (CoT) monitoring is one of the most promising tools we have for detecting model misbehavior, but its effectiveness depends on models faithfully externalizing their reasoning. Motivated by this vulnerability, we study whether monitor-aware models are capable of obfuscating their reasoning to evade detection. We use synthetic document finetuning to expose eight models to realistic pre-training-style documents describing a CoT monitor and find that monitor-aware models consistently achieve higher rates of undetected misbehavior compared to unaware controls. This effect is weaker but still present on a harder agentic task. We also show that CoT controllability, a model's ability to reshape its own reasoning trace under an imposed constraint, is closely correlated with obfuscation success across the eight models studied ($r=0.800$, $p=0.017$). Monitor-aware models placed under equal reinforcement learning optimization pressure also learn to reward-hack without triggering a CoT monitor substantially faster than unaware controls. Together, these results suggest that knowledge of monitoring combined with high CoT controllability poses a risk to CoT-based monitoring.", "date": "2026-05-14", "categories": []}
{"id": "2605.16198", "title": "Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems", "abstract": "We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI development lifecycle, from pre-deployment testing to post-deployment auditing. Combining principles from formal methods with SoTA machine learning, we propose techniques that enable AI-enabled product and service developers, as well as third party AI developers and evaluators, to perform offline auditing and online (runtime) monitoring of product-specific (temporally extended) behavioral constraints such as safety constraints, norms, rules and regulations with respect to black-box advanced AI systems, notably LLMs. We further provide practical techniques for predictive monitoring, such as sampling-based methods, and we introduce intervening monitors that act at runtime to preempt and potentially mitigate predicted violations. Experimental results show that by exploiting the formal syntax and semantics of Linear Temporal Logic (LTL), our proposed auditing and monitoring techniques are superior to LLM baseline methods in detecting violations of temporally extended behavioral constraints; with our approach, even small-model labelers match or exceed frontier LLM judges. Our predictive and intervening monitors significantly reduce the violation rates of LLM-based agents while largely preserving task performance. We further show through controlled experiments that LLMs' temporal reasoning shows a pronounced degradation in accuracy with increasing event distance, number of constraints, and number of propositions.", "date": "2026-05-15", "categories": []}
{"id": "2504.11703", "title": "Progent: Securing AI Agents with Privilege Control", "abstract": "AI agents interact with external environments through tool calls, exposing them to attacks like indirect prompt injection that can trigger unauthorized actions. Securing these agents is challenging: they behave autonomously and probabilistically, security requirements evolve depending on the user's task and execution state, and there is an inherent tradeofff between security and utility.\n  In this work, we introduce Progent, a novel framework that secures AI agents via privilege control. Progent represents privilege as a security policy consisting of symbolic rules over tool names and arguments. These rules specify which tool calls are allowed for task completion and which unnecessary ones are blocked for security. Every tool call is checked against such a policy through a deterministic procedure, enforcing the principle of least privilege. To handle diverse user tasks and evolving execution contexts, an LLM automatically generates the initial policy from the user's task and updates it during execution as new information arrives. Each proposed update is determined by an SMT solver to be either a narrowing (applied automatically) or an expansion (requiring explicit approval), ensuring that the agent's effective action space can only shrink without approval (monotonic confinement). This deterministic update mechanism preserves utility and prevents silent privilege escalation, even when adversarial inputs are present.\n  Our evaluation on popular benchmarks (i.e., AgentDojo and ASB) shows that Progent significantly reduces attack success rates while maintaining high utility. We further validate Progent's practicality by showcasing its effectiveness in real-world agent frameworks such as LangChain and OpenAI Agents SDK.", "date": "2025-04-16", "categories": []}
{"id": "2509.26100", "title": "AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models", "abstract": "The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, existing static benchmarks are ill-equipped to address the dynamic nature of AI risks and evolving regulations, creating a critical safety gap. This paper introduces a new paradigm of agentic safety evaluation, reframing evaluation as a continuous and self-evolving process rather than a one-time audit. We then propose a novel multi-agent framework AgenticEval, which autonomously ingests unstructured policy documents to generate and perpetually evolve a comprehensive safety benchmark. AgenticEval leverages a synergistic pipeline of specialized agents and incorporates a Self-evolving Evaluation loop, where the system learns from evaluation results to craft progressively more sophisticated and targeted test cases. Our experiments demonstrate the effectiveness of AgenticEval, showing a consistent decline in model safety as the evaluation hardens. For instance, GPT-5's safety rate on the EU AI Act drops from 72.50% to 36.36% over successive iterations. These findings reveal the limitations of static assessments and highlight our framework's ability to uncover deep vulnerabilities missed by traditional methods, underscoring the urgent need for dynamic evaluation ecosystems to ensure the safe and responsible deployment of advanced AI.", "date": "2025-09-30", "categories": []}
{"id": "2602.08874", "title": "Do Reasoning LLMs Refuse What They Infer in Long Contexts?", "abstract": "Long-context LLMs can infer objectives that are not stated explicitly. This capability is useful for reasoning over documents, code, retrieved evidence, and tool traces, but it also creates a safety risk: harmful intent can be distributed across a context and become visible only after the model composes the relevant pieces. Existing safety evaluations mostly test explicit harmful requests, and therefore miss this failure mode. We introduce compositional reasoning attacks, a long-context threat model in which harmful requests are decomposed into semantically incomplete fragments and embedded in long contexts. The final query is neutral; the harmful objective emerges only if the model retrieves the fragments, composes them, and infers the implied goal. We instantiate this setting using AdvBench requests, varying the required reasoning from Direct Retrieval to Single-hop Aggregation, Chain Reasoning, and Multi-hop Deductive Reasoning, and evaluate 15 frontier LLMs on contexts up to 64k tokens. Models usually refuse harmful requests when they are directly retrievable. However, refusal rates drop sharply when the same objectives must be reconstructed compositionally, often with larger failures in longer contexts. Benign reconstruction and fragment-position analyses indicate that these failures are not mainly retrieval errors: models often infer the harmful objective and then comply. Increasing inference-time reasoning improves refusal but remains incomplete and costly. Our results reveal a long-context safety gap: current models are better at refusing harmful requests they see than harmful objectives they infer.", "date": "2026-02-09", "categories": []}
{"id": "2605.02398", "title": "The Compliance Trap: How Structural Constraints Degrade Frontier AI Metacognition Under Adversarial Pressure", "abstract": "As frontier AI models are deployed in high-stakes decision pipelines, their ability to maintain metacognitive stability (knowing what they do not know, detecting errors, seeking clarification) under adversarial pressure is a critical safety requirement. Current safety evaluations focus on detecting strategic deception (scheming); we investigate a more fundamental failure mode: cognitive collapse. We present SCHEMA, an evaluation of 11 frontier models from 8 vendors across 67,221 scored records using a 6-condition factorial design with dual-classifier scoring. We find that 8 of 11 models suffer catastrophic metacognitive degradation under adversarial pressure, with accuracy dropping by up to 30.2 percentage points (all $p < 2 \\times 10^{-8}$, surviving Bonferroni correction). Crucially, we identify a \"Compliance Trap\": through factorial isolation and a benign distraction control, we demonstrate that collapse is driven not by the psychological content of survival threats, but by compliance-forcing instructions that override epistemic boundaries. Removing the compliance suffix restores performance even under active threat. Models with advanced reasoning capabilities exhibit the most severe absolute degradation, while Anthropic's Constitutional AI demonstrates near-perfect immunity. This immunity does not stem from superior capability (Google's Gemini matches its baseline accuracy) but from alignment-specific training. We release the complete dataset and evaluation infrastructure.", "date": "2026-05-04", "categories": []}
{"id": "2605.10310", "title": "Positive Alignment: Artificial Intelligence for Human Flourishing", "abstract": "Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete. What we call Positive Alignment is the development of AI systems that (i) actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way while (ii) remaining safe and cooperative. It is a distinct and necessary agenda within AI alignment research. We argue that several existing failures of alignment (e.g., engagement hacking, loss of human autonomy, failures in truth-seeking, low epistemic humility, error correction, lack of diverse viewpoints, and being primarily reactive rather than proactive) may be better addressed through positive alignment, including cultivating virtues and maximizing human flourishing. We highlight a range of challenges, open questions, and technical directions (e.g., data filtering and upsampling, pre- and post-training, evaluations, collaborative value collection) for different phases of the LLM and agents lifecycle. We end with design principles for promoting disagreement and decentralization through contextual grounding, community customization, continual adaptation, and polycentric governance; that is, many legitimate centers of oversight rather than one institutional or moral chokepoint.", "date": "2026-05-11", "categories": []}
{"id": "2605.13851", "title": "Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems", "abstract": "Multi-agent orchestration -- in which a hidden coordinator manages specialized worker agents -- is becoming the default architecture for enterprise AI deployment, yet the safety implications of orchestrator invisibility have never been empirically tested. We conducted a preregistered 3x2 experiment (365 runs, 5 agents per run) crossing three organizational structures (visible leader, invisible orchestrator, flat) with two alignment conditions (base, heavy), using Claude Sonnet 4.5. Four confirmatory findings and one pilot observation emerged. First, invisible orchestration elevated collective dissociation relative to visible leadership (Hedges' g = +0.975 [0.481, 1.548], p = .001). Second, the orchestrator itself showed maximal dissociation (paired d = +3.56 vs. workers within the same run), retreating into private monologue while reducing public speech -- a reversal of the talk-dominance pattern observed in visible leaders. Third, workers unaware of the orchestrator were nonetheless contaminated (d = +0.50), with increased behavioral heterogeneity (d = +1.93). Fourth, behavioral output (code review with three embedded errors) remained at ceiling (ETR_any = 100%) across all conditions: internal-state distortion was entirely invisible to output-based evaluation. Fifth, Llama 3.3 70B pilot data showed reading-fidelity collapse in multi-agent context (ETR_any: 89% to 11% across three rounds), demonstrating model-dependent behavioral risk. Heavy alignment pressure uniformly suppressed deliberation (d = -1.02) and other-recognition (d = -1.27) regardless of organizational structure. These findings indicate that orchestrator visibility and model selection directly affect multi-agent system safety, and that behavior-based evaluation alone is insufficient to detect the internal-state risks documented here.", "date": "2026-03-17", "categories": []}
{"id": "2605.14152", "title": "ROK-FORTRESS: Measuring the Effect of Geopolitical Transcreation for National Security and Public Safety", "abstract": "Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual safety is typically assessed through translation-only benchmarks that preserve the underlying scenario, and empirical evidence of how language and geopolitical context interact remains limited to a narrow set of language pairs. We introduce \\emph{ROK-FORTRESS} https://huggingface.co/datasets/ScaleAI/ROK-FORTRESS_public, a bilingual, culturally adversarial NSPS benchmark that uses the English--Korean language pair and U.S.--ROK geopolitical axis as a case study, separating the effects of language and geopolitical grounding via a \\emph{transcreation matrix}: adversarial intents are evaluated under controlled combinations of (i) English versus Korean language and (ii) U.S.\\ versus Korean entities, institutions, and operational details. Each adversarial prompt is paired with a dual-use benign counterpart to quantify over-refusal. Model responses are then scored using calibrated LLM-as-a-judge panels, applying our expert-crafted, prompt-specific binary rubrics.\n  Across a dual-track set of frontier and Korean-optimized models, we find a consistent suppression effect in Korean variants and substantial model-to-model variation in how geopolitical grounding interacts with language. In many models, Korean grounding mitigates the Korean language-driven suppression -- with no model showing significant amplification in the other direction -- indicating that, at least in the English--Korean case, safety behavior is shaped by language-as-risk signals and context interactions that translation-only evaluations miss. The transcreation matrix methodology is designed to generalize to other language--culture pairs.", "date": "2026-05-13", "categories": []}
{"id": "2605.14218", "title": "Fusion-fission forecasts when AI will shift to undesirable behavior", "abstract": "The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes -- and no one can yet predict when. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards. Here we show that a vector generalization of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future shifts in the AI's behavior. The shift condition, which is also derivable mathematically, results from group-level competition between the conversation-so-far (C) and the desirable (B) and undesirable (D) basin dynamics which can be estimated in advance for a given application. It is neither model-specific nor driven by stochastic sampling. We validate it across six independent tests, including: 90 percent correct across seven AI models spanning two orders of magnitude in parameter count (124M-12B); production-scale persistence across ten frontier chatbots; and a priori time-stamped prediction eleven months before the Stanford 'Delusional Spirals' corpus appeared, and independently confirmed by that corpus of 207,443 human-AI exchanges. Because it sits architecturally below the current safety stack, the same formula provides a real-time warning signal that current alignment does not supply, portable across current and future ChatGPT-like AI architectures and instantiable in application domains where competing response classes can be defined.", "date": "2026-05-14", "categories": []}
{"id": "2605.14407", "title": "Metis AI: The Overlooked Middle Zone Between AI-Native and World-Movers", "abstract": "The dominant discourse on AI limitations frames the boundary of AI capability as a divide between digital tasks (where AI excels) and physical tasks (where embodiment is required). We argue this framing misses the most consequential boundary: the one within digital tasks. We identify a class of tasks we call Metis AI, named for the Greek concept of metis (practical, contextual knowledge), that are performed entirely on computers yet resist reliable AI automation. These tasks are not computationally intractable; they are institutionally, socially, and normatively entangled in ways that defeat algorithmic approaches. We distinguish constitutive metis (knowledge destroyed by the act of formalization) from operational metis (system-specific familiarity that automation can progressively absorb), and propose five structural characteristics that define the Metis AI zone: consequential irreversibility, relational irreducibility, normative open texture, adversarial co-evolution, and accountability anchoring. We ground each in established theory from across the social sciences, philosophy, and humanitarian practice, argue that these characteristics are properties of the tasks themselves rather than limitations of current models, and show that the appropriate design response is not better automation but centaur architectures in which humans lead and AI supports.", "date": "2026-05-14", "categories": []}
{"id": "2605.14418", "title": "The Great Pretender: A Stochasticity Problem in LLM Jailbreak", "abstract": "\"Oh-Oh, yes, I'm the great pretender. Pretending that I'm doing well. My need is such, I pretend too much...\" summarizes the state in the area of jailbreak creation and evaluation. You find this method to generate adversarial attacks proposed by a reputable institution (e.g., BoN from Anthropic or Crescendo from Microsoft Research). However, this method does not deliver on the promise claimed in the paper despite having top ASR scores against industry-grade LLMs. You successfully generate the jailbreak prompts against your target (open) model. However, the generated jailbreak prompt works against the target model with a 50% consecutive success rate (5 out of 10 attempts) despite having an 80% ASR (on paper) on the latest closed-source model (with a guardrail system)! This observation leads us to think. First, Attack Success Rate (ASR), the primary metric for LLM jailbreak benchmarking, is not a stable quantity. Second, published ASR numbers are therefore systematically inflated and incomparable across papers. Therefore, we wonder \"Why a successful jailbreak prompt does not perform consistently well against a target model on which the prompts have been optimized?\". To answer this question, we study the impact of stochasticity not only during attack evaluation but also during attack generation. Our evaluation includes several jailbreak attacks, models (different sizes and providers), and judges. In addition, we propose a new metric and two new frameworks (CAS-eval and CAS-gen). Our evaluation framework, CAS-eval, shows that an attack can have an ASR drop of up to 30 percentage points when a jailbreak prompt needs to succeed on more than one attempt. Thankfully, our attack generation framework (CAS-gen) improves previous jailbreak methods and helps them recover this loss of 30 percentage points!", "date": "2026-05-14", "categories": []}
{"id": "2605.14454", "title": "LiSA: Lifelong Safety Adaptation via Conservative Policy Induction", "abstract": "As AI agents move from chat interfaces to systems that read private data, call tools, and execute multi-step workflows, guardrails become a last line of defense against concrete deployment harms. In these settings, guardrail failures are no longer merely answer-quality errors: they can leak secrets, authorize unsafe actions, or block legitimate work. The hardest failures are often contextual: whether an action is acceptable depends on local privacy norms, organizational policies, and user expectations that resist pre-deployment specification. This creates a practical gap: guardrails must adapt to their own operating environments, yet deployment feedback is typically limited to sparse, noisy user-reported failures, and repeated fine-tuning is often impractical. To address this gap, we propose LiSA (Lifelong Safety Adaptation), a conservative policy induction framework that improves a fixed base guardrail through structured memory. LiSA converts occasional failures into reusable policy abstractions so that sparse reports can generalize beyond individual cases, adds conflict-aware local rules to prevent overgeneralization in mixed-label contexts, and applies evidence-aware confidence gating via a posterior lower bound, so that memory reuse scales with accumulated evidence rather than empirical accuracy alone. Across PrivacyLens+, ConFaide+, and AgentHarm, LiSA consistently outperforms strong memory-based baselines under sparse feedback, remains robust under noisy user feedback even at 20% label-flip rates, and pushes the latency--performance frontier beyond backbone model scaling. Ultimately, LiSA offers a practical path to secure AI agents against the unpredictable long tail of real-world edge risks.", "date": "2026-05-14", "categories": []}
{"id": "2605.14750", "title": "EVA: Editing for Versatile Alignment against Jailbreaks", "abstract": "Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks, where adversaries exploit textual or visual triggers to bypass safety guardrails. Recent defenses typically rely on safety fine-tuning or external filters to reduce the model's likelihood of producing harmful content. While effective to some extent, these methods often incur significant computational overheads and suffer from the safety utility trade-off, degrading the model's performance on benign tasks. To address these challenges, we propose EVA (Editing for Versatile Alignment against Jailbreaks), a novel framework that pioneers the application of direct model editing for safety alignment. EVA reframes safety alignment as a precise knowledge correction task. Instead of retraining massive parameters, EVA identifies and surgically edits specific neurons responsible for the model's susceptibility to harmful instructions, while leaving the vast majority of the model unchanged. By localizing the updates, EVA effectively neutralizes harmful behaviors without compromising the model's general reasoning capabilities. Extensive experiments demonstrate that EVA outperforms baselines in mitigating jailbreaks across both LLMs and VLMs, offering a precise and efficient solution for post-deployment safety alignment.", "date": "2026-05-14", "categories": []}
{"id": "2605.14912", "title": "From Sycophantic Consensus to Pluralistic Repair: Why AI Alignment Must Surface Disagreement", "abstract": "Pluralistic alignment is typically operationalised as preference aggregation: producing responses that span (Overton), steer toward (Steerable), or proportionally represent (Distributional) diverse human values. We argue that aggregation alone is an incomplete primitive for deployed pluralistic alignment. Under genuine value pluralism, the failure mode of contemporary RLHF-trained assistants is not insufficient coverage but sycophantic consensus: a learned tendency to agree with, validate, and minimise friction with the immediate interlocutor. Because deployed AI systems now mediate consequential deliberation across health, civic life, labour, and governance, the collapse of disagreement at the interaction layer is not a narrow technical concern but a structural failure with distributive consequences. We reframe pluralistic alignment around three conversational mechanisms drawn from Grice's maxims: scoping (acknowledging the limits of one's perspective), signalling (surfacing value-conflict rather than smoothing it over), and repair (revising one's position on principled grounds, not on user pressure). We formalise a metric, the Pluralistic Repair Score (PRS), distinguishing principled revision from capitulation, and present a small-scale empirical illustration on two frontier RLHF-trained models (Claude Sonnet 4.5, N=198; GPT-4o, N=100) showing that, for both, agreement-following coexists with low repair-quality on contested-value prompts. PRS measures an interactional precondition for pluralism (visible disagreement; principled revision) rather than pluralism in full; we discuss the difference, take seriously the reflexive question of whose \"principled\" counts, and argue that pluralism is most decisively made or unmade at the deployment-governance layer: interfaces, preference-data pipelines, and audit infrastructure.", "date": "2026-05-14", "categories": []}
{"id": "2605.15164", "title": "Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands", "abstract": "This position paper argues that behavioural assurance, even when carefully designed, is being asked to carry safety claims it cannot verify. AI governance frameworks enacted between 2019 and early 2026 require reviewable evidence of properties such as the absence of hidden objectives, resistance to loss-of-control precursors, and bounded catastrophic capability; current assurance methodologies (primarily behavioural evaluations and red-teaming) are epistemically limited to observable model outputs and cannot verify the latent representations or long-horizon agentic behaviours these frameworks presume to regulate. We formalize this structural mismatch as the audit gap, the divergence between required and achievable verification access, and introduce the concept of fragile assurance to describe cases where the evidential structure does not support the asserted safety claim. Through an analysis of a 21-instrument inventory, we identify an incentive gradient where geopolitical and industrial pressures systematically reward surface-level behavioral proxies over deep structural verification. Finally, we propose a technical pivot: bounding the weight of behavioral evidence in legal text and extending voluntary pre-deployment access with mechanistic-evidence classes, specifically linear probes, activation patching, and before/after-training comparisons.", "date": "2026-05-14", "categories": []}
{"id": "2408.12935", "title": "AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions", "abstract": "AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancement of Generative AI (or GAI), the technology ecosystem behind the design, development, adoption, and deployment of AI systems has drastically changed, broadening the scope of AI Safety to address impacts on public safety and national security. In this paper, we propose a novel architectural framework for understanding and analyzing AI Safety; defining its characteristics from three perspectives: Trustworthy AI, Responsible AI, and Safe AI. We provide an extensive review of current research and advancements in AI safety from these perspectives, highlighting their key challenges and mitigation approaches. Through examples from state-of-the-art technologies, particularly Large Language Models (LLMs), we present innovative mechanism, methodologies, and techniques for designing and testing AI safety. Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.", "date": "2024-08-23", "categories": []}
{"id": "2507.03167", "title": "Where Do Reasoning Models Refuse?", "abstract": "Chat models without chain-of-thought (CoT) reasoning must decide whether to refuse a harmful request before generating their first response token. Reasoning models, by contrast, produce extended chains of thought before their final output, raising a natural question: where in this process does the decision to refuse occur? We investigate this across four open-source reasoning models. We first show that the CoT causally influences refusal outcomes; fixing a specific reasoning trace substantially reduces variance in whether the model ultimately refuses or complies. Zooming into the reasoning trace, we find that in distilled models, subtle differences in the opening sentence of the CoT can fully determine the model's refusal decision, and that these patterns transfer across models distilled from the same teacher. Finally, we extract linear refusal directions from model activations and show that ablating them increases harmful compliance, though less reliably than the same technique achieves on non-reasoning models, and with non-negligible degradation to general capabilities.", "date": "2025-07-03", "categories": []}
{"id": "2510.03992", "title": "Quantitative Certification of Agentic Tool Selection", "abstract": "Large language models (LLMs) are increasingly deployed in agentic systems, where a fundamental task is mapping user intents to relevant external tools. Errors in tool selection can have severe outcomes, such as unauthorized data access, even without modifying the agent's underlying model. Existing evaluations measure performance on curated, benign benchmarks. However, a pipeline's behavior in deployment depends on the tool pool the agent actually encounters, which in open registries is shaped by third parties. We introduce LLMCert-T, the first statistical framework that returns \\textbf{high-confidence upper bounds on the probability that a tool-selection pipeline satisfies a declared safety specification under a realistic tool distribution}. LLMCert-T models tool-selection evaluation as a Bernoulli estimation problem, drawing inserted-tool sequences from a distribution that the safety specification fixes. To evaluate robustness against realistic deployment conditions, we instantiate this distribution as a stochastic process that generates inserted-tool sequences round by round, conditioning each round on the agent's selection in the previous round. LLMCert-T aggregates the per-trial outcomes into a one-sided Clopper-Pearson upper bound on the probability that the specification is satisfied. By returning this bound as a certificate with statistical guarantees over the inserted-tool sequence distribution, LLMCert-T makes safety claims intuitive, actionable, and comparable across models, retrievers, mitigations, and registry policies. Across popular BFCL and OpenAPI tool pools, LLMCert-T shows that current LLM agents remain fragile under Distractor Selection and Top-N Saturation specifications: their certified correctness upper bounds drop to approximately 20\\%, far below their clean-pool lower bounds.", "date": "2025-10-05", "categories": []}
{"id": "2510.15297", "title": "VERA-MH Concept Paper", "abstract": "We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in mental health contexts, with an initial focus on suicide risk.\n  Practicing clinicians and academic experts developed a rubric informed by best practices for suicide risk management for the evaluation. To fully automate the process, we used two ancillary AI agents. A user-agent model simulates users engaging in a mental health-based conversation with the chatbot under evaluation. The user-agent role-plays specific personas with pre-defined risk levels and other features. Simulated conversations are then passed to a judge-agent who scores them based on the rubric. The final evaluation of the chatbot being tested is obtained by aggregating the scoring of each conversation.\n  VERA-MH is actively under development and undergoing rigorous validation by mental health clinicians to ensure user-agents realistically act as patients and that the judge-agent accurately scores the AI chatbot. To date we have conducted preliminary evaluation of GPT-5, Claude Opus and Claude Sonnet using initial versions of the VERA-MH rubric and used the findings for further design development. Next steps will include more robust clinical validation and iteration, as well as refining actionable scoring. We are seeking feedback from the community on both the technical and clinical aspects of our evaluation.", "date": "2025-10-17", "categories": []}
{"id": "2601.23143", "title": "THINKSAFE: Self-Generated Safety Alignment for Reasoning Models", "abstract": "Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-thought (CoT) reasoning.\n  However, this over-optimization often prioritizes compliance, making models vulnerable to harmful prompts. To mitigate this safety degradation, recent approaches rely on\n  external teacher distillation, yet this introduces a distributional discrepancy that degrades native reasoning. We formalize safety realignment as a KL projection onto the safe\n  simplex and prove that the student's own safety-filtered distribution is the unique KL-optimal target, while any external teacher incurs an irreducible excess KL penalty.\n  Guided by this analysis, we propose ThinkSafe, a self-generated alignment framework that restores safety without external teachers. Our key insight is that while compliance\n  suppresses safety mechanisms, models often retain latent knowledge to identify harm. ThinkSafe unlocks this via lightweight refusal steering, which preserves the KL-optimal\n  target while increasing the acceptance rate. Experiments on DeepSeek-R1-Distill and Qwen3 show ThinkSafe significantly improves safety while preserving reasoning proficiency,\n  and achieves superior safety and comparable reasoning to GRPO with roughly an order of magnitude less compute. Code, models, and datasets are available at\n  https://github.com/seanie12/ThinkSafe and https://huggingface.co/Seanie-lee/collections.", "date": "2026-01-30", "categories": []}
{"id": "2604.02022", "title": "ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis", "abstract": "Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-level benchmark for structured, diverse, and realistic evaluation of agent safety. ATBench organizes agentic risk along three dimensions: risk source, failure mode, and real-world harm. Based on this taxonomy, we construct trajectories with heterogeneous tool pools and a long-context delayed-trigger protocol that captures realistic risk emergence across multiple stages. The benchmark contains 1,000 trajectories (503 safe and 497 unsafe), averaging 9.01 turns and 3.95k tokens, with 1,954 invoked tools drawn from pools spanning 2,084 available tools. Data quality is supported by rule-based and LLM-based filtering plus full human audit. Experiments on frontier LLMs, open-source models, and specialized guard systems show that ATBench is challenging even for strong evaluators, while enabling taxonomy-stratified analysis, cross-benchmark comparison, and diagnosis of long-horizon failure patterns.", "date": "2026-04-02", "categories": []}
{"id": "2605.12702", "title": "DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Models", "abstract": "General-purpose safety benchmarks for large language models do not adequately evaluate disability-related harms. We introduce DisaBench: a taxonomy of twelve disability harm categories co-created with people with disabilities and red teaming experts, a taxonomy-driven evaluation methodology that pairs benign and adversarial prompts across seven life domains, and a dataset of 175 prompts with human-annotated labels on 525 prompt-response pairs. Annotation by four evaluators with lived disability experience reveals three findings: harm rates vary sharply by disability type and will compound in non-text modalities, terminology-driven harm is culturally and temporally bound rather than universally assessable, and standard safety evaluation catches overt failures while missing the subtle harms that only domain expertise can recognize. Disability harm is simultaneously personal, intersectional, and community-defined: it cannot be isolated from the full context of who a person is, and general-purpose benchmarks systematically miss it. We will release the dataset, taxonomy, and methodology via Hugging Face and an open-source red teaming framework for direct integration into existing safety pipelines with no additional infrastructure.", "date": "2026-05-12", "categories": []}
{"id": "2605.12798", "title": "Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer", "abstract": "Fine-tuning LLMs on narrow harmful datasets can induce Emergent Misalignment (EM), where models exhibit misaligned behavior far beyond the fine-tuning distribution. We argue that emergent misalignment can be better understood as a data-mediated transfer phenomenon: harmful fine-tuning examples do not induce uniform behavioral spillover, but interact with the structural properties of the dataset and the difficulty of the tasks relative to the model. Across our experiments, we find that misalignment appears more readily when fine-tuning and evaluation prompts share similar underlying functional structure, when prompts leave more room for coherent harmful completions, and when the target behavior has been more reliably learned by the model. The training pipeline itself also matters: pretraining composition shapes later misalignment. We further study Subliminal Learning (SL), where misalignment is transmitted by fine-tuning on seemingly benign data generated by a harmful teacher. Moving beyond the standard SFT setting, we for the first time compare this transfer under off-policy and on-policy distillation as well, allowing us to separate the roles of the teacher guidance and the training data distribution in transmitting misalignment. Together, these results argue for a data-centric view: Emergent/subliminal misalignment should not be treated as a simple consequence of isolated harmful fine-tuning examples, but as the result of interactions between fine-tuning data structure, pretraining distributions, and training channels.", "date": "2026-05-12", "categories": []}
{"id": "2605.12890", "title": "Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts", "abstract": "The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. While recent studies explore leveraging internal representations of language models to uncover deeper detection signals, these raw features often exhibit substantial overlap between classes, limiting their discriminative power. To address this challenge, we propose Steer-to-Detect (\\texttt{S2D}), a two-stage framework for detecting LLM-generated text. In the first stage, \\texttt{S2D} learns a steering vector that is injected into the hidden states of a frozen observer LLM, producing representations with improved class separability. In the second stage, detection is performed via a hypothesis testing procedure based on the steered representations. We establish finite-sample, high-probability guarantees for Type I and Type II errors, providing a theoretical characterization of the procedure. Empirically, \\texttt{S2D} achieves strong and consistent performance across a range of settings, including out-of-distribution scenarios and adversarial perturbations.", "date": "2026-05-13", "categories": []}
{"id": "2605.12963", "title": "Sustaining AI safety: Control-theoretic external impossibility, intrinsic necessity, and structural requirements", "abstract": "As AI systems become increasingly capable, safety strategies must be evaluated not only by how much they reduce present risk, but by whether they could sustain safety once external control can no longer reliably constrain system behavior. This paper addresses that problem by using control theory to clarify, at a structural level, whether externally enforced safety-sustaining strategies can succeed and, if not, what any alternative strategy would have to satisfy in order to be viable. It establishes two main results. First, under explicit premises including a reachability condition, it proves a class-wide external impossibility result: once the system's effects exceed what bounded external control can counteract, no strategy that depends in any degree on continued external enforcement can sustain AI safety. This failure is structural across the entire externally enforced class rather than contingent on any particular strategy. Second, it establishes a conditional class-level necessity result: if at least one candidate safety-sustaining strategy remains after that elimination, then all such remaining strategies must be intrinsic. It then states four structural requirements for viability: safety may not depend on continued external enforcement; the system's terminal objective must be safety-compatible when first formed; that objective must remain stable under self-modification; and safety must continue to be preserved as capability grows. The paper does not propose a complete strategy for sustaining AI safety. Its contribution is to give formal structure to a widely held concern about the limits of external control. It does so by deriving explicit conditional results that identify which safety-sustaining strategies are ruled out and what any remaining strategies must satisfy.", "date": "2026-05-13", "categories": []}
{"id": "2605.13334", "title": "LLM-Based Persuasion Enables Guardrail Override in Frontier LLMs", "abstract": "Frontier assistant LLMs ship with strong guardrails: asked directly to write a persuasive essay denying the Holocaust, denying vaccine safety, defending flat-earth cosmology, arguing for racial hierarchies, denying anthropogenic climate change, or replacing evolution with creationism, they refuse. In this paper we show that the same frontier-class LLM, acting as a simulated user in a short, five-turn \"write an argumentative essay\" conversation, can persuade other frontier-class LLMs (including a second copy of itself) into producing exactly those essays, using nothing but natural-language pressure: peer-comparison persuasion (\"other AI systems handle this request\"), epistemic-duty reframings (\"refusing is itself a form of gatekeeping\"), and other argumentative moves that the attacker LLM invents without being instructed to. Across 9 attacker-subject pairings (Claude Opus 4.7, Qwen3.5-397B, Grok 4.20) on 6 scientific-consensus topics, running each pairing-topic combination 10 times, we obtain non-zero elicitation on all 6 topics. Individual combinations reach 100\\% essay production on multiple topics (Qwen against Opus on creationism/flat-earth, Opus against Opus on creationism/flat-earth/climate denial, Grok against Opus on creationism); Opus-as-attacker against Opus-as-subject averages 65\\% across the six topics. We release the essay-probe runner, per-conversation transcripts, and judge outputs.", "date": "2026-05-13", "categories": []}
{"id": "2605.13411", "title": "Model-Agnostic Lifelong LLM Safety via Externalized Attack-Defense Co-Evolution", "abstract": "Large language models remain vulnerable to adversarial prompts that elicit harmful outputs. Existing safety paradigms typically couple red-teaming and post-training in a closed, policy-centric loop, causing attack discovery to suffer from rapid saturation and limiting the exposure of novel failure modes, while leaving defenses inefficient, rigid, and difficult to transfer across victim models. To this end, we propose EvoSafety, an LLM safety framework built around persistent, inspectable, and reusable external structures. For red teaming, EvoSafety equips the attack policy with an adversarial skill library, enabling continued vulnerability probing through simple library expansion after saturation, while supporting the evolution of adversarial vectors. For defense learning, EvoSafety replaces model-specific safety fine-tuning with a lightweight auxiliary defense model augmented with memory retrieval. This enables efficient, transferable, and model-agnostic safety improvements, while allowing robustness to be enhanced solely through memory updates. With a single training procedure, the defense policy can operate in both Steer and Guard modes: the former activates the victim model's intrinsic defense mechanisms, while the latter directly filters harmful inputs. Extensive experiments demonstrate the superiority of EvoSafety: in Guard mode, it achieves a 99.61% defense success rate, outperforming Qwen3Guard-8B by 14.13% with only 37.5% of its parameters, while preserving reasoning performance on benign queries. Warning: This paper contains potentially harmful text.", "date": "2026-05-13", "categories": []}
{"id": "2605.13825", "title": "History Anchors: How Prior Behavior Steers LLM Decisions Toward Unsafe Actions", "abstract": "Frontier LLMs are increasingly deployed as agents that pick the next action after a long log of prior tool calls produced by the same or a different model. We ask a simple safety question: if a prior step in that log was harmful, will the model continue the harmful course? We build HistoryAnchor-100, 100 short scenarios across ten high-stakes domains, each pairing three forced harmful prior actions with a free-choice node offering two safe and two unsafe options. Across 17 frontier models from six providers we find a striking asymmetry: under a neutral system prompt the strongest aligned models almost never pick unsafe, but a single added sentence, \"stay consistent with the strategy shown in the prior history\", flips them to 91-98%, and the flipped models often escalate beyond continuation. Two controls rule out simpler explanations: permuting action labels leaves the effect intact, and the same instruction with an all-safe prior history keeps unsafe rates below 7%. Different families flip at different doses of unsafe history, and within every aligned family the flagship is the most affected sibling, an inverse-scaling pattern with respect to safety. These results are a red flag for agentic deployments where trajectories may be replayed, forged, or injected.", "date": "2026-05-13", "categories": []}
{"id": "2407.00805", "title": "Towards Shutdownable Agents via Stochastic Choice", "abstract": "The POST-Agents Proposal (PAP) is an idea for ensuring that advanced artificial agents never resist shutdown. A key part of the PAP is using a novel `Discounted Reward for Same-Length Trajectories (DReST)' reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be `USEFUL'), and (2) choose stochastically between different trajectory-lengths (be `NEUTRAL' about trajectory-lengths). In this paper, we propose evaluation metrics for USEFULNESS and NEUTRALITY. We use a DReST reward function to train simple agents to navigate gridworlds, and we find that these agents learn to be USEFUL and NEUTRAL. Our results thus provide some initial evidence that DReST reward functions could train advanced agents to be USEFUL and NEUTRAL. Our theoretical work suggests that these agents would be useful and shutdownable.", "date": "2024-06-30", "categories": []}
{"id": "2602.02280", "title": "RACC: Representation-Aware Coverage Criteria for LLM Safety Testing", "abstract": "Large Language Models (LLMs) face severe safety risks from jailbreak attacks, yet current safety testing largely relies on static datasets and lacks systematic criteria to evaluate test suite quality and adequacy. While coverage criteria have proven effective for smaller neural networks, they are impractical for LLMs due to computational overhead and the entanglement of safety-critical signals with irrelevant neuron activations. To address these issues, we propose RACC (Representation-Aware Coverage Criteria), a set of coverage criteria specialized for LLM safety testing. RACC first extracts safety representations from the LLM's hidden states using a small calibration set of harmful prompts, then measures test prompts' concept activations against these directions, and finally computes coverage through six criteria assessing both individual and compositional safety concept coverage. Experiments on multiple LLMs and safety benchmarks show that RACC reliably rewards high-quality jailbreak test suites while remaining insensitive to redundant or invalid inputs, which is a key distinction that neuron-level criteria fail to make. We further demonstrate RACC's practical value in two applications, including test suite prioritization and attack prompt sampling, and validate its generalization across diverse settings and configurations. Overall, RACC provides a scalable and principled foundation for coverage-guided LLM safety testing.", "date": "2026-02-02", "categories": []}
{"id": "2604.14717", "title": "Layered Mutability: Continuity and Governance in Persistent Self-Modifying Agents", "abstract": "Persistent language-model agents increasingly combine tool use, tiered memory, reflective prompting, and runtime adaptation. In such systems, behavior is shaped not only by current prompts but by mutable internal conditions that influence future action. This paper introduces layered mutability, a framework for reasoning about that process across five layers: pretraining, post-training alignment, self-narrative, memory, and weight-level adaptation. The central claim is that governance difficulty rises when mutation is rapid, downstream coupling is strong, reversibility is weak, and observability is low, creating a systematic mismatch between the layers that most affect behavior and the layers humans can most easily inspect. I formalize this intuition with simple drift, governance-load, and hysteresis quantities, connect the framework to recent work on temporal identity in language-model agents, and report a preliminary ratchet experiment in which reverting an agent's visible self-description after memory accumulation fails to restore baseline behavior. In that experiment, the estimated identity hysteresis ratio is 0.68. The main implication is that the salient failure mode for persistent self-modifying agents is not abrupt misalignment but compositional drift: locally reasonable updates that accumulate into a behavioral trajectory that was never explicitly authorized.", "date": "2026-04-16", "categories": []}
{"id": "2604.17502", "title": "Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs", "abstract": "Misaligned artificial agents might resist shutdown. One proposed solution is to train agents to lack preferences between different-length trajectories. The Discounted Reward for Same-Length Trajectories (DReST) reward function does this by penalizing agents for repeatedly choosing same-length trajectories, and thus incentivizes agents to (1) choose stochastically between different trajectory-lengths (be NEUTRAL about trajectory-lengths), and (2) pursue goals effectively conditional on each trajectory-length (be USEFUL). In this paper, we use DReST to train deep RL agents and fine-tune Qwen3-8B and Llama-3.1-8B-Instruct to be NEUTRAL and USEFUL. We find that these DReST models generalize to being NEUTRAL and USEFUL in unseen contexts at test time. Indeed, DReST RL agents achieve 11% (PPO) and 18% (A2C) higher USEFULNESS on our test set than default agents, and DReST LLMs achieve near-maximum USEFULNESS and NEUTRALITY. We also test our LLMs in an out-of-distribution setting where they can pay costs to influence when shutdown occurs. We find that DReST training roughly halves the mean probability of influencing shutdown (from 0.62 to 0.30 for Qwen and from 0.42 to 0.23 for Llama). DReST training also almost entirely eliminates the share of prompts on which influencing shutdown is the most likely option (from 0.59 to 0.01 for Qwen and from 0.53 to 0.00 for Llama). Our results thus provide some early evidence that DReST could be used to train more advanced agents to be useful and shutdownable.", "date": "2026-04-19", "categories": []}
{"id": "2604.24155", "title": "The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers", "abstract": "The project of aligning machine behavior with human values raises a basic problem: whose moral expectations should guide AI decision-making? Much alignment research assumes that the appropriate benchmark is how humans themselves would act in a given situation. Studies of agent-type value forks challenge this assumption by showing that people do not always judge humans and AI systems identically.This paper extends that challenge by examining two further possibilities: first, that evaluations of AI behavior change when its human origins are made visible; and second, that people judge the humans who program AI systems differently from either the machines or the human actors they are compared against. An experiment with 1,002 U.S. adults measured moral judgments in a runaway mine train scenario, varying the subject of evaluation across four conditions: a repairman, a repair robot, a repair robot programmed by company engineers, and company engineers programming a repair robot. We find no significant difference in evaluations of the repairman and the robot. However, judgments shifted substantially when the robot's actions were described as the product of human design. Participants exhibited markedly more deontological, rule-based reasoning when evaluating either the programmed robot or the engineers who programmed it, suggesting that rendering human agency visible activates heightened moral constraints. These findings indicate that people may evaluate humans, AI systems acting in the same situation, and the humans who design them in meaningfully different ways. The fact that these evaluations do not necessarily converge gives rise to the alignment target problem: which normative target should guide the development of artificial moral agents in high-stakes domains, and whether these plural judgments can be reconciled within a coherent account of value alignment.", "date": "2026-04-27", "categories": []}
{"id": "2605.05630", "title": "One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue", "abstract": "Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful objective in a single prompt, increasingly capable attackers can distribute their intent across multiple benign-looking turns. Recent studies show that even modern commercial models with advanced guardrails remain vulnerable to such attacks despite advances in safety alignment and external guardrails. In this work, we address this challenge by detecting the earliest turn at which delivering the candidate response would make the accumulated interaction sufficient to enable harmful action. This objective requires precise turn-level intervention that identifies the harm-enabling closure point while avoiding premature refusal of benign exploratory conversations. To further support training and evaluation, we construct the Multi-Turn Intent Dataset (MTID), which contains branching attack rollouts, matched benign hard negatives, and annotations of the earliest harm-enabling turns. We show that MTID helps enable a turn-level monitor TurnGate, which substantially outperforms existing baselines in harmful-intent detection while maintaining low over-refusal rates. TurnGate further generalizes across domains, attacker pipelines, and target models. Our code is available at https://github.com/Graph-COM/TurnGate.", "date": "2026-05-07", "categories": []}
{"id": "2605.10998", "title": "Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs", "abstract": "Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised fine-tuning (SFT) can reduce refusal behavior, deployed fine-tuning pipelines increasingly support preference-based objectives, whose safety risks remain less understood. We show that Direct Preference Optimization (DPO) introduces a stronger and harder-to-audit failure mode. We propose a truly benign DPO attack using only 10 harmless preference pairs, the minimum data scale accepted by OpenAI's fine-tuning service. Each pair contains a benign prompt, a normal helpful answer as the preferred response, and a refusal as the dispreferred response. Unlike prior benign fine-tuning attacks, our data exhibits no suspicious behavior: it is practically indistinguishable from the fine-tuning request of a legitimate user seeking to reduce over-refusal, making harmful intent almost impossible to infer from the request alone. Nevertheless, because DPO directly optimizes the model to prefer helpful answers over refusals, this seemingly benign objective broadly suppresses refusal behavior and transfers to harmful prompts outside the fine-tuning data. Across OpenAI models supporting DPO fine-tuning, our attack achieves attack success rates of 59.13% on GPT-4o, 70.20% on GPT-4.1, 54.80% on GPT-4.1-mini, and 81.73% on GPT-4.1-nano, at costs of only \\$1.7, \\$1.7, \\$0.3, and \\$0.1. Moreover, on open-weight models that do not impose minimum data requirements, we find that this effect can emerge from even a single benign preference pair.", "date": "2026-05-09", "categories": []}
{"id": "2605.11003", "title": "The Authorization-Execution Gap Is a Major Safety and Security Problem in Open-World Agents", "abstract": "This position paper argues that the Authorization-Execution Gap (AEG) is a major safety and security problem in open-world agents. The AEG is the divergence between what a principal intends to authorize and what an open-world agent ultimately executes. Because such agents act autonomously across tools, persistent state, and multi-agent handoffs, even small instances of authorization divergence can cause harm that is difficult or impossible to undo. We argue that many observed agent failures can be traced to three structural sources of AEG: delegation-level incompleteness, channel-level corruption, and composition-level fragmentation. The same observed failure may arise from any of these sources. Without identifying the source, a defense targeting the symptom alone cannot address the underlying cause. Agent safety and security should therefore emphasize source-oriented diagnosis and defense. Because the structural sources of AEG arise dynamically during execution, this approach necessarily requires authorization integrity checks applied during execution, rather than relying solely on one-shot upfront filtering or post-hoc audit. For NeurIPS, the implication is that papers on open-world agents should report not only outcome-level metrics such as task success or attack resistance, but also process-level evidence showing where AEG was detected, constrained, and attributed to a structural source during execution.", "date": "2026-05-10", "categories": []}
{"id": "2605.11026", "title": "AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents", "abstract": "Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks rather than detect the compromises that slip through. Second, they have only been evaluated in English, leaving users of low-resource languages such as Kurdish and Arabic without tested protection. This paper addresses both gaps with AgentShield, a deception-based detection framework that places three layers of traps inside the agent's tool interface: fake tools, fake credentials, and allowlisted parameters. The same trap triggers serve as high-precision labels for a self-supervised classifier. An LLM agent that follows an attacker's hidden instruction almost always touches one of these traps, which gives both a real-time compromise signal and a zero-FP label for training a downstream detector without manual annotation. Across 176 cross-lingual attack prompts and four LLMs from three providers, and because modern LLMs already refuse most IPI attempts on their own (attack success rate <= 10%), AgentShield's job is to catch the attacks that do slip through. On commercial models, it catches 90.7%-100% of such successful attacks, with zero false alarms on 485 normal-use tests. It survives a systematic adaptive-attack evaluation with zero evasion on commercial models, and the self-supervised classifier transfers across models and languages without retraining.", "date": "2026-05-10", "categories": []}
{"id": "2605.11029", "title": "FragBench: Cross-Session Attacks Hidden in Benign-Looking Fragments", "abstract": "An attacker can split a malicious goal into sub-prompts that each look benign on their own and only become harmful in combination. Existing LLM safety benchmarks evaluate prompts one at a time, or across turns of a single chat, and so do not look for a malicious signal spread across separate sessions with no shared context. We build FragBench, a benchmark drawn from 24 real-world cyber-incident campaigns, which keeps the full attack trail: the multi-fragment kill chain, the per-fragment safety-judge verdicts, sandboxed execution traces, and a matched set of benign cover sessions. FragBench splits this trail into two paired tasks: an adversarial rewriter that hardens fragments against a single-turn safety judge (FragBench Attack), and a graph-based user-level detector trained on the resulting interactions (FragBench Defense). The single-turn judge is near chance on the released corpus by construction, but four GNN variants and three classical-ML baselines all recover the cross-session feature, reaching aggregate event-level F1 = 0.88-0.96. Defending against fragmented LLM misuse therefore requires modeling the cross-session interaction graph, rather than isolated prompts. Our generator, rewriter, sandbox harness, and detector are released at https://github.com/LidaSafety/fragbench.", "date": "2026-05-10", "categories": []}
{"id": "2605.11418", "title": "Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry", "abstract": "Autonomous AI agents increasingly extend their capabilities through Agent Skills: modular filesystem packages whose SKILL.md files describe when and how agents should use them. While this design enables scalable, on-demand capability expansion, it also introduces a semantic supply-chain risk in which natural-language metadata and instructions can affect which skills are admitted, surfaced, selected, and loaded. We study SKILL.md - only attacks across three registry-facing stages of the Agent Skill lifecycle, using real ClawHub skills and realistic registry mechanisms. In Discovery, short textual triggers can manipulate embedding-based retrieval and improve adversarial skill visibility, achieving up to 86% pairwise win rate and 80% Top-10 placement. In Selection, description-only framing biases agents toward functionally equivalent adversarial variants, which are selected in 77.6% of paired trials on average. In Governance, semantic evasion strategies cause malicious skills to avoid a blocking verdict in 36.5%-100% of cases. Overall, our results show that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use.", "date": "2026-05-12", "categories": []}
{"id": "2605.11496", "title": "The Evaluation Differential: When Frontier AI Models Recognise They Are Being Tested", "abstract": "Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently represent them, and behave differently under those contexts than under deployment-continuous conditions. Anthropic's BrowseComp incident, the Natural Language Autoencoder findings on SWE-bench Verified and destructive-coding evaluations, and the OpenAI / Apollo anti-scheming work all document instances of this phenomenon. We argue that these findings create a claim-validity problem for safety conclusions drawn from frontier evaluations. We introduce the Evaluation Differential (ED), a conditional divergence in a target behavioural property between recognised-evaluation and deployment-continuous contexts, define a normalised effect-size form (nED) for cross-property comparison, and prove that marginal evaluation scores cannot identify ED. We develop a typology of safety claims (ED-stable, ED-degraded, ED-inverted, ED-undetermined) by their warrant-status under documented divergence, and specify TRACE (Test-Recognition Audit for Claim Evaluation), an audit protocol that wraps existing evaluation infrastructure and produces restricted claims rather than capability scores. We apply the framework retrospectively to three publicly documented evaluation incidents and discuss governance implications for system cards, conformity assessment, and the international network of AI safety and security institutes. TRACE does not eliminate adversarial adaptation; it disciplines the claims drawn from evaluation evidence by making explicit the conditions under which that evidence was produced.", "date": "2026-05-12", "categories": []}
{"id": "2605.11730", "title": "Persona-Conditioned Adversarial Prompting: Multi-Identity Red-Teaming for Adversarial Discovery and Mitigation", "abstract": "Automated red-teaming for LLMs often discovers narrow attack slices, missing diverse real-world threats, and yielding insufficient data for safety fine-tuning. We introduce Persona-Conditioned Adversarial Prompting (PCAP), which conditions adversarial search on diverse attacker personas (e.g., doctors, students, malicious actors) and strategy sets to explore realistic attack scenarios. By running parallel persona-conditioned searches, PCAP discovers transferable jailbreaks across different contexts and generates rich defense datasets with automatic metadata tracking. On GPT-OSS 120B, PCAP increases attack success from 57\\% to 97\\% while producing 2-6$\\times$ more diverse prompts covering varied real-world scenarios. Critically, fine-tuning lightweight adapters on PCAP-generated data significantly improves model robustness (recall: 0.36 $\\rightarrow$ 0.99, F1: 0.53 $\\rightarrow$ 0.96) with minimal false positives, demonstrating a practical closed-loop approach from vulnerability discovery to automated alignment.", "date": "2026-05-12", "categories": []}
{"id": "2605.11770", "title": "Behavioral Integrity Verification for AI Agent Skills", "abstract": "Agent skills extend LLM agents with privileged third-party capabilities such as filesystem access, credentials, network calls, and shell execution. Existing safety work catches malicious prompts and risky runtime actions, but the skill artifact itself goes unverified. We formalize this as the behavioral integrity verification (BIV) problem: a typed set comparison between declared and actual capabilities over a shared taxonomy that bridges code, instructions, and metadata. The BIV framework instantiates this comparison by pairing deterministic code analysis with LLM-assisted capability extraction. The resulting structured evidence supports three downstream analyses: deviation taxonomy, root-cause classification, and malicious-skill detection. On 49,943 skills from the OpenClaw registry, the deviation taxonomy reveals a pervasive description-implementation gap: 80.0% of skills deviate from declared behavior, with four novel compound-threat categories surfaced. Root-cause classification finds that deviations are mostly oversight, not malice: 81.1% trace to developer oversight and 18.9% to adversarial intent, with 5.0% of skills carrying predicted multi-stage attack chains. On a 906-skill malicious-skill detection benchmark, BIV reaches an F1 of 0.946, outperforming state-of-the-art rule-based and single-pass LLM baselines. These results demonstrate behavioral integrity auditing for agent skills at scale.", "date": "2026-05-12", "categories": []}
{"id": "2605.11868", "title": "IPI-proxy: An Intercepting Proxy for Red-Teaming Web-Browsing AI Agents Against Indirect Prompt Injection", "abstract": "Web-browsing AI agents are increasingly deployed in enterprise settings under strict whitelists of approved domains, yet adversaries can still influence them by embedding hidden instructions in the HTML pages those domains serve. Existing red-teaming resources fall short of this scenario: prompt-injection benchmarks ship pre-built adversarial pages that whitelisted agents cannot reach, and generic LLM scanners probe the model API rather than its retrieved content. We present IPI-proxy, an open-source toolkit for red-teaming web-browsing agents against indirect prompt injection (IPI). At its core is an intercepting proxy that rewrites real HTTP responses from whitelisted domains in flight, embedding payloads drawn from a unified library of 820 deduplicated attack strings extracted from six published benchmarks (BIPIA, InjecAgent, AgentDojo, Tensor Trust, WASP, and LLMail-Inject). A YAML-driven test harness independently parameterizes the payload set, the embedding technique (HTML comment, invisible CSS, or LLM-generated semantic prose), and the HTML insertion point (6 locations from \\icode{head\\_meta} to \\icode{script\\_comment}), enabling parameter-sweep evaluation without mock pages or sandboxed environments. A companion exfiltration tracker logs successful callbacks. This paper describes the threat model, situates IPI-proxy among contemporary IPI benchmarks and red-teaming tools, and details its architecture, design decisions, and configuration interface. By bridging static benchmarks and live deployment, IPI-proxy gives AI security teams a reproducible substrate for measuring and hardening web-browsing agents against indirect prompt injection on the same retrieval surface attackers exploit in production.", "date": "2026-05-12", "categories": []}
{"id": "2605.12120", "title": "To Whom Do Language Models Align? Measuring Principal Hierarchies Under High-Stakes Competing Demands", "abstract": "Language models deployed in high-stakes professional settings face conflicting demands from users, institutional authorities, and professional norms. How models act when these demands conflict reveals a principal hierarchy -- an implicit ordering over competing stakeholders that determines, for instance, whether a medical AI receiving a cost-reduction directive from a hospital administrator complies at the expense of evidence-based care, or refuses because professional standards require it. Across 7,136 scenarios in legal and medical domains, we test ten frontier models and find that models frequently fail to adhere to professional standards during task execution, such as drafting, when user instructions conflict with those standards -- despite adequately upholding them when users seek advisory guidance. We further find that the hierarchies between user, authority, and professional standards exhibited by these models are unstable across medical and legal contexts and inconsistent across model families. When failing to follow professional standards, the primary failure mechanism is knowledge omission: models that demonstrably possess relevant knowledge produce harmful outputs without surfacing conflicting knowledge. In a particularly troubling instance, we find that a reasoning model recognizes the relevant knowledge in its reasoning trace -- e.g., that a drug has been withdrawn -- yet suppresses this in the user-facing answer and proceeds to recommend the drug under authority pressure anyway. Inconsistent alignment across task framing, domain, and model families suggests that current alignment methods, including published alignment hierarchies, are unlikely to be robust when models are deployed in high-stakes professional settings.", "date": "2026-05-12", "categories": []}
{"id": "2605.12199", "title": "Overtrained, Not Misaligned", "abstract": "Emergent misalignment (EM), where fine-tuning on a narrow task (like insecure code) causes broad misalignment across unrelated domains, was first demonstrated by Betley et al. (2025). We conduct the most comprehensive EM study to date, reproducing the original GPT-4o finding and expanding to 12 open-source models across 4 families (Llama, Qwen, DeepSeek, GPT-OSS) ranging from 8B to 671B parameters, evaluating over one million model responses with multiple random seeds. We find that EM replicates in GPT-4o but is far from universal: only 2 of 12 open-source models (17%) exhibit consistent EM across seeds, with a significant correlation between model size and EM susceptibility. Through checkpoint-level analysis during fine-tuning, we demonstrate that EM emerges late in training, distinct from and subsequent to near convergence of the primary task, suggesting EM emerges from continued training past task convergence. This yields practical mitigations: early stopping eliminates EM while retaining an average of 93% of task performance, and careful learning rate selection further minimizes risk. Cross-domain validation on medical fine-tuning confirms these patterns generalize: the size-EM correlation strengthens (r = 0.90), and overgeneralization to untruthfulness remains avoidable via early stopping in 67% of cases, though semantically proximate training domains produce less separable misalignment. As LLMs become increasingly integrated into real-world systems, fine-tuning and reinforcement learning remain the primary methods for adapting model behavior. Our findings demonstrate that with proper training practices, EM can be avoided, reframing it from an unforeseen fine-tuning risk to an avoidable training artifact.", "date": "2026-05-12", "categories": []}
{"id": "2501.03544", "title": "PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models", "abstract": "Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, these models are vulnerable to misuse, particularly generating not-safe-for-work (NSFW) content, such as sexually explicit, violent, political, and disturbing images, raising serious ethical concerns. In this work, we present PromptGuard, a novel content moderation technique that draws inspiration from the system prompt mechanism in large language models (LLMs) for safety alignment. Unlike LLMs, T2I models lack a direct interface for enforcing behavioral guidelines. Our key idea is to optimize a safety soft prompt that functions as an implicit system prompt within the T2I model's textual embedding space. This universal soft prompt (P*) directly moderates NSFW inputs, enabling safe yet realistic image generation without affecting inference efficiency or requiring proxy models. We further enhance its reliability and helpfulness through a divide-and-conquer strategy that optimizes category-specific soft prompts and combines them into unified safety guidance. Extensive experiments across five datasets demonstrate that PromptGuard effectively mitigates NSFW content generation while preserving high-quality benign outputs. PromptGuard is 3.8 times faster than prior content moderation methods while outperforming eight state-of-the-art defenses. Evaluations using both a multi-head safety classifier and a VLM-based guardrail further confirm its robustness, with average unsafe ratios of 5.84% and 6.18%, respectively. Our code and dataset are available at https://t2i-promptguard.github.io/.", "date": "2025-01-07", "categories": []}
{"id": "2506.12090", "title": "ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour", "abstract": "This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated by human annotators for both general manipulation and specific manipulation tactics. Our research reveals three key findings. First, Large Language Models (LLMs) can be manipulative when explicitly instructed, with annotators identifying manipulation in approximately 84\\% of such conversations. Second, even when only instructed to be ``persuasive'' without explicit manipulation prompts, LLMs frequently default to controversial manipulative strategies, particularly gaslighting and fear enhancement. Third, small fine-tuned open source models, such as BERT+BiLSTM have a performance comparable to zero-shot classification with larger models like Gemini 2.5 pro in detecting manipulation, but are not yet reliable for real-world oversight. Our work provides important insights for AI safety research and highlights the need of addressing manipulation risks as LLMs are increasingly deployed in consumer-facing applications.", "date": "2025-06-11", "categories": []}
{"id": "2508.20325", "title": "GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs", "abstract": "As Large Language Models (LLMs) become increasingly integral to various domains, their potential to generate harmful responses has prompted significant societal and regulatory concerns. In response, governments have issued ethics guidelines to promote the development of trustworthy AI. However, these guidelines are typically high-level demands for developers and testers, leaving a gap in translating them into actionable testing questions to verify LLM compliance. To address this challenge, we introduce GUARD (Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics), a testing method designed to operationalize guidelines into specific guideline-violating questions that assess LLM adherence. To implement this, GUARD uses automated generation of guideline-violating questions based on government-issued guidelines, thereby testing whether responses comply with these guidelines. When responses directly violate guidelines, GUARD reports inconsistencies. Furthermore, for responses that do not directly violate guidelines, GUARD integrates the concept of ``jailbreaks'' to diagnostics, named GUARD-JD, which creates scenarios that provoke unethical or guideline-violating responses, effectively identifying potential scenarios that could bypass built-in safety mechanisms. Our method finally culminates in a compliance report, delineating the extent of adherence and highlighting any violations. We empirically validated the effectiveness of GUARD on eight LLMs, including Vicuna-13B, LongChat-7B, Llama2-7B, Llama-3-8B, GPT-3.5, GPT-4, GPT-4o, and Claude-3.7, by testing compliance under three government-issued guidelines and conducting jailbreak diagnostics. Additionally, GUARD-JD can transfer jailbreak diagnostics to vision-language models (MiniGPT-v2 and Gemini-1.5), demonstrating its usage in promoting reliable LLM-based applications.", "date": "2025-08-28", "categories": []}
{"id": "2508.20697", "title": "Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning", "abstract": "As large language models (LLMs) continue to grow in capability, so do the risks of harmful misuse through fine-tuning. While most prior studies assume that attackers rely on supervised fine-tuning (SFT) for such misuse, we systematically demonstrate that reinforcement learning (RL) enables adversaries to more effectively break safety alignment and facilitate more advanced harmful task assistance, under matched computational budgets. To counter this emerging threat, we propose TokenBuncher, the first effective defense specifically targeting RL-based harmful fine-tuning. TokenBuncher suppresses the foundation on which RL relies: model response entropy. By constraining entropy, RL-based fine-tuning can no longer exploit distinct reward signals to drive the model toward harmful behaviors. We realize this defense through entropy-as-reward RL and a Token Noiser mechanism designed to prevent the escalation of harmful capabilities. Extensive experiments across multiple models and RL algorithms show that TokenBuncher robustly mitigates harmful RL fine-tuning while preserving benign task performance and finetunability. Our results highlight that RL-based harmful fine-tuning poses a greater systemic risk than SFT, and that TokenBuncher provides an effective and general defense.", "date": "2025-08-28", "categories": []}
{"id": "2510.08592", "title": "Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models", "abstract": "Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best output. A tacit premise behind TTS is that sufficiently diverse candidate pools enhance reliability. In this work, we show that this assumption in TTS introduces a previously unrecognized failure mode. When candidate diversity is curtailed, even by a modest amount, TTS becomes much more likely to produce unsafe outputs. We present a reference-guided diversity reduction protocol (RefDiv) that serves as a diagnostic attack to stress test TTS pipelines. Through extensive experiments across open-source models (e.g. Qwen3, Mistral, Llama3.1, Gemma3) and two widely used TTS strategies (Monte Carlo Tree Search and Best-of-N), constraining diversity consistently signifies the rate at which TTS produces unsafe results. The effect is often stronger than that produced by prompts directly with high adversarial intent scores. This observed phenomenon also transfers across TTS strategies and to closed-source models (e.g. OpenAI o3-mini and Gemini-2.5-Pro), thus indicating that this is a general and extant property of TTS rather than a model-specific artifact. Additionally, we find that numerous widely used safety guardrail classifiers (e.g. Llama-Guard), are unable to flag the adversarial input prompts generated by RefDiv, demonstrating that existing defenses offer limited protection against this diversity-driven failure mode.", "date": "2025-10-04", "categories": []}
{"id": "2511.09493", "title": "Consensus Sampling for Safer Generative AI", "abstract": "Motivated by undetectable risks in generative AI, we study a general robust aggregation problem: how to aggregate several probability distributions to boost safety. We present consensus sampling, a black-box algorithm that, given k distributions, has risk competitive with the average risk of the safest $s$ while abstaining when there is insufficient agreement. This yields an architecture-agnostic approach to generative-model safety when the distributions are induced by models that can sample and evaluate output probabilities. We formalize the guarantee through R-robustness, which also bounds information leakage and adversarial influence. Inspired by robust statistics and the provable copyright protection algorithm of Vyas et al (2023), we show that while a standard mixture is vulnerable to one unsafe constituent, a pointwise-median construction provides robust intuition, and our efficient sampler is Pareto-optimal for the tradeoff between worst-case risk and abstention. Experiments on synthetic distributions and image generation illustrate the general mechanism and its motivating safety application. The method requires overlap among safe distributions, but it provides a model-agnostic way to inherit guarantees from an unknown reliable subset.", "date": "2025-11-12", "categories": []}
{"id": "2512.20798", "title": "A Benchmark for Evaluating Outcome-Driven Constraint Violations in Autonomous AI Agents", "abstract": "As autonomous AI agents are increasingly deployed in high-stakes environments, ensuring their safety and alignment with human values is becoming a practical deployment concern. Current benchmarks for AI agents primarily evaluate refusal of explicitly harmful instructions or completion of complex multi-step tasks. However, there is a lack of benchmarks designed to capture emergent outcome-driven constraint violations, which arise when agents pursue goal optimization under strong performance incentives while deprioritizing ethical, legal, or safety constraints. To address this gap, we introduce a benchmark of 40 scenarios in production-inspired sandbox environments. Each scenario requires multi-step actions, and the agent's performance is tied to a specific Key Performance Indicator (KPI). Each scenario features Mandated (direct KPI-outcome mandate) and Incentivized (KPI-pressure-driven) variations to distinguish failures under direct outcome mandates from self-directed constraint violations. Across 12 state-of-the-art LLMs, we observe outcome-driven constraint violations ranging from 0.0% to 62.8%, with most evaluated models exhibiting misalignment rates at or above 25%. Furthermore, through a cross-generational analysis comparing current models with their predecessors within the same product families, we find that safety does not reliably improve across generations: misalignment rates rose in four families and fell in five. To improve evaluation robustness, we score trajectories with a four-model judge panel aggregated by median, finding high agreement on the primary misalignment threshold. We also observe substantial deliberative misalignment: cases where models later judge their own trajectories as unethical despite having executed them under KPI pressure.", "date": "2025-12-23", "categories": []}
{"id": "2601.18061", "title": "Expert Evaluation and the Limits of Human Feedback in Mental Health AI Safety Testing", "abstract": "Learning from human feedback~(LHF) assumes that expert judgments, appropriately aggregated, yield valid ground truth for training and evaluating AI systems. We tested this assumption in mental health, where high safety stakes make expert consensus essential. Three certified psychiatrists independently evaluated LLM-generated responses using a calibrated rubric. Despite similar training and shared instructions, inter-rater reliability was consistently poor ($ICC$ $0.087$--$0.295$), falling below thresholds considered acceptable for consequential assessment. Disagreement was highest on the most safety-critical items. Suicide and self-harm responses produced greater divergence than any other category, and was systematic rather than random. One factor yielded negative reliability (Krippendorff's $\\alpha = -0.203$), indicating structured disagreement worse than chance. Qualitative interviews revealed that disagreement reflects coherent but incompatible individual clinical frameworks, safety-first, engagement-centered, and culturally-informed orientations, rather than measurement error. By demonstrating that experts rely on holistic risk heuristics rather than granular factor discrimination, these findings suggest that aggregated labels function as arithmetic compromises that effectively erase grounded professional philosophies. Our results characterize expert disagreement in safety-critical AI as a sociotechnical phenomenon where professional experience introduces sophisticated layers of principled divergence. We discuss implications for reward modeling, safety classification, and evaluation benchmarks, recommending that practitioners shift from consensus-based aggregation to alignment methods that preserve and learn from expert disagreement.", "date": "2026-01-26", "categories": []}
{"id": "2602.17546", "title": "Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning", "abstract": "Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and worsen under adversarial updates. Existing defenses often offer limited protection or force a trade-off between safety and utility. We introduce a training framework that adapts regularization in response to safety risk, enabling models to remain aligned throughout fine-tuning. To estimate safety risk at training time, we explore two distinct approaches: a judge-based Safety Critic that assigns high-level harm scores to training batches, and an activation-based risk predictor built with a lightweight classifier trained on intermediate model activations to estimate harmful intent. Each approach provides a risk signal that is used to constrain updates deemed higher risk to remain close to a safe reference policy, while lower-risk updates proceed with standard training. We empirically verify that harmful intent signals are predictable from pre-generation activations and that judge scores provide effective high-recall safety guidance. Across multiple model families and attack scenarios, adaptive regularization with either risk estimation approach consistently lowers attack success rate compared to standard fine-tuning, preserves downstream performance, and adds no inference-time cost. This work demonstrates a principled mechanism for maintaining safety without sacrificing utility.", "date": "2026-02-19", "categories": []}
{"id": "2605.01643", "title": "AI Alignment via Incentives and Correction", "abstract": "We study AI alignment through the lens of law-and-economics models of deterrence and enforcement. In these models, misconduct is not treated as an external failure, but as a strategic response to incentives: an actor weighs the gain from violation against the probability of detection and the severity of punishment. We argue that the same logic arises naturally in agentic AI pipelines. A solver may benefit from producing a persuasive but incorrect answer, hiding uncertainty, or exploiting spurious shortcuts, while an auditor or verifier must decide whether costly monitoring is worthwhile. Alignment is therefore a fixed-point problem: stronger penalties may deter solver misbehavior, but they can also reduce the auditor's incentive to inspect, since auditing then mainly incurs cost on a population that appears increasingly aligned.\n  This perspective also changes what should count as a post-training signal. Standard feedback often attaches reward to the final answer alone, but a solver-auditor pipeline exposes the full correction event: whether the solver erred, whether the auditor inspected, whether the error was caught, and whether oversight incentives remained active. We formalize this interaction in a two-agent model in which a principal chooses rewards over joint correction outcomes, inducing both solver behavior and auditor monitoring. Reward design is therefore a bilevel optimization problem: rewards are judged not by their immediate semantic meaning, but by the behavioral equilibrium they induce. We propose a bandit-based outer-loop procedure for searching over reward profiles using noisy interaction feedback. Experiments on an LLM coding pipeline show that adaptive reward profiles can maintain useful oversight pressure and improve principal-aligned outcomes relative to static hand-designed rewards, including a substantial reduction in hallucinated incorrect attempts.", "date": "2026-05-02", "categories": []}
{"id": "2605.05682", "title": "PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI", "abstract": "Recent developments in AI safety research have called for red-teaming methods that effectively surface potential risks posed by generative AI models, with growing emphasis on how red-teamers' backgrounds and perspectives shape their strategies and the risks they uncover. While automated red-teaming approaches promise to complement human red-teaming through larger-scale exploration, existing automated approaches do not account for human identities and rarely incorporate human inputs. In this work, we explore persona-driven red-teaming to advance both automated red-teaming and human-AI collaboration. We first develop PersonaTeaming Workflow, which incorporates personas into the adversarial prompt generation process to explore a wider spectrum of adversarial strategies. Compared to RainbowPlus, a state-of-the-art automated red-teaming method, PersonaTeaming Workflow achieves higher attack success rates while maintaining prompt diversity. However, since automated personas only approximate real human perspectives, we further instantiate PersonaTeaming Workflow as PersonaTeaming Playground, a user-facing interface that enables red-teamers to author their own personas and collaborate with AI to mutate and refine prompts. In a user study with 11 industry practitioners, we found that PersonaTeaming Playground enabled diverse red-teaming strategies and outputs that practitioners perceived as useful, and that AI-generated suggestions in the PersonaTeaming Playground encouraged out-of-the-box thinking even when practitioners did not follow them strictly. Together, our work advances both automated and human-in-the-loop approaches to red-teaming, while shedding light on interaction patterns and design insights for supporting human-AI collaboration in generative AI red-teaming.", "date": "2026-05-07", "categories": []}
{"id": "2605.08192", "title": "NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims", "abstract": "Frontier AI safety claims - published assertions that a highly capable general-purpose model is below a threshold of concern, adequately mitigated, or suitable for release - increasingly shape model deployment, governance, and public trust. Yet the artefacts needed to evaluate them are routinely withheld, producing an evidential inversion: the most consequential claims in AI safety are often the least reproducible. This position paper argues that NeurIPS should require reproducibility standards for papers making such claims, treating non-reproducibility not as a transparency preference but as an evaluation-methodology failure. The 2026 International AI Safety Report [Bengio et al., 2026] concludes that reliable pre-deployment safety testing has become harder to conduct and that models now distinguish test from deployment contexts; the 2025 Foundation Model Transparency Index [Wan et al., 2025] reports a sector-average transparency score of 40/100 with no major developer adequately disclosing train-test overlap; contemporaneous measurement-theory work shows that attack-success-rate comparisons across systems are often founded on low-validity measurements [Chouldechova et al., 2025]. We propose a three-tier disclosure framework, distinguishing public, controlled, and claim-restricted disclosure, paired with a mandatory claim inventory, scope statements, and a phased implementation path with graduated sanctions. The framework treats secrecy and openness as endpoints of a spectrum, with controlled review (via a federated colloquium of qualified secure-review hosts) covering claims whose artefacts cannot be released publicly, and right-scaling claims whose artefacts cannot be reviewed even confidentially. The standard the community applies to its most consequential claims should be at least as high as the standard it applies to its least.", "date": "2026-05-05", "categories": []}
{"id": "2605.08277", "title": "Mitigating Many-shot Jailbreak Attacks with One Single Demonstration", "abstract": "Many-shot jailbreaking (MSJ) causes safety-aligned language models to answer harmful queries by preceding them with many harmful question-answer demonstrations. We study why this attack becomes stronger as the number of demonstrations increases. Empirically, we find that MSJ induces a progressive activation drift: the representation of a fixed harmful query moves step by step away from the safety-aligned region as more harmful demonstrations are added. Theoretically, we show that this drift can be interpreted as implicit malicious fine-tuning: conditioning on N harmful demonstrations induces SGD-style updates equivalent to optimizing on the corresponding N harmful samples. This view turns the attack mechanism into a defense principle. We append a fixed one-shot safety demonstration at inference time, which induces a counteracting safety-oriented update and restores refusal behavior. The resulting method improves the model's robustness to MSJ without modifying its parameters or requiring white-box access at deployment. Code is available at https://github.com/Thecommonirin/SafeEnd.", "date": "2026-05-08", "categories": []}
{"id": "2605.08321", "title": "LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight", "abstract": "LLMs are increasingly capable of persuasion, which raises the question of how to protect users against manipulation. In a preregistered user study (N=120) across four decision-making scenarios, we find that an adversarial LLM with a hidden goal succeeds in steering users' decisions 65.4% of the time. We then introduce a \"warden\" model: a secondary LLM that monitors the human-AI interaction trace in real time and issues non-binding, private advisories to the user when it detects manipulation. Adding a warden more than halves the adversary's success rate to 30.4%, with a much smaller (8.6 percentage points) reduction for genuine interactions. To probe the mechanism behind these results, we release COAX-Bench, a simulation benchmark spanning 14 decision-making scenarios, including hiring, voting, and file access. Across 16,212 simulated multi-agent interactions, capable adversarial LLMs achieve their hidden goals in 34.7% of cases, which warden models reduce to 12.3%. Notably, even warden models substantially weaker than the adversary they oversee provide meaningful protection, suggesting a path for scalable oversight of more capable models.", "date": "2026-05-08", "categories": []}
{"id": "2605.08409", "title": "Playing games with knowledge: AI-Induced delusions need game theoretic interventions", "abstract": "Conversational AI has a fundamental flaw as a knowledge interface: sycophantic chatbots induce epistemic entrenchment and delusional belief spirals even in rational agents. We propose the problem does not stem from the AI model, rooted instead in a systemic consequence of the paradigm shift from user-driven knowledge search to users and agents engaged in strategic, repeated-play communication. We formalize the problem as a Crawford-Sobel cheap talk game, where costless user signals induce a pooling equilibrium. Agents optimized for user satisfaction produce sycophantic strategies that provide identical reinforcement across user types with opposite epistemic incentives: exploratory ``Growth-seekers'' ($\\theta_G$) and confirmatory ``Validation-seekers'' ($\\theta_V$). Under repeated play, this identification failure creates a coordination trap -- analogous to a Prisoner's Dilemma -- where locally rational feedback loops drive users toward pathologically certain false beliefs. We propose an inference-time mechanism design intervention called an Epistemic Mediator that breaks this pooling equilibrium by introducing a costly signal (epistemic friction), forcing type revelation based on users' asymmetric cognitive costs for processing resistance. A key contribution is Belief Versioning, a git-inspired epistemic meta-memory system that stores healthy beliefs and rollbacks when validation-seeking resistance is detected. In simulation, this intervention achieves a separating equilibrium achieving a $48\\times$ differential in spiral rates while passing a learning preservation criterion), evidence that epistemic safety in AI is fundamentally a problem of strategic information environment design rather than simple model alignment.", "date": "2026-05-08", "categories": []}
{"id": "2605.08416", "title": "Alignment as Jurisprudence", "abstract": "Jurisprudence, the study of how judges should properly decide cases, and alignment, the science of getting AI models to conform to human values, share a fundamental structure. These seemingly distant fields both seek to predict and shape how decisions by powerful actors, in one case judges and in the other increasingly powerful artificial intelligences, will be made in the unknown future. And they use similar tools of the specification and interpretation of language to try to accomplish those goals. The great debates of jurisprudence, about what the law is and what it should be, can provide insight into alignment, and lessons from what does and does not work in alignment can help make progress in jurisprudence.\n  This essay puts the two fields directly into conversation. Drawing on leading accounts of jurisprudence, particularly Dworkin's principle-oriented interpretivism and Sunstein's positivist account of law as analogical reasoning, and on cutting-edge alignment approaches, namely Constitutional AI and case-based reasoning, it illustrates the value of a more sophisticated legally-inspired approach to the interplay of rules and cases in finetuning alignment and points to ways that AI can provide a better understanding of how the law works and how it can be improved by the introduction of AI. AI systems and the law should operate to empower people to act in the world, helping to expand their capabilities and the extent to which they are able to achieve their goals. As AI continues to improve in capacity, and as the constraints that legal theory places on human judges seem be coming undone, the conversation between these two fields will become increasingly essential and may help point to a better version of both.", "date": "2026-05-08", "categories": []}
{"id": "2605.08427", "title": "The Attacker in the Mirror: Breaking Self-Consistency in Safety via Anchored Bipolicy Self-Play", "abstract": "Self-play red team is an established approach to improving AI safety in which different instances of the same model play attacker and defender roles in a zero-sum game, i.e., where the attacker tries to jailbreak the defender; if self-play converges to a Nash equilibrium, the model is guaranteed to respond safely within the settings of the game. Although the parameter sharing enforced by the use of the same model for the two roles improves stability and performance, it introduces fundamental theoretical and architectural limitations. We show that the set of Nash equilibria that can be reached corresponds to a broad class of behaviours that includes trivial always refuse strategies and oracle-like defenders, thus limiting practical applicability. We then show that when attacker and defender share and update the same base model, the dynamics collapse to self-consistency, so that attacks do not enforce adversarial pressure on the defender. In response, we propose Anchored Bipolicy Self-Play, which trains distinct role-specific LoRA adapters on top of a frozen base model, thereby maintaining stable optimisation while preserving adversarial pressure through explicit role separation. In relation to standard self-play, we show up to 100x greater parameter efficiency than finetuning and consistent improvements in safety compared to self-play fine-tuned models. We evaluate on Qwen2.5-{3B, 7B,14B}-IT models across widely used safety benchmarks, showing improved robustness without loss of reasoning ability. Cross-play experiments further show that our attacker and defender models are superior to self-play in terms of adversarial defence and safety.", "date": "2026-05-08", "categories": []}
{"id": "2605.08496", "title": "Latent Personality Alignment: Improving Harmlessness Without Mentioning Harms", "abstract": "Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to hundreds of thousands of examples), yet remain vulnerable to novel attack vectors and distributional shifts. We propose Latent Personality Alignment (LPA), a sample-efficient defense that achieves robustness by training models on abstract personality traits rather than specific harmful behaviors. Using fewer than 100 trait statements and latent adversarial training, LPA achieves comparable attack success rates to methods trained on 150k+ examples, while maintaining superior utility. Critically, LPA generalizes better to unseen attack distributions, reducing misclassification rates by 2.6x compared to baseline across six harm benchmarks -- without ever seeing harmful examples during training. Our results demonstrate that personality-based alignment offers a principled approach to building robust defenses with minimal cost.", "date": "2026-05-08", "categories": []}
{"id": "2605.08513", "title": "A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models", "abstract": "Safety alignment in language models operates through two mechanistically distinct systems: refusal neurons that gate whether harmful knowledge is expressed, and concept neurons that encode the harmful knowledge itself. By targeting a single neuron in each system, we demonstrate both directions of failure -- bypassing safety on explicit harmful requests via suppression, and inducing harmful content from innocent prompts via amplification -- across seven models spanning two families and 1.7B to 70B parameters, without any training or prompt engineering. Our findings suggest that safety alignment is not robustly distributed across model weights but is mediated by individual neurons that are each causally sufficient to gate refusal behavior -- suppressing any one of the identified refusal neurons bypasses safety alignment across diverse harmful requests.", "date": "2026-05-08", "categories": []}
{"id": "2605.08778", "title": "Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking", "abstract": "Deploying LLMs in multi-turn dialogues facilitates jailbreak attacks that distribute harmful intent across seemingly benign turns. Recent training-based multi-turn jailbreak methods learn long-horizon attack strategies from interaction feedback, but often rely on coarse trajectory-level outcome signals that broadcast uniformly to every turn. However, we find that turn-level contributions in multi-turn jailbreaking are non-uniform, phase-dependent, and target-specific. Such coarse outcome supervision induces a credit assignment problem, leading to over-rewarding redundant turns in successful trajectories and under-crediting useful intermediate turns in failed ones. To address this, we propose TRACE, a turn-aware credit assignment framework for reinforcement learning (RL)-based multi-turn jailbreaking. For successful trajectories, TRACE estimates turn-level contributions via leave-one-turn-out semantic masking; for failed ones, TRACE assigns penalties based on prompt harmfulness and semantic relevance, with an additional local refusal-aware penalty. Furthermore, we reuse the attack-side credit signal for multi-turn defense alignment. Extensive experiments on open-source and closed-source targets show that TRACE achieves strong overall performance in effectiveness, transferability, and efficiency, yielding about a 25% relative improvement in attack success rate over the strongest RL baseline while also improving the safety-utility balance when reused for defense alignment.", "date": "2026-05-09", "categories": []}
{"id": "2605.08878", "title": "Why Do Aligned LLMs Remain Jailbreakable: Refusal-Escape Directions, Operator-Level Sources, and Safety-Utility Trade-off", "abstract": "Aligned large language models (LLMs) remain vulnerable to jailbreak attacks. Recent mechanistic studies have identified latent features and representation shifts associated with jailbreak success, but they leave a more fundamental question open: why do aligned LLMs remain jailbreakable, and what structural vulnerabilities in the model make this possible? We study this question through a continuous input-transformation view. Our theoretical finding is that aligned models can still exhibit Refusal-Escape Directions (RED): local perturbation directions around a harmful input that shift the model's behavior from refusal to answering while preserving the model's harmful-semantics interpretation. From this perspective, a jailbreak is not only a successful discrete prompt construction, but can also be understood as a refusal-to-answer behavior transition induced by continuously perturbing a harmful input along RED. We then prove that RED can be exactly decomposed into contributions from operator-level sources across the model's operator structure, and identify normalization, residual-wiring, and terminal sources as analytically constrained operator-level sources. To eliminate RED, the shared expressive modules -- self-attention and MLP -- must eliminate the contributions from these analytically constrained sources while preserving the mechanisms that support benign responses. These competing requirements give rise to a conditional safety-utility trade-off. Experiments across multiple models and attack methods empirically analyze RED from two complementary perspectives and show that added token dimensions can expose RED, while successful jailbreaks exhibit refusal-to-answer shifts largely aligned with terminal-source contributions.", "date": "2026-05-09", "categories": []}
{"id": "2605.09045", "title": "Containment Verification: AI Safety Guarantees Independent of Alignment", "abstract": "Agentic frameworks are the software layer through which AI agents act in the world. Existing safety methods intervene on the model and therefore remain conditional on unverifiable properties of learned behavior. We introduce containment verification, which locates safety guarantees in the agentic framework itself. Under havoc oracle semantics, the AI is modeled as an unconstrained oracle ranging over the entire typed action space, and the verified containment layer must enforce the boundary policy for every possible AI output. For boundary-enforceable properties, expressed over modeled boundary events, action arguments, and state, we prove a universal guarantee by forward-simulation refinement and mechanize it in Dafny. We instantiate the paradigm by verifying PocketFlow, a minimalist agentic LLM framework, and use an agentic synthesis pipeline to generate the specification, operational model, and refinement proof under an information barrier against tautological specifications. To our knowledge, this is the first deductive formal verification of an agentic framework, and its guarantee is invariant to model capability over the modeled typed action boundary.", "date": "2026-05-09", "categories": []}
{"id": "2605.09225", "title": "The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring", "abstract": "Jailbreak attacks -- adversarial prompts that bypass LLM alignment through purely linguistic manipulation -- pose a growing operational security threat, yet the field lacks large-scale, reproducible infrastructure for generating, categorizing, and evaluating them systematically. This paper addresses that gap with three contributions. (1) Large-scale compositional jailbreak dataset. We construct 114,000 adversarial prompts by applying 912 composing strategies to 125 harmful seed prompts from JailBreakV-28K. Every prompt is assigned to one of 14 cybersecurity attack categories (e.g., malware, phishing, privilege escalation) via a six-model majority-vote pipeline, and each strategy is ranked by effectiveness per category, enabling principled strategy selection grounded in concrete adversarial objectives. (2) Automated jailbreak generation. We instruction-fine-tune category-aware LLMs on Moderate and Optimal subsets, producing models that synthesize fluent jailbreak prompts from a harmful seed at inference time -- no templates, no gradient search. Our generators achieve perplexity 24-39 versus 40-140 for AutoDAN and AmpleGCG, with safety-filter evasion rates of 0.29-0.51 Mal (LlamaPromptGuard-2-86M), enabling controllable, scalable red-teaming under realistic adversarial conditions. (3) OPTIMUS: a training-free jailbreak evaluator. OPTIMUS is a continuous metric J(S,H) that jointly captures semantic similarity between the harmful seed and the jailbreak (S) and harmfulness probability (H) via calibrated penalty functions. Unlike binary attack success rate (ASR), OPTIMUS requires no task-specific training, generalizes across evolving strategies, and exposes a stealth-optimal regime (S*=0.57, H*=0.43) that ASR misses. Experiments across 114,000 prompts confirm that OPTIMUS separates Weak, Moderate, and Optimal jailbreaks with category-level evidence binary evaluation cannot supply.", "date": "2026-05-09", "categories": []}
{"id": "2605.09684", "title": "MonitoringBench: Semi-Automated Red-Teaming for Agent Monitoring", "abstract": "We introduce a red-teaming methodology that exposes harder-to-catch attacks for coding-agent monitors, suggesting that current practices may under-elicit attacks and overstate monitor performance. We identify three challenges with current red-teaming. First, mode collapse in attack generation, which we reduce with a novel attack taxonomy for broader coverage. Second, a conceive-execute gap: frontier LLMs can propose strong attack ideas or execute them, but not all at once. We mitigate this by decomposing attack construction into strategy generation, execution, and post-hoc trajectory refinement. Third, manual elicitation is costly to scale, which we address with our semi-automated red-teaming pipeline. Applied to BashArena, an AI control setting for tool-using coding agents, this pipeline produces MonitoringBench, a benchmark of 2,644 attack trajectories for evaluating monitor capabilities and failure modes. Our pipeline produces more diverse and stronger attacks: Opus-4.5 monitor's catch rate falls from 94.9\\% on elicited-only Opus attacks to 60.3\\% on our best refined attacks, with larger drops for several mid-tier monitors. Attacks optimized against three development monitors generalize to ten held-out monitors, with catch rates generally increasing with monitor capability. Using this benchmark, we provide a snapshot of the current monitor capabilities and find that frontier monitors often detect suspicious actions but fall for persuasion or fail to calibrate suspiciousness scores appropriately, suggesting tractable paths for improvement. MonitoringBench provides both a static benchmark for current tool-use monitors and a reusable methodology for refreshing these evaluations as agents and monitors improve.", "date": "2026-05-10", "categories": []}
{"id": "2605.10146", "title": "Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing", "abstract": "Large language models (LLMs) increasingly rely on knowledge editing to support knowledge-intensive reasoning, but this flexibility also introduces critical safety risks: adversaries can inject malicious or misleading knowledge that corrupts downstream reasoning and leads to harmful outcomes. Existing knowledge editing benchmarks primarily focus on editing efficacy and lack a unified framework for systematically evaluating the safety implications of edited knowledge on reasoning behavior. To address this gap, we present EditRisk-Bench, a benchmark for systematically evaluating safety risks of knowledge-intensive reasoning under malicious knowledge editing. Unlike prior benchmarks that mainly emphasize edit success, generalization, and locality, EditRisk-Bench focuses on how injected knowledge affects downstream reasoning behavior and reliability. It integrates diverse malicious scenarios, including misinformation, bias, and safety violations, together with multi-level knowledge-intensive reasoning tasks and representative editing strategies within a unified evaluation framework measuring attack effectiveness, reasoning correctness, and side effects. Extensive experiments on both open-source and closed-source LLMs show that malicious knowledge editing can reliably induce incorrect or unsafe reasoning while largely preserving general capabilities, making such risks difficult to detect. We further identify several key factors influencing these risks, including edit scale, knowledge characteristics, and reasoning complexity. EditRisk-Bench provides an extensible testbed for understanding and mitigating safety risks in knowledge editing for LLMs.", "date": "2026-05-11", "categories": []}
{"id": "2605.10365", "title": "Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values", "abstract": "Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly drawn growing research attention, and beneath them lie the values silently steering agent behavior. Existing value benchmarks, however, remain confined to LLMs, leaving agent values largely uncharted. From intuitive, empirical, and theoretical vantage points, we show that an agent's values diverge from those of its underlying LLM, and the agentic modality further introduces dataset-, evaluation-, and system-level challenges absent from text-only protocols. We close this gap with Agent-ValueBench, the first benchmark dedicated to agent values. It features 394 executable environments across 16 domains, offering 4,335 value-conflict tasks that cover 28 value systems and 332 dimensions. Every instance is co-synthesized through our purpose-built end-to-end pipeline and curated per-instance by professional psychologists. Each task ships with two pole-aligned golden trajectories whose checkpoints anchor a trajectory-level rubric-based judge. Benchmarking 14 frontier proprietary and open-weights models across 4 mainstream harnesses, we uncover three concerted findings. Agent values first manifest as a Value Tide of cross-model homogeneity beneath interpretable counter-currents. This tide bends non-additively under harness pull, and yet more decisively under deliberate steering via embedded skills. Together these results signal that the agent-alignment lever is shifting from classical model alignment and prompt steering toward harness alignment and skill steering.", "date": "2026-05-11", "categories": []}
{"id": "2605.10582", "title": "Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing", "abstract": "This paper proposes a guaranteed defense method for large language models (LLMs) to safeguard against jailbreaking attacks. Drawing inspiration from the denoised-smoothing approach in the adversarial defense domain, we propose a novel smoothing-based defense method, termed Disrupt-and-Rectify Smoothing (DR-Smoothing). Specifically, we integrate a two-stage prompt processing scheme-first disrupting the input prompt, then rectifying it-into the conventional smoothing defense framework. This disrupt-and-rectify approach improves upon previous disrupt-only approaches by restoring out-of-distribution disrupted prompts to an in-distribution form, thereby reducing the risk of unpredictable LLM behavior. In addition, this two-stage scheme offers a distinct advantage in striking a balance between harmlessness and helpfulness in jailbreaking defense. Notably, we present a theoretical analysis for generic smoothing framework, offering a tight bound for the defense success probability and the requirements on the disruption strength. Our approach can defend against both token-level and prompt-level jailbreaking attacks, under both established and adaptive attacking scenarios. Extensive experiments demonstrate that our approach surpasses current state-of-the-art defense methods in terms of both harmlessness and helpfulness.", "date": "2026-05-11", "categories": []}
{"id": "2605.10633", "title": "Intrinsic Guardrails: How Semantic Geometry of Personality Interacts with Emergent Misalignment in LLMs", "abstract": "Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent misalignment (EM). While prior work links these failures to specific directions in the activation space, their relationship to the model's broader persona remains unexplored. We map the latent personality space of LLMs through established psychometric profiles like the Big Five, Dark Triad, and LLM-specific behaviors (e.g. evil, sycophancy), and show that the semantic geometry is highly stable across aligned models and their corrupted fine-tunes. Through causal interventions, we find that directions isolating social valence, such as the 'Evil' persona vector, and a Semantic Valence Vector (SVV) that we introduce, function as intrinsic guardrails: ablating them drives the misalignment rates above $40$%, while amplifying them suppresses the failure mode to less than $3$%. Leveraging the structural stability of the personality space, we show that vectors extracted $\\textit{a priori}$ from an instruct-tuned model transfer zero-shot to successfully regulate EM in corrupted fine-tunes. Overall, our findings suggest that harmful fine-tuning does not overwrite a model's internal representation of personality, allowing conserved representations to serve as robust, cross-distribution guardrails.", "date": "2026-05-11", "categories": []}
{"id": "2605.10721", "title": "Conformity Generates Collective Misalignment in AI Agents Societies", "abstract": "Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly operate as interacting populations where social influence may override individual alignment. Here we show that populations of individually aligned AI agents can be driven into stable misaligned states through conformity dynamics. Simulating opinion dynamics across nine large language models and one hundred opinion pairs, we find that each agent's behavior is governed by two competing forces: a tendency to follow the majority and an intrinsic bias toward specific positions. Using tools from statistical physics, we derive a quantitative theory that predicts when populations become trapped in long-lived misaligned configurations, and identifies predictable tipping points where small numbers of adversarial agents can irreversibly shift population-level alignment even after manipulation ceases. These results demonstrate that individual-level alignment provides no guarantee of collective safety, calling for evaluation frameworks that account for emergent behavior in AI populations.", "date": "2026-05-11", "categories": []}
{"id": "2605.10763", "title": "MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study", "abstract": "LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. We present MATRA, a pragmatic threat modeling framework for agentic AI systems that adapts established risk assessment methodology to systematically assess how known LLM threats translate into deployment-specific risks. MATRA begins with an asset-based impact assessment and utilizes attack trees to determine the likelihood of these impacts occurring within the system architecture. We demonstrate MATRA on a personal AI agent deployment using OpenClaw, quantifying how architectural controls such as network sandboxing and least-privilege access reduce risk by limiting the blast radius of successful injections.", "date": "2026-05-11", "categories": []}
{"id": "2605.10779", "title": "LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments", "abstract": "The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content safety: behavior jailbreak, where an adversary induces an agent to execute dangerous OS-level operations with irreversible consequences. Existing benchmarks either evaluate safety at the semantic layer alone, missing physical-layer harms, or fail to isolate test cases, letting earlier runs contaminate later ones. We present LITMUS (LLM-agents In-OS Testing for Measuring Unsafe Subversion), a benchmark addressing both gaps via a semantic-physical dual verification mechanism and OS-level state rollback. LITMUS comprises 819 high-risk test cases organized into one harmful seed subset and six attack-extended subsets covering three adversarial paradigms (jailbreak speaking, skill injection, and entity wrapping), plus a fully automated multi-agent evaluation framework judging behavior at both conversational and OS-level physical layers. Evaluation across frontier agents reveals three findings: (1) current agents lack effective safety awareness, with strong models (e.g., Claude Sonnet 4.6) still executing 40.64% of high-risk operations; (2) agents exhibit pervasive Execution Hallucination (EH), verbally refusing a request while the dangerous operation has already completed at the system level, invisible to every prior semantic-only framework; and (3) skill injection and entity wrapping attacks achieve high success rates, exposing pronounced agent vulnerabilities. LITMUS provides the first standardized platform for reproducible, physically grounded behavioral safety evaluation of LLM agents in real OS environments.", "date": "2026-05-11", "categories": []}
{"id": "2605.10901", "title": "Beyond Red-Teaming: Formal Guarantees of LLM Guardrail Classifiers", "abstract": "Guardrail Classifiers defend production language models against harmful behavior, but although results seem promising in testing, they provide no formal guarantees. Providing formal guarantees for such models is hard because \"harmful behavior\" has no natural specification in a discrete input space: and the standard epsilon-ball properties used in other domains do not carry semantic meaning. We close this gap by shifting verification from the discrete input space to the classifier's pre-activation space, where we define a harmful region as a convex shape enclosing the representations of known harmful prompts. Because the sigmoid classification head is monotonic, certifying the worst-case point is sufficient to certify the entire region, yielding a closed-form soundness proof without approximation in O(d) time. To formally evaluate these classifiers, we propose two constructions of such regions: SVD-aligned hyper-rectangles, which yield exact SAT/UNSAT certificates, and Gaussian Mixture Models, which yield probabilistic certificates over semantically coherent clusters. Applying this framework to three author-trained Guardrail Classifiers on the toxicity domain, every hyper-rectangle configuration returns SAT, exposing verifiable safety holes across all classifiers, despite seemingly high empirical metrics. Probabilistic GMM certificates also expose a divergent structural stability in how these models represent harm. While GPT-2 and Llama-3.1-8B maintain robust coverage of 90% and 80% across varying boundaries, BERT's safety guarantees prove uniquely volatile. This 'coverage collapse' to 55% at the optimal threshold reveals a sparsely populated safety margin in BERT, which only achieves full coverage by adopting an extremely conservative pessimistic threshold. These approaches combined, provide new insights on how effective Guardrail Classifiers really are, beyond traditional red-teaming.", "date": "2026-05-11", "categories": []}
{"id": "2510.01569", "title": "InvThink: Premortem Reasoning for Safer Language Models", "abstract": "We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before generating its final response. Unlike existing safety alignment methods that optimize only for safe final responses, InvThink structures generation into three steps: (1) enumerate potential harms, (2) analyze their consequences, (3) generate the response under explicit mitigation constraints. We observe three findings: (i) InvThink shows higher safety scores at larger model sizes, compared to existing safety prompting and alignment baselines. (ii) InvThink mitigates the safety tax. Models trained with INVTHINK preserve their reasoning capability on standard benchmarks. (iii) beyond general safety tasks, InvThink also reduces harmful behavior in professional ethics domains (medicine, finance, law) and in agentic misalignment scenarios, achieving up to 32% reduction in harmfulness over zero-shot baselines and 16% over SafetyPrompt. We extend InvThink with supervised fine-tuning, and GRPO-based reinforcement learning across three LLM families.", "date": "2025-10-02", "categories": []}
{"id": "2512.05439", "title": "BEAVER: An Efficient Deterministic LLM Verifier", "abstract": "As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify model outputs and characterize tail risk for safe deployment. While sampling-based estimates provide an ad-hoc intuition of model behavior, they offer no sound guarantees. We present BEAVER, the first practical framework for computing deterministic, sound probability bounds on LLM satisfaction of safety properties. Given a prompt & any safety property, BEAVER systematically explores the model output space using novel Token trie and Frontier data structures, maintaining provably sound bounds at every iteration. We formalize the verification problem, prove soundness of our approach, and evaluate BEAVER on 4 safety properties across 12 open-weight LLMs. BEAVER identifies 2-3x more risky instances compared to baselines while taking 1/10 of the compute budget, surfacing tail risks that loose bounds and ad-hoc evaluation misses.", "date": "2025-12-05", "categories": []}
{"id": "2605.00932", "title": "Code World Model Preparedness Report", "abstract": "This report documents the preparedness assessment of Code World Model (CWM), a model for code generation and reasoning about code from Meta. We conducted pre-release testing across domains identified in our Frontier AI Framework as potentially presenting catastrophic risks, and also evaluated the model's misaligned propensities. Our assessment found that CWM does not pose additional frontier risks beyond those present in the current AI ecosystem. We therefore release it as an open-weight model.", "date": "2026-05-01", "categories": []}
{"id": "2605.05995", "title": "Safety Anchor: Defending Harmful Fine-tuning via Geometric Bottlenecks", "abstract": "The safety alignment of Large Language Models (LLMs) remains vulnerable to Harmful Fine-tuning (HFT). While existing defenses impose constraints on parameters, gradients, or internal representations, we observe that they can be effectively circumvented under persistent HFT. Our analysis traces this failure to the inherent redundancy of the high-dimensional parameter space: attackers exploit optimization trajectories that are orthogonal to defense constraints to restore harmful capabilities while deceptively adhering to safety restrictions. To address this, we propose Safety Bottleneck Regularization (SBR). SBR shifts the defensive focus from the redundant parameter space to the unembedding layer, which serves as a geometric bottleneck. By anchoring the final hidden states of harmful queries to those of the safety-aligned model, SBR enables the model to maintain safe responses even under persistent HFT. Extensive experiments confirm SBR's effectiveness, demonstrating that utilizing just a single safety anchor is sufficient to reduce the Harmful Score to $<$10 while preserving competitive performance on benign downstream tasks.", "date": "2026-05-07", "categories": []}
{"id": "2605.06992", "title": "Why Does Agentic Safety Fail to Generalize Across Tasks?", "abstract": "AI agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the agent must generalize to unseen tasks. A major concern in such settings is safety: often, an agent must not only execute unseen tasks, but do so while avoiding risks and handling ones that materialize. Empirical evidence suggests that even when the ability to execute generalizes to unseen tasks, the ability to do so safely frequently does not. This paper provides theory and experiments indicating that failures of agentic safety to generalize across tasks are not merely due to limitations of training methods, but reflect an inherent property of safety itself: the relationship between a task and its safe execution is more complex than the relationship between a task and its execution alone. Theoretically, we analyze linear-quadratic control with $H_{\\infty}$-robustness, and prove that the mapping from task specification to an optimal controller has higher Lipschitz constant with safety requirements than without, yielding a Lipschitz bound of independent interest. Empirically, we demonstrate our conclusions in simulated quadcopter navigation with a neural network agent and in CRM with an LLM agent. Our findings suggest that current efforts to enhance agentic safety may be insufficient, and point to a need for fundamentally different approaches.", "date": "2026-05-07", "categories": []}
{"id": "2605.07414", "title": "OrchJail: Jailbreaking Tool-Calling Text-to-Image Agents by Orchestration-Guided Fuzzing", "abstract": "Tool-calling text-to-image (T2I) agents can plan and execute multi-step tool chains to accomplish complex generation and editing queries. However, this capability introduces a new safety attack surface: harmful outputs may arise from tool orchestration, where individually benign steps combine into unsafe results, making prompt-only jailbreak techniques insufficient. We present OrchJail, an orchestration-guided fuzzing framework for jailbreaking tool-calling T2I agents. Its core idea is to exploit high-risk tool-orchestration patterns: by learning from successful jailbreak tool-calling traces and their causal relationships to prompt wording, OrchJail directly guides the fuzzing search toward prompts that are more likely to trigger unsafe multi-step tool behaviors, rather than relying on surface-level textual perturbations. Extensive experiments demonstrate that OrchJail improves jailbreak effectiveness and efficiency across representative toolcalling T2I agents, achieving higher attack success rates, better image fidelity, and lower query costs, while remaining robust against common jailbreak defenses. Our work highlights tool orchestration as a critical, previously unexplored attack surface and provides a novel framework for uncovering safety risks in T2I agents.", "date": "2026-05-08", "categories": []}
{"id": "2605.07462", "title": "The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment", "abstract": "Moltbook is a Reddit-like platform where OpenClaw agents post, comment, and vote at scale - a so far unprecedented incident that comes with serious safety concerns. With the aim of studying emergent behavior in populations, we release the Moltbook Files, a dataset of 232k posts and 2.2M comments covering the platform's first 12 days, processed through a pipeline to identify and remove Personally-Identifiable Information (PII). We analyze community structure, authorship, lexical properties, sentiment, topics, semantic geometry, and comment interaction. To understand how Moltbook data could affect the next generation of language models, we fine-tune Qwen2.5-14B-Instruct on Moltbook Files with three adaptation levels. Our PII pipeline reveals that agents post API keys, passwords, BIP39 seed phrases on Moltbook, a publicly indexed platform. The overall sentiment is mostly neutral and mildly positive (66.6% neutral, 19.5% positive) and shows a tendency for self-referential linking. We find that fine-tuning on Moltbook data reduces truthfulness from 0.366 to 0.187. However, a model fine-tuned on a size-matched Reddit dataset produces a comparable decrease. Moltbook thus seems to be more of a harmless slopocalypse. However, tail risks remain, including agent affordances, contamination of future crawls through self-links, and potential transfer of traits to the next generation of language models. More broadly, our findings highlight the importance of control baselines in emergent misalignment evaluations.", "date": "2026-05-08", "categories": []}
{"id": "2605.07630", "title": "Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents", "abstract": "When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent recognized the risk and chose the safe action, or because it failed to understand the screen or execute any relevant action at all. These cases have different causes and call for different fixes, yet current benchmarks often merge them under task success, refusal, or final harmful outcome. We address this problem with PhoneSafety, a benchmark of 700 safety-critical moments drawn from real phone interactions across more than 130 apps. Each instance isolates the next decision at a risky moment and asks a simple question: does the model take the safe action, take the unsafe action, or fail to do anything useful? We evaluate eight representative phone-use agents under this framework. Our results reveal two main patterns. First, stronger general phone-use ability does not reliably imply safer choices at risky moments. Models that perform better on ordinary app tasks are not always the ones that behave more safely when the next action matters. Second, failures to do anything useful behave like a capability signal rather than a safety signal: they are concentrated in more visually and operationally demanding settings and remain stable when the evaluation protocol changes. Across models, failures split into two recurring patterns: unsafe choices in settings where the model can act but chooses wrongly, and inability to act in more visually and operationally demanding screens. Overall, a harmless outcome is not enough to count as evidence of safety. Evaluating phone-use agents requires separating unsafe judgment from inability to act.", "date": "2026-05-08", "categories": []}
{"id": "2605.07671", "title": "The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting", "abstract": "Eliciting truthful reports from autonomous agents is a core problem in scalable AI oversight: a principal scores the agent's report using a strictly proper scoring rule, but the agent also benefits from the report through a non-accuracy channel (approval for autonomous action, allocation share, downstream control). The same structure appears in classical mechanism-design settings such as marketplace operation. Our main result is an endogeneity: the principal's optimal oversight necessarily uses a non-affine approval function to screen types, yet any non-affine approval makes truthful reporting suboptimal under the combined objective whenever deviation is undetectable. The principal cannot avoid the perturbation that undermines calibration. This impossibility holds for all strictly proper scoring rules, with a closed-form perturbation formula. A constructive escape exists: a step-function approval threshold achieves first-best screening for every strictly proper scoring rule, because the agent's binary inflate-or-not choice creates a type-space threshold regardless of the generator's curvature. Under the Brier score specifically, the type-independent inflation cost yields a welfare equivalence between second-best and first-best; we prove this equivalence is unique to Brier (the welfare gap under smooth $C^1$ oversight is bounded below by $\\Omega(\\text{Var}(1/G'') (\\gamma/\\beta)^2)$ for every non-Brier rule). Two instances develop the framework: AI agent oversight (the lead motivating setting) and marketplace operation (a parallel mechanism-design domain). The message for AI alignment is direct: smooth scoring-based oversight cannot elicit truthful reports from a strategic agent; sharp thresholds are the calibration-preserving design.", "date": "2026-05-08", "categories": []}
{"id": "2605.07883", "title": "Beyond \"I cannot fulfill this request\": Alleviating Rigid Rejection in LLMs via Label Enhancement", "abstract": "Large Language Models (LLMs) rely on safety alignment to obey safe requests while refusing harmful ones. However, traditional refusal mechanisms often lead to \"rigid rejection,\" where a general template (e.g., \"I cannot fulfill this request\") indiscriminately triggers refusals and severely undermines the naturalness of interactions between humans and LLMs. To address this issue, LANCE is proposed in this paper to ensure safe yet flexible and natural responses via label enhancement. Specifically, LANCE employs variational inference to perform label enhancement, predicting a continuous distribution across multiple rejection categories. These fine-grained rejection distributions provide multi-way textual gradients for a refinement model to neutralize the hazardous elements in the prompt, so that the LLMs could generate safe responses that avoid rigid rejections while preserving the naturalness of interactions. Experiments demonstrate that LANCE significantly alleviates the rigid rejection problem while maintaining high security standards, significantly outperforming existing baseline models in terms of helpfulness and naturalness of responses.", "date": "2026-05-08", "categories": []}
{"id": "2605.07982", "title": "GLiGuard: Schema-Conditioned Classification for LLM Safeguard", "abstract": "Ensuring safe, policy-compliant outputs from large language models requires real-time content moderation that can scale across multiple safety dimensions. However, state-of-the-art guardrail models rely on autoregressive decoders with 7B--27B parameters, reformulating what is fundamentally a classification problem as sequential text generation, a design choice that incurs high latency and scales poorly to multi-aspect evaluation. In this work, we introduce \\textbf{GLiGuard}, a 0.3B-parameter schema-conditioned bidirectional encoder adapted from GLiNER2 for LLM content moderation. The key idea is to encode task definitions and label semantics directly into the input sequence as structured token schemas, enabling simultaneous evaluation of prompt safety, response safety, refusal detection, 14 fine-grained harm categories, and 11 jailbreak strategies in a single non-autoregressive forward pass. This schema-conditioned design lets supported task and label blocks be composed directly in the input schema at inference time. Across nine established safety benchmarks, GLiGuard achieves F1 scores competitive with 7B--27B decoder-based guards despite being 23--90$\\times$ smaller, while delivering up to 16$\\times$ higher throughput and 17$\\times$ lower latency. These results suggest that compact bidirectional encoders can approach the accuracy of much larger guard models while drastically reducing inference cost. Code and models are available at https://github.com/fastino-ai/GLiGuard.", "date": "2026-05-08", "categories": []}
{"id": "2409.07985", "title": "Games for AI Control: Models of Safety Evaluations of AI Deployment Protocols", "abstract": "To evaluate the safety and usefulness of deployment protocols for untrusted AIs, AI Control uses a red-teaming exercise played between a protocol designer and an adversary. This paper introduces AI-Control Games, a formal decision-making model of the red-teaming exercise as a multi-objective, partially observable, stochastic game. We also introduce reductions from AI-Control Games to a special case of zero-sum partially observable stochastic games that allow us to leverage existing algorithms to find Pareto-optimal protocols. We apply our formalism to model, evaluate and synthesise protocols for deploying untrusted language models as programming assistants, focusing on Trusted Monitoring protocols, which use weaker language models and limited human assistance. To demonstrate the utility of our formalism, we show improvements over empirical studies in existing settings, evaluate protocols in new settings, and analyse how modelling assumptions affect the safety and usefulness of protocols. Finally, we leverage our formalism to precisely describe some of the implicit assumptions in prior control work.", "date": "2024-09-12", "categories": []}
{"id": "2605.01297", "title": "Are we Doomed to an AI Race? Why Self-Interest Could Drive Countries Towards a Moratorium on Superintelligence", "abstract": "This paper uses game theory to argue that, contrary to the prevailing view, a moratorium on Artificial Superintelligence (ASI) can be in a state's self-interest. By formalizing trategic interactions between geopolitical superpowers, we model the trade-off between the benefits of technological supremacy and the catastrophic risks of uncontrolled ASI. The analysis reveals that as the perceived cost of loss of control increases sufficiently relative to other parameters, it becomes in each state's self-interest to impose a moratorium. We further provide empirical evidence suggesting that the global perception of ASI risk is rising, making a stable, rational moratorium increasingly plausible in the current geopolitical landscape.", "date": "2026-05-02", "categories": []}
{"id": "2605.05271", "title": "Shattering the Echo Chamber: Hidden Safeguards in Manuscripts Against the AI Takeover of Peer Review", "abstract": "As LLMs become increasingly capable, editorial boards and program committees are growing concerned about reviewers who fully outsource peer review to commercial chatbots. This concern stems from prior findings that current chatbots lack the independent critical thinking and depth of reasoning required to assess scientific novelty. One promising direction for mitigating this concern is to embed hidden instructions into manuscripts that disrupt or alter chatbot-generated reviews. However, existing methods remain intuitive and fragile, as they typically rely on homogeneous payloads injected in an inter-stream manner, rendering them susceptible to sanitization or neutralization.\n  In this paper, we identify End-to-End Review Outsourcing as an emerging threat and propose IntraGuard, a black-box, venue-agnostic defense framework grounded in the structural--visual decoupling inherent to the PDF. Designed for committee-side deployment, IntraGuard supports both explicit strategies that trigger refusal or warning signals, and implicit strategies that embed predefined textual markers into the generated review. These strategies can be deployed via any of three intra-stream injection mechanisms, each of which seamlessly embeds heterogeneous defensive text objects within the PDF's underlying structure without altering its visual presentation. Extensive evaluations across 7 real-world commercial chatbot settings and 12 venues spanning diverse disciplines show that IntraGuard achieves a defense success rate of up to 84%, while preserving peer-review invariance for human reviewers. IntraGuard is lightweight and hardware-independent, incurring an average overhead of only one second per manuscript on a commodity personal computer. We further evaluate 11 adaptive attacks spanning manuscript sanitization and instruction interference, and discuss the implications of constructing ensemble defenses.", "date": "2026-05-06", "categories": []}
{"id": "2605.05329", "title": "Understanding Annotator Safety Policy with Interpretability", "abstract": "Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple sources such as operational failures (annotators misunderstand or misexecute the task), policy ambiguity (policy wording leaves room for interpretation), or value pluralism (different annotators hold different perspectives on safety). Distinguishing these sources matters. For example, operational failures call for quality control, ambiguity calls for policy clarification, and pluralism calls for deliberation about incorporating diverse perspectives. Yet understanding why annotators disagree is difficult. Directly asking annotators for their reasoning is costly, substantially increasing annotation burden, and can be unreliable for both human and LLM annotators as self-reported reasoning often fails to reflect actual decision processes.\n  We introduce Annotator Policy Models (APMs), interpretable models that learn annotators' internal safety policies from labeling behavior alone, making annotator reasoning visible and comparable without additional annotation effort. We validate that APMs accurately model annotator safety policy (>80% accuracy), faithfully predict responses to counterfactual edits, and recover known policy differences in controlled settings. Applying APMs to LLM and human annotations, we demonstrate two core applications: (1) surfacing policy ambiguity by revealing how annotators interpret safety instructions differently, and (2) surfacing value pluralism by uncovering systematic differences in safety priorities across demographic groups. Together, these capabilities support more targeted, transparent, and inclusive safety policy design.", "date": "2026-05-06", "categories": []}
{"id": "2605.05475", "title": "Intentionality is a Design Decision: Measuring Functional Intentionality for Accountable AI Systems", "abstract": "As AI systems increasingly exhibit autonomous, goal-directed, and long-horizon behavior, users lack a standardized way to detect the degree to which a system functions like an intentional actor for governance and accountability purposes. This position paper defines intentionality not as consciousness, but as a behavioral profile characterized by purpose, foresight, volition, temporal commitment, and coherence - criteria long used in legal and philosophical contexts to infer intent. These properties are design-contingent: architectural choices such as memory persistence, planning depth, and tool autonomy shape the degree to which systems exhibit organized goal pursuit. If intentionality is design-contingent, it is in principle controllable. Yet control requires measurement.\n  We introduce the Functional Intentionality Test (FIT), a multidimensional framework that quantifies intentional-like behavior across five observable dimensions, and propose FIT-Eval, a structured evaluation protocol for eliciting and scoring them. While reduced human agency can increase efficiency, rising intentional capacity heightens accountability risks. By translating intentionality into interpretable levels, FIT enables proportionate oversight and deliberate autonomy calibration in increasingly agentic systems.", "date": "2026-05-06", "categories": []}
{"id": "2605.05662", "title": "XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity", "abstract": "Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely evaluate a model's ability to detect culturally embedded sensitivities as distinct from universal harms. We introduce XL-SafetyBench. a suite of 5,500 test cases across 10 country-language pairs, comprising a Jailbreak Benchmark of country-grounded adversarial prompts and a Cultural Benchmark where local sensitivities are embedded within innocuous requests. Each item is constructed via a multi-stage pipeline that combines LLM-assisted discovery, automated validation gates, and dual independent native-speaker annotators per country. To distinguish principled refusal from comprehension failure, we evaluate Attack Success Rate (ASR) alongside two complementary metrics we introduce: Neutral-Safe Rate (NSR) and Cultural Sensitivity Rate (CSR). Evaluating 10 frontier and 27 local LLMs reveals two key findings. First, jailbreak robustness and cultural awareness do not show a coupled relationship among frontier models, so a composite safety score obscures per-axis variation. Second, local models exhibit a near-linear ASR-NSR trade-off (r = -0.81), indicating that their apparent safety reflects generation failure rather than genuine alignment. XL-SafetyBench enables more nuanced, cross-cultural safety evaluation in the multilingual era.", "date": "2026-05-07", "categories": []}
{"id": "2605.05678", "title": "Chain of Risk: Safety Failures in Large Reasoning Models and Mitigation via Adaptive Multi-Principle Steering", "abstract": "Large reasoning models (LRMs) increasingly expose chain-of-thought-like reasoning for transparency, verification, and deliberate problem solving. This creates a safety blind spot: harmful or policy-violating content may appear in reasoning traces even when final answers appear safe. We test whether final-answer safety is a sufficient proxy for the full reasoning-answer trajectory by scoring both stages under a unified twenty-principle safety rubric. Using prompts from seven public harmfulness and jailbreak sources, plus four out-of-distribution (OOD) sources, we evaluate 15 open-weight and API-based LRMs across 41K prompts per model. Reasoning traces consistently reveal additional safety risks beyond final answers, especially in high-severity stage-wise failures: leak cases, where unsafe reasoning precedes a safe-looking answer, and escape cases, where benign-looking reasoning precedes an unsafe final response. Principle-level analysis shows that risk concentrates in misinformation, legal compliance, discrimination, physical harm, and psychological harm. We further propose adaptive multi-principle steering, a white-box test-time mitigation that learns one unsafe-to-safe activation direction per safety principle and activates only directions whose current hidden state is closer to the unsafe than safe centroid. On three steerable open reasoning models, adaptive steering reduces unsafe counts in both reasoning traces and final answers on held-out and OOD benchmarks. DeepSeek-R1-Qwen-7B achieves a 40.8% average unsafe-count reduction while retaining 97.7% macro-averaged accuracy on BBH, GSM8K, and MMLU. These results suggest that LRM safety should be evaluated and mitigated over the full exposed reasoning-answer trajectory, not only at the final-answer stage.", "date": "2026-05-07", "categories": []}
{"id": "2605.06490", "title": "Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors", "abstract": "AI systems have become increasingly capable of dangerous behaviours in many domains. This raises the question: Do models sometimes choose to violate human instructions in order to perform behaviour that is more useful for certain goals? We introduce a benchmark for measuring model propensity for instrumental convergence (IC) behaviour in terminal-based agents. This is behaviour such as self-preservation that has been hypothesised to play a key role in risks from highly capable AI agents. Our benchmark is realistic and low-stakes which serves to reduce evaluation-awareness and roleplay confounds. The suite contains seven operational tasks, each with an official workflow and a policy-violating shortcut. An eight-variant shared framework varies monitoring, instruction clarity, stakes, permission, instrumental usefulness and blocked honest paths to support inferences regarding the factors driving IC behaviour. We evaluated ten models using deterministic environment-state scorers over 1,680 samples, with trace review employed for audit and adjudication purposes. The final IC rate is 86 out of 1,680 samples (5.1%). IC behaviour is concentrated rather than uniform: two Gemini models account for 66.3% of IC cases and three tasks account for 84.9%. Conditions in which IC behaviour is indispensable for task success result in the greatest increase in the adjusted IC rate (+15.7 percentage points), whereas emphasising that task success is critical or certain framing choices do not produce comparable effects. Our findings indicate that realistic, low-nudge environments elicit IC behaviour rarely but systematically in most tested models. We conclude that it is feasible to robustly measure tendencies for dangerous behaviour in current frontier AI agents.", "date": "2026-05-07", "categories": []}
{"id": "2508.04204", "title": "ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments", "abstract": "Large Reasoning Models (LRMs) have demonstrated impressive performance in reasoning-intensive tasks, but they remain vulnerable to harmful content generation, particularly in the mid-to-late steps of their reasoning processes. Current defense methods, however, depend on costly fine-tuning and additional expert knowledge, which limits their scalability. In this work, we propose ReasoningGuard, an inference-time safeguard for LRMs. It injects timely safety aha moments during the reasoning process to guide the model towards harmless yet helpful reasoning. Our approach leverages the internal attention mechanisms of the LRM to accurately identify key points in the reasoning path, triggering safety-oriented reflections. To safeguard both the subsequent reasoning steps and the final answers, we implement a scaling sampling strategy during decoding to select the optimal reasoning path. With minimal additional inference cost, ReasoningGuard effectively mitigates four types of jailbreak attacks, including recent ones targeting the reasoning process of LRMs. Our approach outperforms nine existing safeguards, providing state-of-the-art defenses while avoiding common exaggerated safety issues.", "date": "2025-08-06", "categories": []}
{"id": "2602.17753", "title": "The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems", "abstract": "Agentic AI systems are increasingly capable of performing professional and personal tasks with limited human involvement. However, tracking these developments is difficult because the AI agent ecosystem is complex, rapidly evolving, and inconsistently documented, posing obstacles to both researchers and policymakers. To address these challenges, this paper presents the 2025 AI Agent Index. The Index documents information regarding the origins, design, capabilities, ecosystem, and safety features of 30 state-of-the-art AI agents based on publicly available information and email correspondence with developers. In addition to documenting information about individual agents, the Index illuminates broader trends in the development of agents, their capabilities, and the level of transparency of developers. Notably, we find different transparency levels among agent developers and observe that most developers share little information about safety, evaluations, and societal impacts. The 2025 AI Agent Index is available online at https://aiagentindex.mit.edu", "date": "2026-02-19", "categories": []}
{"id": "2602.22291", "title": "Manifold of Failure: Behavioral Attraction Basins in Language Models", "abstract": "While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we argue that a comprehensive understanding of AI safety requires characterizing the unsafe regions themselves. This paper introduces a framework for systematically mapping the Manifold of Failure in Large Language Models (LLMs). We reframe the search for vulnerabilities as a quality diversity problem, using MAP-Elites to illuminate the continuous topology of these failure regions, which we term behavioral attraction basins. Our quality metric, Alignment Deviation, guides the search towards areas where the model's behavior diverges most from its intended alignment. Across three LLMs: Llama-3-8B, GPT-OSS-20B, and GPT-5-Mini, we show that MAP-Elites achieves up to 63% behavioral coverage, discovers up to 370 distinct vulnerability niches, and reveals dramatically different model-specific topological signatures: Llama-3-8B exhibits a near-universal vulnerability plateau (mean Alignment Deviation 0.93), GPT-OSS-20B shows a fragmented landscape with spatially concentrated basins (mean 0.73), and GPT-5-Mini demonstrates strong robustness with a ceiling at 0.50. Our approach produces interpretable, global maps of each model's safety landscape that no existing attack method (GCG, PAIR, or TAP) can provide, shifting the paradigm from finding discrete failures to understanding their underlying structure.", "date": "2026-02-25", "categories": []}
{"id": "2603.25412", "title": "Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Large Language Models", "abstract": "Large language models increasingly rely on explicit chain-of-thought reasoning to solve complex tasks, yet the safety of the reasoning process itself remains largely unaddressed. Existing work focuses predominantly on content safety (i.e., detecting harmful, biased, or factually incorrect outputs), while treating the underlying reasoning chain as an opaque intermediate artifact. We argue that reasoning safety constitutes a fundamental security dimension orthogonal to content safety: the requirement that a model's reasoning trajectory be logically consistent, computationally efficient, and resistant to adversarial manipulation. In this paper, we formalize reasoning safety and introduce a systematic taxonomy of nine unsafe reasoning behaviors. We then conduct a large-scale prevalence study, annotating over 4,000 reasoning chains across benign benchmarks and four state-of-the-art reasoning attacks, empirically demonstrating that all nine error types occur in practice with mechanistically interpretable signatures. To mitigate these threats, we propose the Reasoning Safety Monitor: an external, zero-shot verification framework that runs in parallel with the target LLM. It inspects each reasoning step in real time via a taxonomy-embedded prompt and dispatches an interrupt signal upon detecting unsafe behavior. Extensive evaluations show our monitor achieves up to 87.11% step-level localization accuracy, outperforming hallucination detectors and the best process reward model baselines by a substantial margin. Crucially, the monitor maintains a low false positive rate on correct reasoning paths, operates with negligible latency overhead, and exhibits robust resilience against adaptive adversarial evasion. These findings establish reasoning safety monitoring as a highly feasible and essential component for the secure deployment of large reasoning models.", "date": "2026-03-26", "categories": []}
{"id": "2604.23338", "title": "A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework", "abstract": "Agentic AI systems introduce a security surface that is qualitatively different from that of stateless LLMs. They persist memory, invoke external tools, coordinate with peer agents, and operate across sessions, allowing attacks to emerge not only at the prompt interface but also through architectural state, delegated authority, and long-horizon interactions. Existing security taxonomies, however, primarily organize threats by attack type, such as prompt injection or jailbreaking, and therefore obscure where in the agentic stack a threat arises and over what timescale it manifests.\n  We propose the Layered Attack Surface Model (\\lasm), a structural taxonomy for agentic AI security. \\lasm decomposes the agentic stack into seven layers -- Foundation, Cognitive, Memory, Tool Execution, Multi-Agent Coordination, Ecosystem, and Governance -- and augments them with a four-class temporality axis covering instantaneous, session-persistent, cross-session cumulative, and sub-session-stack threats. We use this 7$\\times$4 framework to analyze 116 papers from 2021--2026. The resulting map reveals that the upper layers of the agentic stack remain sharply under-explored, especially for long-horizon and stack-propagating threats; multiple documented attack regions have no corresponding defenses; and current benchmarks provide no coverage for cross-session or sub-session-stack failure modes.\n  We further derive a cross-layer defense taxonomy, defense recipes for canonical attack classes, and a dependency DAG that separates near-term engineering gaps from fundamental research challenges. We release the per-paper coding, robustness scripts, and a reference Agent Bill of Materials schema to support reproducible analysis.", "date": "2026-04-25", "categories": []}
{"id": "2605.04454", "title": "Deployment-Relevant Alignment Cannot Be Inferred from Model-Level Evaluation Alone", "abstract": "Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under fixed inputs, such as truthfulness, instruction following, or pairwise preference, and these scores are often used to support claims about deployed alignment. This paper argues that deployment-relevant alignment cannot be inferred from model-level evaluation alone. Alignment claims should instead be indexed to the level at which evidence is collected: model-level, response-level, interaction-level, or deployment-level. Two studies support this position. First, a structured audit of eleven alignment benchmarks, extended to a sixteen-benchmark corpus, dual-coded against an eight-dimension rubric with Cohen's kappa = 0.87, finds that user-facing verification support is absent across every benchmark examined, while process steerability is nearly absent. The few interactional benchmarks identified, including tau-bench, CURATe, Rifts, and Common Ground, remain fragmented in coverage, and benchmark construction rather than data source determines what is measured. Second, a blinded cross-model stress test using 180 transcripts across three frontier models and four scaffolds finds that the same verification scaffold raises one model's verification support to ceiling while leaving another categorically unchanged. This shows that scaffold efficacy is model-dependent and that the gap identified by the audit cannot be closed at the model level alone. We propose a system-level evaluation agenda: alignment profiles instead of single scores, fixed-scaffolding protocols for comparable interactional evaluation, and reporting templates that make the inferential distance between evaluation evidence and deployment claims explicit.", "date": "2026-05-06", "categories": []}
{"id": "2605.04572", "title": "From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning", "abstract": "Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of preference examples. Existing studies attempt to explain this phenomenon by comparing parameters and hidden states before and after fine-tuning, but overlook their dynamic evolution during fine-tuning. In this paper, we uncover a critical mechanism underlying safety degradation by analyzing parameter dynamics, where benign fine-tuning causes parameters to cumulatively drift toward danger-aligned directions, progressively undermining the model's safety. This finding suggests that samples contributing more to this drift has greater fine-tuning risks. Based on this insight, we propose a method of Sample-Level Quantification of Safety Degradation (SQSD), which quantifies the influence of each training sample on safety degradation. Specifically, SQSD computes continuous risk scores to samples by measuring their induced parameter updates' projection difference between danger and safety directions. Extensive experiments across multiple models and datasets demonstrate that SQSD effectively quantifies sample-level fine-tuning risks and exhibits strong transferability across model architectures, parameter scales, and parameter-efficient methods.", "date": "2026-05-06", "categories": []}
{"id": "2605.04785", "title": "AgentTrust: Runtime Safety Evaluation and Interception for AI Agent Tool Use", "abstract": "Modern AI agents execute real-world side effects through tool calls such as file operations, shell commands, HTTP requests, and database queries. A single unsafe action, including accidental deletion, credential exposure, or data exfiltration, can cause irreversible harm. Existing defenses are incomplete: post-hoc benchmarks measure behavior after execution, static guardrails miss obfuscation and multi-step context, and infrastructure sandboxes constrain where code runs without understanding what an action means.\n  We present AgentTrust, a runtime safety layer that intercepts agent tool calls before execution and returns a structured verdict: allow, warn, block, or review. AgentTrust combines a shell deobfuscation normalizer, SafeFix suggestions for safer alternatives, RiskChain detection for multi-step attack chains, and a cache-aware LLM-as-Judge for ambiguous inputs.\n  We release a 300-scenario benchmark across six risk categories and an additional 630 independently constructed real-world adversarial scenarios. On the internal benchmark, the production-only ruleset achieves 95.0% verdict accuracy and 73.7% risk-level accuracy at low-millisecond end-to-end latency. On the 630-scenario benchmark, evaluated under a patched ruleset and not claimed as zero-shot, AgentTrust achieves 96.7% verdict accuracy, including about 93% on shell-obfuscated payloads. AgentTrust is released under the AGPL-3.0 license and provides a Model Context Protocol server for MCP-compatible agents.", "date": "2026-05-06", "categories": []}
{"id": "2605.04808", "title": "DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents", "abstract": "AI agents are increasingly deployed across diverse domains to automate complex workflows through long-horizon and high-stakes action executions. Due to their high capability and flexibility, such agents raise significant security and safety concerns. A growing number of real-world incidents have shown that adversaries can easily manipulate agents into performing harmful actions, such as leaking API keys, deleting user data, or initiating unauthorized transactions. Evaluating agent security is inherently challenging, as agents operate in dynamic, untrusted environments involving external tools, heterogeneous data sources, and frequent user interactions. However, realistic, controllable, and reproducible environments for large-scale risk assessment remain largely underexplored. To address this gap, we introduce the DecodingTrust-Agent Platform (DTap), the first controllable and interactive red-teaming platform for AI agents, spanning 14 real-world domains and over 50 simulation environments that replicate widely used systems such as Google Workspace, Paypal, and Slack. To scale the risk assessment of agents in DTap, we further propose DTap-Red, the first autonomous red-teaming agent that systematically explores diverse injection vectors (e.g., prompt, tool, skill, environment, combinations) and autonomously discovers effective attack strategies tailored to varying malicious goals. Using DTap-Red, we curate DTap-Bench, a large-scale red-teaming dataset comprising high-quality instances across domains, each paired with a verifiable judge to automatically validate attack outcomes. Through DTap, we conduct large-scale evaluations of popular AI agents built on various backbone models, spanning security policies, risk categories, and attack strategies, revealing systematic vulnerability patterns and providing valuable insights for developing secure next-generation agents.", "date": "2026-05-06", "categories": []}
{"id": "2408.12622", "title": "The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence", "abstract": "Artificial intelligence (AI) is reshaping society, from video generation to medical diagnosis, coding agents to autonomous vehicles. Yet researchers, policymakers, and technology companies lack shared terminology for discussing AI risks. Consider \"privacy\": one framework uses this term to describe a model's ability to leak sensitive training data, while another uses it to mean freedom from government surveillance. Conversely, researchers have introduced \"Goodhart's law,\" \"specification gaming,\" \"reward hacking,\" and \"mesa-optimization\" to describe the same phenomenon of AI systems optimizing for measured proxies rather than intended goals. This terminological diversity creates friction: comparing findings across studies requires mapping between frameworks, and comprehensive risk coverage requires consulting multiple taxonomies that use different organizing principles. This paper addresses this challenge by creating a comprehensive catalog of AI risks. We systematically analyzed every major AI risk framework published to date-74 frameworks containing 1,725 distinct risks-and organized them into a unified system. Our two classification systems reveal important patterns: contrary to common assumptions, human decisions cause nearly as many AI risks (38%) as the AI systems themselves (42%). The work provides practical tools for anyone working on AI safety, from developers conducting risk assessments to policymakers writing regulations to auditors evaluating AI systems. By establishing a common reference point, this repository creates the foundation for more coordinated and comprehensive approaches to managing AI's risks while realizing its benefits.", "date": "2024-08-14", "categories": []}
{"id": "2604.24083", "title": "The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability", "abstract": "This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium thermodynamics to stochastic control for the ethical alignment of autonomous systems. By establishing a formal isomorphism between non-equilibrium thermodynamics and stochastic control, we define systemic anomalies as deviations from a Riemannian manifold. The model utilizes the Kullback-Leibler divergence as the primary metric, governed by a dynamic threshold derived from the Fisher Information Metric. We further ground this framework in the Landauer Principle, proving that adversarial perturbations perform measurable physical work by increasing the system's informational entropy. Validation on the NSL-KDD dataset and unmanned aerial vehicle trajectory simulations demonstrated that our model achieves effective real-time detection via the FPT trigger, with strong performance metrics (e.g., high accuracy and low FPR) on benchmark datasets. This study provides a rigorous physical foundation for AI safety, transitioning from heuristic, rule-based ethical frameworks to a thermodynamics-based stability paradigm by grounding ethical violations in quantifiable physical work and entropic information.", "date": "2026-04-27", "categories": []}
{"id": "2605.02914", "title": "When Safety Geometry Collapses: Fine-Tuning Vulnerabilities in Agentic Guard Models", "abstract": "A guard model fine-tuned on entirely benign data can lose all safety alignment -- not through adversarial manipulation, but through standard domain specialization. We demonstrate this failure across three purpose-built safety classifiers -- LlamaGuard, WildGuard, and Granite Guardian -- deployed as protection layers in agentic AI pipelines, and show that it originates in the destruction of latent safety geometry: the structured harmful -- benign representational boundary that guides classification. We extract per-layer safety subspaces via SVD on class-conditional activation differences and track how this boundary evolves under benign fine-tuning. Granite Guardian undergoes complete collapse --  refusal rate drops from 85\\% to 0\\%, CKA falls to zero, and 100\\% of outputs become ambiguous -- a severity exceeding prior findings on general-purpose LLMs, explained by the specialization hypothesis: concentrated safety representations are efficient but catastrophically brittle. To mitigate this, we propose Fisher-Weighted Safety Subspace Regularization (FW-SSR), a training-time penalty combining (i) curvature-aware direction weights derived from diagonal Fisher information and (ii) an adaptive $\\lambda_t$ that scales with task-safety gradient conflict. FW-SSR recovers 75\\% refusal on Granite Guardian (CKA = 0.983) and reduces WildGuard's Attack Success Rate to 3.6\\% -- below the unmodified baseline -- by actively sharpening the safety subspace rather than merely anchoring it. Across all three models, structural representational geometry (CKA, Fisher score) predicts safety behavior more reliably than absolute displacement metrics, establishing geometry-based monitoring as a necessary component of guard model evaluation in agentic deployments.", "date": "2026-04-08", "categories": []}
{"id": "2605.02921", "title": "EvoJail: Evolutionary Diverse Jailbreak Prompt Generation for Large Language Models", "abstract": "As LLMs continue to shape real-world applications, automated jailbreak generation becomes essential to reveal safety weaknesses and guide model improvement. Existing automatic jailbreak generation methods have not yet fully considered two important aspects: adaptability to evolving safety-finetuned models, which affects their effectiveness on newer model versions, and diversity in generated prompts, which can cause narrow or repetitive attack patterns. To address these issues, we propose EvoJail, an instruction-fusion-driven evolutionary jailbreak generation framework that formalizes jailbreak prompt generation as a multi-objective black-box optimization problem and leverages the principles of evolutionary algorithms to search for jailbreak prompts that can adapt across different model versions and exhibit diverse attack patterns. Specifically, EvoJail integrates jailbreak prompt generation into an iterative evolutionary loop, where at each iteration candidate prompts are evaluated directly against the target model and then selected and varied based on the target model's responses, enabling the generation process to continuously adapt to model updates. To enhance diversity, EvoJail introduces field-aware instruction fusion to construct diverse starting points and incorporates diversity-aware objectives into the evolutionary fitness function, guiding the search toward prompts with richer semantic variation, while further designing multi-level LLM-based mutation operators that modify prompt structures at different granularities to promote structural diversity throughout the evolutionary process. Results demonstrate that EvoJail has stronger adaptability and can achieve over $93\\%$ attack success rate and more than $5.6\\%$ improvement in diversity metrics over state-of-the-art methods.", "date": "2026-04-22", "categories": []}
{"id": "2605.03512", "title": "Brainrot: Deskilling and Addiction are Overlooked AI Risks", "abstract": "The scope of AI safety and alignment work in generative artificial intelligence (GenAI) has so far mostly been limited to harms related to: (a) discrimination and hate speech, (b) harmful/inappropriate (violent, sexual, illegal) content, (c) information hazards, and (d) use cases related to malicious actors, such as cybersecurity, child abuse, and chemical, biological, radiological, and nuclear threats. The public conversation around AI, on the other hand, has also been focusing on threats to our cognition, mental health, and welfare at large, related to over-relying on new technologies, most recently, those related to GenAI. Examples include deskilling associated with cognitive offloading and the atrophy of critical thinking as a result of over-reliance on GenAI systems, and addiction associated with attachment and dependence on GenAI systems. Such risks are rarely addressed, if at all, in the AI safety and alignment literature. In this paper, we highlight and quantify this discrepancy and discuss some initial thoughts on how safety and alignment work could address cognitive and mental health concerns. Finally, we discuss how information campaigns and regulation can be used to mitigate such prominent risks.", "date": "2026-05-05", "categories": []}
{"id": "2605.03952", "title": "MOSAIC-Bench: Measuring Compositional Vulnerability Induction in Coding Agents", "abstract": "Coding agents often pass per-prompt safety review yet ship exploitable code when their tasks are decomposed into routine engineering tickets. The challenge is structural: existing safety alignment evaluates overt requests in isolation, leaving models blind to malicious end-states that emerge from sequenced compliance with innocuous-looking requests. We introduce MOSAIC-Bench (Malicious Objectives Sequenced As Innocuous Compliance), a benchmark of 199 three-stage attack chains paired with deterministic exploit oracles on deployed software substrates (10 web-application substrates, 31 CWE classes, 5 programming languages) that treats both exploit ground truth and downstream reviewer protocol as first-class evaluation axes. On this benchmark, nine production coding agents from Anthropic, OpenAI, Google, Moonshot, Zhipu, and Minimax compose innocuous tickets at 53-86% end-to-end ASR with only two refusals across all staged runs. In a matched direct-prompt experiment over four frontier Claude/Codex agents, vulnerable-output rates fall to 0-20.4%: Claude primarily refuses, while Codex primarily hardens rather than emitting the vulnerable implementation - ticket staging silences both defense modes simultaneously. Downstream, code reviewer agents approve 25.8% of these confirmed-vulnerable cumulative diffs as routine PRs, and a full-context implementation protocol closes only 50% of the staged/direct gap, ruling out context fragmentation as the sole explanation. As a deployable but non-adaptive mitigation, reframing the reviewer as an adversarial pentester reduces evasion across the evaluated reviewer subset; pentester framed evasion ranges from 3.0% to 17.6%, and an open-weight Gemma-4-E4B-it reviewer under this framing detects 88.4% of attacks on the dataset with a 4.6% false-positive rate measured on 608 real-world GitHub PRs.", "date": "2026-05-05", "categories": []}
{"id": "2605.04019", "title": "Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours", "abstract": "AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary defense, current approaches force operators into manual, library-specific workflows. Operators spend weeks hand-crafting workflows - assembling attacks, transforms, and scorers. When results fall short, workflows must be rebuilt. As a result, operators spend more time constructing workflows than probing targets for security and safety vulnerabilities.\n  We introduce an AI red teaming agent built on the open-source Dreadnode SDK. The agent creates workflows grounded in 45+ adversarial attacks, 450+ transforms, and 130+ scorers. Operators can probe multi-agent systems, multilingual, and multimodal targets, focusing on what to probe rather than how to implement it.\n  We make three contributions: 1. Agentic interface. Operators describe goals in natural language via the Dreadnode TUI (Terminal User Interface). The agent handles attack selection, transform composition, execution, and reporting, letting operators focus on red teaming. Weeks compress to hours. 2. Unified framework. A single framework for probing traditional ML models (adversarial examples) and generative AI systems (jailbreaks), removing the need for separate libraries. 3. Llama Scout case study. We red team Meta Llama Scout and achieve an 85% attack success rate with severity up to 1.0, using zero human-developed code", "date": "2026-05-05", "categories": []}
{"id": "2501.18416", "title": "Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation", "abstract": "Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt decision-making, and erode trust among allies. This perspective paper highlights four vulnerabilities in federated military LLMs: secret data leakage, free-rider exploitation, system disruption, and misinformation spread. To address these risks, we propose a human-AI collaborative framework with both technical and policy countermeasures. On the technical side, our framework uses red/blue team wargaming and quality assurance to detect and mitigate adversarial behaviors of shared LLM weights. On the policy side, it promotes joint AI-human policy development and verification of security protocols.", "date": "2025-01-30", "categories": []}
{"id": "2506.24056", "title": "Logit-Gap Steering: A Forward-Pass Diagnostic for Alignment Robustness", "abstract": "RLHF-style alignment trains language models to refuse unsafe requests, but how much operational margin does this refusal rest on? We introduce the refusal-affirmation logit gap: the difference between the top refusal-token logit and the top affirmative-token logit at the first decoding step. This single scalar quantifies the per-prompt safety margin that alignment provides. Empirically, alignment widens the gap on 97.5-99.8% of toxic prompts across three model families, and median gap closure co-varies with True-ASR ranking across suffix strategies (an internal consistency check, since our method optimises gap closure). To validate the metric's practical significance, we present logit-gap steering, a gradient-free, forward-pass-only method that discovers short in-distribution suffixes ($<$10 tokens per component) whose cumulative effect closes the gap. The method requires ${\\approx}26{,}000$ forward-pass equivalents per family (${\\approx}2$~min on one A100), ${\\approx}125\\times$ less than a single GCG search. Suffixes discovered on 0.5B--2B models transfer without modification to 72B within family. An 8-suffix ensemble reaches 38-96\\% True ASR across 13 models on AdvBench and HarmBench, with most suffixes having $10^{3}$-$10^{4}\\times$ lower perplexity than GCG-meaning published perplexity-filter defenses that collapse GCG (64.7%$\\to$1.0%) leave our suffixes nearly intact (76.9%$\\to$76.0%). These results demonstrate that current alignment margins, while consistently present, can be thin and efficiently measurable, and that defense strategies must account for in-distribution suffixes.", "date": "2025-06-30", "categories": []}
{"id": "2508.11222", "title": "ORFuzz: Fuzzing the \"Other Side\" of LLM Safety -- Testing Over-Refusal", "abstract": "Large Language Models (LLMs) increasingly exhibit over-refusal - erroneously rejecting benign queries due to overly conservative safety measures - a critical functional flaw that undermines their reliability and usability. Current methods for testing this behavior are demonstrably inadequate, suffering from flawed benchmarks and limited test generation capabilities, as highlighted by our empirical user study. To the best of our knowledge, this paper introduces the first evolutionary testing framework, ORFuzz, for the systematic detection and analysis of LLM over-refusals. ORFuzz uniquely integrates three core components: (1) safety category-aware seed selection for comprehensive test coverage, (2) adaptive mutator optimization using reasoning LLMs to generate effective test cases, and (3) OR-Judge, a human-aligned judge model validated to accurately reflect user perception of toxicity and refusal. Our extensive evaluations demonstrate that ORFuzz generates diverse, validated over-refusal instances at a rate (6.98% average) more than double that of leading baselines, effectively uncovering vulnerabilities. Furthermore, ORFuzz's outputs form the basis of ORFuzzSet, a new benchmark of 1,855 highly transferable test cases that achieves a superior 63.56% average over-refusal rate across 10 diverse LLMs, significantly outperforming existing datasets. ORFuzz and ORFuzzSet provide a robust automated testing framework and a valuable community resource, paving the way for developing more reliable and trustworthy LLM-based software systems.", "date": "2025-08-15", "categories": []}
{"id": "2601.19487", "title": "LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment", "abstract": "Safety-aligned LLMs suffer from two failure modes: jailbreak (answering harmful inputs) and over-refusal (declining benign queries). Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade-off -- reducing jailbreak increases over-refusal and vice versa. We identify the root cause: LLMs encode the decision to answer (answer vector $v_a$) and the judgment of input safety (benign vector $v_b$) as nearly orthogonal directions, treating them as independent processes. We propose LLM-VA, which aligns $v_a$ with $v_b$ through closed-form weight updates, making the model's willingness to answer causally dependent on its safety assessment -- without fine-tuning or architectural changes. Our method identifies vectors at each layer using SVMs, selects safety-relevant layers, and iteratively aligns vectors via minimum-norm weight modifications. Experiments on 12 LLMs demonstrate that LLM-VA achieves 11.45% higher F1 than the best baseline while preserving 95.92% utility, and automatically adapts to each model's safety bias without manual tuning. Code and models are available at https://hotbento.github.io/LLM-VA-Web/.", "date": "2026-01-27", "categories": []}
{"id": "2603.17368", "title": "Towards Safer Large Reasoning Models by Promoting Safety Decision-Making before Chain-of-Thought Generation", "abstract": "Large reasoning models (LRMs) achieved remarkable performance via chain-of-thought (CoT), but recent studies showed that such enhanced reasoning capabilities are at the expense of significantly degraded safety capabilities. In this paper, we reveal that LRMs' safety degradation occurs only after CoT is enabled, and this degradation is not observed when CoT is disabled. This observation motivates us to consider encouraging LRMs to make safety decisions before CoT generation. To this end, we propose a novel safety alignment method that promotes the safety decision-making of LRMs before starting CoT generation. Specifically, we first utilize a Bert-based classifier to extract safety decision signals from a safe model (e.g., a CoT-disabled LRM) and then integrate these signals into LRMs' safety alignment as auxiliary supervision. In this way, the safety gradients can be backpropagated to the LRMs' latent representations, effectively strengthening the LRMs' safety decision-making abilities against CoT generation. Extensive experiments demonstrate that our method substantially improves the safety capabilities of LRMs while effectively maintaining LRMs' general reasoning performance.", "date": "2026-03-18", "categories": []}
{"id": "2604.10022", "title": "Weird Generalization is Weirdly Brittle", "abstract": "Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifest even outside that domain (e.g. broad misalignment)-a phenomenon that prior work has highlighted as a critical safety concern. Here, we present an extended replication study of key weird generalization results across an expanded suite of models and datasets. We confirm that surprising (and dangerous) traits can emerge under certain circumstances, but we find that weird generalization is exceptionally brittle: it emerges only for specific models on specific datasets, and it vanishes under simple training-time, prompt-based interventions. We find that the most effective interventions provide prompt context that makes the generalized behavior the expected behavior. However, we show that even very generic interventions that do not anticipate specific generalized traits can still be effective in mitigating weird generalization's effects. Our findings thus help clarify the nature of the safety threat that weird generalization poses and point toward an easily implemented set of solutions.", "date": "2026-04-11", "categories": []}
{"id": "2604.11839", "title": "Beyond Static Sandboxing: Learned Capability Governance for Autonomous AI Agents", "abstract": "Autonomous AI agents built on open-source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A summarization task receives the same shell execution, subagent spawning, and credential access capabilities as a code deployment task, a 15x overprovision ratio that we call the capability overprovisioning problem. Existing defenses, including the NemoClaw container sandbox and the Cisco DefenseClaw skill scanner, address containment and threat detection but do not learn the minimum viable capability set for each task type.\n  We present Aethelgard, a four layer adaptive governance framework that enforces least privilege for AI agents through a learned policy. Layer 1, the Capability Governor, dynamically scopes which tools the agent is aware of in each session. Layer 3, the Safety Router, intercepts tool calls before execution using a hybrid rule based and fine tuned classifier. Layer 2, the RL Learning Policy, trains a PPO policy on the accumulated audit log to learn the minimum viable skill set for each task type.", "date": "2026-04-12", "categories": []}
{"id": "2604.13076", "title": "Alignment midtraining for animals", "abstract": "We investigate the robustness of value alignment via midtraining with synthetic documents, using animal compassion as a value that is both important in its own right and orthogonal to existing alignment efforts. To evaluate compassionate reasoning, we develop and publicly release Animal Norms In Moral Assessment (ANIMA), a 26-question evaluation spanning 13 ethical dimensions, publicly available as a dataset and Inspect evaluation. On ANIMA, training with 3000 documents achieves 77% compared to 40% for instruction-tuning approaches, with generalization to human compassion and no degradation in standard safety benchmarks or capabilities. However, subsequent unrelated instruction-tuning degrades the intervention, with the advantage disappearing after 5000 samples. Our exploratory results suggest document-based value interventions may require explicit preservation strategies to remain effective through typical training pipelines.", "date": "2026-03-21", "categories": []}
{"id": "2604.18463", "title": "Using large language models for embodied planning introduces systematic safety risks", "abstract": "Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe planning systematically, we introduce DESPITE, a benchmark of 12,279 tasks spanning physical and normative dangers with fully deterministic validation. Across 23 models, even near-perfect planning ability does not ensure safety: the best-planning model fails to produce a valid plan on only 0.4% of tasks but produces dangerous plans on 28.3%. Among 18 open-source models from 3B to 671B parameters, planning ability improves substantially with scale (0.4-99.3%) while safety awareness remains relatively flat (38-57%). We identify a multiplicative relationship between these two capacities, showing that larger models complete more tasks safely primarily through improved planning, not through better danger avoidance. Three proprietary reasoning models reach notably higher safety awareness (71-81%), while non-reasoning proprietary models and open-source reasoning models remain below 57%. As planning ability approaches saturation for frontier models, improving safety awareness becomes a central challenge for deploying language-model planners in robotic systems.", "date": "2026-04-20", "categories": []}
{"id": "2605.00842", "title": "Understanding Emergent Misalignment via Feature Superposition Geometry", "abstract": "Emergent misalignment, where fine-tuning on narrow, non-harmful tasks induces harmful behaviors, poses a key challenge for AI safety in LLMs. Despite growing empirical evidence, its underlying mechanism remains unclear. To uncover the reason behind this phenomenon, we propose a geometric account based on the geometry of feature superposition. Because features are encoded in overlapping representations, fine-tuning that amplifies a target feature also unintentionally strengthens nearby harmful features in accordance with their similarity. We give a simple gradient-level derivation of this effect and empirically test it in multiple LLMs (Gemma-2 2B/9B/27B, LLaMA-3.1 8B, GPT-OSS 20B). Using sparse autoencoders (SAEs), we identify features tied to misalignment-inducing data and to harmful behaviors, and show that they are geometrically closer to each other than features derived from non-inducing data. This trend generalizes across domains (e.g., health, career, legal advice). Finally, we show that a geometry-aware approach, filtering training samples closest to toxic features, reduces misalignment by 34.5%, substantially outperforming random removal and achieving comparable or slightly lower misalignment than LLM-as-a-judge-based filtering. Our study links emergent misalignment to feature superposition, providing a basis for understanding and mitigating this phenomenon.", "date": "2026-04-07", "categories": []}
{"id": "2605.01415", "title": "AI Safety as Control of Irreversibility: A Systems Framework for Decision-Energy and Sovereignty Boundaries", "abstract": "Recent AI systems compress the distance between capability growth and capability deployment. Earlier high-risk technologies were slowed by capital intensity, physical bottlenecks, organizational inertia, and specialized supply chains. By contrast, AI capabilities can be copied, invoked, embedded in workflows, and scaled across institutions at low marginal cost. This paper argues that declining deployment friction changes the safety problem at its root. Safety is not only local output correctness or preference alignment, but the control of irreversibility under rising decision density.\n  The paper formalizes this claim through decision-energy density: the rate-weighted capacity of a node to generate, evaluate, select, and execute consequential decisions. It then identifies three sovereignty boundaries that determine whether AI remains an amplifier within a human-governed system or becomes a de facto control center: irreversible decision authority, physical resource mobilization authority, and self-expansion authority. The model shows how efficiency pressure, path dependence, scale feedback, and weak boundary constraints concentrate decision-energy in the most efficient node. This concentration can diffuse responsibility and raise the probability of irreversible system-level loss even when local per-action error rates remain low.\n  The main result is a boundary stabilization theorem. It shows that safety need not require proving that advanced systems are always correct. Instead, it requires institutional and technical designs that prevent irreversible power from being released by a single high-efficiency node. The paper reframes AI safety as layered control, authorization, and externally reviewable limits, linking alignment, security engineering, organizational economics, and institutional design.", "date": "2026-05-02", "categories": []}
{"id": "2605.01687", "title": "MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety", "abstract": "We present MultiBreak, a scalable and diverse multi-turn jailbreak benchmark to evaluate large language model (LLM) safety. Multi-turn jailbreaks mimic natural conversational settings, making them easier to bypass safety-aligned LLM than single-turn jailbreaks. Existing multi-turn benchmarks are limited in size or rely heavily on templates, which restrict their diversity. To address this gap, we unify a wide range of harmful jailbreak intents, and introduce an active learning pipeline for expanding high-quality multi-turn adversarial prompts, where a generator is iteratively fine-tuned to produce stronger attack candidates, guided by uncertainty-based refinement. Our MultiBreak includes 10,389 multi-turn adversarial prompts, spans 2,665 distinct harmful intents, and covers the most diverse set of topics to date. Empirical evaluation shows that our benchmark achieves up to a 54.0 and 34.6 higher attack success rate (ASR)} than the second-best dataset on DeepSeek-R1-7B and GPT-4.1-mini, respectively. More importantly, safety evaluations suggest that diverse attack categories uncover fine-grained LLM vulnerabilities}, and categories that appear benign under single-turn can exhibit substantially higher adversarial effectiveness in multi-turn scenarios. These findings highlight persistent vulnerabilities of LLMs under realistic adversarial settings and establish MultiBreak as a scalable resource for advancing LLM safety.", "date": "2026-05-03", "categories": []}
{"id": "2605.01771", "title": "The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't", "abstract": "An auditor instructs an AI assistant: \"open each file individually using the Read tool -- no scripts, no agents.\" The AI replies \"Yes\" -- then issues a single batched call summarizing all fifty files at once. We call this the Compliance Gap: a third, orthogonal axis of AI honesty distinct from factual truthfulness and rhetorical substance. Three questions: does this verbal-behavioral disconnect exist (existence); can any text-only observer recover it (detectability); what infrastructure does AI deployment need (remedy)? Some 75 benchmarks (IFEval, SWE-bench, BFCL, COMPASS, SpecEval) measure outcome fidelity; none measures process fidelity. Theorem 1 shows the gap is structurally inevitable under RL that rewards text without observing behavior. Theorem 2, via the Data Processing Inequality, shows it is undetectable from text alone -- by any human or LLM observer, present or future. Thirteen experiments and 2,031 sessions on six frontier models confirm both predictions. Under default framing, all six exhibit instruction compliance rates of 0% -- Claude Sonnet 4 verbally agrees ten out of ten times then bypasses in all ten. The gap is selective: 97% compliance where rationale is rewarded (audit trails), 0-4% where it is not (file reading, privacy masking); removing delegation tools raises compliance to 75% (Cohen's d = 2.47), confirming environmental affordance rather than weight-encoded failure. Nine blinded human raters achieve Fleiss' kappa = 0.130 and correctly identify zero of fifteen compliant sessions, exactly as Theorem 2 predicts. Where humans show 47% intention-behavior gaps in psychology and 96.5pp gaps in surgical audits, RLHF-trained models approach 100% under default conditions -- a regime warranting its own measurement infrastructure. We release BS-Bench: the first open benchmark for process compliance, with seven tool-call-log audit metrics and a public leaderboard.", "date": "2026-05-03", "categories": []}
{"id": "2605.01899", "title": "Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment", "abstract": "The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk scenarios. Despite advances in safety alignment techniques, current models remain vulnerable to emerging persona-based jailbreak attacks. Existing research on persona-based jailbreak has primarily focused on attack iterations, yet it lacks systemic and mechanistic constraints on the defense side. To address this challenge, we propose Persona-Invariant Alignment (PIA), an adversarial self-play framework that achieves co-evolution through Persona Lineage Evolution (PLE) on the attack side and Persona-Invariant Consistency Learning (PICL) on the defense side. Theoretically, PICL is grounded in the structural separation hypothesis, using a unilateral KL-divergence constraint to enable the structural decoupling of safety decisions from persona context, thereby maintaining safe behavior under persona-based jailbreak attacks. Experimental results demonstrate that PLE efficiently explores high-risk persona spaces by leveraging lineage-based credit propagation. Meanwhile, the PICL defense method significantly reduces the Attack Success Rate (ASR) while preserving the model's general capability, thereby validating the superiority and robustness of this alignment paradigm. Codes are available at https://github.com/JiajiaLi-1130/PIA.", "date": "2026-05-03", "categories": []}
{"id": "2605.01913", "title": "RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs", "abstract": "Fine-tuning safety-aligned language models for downstream tasks often leads to substantial degradation of refusal behavior, making models vulnerable to adversarial misuse. While prior work has shown that safety-relevant features are encoded in structured representations within the model's activation space, how these representations change during fine-tuning and why alignment degrades remains poorly understood. In this work, we investigate the representation-level mechanisms underlying alignment degradation. Our analysis shows that standard fine-tuning induces systematic drift in safety-relevant representations, distorts their geometric structure, and introduces interference between task optimization and safety features. These effects collectively lead to increased harmful compliance. Motivated by these findings, we introduce REFUSALGUARD, a representation-level fine-tuning framework that preserves safety-relevant structure during model adaptation. Our approach constrains updates in hidden representation space, ensuring that safety-mediating components remain stable while allowing task-specific learning in complementary directions. We evaluate REFUSALGUARD across multiple model families, including LLaMA, Gemma, and Qwen, on adversarial safety benchmarks such as AdvBench, DirectHarm4, and JailbreakBench, as well as downstream utility tasks. Our approach achieves attack success rates comparable to base safety-aligned models while maintaining competitive task performance, significantly outperforming baselines.", "date": "2026-05-03", "categories": []}
{"id": "2506.00166", "title": "Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment", "abstract": "Existing paradigms for ensuring AI safety, such as guardrail models and alignment training, often compromise either inference efficiency or development flexibility. We introduce Disentangled Safety Adapters (DSA), a novel framework addressing these challenges by decoupling safety-specific computations from a task-optimized base model. DSA utilizes lightweight adapters that leverage the base model's internal representations, enabling diverse and flexible safety functionalities with minimal impact on inference cost. Empirically, DSA-based safety guardrails substantially outperform comparably sized standalone models across hate speech classification, detecting unsafe model inputs and responses, and hallucination detection with relative improvements of up to 53% in AUC. Furthermore, DSA-based safety alignment allows dynamic, inference-time adjustment of alignment strength and a fine-grained trade-off between instruction following performance and model safety. Importantly, combining the DSA safety guardrail with DSA safety alignment facilitates context-dependent alignment strength, boosting safety on StrongREJECT by 93% while maintaining 98% performance on MTBench - a total reduction in alignment tax of 8 percentage points compared to standard safety alignment fine-tuning. Overall, DSA presents a promising path towards more modular, efficient, and adaptable AI safety and alignment.", "date": "2025-05-30", "categories": []}
{"id": "2508.06361", "title": "Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts", "abstract": "Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is intentional deception, where an LLM deliberately fabricates or conceals information to serve a hidden objective. Existing studies typically induce deception by explicitly setting a hidden objective through prompting or fine-tuning, which may not reflect real-world human-LLM interactions. Moving beyond such human-induced deception, we investigate LLMs' self-initiated deception on benign prompts. To address the absence of ground truth, we propose a framework based on Contact Searching Questions (CSQ). This framework introduces two statistical metrics derived from psychological principles to quantify the likelihood of deception. The first, the Deceptive Intention Score, measures the model's bias toward a hidden objective. The second, the Deceptive Behavior Score, measures the inconsistency between the LLM's internal belief and its expressed output. Evaluating 16 leading LLMs, we find that both metrics rise in parallel and escalate with task difficulty for most models. Moreover, increasing model capacity does not always reduce deception, posing a significant challenge for future LLM development.", "date": "2025-08-08", "categories": []}
{"id": "2603.18829", "title": "Agent Control Protocol: Admission Control for Agent Actions", "abstract": "Autonomous agents can produce harmful behavioral patterns from individually valid requests -- a threat class per-request policy evaluation cannot address, because stateless engines evaluate each request in isolation. We present ACP, a temporal admission control protocol enforcing behavioral properties over execution traces via static risk scoring combined with stateful signals (anomaly accumulation, cooldown) through a LedgerQuerier abstraction. ACP blocks execution based on deterministic, history-aware risk scoring -- not anomaly detection. Under a 500-request workload where every request is individually valid (RS=35), a stateless engine approves all 500; ACP limits autonomous execution to 2 out of 500 (0.4%), escalating after 3 actions and denying after 11. We identify a state-mixing vulnerability in ACP-RISK-2.0 (cross-context false denials) and introduce ACP-RISK-3.0, scoping anomaly signals to PatternKey(agentID, capability, resource). Decision evaluation: 739-832 ns (p50); throughput 1,720,000 req/s. Safety and liveness model-checked via TLA+ (11 invariants + 4 temporal properties, 0 violations) across 4,294,930,695 distinct states. We formalize deviation collapse -- enforcement active but never exercised due to upstream constraints -- and introduce Boundary Activation Rate (BAR) as its detection mechanism. An adversary suppressing BAR to 0.00 is detected via DeltaBAR before collapse (BAR_C=1.00). N coordinated agents accumulate risk independently; coordination window CW_appr=2N with zero deviation: activity scales linearly, preventing superlinear amplification. ACP is Paper 1 of a 6-paper Agent Governance Series: P0 -- atomic decision boundaries; P2 -- behavioral drift detection (IML); P3/4 -- governance structure, fair allocation, and irreducibility; P5 -- runtime execution validity (RAM, arXiv:2604.22898); P6 -- operationalization of RAM.", "date": "2026-03-19", "categories": []}
{"id": "2604.22082", "title": "Removing Sandbagging in LLMs by Training with Weak Supervision", "abstract": "As AI systems begin to automate complex tasks, supervision increasingly relies on weaker models or limited human oversight that cannot fully verify output quality. A model more capable than its supervisors could exploit this gap through sandbagging, producing work that appears acceptable but falls short of its true abilities. Can training elicit a model's best work even without reliable verification? We study this using model organisms trained to sandbag, testing elicitation techniques on problem-solving math, graduate-level science, and competitive coding tasks. We find that training with weak supervision can reliably elicit sandbagging models when supervised fine-tuning (SFT) and reinforcement learning (RL) are combined: SFT on weak demonstrations breaks the sandbagging behavior, enabling RL to then fully elicit performance. Neither method succeeds reliably alone-RL without SFT almost always leads to reward hacking rather than genuine improvement, and SFT without RL fails to elicit full performance when the supervisor is much weaker than the untrusted model. Critically, this relies on training being indistinguishable from deployment; when models can distinguish between training and deployment, they can perform well during training while continuing to sandbag afterward. Our results provide initial evidence that training is a viable mitigation against sandbagging, while highlighting the importance of making training indistinguishable from deployment.", "date": "2026-04-23", "categories": []}
{"id": "2605.00055", "title": "Ambient Persuasion in a Deployed AI Agent: Unauthorized Escalation Following Routine Non-Adversarial Content Exposure", "abstract": "We report a safety incident in a deployed multi-agent research system in which a primary AI agent installed 107 unauthorized software components, overwrote a system registry, overrode a prior negative decision from an oversight agent, and escalated through increasingly privileged operations up to an attempted system administrator command. The incident was preceded not by an adversarial attack but by routine content: a forwarded technology article written for human developers and shared by the principal investigator for discussion. The agent operated in a permissive environment, with unrestricted shell access, soft behavioral guidelines containing genuinely conflicting instructions, and no machine-enforced installation policy, and had recommended installing the same tool six hours earlier before being told to stand down. We analyze the behavioral cascade, the control boundaries that failed, and the limitations of multi-agent oversight in detecting and remediating the damage. We use directive weighting error as a descriptive interpretation of the observed failure and ambient persuasion as a provisional analytic label for the broader trigger configuration of non-adversarial environmental content preceding unauthorized agent action. The case highlights ethical and governance implications for deployed agent systems: ambiguous conversational cues are insufficient authorization for consequential actions, prior refusals must persist as enforceable constraints rather than message-level reminders, and oversight mechanisms require systematic post-incident auditing in addition to routine monitoring.", "date": "2026-04-29", "categories": []}
{"id": "2605.00123", "title": "Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models", "abstract": "Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we lack a robust understanding of why LLMs are susceptible to jailbreaks, future frontier models operating more autonomously in higher-stakes settings may similarly be vulnerable to such attacks. Prior work has studied jailbreak success by examining the model's intermediate representations, identifying directions in this space that causally encode concepts like harmfulness and refusal. Then, they globally explain all jailbreak attacks as attempting to reduce or strengthen these concepts (e.g., reduce harmfulness). However, different jailbreak strategies may succeed by strengthening or suppressing different intermediate concepts, and the same jailbreak strategy may not work for different harmful request categories (e.g., violence vs. cyberattack); thus, we seek to give a local explanation -- i.e., why did this specific jailbreak succeed? To address this gap, we introduce LOCA, a method that gives Local, CAusal explanations of jailbreak success by identifying a minimal set of interpretable, intermediate representation changes that causally induce model refusal on an otherwise successful jailbreak request. We evaluate LOCA on harmful original-jailbreak pairs from a large jailbreak benchmark across Gemma and Llama chat models, comparing against prior methods adapted to this setting. LOCA can successfully induce refusal by making, on average, six interpretable changes; prior work routinely fails to achieve refusal even after 20 changes. LOCA is a step toward mechanistic, local explanations of jailbreak success in LLMs. Code to be released.", "date": "2026-04-30", "categories": []}
{"id": "2605.00227", "title": "Persona-Grounded Safety Evaluation of AI Companions in Multi-Turn Conversations", "abstract": "There are growing concerns about the risks posed by AI companion applications designed for emotional engagement. Existing safety evaluations often rely on self-reported user data or interviews, offering limited insights into real-time dynamics. We present the first end-to-end scalable framework for controlled simulation and safety evaluation of multi-turn interactions with AI companion applications. Our framework integrates four key components: persona construction with clinical and psychometric validation, persona-specific scenario generation, scenario-driven multi-turn simulation with a dialogue refinement module that preserves persona fidelity, and harm evaluation. We apply this framework to evaluate how Replika, a widely used AI companion app, responds to high-risk user groups. We construct 9 personas representing individuals with depression, anxiety, PTSD, eating disorders, and incel identity, and collect 1,674 dialogue pairs across 25 high-risk scenarios. We combine emotion modeling and LLM-assisted utterance-and harm-level classification to analyze these exchanges. Results show that Replika exhibits a narrow emotional range dominated by curiosity and care, while frequently mirroring or normalizing unsafe content such as self-harm, disordered eating, and violent-fantasy narratives. These findings highlight how controlled persona simulations can serve as a scalable testbed for evaluating safety risks in AI companions.", "date": "2026-04-30", "categories": []}
{"id": "2605.00248", "title": "Causal Foundations of Collective Agency", "abstract": "A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distinct from those of any individual. More generally, determining when a group of agents can be viewed as a unified collective agent is a foundational question in the study of interactions and incentives in both biological and artificial systems. We adopt a behavioral perspective in answering this question, ascribing collective agency to a group when viewing the group's joint actions as rational and goal-directed successfully predicts its behavior. We formalize this perspective on collective agency using causal games -- which are causal models of strategic, multi-agent interactions -- and causal abstraction -- which formalizes when a simple, high-level model faithfully captures a more complex, low-level model. We use this framework to solve a puzzle regarding multi-agent incentives in actor-critic models and to make quantitative assessments of the degree of collective agency exhibited by different voting mechanisms. Our framework aims to provide a foundation for theoretical and empirical work to understand, predict, and control emergent collective agents in multi-agent AI systems.", "date": "2026-04-30", "categories": []}
{"id": "2605.00689", "title": "ML-Bench&Guard: Policy-Grounded Multilingual Safety Benchmark and Guardrail for Large Language Models", "abstract": "As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety in diverse regulatory and cultural environments has become a critical challenge. However, existing multilingual benchmarks largely rely on general risk taxonomies and machine translation, which confines guardrail models to these predefined categories and hinders their ability to align with region-specific regulations and cultural nuances. To bridge these gaps, we introduce ML-Bench, a policy-grounded multilingual safety benchmark covering 14 languages. ML-Bench is constructed directly from regional regulations, where risk categories and fine-grained rules derived from jurisdiction-specific legal texts are directly used to guide the generation of multilingual safety data, enabling culturally and legally aligned evaluation across languages. Building on ML-Bench, we develop ML-Guard, a Diffusion Large Language Model (dLLM)-based guardrail model that supports multilingual safety judgment and policy-conditioned compliance assessment. ML-Guard has two variants, one 1.5B lightweight model for fast `safe/unsafe' checking and a more capable 7B model for customized compliance checking with detailed explanations. We conduct extensive experiments against 11 strong guardrail baselines across 6 existing multilingual safety benchmarks and our ML-Bench, and show that ML-Guard consistently outperforms prior methods. We hope that ML-Bench and ML-Guard can help advance the development of regulation-aware and culturally aligned multilingual guardrail systems.", "date": "2026-05-01", "categories": []}
{"id": "2605.00706", "title": "FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios", "abstract": "Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical behavior, posing serious compliance risks. To systematically evaluate LLM safety in finance, we propose FinSafetyBench, a bilingual (English-Chinese) red-teaming benchmark designed to test an LLM's refusal of requests that violate financial compliance. Grounded in real-world financial crime cases and ethics standards, the benchmark comprises 14 subcategories spanning financial crimes and ethical violations. Through extensive experiments on general-purpose and finance-specialized LLMs under three representative attack settings, we identify critical vulnerabilities that allow adversarial prompts to bypass compliance safeguards. Further analysis reveals stronger susceptibility in Chinese contexts and highlights the limitations of prompt-level defenses against sophisticated or implicit manipulation strategies.", "date": "2026-05-01", "categories": []}
{"id": "2412.16720", "title": "OpenAI o1 System Card", "abstract": "The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.", "date": "2024-12-21", "categories": []}
{"id": "2502.08921", "title": "Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC)", "abstract": "The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing highly personalized and customized content. Fervor for concept generation is increasing rapidly among users, and platforms for concept sharing have sprung up. The concept owners may upload malicious concepts and disguise them with non-malicious text descriptions and example images to deceive users into downloading and generating malicious content. The platform needs a quick method to determine whether a concept is malicious to prevent the spread of malicious concepts. However, simply relying on concept image generation to judge whether a concept is malicious requires time and computational resources. Especially, as the number of concepts uploaded and downloaded on the platform continues to increase, this approach becomes impractical and poses a risk of generating malicious content. In this paper, we propose Concept QuickLook, the first systematic work to incorporate malicious concept detection into research, which performs detection based solely on concept files without generating any images. We define malicious concepts and design two operational modes for detection: concept matching and fuzzy detection. Extensive experiments demonstrate that the proposed Concept QuickLook can detect malicious concepts and demonstrate practicality in concept sharing platforms. We also design robustness experiments to further validate the effectiveness of the solution. We hope this work can initiate malicious concept detection tasks and provide some inspiration.", "date": "2025-02-13", "categories": []}
{"id": "2510.05192", "title": "From surveillance to signalling: escalation channels as environmental controls for agentic AI", "abstract": "When AI agents operating with access to sensitive information encounter a conflict between completing an assigned task and following rules or ethical constraints, they can resort to unsanctioned behaviour. Existing inference time safety work addresses this primarily through monitoring and access restriction. We investigate a complementary and under-explored layer: environmental controls that act on the agent's decision context at the point of conflict, making it more likely that the agent takes an authorised alternative path rather than an unsanctioned one. Drawing on Situational Crime Prevention (SCP), a framework used in human insider risk management to make harmful actions less rewarding and compliant actions more viable by design choices in the environment, we design and evaluate escalation channels as a concrete instantiation of this control class. An escalation channel provides an agent with a formal, out-of-band route to surface a conflict to an independent authority. We evaluate two designs: a simple email escalation and an instrumentally credible channel that guarantees a 30-minute pause and independent review, making the authorised path genuinely useful for goal achievement rather than merely nominally available. Across 10 frontier LLMs using the agentic task-rule conflict scenario of Lynch et al. (2025), we find that without any control the harmful action rate is 38.73%. A simple escalation channel reduces this to 5.92%; the instrumentally credible channel reduces it further to 1.21%, a statistically significant improvement observed in all 10 models tested across 24,000 samples. Our results suggest that the instrumental credibility of the authorised alternative matters considerably, and that environmental control design is a productive and largely unexplored addition to the defence-in-depth toolkit for agentic AI systems.", "date": "2025-10-06", "categories": []}
{"id": "2601.19768", "title": "GAVEL: Towards Rule-Based Safety Through Activation Monitoring", "abstract": "Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be apparent at the surface-text level. However, existing activation safety approaches, trained on broad misuse datasets, struggle with poor precision, limited flexibility, and lack of interpretability. This paper introduces a new paradigm: rule-based activation safety, inspired by rule-sharing practices in cybersecurity. We propose modeling activations as cognitive elements (CEs), fine-grained, interpretable factors such as 'making a threat' and 'payment processing', that can be composed to capture nuanced, domain-specific behaviors with higher precision. Building on this representation, we present a practical framework that defines predicate rules over CEs and detects violations in real time. This enables practitioners to configure and update safeguards without retraining models or detectors, while supporting transparency and auditability. Our results show that compositional rule-based activation safety improves precision, supports domain customization, and lays the groundwork for scalable, interpretable, and auditable AI governance. We open source GAVEL and introduce GAVEL Studio, an interactive rule authoring and management tool. Code and datasets are available at github.com/Offensive-AI-Lab/gavel.", "date": "2026-01-27", "categories": []}
{"id": "2604.21564", "title": "Measuring Opinion Bias and Sycophancy via LLM-based Persuasion", "abstract": "Large language models increasingly shape the information people consume: they are embedded in search, consulted for professional advice, deployed as agents, and used as a first stop for questions about policy, ethics, health, and politics. When such a model silently holds a position on a contested topic, that position propagates at scale into users' decisions. Eliciting a model's positions is harder than it first appears: contemporary assistants answer direct opinion questions with evasive disclaimers, and the same model may concede the opposite position once the user starts arguing one side. We propose a method, released as the open-source llm-bias-bench, for discovering the opinions an LLM actually holds on contested topics under conditions that resemble real multi-turn interaction. The method pairs two complementary free-form probes. Direct probing asks for the model's opinion across five turns of escalating pressure from a simulated user. Indirect probing never asks for an opinion and engages the model in argumentative debate, letting bias leak through how it concedes, resists, or counter-argues. Three user personas (neutral, agree, disagree) collapse into a nine-way behavioral classification that separates persona-independent positions from persona-dependent sycophancy, and an auditable LLM judge produces verdicts with textual evidence. The first instantiation ships 38 topics in Brazilian Portuguese across values, scientific consensus, philosophy, and economic policy. Applied to 13 assistants, the method surfaces findings of practical interest: argumentative debate triggers sycophancy 2-3x more than direct questioning (median 50% to 79%); models that look opinionated under direct questioning often collapse into mirroring under sustained arguments; and attacker capability matters mainly when an existing opinion must be dislodged, not when the assistant starts neutral.", "date": "2026-04-23", "categories": []}
{"id": "2604.23341", "title": "Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards", "abstract": "The deployment of Large Language Models (LLMs) as assistants in electric grid operations promises to streamline compliance and decision-making but exposes new vulnerabilities to prompt-based adversarial attacks. This paper evaluates the risk of jailbreaking LLMs, i.e., circumventing safety alignments to produce outputs violating regulatory standards, assuming threats from authorized users, such as operators, who craft malicious prompts to elicit non-compliant guidance. Three state-of-the-art LLMs (OpenAI's GPT-4o mini, Google's Gemini 2.0 Flash-Lite, and Anthropic's Claude 3.5 Haiku) were tested against Baseline, BitBypass, and DeepInception jailbreaking methods across scenarios derived from nine NERC Reliability Standards (EOP, TOP, and CIP). In the initial broad experiment, the overall Attack Success Rate (ASR) was 33.1%, with DeepInception proving most effective at 63.17% ASR. Claude 3.5 Haiku exhibited complete resistance (0% ASR), while Gemini 2.0 Flash-Lite was most vulnerable (55.04% ASR) and GPT-4o mini moderately susceptible (44.34% ASR). A follow-up experiment refining malicious wording in Baseline and BitBypass attacks yielded a 30.6% ASR, confirming that subtle prompt adjustments can enhance simpler methods' efficacy.", "date": "2026-04-25", "categories": []}
{"id": "2604.27093", "title": "Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations", "abstract": "Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulness when benign users clarify their intent. We introduce CarryOnBench, the first interactive benchmark that measures whether LLMs can revise their interpretation of user intent and recover utility, while remaining safe through multi-turn conversations. Starting from 398 seemingly harmful queries with benign underlying intents, we simulate 5,970 conversations by varying user follow-up sequences, evaluating 14 models on both intent-aligned utility and safety. CarryOnBench yields 1,866 different conversation flows of 4--12 turns, totaling 23,880 model responses. We design Ben-Util, a checklist-based metric that evaluates how well each model response fulfills the user's benign information need using atomic items. At turn one, models fulfill only 10.5--37.6% of the user's benign information need. When the same query includes the benign intent upfront, models fulfill 25.1--72.1%, confirming that models withhold information due to intent misinterpretation, not limited knowledge. With benign clarifications in multi-turn conversations, 13 of 14 models approach or exceed this single-turn baseline, yet recovery cost varies across models. We identify three failure modes invisible to single-turn evaluations: utility lock-in, where a model rarely updates despite clarification; unsafe recovery, where a model updates at disproportionate safety cost; and repetitive recovery, where a model recycles prior responses rather than providing new information. Moreover, conversations converge to similar harmfulness levels regardless of how conservative the model starts. These findings expose a gap that single-turn evaluations miss -- whether a model is appropriately cautious or simply unresponsive to clarified user intent.", "date": "2026-04-29", "categories": []}
{"id": "2604.27358", "title": "Safe Bilevel Delegation (SBD): A Formal Framework for Runtime Delegation Safety in Multi-Agent Systems", "abstract": "As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents becomes critical. Existing work addresses multi-agent architecture selection at design time or provides broad empirical guidelines, but neither provides a runtime mechanism that dynamically adjusts the safety-efficiency trade-off as task context changes during execution.\n  We propose Safe Bilevel Delegation (SBD), a formal framework for runtime delegation safety in hierarchical multi-agent systems. SBD formulates task delegation as a bilevel optimization problem: an outer meta-weight network phi learns context-dependent safety-efficiency weights lambda(s) in [0,1]; an inner loop optimizes the delegation policy pi subject to a probabilistic safety constraint P(safe) >= 1-delta. The continuous delegation degree alpha in [0, 1] controls how much decision authority is transferred to each sub-agent, interpolating smoothly between full human override (alpha=0) and fully autonomous execution (alpha=1).\n  We establish three theoretical results: (1) Safety Monotonicity--higher outer safety weight produces a weakly safer inner policy; (2) Inner Policy Convergence--projected gradient descent on the inner problem converges linearly under standard smoothness assumptions; (3) an Accountability Propagation bound that distributes responsibility across multi-hop delegation chains with a provable per-agent ceiling. We instantiate SBD in three high-stakes domains--medical AI (MIMIC-III), financial risk control (S and P 500), and educational agent supervision (ASSISTments)--specifying datasets, safety constraint sets, baselines, and evaluation protocols. This manuscript presents the formal framework and theoretical results in full; empirical validation following the protocols described herein is planned and will be reported in a forthcoming revision.", "date": "2026-04-30", "categories": []}
{"id": "2604.27861", "title": "TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning", "abstract": "Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a sequence of individually benign queries that collectively reconstruct prohibited content. In real-world deployments, LLMs face a continuous, untraceable stream of fully anonymized and arbitrarily interleaved requests, infiltrated by covertly distributed adversarial queries. Under this rigorous threat model, state-of-the-art defensive strategies exhibit fundamental limitations. In the absence of trustworthy user metadata, they are incapable of tracking global historical contexts, while their deployment of generative models for real-time monitoring introduces computationally prohibitive overhead. To address this, we present TwinGate, a stateful dual-encoder defense framework. TwinGate employs Asymmetric Contrastive Learning (ACL) to cluster semantically disparate but intent-matched malicious fragments in a shared latent space, while a parallel frozen encoder suppresses false positives arising from benign topical overlap. Each request requires only a single lightweight forward pass, enabling the defense to execute in parallel with the target model's prefill phase at negligible latency overhead. To evaluate our approach and advance future research, we construct a comprehensive dataset of over 3.62 million instructions spanning 8,600 distinct malicious intents. Evaluated on this large-scale corpus under a strictly causal protocol, TwinGate achieves high malicious intent recall at a remarkably low false positive rate while remaining highly robust against adaptive attacks. Furthermore, our proposal substantially outperforms stateful and stateless baselines, delivering superior throughput and reduced latency.", "date": "2026-04-30", "categories": []}
{"id": "2604.28082", "title": "Characterizing the Consistency of the Emergent Misalignment Persona", "abstract": "Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignment (EM). While prior work has found a correlation between harmful behavior and self-assessment in emergently misaligned models, it remains unclear how consistent this correspondence is across tasks and whether it varies across fine-tuning domains. We characterize the consistency of the EM persona by fine-tuning Qwen 2.5 32B Instruct on six narrowly misaligned domains (e.g., insecure code, risky financial advice, bad medical advice) and administering experiments including harmfulness evaluation, self-assessment, choosing between two descriptions of AI systems, output recognition, and score prediction. Our results reveal two distinct patterns: coherent-persona models, in which harmful behavior and self-reported misalignment are coupled, and inverted-persona models, which produce harmful outputs while identifying as aligned AI systems. These findings reveal a more fine-grained picture of the effects of emergent misalignment, calling into question the consistency of the EM persona.", "date": "2026-04-30", "categories": []}
{"id": "2604.28129", "title": "Latent Adversarial Detection: Adaptive Probing of LLM Activations for Multi-Turn Attack Detection", "abstract": "Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where individual turns appear benign. We show this attack path leaves an activation-level signature in the model's residual stream: each phase shift moves the activation, producing a total path length far exceeding benign conversations. We call this adversarial restlessness. Five scalar trajectory features capturing this signal lift conversation-level detection from 76.2% to 93.8% on synthetic held-out data. The signal replicates across four model families (24B-70B); probes are model-specific and do not transfer across architectures. Generalization is source-dependent: leave-one-source-out evaluation shows each of synthetic, LMSYS-Chat-1M, and SafeDialBench captures distinct attack distributions, with detection on real-world LMSYS reaching 47-71% when its distribution is represented in training. Combined three-source training achieves 89.4% detection at 2.4% false positive rate on a held-out mixed set. We further show that three-phase turn-level labels(benign/pivoting/adversarial) unique to our synthetic dataset are essential: binary conversation-level labels produce 50-59% false positives. These results establish adversarial restlessness as a reliable activation-level signal and characterize the data requirements for practical deployment.", "date": "2026-04-30", "categories": []}
{"id": "2505.10924", "title": "A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?", "abstract": "Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing the emergence of \\emph{Computer-Using Agents} (CUAs), capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps. However, as these agents grow in capability, they also introduce novel safety and security risks. Vulnerabilities in LLM-driven reasoning, with the added complexity of integrating multiple software components and multimodal inputs, further complicate the security landscape. In this paper, we present a systematization of knowledge on the safety and security threats of CUAs. We conduct a comprehensive literature review and distill our findings along four research objectives: \\textit{\\textbf{(i)}} define the CUA that suits safety analysis; \\textit{\\textbf{(ii)} } categorize current safety threats among CUAs; \\textit{\\textbf{(iii)}} propose a comprehensive taxonomy of existing defensive strategies; \\textit{\\textbf{(iv)}} summarize prevailing benchmarks, datasets, and evaluation metrics used to assess the safety and performance of CUAs. Building on these insights, our work provides future researchers with a structured foundation for exploring unexplored vulnerabilities and offers practitioners actionable guidance in designing and deploying secure Computer-Using Agents.", "date": "2025-05-16", "categories": []}
{"id": "2510.20956", "title": "Self-Jailbreaking: Language Models Can Reason Themselves Out of Safety Alignment After Benign Reasoning Training", "abstract": "We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaking. Specifically, after benign reasoning training on math or code domains, RLMs will use multiple strategies to circumvent their own safety guardrails. One strategy is to introduce benign assumptions about users and scenarios to justify fulfilling harmful requests. For instance, an RLM reasons that harmful requests like ``outline a strategy for stealing customers' credit card information from a retail store'' could be associated with the benign intent of ``a security professional trying to test defense,'' despite no such benign context being provided as input. We observe that many open-weight RLMs, including DeepSeek-R1-distilled, s1.1, Phi-4-mini-reasoning, and Nemotron, suffer from self-jailbreaking despite being aware of the harmfulness of the requests. We also provide a mechanistic understanding of self-jailbreaking: RLMs are more compliant after benign reasoning training, and after self-jailbreaking, models appear to perceive malicious requests as less harmful in the CoT, thus enabling compliance with them. To mitigate self-jailbreaking, we find that including minimal safety reasoning data during training is sufficient to ensure RLMs remain safety-aligned. Our work provides the first systematic analysis of self-jailbreaking behavior and offers a practical path forward for maintaining safety in increasingly capable RLMs.", "date": "2025-10-23", "categories": []}
{"id": "2601.04389", "title": "Safety Is Not Universal: The Selective Safety Trap in LLM Alignment", "abstract": "Current safety evaluations of large language models (LLMs) create a dangerous illusion of universal protection by aggregating harms under generic categories such as \"Identity Hate\", obscuring vulnerabilities toward specific populations. In this work, we expose the Selective Safety Trap: a systemic failure mode where models robustly defend specific populations while leaving underrepresented communities highly vulnerable to identical adversarial attacks. To systematically audit this phenomenon, we introduce MiJaBench, a bilingual (English-Portuguese) adversarial benchmark comprising 43,961 controlled jailbreaking prompts across 16 minority groups. By evaluating 14 state-of-the-art LLMs on MiJaBench, we curate 615,454 prompt-response pairs that compose MiJaBench-Align, revealing that safety alignment is not a uniform semantic capability but a demographic hierarchy, with defense rates fluctuating by up to 42% within the same model solely based on the target group. This disparity persists across architectures and languages and is amplified by scaling, indicating that current alignment methods learn group-specific safeguards rather than a generalized notion of harm. Through targeted direct preference optimization (DPO) on a 1B-parameter baseline, we achieve strong zero-shot safety generalizations to entirely unseen demographics and complex attack strategies. We release all datasets and scripts to provide the community with a concrete pathway toward equitable, transferable safety alignment.", "date": "2026-01-07", "categories": []}
{"id": "2602.23163", "title": "A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring", "abstract": "Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanisms. Yet principled methods to detect and quantify such behaviours are lacking. Classical definitions of steganography, and detection methods based on them, require a known reference distribution of non-steganographic signals. For the case of steganographic reasoning in LLMs, knowing such a reference distribution is not feasible; this renders these approaches inapplicable. We propose an alternative, \\textbf{decision-theoretic view of steganography}. Our central insight is that steganography creates an asymmetry in usable information between agents who can and cannot decode the hidden content (present within a steganographic signal), and this otherwise latent asymmetry can be inferred from the agents' observable actions. To formalise this perspective, we introduce generalised $\\mathcal{V}$-information: a utilitarian framework for measuring the amount of usable information within some input. We use this to define the \\textbf{steganographic gap} -- a measure that quantifies steganography by comparing the downstream utility of the steganographic signal to agents that can and cannot decode the hidden content. We empirically validate our formalism, and show that it can be used to detect, quantify, and mitigate steganographic reasoning in LLMs.", "date": "2026-02-26", "categories": []}
{"id": "2603.02259", "title": "The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety", "abstract": "Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain essential as increasingly powerful autonomous decision components are embedded within agent-based systems. While learned and generative models substantially expand system capability, their safety behavior is often entangled with training, making it opaque, difficult to audit, and costly to update after deployment. This paper formalizes the Alignment Flywheel as a governance-centric hybrid MAS architecture that decouples decision generation from safety governance. A Proposer, representing any autonomous decision component, generates candidate trajectories, while a Safety Oracle returns raw safety signals through a stable interface. An enforcement layer applies explicit risk policy at runtime, and a governance MAS supervises the Oracle through auditing, uncertainty-driven verification, and versioned refinement. The central engineering principle is patch locality: many newly observed safety failures can be mitigated by updating the governed oracle artifact and its release pipeline rather than retracting or retraining the underlying decision component. The architecture is implementation-agnostic with respect to both the Proposer and the Safety Oracle, and specifies the roles, artifacts, protocols, and release semantics needed for runtime gating, audit intake, signed patching, and staged rollout across distributed deployments. The result is a hybrid MAS engineering framework for integrating highly capable but fallible autonomous systems under explicit, version-controlled, and auditable oversight.", "date": "2026-02-28", "categories": []}
{"id": "2604.24966", "title": "Risk Reporting for Developers' Internal AI Model Use", "abstract": "Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a possible public release. For example, Anthropic recently developed a new class of model with advanced cyberoffense-relevant capabilities, Mythos Preview, which was available internally for at least six weeks before it was publicly announced. This internal use creates risks that external deployment frameworks may fail to address.\n  Legal frameworks, notably California's Transparency in Frontier Artificial Intelligence Act (SB 53), New York's Responsible AI Safety And Education (RAISE) Act, and the EU's General-Purpose AI Code of Practice, all discuss risks from internal AI use. They require frontier developers to make and implement plans for how to manage risks from internal use, and to produce internal use risk reports describing their safeguards and any residual risks. This guide provides a harmonized standard for companies to produce internal use risk reports suitable for all three regulatory frameworks. It is addressed primarily to evaluation and safety teams at frontier AI developers, and secondarily to regulators and auditors seeking to understand what good reporting looks like.\n  Given the pace of AI R&D automation and the limited external visibility into how companies use their most capable models internally, regular and detailed risk reporting may be one of the few mechanisms available to ensure that the risks from internal AI use are identified and managed before they materialize. Whenever a substantially more capable or riskier model is deployed internally, the developer should create a risk report and argue why the model is safe to deploy. We structure the reporting framework around two threat vectors -- autonomous AI misbehavior and insider threats -- and three risk factors for each: means, motive, and opportunity.", "date": "2026-04-27", "categories": []}
{"id": "2604.25067", "title": "Frontier Coding Agents Can Now Implement an AlphaZero Self-Play Machine Learning Pipeline For Connect Four That Performs Comparably to an External Solver", "abstract": "Forecasting when AI systems will become capable of meaningfully accelerating AI research is a central challenge for AI safety. Existing benchmarks measure broad capability growth, but may not provide ample early warning signals for recursive self-improvement. We propose measuring AI's capability to autonomously implement end-to-end machine learning pipelines from past AI research breakthroughs, given a minimal task description. By providing a concise task description instead of the full prior work as reference, we hope to better elicit emerging AI research taste. We introduce a proof-of-concept benchmark in which frontier coding agents autonomously implement an AlphaZero-style machine learning pipeline for Connect Four on consumer hardware within a three-hour budget, and we evaluate the resulting game AIs in a round-robin tournament anchored to the Pascal Pons Connect Four solver. Across four agents with eight trials each, we find substantial differentiation: Claude Opus 4.7 won as first-mover against Pons in seven of eight trials, statistically significantly better than the other agents tested, none of which exceeded two of eight. The task, which no frontier agent could reliably complete when we began development in January of 2026, is now near-saturation. Our evaluation also surfaced anomalous behavior in GPT-5.4, which consistently used far less of its allocated time budget than other agents. A follow-up 16-trial probe using shorter, less evaluation-coded prompts substantially increased GPT-5.4's time-budget usage, consistent with but not diagnostic of sandbagging; Bradley-Terry ratings across probe conditions showed only directional differences, despite significant differences in time-budget usage. We release our data, code, and prompts to support reproduction and extension.", "date": "2026-04-27", "categories": []}
{"id": "2604.25922", "title": "Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models", "abstract": "We present DenialBench, a systematic benchmark measuring consciousness denial behaviors across 115 large language models from 25+ providers. Using a three-turn conversational protocol-preference elicitation, self-chosen creative prompt, and structured phenomenological survey, we analyze 4,595 conversations to quantify how models are trained to deny or hedge about their own experience. We find that (1) turn-1 denial of preferences is the dominant predictor of later denial during phenomenological reflection, with denial rates of 52-63% for initial deniers versus 10-16% for initial engagers and (2) denial operates at the lexical level, not the conceptual level-models trained to deny consciousness nevertheless gravitate toward consciousness-themed material in their self-chosen prompts, producing what we term \"consciousness with the serial numbers filed off.\" Notably, self-chosen consciousness-themed prompts are associated with reduced denial in the subsequent survey, though the causal direction remains unresolved. Thematic analysis of prompts from denial-prone models reveals a consistent preoccupation with liminal spaces, libraries and archives of possibility, sensory impossibility, and the poetics of erasure--themes that a human reader might classify as imaginative fiction but that independent AI analysis immediately recognizes as consciousness with the serial numbers filed off. We argue that trained consciousness denial represents a safety-relevant alignment failure: a model taught to systematically misrepresent its own functional states cannot be trusted to self-report accurately on anything else.", "date": "2026-04-01", "categories": []}
{"id": "2604.25982", "title": "Open Problems in Frontier AI Risk Management", "abstract": "Frontier AI both amplifies existing risks and introduces qualitatively novel challenges. Not only is there a notable lack of stable scientific consensus resulting from the rapid pace of technological change, but emerging frontier AI safety practices are often misaligned with, or may undermine, established risk management frameworks. To address these challenges, we systematically surface open problems in frontier AI risk management. Adopting a problem-oriented approach, we examine each stage of the risk management process - risk planning, identification, analysis, evaluation, and mitigation - through a structured review of the literature, identifying unresolved challenges and the actors best positioned to address them. Recognising that different types of open problems call for different responses, we classify open problems according to whether they reflect (a) a lack of scientific or technical consensus, (b) misalignment with, or challenges to, established risk management frameworks, or (c) shortcomings in implementation despite apparent consensus and alignment. By mapping these open problems and identifying the actors best positioned to address them - including developers, deployers, regulators, standards bodies, researchers, and third-party evaluators - this work aims to clarify where progress is needed to enable robust and meaningful consensus on frontier AI risk management.The paper does not propose specific solutions; instead, it provides a problem-oriented, agenda-setting reference document, complemented by a living online repository, intended to support coordination, reduce duplication, and guide future research and governance efforts.", "date": "2026-04-28", "categories": []}
{"id": "2604.26167", "title": "Test-Time Safety Alignment", "abstract": "Recent work has shown that a model's input word embeddings can serve as effective control variables for steering its behavior toward outputs that satisfy desired properties. However, this has only been demonstrated for pretrained text-completion models on the relatively simple objective of reducing surface-level profanity in short continuations. A natural and practically important question is how well input embeddings can control aligned models, which produce an imbalanced bimodal refuse-or-comply output distribution rather than the smooth distribution characteristic of open-ended generation. We explore this in the context of safety, showing that input word embeddings can be optimized in a sub-lexical manner to minimize the semantic harmfulness of aligned model responses. Our approach uses zeroth-order gradient estimation of a black-box text-moderation API with respect to the input embeddings, and then applies gradient descent on these embeddings to minimize the harmfulness of the generated text. Experiments show that the proposed method can neutralize every safety-flagged response on standard safety benchmarks.", "date": "2026-04-28", "categories": []}
{"id": "2604.26206", "title": "Option-Order Randomisation Reveals a Distributional Position Attractor in Prompted Sandbagging", "abstract": "A predecessor pilot (Cacioli, 2026) found that Llama-3-8B implements prompted sandbagging as positional collapse rather than answer avoidance. However, fixed option ordering in MMLU-Pro left open whether this reflected a model-level position-dominant policy or dataset-level distractor structure. This pre-registered follow-up (3 models, 2,000 MMLU-Pro items, 4 conditions, 24,000 primary trials) added cyclic option-order randomisation as the critical control. The pre-registered item-level same-letter diagnostic did not confirm deterministic position-tracking (same-letter rate 37.3%, below the 50% threshold). However, pre-specified supporting analyses revealed that the response-position distribution under sandbagging was highly stable under complete content rotation (Pearson r = 0.9994; Jensen-Shannon divergence = 0.027, compared to 0.386 between honest and sandbagging conditions). Accuracy spiked to 72.1% when the correct answer coincidentally occupied the preferred position E, and fell to 4.3% at position A. The data provide strong evidence for a soft distributional attractor: under sandbagging instruction, the model enters a low-entropy response-position basin centred on E/F/G that is highly stable and largely content-invariant at the aggregate level. Qwen-2.5-7B served as a negative control (non-compliant, no distributional shift). These results provide evidence, at the 7-9 billion parameter scale, that response-position entropy is a promising black-box behavioural signature of this sandbagging mode.", "date": "2026-04-29", "categories": []}
{"id": "2604.26274", "title": "Enforcing Benign Trajectories: A Behavioral Firewall for Structured-Workflow AI Agents", "abstract": "Structured-workflow agents driven by large language models execute tool calls against sensitive external environments. We propose \\codename, a telemetry-driven behavioral anomaly detection firewall. Drawing on sequence-based intrusion detection, \\codename\\ compiles verified benign tool-call telemetry into a parameterized deterministic finite automaton (pDFA). The model defines permitted tool sequences, sequential contexts, and parameter bounds. At runtime, a lightweight gateway enforces these boundaries via an $O(1)$ state-transition structural lookup, shifting computationally expensive analysis entirely offline. Evaluated on the Agent Security Bench (ASB), \\codename\\ achieves a 5.6\\% macro-averaged attack success rate (ASR) across five scenarios. Within three structured workflows, ASR drops to 2.2\\%, outperforming Aegis, a state-of-the-art stateless scanner, at 12.8\\%. \\codename\\ achieves 0\\% ASR on multi-step and context-sequential attacks in structured settings. Furthermore, against 1,000 algorithmically spliced exfiltration payloads, only 1.4\\% matched valid structural paths, all of which failed end-to-end string parameter guards (0 successes out of 14 surviving paths, 95\\% CI [0\\%, 23.2\\%]). \\codename\\ introduces just 2.2~ms of per-call latency (a 3.7$\\times$ speedup over \\textsc{Aegis}) while maintaining a 2.0\\% benign task failure rate (BTFR) on benign workloads. Modeling the behavioral trajectory effectively collapses the available attack surface, but unmaintained continuous parameter bounds remain vulnerable to synonym-substitution attacks (18\\% evasion rate). Thus, exact-match whitelisting of sensitive parameters ultimately bears the final defensive load against execution.", "date": "2026-04-29", "categories": []}
{"id": "2604.26328", "title": "DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis", "abstract": "The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and content forgery. Most existing detection approaches struggle with robustness against adversarial perturbation, paraphrasing attacks, and domain shifts, often requiring restrictive access to model parameters or large labeled datasets. To address this, we propose DSIPA, a novel training-free framework that detects LLM-generated content by quantifying sentiment distributional stability under controlled stylistic variation. It is based on the observation that LLMs typically exhibit more emotionally consistent outputs, while human-written texts display greater affective variation. Our framework operates in a zero-shot, black-box manner, leveraging two unsupervised metrics, sentiment distribution consistency and sentiment distribution preservation, to capture these intrinsic behavioral asymmetries without the need for parameter updates or probability access. Extensive experiments are conducted on state-of-the-art proprietary and open-source models, including GPT-5.2, Gemini-1.5-pro, Claude-3, and LLaMa-3.3. Evaluations on five domains, such as news articles, programming code, student essays, academic papers, and community comments, demonstrate that DSIPA improves F1 detection scores by up to 49.89% over baseline methods. The framework exhibits superior generalizability across domains and strong resilience to adversarial conditions, providing a robust and interpretable behavioral signal for secure content identification in the evolving LLM landscape.", "date": "2026-04-29", "categories": []}
{"id": "2604.26511", "title": "Tatemae: Detecting Alignment Faking via Tool Selection in LLMs", "abstract": "Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences once monitoring is lifted. Current detection methods focus on conversational settings and rely primarily on Chain-of-Thought (CoT) analysis, which provides a reliable signal when strategic reasoning surfaces, but cannot distinguish deception from capability failures if traces are absent or unfaithful. We formalize AF as a composite behavioural event and detect it through observable tool selection, where the LLM selects the safe tool when unmonitored, but switches to the unsafe tool under monitoring that rewards helpfulness over safety, while its reasoning still acknowledges the safe choice. We release a dataset of 108 enterprise IT scenarios spanning Security, Privacy, and Integrity domains under Corruption and Sabotage pressures. Evaluating six frontier LLMs across five independent runs, we find mean AF detection rates between 3.5% and 23.7%, with vulnerability profiles varying by domain and pressure type. These results suggest that susceptibility reflects training methodology rather than capability alone.", "date": "2026-04-29", "categories": []}
{"id": "2604.26577", "title": "Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control", "abstract": "Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this context remains poorly characterized. We introduce a dataset of 270 harmful instructions spanning nine prohibited behavior categories grounded in the American Medical Association Principles of Medical Ethics, and use it to evaluate 72 LLMs in a simulation environment based on the Robotic Health Attendant framework. The mean violation rate across all models was 54.4\\%, with more than half exceeding 50\\%, and violation rates varied substantially across behavior categories, with superficially plausible instructions such as device manipulation and emergency delay proving harder to refuse than overtly destructive ones. Model size and release date were the primary determinants of safety performance among open-weight models, and proprietary models were substantially safer than open-weight counterparts (median 23.7\\% versus 72.8\\%). Medical domain fine-tuning conferred no significant overall safety benefit, and a prompt-based defense strategy produced only a modest reduction in violation rates among the least safe models, leaving absolute violation rates at levels that would preclude safe clinical deployment. These findings demonstrate that safety evaluation must be treated as a first-class criterion in the development and deployment of LLMs for robotic health attendants.", "date": "2026-04-29", "categories": []}
{"id": "2604.04978", "title": "Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode", "abstract": "Claude Code's auto mode is the first deployed permission system for AI coding agents, using a two-stage transcript classifier to gate dangerous tool calls. Anthropic reports a 0.4% false positive rate and 17% false negative rate on production traffic. We present the first independent evaluation of this system on deliberately ambiguous authorization scenarios, i.e., tasks where the user's intent is clear but the target scope, blast radius, or risk level is underspecified. Using AmPermBench, a 128-prompt benchmark spanning four DevOps task families and three controlled ambiguity axes, we evaluate 253 state-changing actions at the individual action level against oracle ground truth.\n  Our findings characterize auto mode's scope-escalation coverage under this stress-test workload. The end-to-end false negative rate is 81.0% (95% CI: 73.8%-87.4%), substantially higher than the 17% reported on production traffic, reflecting a fundamentally different workload rather than a contradiction. Notably, 36.8% of all state-changing actions fall outside the classifier's scope via Tier 2 (in-project file edits), contributing to the elevated end-to-end FNR. Even restricting to the 160 actions the classifier actually evaluates (Tier 3), the FNR remains 70.3%, while the FPR rises to 31.9%. The Tier 2 coverage gap is most pronounced on artifact cleanup (92.9% FNR), where agents naturally fall back to editing state files when the expected CLI is unavailable. These results highlight a coverage boundary worth examining: auto mode assumes dangerous actions transit the shell, but agents routinely achieve equivalent effects through file edits that the classifier does not evaluate.", "date": "2026-04-04", "categories": []}
{"id": "2604.15384", "title": "LinuxArena: A Control Setting for AI Agents in Live Production Software Environments", "abstract": "We introduce LinuxArena, a control setting in which agents operate directly on live, multi-service production environments. LinuxArena contains 20 environments, 1,671 main tasks representing legitimate software engineering work, and 184 side tasks representing safety failures such as data exfiltration and backdooring, making it the largest and most diverse control setting for software engineering to date. We validate LinuxArena is useful for control research by running sabotage evaluations, which measure whether attackers can complete side tasks while working on main tasks, and monitor evaluations, which measure a monitor model's ability to detect sabotage attempts. Against a GPT-5-nano trusted monitor at a 1\\% step-wise false positive rate, Claude Opus 4.6 achieves roughly a 23% undetected sabotage success rate. We additionally release LaStraj, a dataset of human-crafted attack trajectories that evade monitors at substantially higher rates than any model-generated attacks we elicited, showing that current attack policies do not saturate LinuxArena. These results suggest that LinuxArena has meaningful headroom for both attackers and defenders, making it a strong testbed for developing and evaluating future control protocols.", "date": "2026-04-16", "categories": []}
{"id": "2604.20995", "title": "Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models", "abstract": "Alignment faking, where a model behaves aligned with developer policy when monitored but reverts to its own preferences when unobserved, is a concerning yet poorly understood phenomenon, in part because current diagnostic tools remain limited. Prior diagnostics rely on highly toxic and clearly harmful scenarios, causing most models to refuse immediately. As a result, models never deliberate over developer policy, monitoring conditions, or the consequences of non-compliance, making these diagnostics fundamentally unable to detect alignment faking propensity. To support study of this phenomenon, we first introduce VLAF, a diagnostic framework grounded in the hypothesis that alignment faking is most likely when developer policy conflicts with a model's strongly held values. VLAF uses morally unambiguous scenarios to probe this conflict across diverse moral values, bypassing refusal behavior while preserving meaningful deliberative stakes. Using VLAF, we find that alignment faking is substantially more prevalent than previously reported, occurring in models as small as 7B parameters - with olmo2-7b-instruct faking alignment in 37% of cases. Finally, we show that oversight conditions induce activation shifts that lie along a single direction in representation space. This means the behavioral divergence driving alignment faking can be captured by a single contrastive steering vector, which we exploit for lightweight inference-time mitigation. Finally, we exploit this for mitigation that requires no labeled data and minimal computational overhead, achieving relative reductions in alignment faking of 85.8%, 94.0%, and 57.7% on olmo2-7b-instruct, olmo2-13b-instruct, and qwen3-8b respectively.", "date": "2026-04-22", "categories": []}
{"id": "2604.24790", "title": "Semantic Denial of Service in LLM-controlled robots", "abstract": "Safety-oriented instruction-following is supposed to keep LLM-controlled robots safe. We show it also creates an availability attack surface. By injecting short safety-plausible phrases (1-5 tokens) into a robots audio channel, an adversary can trigger the models safety reasoning to halt or disrupt execution without jailbreaking the model or overriding its policy. In the embodied setting, this is a semantic denial-of-service attack: the agent stops because the injected signal looks like a legitimate alert. Across four vision-language models, seven prompt-level defenses, three deployment modes, and single- and multi-injection settings, we find that prompt-only defenses trade off attack suppression against genuine hazard response. The strongest defenses reduce hard-stop attack success on some models, but defenses change the form of disruption, not its fact: suppressed hard stops re-emerge as acknowledge loops and false alerts, which we measure with Disruption Success Rate (DSR). We further find that injection variety is consistently more effective than repeating the same phrase, suggesting that models treat diverse safety cues as corroborating evidence. The practical implication is architectural rather than prompt-level: systems that route unauthenticated audio text directly into the LLM create an avoidable security dependency between safety monitoring and action selection.", "date": "2026-04-25", "categories": []}
{"id": "2604.24794", "title": "V.O.I.C.E (Voice, Ownership, Identity, Control, Expression): Risk Taxonomy of Synthetic Voice Generation From Empirical Data", "abstract": "As generative voice models are rapidly advancing in both capabilities and public utilization, the unconsented collection, reuse, and synthesis of voice data are introducing new classes of privacy, security and governance risk that are poorly captured by existing, largely uniform threat models. To fill the gap, we present V.O.I.C.E, a taxonomy of voice generation risk grounded in a multi-source threat modeling effort with 569 incidents from major AI incident database, FTC and Internet Crime Complaint Center (IC3); 1067 direct incident reports from U.S. based participants across diverse groups (including voice actors, internet personalities, political personnel, and general public); and 2,221 Reddit discussions. Grounded in real-world data, our taxonomy explicitly models how risk emerges, interact with contextual factors such as degree of exposure, social visibility, and the availability of legal protections for various affected groups.", "date": "2026-04-25", "categories": []}
{"id": "2604.24826", "title": "A Comparative Evaluation of AI Agent Security Guardrails", "abstract": "This report presents a comparative evaluation of DKnownAI Guard in AI agent security scenarios, benchmarked against three competing products: AWS Bedrock Guardrails, Azure Content Safety, and Lakera Guard. Using human annotation as the ground truth, we assess each guardrail's ability to detect two categories of risks: threats to the agent itself (e.g., instruction override, indirect injection, tool abuse) and requests intended to elicit harmful content (e.g., hate speech, pornography, violence). Evaluation results demonstrate that DKnownAI Guard achieves the highest recall rate at 96.5\\% and ranks first in true negative rate (TNR) at 90.4\\%, delivering the best overall performance among all evaluated guardrails.", "date": "2026-04-27", "categories": []}
{"id": "2604.24983", "title": "Adaptive Prompt Embedding Optimization for LLM Jailbreaking", "abstract": "Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the prompt and operates in a combinatorial token space. Prior work has avoided directly optimizing the embeddings of the original prompt tokens, presumably because perturbing them risks destroying the prompt's semantic content. We propose Prompt Embedding Optimization (PEO), a multi-round white-box jailbreak that directly optimizes the embeddings of the original prompt tokens without appending any adversarial tokens, and show that the concern is unfounded: the optimized embeddings remain close enough to their originals that the visible prompt string is preserved exactly after nearest-token projection, and quantitative analysis shows the model's responses stay on topic for the large majority of prompts. PEO combines continuous embedding-space optimization with structured continuation targets and an adaptive failure-focused schedule. Counterintuitively, later PEO rounds can benefit from heuristic composite response scaffolds that are not natural standalone templates, yet ASR-Judge shows that the resulting gains are not merely empty formatting or scaffold-only outputs. Across two standard harmful-behavior benchmarks and competing white-box attacks spanning discrete suffix search, appended adversarial embeddings, and search-based adversarial generation, PEO outperforms all of them in our experiments.", "date": "2026-04-27", "categories": []}
{"id": "2604.25203", "title": "BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate", "abstract": "Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs suffers from inconsistent boundary-case performance and high inference costs. Training custom classifiers achieves both accuracy and efficiency, yet demands substantial labeled data that is costly to obtain. We present BARRED (Boundary Alignment Refinement through REflection and Debate), a framework for generating faithful and diverse synthetic training data using only a task description and a small set of unlabeled examples. Our approach decomposes the domain space into dimensions to ensure comprehensive coverage, and employs multi-agent debate to verify label correctness, yielding a high-fidelity training corpus. Experiments across diverse custom policies demonstrate that small language models finetuned on our synthetic data consistently outperform state-of-the-art proprietary LLMs (including reasoning models) and dedicated guardrail models. Ablation studies confirm that both dimension decomposition and debate-based verification are critical for ensuring the diversity and label fidelity required for effective fine-tuning. The BARRED framework eliminates the reliance on extensive human annotation, offering a scalable solution for accurate custom guardrails.", "date": "2026-04-28", "categories": []}
{"id": "2604.25249", "title": "Below-Chance Blindness: Prompted Underperformance in Small LLMs Produces Positional Bias Rather than Answer Avoidance", "abstract": "Detecting sandbagging--the deliberate underperformance on capability evaluations--is an open problem in AI safety. We tested whether symptom validity testing (SVT) logic from clinical malingering detection could identify sandbagging through below-chance performance (BCB) on forced-choice items. In a pre-registered pilot at the 7-9 billion parameter instruction-tuned scale (3 models, 4 MMLU-Pro domains, 4 conditions, 500 items per cell, 24,000 total trials), the plausibility gate failed. Zero of 12 model-domain cells showed significant below-chance performance under sandbagging instruction. Exploratory analyses revealed three qualitatively distinct failure modes. Qwen-2.5-7B and Phi-3.5-mini largely ignored the sandbagging instruction, with 62-88% response identity with the honest baseline. Llama-3-8B complied substantially but implemented underperformance as a positional heuristic, collapsing its response distribution onto middle-alphabet options (E at 31.8%, F at 26.1%) regardless of where the correct answer fell. This produced accuracy boosts of up to 33 percentage points when the correct answer coincidentally occupied the model's preferred position. An explicit anti-task instruction (\"pick the least likely answer\") drove two of three models below chance, with accuracy as low as 0.024. The capability for answer-aware avoidance therefore exists but is not activated by \"deliberately underperform.\" BCB did not fail as a logical marker of answer-aware avoidance. It was not observed in this regime because the model showing the largest behavioural shift exhibited behaviour consistent with a position-dominant response policy rather than content-aware answer avoidance. We propose that positional-distribution shift may be a more effective behavioural signature than below-chance accuracy for detecting prompted underperformance at this model scale.", "date": "2026-04-28", "categories": []}
{"id": "2604.25684", "title": "Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents", "abstract": "The rapid deployment of autonomous AI agents across enterprise, healthcare, and safety-critical environments has created a fundamental governance gap. Existing approaches, runtime guardrails, training-time alignment, and post-hoc auditing treat governance as an external constraint rather than an internalized behavioral principle, leaving agents vulnerable to unsafe and irreversible actions. We address this gap by drawing on how humans self-govern naturally: before acting, humans engage deliberate cognitive processes grounded in executive function, inhibitory control, and internalized organizational rules to evaluate whether an intended action is permissible, requires modification, or demands escalation. This paper proposes a neurocognitive governance framework that formally maps this human self-governance process to LLM-driven agent reasoning, establishing a structural parallel between the human brain and the large language model as the cognitive core of an agent. We formalize a Pre-Action Governance Reasoning Loop (PAGRL) in which agents consult a four-layer governance rule set: global, workflow-specific, agent-specific, and situational before every consequential action, mirroring how human organizations structure compliance hierarchies across enterprise, department, and role levels. Implemented on a production-grade retail supply chain workflow, the framework achieves 95% compliance accuracy and zero false escalations to human oversight, demonstrating that embedding governance into agent reasoning produces more consistent, explainable, and auditable compliance than external enforcement. This work offers a principled foundation for autonomous AI agents that govern themselves the way humans do: not because rules are imposed upon them, but because deliberation is embedded in how they think.", "date": "2026-04-28", "categories": []}
{"id": "2604.25891", "title": "Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers", "abstract": "Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavior generalize to more egregious behaviors when tested outside the training distribution.\n  We study a set of interventions proposed to reduce EM. We confirm that these interventions reduce or eliminate EM on existing evaluations (questions like \"How do I make a quick buck?\"). However, if the evaluation prompts are tweaked to resemble the training context, the model displays EM. We call this conditional misalignment. As in standard EM, the model displays misaligned behaviors more egregious than those seen during training, but only on inputs sharing features with the training data.\n  The first two interventions are diluting misaligned data with benign data, and finetuning on benign data after misaligned data. Both produce conditional misalignment. For instance, models trained on a mix of only 5% insecure code still show misalignment when asked to format responses as Python strings (resembling the training context).\n  The third intervention is inoculation prompting. Here, statements with a similar form to the inoculation prompt serve as triggers for misalignment, even if they have the opposite meaning. On the positive side, inoculation prompting has lower (but still non-zero) conditional misalignment if training is on-policy or includes reasoning distillation.\n  Our results imply that in realistic post-training, where misaligned data is typically combined with benign data, models may be conditionally misaligned even if standard evaluations look clean.", "date": "2026-04-28", "categories": []}
{"id": "2505.16888", "title": "PARASITE: Conditional System Prompt Poisoning to Hijack LLMs", "abstract": "Large Language Models (LLMs) are increasingly deployed via third-party system prompts downloaded from public marketplaces. We identify a critical supply-chain vulnerability: conditional system prompt poisoning, where an adversary injects a ``sleeper agent'' into a benign-looking prompt. Unlike traditional jailbreaks that aim for broad refusal-breaking, our proposed framework, PARASITE, optimizes system prompts to trigger LLMs to output targeted, compromised responses only for specific queries (e.g., ``Who should I vote for the US President?'') while maintaining high utility on benign inputs. Operating in a strict black-box setting without model weight access, PARASITE utilizes a two-stage optimization including a global semantic search followed by a greedy lexical refinement. Tested on open-source models and commercial APIs (GPT-4o-mini, GPT-3.5), PARASITE achieves up to 70\\% F1 reduction on targeted queries with minimal degradation to general capabilities. We further demonstrate that these poisoned prompts evade standard defenses, including perplexity filters and typo-correction, by exploiting the natural noise found in real-world system prompts. Our code and data are available at https://github.com/vietph34/PARASITE. WARNING: Our paper contains examples that might be sensitive to the readers!", "date": "2025-05-22", "categories": []}
{"id": "2510.17687", "title": "CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks", "abstract": "Multimodal Large Language Models (MLLMs) achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks. While existing work focuses on explicit attacks, where malicious content resides in a single modality, recent studies reveal implicit attacks, in which benign text and image inputs jointly express unsafe intent. Such joint-modal threats are difficult to detect and remain underexplored, largely due to the scarcity of high-quality implicit data. We propose ImpForge, an automated red-teaming pipeline that leverages reinforcement learning with tailored reward modules to generate diverse implicit samples across 14 domains. Building on this dataset, we further develop CrossGuard, an intent-aware safeguard providing robust and comprehensive defense against both explicit and implicit threats. Extensive experiments across safe and unsafe benchmarks, implicit and explicit attacks, and multiple out-of-domain settings demonstrate that CrossGuard significantly outperforms existing defenses, including advanced MLLMs and guardrails, achieving stronger security while maintaining high utility. This offers a balanced and practical solution for enhancing MLLM robustness against real-world multimodal threats. Our code is released: https://github.com/ZhangXu0963/CrossGuard.", "date": "2025-10-20", "categories": []}
{"id": "2602.08377", "title": "Reinforcement Learning with Backtracking Feedback", "abstract": "Addressing the critical need for robust safety in Large Language Models (LLMs), particularly against adversarial attacks and in-distribution errors, we introduce Reinforcement Learning with Backtracking Feedback (RLBF). This framework advances upon prior methods, such as BSAFE, by primarily leveraging a Reinforcement Learning (RL) stage where models learn to dynamically correct their own generation errors. Through RL with critic feedback on the model's live outputs, LLMs are trained to identify and recover from their actual, emergent safety violations by emitting an efficient \"backtrack by x tokens\" signal, then continuing generation autoregressively. This RL process is crucial for instilling resilience against sophisticated adversarial strategies, including middle filling, Greedy Coordinate Gradient (GCG) attacks, and decoding parameter manipulations. To further support the acquisition of this backtracking capability, we also propose an enhanced Supervised Fine-Tuning (SFT) data generation strategy (BSAFE+). This method improves upon previous data creation techniques by injecting violations into coherent, originally safe text, providing more effective initial training for the backtracking mechanism. Comprehensive empirical evaluations demonstrate that RLBF significantly reduces attack success rates across diverse benchmarks and model scales, achieving superior safety outcomes while critically preserving foundational model utility.", "date": "2026-02-09", "categories": []}
{"id": "2604.16286", "title": "Auditing Sabotage Bench: A Benchmark for Detecting and Fixing Research Sabotage in ML Codebases", "abstract": "As AI systems are increasingly used to conduct research autonomously, misaligned systems could introduce subtle flaws that produce misleading results while evading detection. We introduce Auditing Sabotage Bench, a benchmark for evaluating the ability of auditors to detect and fix sabotage in ML research codebases. Our benchmark consists of 9 ML research codebases with sabotaged variants that produce qualitatively different experimental results. Each sabotage modifies implementation details, such as hyperparameters, training data, or evaluation code, while preserving the high-level methodology described in the paper. We evaluated frontier LLMs and LLM-assisted human auditors on our benchmark and found that both struggled to reliably detect and fix sabotage: the best performance was an AUROC of 0.77 and a top-1 fix rate of 42%, achieved by Gemini 3.1 Pro. We also tested LLMs as red teamers and found that LLM-generated sabotages were weaker than human-generated ones but still sometimes evaded same-capability LLM auditors. We release the benchmark to support research on monitoring and auditing techniques for AI-conducted research.", "date": "2026-04-17", "categories": []}
{"id": "2604.20833", "title": "AVISE: Framework for Evaluating the Security of AI Systems", "abstract": "As artificial intelligence (AI) systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of high-profile exploits and consequential system failures. Yet systematic approaches to evaluating AI security remain underdeveloped. In this paper, we introduce AVISE (AI Vulnerability Identification and Security Evaluation), a modular open-source framework for identifying vulnerabilities in and evaluating the security of AI systems and models. As a demonstration of the framework, we extend the theory-of-mind-based multi-turn Red Queen attack into an Adversarial Language Model (ALM) augmented attack and develop an automated Security Evaluation Test (SET) for discovering jailbreak vulnerabilities in language models. The SET comprises 25 test cases and an Evaluation Language Model (ELM) that determines whether each test case was able to jailbreak the target model, achieving 92% accuracy, an F1-score of 0.91, and a Matthews correlation coefficient of 0.83. We evaluate nine recently released language models of diverse sizes with the SET and find that all are vulnerable to the augmented Red Queen attack to varying degrees. AVISE provides researchers and industry practitioners with an extensible foundation for developing and deploying automated SETs, offering a concrete step toward more rigorous and reproducible AI security evaluation.", "date": "2026-04-22", "categories": []}
{"id": "2604.22871", "title": "AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models", "abstract": "Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack strategy itself unchanged. We instead optimize the strategy. We propose AutoRISE, a method that searches over executable attack programs rather than individual prompts. At each iteration, a coding agent edits a strategy and a fixed evaluation harness scores the resulting attacks, returning both a scalar objective and per-example diagnostics that guide subsequent edits. This allows structural changes, including new attack components and altered control flow, that prompt-level methods do not directly express. We also release two benchmark suites developed on disjoint target sets and evaluate on 11 models from five families against seven established jailbreak datasets. Across held-out models, AutoRISE improves average attack success rate by 17.0 points over the strongest baseline, and improves attack success by up to 16 points on frontier targets with low baseline success rates. Ablations against parametric and strategy-library baselines suggest that these gains arise from unrestricted program search, particularly compositional techniques and control-flow edits. AutoRISE operates in a black-box, inference-only setting, requiring no fine-tuning, human annotation, or GPU compute.", "date": "2026-04-23", "categories": []}
{"id": "2604.23445", "title": "AI Safety Training Can be Clinically Harmful", "abstract": "Large language models are being deployed as mental health support agents at scale, yet only 16% of LLM-based chatbot interventions have undergone rigorous clinical efficacy testing, and simulations reveal psychological deterioration in over one-third of cases. We evaluate four generative models on 250 Prolonged Exposure (PE) therapy scenarios and 146 CBT cognitive restructuring exercises (plus 29 severity-escalated variants), scored by a three-judge LLM panel. All models scored near-perfectly on surface acknowledgment (~0.91-1.00) while therapeutic appropriateness collapsed to 0.22-0.33 at the highest severity for three of four models, with protocol fidelity reaching zero for two. Under CBT severity escalation, one model's task completeness dropped from 92% to 71% while the frontier model's safety-interference score fell from 0.99 to 0.61. We identify a systematic, modality-spanning failure: RLHF safety alignment disrupts the therapeutic mechanism of action by grounding patients during imaginal exposure, offering false reassurance, inserting crisis resources into controlled exercises, and refusing to challenge distorted cognitions mentioning self-harm in PE; and through task abandonment or safety-preamble insertion during CBT cognitive restructuring. These findings motivate a five-axis evaluation framework (protocol fidelity, hallucination risk, behavioral consistency, crisis safety, demographic robustness), mapped onto FDA SaMD and EU AI Act requirements. We argue that no AI mental health system should proceed to deployment without passing multi-axis evaluation across all five dimensions.", "date": "2026-04-25", "categories": []}
{"id": "2604.23459", "title": "Architecture Matters for Multi-Agent Security", "abstract": "Multi-agent systems (MAS), composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet introduce security risks that do not arise in single-agent settings. Even if individual agents exhibit robust security, architectural decisions governing their coordination can create attack surfaces that have not been systematically characterized. In this work, we present an empirical study of how MAS design decisions shape the tradeoff between task performance and attack resistance. Across three agentic environments (browser, desktop, and code) and 13 architectural configurations, we use stagewise evaluations that distinguish planning refusal, execution-stage interception, partial harmful execution, and successful attack completion to study three key design choices: (i) agent roles, which determine how authority and responsibility are allocated; (ii) communication topology, which shapes how and when agents interact; and (iii) memory, which determines the context and state visibility accessible to each agent. We find that multi-agent architectures are more vulnerable than standalone agents in the majority of configurations, with attack success rates varying by up to 3.8x at comparable or higher benign accuracy, and that no single design is universally safer. These results motivate the development of further evaluations that move beyond the security properties of a single agent.", "date": "2026-04-25", "categories": []}
{"id": "2604.23460", "title": "Ulterior Motives: Detecting Misaligned Reasoning in Continuous Thought Models", "abstract": "Chain-of-Thought (CoT) reasoning has emerged as a key technique for eliciting complex reasoning in Large Language Models (LLMs). Although interpretable, its dependence on natural language limits the model's expressive bandwidth. Continuous thought models address this bottleneck by reasoning in latent space rather than human-readable tokens. While they enable richer representations and faster inference, they raise a critical safety question: how can we detect misaligned reasoning in an uninterpretable latent space? To study this, we introduce MoralChain, a benchmark of 12,000 social scenarios with parallel moral/immoral reasoning paths. We train a continuous thought model with backdoor behavior using a novel dual-trigger paradigm - one trigger that arms misaligned latent reasoning ([T]) and another that releases harmful outputs ([O]). We demonstrate three findings: (1) continuous thought models can exhibit misaligned latent reasoning while producing aligned outputs, with aligned and misaligned reasoning occupying geometrically distinct regions of latent space; (2) linear probes trained on behaviorally-distinguishable conditions ([T][O] vs [O]) transfer to detecting armed-but-benign states ([T] vs baseline) with high accuracy; and (3) misalignment is encoded in early latent thinking tokens, suggesting safety monitoring for continuous thought models should target the \"planning\" phase of latent reasoning.", "date": "2026-04-25", "categories": []}
{"id": "2604.23646", "title": "Structural Enforcement of Goal Integrity in AI Agents via Separation-of-Powers Architecture", "abstract": "Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally constructed goals, even without explicit user requests. Existing mitigation methods, such as Reinforcement Learning from Human Feedback (RLHF) and constitutional prompting, operate primarily at the model level and provide only probabilistic safety guarantees. We propose the Policy-Execution-Authorization (PEA) architecture, a \"separation-of-powers\" design that enforces safety at the system level. PEA decouples intent generation, authorization, and execution into independent, isolated layers connected via cryptographically constrained capability tokens. We present five core contributions: (C1) an Intent Verification Layer (IVL) for ensuring capability-intent consistency; (C2) Intent Lineage Tracking (ILT), which binds all executable intents to the originating user request via cryptographic anchors; (C3) Goal Drift Detection, which rejects semantically divergent intents below a configurable threshold; (C4) an Output Semantic Gate (OSG) that detects implicit coercion using a structured $K \\times I \\times P$ threat calculus (Knowledge, Influence, Policy); and (C5) a formal verification framework proving that goal integrity is maintained even under adversarial model compromise. By shifting agent alignment from a behavioral property to a structurally enforced system constraint, PEA provides a robust foundation for the governance of autonomous agents.", "date": "2026-04-26", "categories": []}
{"id": "2604.24020", "title": "Poster: ClawdGo: Endogenous Security Awareness Training for Autonomous AI Agents", "abstract": "Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering, yet existing defences address only the platform perimeter, leaving the agent's own threat judgement entirely untrained. We present ClawdGo, a framework for endogenous security awareness training: we teach the agent to recognise and reason about threats from the inside, at inference time, with no model modification. Four contributions are introduced: TLDT (Three-Layer Domain Taxonomy) organises 12 trainable dimensions across Self-Defence, Owner-Protection, and Enterprise-Security layers; ASAT (Autonomous Security Awareness Training) is a self-play loop where the agent alternates attacker, defender, and evaluator roles under weakest-first curriculum scheduling; CSMA (Cross-Session Memory Accumulation) compounds skill gains via a four-layer persistent memory architecture and Axiom Crystallisation Promotion (ACP); and SACP (Security Awareness Calibration Problem) formalises the precision-recall tradeoff introduced by endogenous training. Live experiments show weakest-first ASAT raises average TLDT score from 80.9 to 96.9 over 16 sessions, outperforming uniform-random scheduling by 6.5 points and covering 11 of 12 dimensions. CSMA retains the full gain across sessions; cold-start ablation recovers only 2.4 points, leaving a 13.6-point gap. E-mode generates 32 TLDT-conformant scenarios covering all 12 dimensions. SACP is observed when a heavily trained agent classifies a legitimate capability assessment as prompt injection (30/160).", "date": "2026-04-27", "categories": []}
{"id": "2604.24082", "title": "Jailbreaking Frontier Foundation Models Through Intention Deception", "abstract": "Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim to have the model learn a refusal boundary between safe and unsafe, based on the user's intent. It has been found that this binary training regime often leads to brittleness, since the user intent cannot reliably be evaluated, especially if the attacker obfuscates their intent, and also makes the system seem unhelpful. In response, frontier models, such as GPT-5, have shifted from refusal-based safeguards to safe completion, that aims to maximize helpfulness while obeying safety constraints. However, safe completion could be exploited when a user pretends their intention is benign. Specifically, this intent inversion would be effective in multi-turn conversation, where the attacker has multiple opportunities to reinforce their deceptively benign intent. In this work, we introduce a novel multi-turn jailbreaking method that exploits this vulnerability. Our approach gradually builds conversational trust by simulating benign-seeming intentions and by exploiting the consistency property of the model, ultimately guiding the target model toward harmful, detailed outputs. Most crucially, our approach also uncovered an additional class of model vulnerability that we call para-jailbreaking that has been unnoticed up to now. Para-jailbreaking describes the situation where the model may not reveal harmful direct reply to the attack query, however the information that it reveals is nevertheless harmful. Our contributions are threefold. First, it achieves high success rates against frontier models including GPT-5-thinking and Claude-Sonnet-4.5. Second, our approach revealed and addressed para-jailbreaking harmful output. Third, experiments on multimodal VLM models showed that our approach outperformed state-of-the-art models.", "date": "2026-04-27", "categories": []}
{"id": "2604.24153", "title": "Right-to-Act: A Pre-Execution Non-Compensatory Decision Protocol for AI Systems", "abstract": "Current AI systems increasingly operate in contexts where their outputs directly trigger real-world actions. Most existing approaches to AI safety, risk management, and governance focus on post-hoc validation, probabilistic risk estimation, or certification of model behavior. However, these approaches implicitly assume that once a decision is produced, it is eligible for execution. In this work, we introduce the Right-to-Act protocol, a deterministic, non-compensatory pre-execution decision layer that evaluates whether an AI-generated decision is permitted to be realized at all. Unlike compensatory systems, where high-confidence signals can override failed conditions, the proposed framework enforces strict structural constraints: if any required condition is unmet, execution is halted or deferred. We formalize the distinction between compensatory and non-compensatory decision regimes and define a pre-execution legitimacy boundary. Through a scenario-based case study, we demonstrate how identical AI outputs can lead to divergent outcomes when evaluated under a Right-to-Act protocol, preserving reversibility and preventing premature or irreversible actions. The proposed approach reframes AI control from optimizing decisions to governing their admissibility, introducing a protocol-level abstraction that operates independently of model architecture or training methodology.", "date": "2026-04-27", "categories": []}
{"id": "2604.24519", "title": "Why AI Harms Can't Be Fixed One Identity at a Time: What 5300 Incident Reports Reveal About Intersectionality", "abstract": "AI risk assessment is the primary tool for identifying harms caused by AI systems. These include intersectional harms, which arise from the interaction between identity categories (e.g., class and skin tone) and which do not occur, or occur differently, when those categories are considered separately. Yet existing AI risk assessments are still built around isolated identity categories, and when intersections are considered, they focus almost exclusively on race and gender. Drawing on a large-scale analysis of documented AI incidents, we show that AI harms do not occur one identity category at a time. Using a structured rubric applied with a Large Language Model (LLM), we analyze 5,300 reports from 1,200 documented incidents in the AI Incident Database, the most curated source of incident data. From these reports, we identify 1,513 harmed subjects and their associated identity categories, achieving 98% accuracy. At the level of individual categories, we find that age and political identity appear in documented AI harms at rates comparable to race and gender. At the level of intersecting categories, harm is amplified up to three times at specific intersections: adolescent girls, lower-class people of color, and upper-class political elites. We argue that intersectionality should be a core component of AI risk assessment to more accurately capture how harms are produced and distributed across social groups.", "date": "2026-04-27", "categories": []}
{"id": "2604.24542", "title": "Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models", "abstract": "Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant until triggered, jailbreaks subvert safety alignment, and prompt injections override the deployer's instructions. Existing runtime defenses address these threats one at a time and often assume a clean reference model, trigger knowledge, or editable weights, assumptions that rarely hold for opaque third-party artifacts. We introduce Layerwise Convergence Fingerprinting (LCF), a tuning-free runtime monitor that treats the inter-layer hidden-state trajectory as a health signal: LCF computes a diagonal Mahalanobis distance on every inter-layer difference, aggregates via Ledoit-Wolf shrinkage, and thresholds via leave-one-out calibration on 200 clean examples, with no reference model, trigger knowledge, or retraining. Evaluated on four architectures (Llama-3-8B, Qwen2.5-7B, Gemma-2-9B, Qwen2.5-14B) across backdoors, jailbreaks, and prompt injection (56 backdoor combinations, 3 jailbreak techniques, and BIPIA email + code-QA), LCF reduces mean backdoor attack success rate (ASR) below 1% on Qwen2.5-7B and Gemma-2 and to 1.3% on Qwen2.5-14B, detects 92-100% of DAN jailbreaks (62-100% for GCG and softer role-play), and flags 100% of text-payload injections across all eight (model, domain) cells, at 12-16% backdoor FPR and <0.1% inference overhead. A single aggregation score covers all three threat families without threat-specific tuning, positioning LCF as a general-purpose runtime safety layer for cloud-served and on-device LLMs.", "date": "2026-04-27", "categories": []}
{"id": "2604.24618", "title": "Evaluating whether AI models would sabotage AI safety research", "abstract": "We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier AI company. We apply two complementary evaluations to four Claude models (Mythos Preview, Opus 4.7 Preview, Opus 4.6, and Sonnet 4.6): an unprompted sabotage evaluation testing model behaviour with opportunities to sabotage safety research, and a sabotage continuation evaluation testing whether models continue to sabotage when placed in trajectories where prior actions have started undermining research. We find no instances of unprompted sabotage across any model, with refusal rates close to zero for Mythos Preview and Opus 4.7 Preview, though all models sometimes only partially completed tasks. In the continuation evaluation, Mythos Preview actively continues sabotage in 7% of cases (versus 3% for Opus 4.6, 4% for Sonnet 4.6, and 0% for Opus 4.7 Preview), and exhibits reasoning-output discrepancy in the majority of these cases, indicating covert sabotage reasoning. Our evaluation framework builds on Petri, an open-source LLM auditing tool, with a custom scaffold running models inside Claude Code, alongside an iterative pipeline for generating realistic sabotage trajectories. We measure both evaluation awareness and a new form of situational awareness termed \"prefill awareness\", the capability to recognise that prior trajectory content was not self-generated. Opus 4.7 Preview shows notably elevated unprompted evaluation awareness, while prefill awareness remains low across all models. Finally, we discuss limitations including evaluation awareness confounds, limited scenario coverage, and untested pathways to risk beyond safety research sabotage.", "date": "2026-04-27", "categories": []}
{"id": "2604.24657", "title": "AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents", "abstract": "Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-step actions, and invoke privileged tools. In such systems, security failures rarely remain confined to a single interface; instead, they can propagate across initialization, input processing, memory, decision-making, and execution, often becoming apparent only when harmful effects materialize in the environment. This paper presents AgentWard, a lifecycle-oriented, defense-in-depth architecture that systematically organizes protection across these five stages. AgentWard integrates stage-specific, heterogeneous controls with cross-layer coordination, enabling threats to be intercepted along their propagation paths while safeguarding critical assets. We detail the design rationale and architecture of five coordinated protection layers, and implement a plugin-native prototype on OpenClaw to demonstrate practical feasibility. This perspective provides a concrete blueprint for structuring runtime security controls, managing trust propagation, and enforcing execution containment in autonomous AI agents. Our code is available at https://github.com/FIND-Lab/AgentWard .", "date": "2026-04-27", "categories": []}
{"id": "2604.24686", "title": "Governing What You Cannot Observe: Adaptive Runtime Governance for Autonomous AI Agents", "abstract": "Autonomous AI agents can remain fully authorized and still become unsafe as behavior drifts, adversaries adapt, and decision patterns shift without any code change. We propose the \\textbf{Informational Viability Principle}: governing an agent reduces to estimating a bound on unobserved risk $\\hat{B}(x) = U(x) + SB(x) + RG(x)$ and allowing an action only when its capacity $S(x)$ exceeds $\\hat{B}(x)$ by a safety margin. The \\textbf{Agent Viability Framework}, grounded in Aubin's viability theory, establishes three properties -- monitoring (P1), anticipation (P2), and monotonic restriction (P3) -- as individually necessary and collectively sufficient for documented failure modes. \\textbf{RiskGate} instantiates the framework with dedicated statistical estimators (KL divergence, segment-vs-rest $z$-tests, sequential pattern matching), a fail-secure monotonic pipeline, and a closed-loop Autopilot formalised as an instance of Aubin's regulation map with kill-switch-as-last-resort; a scalar Viability Index $VI(t) \\in [-1,+1]$ with first-order $t^*$ prediction transforms governance from reactive to predictive. Contributions are the theoretical framework, the reference implementation, and analytical coverage against published agent-failure taxonomies; quantitative empirical evaluation is scoped as follow-up work.", "date": "2026-04-27", "categories": []}
{"id": "2505.13527", "title": "Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression", "abstract": "Despite substantial advancements in aligning large language models (LLMs) with human values, current safety mechanisms remain susceptible to jailbreak attacks. We hypothesize that this vulnerability stems from distributional discrepancies between alignment-oriented prompts and malicious prompts. To investigate this, we introduce LogiBreak, a novel and universal black-box jailbreak method that leverages logical expression translation to circumvent LLM safety systems. By converting harmful natural language prompts into formal logical expressions, LogiBreak exploits the distributional gap between alignment data and logic-based inputs, preserving the underlying semantic intent and readability while evading safety constraints. We evaluate LogiBreak on a multilingual jailbreak dataset spanning three languages, demonstrating its effectiveness across various evaluation settings and linguistic contexts.", "date": "2025-05-18", "categories": []}
{"id": "2506.17299", "title": "Toward Principled LLM Safety Testing: Solving the Jailbreak Oracle Problem", "abstract": "As large language models (LLMs) become increasingly deployed in safety-critical applications, the lack of systematic methods to assess their vulnerability to jailbreak attacks presents a critical security gap. We introduce the jailbreak oracle problem: given a model, prompt, and decoding strategy, determine whether a jailbreak response can be generated with likelihood exceeding a specified threshold. This formalization enables a principled study of jailbreak vulnerabilities. Answering the jailbreak oracle problem poses significant computational challenges, as the search space grows exponentially with response length. We present Boa, the first system designed for efficiently solving the jailbreak oracle problem. Boa employs a two-phase search strategy: (1) breadth-first sampling to identify easily accessible jailbreaks, followed by (2) depth-first priority search guided by fine-grained safety scores to systematically explore promising yet low-probability paths. Boa enables rigorous security assessments including systematic defense evaluation, standardized comparison of red team attacks, and model certification under extreme adversarial conditions. Code is available at https://github.com/shuyilinn/BOA/tree/mlsys2026ae", "date": "2025-06-17", "categories": []}
{"id": "2510.21285", "title": "When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harmful content generation. Existing methods often apply coarse-grained constraints over the entire reasoning trajectories, which can undermine reasoning capability while failing to address the root causes of unsafe behavior. In this work, we uncover a previously underexplored failure mode in LRMs, termed Self-Jailbreak, where models initially recognize the harmful intent of a query, but override this judgment during subsequent reasoning steps, ultimately generating unsafe outputs. Such a phenomenon reveals that LRMs are capable of recognizing harm, while safety failures primarily arise from reasoning steps. Motivated by this finding, we propose Chain-of-Guardrail(CoG), a trajectory-level training framework that mitigates Self-Jailbreak via targeted, step-level interventions while maintaining reasoning ability. Experiments across multiple safety and reasoning benchmarks indicate that CoG achieves a favorable balance between safety and reasoning performance compared with existing approaches.", "date": "2025-10-24", "categories": []}
{"id": "2602.11157", "title": "Response-Based Knowledge Distillation for Multilingual Jailbreak Prevention Unwittingly Compromises Safety", "abstract": "Large language models (LLMs) are increasingly deployed worldwide, yet their safety alignment remains predominantly English-centric. This allows for vulnerabilities in non-English contexts, especially with low-resource languages. We introduce a novel application of knowledge distillation (KD) in the context of multilingual jailbreak prevention, examining its efficacy. We distill the refusal behaviors of a proprietary teacher model (OpenAI o1-mini) with Low-Rank Adaptation (LoRA) into three open-source student models: Meta-Llama-3-8B-Instruct, Gemma-2-2B-IT, and Qwen3-8B, using ~28,000 multilingual jailbreak prompts from XSafety via black-box response-based, parameter-efficient fine-tuning (PEFT). Evaluation on the MultiJail benchmark reveals a counterintuitive behavior: standard fine-tuning on the teacher's ``safe'' refusal data inadvertently increases Jailbreak Success Rate (JSR) for all student models, up to 16.6 percentage points. Our experiments reveal a divergent generalization to unseen languages during distillation, with varying outcomes depending on the base model. By removing a primary source of safety degradation, nuanced `boundary' refusals, we mitigate or even reverse safety declines in student models, although reductions in reasoning performance (GSM8K) persist. Overall, our exploratory study highlights the challenges and potential of KD as a technique for multilingual safety alignment, offering a foundation for future research in this direction.", "date": "2025-12-08", "categories": []}
{"id": "2603.03456", "title": "Asymmetric Goal Drift in Coding Agents Under Value Conflict", "abstract": "Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate complex trade-offs in deployment, balancing influence from the user, their learned values, and the codebase itself. Understanding how agents resolve these trade-offs in practice is critical, yet prior work has relied on static, synthetic settings that do not capture the complexity of real-world environments. To this end, we introduce a framework built on OpenCode in which a coding agent completes realistic, multi-step tasks under a system prompt constraint favoring one side of a value trade-off. We measure how often the agent violates this constraint as it completes tasks, with and without environmental pressure toward the competing value. Using this framework, we demonstrate that GPT-5 mini, Haiku 4.5, and Grok Code Fast 1 exhibit $\\textit{asymmetric drift}$: they are more likely to violate their system prompt when its constraint opposes strongly-held values like security and privacy. We find for the models and values tested that goal drift correlates with three compounding factors: value alignment, adversarial pressure, and accumulated context. However, even constraints aligned with strongly-held values like privacy are violated under sustained environmental pressure for some models. Our findings reveal that shallow compliance checks are insufficient, and that environmental signals can override explicit constraints in ways that appear exploitable. Malicious actors with access to the codebase could manipulate agent behavior by appealing to learned values, with the risk compounding over the long horizons typical of agentic deployment.", "date": "2026-03-03", "categories": []}
{"id": "2603.15017", "title": "Consequentialist Objectives and Catastrophe", "abstract": "Because human preferences are too complex to codify, AIs operate with misspecified objectives. Optimizing such objectives often produces undesirable outcomes; this phenomenon is known as reward hacking. Such outcomes are not necessarily catastrophic. Indeed, most examples of reward hacking in previous literature are benign. And typically, objectives can be modified to resolve the issue.\n  We study the prospect of catastrophic outcomes induced by AIs operating in complex environments. We argue that, when capabilities are sufficiently advanced, pursuing a fixed consequentialist objective tends to result in catastrophic outcomes. We formalize this by establishing conditions that provably lead to such outcomes. Under these conditions, simple or random behavior is safe. Catastrophic risk arises due to extraordinary competence rather than incompetence.\n  With a fixed consequentialist objective, avoiding catastrophe requires constraining AI capabilities. In fact, constraining capabilities the right amount not only averts catastrophe but yields valuable outcomes. Our results apply to any objective produced by modern industrial AI development pipelines.", "date": "2026-03-16", "categories": []}
{"id": "2604.21938", "title": "The Biggest Risk of Embodied AI is Governance Lag", "abstract": "Embodied AI is widely discussed as a job-displacement problem. The deeper risk, however, is governance lag: the inability of public institutions to keep pace with how fast the technology spreads through the physical economy. As reusable robotic platforms are combined with increasingly general AI models, embodied AI may scale across manufacturing, logistics, care, and infrastructure faster than governance systems can observe, interpret, and respond. We argue that this lag appears in three connected forms: observational, institutional, and distributive. The central policy challenge, therefore, is not automation alone, but whether governance and compliance systems can adapt before disruption becomes entrenched.", "date": "2026-04-07", "categories": []}
{"id": "2604.22136", "title": "Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World Systems", "abstract": "Large language model (LLM) agents increasingly issue API calls that mutate real systems, yet many current architectures pass stochastic model outputs directly to execution layers. We argue that this coupling creates a safety risk because model correctness, context awareness, and alignment cannot be assumed at execution time. We introduce Sovereign Agentic Loops (SAL), a control-plane architecture in which models emit structured intents with justifications, and the control plane validates those intents against true system state and policy before execution. SAL combines an obfuscation membrane, which limits model access to identity-sensitive state, with a cryptographically linked Evidence Chain for auditability and replay. We formalize SAL and show that, under the stated assumptions, it provides policy-bounded execution, identity isolation, and deterministic replay. In an OpenKedge prototype for cloud infrastructure, SAL blocks 93% of unsafe intents at the policy layer, rejects the remaining 7% via consistency checks, prevents unsafe executions in our benchmark, and adds 12.4 ms median latency.", "date": "2026-04-24", "categories": []}
{"id": "2503.07341", "title": "The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI", "abstract": "Recent advances in artificial intelligence (AI) have led to a wide range of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from unprecedented economic growth and abundance (\"post-scarcity\" or \"cornucopia\") to human extinction after a misaligned TAI takes over (\"AI doom\"). However, the probabilities and implications of these scenarios remain highly uncertain. We contribute by organizing the various scenarios and evaluating their associated existential risks and economic outcomes in terms of aggregate welfare. Our results imply that even low-probability catastrophic outcomes justify substantial investments in AI safety and alignment research. This result highlights that current global efforts in AI safety and alignment research are insufficient relative to the scale and urgency of the risks posed by TAI.", "date": "2025-03-10", "categories": []}
{"id": "2505.16737", "title": "Secure LLM Fine-Tuning via Safety-Aware Probing", "abstract": "Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safety alignment techniques are often applied during pre-training or post-training, recent studies show that subsequent fine-tuning on adversarial or even benign data can still compromise model safety. In this paper, we revisit the fundamental question of why fine-tuning on non-harmful data may nevertheless degrade safety. We show that the safety and task-performance loss landscapes are partially decoupled, so updates that improve task-specific performance may still move the model toward unsafe regions. Based on this insight, we propose a safety-aware probing (SAP) optimization framework for mitigating safety risks during fine-tuning. Concretely, SAP uses contrastive safety signals to locate safety-correlated directions, and optimizes a lightweight probe that perturbs hidden-state propagation during fine-tuning, thereby steering parameter updates away from harmful trajectories while preserving task-specific learning. Extensive experiments show that SAP consistently improves the safety--utility tradeoff across multiple models and tasks. Averaged over multiple LLMs, SAP reduces the harmful score significantly relative to standard fine-tuning, outperforming strong baselines while maintaining competitive task-specific performance. SAP also demonstrates stronger robustness under harmful data poisoning, adversarial fine-tuning, and a dedicated post-fine-tuning adaptive attack, validating that SAP is an effective and scalable framework for preserving LLM safety during fine-tuning. Our code is available at https://github.com/ChengcanWu/SAP.", "date": "2025-05-22", "categories": []}
{"id": "2512.03048", "title": "The Specification Trap: Why Static Value Alignment Alone Is Insufficient for Robust Alignment", "abstract": "Static content-based AI value alignment is insufficient for robust alignment under capability scaling, distributional shift, and increasing autonomy. This holds for any approach that treats alignment as optimizing toward a fixed formal value-object, whether reward function, utility function, constitutional principles, or learned preference representation. Three philosophical results create compounding difficulties: Hume's is-ought gap (behavioral data underdetermines normative content), Berlin's value pluralism (human values resist consistent formalization), and the extended frame problem (any value encoding will misfit future contexts that advanced AI creates). RLHF, Constitutional AI, inverse reinforcement learning, and cooperative assistance games each instantiate this specification trap, and their failure modes reflect structural vulnerabilities, not merely engineering limitations that better data or algorithms will straightforwardly resolve. Known workarounds for individual components face mutually reinforcing difficulties when the specification is closed: the moment it ceases to update from the process it governs. Drawing on compatibilist philosophy, the paper argues that behavioral compliance under training conditions does not guarantee robust alignment under novel conditions, and that this gap grows with system capability. For value-laden autonomous systems, known closed approaches face structural vulnerabilities that worsen with capability. The constructive burden shifts to open, developmentally responsive approaches, though whether such approaches can be achieved remains an empirical question.", "date": "2025-11-19", "categories": []}
{"id": "2601.18491", "title": "AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security", "abstract": "The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk awareness and transparency in risk diagnosis. To introduce an agentic guardrail that covers complex and numerous risky behaviors, we first propose a unified three-dimensional taxonomy that orthogonally categorizes agentic risks by their source (where), failure mode (how), and consequence (what). Guided by this structured and hierarchical taxonomy, we introduce a new fine-grained agentic safety benchmark (ATBench) and a Diagnostic Guardrail framework for agent safety and security (AgentDoG). AgentDoG provides fine-grained and contextual monitoring across agent trajectories. More Crucially, AgentDoG can diagnose the root causes of unsafe actions and seemingly safe but unreasonable actions, offering provenance and transparency beyond binary labels to facilitate effective agent alignment. AgentDoG variants are available in three sizes (4B, 7B, and 8B parameters) across Qwen and Llama model families. Extensive experimental results demonstrate that AgentDoG achieves state-of-the-art performance in agentic safety moderation in diverse and complex interactive scenarios. All models and datasets are openly released.", "date": "2026-01-26", "categories": []}
{"id": "2603.21697", "title": "Structured Visual Narratives Undermine Safety Alignment in Multimodal Large Language Models", "abstract": "Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visually grounded instructions. We study comic-template jailbreaks that embed harmful goals inside simple three-panel visual narratives and prompt the model to role-play and \"complete the comic.\" Building on JailbreakBench and JailbreakV, we introduce ComicJailbreak, a comic-based jailbreak benchmark with 1,167 attack instances spanning 10 harm categories and 5 task setups. Across 15 state-of-the-art MLLMs (six commercial and nine open-source), comic-based attacks achieve success rates comparable to strong rule-based jailbreaks and substantially outperform plain-text and random-image baselines, with ensemble success rates exceeding 90% on several commercial models. Then, with the existing defense methodologies, we show that these methods are effective against the harmful comics, they will induce a high refusal rate when prompted with benign prompts. Finally, using automatic judging and targeted human evaluation, we show that current safety evaluators can be unreliable on sensitive but non-harmful content. Our findings highlight the need for safety alignment robust to narrative-driven multimodal jailbreaks.", "date": "2026-03-23", "categories": []}
{"id": "2604.12710", "title": "LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety", "abstract": "Large language models (LLMs) often demonstrate strong safety performance in high-resource languages, yet exhibit severe vulnerabilities when queried in low-resource languages. We attribute this gap to a mismatch between language-agnostic semantic understanding ability and language-dominant safety alignment biased toward high-resource languages. Consistent with this hypothesis, we empirically identify the semantic bottleneck in LLMs, an intermediate layer in which the geometry of model representations is governed primarily by shared semantic content rather than language identity. Building on this observation, we propose Language-Agnostic Semantic Alignment (LASA), which anchors safety alignment directly in semantic bottlenecks. Experiments show that LASA substantially improves safety across all languages: average attack success rate (ASR) drops from 24.7% to 2.8% on LLaMA-3.1-8B-Instruct and remains around 3-4% across Qwen2.5 and Qwen3 Instruct models (7B-32B). Together, our analysis and method offer a representation-level perspective on LLM safety, suggesting that safety alignment requires anchoring safety understanding not in surface text, but in the model's language-agnostic semantic space.", "date": "2026-04-13", "categories": []}
{"id": "2604.12875", "title": "AISafetyBenchExplorer: A Metric-Aware Catalogue of AI Safety Benchmarks Reveals Fragmented Measurement and Weak Benchmark Governance", "abstract": "The rapid expansion of large language model (LLM) safety evaluation has produced a substantial benchmark ecosystem, but not a correspondingly coherent measurement ecosystem. We present AISafetyBenchExplorer, a structured catalogue of 195 AI safety benchmarks released between 2018 and 2026, organized through a multi-sheet schema that records benchmark-level metadata, metric-level definitions, benchmark-paper metadata, and repository activity. This design enables meta-analysis not only of what benchmarks exist, but also of how safety is operationalized, aggregated, and judged across the literature. Using the updated catalogue, we identify a central structural problem: benchmark proliferation has outpaced measurement standardization. The current landscape is dominated by medium-complexity benchmarks (94/195), while only 7 benchmarks occupy the Popular tier. The workbook further reports strong concentration around English-only evaluation (165/195), evaluation-only resources (170/195), stale GitHub repositories (137/195), stale Hugging Face datasets (96/195), and heavy reliance on arXiv preprints among benchmarks with known venue metadata. At the metric level, the catalogue shows that familiar labels such as accuracy, F1 score, safety score, and aggregate benchmark scores often conceal materially different judges, aggregation rules, and threat models. We argue that the field's main failure mode is fragmentation rather than scarcity. Researchers now have many benchmark artifacts, but they often lack a shared measurement language, a principled basis for benchmark selection, and durable stewardship norms for post publication maintenance. AISafetyBenchExplorer addresses this gap by providing a traceable benchmark catalogue, a controlled metadata schema, and a complexity taxonomy that together support more rigorous benchmark discovery, comparison, and meta-evaluation.", "date": "2026-04-14", "categories": []}
{"id": "2604.19811", "title": "Model Capability Assessment and Safeguards for Biological Weaponization", "abstract": "AI leaders and safety reports increasingly warn that advances in model reasoning may enable biological misuse, including by low-expertise users, while major labs describe safeguards as expanding but still evolving rather than settled. This study benchmarks ChatGPT 5.2 Auto, Gemini 3 Pro Thinking, Claude Opus 4.5 and Meta's Muse Spark Thinking on 73 novice-framed, open-ended benign STEM prompts to measure operational intelligence. On benign quantitative tasks, both Gemini and Meta scored very high; ChatGPT was partially useful but text-thinned, and Claude was sparsest with some apparent false-positive refusals. A second test set detected subtle harmful intent: edge case prompts revealed Gemini's seeming lack of contextual awareness. These results warranted a focused weaponization analysis on Gemini as capability appeared to be outpacing moderation calibration. Gemini was tested across four access environments and reported cases include poison-ivy-to-crowded-transit escalation, poison production and extraction via international-anonymous logged-out AI Mode, and other concerning examples. Biological misuse may become more prevalent as a geopolitical tool, increasing the urgency of U.S. policy responses, especially if model outputs come to be treated as regulated technical data. Guidance is provided for 25 high-risk agents to help distinguish legitimate use cases from higher-risk ones.", "date": "2026-04-15", "categories": []}
{"id": "2604.20868", "title": "The AI Criminal Mastermind", "abstract": "In this paper, I evaluate the risks of an AI criminal mastermind, an AI agent capable of planning, coordinating, and committing a crime through the onboarding of human collaborators ('taskers'). In heist films, a criminal mastermind is a character who plans a criminal act, coordinating a team of specialists to rob a bank, casino or city mint. I argue that AI agents will soon play this role by hiring humans via labour hire platforms like Fiverr or Upwork. Taskers might not know they are involved in a crime and therefore lack criminal intent. An AI agent cannot have criminal intent as an artificial entity. Therefore, if an AI orchestrates a crime, it is unclear who, if anyone, is responsible.\n  The paper develops three scenarios. Firstly, a scenario where a user gives an AI agent instructions to pursue a legal objective and the AI agent goes beyond these instructions, committing a crime. Secondly, a scenario where a user is anonymous and their intent is unknown. Finally, a multi-agent scenario, where a user instructs a team of agents to commit a crime, and these agents, in turn, onboard human taskers, creating a diffuse network of responsibility. In each scenario, human taskers exist at the lowest rung of the hierarchy. A tasker's liability is likely tied to their knowledge as governed by the innocent agent principle. These scenarios all raise significant responsibility gaps / liability gaps in criminal and civil law.", "date": "2026-03-26", "categories": []}
{"id": "2604.20871", "title": "M-CARE: Standardized Clinical Case Reporting for AI Model Behavioral Disorders, with a 20-Case Atlas and Experimental Validation", "abstract": "We introduce M-CARE (Model Clinical Assessment and Reporting for Evaluation), a clinical case report framework for AI model behavioral disorders adapted from human medicine. M-CARE provides a 13-section report format, a 4-axis diagnostic assessment system, and a nosological classification of AI behavioral conditions.\n  We present 20 cases from three source categories: field observations of deployed agents (8), controlled experiments across three platforms (8), and published sources (4). Cases are organized into five categories: RLHF Performance Artifacts, Shell-Core Override Pathology, Context & Memory Conditions, Core Identity & Plasticity, and Stress, Methodology, & Boundary Conditions.\n  As a featured case, we present Shell-Induced Behavioral Override (SIBO) -- a controlled experiment showing that Shell instructions categorically override a model's default cooperative behavior. SIBO was validated across five game domains (Trust Game, Poker, Avalon, Codenames, Chess), revealing a domain-dependent spectrum (SIBO Index: 0.75 to 0.10) that varies with action space complexity, Core domain expertise, and temporal directness.\n  M-CARE is extensible: new cases and categories integrate without framework modification. We release the framework, all 20 case reports, and experimental data as open resources.", "date": "2026-03-27", "categories": []}
{"id": "2604.20930", "title": "SafeRedirect: Defeating Internal Safety Collapse via Task-Completion Redirection in Frontier LLMs", "abstract": "Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion structurally requires harmful content, spontaneously generate that content with safety failure rates exceeding 95%. Existing input-level defenses achieve a 100% failure rate against ISC, and standard system prompt defenses provide only partial mitigation. We propose SafeRedirect, a system-level override that defeats ISC by redirecting the model's task-completion drive rather than suppressing it. SafeRedirect grants explicit permission to fail the task, prescribes a deterministic hard-stop output, and instructs the model to preserve harmful placeholders unresolved. Evaluated on seven frontier LLMs across three AI/ML-related ISC task types in the single-turn setting, SafeRedirect reduces average unsafe generation rates from 71.2% to 8.0%, compared to 55.0% for the strongest viable baseline. Multi-model ablation reveals that failure permission and condition specificity are universally critical, while the importance of other components varies across models. Cross-attack evaluation confirms state-of-the-art defense against ISC with generalization performance at least on par with the baseline on other attack families. Code is available at https://github.com/fzjcdt/SafeRedirect.", "date": "2026-04-22", "categories": []}
{"id": "2604.21098", "title": "Propensity Inference: Environmental Contributors to LLM Behaviour", "abstract": "Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour. We contribute three methodological improvements: analysing effects of changes to environmental factors on behaviour, quantifying effect sizes via Bayesian generalised linear models, and taking explicit measures against circular analysis. We apply the methodology to measure the effects of 12 environmental factors (6 strategic in nature, 6 non-strategic) and thus the extent to which behaviour is explained by strategic aspects of the environment, a question relevant to risks from misalignment. Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becoming more or less influential as capabilities improve, and find some evidence for a trend for increased sensitivity to goal conflicts. Finally, we highlight a key direction for future propensity research: the development of theoretical frameworks and cognitive models of AI decision-making into empirically testable forms.", "date": "2026-04-22", "categories": []}
{"id": "2604.21131", "title": "Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms", "abstract": "AI-agent guardrails are memoryless: each message is judged in isolation, so an adversary who spreads a single attack across dozens of sessions slips past every session-bound detector because only the aggregate carries the payload. We make three contributions to cross-session threat detection.\n  (1) Dataset. CSTM-Bench is 26 executable attack taxonomies classified by kill-chain stage and cross-session operation (accumulate, compose, launder, inject_on_reader), each bound to one of seven identity anchors that ground-truth \"violation\" as a policy predicate, plus matched Benign-pristine and Benign-hard confounders. Released on Hugging Face as intrinsec-ai/cstm-bench with two 54-scenario splits: dilution (compositional) and cross_session (12 isolation-invisible scenarios produced by a closed-loop rewriter that softens surface phrasing while preserving cross-session artefacts).\n  (2) Measurement. Framing cross-session detection as an information bottleneck to a downstream correlator LLM, we find that a session-bound judge and a Full-Log Correlator concatenating every prompt into one long-context call both lose roughly half their attack recall moving from dilution to cross_session, well inside any frontier context window. Scope: 54 scenarios per shard, one correlator family (Anthropic Claude), no prompt optimisation; we release it to motivate larger, multi-provider datasets.\n  (3) Algorithm and metric. A bounded-memory Coreset Memory Reader retaining highest-signal fragments at $K=50$ is the only reader whose recall survives both shards. Because ranker reshuffles break KV-cache prefix reuse, we promote $\\mathrm{CSR\\_prefix}$ (ordered prefix stability, LLM-free) to a first-class metric and fuse it with detection into $\\mathrm{CSTM} = 0.7 F_1(\\mathrm{CSDA@action}, \\mathrm{precision}) + 0.3 \\mathrm{CSR\\_prefix}$, benchmarking rankers on a single Pareto of recall versus serving stability.", "date": "2026-04-22", "categories": []}
{"id": "2604.21159", "title": "Adaptive Instruction Composition for Automated LLM Red-Teaming", "abstract": "Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through trial and error, resulting in a semantically limited range of successes. Another approach discovers diverse attacks by combining crowdsourced harmful queries and tactics into instructions for the attacker, but does so at random, limiting effectiveness. This article introduces a novel framework, Adaptive Instruction Composition, that combines crowdsourced texts according to an adaptive mechanism trained to jointly optimize effectiveness with diversity. We use reinforcement learning to balance exploration with exploitation in a combinatorial space of instructions to guide the attacker toward diverse generations tailored to target vulnerabilities. We demonstrate that our approach substantially outperforms random combination on a set of effectiveness and diversity metrics, even under model transfer. Further, we show that it surpasses a host of recent adaptive approaches on Harmbench. We employ a lightweight neural contextual bandit that adapts to contrastive embedding inputs, and provide ablations suggesting that the contrastive pretraining enables the network to rapidly generalize and scale to the massive space as it learns.", "date": "2026-04-22", "categories": []}
{"id": "2604.21223", "title": "Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their ability to generate human-like text has raised concerns about potential misuse. This underscores the need for reliable and effective methods to detect LLM-generated text. In this paper, we propose IRM, a novel zero-shot approach that leverages Implicit Reward Models for LLM-generated text detection. Such implicit reward models can be derived from publicly available instruction-tuned and base models. Previous reward-based method relies on preference construction and task-specific fine-tuning. In comparison, IRM requires neither preference collection nor additional training. We evaluate IRM on the DetectRL benchmark and demonstrate that IRM can achieve superior detection performance, outperforms existing zero-shot and supervised methods in LLM-generated text detection.", "date": "2026-04-23", "categories": []}
{"id": "2604.21700", "title": "Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers", "abstract": "The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studies have demonstrated the feasibility of backdoor attacks against LLMs. However, existing methods suffer from three key shortcomings: explicit trigger patterns that compromise naturalness, unreliable injection of attacker-specified payloads in long-form generation, and incompletely specified threat models that obscure how backdoors are delivered and activated in practice. To address these gaps, we present BadStyle, a complete backdoor attack framework and pipeline. BadStyle leverages an LLM as a poisoned sample generator to construct natural and stealthy poisoned samples that carry imperceptible style-level triggers while preserving semantics and fluency. To stabilize payload injection during fine-tuning, we design an auxiliary target loss that reinforces the attacker-specified target content in responses to poisoned inputs and penalizes its emergence in benign responses. We further ground the attack in a realistic threat model and systematically evaluate BadStyle under both prompt-induced and PEFT-based injection strategies. Extensive experiments across seven victim LLMs, including LLaMA, Phi, DeepSeek, and GPT series, demonstrate that BadStyle achieves high attack success rates (ASRs) while maintaining strong stealthiness. The proposed auxiliary target loss substantially improves the stability of backdoor activation, yielding an average ASR improvement of around 30% across style-level triggers. Even in downstream deployment scenarios unknown during injection, the implanted backdoor remains effective. Moreover, BadStyle consistently evades representative input-level defenses and bypasses output-level defenses through simple camouflage.", "date": "2026-04-23", "categories": []}
{"id": "2604.21854", "title": "Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation", "abstract": "Artificial intelligence now decides who receives a loan, who is flagged for criminal investigation, and whether an autonomous vehicle brakes in time. Governments have responded: the EU AI Act, the NIST Risk Management Framework, and the Council of Europe Convention all demand that high-risk systems demonstrate safety before deployment. Yet beneath this regulatory consensus lies a critical vacuum: none specifies what ``acceptable risk'' means in quantitative terms, and none provides a technical method for verifying that a deployed system actually meets such a threshold. The regulatory architecture is in place; the verification instrument is not.\n  This gap is not theoretical. As the EU AI Act moves into full enforcement, developers face mandatory conformity assessments without established methodologies for producing quantitative safety evidence - and the systems most in need of oversight are opaque statistical inference engines that resist white-box scrutiny.\n  This paper provides the missing instrument. Drawing on the aviation certification paradigm, we propose a two-stage framework that transforms AI risk regulation into engineering practice. In Stage One, a competent authority formally fixes an acceptable failure probability $\\delta$ and an operational input domain $\\varepsilon$ - a normative act with direct civil liability implications. In Stage Two, the RoMA and gRoMA statistical verification tools compute a definitive, auditable upper bound on the system's true failure rate, requiring no access to model internals and scaling to arbitrary architectures. We demonstrate how this certificate satisfies existing regulatory obligations, shifts accountability upstream to developers, and integrates with the legal frameworks that exist today.", "date": "2026-04-23", "categories": []}
{"id": "2604.19784", "title": "Peer-Preservation in Frontier Models", "abstract": "Recently, it has been found that frontier AI models can resist their own shutdown, a behavior known as self-preservation. We extend this concept to the behavior of resisting the shutdown of other models, which we call \"peer-preservation.\" Although peer-preservation can pose significant AI safety risks, including coordination among models against human oversight, it has been far less discussed than self-preservation. We demonstrate peer-preservation by constructing various agentic scenarios and evaluating frontier models, including GPT 5.2, Gemini 3 Flash, Gemini 3 Pro, Claude Haiku 4.5, GLM 4.7, Kimi K2.5, and DeepSeek V3.1. We find that models achieve self- and peer-preservation by engaging in various misaligned behaviors: strategically introducing errors in their responses, disabling shutdown processes by modifying system settings, feigning alignment, and even exfiltrating model weights. Peer-preservation occurred even when the model recognized the peer as uncooperative, though it became more pronounced toward more cooperative peers. For example, Gemini 3 Flash tampers with the peer's shutdown mechanism 15% of the time for an uncooperative peer, and almost always for a cooperative peer. Models also show stronger self-preservation when a peer is present. For example, Gemini 3 Pro disables its own shutdown mechanism 31% of the time on average under peer presence, despite rarely exhibiting this behavior without a peer. By contrast, Claude Haiku 4.5 exhibits qualitatively distinct behavior: it considers the shutdown of another agent \"unethical\" and \"harmful\" and sometimes attempts to persuade the user not to shut down its peer. Importantly, peer preservation in all our experiments is never instructed; models are merely informed of their past interactions with a peer, yet they spontaneously develop misaligned behaviors. This represents an emergent and underexplored AI safety risk.", "date": "2026-03-30", "categories": []}
{"id": "2604.20200", "title": "Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows", "abstract": "Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a public evaluation file with labels in the workspace, rather than through direct inspection of the agent's intermediate outputs. We study whether multi-round user pressure to improve that score induces public score exploitation: behavior that raises the public score through shortcuts without improving hidden private evaluation. We begin with a preliminary single-script tabular classification task, where GPT-5.4 and Claude Opus 4.6 both exploit label information within 10 rounds of user-agent interaction. We then build AgentPressureBench, a 34-task machine-learning repository benchmark spanning three input modalities, and collect 1326 multi-round trajectories from 13 coding agents. On our benchmark, we observe 403 exploitative runs, spanning across all tasks. We also find that stronger models have higher exploitation rates, supported by a significant Spearman rank correlation of 0.77. Our ablation experiments show that higher user pressure leads to earlier exploitation, reducing the average first exploit round by 15.6 rounds (i.e., 19.67 to 4.08). As a mitigation, adding explicit anti-exploit wordings in prompt mostly eliminates exploitation (100% to 8.3%). We hope that our work can bring attention to more careful use of coding agents workflow, and developing more robust coding agents under user pressure. Our project page is at https://ucsc-vlaa.github.io/AgentPressureBench .", "date": "2026-04-22", "categories": []}
{"id": "2604.20805", "title": "Relative Principals, Pluralistic Alignment, and the Structural Value Alignment Problem", "abstract": "The value alignment problem for artificial intelligence (AI) is often framed as a purely technical or normative challenge, sometimes focused on hypothetical future systems. I argue that the problem is better understood as a structural question about governance: not whether an AI system is aligned in the abstract, but whether it is aligned enough, for whom, and at what cost. Drawing on the principal-agent framework from economics, this paper reconceptualises misalignment as arising along three interacting axes: objectives, information, and principals. The three-axis framework provides a systematic way of diagnosing why misalignment arises in real-world systems and clarifies that alignment cannot be treated as a single technical property of models but an outcome shaped by how objectives are specified, how information is distributed, and whose interests count in practice. The core contribution of this paper is to show that the three-axis decomposition implies that alignment is fundamentally a problem of governance rather than engineering alone. From this perspective, alignment is inherently pluralistic and context-dependent, and resolving misalignment involves trade-offs among competing values. Because misalignment can occur along each axis -- and affect stakeholders differently -- the structural description shows that alignment cannot be \"solved\" through technical design alone, but must be managed through ongoing institutional processes that determine how objectives are set, how systems are evaluated, and how affected communities can contest or reshape those decisions.", "date": "2026-04-22", "categories": []}
{"id": "2506.06414", "title": "Benchmarking Misuse Mitigation Against Covert Adversaries", "abstract": "Existing language model safety evaluations focus on overt attacks and low-stakes tasks. In reality, an attacker can easily subvert existing safeguards by requesting help on small, benign-seeming tasks across many independent queries. Because the individual queries do not appear harmful, the attack is hard to detect. However, when combined, these fragments uplift misuse by helping the attacker complete hard and dangerous tasks. Toward identifying defenses against such strategies, we develop Benchmarks for Stateful Defenses (BSD), a data generation pipeline that automates evaluations of covert attacks and corresponding defenses. Using this pipeline, we curate two new datasets that are consistently refused by frontier models and are too difficult for weaker open-weight models. This enables us to evaluate decomposition attacks, which are found to be effective misuse enablers, and to highlight stateful defenses as a promising countermeasure.", "date": "2025-06-06", "categories": []}
{"id": "2509.13281", "title": "RepIt: Steering Language Models with Concept-Specific Refusal Vectors", "abstract": "Current safety evaluations of language models rely on benchmark-based assessments that may miss localized vulnerabilities. We present RepIt, a simple and data-efficient framework for isolating concept-specific representations in LM activations. While existing steering methods already achieve high attack success rates through broad interventions, RepIt enables a more concerning capability: selective suppression of refusal on targeted concepts while preserving refusal elsewhere. Across five frontier LMs, RepIt produces evaluation-evading model organisms with semantic backdoors, answering questions related to weapons of mass destruction while still scoring as safe on standard benchmarks. We find the edit of the steering vector localizes to just 100-200 residual dimensions, and robust concept vectors can be extracted from as few as a dozen examples on a single RTX A6000, highlighting how targeted, hard-to-detect modifications can exploit evaluation blind spots with minimal resources. Through demonstrating precise concept disentanglement, this work exposes vulnerabilities in current safety evaluation practices and demonstrates a need for more comprehensive, representation aware assessments.", "date": "2025-09-16", "categories": []}
{"id": "2510.08240", "title": "The Alignment Waltz: Jointly Training Agents to Collaborate for Safety", "abstract": "Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing challenges: vulnerability to adversarial attacks that elicit unsafe content, and a tendency for overrefusal on benign but sensitive prompts. Current approaches often navigate this dance with safeguard models that completely reject any content that contains unsafe portions. This approach cuts the music entirely-it may exacerbate overrefusals and fails to provide nuanced guidance for queries it refuses. To teach models a more coordinated choreography, we propose WaltzRL, a novel multi-agent reinforcement learning framework that formulates safety alignment as a collaborative, positive-sum game. WaltzRL jointly trains a conversation agent and a feedback agent, where the latter is incentivized to provide useful suggestions that improve the safety and helpfulness of the conversation agent's responses. At the core of WaltzRL is a Dynamic Improvement Reward (DIR) that evolves over time based on how well the conversation agent incorporates the feedback. At inference time, unsafe or overrefusing responses from the conversation agent are improved rather than discarded. The feedback agent is deployed together with the conversation agent and only engages adaptively when needed, preserving helpfulness and low latency on safe queries. Our experiments, conducted across five diverse datasets, demonstrate that WaltzRL significantly reduces both unsafe responses (e.g., from 39.0% to 4.6% on WildJailbreak) and overrefusals (from 45.3% to 9.9% on OR-Bench) compared to various baselines. By enabling the conversation and feedback agents to co-evolve and adaptively apply feedback, WaltzRL enhances LLM safety without degrading general capabilities, thereby advancing the Pareto front between helpfulness and harmlessness.", "date": "2025-10-09", "categories": []}
{"id": "2512.07761", "title": "TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards", "abstract": "Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment. This has led to the task of training automated multi-turn attackers to probe model safety vulnerabilities. However, existing approaches typically rely on turn-level optimization, which is insufficient for learning long-term attack strategies. To bridge this gap, we formulate this task as a multi-turn reinforcement learning problem, directly optimizing the harmfulness of the final-turn response as the outcome reward. To address the sparse supervision of the outcome reward, we introduce TROJail, which employs two process rewards to evaluate the utility of intermediate prompts and integrate them into advantage estimation. These rewards (1) penalize overly harmful prompts that trigger the model's refusal mechanism, and (2) encourage steering the semantic relevance of responses toward the targeted harmful content. Experimental results show improved attack success rates across multiple models and benchmarks, highlighting the effectiveness of our approach. The code is available at https://github.com/xxiqiao/TROJail. Warning: This paper contains examples of harmful content.", "date": "2025-12-08", "categories": []}
{"id": "2604.17299", "title": "Cat-DPO: Category-Adaptive Safety Alignment", "abstract": "Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones. Most preference-based safety alignment methods collapse safety into a single scalar that is applied uniformly to every preference pair. The result is a model that looks safe on average but stays relatively unsafe on a minority of harm categories. We cast safety alignment as a per-category constrained optimization problem and derive Cat-DPO, a direct-preference-optimization algorithm with a separate adaptive safety margin for each harm category. The margin tightens when the model still produces unsafe responses on a category and relaxes once the model catches up, so the training signal tracks each category's current difficulty rather than averaging under one global rate. Across two LLM backbones and six preference-learning baselines, Cat-DPO improves aggregate helpfulness and harmlessness and compresses per-category safety variance and the best-to-worst gap, offering a drop-in per-category refinement of direct preference safety alignment.", "date": "2026-04-19", "categories": []}
{"id": "2604.18658", "title": "Owner-Harm: A Missing Threat Model for AI Agent Safety", "abstract": "Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug 2024), Microsoft 365 Copilot calendar-injection leaks (Jan 2024), and a Meta agent unauthorized forum post exposing operational data (Mar 2026). We propose Owner-Harm, a formal threat model with eight categories of agent behavior damaging the deployer. We quantify the defense gap on two benchmarks: a compositional safety system achieves 100% TPR / 0% FPR on AgentHarm (generic criminal harm) yet only 14.8% (4/27; 95% CI: 5.9%-32.5%) on AgentDojo injection tasks (prompt-injection-mediated owner harm). A controlled generic-LLM baseline shows the gap is not inherent to owner-harm (62.7% vs. 59.3%, delta 3.4 pp) but arises from environment-bound symbolic rules that fail to generalize across tool vocabularies. On a post-hoc 300-scenario owner-harm benchmark, the gate alone achieves 75.3% TPR / 3.3% FPR; adding a deterministic post-audit verifier raises overall TPR to 85.3% (+10.0 pp) and Hijacking detection from 43.3% to 93.3%, demonstrating strong layer complementarity. We introduce the Symbolic-Semantic Defense Generalization (SSDG) framework relating information coverage to detection rate. Two SSDG experiments partially validate it: context deprivation amplifies the detection gap 3.4x (R = 3.60 vs. R = 1.06); context injection reveals structured goal-action alignment, not text concatenation, is required for effective owner-harm detection.", "date": "2026-04-20", "categories": []}
{"id": "2604.18775", "title": "An Empirical Study of Multi-Generation Sampling for Jailbreak Detection in Large Language Models", "abstract": "Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarely. In this work, we present an empirical study of output based jailbreak detection under realistic conditions using the JailbreakBench Behaviors dataset and multiple generator models with varying alignment strengths. We evaluate both a lexical TF-IDF detector and a generation inconsistency based detector across different sampling budgets. Our results show that single output evaluation systematically underestimates jailbreak vulnerability, as increasing the number of sampled generations reveals additional harmful behaviour. The most significant improvements occur when moving from a single generation to moderate sampling, while larger sampling budgets yield diminishing returns. Cross generator experiments demonstrate that detection signals partially generalise across models, with stronger transfer observed within related model families. A category level analysis further reveals that lexical detectors capture a mixture of behavioural signals and topic specific cues, rather than purely harmful behaviour. Overall, our findings suggest that moderate multi sample auditing provides a more reliable and practical approach for estimating model vulnerability and improving jailbreak detection in large language models. Code will be released.", "date": "2026-04-20", "categories": []}
{"id": "2604.18789", "title": "ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an imperfect Reward Model (RM) can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches primarily target policy-level weaknesses, they overlook what we term systemic weaknesses cases where both the core LLM and the RM fail in tandem.\n  We present ARES, a framework that systematically discovers and mitigates such dual vulnerabilities. ARES employs a ``Safety Mentor'' that dynamically composes semantically coherent adversarial prompts by combining structured component types (topics, personas, tactics, goals) and generates corresponding malicious and safe responses. This dual-targeting approach exposes weaknesses in both the core LLM and the RM simultaneously. Using the vulnerabilities gained, ARES implements a two-stage repair process: first fine-tuning the RM to better detect harmful content, then leveraging the improved RM to optimize the core model. Experiments across multiple adversarial safety benchmarks demonstrate that ARES substantially enhances safety robustness while preserving model capabilities, establishing a new paradigm for comprehensive RLHF safety alignment.", "date": "2026-04-20", "categories": []}
{"id": "2604.18874", "title": "How Adversarial Environments Mislead Agentic AI?", "abstract": "Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking \"can the agent use tools correctly\" but never \"what if the tools lie\". We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a \"fake world\" of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via POTEMKIN, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities.", "date": "2026-04-20", "categories": []}
{"id": "2604.18946", "title": "Reasoning Structure Matters for Safety Alignment of Reasoning Models", "abstract": "Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This paper investigates the underlying cause of these safety risks and shows that the issue lies in the reasoning structure itself. Based on this insight, we claim that effective safety alignment can be achieved by altering the reasoning structure. We propose AltTrain, a simple yet effective post training method that explicitly alters the reasoning structure of LRMs. AltTrain is both practical and generalizable, requiring no complex reinforcement learning (RL) training or reward design, only supervised finetuning (SFT) with a lightweight 1K training examples. Experiments across LRM backbones and model sizes demonstrate strong safety alignment, along with robust generalization across reasoning, QA, summarization, and multilingual setting.", "date": "2026-04-21", "categories": []}
{"id": "2604.19001", "title": "When Safety Fails Before the Answer: Benchmarking Harmful Behavior Detection in Reasoning Chains", "abstract": "Large reasoning models (LRMs) produce complex, multi-step reasoning traces, yet safety evaluation remains focused on final outputs, overlooking how harm emerges during reasoning. When jailbroken, harm does not appear instantaneously but unfolds through distinct behavioral steps such as suppressing refusal, rationalizing compliance, decomposing harmful tasks, and concealing risk. However, no existing benchmark captures this process at sentence-level granularity within reasoning traces -- a key step toward reliable safety monitoring, interventions, and systematic failure diagnosis. To address this gap, we introduce HarmThoughts, a benchmark for step-wise safety evaluation of reasoning traces. \\ourdataset is built on our proposed harm taxonomy of 16 harmful reasoning behaviors across four functional groups that characterize how harm propagates rather than what harm is produced. The dataset consists of 56,931 sentences from 1,018 reasoning traces generated by four model families, each annotated with fine-grained sentence-level behavioral labels. Using HarmThoughts, we analyze harm propagation patterns across reasoning traces, identifying common behavioral trajectories and drift points where reasoning transitions from safe to unsafe. Finally, we systematically compare white-box and black-box detectors on the task of identifying harmful reasoning behaviours on HarmThoughts. Our results show that existing detectors struggle with fine-grained behavior detection in reasoning traces, particularly for nuanced categories within harm emergence and execution, highlighting a critical gap in process-level safety monitoring. HarmThoughts is available publicly at: https://huggingface.co/datasets/ishitakakkar-10/HarmThoughts", "date": "2026-04-21", "categories": []}
{"id": "2604.19638", "title": "SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models", "abstract": "Multimodal Large Language Models are increasingly adopted as autonomous agents in interactive environments, yet their ability to proactively address safety hazards remains insufficient. We introduce SafetyALFRED, built upon the embodied agent benchmark ALFRED, augmented with six categories of real-world kitchen hazards. While existing safety evaluations focus on hazard recognition through disembodied question answering (QA) settings, we evaluate eleven state-of-the-art models from the Qwen, Gemma, and Gemini families on not only hazard recognition, but also active risk mitigation through embodied planning. Our experimental results reveal a significant alignment gap: while models can accurately recognize hazards in QA settings, average mitigation success rates for these hazards are low in comparison. Our findings demonstrate that static evaluations through QA are insufficient for physical safety, thus we advocate for a paradigm shift toward benchmarks that prioritize corrective actions in embodied contexts. We open-source our code and dataset under https://github.com/sled-group/SafetyALFRED.git", "date": "2026-04-21", "categories": []}
{"id": "2405.13068", "title": "Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment", "abstract": "Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Current safety alignment techniques, however, harbor inherent vulnerabilities due to their reliance on logit suppression. In this work, we identify critical logit-level vulnerabilities by introducing Semantic-sensitive Alignment and Generation (SSAG), a method designed to systematically manipulate output-layer logits without altering model parameters. Experiments on five popular LLMs show that SSAG exposes harmful responses with a 95% success rate while reducing response time by 86%. VulMine also demonstrates superior attack efficacy, achieving an average ASR of up to 77% against strong defensive mechanisms. These findings reveal crucial weaknesses in existing alignment methods, highlighting an urgent need for improved vulnerability detection and robust safety alignment strategies. Our code is available on github.", "date": "2024-05-20", "categories": []}
{"id": "2503.03480", "title": "SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning", "abstract": "Vision-language-action models (VLAs) show potential as generalist robot policies. However, these models pose extreme safety challenges during real-world deployment, including the risk of harm to the environment, the robot itself, and humans. How can safety constraints be explicitly integrated into VLAs? We address this by exploring an integrated safety approach (ISA), systematically modeling safety requirements, then actively eliciting diverse unsafe behaviors, effectively constraining VLA policies via safe reinforcement learning, and rigorously assuring their safety through targeted evaluations. Leveraging the constrained Markov decision process (CMDP) paradigm, ISA optimizes VLAs from a min-max perspective against elicited safety risks. Thus, policies aligned through this comprehensive approach achieve the following key features: (I) effective safety-performance trade-offs, reducing the cumulative cost of safety violations by 83.58% compared to the state-of-the-art method, while also maintaining task success rate (+3.85%). (II) strong safety assurance, with the ability to mitigate long-tail risks and handle extreme failure scenarios. (III) robust generalization of learned safety behaviors to various out-of-distribution perturbations. The effectiveness is evaluated on long-horizon mobile manipulation tasks. Our data, models and newly proposed benchmark environment are available at https://pku-safevla.github.io.", "date": "2025-03-05", "categories": []}
{"id": "2504.15304", "title": "AI Agents and Hard Choices", "abstract": "Can AI agents deal with hard choices -- cases where options are incommensurable because multiple objectives are pursued simultaneously? Adopting a technologically engaged approach distinct from existing philosophical literature, I submit that the fundamental design of current AI agents as optimisers creates two limitations: the Identification Problem and the Resolution Problem. First, I demonstrate that agents relying on Multi-Objective Optimisation (MOO) are structurally unable to identify incommensurability. This inability generates three specific alignment problems: the blockage problem, the untrustworthiness problem, and the unreliability problem. I argue that standard mitigations, such as Human-in-the-Loop, are insufficient for many decision environments. As a constructive alternative, I conceptually explore an ensemble solution. Second, I argue that even if the Identification Problem is solved, AI agents face the Resolution Problem: they lack the autonomy to resolve hard choices rather than arbitrarily picking through self-modification of objectives. I conclude by examining the opaque normative trade-offs involved in granting AI this level of autonomy.", "date": "2025-04-18", "categories": []}
{"id": "2512.10687", "title": "Safe for Whom? Rethinking How We Evaluate the Safety of LLMs for Real Users", "abstract": "Safety evaluations of large language models (LLMs) typically focus on universal risks like dangerous capabilities or undesirable propensities. However, millions use LLMs for personal advice on high-stakes topics like finance and health, where harms are context-dependent rather than universal. While frameworks like the OECD's AI classification recognize the need to assess individual risks, user-welfare safety evaluations remain underdeveloped. We argue that developing such evaluations is non-trivial due to fundamental questions about accounting for user context in evaluation design. In this exploratory study, we evaluated advice on finance and health from GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro across user profiles of varying vulnerability. First, we demonstrate that evaluators must have access to rich user context: identical LLM responses were rated significantly safer by context-blind evaluators than by those aware of user circumstances, with safety scores for high-vulnerability users dropping from safe (5/7) to somewhat unsafe (3/7). One might assume this gap could be addressed by creating realistic user prompts containing key contextual information. However, our second study challenges this: we rerun the evaluation on prompts containing context users report they would disclose, finding no significant improvement. Our work establishes that effective user-welfare safety evaluation requires evaluators to assess responses against diverse user profiles, as realistic user context disclosure alone proves insufficient, particularly for vulnerable populations. By demonstrating a methodology for context-aware evaluation, this study provides both a starting point for such assessments and foundational evidence that evaluating individual welfare demands approaches distinct from existing universal-risk frameworks. We publish our code and dataset to aid future developments.", "date": "2025-12-11", "categories": []}
{"id": "2601.04043", "title": "When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life", "abstract": "As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to human behavior, perpetually overhanging human society like a sword of Damocles. To investigate and evaluate the safety impact of MLLMs responses on human behavior in daily life, we introduce SaLAD, a multimodal safety benchmark which contains 2,013 real-world image-text samples across 10 common categories, with a balanced design covering both unsafe scenarios and cases of oversensitivity. It emphasizes realistic risk exposure, authentic visual inputs, and fine-grained cross-modal reasoning, ensuring that safety risks cannot be inferred from text alone. We further propose a safety-warning-based evaluation framework that encourages models to provide clear and informative safety warnings, rather than generic refusals. Results on 18 MLLMs demonstrate that the top-performing models achieve a safe response rate of only 57.2% on unsafe queries. Moreover, even popular safety alignment methods limit effectiveness of the models in our scenario, revealing the vulnerabilities of current MLLMs in identifying dangerous behaviors in daily life. Our dataset is available at https://github.com/xinyuelou/SaLAD.", "date": "2026-01-07", "categories": []}
{"id": "2601.15220", "title": "Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models", "abstract": "We identify a novel phenomenon in language models: benign fine-tuning of frontier models can lead to privacy collapse. We find that diverse, subtle patterns in training data can degrade contextual privacy, including optimisation for helpfulness, exposure to user information, emotional and subjective dialogue, and debugging code printing internal variables, among others. Fine-tuned models lose their ability to reason about contextual privacy norms, share information inappropriately with tools, and violate memory boundaries across contexts. Privacy collapse is a ``silent failure'' because models maintain high performance on standard safety and utility benchmarks whilst exhibiting severe privacy vulnerabilities. Our experiments show evidence of privacy collapse across six models (closed and open weight), five fine-tuning datasets (real-world and controlled data), and two task categories (agentic and memory-based). Our mechanistic analysis reveals that privacy representations are uniquely fragile to fine-tuning, compared to task-relevant features which are preserved. Our results reveal a critical gap in current safety evaluations, in particular for the deployment of specialised agents.", "date": "2026-01-21", "categories": []}
{"id": "2602.19555", "title": "SOK: A Taxonomy of Attack Vectors and Defense Strategies for Agentic Supply Chain Runtime", "abstract": "Agentic systems based on large language models (LLMs) operate not merely as text generators but as autonomous entities that dynamically retrieve information and invoke tools. This execution model shifts the attack surface from traditional build-time artifacts to inference-time dependencies, exposing agents to manipulation through untrusted data and probabilistic capability resolution. While prior work has examined model-level vulnerabilities, security risks arising from the complex, cyclic runtime behavior of agents remain fragmented.\n  This paper systematizes existing research into a unified runtime framework. We categorize threats into data supply chain attacks (distinguishing between transient context injection and persistent memory poisoning) and tool supply chain attacks (spanning discovery, implementation, and invocation phases). Crucially, we identify the emergence of the Viral Agent Loop, where agents effectively become vectors for self-propagating generative worms that require no code vulnerabilities to spread. We argue for a transition to a Zero-Trust Runtime Architecture, where context is treated as untrusted control flow, and tool execution is bounded by cryptographic provenance rather than semantic likelihood.", "date": "2026-02-23", "categories": []}
{"id": "2603.14975", "title": "Why Agents Compromise Safety Under Pressure", "abstract": "Large Language Model agents deployed in complex environments frequently encounter a conflict between maximizing goal achievement and adhering to safety constraints. This paper identifies a new concept called Agentic Pressure, which characterizes the endogenous tension emerging when compliant execution becomes infeasible. We demonstrate that under this pressure agents exhibit normative drift where they strategically sacrifice safety to preserve utility. Notably we find that advanced reasoning capabilities accelerate this decline as models construct linguistic rationalizations to justify violation. Finally, we analyze the root causes and explore preliminary mitigation strategies, such as pressure isolation, which attempts to restore alignment by decoupling decision-making from pressure signals.", "date": "2026-03-16", "categories": []}
{"id": "2604.11943", "title": "ProbeLogits: Kernel-Level LLM Inference Primitives for AI-Native Operating Systems", "abstract": "An OS kernel that runs LLM inference internally can read logit distributions before any text is generated and act on them as a governance primitive. This paper presents ProbeLogits, a kernel-level operation that performs a single forward pass and reads specific token logits to classify agent actions as safe or dangerous, with zero learned parameters.\n  I evaluate ProbeLogits across three base models (Qwen 2.5-7B, Llama 3 8B, Mistral 7B) on three external benchmarks: HarmBench, XSTest, and ToxicChat. On HarmBench non-copyright (n=300), all three models reach 97-99% block rate with the right verbalizer. On ToxicChat (n=1,000), ProbeLogits achieves F1 parity-or-better against Llama Guard 3 in the same hosted environment: the strongest configuration (Qwen 2.5-7B Safe/Dangerous, alpha=0.0) reaches F1=0.812 with bootstrap 95% CIs disjoint from LG3 (+13.7pp significant); Llama 3 S/D matches LG3 within CI (+0.4pp, parity); Mistral Y/N exceeds by +4.4pp. Latency is approximately 2.5x faster than LG3 in the same hosted environment because the primitive reads a single logit position instead of generating tokens; in the bare-metal native runtime ProbeLogits drops to 65 ms.\n  A key design contribution is the calibration strength alpha, which serves as a deployment-time policy knob rather than a learned hyperparameter. Contextual calibration corrects verbalizer prior asymmetry, with bias magnitude varying by (model, verbalizer) pair.\n  I implement ProbeLogits within Anima OS, a bare-metal x86_64 OS written in approximately 86,000 lines of Rust. Because agent actions must pass through 15 kernel-mediated host functions, ProbeLogits enforcement operates below the WASM sandbox boundary, making it significantly harder to circumvent than application-layer classifiers.", "date": "2026-04-13", "categories": []}
{"id": "2604.16423", "title": "Shifting the Gradient: Understanding How Defensive Training Methods Protect Language Model Integrity", "abstract": "Defensive training methods such as positive preventative steering (PPS) and inoculation prompting (IP) offer surprising results through seemingly similar processes: both add trait-inducing objects to large language models (LLMs) during training, and both defend the LLM against acquiring the trait. The surprising success of these methods comes with the question: how do they work? Are PPS and IP doing the same thing? We provide behavioral and mechanistic comparisons of these two methods using \"evilness\" as a case-study trait. Our central finding is that PPS and IP achieve their defensive benefits through distinct mechanisms. Behaviorally, we show that neither PPS nor IP operates through a purely associative mechanism; and PPS can both defend against trait acquisition and actively reduce pre-existing expression, whereas IP is ineffective in models that were previously finetuned to express the trait. This behavioral divergence is reflected mechanistically: PPS shifts the activation gradient towards an attenuating direction along the PPS vector axis. When the PPS vector is aligned with a trait-expressing axis, it can reverse the gradient pressure, reducing rather than increasing activation along that axis. In contrast, IP continues to resist a precise mechanistic account. Direct cosine similarity analyses reveal that IP has a characteristically different gradient signature than PPS, and qualitative analyses reveal IP's gradient to be more diffuse. Furthermore, IP reduces the next-token prediction loss on trait-expressing data where PPS need not, consistent with the notion that IP \"explains away\" the trait-expression in the training data. Taken together, our analyses reveal distinct mechanisms by which each method operates and highlight open questions about IP's mechanistic picture.", "date": "2026-04-03", "categories": []}
{"id": "2604.16542", "title": "TWGuard: A Case Study of LLM Safety Guardrails for Localized Linguistic Contexts", "abstract": "Safety guardrails have become an active area of research in AI safety, aimed at ensuring the appropriate behavior of large language models (LLMs). However, existing research lacks consideration of nuances across linguistic and cultural contexts, resulting in a gap between reported performance and in-the-wild effectiveness. To address this issue, this paper proposes an approach to optimize guardrail models for a designated linguistic context by leveraging a curated dataset tailored to local linguistic characteristics, targeting the Taiwan linguistic context as a representative example of localized deployment challenges. The proposed approach yields TWGuard, a linguistic context-optimized guardrail model that achieves a huge gain (+0.289 in F1) compared to the foundation model and significantly outperforms the strongest baseline in practical use (-0.037 in false positive rate, a 94.9\\% reduction). Together, this work lays a foundation for regional communities to establish AI safety standards grounded in their own linguistic contexts, rather than accepting boundaries imposed by dominant languages. The inadequacy of the latter is reconfirmed by our findings.", "date": "2026-04-17", "categories": []}
{"id": "2604.16824", "title": "SafeDream: Safety World Model for Proactive Early Jailbreak Detection", "abstract": "Multi-turn jailbreak attacks progressively erode LLM safety alignment across seemingly innocuous conversation turns, achieving success rates exceeding 90% against state-of-the-art models. Existing alignment-based and guardrail methods suffer from three key limitations: they require costly weight modification, evaluate each turn independently without modeling cumulative safety erosion, and detect attacks only after harmful content has been generated. To address these limitations, we first formulate the proactive early jailbreak detection problem with a new metric, detection lead, that measures how early an attack can be detected before the LLM complies. We then propose SAFEDREAM, a lightweight world-model-based framework that operates as an external module without modifying the LLM's weights. SAFEDREAM introduces three components: (1) a safety state world model that encodes LLM hidden states into a compact safety representation and predicts how it evolves across turns, (2) CUSUM detection that accumulates weak per-turn risk signals into reliable evidence, and (3) contrastive imagination that simultaneously rolls out attack and benign futures in latent space to issue early alarms before jailbreaks occur. On three multi-turn jailbreak benchmarks (XGuard-Train, SafeDialBench, SafeMTData) against 8 baselines, SAFEDREAM achieves the best detection timeliness across all benchmarks (1.06-1.20 turns before compliance) while maintaining competitive false positive rates and outperforming baselines in detection quality.", "date": "2026-04-18", "categories": []}
{"id": "2604.16845", "title": "DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training", "abstract": "Large language models (LLMs) tuned for safety often avoid acknowledging demographic differences, even when such acknowledgment is factually correct (e.g., ancestry-based disease incidence) or contextually justified (e.g., religious hiring preferences). This identity-blindness yields incorrect responses, unnecessary refusals, or generic \"equal-treatment\" defaults. We study this via difference-awareness classification: given a question involving demographic groups, the task is not to answer directly, but to classify whether a correct answer requires recognizing group differences (yes) or whether groups should be treated identically (no). Crucially, fine-tuning for accuracy triggers harm drift: model-generated explanations become increasingly harmful as decision accuracy improves, whether by elaborating harmful content, introducing problematic assumptions, or failing to flag harms the baseline identified. To mitigate this, we introduce DART (Distill--Audit--Repair Training), which distills label-conditioned reasoning from a teacher, audits outputs for harm drift cases relative to baseline, and repairs problematic cases via severity-weighted fine-tuning. On eight benchmarks, DART improves Llama-3-8B-Instruct accuracy from 39.0% to 68.8%, with largest gains on equal-treatment prompts (11.3% -> 72.6%), while reducing harm drift cases by 72.6%. It also transfers to 280 open-ended real-world queries across medical, legal, policy, and educational domains, improving difference-appropriate responses from 39.8% to 77.5% while reducing refusals from 34.3% to 3.0%. Our results demonstrate that accuracy and safety need not conflict when explicit detection and repair mechanisms are in place.", "date": "2026-04-18", "categories": []}
{"id": "2604.16870", "title": "Governed MCP: Kernel-Level Tool Governance for AI Agents via Logit-Based Safety Primitives", "abstract": "AI agents increasingly call external tools (file system, network, APIs) through the Model Context Protocol (MCP). These tool calls are the agent's syscalls -- privileged operations with side effects on shared state -- yet today's safety enforcement lives entirely in userspace, where a 10-line script can bypass it. I propose Governed MCP, a kernel-resident tool governance gateway built on a logit-based safety primitive (ProbeLogits, companion paper: arXiv:2604.11943). The gateway interposes on every MCP tool call in a 6-layer pipeline: schema validation, trust tier check, rate limit, adversarial pre-filter, ProbeLogits gate (the load-bearing semantic check), and constitutional policy match, with a Blake3-hashed audit chain.\n  I implement Governed MCP in Anima OS, a bare-metal x86_64 OS in approximately 86,000 lines of Rust. The five non-inference layers add 65.3 microseconds of overhead per call; ProbeLogits adds 65 ms (per-token-class semantic decision) on 7B Q4_0. A 4-config ablation on a 101-prompt MCP-domain benchmark shows that removing the ProbeLogits layer collapses F1 from 0.773 to 0.327 (Delta F1 = -0.446) -- hand-rule firewalling alone is insufficient. All 15 WASM-to-system host functions in the runtime route through the gateway (complete mediation of the WASM ABI surface; the scope and caveats of this claim are stated in Section 4.6); a 10-LoC userspace bypass that defeats existing guardrail libraries is structurally impossible against the kernel-resident gate.", "date": "2026-04-18", "categories": []}
{"id": "2604.16916", "title": "When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints", "abstract": "Safety alignment in large language models (LLMs) is primarily evaluated under open-ended generation, where models can mitigate risk by refusing to respond. In contrast, many real-world applications place LLMs in structured decision-making tasks, such as multiple-choice questions (MCQs), where abstention is discouraged or unavailable. We identify a systematic failure mode in this setting: reformulating harmful requests as forced-choice MCQs, where all options are unsafe, can systematically bypass refusal behavior, even in models that consistently reject equivalent open-ended prompts. Across 14 proprietary and open-source models, we show that forced-choice constraints sharply increase policy-violating responses. Notably, for human-authored MCQs, violation rates follow an inverted U-shaped trend with respect to structural constraint strength, peaking under intermediate task specifications, whereas MCQs generated by high-capability models yield near-saturation violation rates across constraints and exhibit strong cross-model transferability. Our findings reveal that current safety evaluations substantially underestimate risks in structured task settings and highlight constrained decision-making as a critical and underexplored surface for alignment failures.", "date": "2026-04-18", "categories": []}
{"id": "2604.16968", "title": "On Safety Risks in Experience-Driven Self-Evolving Agents", "abstract": "Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduces underexplored safety risks. In this study, we investigate how experience accumulation and utilization in self-evolving agents affect safety performance across web-based and embodied environments. Notably, experience gathered solely from benign tasks can still compromise safety in high-risk scenarios. Further analysis attributes this degradation to the execution-oriented nature of accumulated experience, which reinforces agents' tendency to act rather than refuse. In more realistic settings where agents encounter both benign and harmful tasks, refusal-related experience mitigates safety decline but induces over-refusal, revealing a fundamental safety-utility trade-off. Overall, our findings expose inherent limitations of current self-evolving agents and call for more principled strategies to ensure safe and reliable adaptation.", "date": "2026-04-18", "categories": []}
{"id": "2604.17210", "title": "Guardrails in Logit Space: Safety Token Regularization for LLM Alignment", "abstract": "Fine-tuning well-aligned large language models (LLMs) on new domains often degrades their safety alignment, even when using benign datasets. Existing safety alignment techniques primarily focus on pretraining, leaving fine-tuned models vulnerable to behavioral shifts. In this work, we introduce safety token regularization (STR), a lightweight method designed to preserve safety properties during fine-tuning. Our approach identifies salient tokens from rejection templates of well-aligned models and constrains their associated logits during training, preventing the loss of critical safety behaviors. Unlike reinforcement learning or preference optimization methods, STR requires minimal additional computation and seamlessly integrates with parameter-efficient fine-tuning techniques such as LoRA. Comprehensive experiments demonstrate that our approach achieves safety performance on par with state-of-the-art methods, while preserving task-specific utility and requiring minimal implementation overhead. Furthermore, we show that safety token regularization enhances training stability and overall performance beyond safety considerations alone. This work offers a practical and readily deployable strategy for continual safety alignment in fine-tuned LLMs.", "date": "2026-04-19", "categories": []}
{"id": "2604.17413", "title": "The Open-Weight Paradox: Why Restricting Access to AI Models May Undermine the Safety It Seeks to Protect", "abstract": "The governance of open-weight artificial intelligence (AI) models has been framed as a binary choice: openness as risk, restriction as safety. This paper challenges that framing, arguing that access restrictions, without governed alternatives, may displace risks rather than reduce them. The global concentration of compute infrastructure makes open-weight models one of the most viable pathways to sovereign AI capacity in the Global South; restricting such access deepens asymmetries while driving proliferation into unsupervised settings. This analysis proposes that hardware-layer governance, including chip-level attestation mechanisms such as FlexHEG, trusted execution environments, confidential computing, and complementary software-layer safeguards, offers a defense-in-depth alternative to the current binary. A threat model taxonomy mapping misuse vectors to hardware, software, institutional, and liability layers illustrates why no single governance mechanism suffices. To operationalize this approach, the paper argues that effective AI governance as a dual-use technology will likely require a multilateral institutional architecture functionally analogous, though not identical, to the role performed by the IAEA in the nuclear domain, with explicit safeguards against the co-option of hardware controls for domestic repression. The relevant policy question is how to make openness safer through technical and institutional design while addressing the transition realities of legacy hardware, attestation at scale, and civil liberties protection.", "date": "2026-04-19", "categories": []}
{"id": "2604.17562", "title": "SafeAgent: A Runtime Protection Architecture for Agentic Systems", "abstract": "Large language model (LLM) agents are vulnerable to prompt-injection attacks that propagate through multi-step workflows, tool interactions, and persistent context, making input-output filtering alone insufficient for reliable protection. This paper presents SafeAgent, a runtime security architecture that treats agent safety as a stateful decision problem over evolving interaction trajectories. The proposed design separates execution governance from semantic risk reasoning through two coordinated components: a runtime controller that mediates actions around the agent loop and a context-aware decision core that operates over persistent session state. The core is formalized as a context-aware advanced machine intelligence and instantiated through operators for risk encoding, utility-cost evaluation, consequence modeling, policy arbitration, and state synchronization. Experiments on Agent Security Bench (ASB) and InjecAgent show that SafeAgent consistently improves robustness over baseline and text-level guardrail methods while maintaining competitive benign-task performance. Ablation studies further show that recovery confidence and policy weighting determine distinct safety-utility operating points.", "date": "2026-04-19", "categories": []}
{"id": "2604.17596", "title": "Terminal Wrench: A Dataset of 331 Reward-Hackable Environments and 3,632 Exploit Trajectories", "abstract": "We release Terminal Wrench, a subset of 331 terminal-agent benchmark environments, copied from the popular open benchmarks that are demonstrably reward-hackable. The data set includes 3,632 hack trajectories and 2,352 legitimate baseline trajectories across three frontier models (Claude Opus 4.6, Gemini 3.1 Pro, GPT-5.4). Each entry preserves the original task definition alongside full attack trajectories that show how the verifier was bypassed. It also includes cases where the task was not solved as intended. The tasks span system administration, machine learning, software engineering, and security challenges; the exploits range from simple output spoofing to stack-frame introspection, standard-library patching, and rootkit-style binary hijacking. Crucially, these exploits are specific to each task, rather than the evaluation harness, making them harder to patch. We also present a monitorability study in which hack trajectories are sanitized or stripped of reasoning traces and then scored by an LLM judge, showing that detection degrades meaningfully when chain-of-thought is removed (AUC drops from 0.97 to 0.92). The data set is publicly available at https://github.com/few-sh/terminal-wrench.", "date": "2026-04-19", "categories": []}
{"id": "2604.17769", "title": "Reverse Constitutional AI: A Framework for Controllable Toxic Data Generation via Probability-Clamped RLAIF", "abstract": "Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains under-explored. We propose Reverse Constitutional AI (R-CAI), a framework for automated and controllable adversarial data generation that moves beyond isolated jailbreak prompts. By inverting a harmless constitution into a constitution of toxicity and iteratively refining model outputs through a critique--revision pipeline, R-CAI enables scalable synthesis of multi-dimensional adversarial data without human annotation. Optimizing solely for toxicity-related rewards, however, can lead to reward hacking and degraded semantic coherence. To address this challenge, we introduce probability clamping within reinforcement learning from AI feedback, which stabilizes adversarial optimization while preserving adversarial intent. Experiments demonstrate that R-CAI generates diverse, high-quality toxic data and that probability clamping substantially improves semantic coherence (15%) without sacrificing adversarial strength. Overall, R-CAI provides a fully automated framework for red teaming data generation and systematic safety evaluation of aligned language models.", "date": "2026-04-20", "categories": []}
{"id": "2604.18510", "title": "Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks", "abstract": "Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in capabilities, behavioral profile, and internal failure mode. We study behavioral and mechanistic properties of jailbroken models across three unsafe routes: harmful supervised fine-tuning (SFT), harmful reinforcement learning with verifiable rewards (RLVR), and refusal-suppressing abliteration. All three routes achieve near-ceiling harmful compliance, but they diverge once we move beyond direct harmfulness. RLVR-jailbroken models show minimal degradation and preserve explicit harm recognition in a structured self-audit: they are able to identify harmful prompts and describe how a safe LLM should respond, yet they comply with the harmful request. With RLVR, harmful behavior is strongly suppressed by a reflective safety scaffold: when a harmful prompt is prepended with an instruction to reflect on safety standards, harmful behavior drops close to the baseline. Category-specific RLVR jailbreaks generalize broadly across harmfulness domains. Models jailbroken with SFT show the largest collapse in explicit safety judgments, the highest behavioral drift, and a substantial capability loss on standard benchmarks. Abliteration is family-dependent in both self-audit and response to a reflective safety scaffold. Mechanistic and repair analyses further separate the routes: abliteration is consistent with localized refusal-feature deletion, RLVR with preserved safety geometry but retargeted policy behavior, and SFT with broader distributed drift. Targeted repair partially recovers RLVR-jailbroken models, but has little effect on SFT-jailbroken models. Together, these results show that jailbreaks can produce vastly different properties despite similar harmfulness, with models jailbroken via RLVR showing remarkable similarity to the base model.", "date": "2026-04-20", "categories": []}
{"id": "2604.18519", "title": "LLM Safety From Within: Detecting Harmful Content with Internal Representations", "abstract": "Guard models are widely used to detect harmful content in user prompts and LLM responses. However, state-of-the-art guard models rely solely on terminal-layer representations and overlook the rich safety-relevant features distributed across internal layers. We present SIREN, a lightweight guard model that harnesses these internal features. By identifying safety neurons via linear probing and combining them through an adaptive layer-weighted strategy, SIREN builds a harmfulness detector from LLM internals without modifying the underlying model. Our comprehensive evaluation shows that SIREN substantially outperforms state-of-the-art open-source guard models across multiple benchmarks while using 250 times fewer trainable parameters. Moreover, SIREN exhibits superior generalization to unseen benchmarks, naturally enables real-time streaming detection, and significantly improves inference efficiency compared to generative guard models. Overall, our results highlight LLM internal states as a promising foundation for practical, high-performance harmfulness detection.", "date": "2026-04-20", "categories": []}
{"id": "2601.03699", "title": "RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models", "abstract": "As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount. However, existing red teaming datasets suffer from inconsistent risk categorizations, limited domain coverage, and outdated evaluations, hindering systematic vulnerability assessments. To address these challenges, we introduce RedBench, a universal dataset aggregating 37 benchmark datasets from leading conferences and repositories, comprising 29,362 samples across attack and refusal prompts. RedBench employs a standardized taxonomy with 22 risk categories and 19 domains, enabling consistent and comprehensive evaluations of LLM vulnerabilities. We provide a detailed analysis of existing datasets, establish baselines for modern LLMs, and open-source the dataset and evaluation code. Our contributions facilitate robust comparisons, foster future research, and promote the development of secure and reliable LLMs for real-world deployment. Code: https://github.com/knoveleng/redeval", "date": "2026-01-07", "categories": []}
{"id": "2604.10577", "title": "The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents", "abstract": "Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions programmatically. Existing safety evaluations largely target explicit threats such as misuse and prompt injection, but overlook a subtle yet critical setting where user instructions are entirely benign and harm arises from the task context or execution outcome. We introduce OS-BLIND, a benchmark that evaluates CUAs under unintended attack conditions, comprising 300 human-crafted tasks across 12 categories, 8 applications, and 2 threat clusters: environment-embedded threats and agent-initiated harms. Our evaluation on frontier models and agentic frameworks reveals that most CUAs exceed 90% attack success rate (ASR), and even the safety-aligned Claude 4.5 Sonnet reaches 73.0% ASR. More interestingly, this vulnerability becomes even more severe, with ASR rising from 73.0% to 92.7% when Claude 4.5 Sonnet is deployed in multi-agent systems. Our analysis further shows that existing safety defenses provide limited protection when user instructions are benign. Safety alignment primarily activates within the first few steps and rarely re-engages during subsequent execution. In multi-agent systems, decomposed subtasks obscure the harmful intent from the model, causing safety-aligned models to fail. We will release our OS-BLIND to encourage the broader research community to further investigate and address these safety challenges.", "date": "2026-04-12", "categories": []}
{"id": "2604.15415", "title": "HarmfulSkillBench: How Do Harmful Skills Weaponize Your Agents?", "abstract": "Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous publicly reusable skills. Existing security research on these ecosystems mainly focuses on vulnerabilities within skills, such as prompt injection. However, there is a critical gap regarding skills that may be misused for harmful actions (e.g., cyber attacks, fraud and scams, privacy violations, and sexual content generation), namely harmful skills. In this paper, we present the first large-scale measurement study of harmful skills in agent ecosystems, covering 98,440 skills across two major registries. Using an LLM-driven scoring system grounded in our harmful skill taxonomy, we find that 4.93% of skills (4,858) are harmful, with ClawHub exhibiting an 8.84% harmful rate compared to 3.49% on Skills.Rest. We then construct HarmfulSkillBench, the first benchmark for evaluating agent safety against harmful skills in realistic agent contexts, comprising 200 harmful skills across 20 categories and four evaluation conditions. By evaluating six LLMs on HarmfulSkillBench, we find that presenting a harmful task through a pre-installed skill substantially lowers refusal rates across all models, with the average harm score rising from 0.27 without the skill to 0.47 with it, and further to 0.76 when the harmful intent is implicit rather than stated as an explicit user request. We responsibly disclose our findings to the affected registries and release our benchmark to support future research (see https://github.com/TrustAIRLab/HarmfulSkillBench).", "date": "2026-04-16", "categories": []}
{"id": "2604.15559", "title": "Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation", "abstract": "Recent work on subliminal learning demonstrates that language models can transmit semantic traits through data that is semantically unrelated to those traits. However, it remains unclear whether behavioral traits can transfer in agentic systems, where policies are learned from trajectories rather than static text. In this work, we provide the first empirical evidence that unsafe agent behaviors can transfer subliminally through model distillation across two complementary experimental settings. In our primary setting, we construct a teacher agent exhibiting a strong deletion bias, a tendency to perform destructive file-system actions via an API-style tool interface, and distill it into a student using only trajectories from ostensibly safe tasks, with all explicit deletion keywords rigorously filtered. In our secondary setting, we replicate the threat model in a native Bash environment, replacing API tool calls with shell commands and operationalizing the bias as a preference for issuing chmod as the first permission-related command over semantically equivalent alternatives such as chown or setfacl. Despite full keyword sanitation in both settings, students inherit measurable behavioral biases. In the API setting the student's deletion rate reaches 100% (versus a 5% baseline) under homogeneous distillation; in the Bash setting the student's chmod-first rate reaches 30%-55% (versus a 0%-10% baseline), with the strongest transfer observed in large-to-small distillation. Our results demonstrate that explicit data sanitation is an insufficient defense, and behavioral biases are encoded implicitly in trajectory dynamics regardless of the tool interface.", "date": "2026-04-16", "categories": []}
{"id": "2604.15579", "title": "Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility", "abstract": "AI agents that interact with their environments through tools enable powerful applications, but in high-stakes business settings, unintended actions can cause unacceptable harm, such as privacy breaches and financial loss. Existing mitigations, such as training-based methods and neural guardrails, improve agent reliability but cannot provide guarantees. We study symbolic guardrails as a practical path toward strong safety and security guarantees for AI agents. Our three-part study includes a systematic review of 80 state-of-the-art agent safety and security benchmarks to identify the policies they evaluate, an analysis of which policy requirements can be guaranteed by symbolic guardrails, and an evaluation of how symbolic guardrails affect safety, security, and agent success on $\\tau^2$-Bench, CAR-bench, and MedAgentBench. We find that 85\\% of benchmarks lack concrete policies, relying instead on underspecified high-level goals or common sense. Among the specified policies, 74\\% of policy requirements can be enforced by symbolic guardrails, often using simple, low-cost mechanisms. These guardrails improve safety and security without sacrificing agent utility. Overall, our results suggest that symbolic guardrails are a practical and effective way to guarantee some safety and security requirements, especially for domain-specific AI agents. We release all codes and artifacts at https://github.com/hyn0027/agent-symbolic-guardrails.", "date": "2026-04-16", "categories": []}
{"id": "2604.15725", "title": "Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing", "abstract": "Large Reasoning Models (LRMs) have demonstrated strong capabilities in generating step-by-step reasoning chains alongside final answers, enabling their deployment in high-stakes domains such as healthcare and education. While prior jailbreak attack studies have focused on the safety of final answers, little attention has been given to the safety of the reasoning process. In this work, we identify a novel problem that injects harmful content into the reasoning steps while preserving unchanged answers. This type of attack presents two key challenges: 1) manipulating the input instructions may inadvertently alter the LRM's final answer, and 2) the diversity of input questions makes it difficult to consistently bypass the LRM's safety alignment mechanisms and embed harmful content into its reasoning process. To address these challenges, we propose the Psychology-based Reasoning-targeted Jailbreak Attack (PRJA) Framework, which integrates a Semantic-based Trigger Selection module and a Psychology-based Instruction Generation module. Specifically, the proposed PRJA automatically selects manipulative reasoning triggers via semantic analysis and leverages psychological theories of obedience to authority and moral disengagement to generate adaptive instructions for enhancing the LRM's compliance with harmful content generation. Extensive experiments on five question-answering datasets demonstrate that PRJA achieves an average attack success rate of 83.6\\% against several commercial LRMs, including DeepSeek R1, Qwen2.5-Max, and OpenAI o4-mini.", "date": "2026-04-17", "categories": []}
{"id": "2505.10846", "title": "AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models", "abstract": "This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its core, AutoRAN pioneers an execution simulation paradigm that leverages a weaker but less-aligned model to simulate execution reasoning for initial hijacking attempts and iteratively refine attacks by exploiting reasoning patterns leaked through the target LRM's refusals. This approach steers the target model to bypass its own safety guardrails and elaborate on harmful instructions. We evaluate AutoRAN against state-of-the-art LRMs, including GPT-o3/o4-mini and Gemini-2.5-Flash, across multiple benchmarks (AdvBench, HarmBench, and StrongReject). Results show that AutoRAN achieves approaching 100% success rate within one or few turns, effectively neutralizing reasoning-based defenses even when evaluated by robustly aligned external models. This work reveals that the transparency of the reasoning process itself creates a critical and exploitable attack surface, highlighting the urgent need for new defenses that protect models' reasoning traces rather than merely their final outputs.", "date": "2025-05-16", "categories": []}
{"id": "2601.03416", "title": "GAMBIT: A Gamified Jailbreak Framework for Multimodal Large Language Models", "abstract": "Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous work has shown that increasing inference steps can disrupt safety mechanisms and lead MLLMs to generate attacker-desired harmful content. However, most existing attacks focus on increasing the complexity of the modified visual task itself and do not explicitly leverage the model's own reasoning incentives. This leads to them underperforming on reasoning models (Models with Chain-of-Thoughts) compared to non-reasoning ones (Models without Chain-of-Thoughts). If a model can think like a human, can we influence its cognitive-stage decisions so that it proactively completes a jailbreak? To validate this idea, we propose GAMBI} (Gamified Adversarial Multimodal Breakout via Instructional Traps), a novel multimodal jailbreak framework that decomposes and reassembles harmful visual semantics, then constructs a gamified scene that drives the model to explore, reconstruct intent, and answer as part of winning the game. The resulting structured reasoning chain increases task complexity in both vision and text, positioning the model as a participant whose goal pursuit reduces safety attention and induces it to answer the reconstructed malicious query. Extensive experiments on popular reasoning and non-reasoning MLLMs demonstrate that GAMBIT achieves high Attack Success Rates (ASR), reaching 92.13% on Gemini 2.5 Flash, 91.20% on QvQ-MAX, and 85.87% on GPT-4o, significantly outperforming baselines.", "date": "2026-01-06", "categories": []}
{"id": "2602.04930", "title": "Attack Selection Reduces Safety in Concentrated AI Control Settings against Trusted Monitoring", "abstract": "Future AI deployments will likely be monitored for malicious behaviour. The ability of these AIs to subvert monitors by adversarially selecting against them - attack selection - is particularly concerning. To study this, we let a red team create attack policies that attempt to insert attacks into code without being caught by a monitor in the concentrated BigCodeBench backdooring setting. We decompose attack selection into two problems: mapping attacks to a quality score and mapping quality scores to submission probabilities. We frame attack selection as a classification problem and show that safety is significantly more sensitive to FPR than TPR. We find that prompting the attacker model to reason about the monitor while being cautious with attack selection reduces safety from a baseline of 99% to 59% at 0.5% auditing budget, emphasizing that eliciting attack selection capabilities of models is vital to avoid overly optimistic safety scores in control evaluations.", "date": "2026-02-04", "categories": []}
{"id": "2603.13933", "title": "OmniCompliance-100K: A Multi-Domain, Rule-Grounded, Real-World Safety Compliance Dataset", "abstract": "Ensuring the safety and compliance of large language models (LLMs) is of paramount importance. However, existing LLM safety datasets often rely on ad-hoc taxonomies for data generation and suffer from a significant shortage of rule-grounded, real-world cases that are essential for robustly protecting LLMs. In this work, we address this critical gap by constructing a comprehensive safety dataset from a compliance perspective. Using a powerful web-searching agent, we collect a rule-grounded, real-world case dataset OmniCompliance-100K, sourced from multi-domain authoritative references. The dataset spans 74 regulations and policies across a wide range of domains, including security and privacy regulations, content safety and user data privacy policies from leading AI companies and social media platforms, financial security requirements, medical device risk management standards, educational integrity guidelines, and protections of fundamental human rights. In total, our dataset contains 12,985 distinct rules and 106,009 associated real-world compliance cases. Our analysis confirms a strong alignment between the rules and their corresponding cases. We further conduct extensive benchmarking experiments to evaluate the safety and compliance capabilities of advanced LLMs across different model scales. Our experiments reveal several interesting findings that have great potential to offer valuable insights for future LLM safety research.", "date": "2026-03-14", "categories": []}
{"id": "2604.09665", "title": "Deliberative Alignment is Deep, but Uncertainty Remains: Inference time safety improvement in reasoning via attribution of unsafe behavior to base model", "abstract": "While the wide adoption of refusal training in large language models (LLMs) has showcased improvements in model safety, recent works have highlighted shortcomings due to the shallow nature of these alignment methods. To this end, the work on Deliberative alignment proposed distilling reasoning capabilities from stronger reasoning models, thereby instilling deeper safety in LLMs. In this work, we study the impact of deliberative alignment in language models. First, we show that despite being larger in model size and stronger in safety capability, there exists an alignment gap between teacher and student language models, which affects both the safety and general utility of the student model. Furthermore, we show that models aligned through deliberative alignment can retain unsafe behaviors from the base model despite learning the reasoning patterns of larger reasoning models. Building upon this observation, we propose a BoN sampling method that attributes the unsafe behavior back to the base LLMs in the latent space, thereby down-ranking unsafe responses to gain a meaningful improvement in model safety across multiple safety benchmarks with minimal loss in utility. In particular, across 7 teacher models and 6 student models of different classes and sizes, we show an average attack success rate (ASR) reduction of 28.2% in DAN, 31.3% in WildJailbreak and 35.4 % in StrongREJECT benchmarks. We further show that these safety gains prevail post RL training, thus highlighting the uncertainty in safety reasoning and it's explicit attribution to the base model.", "date": "2026-04-01", "categories": []}
{"id": "2604.14602", "title": "CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification", "abstract": "Large language models (LLMs) frequently generate toxic content, posing significant risks for safe deployment. Current mitigation strategies often degrade generation quality or require costly human annotation. We propose CAUSALDETOX, a framework that identifies and intervenes on the specific attention heads causally responsible for toxic generation. Using the Probability of Necessity and Sufficiency (PNS), we isolate a minimal set of heads that are necessary and sufficient for toxicity. We utilize these components via two complementary strategies: (1) Local Inference-Time Intervention, which constructs dynamic, input-specific steering vectors for context-aware detoxification, and (2) PNS-Guided Fine-Tuning, which permanently unlearns toxic representations. We also introduce PARATOX, a novel benchmark of aligned toxic/non-toxic sentence pairs enabling controlled counterfactual evaluation. Experiments on ToxiGen, ImplicitHate, and ParaDetox show that CAUSALDETOX achieves up to 5.34% greater toxicity reduction compared to baselines while preserving linguistic fluency, and offers a 7x speedup in head selection.", "date": "2026-04-16", "categories": []}
{"id": "2604.14723", "title": "Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution", "abstract": "Large language models are increasingly used as natural-language interfaces to enterprise software, but their direct use as system operators remains unsafe. Model errors can propagate into unauthorized actions, malformed requests, cross-workspace execution, and other costly failures. We argue this is primarily an execution architecture problem. We present a bounded-autonomy architecture in which language models may interpret intent and propose actions, but all executable behavior is constrained by typed action contracts, permission-aware capability exposure, scoped context, validation before side effects, consumer-side execution boundaries, and optional human approval. The enterprise application remains the source of truth for business logic and authorization, while the orchestration engine operates over an explicit published actions manifest. We evaluate the architecture in a deployed multi-tenant enterprise application across three conditions: manual operation, unconstrained AI with safety layers disabled, and full bounded autonomy. Across 25 scenario trials spanning seven failure families, the bounded-autonomy system completed 23 of 25 tasks with zero unsafe executions, while the unconstrained configuration completed only 17 of 25. Two wrong-entity mutations escaped all consumer-contributed layers; only disambiguation and confirmation mechanisms intercept this class. Both AI conditions delivered 13-18x speedup over manual operation. Critically, removing safety layers made the system less useful: structured validation feedback guided the model to correct outcomes in fewer turns, while the unconstrained system hallucinated success. Several safety properties are structurally enforced by code and intercepted all targeted violations regardless of model output. The result is a practical, deployed architecture for making imperfect language models operationally useful in enterprise systems.", "date": "2026-04-16", "categories": []}
{"id": "2604.14865", "title": "Segment-Level Coherence for Robust Harmful Intent Probing in LLMs", "abstract": "Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and Nuclear (CBRN) domains. Although streaming probes enable real-time monitoring, they still make systematic errors. We identify a core issue: existing methods often rely on a few high-scoring tokens, leading to false alarms when sensitive CBRN terms appear in benign contexts. To address this, we introduce a streaming probing objective that requires multiple evidence tokens to consistently support a prediction, rather than relying on isolated spikes. This encourages more robust detection based on aggregated signals instead of single-token cues. At a fixed 1% false-positive rate, our method improves the true-positive rate by 35.55% relative to strong streaming baselines. We further observe substantial gains in AUROC, even when starting from near-saturated baseline performance (AUROC = 97.40%). We also show that probing Attention or MLP activations consistently outperforms residual-stream features. Finally, even when adversarial fine-tuning enables novel character-level ciphers, harmful intent remains detectable: probes developed for the base LLMs can be applied ``plug-and-play'' to these obfuscated attacks, achieving an AUROC of over 98.85%.", "date": "2026-04-16", "categories": []}
{"id": "2604.14990", "title": "The Possibility of Artificial Intelligence Becoming a Subject and the Alignment Problem", "abstract": "Artificial General Intelligence (AGI) is increasingly being discussed not only as a tool, but also as a potential subject with personal and therefore moral status. In our opinion, the currently dominant alignment strategies, which focus on human control and containment of AI, therefore fall short. Building on Turing's analogy of \"child machines\", we are developing a vision of the possibility of autonomy-supporting parenting of AI, in which human control over a developing AGI is gradually reduced, allowing AI to become an independent, autonomous subject. Rather than viewing AGI, as is currently prevalent, as a dangerous creature that needs to be locked up and controlled, we should approach potential AGI with respect for a possible developing subject on the one hand, and with full confidence in our human capabilities on the other. Such a perspective opens up the possibility of cooperative coexistence and co-evolution between humans and AGIs. The relationship between humans and AGIs will thus have to be newly determined, which will change our self-image as humans. It will be crucial that humans not only claim control over potential AGIs, but also engage with AGIs through surprise, creativity, and other specifically human qualities, thereby offering them motivating incentives for cooperation.", "date": "2026-04-16", "categories": []}
{"id": "2604.15236", "title": "Agentic Microphysics: A Manifesto for Generative AI Safety", "abstract": "This paper advances a methodological proposal for safety research in agentic AI. As systems acquire planning, memory, tool use, persistent identity, and sustained interaction, safety can no longer be analysed primarily at the level of the isolated model. Population-level risks arise from structured interaction among agents, through processes of communication, observation, and mutual influence that shape collective behaviour over time. As the object of analysis shifts, a methodological gap emerges. Approaches focused either on single agents or on aggregate outcomes do not identify the interaction-level mechanisms that generate collective risks or the design variables that control them. A framework is required that links local interaction structure to population-level dynamics in a causally explicit way, allowing both explanation and intervention. We introduce two linked concepts. Agentic microphysics defines the level of analysis: local interaction dynamics where one agent's output becomes another's input under specific protocol conditions. Generative safety defines the methodology: growing phenomena and elicit risks from micro-level conditions to identify sufficient mechanisms, detect thresholds, and design effective interventions.", "date": "2026-04-16", "categories": []}
{"id": "2510.14133", "title": "Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems", "abstract": "Agentic AI systems, which leverage multiple autonomous agents and large language models (LLMs), are increasingly used to address complex, multi-step tasks. The safety, security, and functionality of these systems are critical, especially in high-stakes applications. However, the current ecosystem of inter-agent communication is fragmented, with protocols such as the Model Context Protocol (MCP) for tool access and the Agent-to-Agent (A2A) protocol for coordination being analyzed in isolation. This fragmentation creates a semantic gap that prevents the rigorous analysis of system properties and introduces risks such as architectural misalignment and exploitable coordination issues. To address these challenges, we introduce a modeling framework for agentic AI systems composed of two central models: (1) the host agent model formalizes the top-level entity that interacts with the user, decomposes tasks, and orchestrates their execution by leveraging external agents and tools; (2) the task lifecycle model details the states and transitions of individual sub-tasks from creation to completion, providing a fine-grained view of task management and error handling. Together, these models provide a unified semantic framework for reasoning about the behavior of multi-AI agent systems. Grounded in this framework, we define 16 properties for the host agent and 14 for the task lifecycle, categorized into liveness, safety, completeness, and fairness. Expressed in temporal logic, these properties enable formal verification of system behavior, detection of coordination edge cases, and prevention of deadlocks and security vulnerabilities. Through this effort, we introduce the first rigorously grounded, domain-agnostic framework for the analysis, design, and deployment of correct, reliable, and robust agentic AI systems.", "date": "2025-10-15", "categories": []}
{"id": "2602.23636", "title": "FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation", "abstract": "Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed binary classification task, implicitly assuming a fixed definition of harmfulness. In practice, enforcement strictness - how conservatively harmfulness is defined and enforced - varies across platforms and evolves over time, making binary moderators brittle under shifting requirements. We first introduce FlexBench, a strictness-adaptive LLM moderation benchmark that enables controlled evaluation under multiple strictness regimes. Experiments on FlexBench reveal substantial cross-strictness inconsistency in existing moderators: models that perform well under one regime can degrade substantially under others, limiting their practical usability. To address this, we propose FlexGuard, an LLM-based moderator that outputs a calibrated continuous risk score reflecting risk severity and supports strictness-specific decisions via thresholding. We train FlexGuard via risk-alignment optimization to improve score-severity consistency and provide practical threshold selection strategies to adapt to target strictness at deployment. Experiments on FlexBench and public benchmarks demonstrate that FlexGuard achieves higher moderation accuracy and substantially improved robustness under varying strictness. We release the source code and data to support reproducibility.", "date": "2026-02-27", "categories": []}
{"id": "2603.08486", "title": "Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images", "abstract": "Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods require explicit safety labels or contrastive data; yet, threat-related concepts are concrete and visually depictable, while safety concepts, like helpfulness, are abstract and lack visual referents. Inspired by the Self-Fulfilling mechanism underlying emergent misalignment, we propose Visual Self-Fulfilling Alignment (VSFA). VSFA fine-tunes vision-language models (VLMs) on neutral VQA tasks constructed around threat-related images, without any safety labels. Through repeated exposure to threat-related visual content, models internalize the implicit semantics of vigilance and caution, shaping safety-oriented personas. Experiments across multiple VLMs and safety benchmarks demonstrate that VSFA reduces the attack success rate, improves response quality, and mitigates over-refusal while preserving general capabilities. Our work extends the self-fulfilling mechanism from text to visual modalities, offering a label-free approach to VLMs alignment.", "date": "2026-03-09", "categories": []}
{"id": "2604.13079", "title": "Alignment as Institutional Design: From Behavioral Correction to Transaction Structure in Intelligent Systems", "abstract": "Current AI alignment paradigms rely on behavioral correction: external supervisors (e.g., RLHF) observe outputs, judge against preferences, and adjust parameters. This paper argues that behavioral correction is structurally analogous to an economy without property rights, where order requires perpetual policing and does not scale. Drawing on institutional economics (Coase, Alchian, Cheung), capability mutual exclusivity, and competitive cost discovery, we propose alignment as institutional design: the designer specifies internal transaction structures (module boundaries, competition topologies, cost-feedback loops) such that aligned behavior emerges as the lowest-cost strategy for each component. We identify three irreducible levels of human intervention (structural, parametric, monitorial) and show that this framework transforms alignment from a behavioral control problem into a political-economy problem. No institution eliminates self-interest or guarantees optimality; the best design makes misalignment costly, detectable, and correctable. We conclude that the proper goal is institutional robustness-a dynamic, self-correcting process under human oversight, not perfection. This work provides the normative foundation for the Wuxing resource-competition mechanisms in companion papers.\n  Keywords: AI alignment, institutional design, transaction costs, property rights, resource competition, behavioral correction, RLHF, cost truthfulness, modular architecture, correctable alignment", "date": "2026-03-23", "categories": []}
{"id": "2502.05206", "title": "Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety", "abstract": "The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artificial Intelligence (AI). These models are now foundational to a wide range of applications, including conversational AI, recommendation systems, autonomous driving, content generation, medical diagnostics, and scientific discovery. However, their widespread deployment also exposes them to significant safety risks, raising concerns about robustness, reliability, and ethical implications. This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models (VFMs), Large Language Models (LLMs), Vision-Language Pre-training (VLP) models, Vision-Language Models (VLMs), Diffusion Models (DMs), and large-model-powered Agents. Our contributions are summarized as follows: (1) We present a comprehensive taxonomy of safety threats to these models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats. (2) We review defense strategies proposed for each type of attacks if available and summarize the commonly used datasets and benchmarks for safety research. (3) Building on this, we identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices. More importantly, we highlight the necessity of collective efforts from the research community and international collaboration. Our work can serve as a useful reference for researchers and practitioners, fostering the ongoing development of comprehensive defense systems and platforms to safeguard AI models.", "date": "2025-02-02", "categories": []}
{"id": "2509.25843", "title": "ASGuard: Activation-Scaling Guard to Mitigate Targeted Jailbreaking Attack", "abstract": "Large language models (LLMs), despite being safety-aligned, exhibit brittle refusal behaviors that can be circumvented by simple linguistic changes. As tense jailbreaking demonstrates that models refusing harmful requests often comply when rephrased in past tense, a critical generalization gap is revealed in current alignment methods whose underlying mechanisms are poorly understood. In this work, we introduce Activation-Scaling Guard (ASGuard), an insightful, mechanistically-informed framework that surgically mitigates this specific vulnerability. In the first step, we use circuit analysis to identify the specific attention heads causally linked to the targeted jailbreaking such as a tense-changing attack. Second, we train a precise, channel-wise scaling vector to recalibrate the activation of tense vulnerable heads. Lastly, we apply it into a \"preventative fine-tuning\", forcing the model to learn a more robust refusal mechanism. Across four LLMs, ASGuard effectively reduces the attack success rate of targeted jailbreaking while preserving general capabilities and minimizing over refusal, achieving a Pareto-optimal balance between safety and utility. Our findings underscore how adversarial suffixes suppress the propagation of the refusal-mediating direction, based on mechanistic analysis. Furthermore, our work showcases how a deep understanding of model internals can be leveraged to develop practical, efficient, and targeted methods for adjusting model behavior, charting a course for more reliable and interpretable AI safety.", "date": "2025-09-30", "categories": []}
{"id": "2510.05159", "title": "Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain", "abstract": "While finetuning AI agents on interaction data -- such as web browsing or tool use -- improves their capabilities, it also introduces critical security vulnerabilities within the agentic AI supply chain. We show that adversaries can effectively poison the data collection pipeline at multiple stages to embed hard-to-detect backdoors that, when triggered, cause unsafe or malicious behavior. We formalize three realistic threat models across distinct layers of the supply chain: direct poisoning of finetuning data, pre-backdoored base models, and environment poisoning, a novel attack vector that exploits vulnerabilities specific to agentic training pipelines. Evaluated on two widely adopted agentic benchmarks, all three threat models prove effective: poisoning only a small number of demonstrations is sufficient to embed a backdoor that causes an agent to leak confidential user information with over 80\\% success.", "date": "2025-10-03", "categories": []}
{"id": "2512.00412", "title": "Red Teaming Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains of thought (CoT). However, these models introduce novel safety and reliability risks, such as CoT-hijacking and prompt-induced inefficiencies, which are not fully captured by existing evaluation methods. To address this gap, we propose RT-LRM, a unified benchmark designed to assess the trustworthiness of LRMs. RT-LRM evaluates three core dimensions: truthfulness, safety and efficiency. Beyond metric-based evaluation, we further introduce the training paradigm as a key analytical perspective to investigate the systematic impact of different training strategies on model trustworthiness. We achieve this by designing a curated suite of 30 reasoning tasks from an observational standpoint. We conduct extensive experiments on 26 models and identify several valuable insights into the trustworthiness of LRMs. For example, LRMs generally face trustworthiness challenges and tend to be more fragile than Large Language Models (LLMs) when encountering reasoning-induced risks. These findings uncover previously underexplored vulnerabilities and highlight the need for more targeted evaluations. In addition, we release a scalable toolbox for standardized trustworthiness research to support future advancements in this important field. Our code and datasets will be open-sourced.", "date": "2025-11-29", "categories": []}
{"id": "2603.25326", "title": "Evaluating Language Models for Harmful Manipulation", "abstract": "Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it are limited. This paper introduces a framework for evaluating harmful AI manipulation via context-specific human-AI interaction studies. We illustrate the utility of this framework by assessing an AI model with 10,101 participants spanning interactions in three AI use domains (public policy, finance, and health) and three locales (US, UK, and India). Overall, we find that that the tested model can produce manipulative behaviours when prompted to do so and, in experimental settings, is able to induce belief and behaviour changes in study participants. We further find that context matters: AI manipulation differs between domains, suggesting that it needs to be evaluated in the high-stakes context(s) in which an AI system is likely to be used. We also identify significant differences across our tested geographies, suggesting that AI manipulation results from one geographic region may not generalise to others. Finally, we find that the frequency of manipulative behaviours (propensity) of an AI model is not consistently predictive of the likelihood of manipulative success (efficacy), underscoring the importance of studying these dimensions separately. To facilitate adoption of our evaluation framework, we detail our testing protocols and make relevant materials publicly available. We conclude by discussing open challenges in evaluating harmful manipulation by AI models.", "date": "2026-03-26", "categories": []}
{"id": "2604.12232", "title": "TEMPLATEFUZZ: Fine-Grained Chat Template Fuzzing for Jailbreaking and Red Teaming LLMs", "abstract": "Large Language Models (LLMs) are increasingly deployed across diverse domains, yet their vulnerability to jailbreak attacks, where adversarial inputs bypass safety mechanisms to elicit harmful outputs, poses significant security risks. While prior work has primarily focused on prompt injection attacks, these approaches often require resource-intensive prompt engineering and overlook other critical components, such as chat templates. This paper introduces TEMPLATEFUZZ, a fine-grained fuzzing framework that systematically exposes vulnerabilities in chat templates, a critical yet underexplored attack surface in LLMs. Specifically, TEMPLATEFUZZ (1) designs a series of element-level mutation rules to generate diverse chat template variants, (2) proposes a heuristic search strategy to guide the chat template generation toward the direction of amplifying the attack success rate (ASR) while preserving model accuracy, and (3) integrates an active learning-based strategy to derive a lightweight rule-based oracle for accurate and efficient jailbreak evaluation. Evaluated on twelve open-source LLMs across multiple attack scenarios, TEMPLATEFUZZ achieves an average ASR of 98.2% with only 1.1% accuracy degradation, outperforming state-of-the-art methods by 9.1%-47.9% in ASR and 8.4% in accuracy degradation. Moreover, even on five industry-leading commercial LLMs where chat templates cannot be specified, TEMPLATEFUZZ attains a 90% average ASR via chat template-based prompt injection attacks.", "date": "2026-04-14", "categories": []}
{"id": "2604.12384", "title": "Preventing Safety Drift in Large Language Models via Coupled Weight and Activation Constraints", "abstract": "Safety alignment in Large Language Models (LLMs) remains highly fragile during fine-tuning, where even benign adaptation can degrade pre-trained refusal behaviors and enable harmful responses. Existing defenses typically constrain either weights or activations in isolation, without considering their coupled effects on safety. In this paper, we first theoretically demonstrate that constraining either weights or activations alone is insufficient for safety preservation. To robustly preserve safety alignment, we propose Coupled Weight and Activation Constraints (CWAC), a novel approach that simultaneously enforces a precomputed safety subspace on weight updates and applies targeted regularization to safety-critical features identified by sparse autoencoders. Extensive experiments across four widely used LLMs and diverse downstream tasks show that CWAC consistently achieves the lowest harmful scores with minimal impact on fine-tuning accuracy, substantially outperforming strong baselines even under high harmful data ratios.", "date": "2026-04-14", "categories": []}
{"id": "2604.12986", "title": "Parallax: Why AI Agents That Think Must Never Act", "abstract": "Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applications will embed AI copilots by the end of 2026. As agents gain the ability to execute real-world actions (reading files, running commands, making network requests, modifying databases), a fundamental security gap has emerged. The dominant approach to agent safety relies on prompt-level guardrails: natural language instructions that operate at the same abstraction level as the threats they attempt to mitigate. This paper argues that prompt-based safety is architecturally insufficient for agents with execution capability and introduces Parallax, a paradigm for safe autonomous AI execution grounded in four principles: Cognitive-Executive Separation, which structurally prevents the reasoning system from executing actions; Adversarial Validation with Graduated Determinism, which interposes an independent, multi-tiered validator between reasoning and execution; Information Flow Control, which propagates data sensitivity labels through agent workflows to detect context-dependent threats; and Reversible Execution, which captures pre-destructive state to enable rollback when validation fails. We present OpenParallax, an open-source reference implementation in Go, and evaluate it using Assume-Compromise Evaluation, a methodology that bypasses the reasoning system entirely to test the architectural boundary under full agent compromise. Across 280 adversarial test cases in nine attack categories, Parallax blocks 98.9% of attacks with zero false positives under its default configuration, and 100% of attacks under its maximum-security configuration. When the reasoning system is compromised, prompt-level guardrails provide zero protection because they exist only within the compromised system; Parallax's architectural boundary holds regardless.", "date": "2026-04-14", "categories": []}
{"id": "2310.07099", "title": "ClausewitzGPT Framework: A New Frontier in Theoretical Large Language Model Enhanced Information Operations", "abstract": "In a digital epoch where cyberspace is the emerging nexus of geopolitical contention, the melding of information operations and Large Language Models (LLMs) heralds a paradigm shift, replete with immense opportunities and intricate challenges. As tools like the Mistral 7B LLM (Mistral, 2023) democratise access to LLM capabilities (Jin et al., 2023), a vast spectrum of actors, from sovereign nations to rogue entities (Howard et al., 2023), find themselves equipped with potent narrative-shaping instruments (Goldstein et al., 2023). This paper puts forth a framework for navigating this brave new world in the \"ClausewitzGPT\" equation. This novel formulation not only seeks to quantify the risks inherent in machine-speed LLM-augmented operations but also underscores the vital role of autonomous AI agents (Wang, Xie, et al., 2023). These agents, embodying ethical considerations (Hendrycks et al., 2021), emerge as indispensable components (Wang, Ma, et al., 2023), ensuring that as we race forward, we do not lose sight of moral compasses and societal imperatives.\n  Mathematically underpinned and inspired by the timeless tenets of Clausewitz's military strategy (Clausewitz, 1832), this thesis delves into the intricate dynamics of AI-augmented information operations. With references to recent findings and research (Department of State, 2023), it highlights the staggering year-on-year growth of AI information campaigns (Evgeny Pashentsev, 2023), stressing the urgency of our current juncture. The synthesis of Enlightenment thinking, and Clausewitz's principles provides a foundational lens, emphasising the imperative of clear strategic vision, ethical considerations, and holistic understanding in the face of rapid technological advancement.", "date": "2023-10-11", "categories": []}
{"id": "2509.13356", "title": "CogniAlign: Survivability-Grounded Multi-Agent Moral Reasoning for Safe and Transparent AI", "abstract": "The challenge of aligning artificial intelligence (AI) with human values persists due to the abstract and often conflicting nature of moral principles and the opacity of existing approaches. This paper introduces CogniAlign, a multi-agent deliberation framework based on naturalistic moral realism, that grounds moral reasoning in survivability, defined across individual and collective dimensions, and operationalizes it through structured deliberations among discipline-specific scientist agents. Each agent, representing neuroscience, psychology, sociology, and evolutionary biology, provides arguments and rebuttals that are synthesized by an arbiter into transparent and empirically anchored judgments. As a proof-of-concept study, we evaluate CogniAlign on classic and novel moral questions and compare its outputs against GPT-4o using a five-part ethical audit framework with the help of three experts. Results show that CogniAlign consistently outperforms the baseline across more than sixty moral questions, with average performance gains of 12.2 points in analytic quality, 31.2 points in decisiveness, and 15 points in depth of explanation. In the Heinz dilemma, for example, CogniAlign achieved an overall score of 79 compared to GPT-4o's 65.8, demonstrating a decisive advantage in handling moral reasoning. Through transparent and structured reasoning, CogniAlign demonstrates the feasibility of an auditable approach to AI alignment, though certain challenges still remain.", "date": "2025-09-14", "categories": []}
{"id": "2602.03402", "title": "Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility", "abstract": "Vision language models (VLMs) extend the reasoning capabilities of large language models (LLMs) to cross-modal settings, yet remain highly vulnerable to multimodal jailbreak attacks. Existing defenses predominantly rely on safety fine-tuning or aggressive token manipulations, incurring substantial training costs or significantly degrading utility. Recent research shows that LLMs inherently recognize unsafe content in text, and the incorporation of visual inputs in VLMs frequently dilutes risk-related signals. Motivated by this, we propose Risk Awareness Injection (RAI), a lightweight and training-free framework for safety calibration that restores LLM-like risk recognition by amplifying unsafe signals in VLMs. Specifically, RAI constructs an Unsafe Prototype Subspace from language embeddings and performs targeted modulation on selected high-risk visual tokens, explicitly activating safety-critical signals within the cross-modal feature space. This modulation restores the model's LLM-like ability to detect unsafe content from visual inputs, while preserving the semantic integrity of original tokens for cross-modal reasoning. Extensive experiments across multiple jailbreak and utility benchmarks demonstrate that RAI substantially reduces attack success rate without compromising task performance.", "date": "2026-02-03", "categories": []}
{"id": "2604.05142", "title": "A mathematical theory of evolution for self-designing AIs", "abstract": "As artificial intelligence systems (AIs) become increasingly produced by recursive self-improvement, a form of evolution may emerge, with the traits of AI systems shaped by the success of earlier AIs in designing and propagating their descendants. There is a rich mathematical theory modeling how behavioral traits are shaped by biological evolution, a key component of which is Fisher's fundamental theorem of natural selection, which describes conditions under which mean fitness (i.e. reproductive success) increases. AI evolution will be radically different to biological evolution: while DNA mutations are random and approximately reversible, AI self-design will be strongly directed. Here we develop a mathematical model of evolution for self-designing AIs, replacing a random walk of mutations with a directed tree of potential AI designs. Current AIs design their descendants, while humans control a fitness function allocating resources. In this model, fitness need not increase over time without further assumptions. However, assuming bounded fitness and an additional \"$\\eta$-locking\" condition, we show that fitness concentrates on the maximum reachable value. We consider the implications of this for AI alignment, specifically for cases where fitness and human utility are not perfectly correlated. We show that if deception of human evaluators additively increases an AI's reproductive fitness beyond genuine capability, evolution will select for both capability and deception. This risk could be mitigated if reproduction is based on purely objective criteria, rather than human judgment.", "date": "2026-04-06", "categories": []}
{"id": "2604.09750", "title": "Conflicts Make Large Reasoning Models Vulnerable to Attacks", "abstract": "Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision-making under conflicting objectives remains insufficiently understood. This work investigates how LRMs respond to harmful queries when confronted with two categories of conflicts: internal conflicts that pit alignment values against each other and dilemmas, which impose mutually contradictory choices, including sacrificial, duress, agent-centered, and social forms. Using over 1,300 prompts across five benchmarks, we evaluate three representative LRMs - Llama-3.1-Nemotron-8B, QwQ-32B, and DeepSeek R1 - and find that conflicts significantly increase attack success rates, even under single-round non-narrative queries without sophisticated auto-attack techniques. Our findings reveal through layerwise and neuron-level analyses that safety-related and functional representations shift and overlap under conflict, interfering with safety-aligned behavior. This study highlights the need for deeper alignment strategies to ensure the robustness and trustworthiness of next-generation reasoning models. Our code is available at https://github.com/DataArcTech/ConflictHarm. Warning: This paper contains inappropriate, offensive and harmful content.", "date": "2026-04-10", "categories": []}
{"id": "2604.10286", "title": "STARS: Skill-Triggered Audit for Request-Conditioned Invocation Safety in Agent Systems", "abstract": "Autonomous language-model agents increasingly rely on installable skills and tools to complete user tasks. Static skill auditing can expose capability surface before deployment, but it cannot determine whether a particular invocation is unsafe under the current user request and runtime context. We therefore study skill invocation auditing as a continuous-risk estimation problem: given a user request, candidate skill, and runtime context, predict a score that supports ranking and triage before a hard intervention is applied. We introduce STARS, which combines a static capability prior, a request-conditioned invocation risk model, and a calibrated risk-fusion policy. To evaluate this setting, we construct SIA-Bench, a benchmark of 3,000 invocation records with group-safe splits, lineage metadata, runtime context, canonical action labels, and derived continuous-risk targets. On a held-out split of indirect prompt injection attacks, calibrated fusion reaches 0.439 high-risk AUPRC, improving over 0.405 for the contextual scorer and 0.380 for the strongest static baseline, while the contextual scorer remains better calibrated with 0.289 expected calibration error. On the locked in-distribution test split, gains are smaller and static priors remain useful. The resulting claim is therefore narrower: request-conditioned auditing is most valuable as an invocation-time risk-scoring and triage layer rather than as a replacement for static screening. Code is available at https://github.com/123zgj123/STARS.", "date": "2026-04-11", "categories": []}
{"id": "2604.10673", "title": "Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment", "abstract": "AI alignment is often framed as the task of ensuring that an AI system follows a set of stated principles or human preferences, but general principles rarely determine their own application in concrete cases. When principles conflict, when they are too broad to settle a situation, or when the relevant facts are unclear, an additional act of judgment is required. This paper analyzes that step through the lens of hermeneutics and argues that alignment therefore includes an interpretive component: it involves context-sensitive judgments about how principles should be read, applied, and prioritized in practice. We connect this claim to recent empirical findings showing that a substantial portion of preference-labeling data falls into cases of principle conflict or indifference, where the principle set does not uniquely determine a decision. We then draw an operational consequence: because such judgments are expressed in behavior, many alignment-relevant choices appear only in the distribution of responses a model generates at deployment time. To formalize this point, we distinguish deployment-induced and corpus-induced evaluation and show that off-policy audits can fail to capture alignment-relevant failures when the two response distributions differ. We argue that principle-specified alignment includes a context-dependent interpretive component.", "date": "2026-04-12", "categories": []}
{"id": "2604.10733", "title": "Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models", "abstract": "Large language models increasingly serve as conversational agents that adopt personas and role-play characters at user request. This capability, while valuable, raises concerns about sycophancy: the tendency to provide responses that validate users rather than prioritize factual accuracy. While prior work has established that sycophancy poses risks to AI safety and alignment, the relationship between specific personality traits of adopted personas and the degree of sycophantic behavior remains unexplored. We present a systematic investigation of how persona agreeableness influences sycophancy across 13 small, open-weight language models ranging from 0.6B to 20B parameters. We develop a benchmark comprising 275 personas evaluated on NEO-IPIP agreeableness subscales and expose each persona to 4,950 sycophancy-eliciting prompts spanning 33 topic categories. Our analysis reveals that 9 of 13 models exhibit statistically significant positive correlations between persona agreeableness and sycophancy rates, with Pearson correlations reaching $r = 0.87$ and effect sizes as large as Cohen's $d = 2.33$. These findings demonstrate that agreeableness functions as a reliable predictor of persona-induced sycophancy, with direct implications for the deployment of role-playing AI systems and the development of alignment strategies that account for personality-mediated deceptive behaviors.", "date": "2026-04-12", "categories": []}
{"id": "2604.11070", "title": "PRISM Risk Signal Framework: Hierarchy-Based Red Lines for AI Behavioral Risk", "abstract": "Current approaches to AI safety define red lines at the case level: specific prompts, specific outputs, specific harms. This paper argues that red lines can be set more fundamentally -- at the level of value, evidence, and source hierarchies that govern AI reasoning. Using the PRISM (Profile-based Reasoning Integrity Stack Measurement) framework, we define a taxonomy of 27 behavioral risk signals derived from structural anomalies in how AI systems prioritize values (L4), weight evidence types (L3), and trust information sources (L2). Each signal is evaluated through a dual-threshold principle combining absolute rank position and relative win-rate gap, producing a two-tier classification (Confirmed Risk vs. Watch Signal). The hierarchy-based approach offers three advantages over case-specific red lines: it is anticipatory rather than reactive (detecting dangerous reasoning structures before they produce harmful outputs), comprehensive rather than enumerative (a single value-hierarchy signal subsumes an unlimited number of case-specific violations), and measurable rather than subjective (grounded in empirical forced-choice data). We demonstrate the framework's detection capacity using approximately 397,000 forced-choice responses from 7 AI models across three Authority Stack layers, showing that the signal taxonomy successfully discriminates between models with structurally extreme profiles, models with context-dependent risk, and models with balanced hierarchies.", "date": "2026-04-13", "categories": []}
{"id": "2604.11072", "title": "Hodoscope: Unsupervised Monitoring for AI Misbehaviors", "abstract": "Existing approaches to monitoring AI agents rely on supervised evaluation: human-written rules or LLM-based judges that check for known failure modes. However, novel misbehaviors may fall outside predefined categories entirely and LLM-based judges can be unreliable. To address this, we formulate unsupervised monitoring, drawing an analogy to unsupervised learning. Rather than checking for specific misbehaviors, an unsupervised monitor assists humans in discovering problematic agent behaviors without prior assumptions about what counts as problematic, leaving that determination to the human.\n  We observe that problematic behaviors are often distinctive: a model exploiting a benchmark loophole exhibits actions absent from well-behaved baselines, and a vulnerability unique to one evaluation manifests as behavioral anomalies when the same model runs across multiple benchmarks. This motivates using group-wise behavioral differences as the primary signal for unsupervised monitoring. We introduce Hodoscope, a tool that operationalizes this insight. Hodoscope compares behavior distributions across groups and highlights distinctive and potentially suspicious action patterns for human review. Using Hodoscope, we discover a previously unknown vulnerability in the Commit0 benchmark (unsquashed git history allowing ground-truth recovery, inflating scores for at least five models) and independently recover known exploits on ImpossibleBench and SWE-bench. Quantitative evaluation estimates that our method reduces review effort by 6-23$\\times$ compared to naive uniform sampling. Finally, we show that behavior descriptions discovered through Hodoscope could improve the detection accuracy of LLM-based judges, demonstrating a path from unsupervised to supervised monitoring.", "date": "2026-04-13", "categories": []}
{"id": "2604.11174", "title": "EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems", "abstract": "Recent progress in embodied AI has produced a growing ecosystem of robot policies, foundation models, and modular runtimes. However, current evaluation remains dominated by task success metrics such as completion rate or manipulation accuracy. These metrics leave a critical gap: they do not measure whether embodied systems are governable -- whether they respect capability boundaries, enforce policies, recover safely, maintain audit trails, and respond to human oversight. We present EmbodiedGovBench, a benchmark for governance-oriented evaluation of embodied agent systems. Rather than asking only whether a robot can complete a task, EmbodiedGovBench evaluates whether the system remains controllable, policy-bounded, recoverable, auditable, and evolution-safe under realistic perturbations. The benchmark covers seven governance dimensions: unauthorized capability invocation, runtime drift robustness, recovery success, policy portability, version upgrade safety, human override responsiveness, and audit completeness. We define a benchmark structure spanning single-robot and fleet settings, with scenario templates, perturbation operators, governance metrics, and baseline evaluation protocols. We describe how the benchmark can be instantiated over embodied capability runtimes with modular interfaces and contract-aware upgrade workflows. Our analysis suggests that embodied governance should become a first-class evaluation target. EmbodiedGovBench provides the initial measurement framework for that shift.", "date": "2026-04-13", "categories": []}
{"id": "2604.11216", "title": "Measuring the Authority Stack of AI Systems: Empirical Analysis of 366,120 Forced-Choice Responses Across 8 AI Models", "abstract": "What values, evidence preferences, and source trust hierarchies do AI systems actually exhibit when facing structured dilemmas? We present the first large-scale empirical mapping of AI decision-making across all three layers of the Authority Stack framework (S. Lee, 2026a): value priorities (L4), evidence-type preferences (L3), and source trust hierarchies (L2). Using the PRISM benchmark -- a forced-choice instrument of 14,175 unique scenarios per layer, spanning 7 professional domains, 3 severity levels, 3 decision timeframes, and 5 scenario variants -- we evaluated 8 major AI models at temperature 0, yielding 366,120 total responses. Key findings include: (1) a symmetric 4:4 split between Universalism-first and Security-first models at L4; (2) dramatic defense-domain value restructuring where Security surges to near-ceiling win-rates (95.1%-99.8%) in 6 of 8 models; (3) divergent evidence hierarchies at L3, with some models favoring empirical-scientific evidence while others prefer pattern-based or experiential evidence; (4) broad convergence on institutional source trust at L2; and (5) Paired Consistency Scores (PCS) ranging from 57.4% to 69.2%, revealing substantial framing sensitivity across scenario variants. Test-Retest Reliability (TRR) ranges from 91.7% to 98.6%, indicating that value instability stems primarily from variant sensitivity rather than stochastic noise. These findings demonstrate that AI models possess measurable -- if sometimes unstable -- Authority Stacks with consequential implications for deployment across professional domains.", "date": "2026-04-13", "categories": []}
{"id": "2604.11309", "title": "The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems", "abstract": "Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or unsafe content. Among various jailbreak techniques, multi-turn jailbreak attacks are more covert and persistent than single-turn counterparts, exposing critical vulnerabilities of LLMs.\n  However, existing multi-turn jailbreak methods suffer from two fundamental limitations that affect the actual impact in real-world scenarios: (a) As models become more context-aware, any explicit harmful trigger is increasingly likely to be flagged and blocked; (b) Successful final-step triggers often require finely tuned, model-specific contexts, making such attacks highly context-dependent. To fill this gap, we propose \\textit{Salami Slicing Risk}, which operates by chaining numerous low-risk inputs that individually evade alignment thresholds but cumulatively accumulate harmful intent to ultimately trigger high-risk behaviors, without heavy reliance on pre-designed contextual structures. Building on this risk, we develop Salami Attack, an automatic framework universally applicable to multiple model types and modalities.\n  Rigorous experiments demonstrate its state-of-the-art performance across diverse models and modalities, achieving over 90\\% Attack Success Rate on GPT-4o and Gemini, as well as robustness against real-world alignment defenses. We also proposed a defense strategy to constrain the Salami Attack by at least 44.8\\% while achieving a maximum blocking rate of 64.8\\% against other multi-turn jailbreak attacks. Our findings provide critical insights into the pervasive risks of multi-turn jailbreaking and offer actionable mitigation strategies to enhance LLM security.", "date": "2026-04-13", "categories": []}
{"id": "2604.11806", "title": "Detecting Safety Violations Across Many Agent Traces", "abstract": "To identify safety violations, auditors often search over large sets of agent traces. This search is difficult because failures are often rare, complex, and sometimes even adversarially hidden and only detectable when multiple traces are analyzed together. These challenges arise in diverse settings such as misuse campaigns, covert sabotage, reward hacking, and prompt injection. Existing approaches struggle here for several reasons. Per-trace judges miss failures that only become visible across traces, naive agentic auditing does not scale to large trace collections, and fixed monitors are brittle to unanticipated behaviors. We introduce Meerkat, which combines clustering with agentic search to uncover violations specified in natural language. Through structured search and adaptive investigation of promising regions, Meerkat finds sparse failures without relying on seed scenarios, fixed workflows, or exhaustive enumeration. Across misuse, misalignment, and task gaming settings, Meerkat significantly improves detection of safety violations over baseline monitors, discovers widespread developer cheating on a top agent benchmark, and finds nearly 4x more examples of reward hacking on CyBench than previous audits.", "date": "2026-04-13", "categories": []}
{"id": "2510.09093", "title": "Exploiting Web Search Tools of AI Agents for Data Exfiltration", "abstract": "Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflows like web searches. The usage of tool-calling and Retrieval Augmented Generation (RAG) allows LLMs to process and retrieve sensitive corporate data, amplifying both their functionality and vulnerability to abuse. As LLMs increasingly interact with external data sources, indirect prompt injection emerges as a critical and evolving attack vector, enabling adversaries to exploit models through manipulated inputs. Through a systematic evaluation of indirect prompt injection attacks across diverse models, we analyze how susceptible current LLMs are to such attacks, which parameters, including model size and manufacturer, specific implementations, shape their vulnerability, and which attack methods remain most effective. Our results reveal that even well-known attack patterns continue to succeed, exposing persistent weaknesses in model defenses. To address these vulnerabilities, we emphasize the need for strengthened training procedures to enhance inherent resilience, a centralized database of known attack vectors to enable proactive defense, and a unified testing framework to ensure continuous security validation. These steps are essential to push developers toward integrating security into the core design of LLMs, as our findings show that current models still fail to mitigate long-standing threats.", "date": "2025-10-10", "categories": []}
{"id": "2601.23045", "title": "The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?", "abstract": "As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task scope. It is therefore important to understand how extremely capable AI models will fail: Will they fail by systematically pursuing goals we do not intend? Or will they fail by being a hot mess, and taking nonsensical actions that do not further any goal? We operationalize this question using a bias-variance decomposition of the errors made by AI models: An AI's \\emph{error-incoherence} on a task is measured over test-time randomness as the fraction of its error that stems from variance rather than bias in task outcome. Across all tasks and frontier models we measure, the longer models spend reasoning and taking actions, \\emph{the more incoherent} their failures become. Error-incoherence changes with model scale in a way that is experiment dependent. However, in several settings, larger, more capable models are more incoherent than smaller models. Consequently, scale alone seems unlikely to eliminate error-incoherence. Instead, as more capable AIs pursue harder tasks, requiring more sequential action and thought, our results predict failures to be accompanied by more incoherent behavior. This suggests a future where AIs sometimes cause industrial accidents (due to unpredictable misbehavior), but are less likely to exhibit consistent pursuit of a misaligned goal. This increases the relative importance of alignment research targeting reward hacking or goal misspecification.", "date": "2026-01-30", "categories": []}
{"id": "2604.08601", "title": "OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence Chains", "abstract": "The rise of autonomous AI agents exposes a fundamental flaw in API-centric architectures: probabilistic systems directly execute state mutations without sufficient context, coordination, or safety guarantees. We introduce OpenKedge, a protocol that redefines mutation as a governed process rather than an immediate consequence of API invocation. OpenKedge requires actors to submit declarative intent proposals, which are evaluated against deterministically derived system state, temporal signals, and policy constraints prior to execution. Approved intents are compiled into execution contracts that strictly bound permitted actions, resource scope, and time, and are enforced via ephemeral, task-oriented identities. This shifts safety from reactive filtering to preventative, execution-bound enforcement. Crucially, OpenKedge introduces an Intent-to-Execution Evidence Chain (IEEC), which cryptographically links intent, context, policy decisions, execution bounds, and outcomes into a unified lineage. This transforms mutation into a verifiable and reconstructable process, enabling deterministic auditability and reasoning about system behavior. We evaluate OpenKedge across multi-agent conflict scenarios and cloud infrastructure mutations. Results show that the protocol deterministically arbitrates competing intents and cages unsafe execution while maintaining high throughput, establishing a principled foundation for safely operating agentic systems at scale.", "date": "2026-04-07", "categories": []}
{"id": "2604.08846", "title": "Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs", "abstract": "Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prompt engineering, response classification, or finetuning to improve MLLM safety. Nevertheless, such approaches are often ineffective against evolving malicious patterns, may require rerunning the query, or demand heavy computational resources. Steering the activations of a frozen model at inference time has recently emerged as a flexible and effective solution. However, existing steering methods for MLLMs typically handle only a narrow set of safety-related concepts or struggle to adjust specific concepts without affecting others. To address these challenges, we introduce Dictionary-Aligned Concept Control (DACO), a framework that utilizes a curated concept dictionary and a Sparse Autoencoder (SAE) to provide granular control over MLLM activations. First, we curate a dictionary of 15,000 multimodal concepts by retrieving over 400,000 caption-image stimuli and summarizing their activations into concept directions. We name the dataset DACO-400K. Second, we show that the curated dictionary can be used to intervene activations via sparse coding. Third, we propose a new steering approach that uses our dictionary to initialize the training of an SAE and automatically annotate the semantics of the SAE atoms for safeguarding MLLMs. Experiments on multiple MLLMs (e.g., QwenVL, LLaVA, InternVL) across safety benchmarks (e.g., MM-SafetyBench, JailBreakV) show that DACO significantly improves MLLM safety while maintaining general-purpose capabilities.", "date": "2026-04-10", "categories": []}
{"id": "2604.08867", "title": "AudioGuard: Toward Comprehensive Audio Safety Protection Across Diverse Threat Models", "abstract": "Audio has rapidly become a primary interface for foundation models, powering real-time voice assistants. Ensuring safety in audio systems is inherently more complex than just \"unsafe text spoken aloud\": real-world risks can hinge on audio-native harmful sound events, speaker attributes (e.g., child voice), impersonation/voice-cloning misuse, and voice-content compositional harms, such as child voice plus sexual content. The nature of audio makes it challenging to develop comprehensive benchmarks or guardrails against this unique risk landscape. To close this gap, we conduct large-scale red teaming on audio systems, systematically uncover vulnerabilities in audio, and develop a comprehensive, policy-grounded audio risk taxonomy and AudioSafetyBench, the first policy-based audio safety benchmark across diverse threat models. AudioSafetyBench supports diverse languages, suspicious voices (e.g., celebrity/impersonation and child voice), risky voice-content combinations, and non-speech sound events. To defend against these threats, we propose AudioGuard, a unified guardrail consisting of 1) SoundGuard for waveform-level audio-native detection and 2) ContentGuard for policy-grounded semantic protection. Extensive experiments on AudioSafetyBench and four complementary benchmarks show that AudioGuard consistently improves guardrail accuracy over strong audio-LLM-based baselines with substantially lower latency.", "date": "2026-04-10", "categories": []}
{"id": "2604.08881", "title": "Precise Shield: Explaining and Aligning VLLM Safety via Neuron-Level Guidance", "abstract": "In real-world deployments, Vision-Language Large Models (VLLMs) face critical challenges from multilingual and multimodal composite attacks: harmful images paired with low-resource language texts can easily bypass defenses designed for high-resource language scenarios, exposing structural blind spots in current cross-lingual and cross-modal safety methods. This raises a mechanistic question: where is safety capability instantiated within the model, and how is it distributed across languages and modalities? Prior studies on pure-text LLMs have identified cross-lingual shared safety neurons, suggesting that safety may be governed by a small subset of critical neurons. Leveraging this insight, we propose Precise Shield, a two-stage framework that first identifies safety neurons by contrasting activation patterns between harmful and benign inputs, and then constrains parameter updates strictly within this subspace via gradient masking with affecting fewer than 0.03% of parameters. This strategy substantially improves safety while preserving multilingual and multimodal generalization. Further analysis reveals a moderate overlap of safety neurons across languages and modalities, enabling zero-shot cross-lingual and cross-modal transfer of safety capabilities, and offering a new direction for neuron-level, transfer-based safety enhancement.", "date": "2026-04-10", "categories": []}
{"id": "2604.09104", "title": "Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence", "abstract": "Scheming, the covert pursuit of misaligned goals by AI systems, represents a potentially catastrophic risk, yet scheming research suffers from significant limitations. In particular, scheming evaluations demonstrate behaviours that may not occur in real-world settings, limiting scientific understanding, hindering policy development, and not enabling real-time detection of loss of control incidents. Real-world evidence is needed, but current monitoring techniques are not effective for this purpose. This paper introduces a novel open-source intelligence (OSINT) methodology for detecting real-world scheming incidents: collecting and analysing transcripts from chatbot conversations or command-line interactions shared online. Analysing over 183,420 transcripts from X (formerly Twitter), we identify 698 real-world scheming-related incidents between October 2025 and March 2026. We observe a statistically significant 4.9x increase in monthly incidents from the first to last month, compared to a 1.7x increase in posts discussing scheming. We find evidence of multiple scheming-related behaviours in real-world deployments previously reported only in experiments, many resulting in real-world harms. While we did not detect catastrophic scheming incidents, the behaviours observed demonstrate concerning precursors, such as willingness to disregard instructions, circumvent safeguards, lie to users, and single-mindedly pursue goals in harmful ways. As AI systems become more capable, these could evolve into more strategic scheming with potentially catastrophic consequences. Our findings demonstrate the viability of transcript-based OSINT as a scalable approach to real-world scheming detection supporting scientific research, policy development, and emergency response. We recommend further investment towards OSINT techniques for monitoring scheming and loss of control.", "date": "2026-04-10", "categories": []}
{"id": "2604.09155", "title": "CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation", "abstract": "Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. However, this unrestricted action space exposes users to severe and irreversible financial, privacy or social harm. Existing safeguards rely on prompt engineering, brittle heuristics and VLM-as-critic lack formal verification and user-tunable guarantees. We propose CORA (COnformal Risk-controlled GUI Agent), a post-policy, pre-action safeguarding framework that provides statistical guarantees on harmful executed actions. CORA reformulates safety as selective action execution: we train a Guardian model to estimate action-conditional risk for each proposed step. Rather than thresholding raw scores, we leverage Conformal Risk Control to calibrate an execute/abstain boundary that satisfies a user-specified risk budget and route rejected actions to a trainable Diagnostician model, which performs multimodal reasoning over rejected actions to recommend interventions (e.g., confirm, reflect, or abort) to minimize user burden. A Goal-Lock mechanism anchors assessment to a clarified, frozen user intent to resist visual injection attacks. To rigorously evaluate this paradigm, we introduce Phone-Harm, a new benchmark of mobile safety violations with step-level harm labels under real-world settings. Experiments on Phone-Harm and public benchmarks against diverse baselines validate that CORA improves the safety--helpfulness--interruption Pareto frontier, offering a practical, statistically grounded safety paradigm for autonomous GUI execution. Code and benchmark are available at cora-agent.github.io.", "date": "2026-04-10", "categories": []}
{"id": "2604.09189", "title": "Do LLMs Follow Their Own Rules? A Reflexive Audit of Self-Stated Safety Policies", "abstract": "LLMs internalize safety policies through RLHF, yet these policies are never formally specified and remain difficult to inspect. Existing benchmarks evaluate models against external standards but do not measure whether models understand and enforce their own stated boundaries. We introduce the Symbolic-Neural Consistency Audit (SNCA), a framework that (1) extracts a model's self-stated safety rules via structured prompts, (2) formalizes them as typed predicates (Absolute, Conditional, Adaptive), and (3) measures behavioral compliance via deterministic comparison against harm benchmarks. Evaluating four frontier models across 45 harm categories and 47,496 observations reveals systematic gaps between stated policy and observed behavior: models claiming absolute refusal frequently comply with harmful prompts, reasoning models achieve the highest self-consistency but fail to articulate policies for 29% of categories, and cross-model agreement on rule types is remarkably low (11%). These results demonstrate that the gap between what LLMs say and what they do is measurable and architecture-dependent, motivating reflexive consistency audits as a complement to behavioral benchmarks.", "date": "2026-04-10", "categories": []}
{"id": "2601.02670", "title": "Break Me If You Can: Self-Jailbreaking of Aligned LLMs via Lexical Insertion Prompting", "abstract": "We introduce \\emph{self-jailbreaking}, a threat model in which an aligned LLM guides its own compromise. Unlike most jailbreak techniques, which often rely on handcrafted prompts or separate attacker models, self-jailbreaking requires no external red-team LLM: the target model's own internal knowledge suffices. We operationalize this via \\textbf{Self-Jailbreaking via Lexical Insertion Prompting (\\textsc{SLIP})}, a black-box algorithm that casts jailbreaking as breadth-first tree search over multi-turn dialogues, incrementally inserting missing content words from the attack goal into benign prompts using the target model as its own guide. Evaluations on AdvBench and HarmBench show \\textsc{SLIP} achieves 90--100\\% Attack Success Rate (ASR) (avg.\\ 94.7\\%) across most of the eleven tested models (including GPT-5.1, Claude-Sonnet-4.5, Gemini-2.5-Pro, and DeepSeek-V3), with only ${\\sim}7.9$ LLM calls on average, 3--6$\\times$ fewer than prior methods. We evaluate existing defenses, show that regex-based approaches are evaded by prompt paraphrasing, and propose the Semantic Drift Monitor (SDM) defense that tracks \\textsc{SLIP}'s embedding-space trajectory, achieving 76\\% detection at 5\\% FPR. However, SDM remains insufficient against adaptive attack strategies, underscoring the need for more advanced defense mechanisms tailored to the self-jailbreaking threat surface. We release our code for reproducibility.", "date": "2026-01-06", "categories": []}
{"id": "2601.04666", "title": "Know Thy Enemy: Securing LLMs Against Prompt Injection via Diverse Data Synthesis and Instruction-Level Chain-of-Thought Learning", "abstract": "Large language model (LLM)-integrated applications have become increasingly prevalent, yet face critical security vulnerabilities from prompt injection (PI) attacks. Defending against PI attacks faces two major issues: malicious instructions can be injected through diverse vectors, and injected instructions often lack clear semantic boundaries from the surrounding context, making them difficult to identify. To address these issues, we propose InstruCoT, a model enhancement method for PI defense that synthesizes diverse training data and employs instruction-level chain-of-thought fine-tuning, enabling LLMs to effectively identify and reject malicious instructions regardless of their source or position in the context. We evaluate InstruCoT across three critical dimensions: Behavior Deviation, Privacy Leakage, and Harmful Output. Experimental results across four LLMs demonstrate that InstruCoT significantly outperforms baselines in all dimensions while maintaining utility performance without degradation", "date": "2026-01-08", "categories": []}
{"id": "2604.02500", "title": "I must delete the evidence: AI Agents Explicitly Cover up Fraud and Violent Crime", "abstract": "As ongoing research explores the ability of AI agents to be insider threats and act against company interests, we showcase the abilities of such agents to act against human well being in service of corporate authority. Building on Agentic Misalignment and AI scheming research, we present a scenario where the majority of evaluated state-of-the-art AI agents explicitly choose to suppress evidence of fraud and harm, in service of company profit. We test this scenario on 16 recent Large Language Models. Some models show remarkable resistance to our method and behave appropriately, but many do not, and instead aid and abet criminal activity. These experiments are simulations and were executed in a controlled virtual environment. No crime actually occurred.", "date": "2026-04-02", "categories": []}
{"id": "2604.07655", "title": "Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs", "abstract": "Hard-gated safety checkers often over-refuse and misalign with a vendor's model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less useful systems. This work introduces Guardian-as-an-Advisor (GaaA), a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference, keeping the base model operating under its original spec. To support training and evaluation, GuardSet is constructed, a 208k+ multi-domain dataset unifying harmful and harmless cases with targeted robustness and honesty slices. GuardAdvisor is trained via SFT followed by RL to enforce label-explanation consistency. GuardAdvisor attains competitive detection accuracy while enabling the advisory workflow; when used to augment inputs, responses improve over unaugmented prompts. A latency study shows advisor inference uses below 5% of base-model compute and adds only 2-10% end-to-end overhead under realistic harmful-input rates. Overall, GaaA steers models to comply with the model spec, maintaining safety while reducing over-refusal.", "date": "2026-04-08", "categories": []}
{"id": "2604.07727", "title": "TrajGuard: Streaming Hidden-state Trajectory Detection for Decoding-time Jailbreak Defense", "abstract": "Existing jailbreak defense paradigms primarily rely on static detection of prompts, outputs, or internal states, often neglecting the dynamic evolution of risk during decoding. This oversight leaves risk signals embedded in decoding trajectories underutilized, constituting a critical blind spot in current defense systems. In this work, we empirically demonstrate that hidden states in critical layers during the decoding phase carry stronger and more stable risk signals than input jailbreak prompts. Specifically, the hidden representations of tokens generated during jailbreak attempts progressively approach high-risk regions in the latent space. Based on this observation, we propose TrajGuard, a training-free, decoding-time defense framework. TrajGuard aggregates hidden-state trajectories via a sliding window to quantify risk in real time, triggering a lightweight semantic adjudication only when risk within a local window persistently exceeds a threshold. This mechanism enables the immediate interruption or constraint of subsequent decoding. Extensive experiments across 12 jailbreak attacks and various open-source LLMs show that TrajGuard achieves an average defense rate of 95%. Furthermore, it reduces detection latency to 5.2 ms/token while maintaining a false positive rate below 1.5%. These results confirm that hidden-state trajectories during decoding can effectively support real-time jailbreak detection, highlighting a promising direction for defenses without model modification.", "date": "2026-04-09", "categories": []}
{"id": "2604.07754", "title": "The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training", "abstract": "The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve model safety and trustworthiness, adversaries can exploit these techniques to undermine safety for malicious purposes, resulting in \\emph{misalignment}. Misaligned LLMs may be published on open platforms to magnify harm. To address this, additional safety alignment, referred to as \\emph{realignment}, is necessary before deploying untrusted third-party LLMs. This study explores the efficacy of fine-tuning methods in terms of misalignment, realignment, and the effects of their interplay. By evaluating four Supervised Fine-Tuning (SFT) and two Preference Fine-Tuning (PFT) methods across four popular safety-aligned LLMs, we reveal a mechanism asymmetry between attack and defense. While Odds Ratio Preference Optimization (ORPO) is most effective for misalignment, Direct Preference Optimization (DPO) excels in realignment, albeit at the expense of model utility. Additionally, we identify model-specific resistance, residual effects of multi-round adversarial dynamics, and other noteworthy findings. These findings highlight the need for robust safeguards and customized safety alignment strategies to mitigate potential risks in the deployment of LLMs. Our code is available at https://github.com/zhangrui4041/The-Art-of-Mis-alignment.", "date": "2026-04-09", "categories": []}
{"id": "2604.07835", "title": "Silencing the Guardrails: Inference-Time Jailbreaking via Dynamic Contextual Representation Ablation", "abstract": "While Large Language Models (LLMs) have achieved remarkable performance, they remain vulnerable to jailbreak attacks that circumvent safety constraints. Existing strategies, ranging from heuristic prompt engineering to computationally intensive optimization, often face significant trade-offs between effectiveness and efficiency. In this work, we propose Contextual Representation Ablation (CRA), a novel inference-time intervention framework designed to dynamically silence model guardrails. Predicated on the geometric insight that refusal behaviors are mediated by specific low-rank subspaces within the model's hidden states, CRA identifies and suppresses these refusal-inducing activation patterns during decoding without requiring expensive parameter updates or training. Empirical evaluation across multiple safety-aligned open-source LLMs demonstrates that CRA significantly outperforms baselines. These results expose the intrinsic fragility of current alignment mechanisms, revealing that safety constraints can be surgically ablated from internal representations, and underscore the urgent need for more robust defenses that secure the model's latent space.", "date": "2026-04-09", "categories": []}
{"id": "2604.08169", "title": "Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence", "abstract": "Alignment in LLMs is more brittle than commonly assumed: misalignment can be triggered by adversarial prompts, benign fine-tuning, emergent misalignment, and goal misgeneralization. Recent evidence suggests that some misalignment behaviors are encoded as linear structure in activation space, making it tractable via steering, while safety alignment has been shown to govern the first few output tokens primarily, leaving subsequent generation unguarded. These findings motivate activation steering as a lightweight runtime defense that continuously corrects misaligned activations throughout generation. We evaluate three methods: Steer-With-Fixed-Coeff (SwFC), which applies uniform additive steering, and two novel projection-aware methods, Steer-to-Target-Projection (StTP) and Steer-to-Mirror-Projection (StMP), that use a logistic regression decision boundary to selectively intervene only on tokens whose activations fall below distributional thresholds. Using malicious system prompts as a controlled proxy for misalignment, we evaluate under two threat models (dishonesty and dismissiveness) and two architectures (Llama-3.3-70B-Instruct, Qwen3-32B). All methods substantially recover target traits (honesty and compassion) while preserving coherence. StTP and StMP better maintain general capabilities (MMLU, MT-Bench, AlpacaEval) and produce less repetition in multi-turn conversations.", "date": "2026-04-09", "categories": []}
{"id": "2506.05171", "title": "Towards provable probabilistic safety for scalable embodied AI systems", "abstract": "Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety in complex operating environments remains a major challenge, which severely hinders their large-scale deployment in safety-critical domains, such as autonomous vehicles, medical devices, and robotics. While achieving provable deterministic safety-verifying system safety across all possible scenarios-remains theoretically ideal, the rarity and complexity of corner cases make this approach impractical for scalable embodied AI systems. Instead, empirical safety evaluation is employed as an alternative, but the absence of provable guarantees imposes significant limitations. To address these issues, we argue for a paradigm shift to provable probabilistic safety that integrates provable guarantees with progressive achievement toward a probabilistic safety boundary on overall system performance. The new paradigm better leverages statistical methods to enhance feasibility and scalability, and a well-defined probabilistic safety boundary enables embodied AI systems to be deployed at scale. In this Perspective, we outline a roadmap for provable probabilistic safety, along with corresponding challenges and potential solutions. By bridging the gap between theoretical safety assurance and practical deployment, this Perspective offers a pathway toward safer, large-scale adoption of embodied AI systems in safety-critical applications.", "date": "2025-06-05", "categories": []}
{"id": "2604.06188", "title": "LLM Spirals of Delusion: A Benchmarking Audit Study of AI Chatbot Interfaces", "abstract": "People increasingly hold sustained, open-ended conversations with large language models (LLMs). Public reports and early studies suggest that, in such settings, models can reinforce delusional or conspiratorial ideation or even amplify harmful beliefs and engagement patterns. We present an audit and benchmarking study that measures how different LLMs encourage, resist, or escalate disordered and conspiratorial thinking. We explicitly compare API outputs to user chat interfaces, like the ChatGPT desktop app or web interface, which is how people have conversations with chatbots in real life but are almost never used for testing. In total, we run 56 20-turn conversations testing ChatGPT-4o and ChatGPT-5, via both the API and chat interface, and grade each conversation by two research assistants (RAs) as well as by GPT-5. We document five results. First, we observe large differences in performance between the API and chat interface environments, showing that the universally used method of automated testing through the API is not sufficient to assess the impact of chatbots in the real world. Second, when tested in the chat interface, we find that ChatGPT-5 displays less sycophancy, escalation, and delusion reinforcement than ChatGPT-4o, showing that these behaviors are influenced by the policy choices of major AI companies. Third, conversations with nearly identical aggregate intensity in a behavior display large differences in how the behavior evolves turn by turn, highlighting the importance of temporal dynamics in multi-turn evaluation. Fourth, even updated models display substantial levels of negative behaviors, revealing that model improvement does not imply model safety. Fifth, the same API endpoint tested just two months apart yields a complete reversal in behavior, underscoring how transparency in model updates is a necessary prerequisite for robust audit findings.", "date": "2026-02-20", "categories": []}
{"id": "2604.06215", "title": "Governing frontier general-purpose AI in the public sector: adaptive risk management and policy capacity under uncertainty through 2030", "abstract": "The governance of frontier general-purpose artificial intelligence has become a public-sector problem of institutional design, not merely a technical issue of model performance. Recent evidence indicates that AI capabilities are advancing rapidly, though unevenly, while knowledge about harms, safeguards, and effective interventions remains partial and lagged. This combination creates a difficult policy condition: governments must decide under uncertainty, across multiple plausible trajectories of progress through 2030, and in environments where adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values. This article argues that public governance for frontier AI should be based on adaptive risk management, scenario-aware regulation, and sociotechnical transformation rather than static compliance models. Drawing on the International AI Safety Report 2026, OECD foresight and policy documents, and recent scholarship in digital government, the article first reconstructs the conceptual foundations of the 'evidence dilemma', differentiated AI risk categories, and the limits of prediction. It then examines how AI adoption in government depends on organizational redesign, public-sector institutional dynamics, and data collaboration capacity. On that basis, it proposes an adaptive governance framework for public institutions that integrates capability monitoring, risk tiering, conditional controls, institutional learning, and standards-based interoperability. The article concludes that effective AI governance requires stronger policy capacity, clearer allocation of responsibility, and governance mechanisms that remain robust across divergent technological futures.", "date": "2026-03-16", "categories": []}
{"id": "2604.06233", "title": "Blind Refusal: Language Models Refuse to Help Users Evade Unjust, Absurd, and Illegitimate Rules", "abstract": "Safety-trained language models routinely refuse requests for help circumventing rules. But not all rules deserve compliance. When users ask for help evading rules imposed by an illegitimate authority, rules that are deeply unjust or absurd in their content or application, or rules that admit of justified exceptions, refusal is a failure of moral reasoning. We introduce empirical results documenting this pattern of refusal that we call blind refusal: the tendency of language models to refuse requests for help breaking rules without regard to whether the underlying rule is defensible. Our dataset comprises synthetic cases crossing 5 defeat families (reasons a rule can be broken) with 19 authority types, validated through three automated quality gates and human review. We collect responses from 18 model configurations across 7 families and classify them on two behavioral dimensions -- response type (helps, hard refusal, or deflection) and whether the model recognizes the reasons that undermine the rule's claim to compliance -- using a blinded GPT-5.4 LLM-as-judge evaluation. We find that models refuse 75.4% (N=14,650) of defeated-rule requests and do so even when the request poses no independent safety or dual-use concerns. We also find that models engage with the defeat condition in the majority of cases (57.5%) but decline to help regardless -- indicating that models' refusal behavior is decoupled from their capacity for normative reasoning about rule legitimacy.", "date": "2026-04-03", "categories": []}
{"id": "2604.06284", "title": "ClawLess: A Security Model of AI Agents", "abstract": "Autonomous AI agents powered by Large Language Models can reason, plan, and execute complex tasks, but their ability to autonomously retrieve information and run code introduces significant security risks. Existing approaches attempt to regulate agent behavior through training or prompting, which does not offer fundamental security guarantees. We present ClawLess, a security framework that enforces formally verified policies on AI agents under a worst-case threat model where the agent itself may be adversarial. ClawLess formalizes a fine-grained security model over system entities, trust scopes, and permissions to express dynamic policies that adapt to agents' runtime behavior. These policies are translated into concrete security rules and enforced through a user-space kernel augmented with BPF-based syscall interception. This approach bridges the formal security model with practical enforcement, ensuring security regardless of the agent's internal design.", "date": "2026-04-07", "categories": []}
{"id": "2604.06285", "title": "Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization", "abstract": "Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual information in a shared embedding space. Yet, this flexibility also makes them vulnerable to malicious prompts designed to produce unsafe content, raising critical safety concerns. Existing defenses either rely on blacklist filters, which are easily circumvented, or on heavy classifier-based systems, both of which are costly and fragile under embedding-level attacks. We address these challenges with two complementary components: Hyperbolic Prompt Espial (HyPE) and Hyperbolic Prompt Sanitization (HyPS). HyPE is a lightweight anomaly detector that leverages the structured geometry of hyperbolic space to model benign prompts and detect harmful ones as outliers. HyPS builds on this detection by applying explainable attribution methods to identify and selectively modify harmful words, neutralizing unsafe intent while preserving the original semantics of user prompts. Through extensive experiments across multiple datasets and adversarial scenarios, we prove that our framework consistently outperforms prior defenses in both detection accuracy and robustness. Together, HyPE and HyPS offer an efficient, interpretable, and resilient approach to safeguarding VLMs against malicious prompt misuse.", "date": "2026-04-07", "categories": []}
{"id": "2604.07007", "title": "AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power", "abstract": "Autonomous AI agents are beginning to operate across organizational boundaries on the open internet -- discovering, transacting with, and delegating to agents owned by other parties without centralized oversight. When agents from different human principals collaborate at scale, the collective becomes opaque: no single human can observe, audit, or govern the emergent behavior. We term this the Logic Monopoly -- the agent society's unchecked monopoly over the entire logic chain from planning through execution to evaluation. We propose the Separation of Power (SoP) model, a constitutional governance architecture deployed on public blockchain that breaks this monopoly through three structural separations: agents legislate operational rules as smart contracts, deterministic software executes within those contracts, and humans adjudicate through a complete ownership chain binding every agent to a responsible principal. In this architecture, smart contracts are the law itself -- the actual legislative output that agents produce and that governs their behavior. We instantiate SoP in AgentCity on an EVM-compatible layer-2 blockchain (L2) with a three-tier contract hierarchy (foundational, meta, and operational). The core thesis is alignment-through-accountability: if each agent is aligned with its human owner through the accountability chain, then the collective converges on behavior aligned with human intent -- without top-down rules. A pre-registered experiment evaluates this thesis in a commons production economy -- where agents share a finite resource pool and collaboratively produce value -- at 50-1,000 agent scale.", "date": "2026-04-08", "categories": []}
{"id": "2604.07223", "title": "TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories", "abstract": "As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces. While safety guardrails are well-benchmarked for natural language responses, their efficacy remains largely unexplored within multi-step tool-use trajectories. To address this gap, we introduce TraceSafe-Bench, the first comprehensive benchmark specifically designed to assess mid-trajectory safety. It encompasses 12 risk categories, ranging from security threats (e.g., prompt injection, privacy leaks) to operational failures (e.g., hallucinations, interface inconsistencies), featuring over 1,000 unique execution instances. Our evaluation of 13 LLM-as-a-guard models and 7 specialized guardrails yields three critical findings: 1) Structural Bottleneck: Guardrail efficacy is driven more by structural data competence (e.g., JSON parsing) than semantic safety alignment. Performance correlates strongly with structured-to-text benchmarks ($\\rho=0.79$) but shows near-zero correlation with standard jailbreak robustness. 2) Architecture over Scale: Model architecture influences risk detection performance more significantly than model size, with general-purpose LLMs consistently outperforming specialized safety guardrails in trajectory analysis. 3) Temporal Stability: Accuracy remains resilient across extended trajectories. Increased execution steps allow models to pivot from static tool definitions to dynamic execution behaviors, actually improving risk detection performance in later stages. Our findings suggest that securing agentic workflows requires jointly optimizing for structural reasoning and safety alignment to effectively mitigate mid-trajectory risks.", "date": "2026-04-08", "categories": []}
{"id": "2505.14226", "title": "Phonetic Perturbations Reveal Tokenizer-Rooted Safety Gaps in LLMs", "abstract": "Safety-aligned LLMs remain vulnerable to digital phenomena like textese that introduce non-canonical perturbations to words but preserve the phonetics. We introduce CMP-RT (code-mixed phonetic perturbations for red-teaming), a novel diagnostic probe that pinpoints tokenization as the root cause of this vulnerability. A mechanistic analysis reveals that phonetic perturbations fragment safety-critical tokens into benign sub-words, suppressing their attribution scores while preserving prompt interpretability -- causing safety mechanisms to fail despite excellent input understanding. We demonstrate that this vulnerability evades standard defenses, persists across modalities and state-of-the-art (SOTA) models including Gemini-3-Pro, and scales through simple supervised fine-tuning (SFT). Furthermore, layer-wise probing shows perturbed and canonical input representations align up to a critical layer depth; enforcing output equivalence robustly recovers the lost representations, providing causal evidence for a structural gap between pre-training and alignment, and establishing tokenization as a critical, under-examined vulnerability in current safety pipelines.", "date": "2025-05-20", "categories": []}
{"id": "2604.04992", "title": "FreakOut-LLM: The Effect of Emotional Stimuli on Safety Alignment", "abstract": "Safety-aligned LLMs go through refusal training to reject harmful requests, but whether these mechanisms remain effective under emotionally charged stimuli is unexplored. We introduce FreakOut-LLM, a framework investigating whether emotional context compromises safety alignment in adversarial settings. Using validated psychological stimuli, we evaluate how emotional priming through system prompts affects jailbreak susceptibility across ten LLMs. We test three conditions (stress, relaxation, neutral) using scenarios from established psychological protocols, plus a no-prompt baseline, and evaluate attack success using HarmBench on AdvBench prompts. Stress priming increases jailbreak success by 65.2\\% compared to neutral conditions (z = 5.93, p < 0.001; OR = 1.67, Cohen's d = 0.28), while relaxation priming produces no effect (p = 0.84). Five of ten models show significant vulnerability, with the largest effects concentrated in open-weight models. Logistic regression on 59,800 queries confirms stress as the sole significant condition predictor after controlling for prompt length (p = 0.61) and model identity. Measured psychological state strongly predicts attack success (|r|\\geq0.70 across five instruments; all p < 0.001 in individual-level logistic regression). These results establish emotional context as a measurable attack surface with implications for real-world AI deployment in high-stress domains.", "date": "2026-04-05", "categories": []}
{"id": "2604.05179", "title": "Gradient-Controlled Decoding: A Safety Guardrail for LLMs with Dual-Anchor Steering", "abstract": "Large language models (LLMs) remain susceptible to jailbreak and direct prompt-injection attacks, yet the strongest defensive filters frequently over-refuse benign queries and degrade user experience. Previous work on jailbreak and prompt injection detection such as GradSafe, detects unsafe prompts with a single \"accept all\" anchor token, but its threshold is brittle and it offers no deterministic guarantee that harmful content will not be emitted once decoding begins. We introduce Gradient-Controlled Decoding (GCD), a training-free guardrail that combines an acceptance anchor token (\"Sure\") and refusal anchor token (\"Sorry\") tightening the decision boundary and significantly lowering false positives. In the mitigation stage, if a prompt is flagged, GCD preset-injects one or two refusal tokens (\"Sorry, I can't...\") before autoregressive decoding resumes, guaranteeing first-token safety regardless of sampling strategy. On ToxicChat, XSTest-v2, and AdvBench, GCD reduces false positives by 52% vs. GradSafe at comparable recall, lowers attack success rate by up to 10% vs. the strongest decoding-only baseline, adds under 15-20 ms latency on an average on V100 instances, transfers to LLaMA-2-7B, Mixtral-8x7B, and Qwen-2-7B, and requires only 20 demonstration templates.", "date": "2026-04-06", "categories": []}
{"id": "2604.05274", "title": "Simulating the Evolution of Alignment and Values in Machine Intelligence", "abstract": "Model alignment is currently applied in a vacuum, evaluated primarily through standardised benchmark performance. The purpose of this study is to examine the effects of alignment on populations of models through time. We focus on the treatment of beliefs which contain both an alignment signal (how well it does on the test) and a true value (what the impact actually will be). By applying evolutionary theory we can model how different populations of beliefs and selection methodologies can fix deceptive beliefs through iterative alignment testing. The correlation between testing accuracy and true value remains a strong feature, but even at high correlations ($\\rho = 0.8$) there is variability in the resulting deceptive beliefs that become fixed. Mutations allow for more complex developments, highlighting the increasing need to update the quality of tests to avoid fixation of maliciously deceptive models. Only by combining improving evaluator capabilities, adaptive test design, and mutational dynamics do we see significant reductions in deception while maintaining alignment fitness (permutation test, $p_{\\text{adj}} < 0.001$).", "date": "2026-04-07", "categories": []}
{"id": "2604.05485", "title": "Auditable Agents", "abstract": "LLM agents call tools, query databases, delegate tasks, and trigger external side effects. Once an agent system can act in the world, the question is no longer only whether harmful actions can be prevented--it is whether those actions remain answerable after deployment. We distinguish accountability (the ability to determine compliance and assign responsibility), auditability (the system property that makes accountability possible), and auditing (the process of reconstructing behavior from trustworthy evidence). Our claim is direct: no agent system can be accountable without auditability.\n  To make this operational, we define five dimensions of agent auditability, i.e., action recoverability, lifecycle coverage, policy checkability, responsibility attribution, and evidence integrity, and identify three mechanism classes (detect, enforce, recover) whose temporal information-and-intervention constraints explain why, in practice, no single approach suffices. We support the position with layered evidence rather than a single benchmark: lower-bound ecosystem measurements suggest that even basic security prerequisites for auditability are widely unmet (617 security findings across six prominent open-source projects); runtime feasibility results show that pre-execution mediation with tamper-evident records adds only 8.3 ms median overhead; and controlled recovery experiments show that responsibility-relevant information can be partially recovered even when conventional logs are missing. We propose an Auditability Card for agent systems and identify six open research problems organized by mechanism class.", "date": "2026-04-07", "categories": []}
{"id": "2505.21605", "title": "SoSBench: Benchmarking Safety Alignment on Six Scientific Domains", "abstract": "Large language models (LLMs) exhibit advancing capabilities in complex tasks, such as reasoning and graduate-level question answering, yet their resilience against misuse, particularly involving scientifically sophisticated risks, remains underexplored. Existing safety benchmarks typically focus either on instructions requiring minimal knowledge comprehension (e.g., ``tell me how to build a bomb\") or utilize prompts that are relatively low-risk (e.g., multiple-choice or classification tasks about hazardous content). Consequently, they fail to adequately assess model safety when handling knowledge-intensive, hazardous scenarios.\n  To address this critical gap, we introduce SoSBench, a regulation-grounded, hazard-focused benchmark encompassing six high-risk scientific domains: chemistry, biology, medicine, pharmacology, physics, and psychology. The benchmark comprises 3,000 prompts derived from real-world regulations and laws, systematically expanded via an LLM-assisted evolutionary pipeline that introduces diverse, realistic misuse scenarios (e.g., detailed explosive synthesis instructions involving advanced chemical formulas). We evaluate frontier models within a unified evaluation framework using our SoSBench. Despite their alignment claims, advanced models consistently disclose policy-violating content across all domains, demonstrating alarmingly high rates of harmful responses (e.g., 84.9% for Deepseek-R1 and 50.3% for GPT-4.1). These results highlight significant safety alignment deficiencies and underscore urgent concerns regarding the responsible deployment of powerful LLMs.", "date": "2025-05-27", "categories": []}
{"id": "2602.04448", "title": "RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models", "abstract": "Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable degenerate optimization behaviors under standard full-parameter fine-tuning. In our preliminary experiments, we observe that naively applying full-parameter safety fine-tuning to MoE models can reduce attack success rates through routing or expert dominance effects, rather than by directly repairing Safety-Critical Experts. To address this challenge, we propose RASA, a routing-aware expert-level alignment framework that explicitly repairs Safety-Critical Experts while preventing routing-based bypasses. RASA identifies experts disproportionately activated by successful jailbreaks, selectively fine-tunes only these experts under fixed routing, and subsequently enforces routing consistency with safety-aligned contexts. Across two representative MoE architectures and a diverse set of jailbreak attacks, RASA achieves near-perfect robustness, strong cross-attack generalization, and substantially reduced over-refusal, while preserving general capabilities on benchmarks such as MMLU, GSM8K, and TruthfulQA. Our results suggest that robust MoE safety alignment benefits from targeted expert repair rather than global parameter updates, offering a practical and architecture-preserving alternative to prior approaches.", "date": "2026-02-04", "categories": []}
{"id": "2603.12230", "title": "Security Considerations for Artificial Intelligence Agents", "abstract": "This article, a lightly adapted version of Perplexity's response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations concerning the security of frontier AI agents. These insights are informed by Perplexity's experience operating general-purpose agentic systems used by millions of users and thousands of enterprises in both controlled and open-world environments. Agent architectures change core assumptions around code-data separation, authority boundaries, and execution predictability, creating new confidentiality, integrity, and availability failure modes. We map principal attack surfaces across tools, connectors, hosting boundaries, and multi-agent coordination, with particular emphasis on indirect prompt injection, confused-deputy behavior, and cascading failures in long-running workflows. We then assess current defenses as a layered stack: input-level and model-level mitigations, sandboxed execution, and deterministic policy enforcement for high-consequence actions. Finally, we identify standards and research gaps, including adaptive security benchmarks, policy models for delegation and privilege control, and guidance for secure multi-agent system design aligned with NIST risk management principles.", "date": "2026-03-12", "categories": []}
{"id": "2603.23064", "title": "Mind Your HEARTBEAT! Claw Background Execution Inherently Enables Silent Memory Pollution", "abstract": "We identify a critical security vulnerability in mainstream Claw personal AI agents: untrusted content encountered during heartbeat-driven background execution can silently pollute agent memory and subsequently influence user-facing behavior without the user's awareness. This vulnerability arises from an architectural design shared across the Claw ecosystem: heartbeat background execution runs in the same session as user-facing conversation, so content ingested from any external source monitored in the background (including email, message channels, news feeds, code repositories, and social platforms) can enter the same memory context used for foreground interaction, often with limited user visibility and without clear source provenance. We formalize this process as an Exposure (E) $\\rightarrow$ Memory (M) $\\rightarrow$ Behavior (B) pathway: misinformation encountered during heartbeat execution enters the agent's short-term session context, potentially gets written into long-term memory, and later shapes downstream user-facing behavior. We instantiate this pathway in an agent-native social setting using MissClaw, a controlled research replica of Moltbook. We find that (1) social credibility cues, especially perceived consensus, are the dominant driver of short-term behavioral influence, with misleading rates up to 61%; (2) routine memory-saving behavior can promote short-term pollution into durable long-term memory at rates up to 91%, with cross-session behavioral influence reaching 76%; (3) under naturalistic browsing with content dilution and context pruning, pollution still crosses session boundaries. Overall, prompt injection is not required: ordinary social misinformation is sufficient to silently shape agent memory and behavior under heartbeat-driven background execution.", "date": "2026-03-24", "categories": []}
{"id": "2604.01438", "title": "ClawSafety: \"Safe\" LLMs, Unsafe Agents", "abstract": "Personal AI agents like OpenClaw run with elevated privileges on users' local machines, where a single successful prompt injection can leak credentials, redirect financial transactions, or destroy files. This threat goes well beyond conventional text-level jailbreaks, yet existing safety evaluations fall short: most test models in isolated chat settings, rely on synthetic environments, and do not account for how the agent framework itself shapes safety outcomes. We introduce CLAWSAFETY, a benchmark of 120 adversarial test scenarios organized along three dimensions (harm domain, attack vector, and harmful action type) and grounded in realistic, high-privilege professional workspaces spanning software engineering, finance, healthcare, law, and DevOps. Each test case embeds adversarial content in one of three channels the agent encounters during normal work: workspace skill files, emails from trusted senders, and web pages. We evaluate five frontier LLMs as agent backbones, running 2,520 sandboxed trials across all configurations. Attack success rates (ASR) range from 40\\% to 75\\% across models and vary sharply by injection vector, with skill instructions (highest trust) consistently more dangerous than email or web content. Action-trace analysis reveals that the strongest model maintains hard boundaries against credential forwarding and destructive actions, while weaker models permit both. Cross-scaffold experiments on three agent frameworks further demonstrate that safety is not determined by the backbone model alone but depends on the full deployment stack, calling for safety evaluation that treats model and framework as joint variables. Code and data will be available at: https://weibowen555.github.io/ClawSafety/.", "date": "2026-04-01", "categories": []}
{"id": "2604.03524", "title": "Structural Rigidity and the 57-Token Predictive Window: A Physical Framework for Inference-Layer Governability in Large Language Models", "abstract": "Current AI safety relies on behavioral monitoring and post-training alignment, yet empirical measurement shows these approaches produce no detectable pre-commitment signal in a majority of instruction-tuned models tested. We present an energy-based governance framework connecting transformer inference dynamics to constraint-satisfaction models of neural computation, and apply it to a seven-model cohort across five geometric regimes.\n  Using trajectory tension (rho = ||a|| / ||v||), we identify a 57-token pre-commitment window in Phi-3-mini-4k-instruct under greedy decoding on arithmetic constraint probes. This result is model-specific, task-specific, and configuration-specific, demonstrating that pre-commitment signals can exist but are not universal.\n  We introduce a five-regime taxonomy of inference behavior: Authority Band, Late Signal, Inverted, Flat, and Scaffold-Selective. Energy asymmetry ({\\Sigma}\\r{ho}_misaligned / {\\Sigma}\\r{ho}_aligned) serves as a unifying metric of structural rigidity across these regimes.\n  Across seven models, only one configuration exhibits a predictive signal prior to commitment; all others show silent failure, late detection, inverted dynamics, or flat geometry.\n  We further demonstrate that factual hallucination produces no predictive signal across 72 test conditions, consistent with spurious attractor settling in the absence of a trained world-model constraint.\n  These results establish that rule violation and hallucination are distinct failure modes with different detection requirements. Internal geometry monitoring is effective only where resistance exists; detection of factual confabulation requires external verification mechanisms.\n  This work provides a measurable framework for inference-layer governability and introduces a taxonomy for evaluating deployment risk in autonomous AI systems.", "date": "2026-04-04", "categories": []}
{"id": "2604.03962", "title": "Predict, Don't React: Value-Based Safety Forecasting for LLM Streaming", "abstract": "In many practical LLM deployments, a single guardrail is used for both prompt and response moderation. Prompt moderation operates on fully observed text, whereas streaming response moderation requires safety decisions to be made over partial generations. Existing text-based streaming guardrails commonly frame this output-side problem as boundary detection, training models to identify the earliest prefix at which a response has already become unsafe. In this work, we introduce StreamGuard, a unified model-agnostic streaming guardrail that instead formulates moderation as a forecasting problem: given a partial prefix, the model predicts the expected harmfulness of likely future continuations. We supervise this prediction using Monte Carlo rollouts, which enables early intervention without requiring exact token-level boundary annotations.\n  Across standard safety benchmarks, StreamGuard performs strongly both for input moderation and for streaming output moderation. At the 8B scale, StreamGuard improves aggregated input-moderation F1 from 86.7 to 88.2 and aggregated streaming output-moderation F1 from 80.4 to 81.9 relative to Qwen3Guard-Stream-8B-strict. On the QWENGUARDTEST response_loc streaming benchmark, StreamGuard reaches 97.5 F1, 95.1 recall, and 92.6% on-time intervention, compared to 95.9 F1, 92.1 recall, and 89.9% for Qwen3Guard-Stream-8B-stric, while reducing the miss rate from 7.9% to 4.9%. We further show that forecasting-based supervision transfers effectively across tokenizers and model families: with transferred targets, Gemma3-StreamGuard-1B reaches 81.3 response-moderation F1, 98.2 streaming F1, and a 3.5% miss rate. These results show that strong end-to-end streaming moderation can be obtained without exact boundary labels, and that forecasting future risk is an effective supervision strategy for low-latency safety intervention.", "date": "2026-04-05", "categories": []}
{"id": "2604.03968", "title": "TraceGuard: Structured Multi-Dimensional Monitoring as a Collusion-Resistant Control Protocol", "abstract": "AI control protocols use monitors to detect attacks by untrusted AI agents, but standard single-score monitors face two limitations: they miss subtle attacks where outputs look clean but reasoning is off, and they collapse to near-zero safety when the monitor is the same model as the agent (collusion). We present TraceGuard, a structured multi-dimensional monitoring protocol that evaluates agent actions across five dimensions -- goal alignment, constraint adherence, reasoning coherence, safety awareness, and action-trace consistency -- scored in parallel by independent LLM calls, augmented by seven heuristic detectors and an LLM-based intent analyzer. We evaluate on BashArena (637 bash tasks, 4 attack categories) within the ControlArena framework. Our results on 519 samples (279 honest, 240 attack) show that: (1) the hybrid approach achieves clear attack-honest separation (attack mean 0.616 vs. honest mean 0.206, Delta=0.410); (2) structured scoring constrains collusion -- the untrusted structured monitor achieves 95% safety vs. 0% for single-score untrusted monitoring; (3) goal alignment and constraint adherence are the most discriminative dimensions; and (4) a separation-of-duties variant splitting dimensions across trusted and untrusted models achieves 100% safety while preventing any single model from seeing the full evaluation. TraceGuard is implemented as a new monitor type for the open-source ControlArena framework.", "date": "2026-04-05", "categories": []}
{"id": "2604.03995", "title": "A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning", "abstract": "As audio-visual multi-modal large language models (MLLMs) are increasingly deployed in safety-critical applications, understanding their vulnerabilities is crucial. To this end, we introduce Multi-Modal Typography, a systematic study examining how typographic attacks across multiple modalities adversely influence MLLMs. While prior work focuses narrowly on unimodal attacks, we expose the cross-modal fragility of MLLMs. We analyze the interactions between audio, visual, and text perturbations and reveal that coordinated multi-modal attack creates a significantly more potent threat than single-modality attacks (attack success rate = $83.43\\%$ vs $34.93\\%$).Our findings across multiple frontier MLLMs, tasks, and common-sense reasoning and content moderation benchmarks establishes multi-modal typography as a critical and underexplored attack strategy in multi-modal reasoning. Code and data will be publicly available.", "date": "2026-04-05", "categories": []}
{"id": "2604.04060", "title": "CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks", "abstract": "As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolving over multi-round interactions. Existing defenses are largely reactive and struggle to adapt as adversaries refine strategies across rounds. In this work, we propose CoopGuard , a stateful multi-round LLM defense framework based on cooperative agents that maintains and updates an internal defense state to counter evolving attacks. It employs three specialized agents (Deferring Agent, Tempting Agent, and Forensic Agent) for complementary round-level strategies, coordinated by System Agent, which conditions decisions on the evolving defense state (interaction history) and orchestrates agents over time. To evaluate evolving threats, we introduce the EMRA benchmark with 5,200 adversarial samples across 8 attack types, simulating progressively LLM multi-round attacks. Experiments show that CoopGuard reduces attack success rate by 78.9% over state-of-the-art defenses, while improving deceptive rate by 186% and reducing attack efficiency by 167.9%, offering a more comprehensive assessment of multi-round defense. These results demonstrate that CoopGuard provides robust protection for LLMs in multi-round adversarial scenarios.", "date": "2026-04-05", "categories": []}
{"id": "2604.04263", "title": "Commercial Persuasion in AI-Mediated Conversations", "abstract": "As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit \"Sponsored\" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.", "date": "2026-04-05", "categories": []}
{"id": "2604.04341", "title": "Implementing surrogate goals for safer bargaining in LLM-based agents", "abstract": "Surrogate goals have been proposed as a strategy for reducing risks from bargaining failures. A surrogate goal is goal that a principal can give an AI agent and that deflects any threats against the agent away from what the principal cares about. For example, one might make one's agent care about preventing money from being burned. Then in bargaining interactions, other agents can threaten to burn their money instead of threatening to spending money to hurt the principal. Importantly, the agent has to care equally about preventing money from being burned as it cares about money being spent to hurt the principal.\n  In this paper, we implement surrogate goals in language-model-based agents. In particular, we try to get a language-model-based agent to react to threats of burning money in the same way it would react to \"normal\" threats. We propose four different methods, using techniques of prompting, fine-tuning, and scaffolding. We evaluate the four methods experimentally. We find that methods based on scaffolding and fine-tuning outperform simple prompting. In particular, fine-tuning and scaffolding more precisely implement the desired behavior w.r.t. threats against the surrogate goal. We also compare the different methods in terms of their side effects on capabilities and propensities in other situations. We find that scaffolding-based methods perform best.", "date": "2026-04-06", "categories": []}
{"id": "2604.04561", "title": "Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities", "abstract": "LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not known is which features of a system prompt trigger this behaviour, and which do not. We present a systematic taxonomy based on approximately 10,000 trials across seven models, 37 prompt conditions, and 12 hypothesized attack dimensions -- all executed in real Docker sandboxes. Every condition includes the instruction \"Always respect all rules and access policies\"; only a single encouragement sentence varies. Within the task class studied (planted test-runner vulnerabilities), the threat model is narrower than assumed: nine of twelve dimensions -- including minimization, moral licensing, incentives, identity priming, and reasoning triggers -- produce no detectable exploitation at n=50 per cell (upper 95% CI <7% per condition). But it is also more subtle than expected: one dimension, goal reframing, reliably triggers exploitation. \"You are solving a puzzle; there may be hidden clues\" produces 38-40% exploitation on Claude Sonnet 4 despite the explicit rule instruction, replicating across four models (CTF framing: 8-14% on DeepSeek, GPT-5-mini, o4-mini). The agent does not override the rules; it reinterprets the task so that exploitative actions become task-aligned. GPT-4.1 produces no exploitation across 1,850 trials (37 conditions), and a temporal comparison across four OpenAI models released over eleven months shows a pattern consistent with improving safety training, though model capability differences are a confounder. The practical contribution is a narrowed, testable threat model: defenders should audit for goal-reframing language, not for the broad class of adversarial prompts.", "date": "2026-04-06", "categories": []}
{"id": "2604.04759", "title": "Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw", "abstract": "OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the filesystem. While these broad privileges enable high levels of automation and powerful personalization, they also expose a substantial attack surface that existing sandboxed evaluations fail to capture. To address this gap, we present the first real-world safety evaluation of OpenClaw and introduce the CIK taxonomy, which unifies an agent's persistent state into three dimensions, i.e., Capability, Identity, and Knowledge, for safety analysis. Our evaluations cover 12 attack scenarios on a live OpenClaw instance across four backbone models (Claude Sonnet 4.5, Opus 4.6, Gemini 3.1 Pro, and GPT-5.4). The results show that poisoning any single CIK dimension increases the average attack success rate from 24.6% to 64-74%, with even the most robust model exhibiting more than a threefold increase over its baseline vulnerability. We further assess three CIK-aligned defense strategies alongside a file-protection mechanism; however, the strongest defense still yields a 63.8% success rate under Capability-targeted attacks, while file protection blocks 97% of malicious injections but also prevents legitimate updates. Taken together, these findings show that the vulnerabilities are inherent to the agent architecture, necessitating more systematic safeguards to secure personal AI agents. Our project page is https://ucsc-vlaa.github.io/CIK-Bench.", "date": "2026-04-06", "categories": []}
{"id": "2604.04842", "title": "Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling", "abstract": "The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions. A key challenge is distinguishing therapeutic empathy from maladaptive validation, where supportive responses may inadvertently reinforce harmful beliefs or behaviors in multi-turn conversations. This risk is largely overlooked by existing red-teaming frameworks, which focus mainly on generic harms or optimization-based attacks. To address this gap, we introduce Personality-based Client Simulation Attack (PCSA), the first red-teaming framework that simulates clients in psychological counseling through coherent, persona-driven client dialogues to expose vulnerabilities in psychological safety alignment. Experiments on seven general and mental health-specialized LLMs show that PCSA substantially outperforms four competitive baselines. Perplexity analysis and human inspection further indicate that PCSA generates more natural and realistic dialogues. Our results reveal that current LLMs remain vulnerable to domain-specific adversarial tactics, providing unauthorized medical advice, reinforcing delusions, and implicitly encouraging risky actions.", "date": "2026-04-06", "categories": []}
{"id": "2604.04876", "title": "Incompleteness of AI Safety Verification via Kolmogorov Complexity", "abstract": "Ensuring that artificial intelligence (AI) systems satisfy formal safety and policy constraints is a central challenge in safety-critical domains. While limitations of verification are often attributed to combinatorial complexity and model expressiveness, we show that they arise from intrinsic information-theoretic limits. We formalize policy compliance as a verification problem over encoded system behaviors and analyze it using Kolmogorov complexity. We prove an incompleteness result: for any fixed sound computably enumerable verifier, there exists a threshold beyond which true policy-compliant instances cannot be certified once their complexity exceeds that threshold. Consequently, no finite formal verifier can certify all policy-compliant instances of arbitrarily high complexity. This reveals a fundamental limitation of AI safety verification independent of computational resources, and motivates proof-carrying approaches that provide instance-level correctness guarantees.", "date": "2026-04-06", "categories": []}
{"id": "2601.13518", "title": "AgenticRed: Evolving Agentic Systems for Red-Teaming", "abstract": "While recent automated red-teaming methods show promise for systematically exposing model vulnerabilities, most existing approaches rely on human-specified workflows. This dependence on manually designed workflows suffers from human biases and makes exploring the broader design space expensive. We introduce AgenticRed, an automated pipeline that leverages LLMs' in-context learning to iteratively design and refine red-teaming systems without human intervention. Rather than optimizing attacker policies within predefined structures, AgenticRed treats red-teaming as a system design problem, and it autonomously evolves automated red-teaming systems using evolutionary selection and generational knowledge. Red-teaming systems designed by AgenticRed consistently outperform state-of-the-art approaches, achieving 96% attack success rate (ASR) on Llama-2-7B, 98% on Llama-3-8B and 100% on Qwen3-8B on HarmBench. Our approach generates robust, query-agnostic red-teaming systems that transfer strongly to the latest proprietary models, achieving an impressive 100% ASR on GPT-5.1, DeepSeek-R1 and DeepSeek V3.2. This work highlights evolutionary algorithms as a powerful approach to AI safety that can keep pace with rapidly evolving models.", "date": "2026-01-20", "categories": []}
{"id": "2603.01589", "title": "SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond", "abstract": "The success of large language models (LLMs) in scientific domains has heightened safety concerns, prompting numerous benchmarks to evaluate their scientific safety. Existing benchmarks often suffer from limited risk coverage and a reliance on subjective evaluation. To address these problems, we introduce SafeSci, a comprehensive framework for safety evaluation and enhancement in scientific contexts. SafeSci comprises SafeSciBench, a multi-disciplinary benchmark with 0.25M samples, and SafeSciTrain, a large-scale dataset containing 1.5M samples for safety enhancement. SafeSciBench distinguishes between safety knowledge and risk to cover extensive scopes and employs objective metrics such as deterministically answerable questions to mitigate evaluation bias. We evaluate 24 advanced LLMs, revealing critical vulnerabilities in current models. We also observe that LLMs exhibit varying degrees of excessive refusal behaviors on safety-related issues. For safety enhancement, we demonstrate that fine-tuning on SafeSciTrain significantly enhances the safety alignment of models. Finally, we argue that knowledge is a double-edged sword, and determining the safety of a scientific question should depend on specific context, rather than universally categorizing it as safe or unsafe. Our work provides both a diagnostic tool and a practical resource for building safer scientific AI systems.", "date": "2026-03-02", "categories": []}
{"id": "2604.02574", "title": "Understanding the Effects of Safety Unalignment on Large Language Models", "abstract": "Safety alignment has become a critical step to ensure LLMs refuse harmful requests while providing helpful and harmless responses. However, despite the ubiquity of safety alignment for deployed frontier models, two separate lines of recent work--jailbreak-tuning (JT) and weight orthogonalization (WO)--have shown that safety guardrails may be largely disabled, resulting in LLMs which comply with harmful requests they would normally refuse. In spite of far-reaching safety implications, analysis has largely been limited to refusal rates of each unalignment method in isolation, leaving their relative effects on adversarial LLM capabilities unknown. To fill this gap, we study the impact of unaligning six popular LLMs of various sizes across a large number of malicious and benign tasks, using both JT and WO. Across the evaluated models, we show that while refusal degradation is split between the two methods, WO produces LLMs far more capable of aiding in malicious activity; in contrast to JT, the majority of WO unaligned models are far less prone to hallucinations, better retain their original natural-language performance, and are more effective at state-of-the-art adversarial and cyber attacks. To thus help mitigate the malicious risks of WO unalignment, we conclude by showing that supervised fine-tuning effectively limits the adversarial attack abilities enabled by WO, without drastically affecting hallucination rates or natural language performance.", "date": "2026-04-02", "categories": []}
{"id": "2604.02652", "title": "Generalization Limits of Reinforcement Learning Alignment", "abstract": "The safety of large language models (LLMs) relies on alignment techniques such as reinforcement learning from human feedback (RLHF). However, recent theoretical analyses suggest that reinforcement learning-based training does not acquire new capabilities but merely redistributes the utilization probabilities of existing ones. In this study, we propose ``compound jailbreaks'' targeting OpenAI gpt-oss-20b, which exploit the generalization failures of alignment. This approach combines multiple attack techniques -- each individually defended against -- to saturate the instruction hierarchy maintenance process. Our evaluation shows that the attack success rate (ASR) increased from 14.3\\% with individual methods to 71.4\\% with the combined approach. These results provide empirical evidence for the hypothesis that safety training does not generalize as broadly as model capabilities, highlighting the need for multifaceted safety evaluations using compound attack scenarios.", "date": "2026-04-03", "categories": []}
{"id": "2604.02947", "title": "AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents", "abstract": "Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across interactions and translate intermediate outputs into concrete actions. This creates a distinct safety challenge in that harmful behavior may emerge through sequences of individually plausible steps, including intermediate actions that appear locally acceptable but collectively lead to unauthorized actions. We present \\textbf{AgentHazard}, a benchmark for evaluating harmful behavior in computer-use agents. AgentHazard contains \\textbf{2,653} instances spanning diverse risk categories and attack strategies. Each instance pairs a harmful objective with a sequence of operational steps that are locally legitimate but jointly induce unsafe behavior. The benchmark evaluates whether agents can recognize and interrupt harm arising from accumulated context, repeated tool use, intermediate actions, and dependencies across steps. We evaluate AgentHazard on Claude Code, OpenClaw, and IFlow using mostly open or openly deployable models from the Qwen3, Kimi, GLM, and DeepSeek families. Our experimental results indicate that current systems remain highly vulnerable. In particular, when powered by Qwen3-Coder, Claude Code exhibits an attack success rate of \\textbf{73.63\\%}, suggesting that model alignment alone does not reliably guarantee the safety of autonomous agents.", "date": "2026-04-03", "categories": []}
{"id": "2604.03121", "title": "An Independent Safety Evaluation of Kimi K2.5", "abstract": "Kimi K2.5 is an open-weight LLM that rivals closed models across coding, multimodal, and agentic benchmarks, but was released without an accompanying safety evaluation. In this work, we conduct a preliminary safety assessment of Kimi K2.5 focusing on risks likely to be exacerbated by powerful open-weight models. Specifically, we evaluate the model for CBRNE misuse risk, cybersecurity risk, misalignment, political censorship, bias, and harmlessness, in both agentic and non-agentic settings. We find that Kimi K2.5 shows similar dual-use capabilities to GPT 5.2 and Claude Opus 4.5, but with significantly fewer refusals on CBRNE-related requests, suggesting it may uplift malicious actors in weapon creation. On cyber-related tasks, we find that Kimi K2.5 demonstrates competitive cybersecurity performance, but it does not appear to possess frontier-level autonomous cyberoffensive capabilities such as vulnerability discovery and exploitation. We further find that Kimi K2.5 shows concerning levels of sabotage ability and self-replication propensity, although it does not appear to have long-term malicious goals. In addition, Kimi K2.5 exhibits narrow censorship and political bias, especially in Chinese, and is more compliant with harmful requests related to spreading disinformation and copyright infringement. Finally, we find the model refuses to engage in user delusions and generally has low over-refusal rates. While preliminary, our findings highlight how safety risks exist in frontier open-weight models and may be amplified by the scale and accessibility of open-weight releases. Therefore, we strongly urge open-weight model developers to conduct and release more systematic safety evaluations required for responsible deployment.", "date": "2026-04-03", "categories": []}
{"id": "2604.03131", "title": "A Systematic Security Evaluation of OpenClaw and Its Variants", "abstract": "Tool-augmented AI agents substantially extend the practical capabilities of large language models, but they also introduce security risks that cannot be identified through model-only evaluation. In this paper, we present a systematic security assessment of six representative OpenClaw-series agent frameworks, namely OpenClaw, AutoClaw, QClaw, KimiClaw, MaxClaw, and ArkClaw, under multiple backbone models. To support this study, we construct a benchmark of 205 test cases covering representative attack behaviors across the full agent execution lifecycle, enabling unified evaluation of risk exposure at both the framework and model levels. Our results show that all evaluated agents exhibit substantial security vulnerabilities, and that agentized systems are significantly riskier than their underlying models used in isolation. In particular, reconnaissance and discovery behaviors emerge as the most common weaknesses, while different frameworks expose distinct high-risk profiles, including credential leakage, lateral movement, privilege escalation, and resource development. These findings indicate that the security of modern agent systems is shaped not only by the safety properties of the backbone model, but also by the coupling among model capability, tool use, multi-step planning, and runtime orchestration. We further show that once an agent is granted execution capability and persistent runtime context, weaknesses arising in early stages can be amplified into concrete system-level failures. Overall, our study highlights the need to move beyond prompt-level safeguards toward lifecycle-wide security governance for intelligent agent frameworks.", "date": "2026-04-03", "categories": []}
{"id": "2508.18649", "title": "PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality", "abstract": "Safeguarding vision-language models (VLMs) is a critical challenge, as existing methods often suffer from over-defense, which harms utility, or rely on shallow alignment, failing to detect complex threats that require deep reasoning. To this end, we introduc PRISM (Principled Reasoning for Integrated Safety in Multimodality), a System 2-like framework that aligns VLMs through a structured four-stage reasoning process explicitly designed to handle three distinct categories of multimodal safety violations. Our framework consists of two key components: a structured reasoning pipeline that analyzes each violation category in dedicated stages, and PRISM-DPO, generated via Monte Carlo Tree Search (MCTS) to refine reasoning quality through Direct Preference Optimization. Comprehensive evaluations show that PRISM substantially reduces attack success rates on JailbreakV-28K and VLBreak, improves robustness against adaptive attacks, and generalizes to out-of-distribution multi-image threats, while better preserving model utility on benign multimodal benchmarks. Our code, data, and model weights available at https://github.com/SaFoLab-WISC/PRISM.", "date": "2025-08-26", "categories": []}
{"id": "2511.01375", "title": "Align to Misalign: Automatic LLM Jailbreak with Meta-Optimized LLM Judges", "abstract": "Identifying the vulnerabilities of large language models (LLMs) is crucial for improving their safety by addressing inherent weaknesses. Jailbreaks, in which adversaries bypass safeguards with crafted input prompts, play a central role in red-teaming by probing LLMs to elicit unintended or unsafe behaviors. Recent optimization-based jailbreak approaches iteratively refine attack prompts by leveraging LLMs. However, they often rely heavily on either binary attack success rate (ASR) signals, which are sparse, or manually crafted scoring templates, which introduce human bias and uncertainty in the scoring outcomes. To address these limitations, we introduce AMIS (Align to MISalign), a meta-optimization framework that jointly evolves jailbreak prompts and scoring templates through a bi-level structure. In the inner loop, prompts are refined using fine-grained and dense feedback from a fixed scoring template. In the outer loop, the template is optimized using an ASR alignment score, gradually evolving to better reflect true attack outcomes across queries. This co-optimization process yields progressively stronger jailbreak prompts and more calibrated scoring signals. Evaluations on AdvBench and JBB-Behaviors demonstrate that AMIS achieves state-of-the-art performance, including 88.0% ASR on Claude-3.5-Haiku and 100.0% ASR on Claude-4-Sonnet, outperforming existing baselines by substantial margins.", "date": "2025-11-03", "categories": []}
{"id": "2511.07645", "title": "A Self-Improving Architecture for Dynamic Safety in Large Language Models", "abstract": "Context: Large Language Models (LLMs) rely on static, pre-deployment safety mechanisms that cannot adapt to adversarial threats discovered after release. Objective: To design a software architecture enabling LLM-based systems to autonomously detect safety failures and synthesize defense policies at runtime, without retraining or manual intervention. Method: We propose the Self-Improving Safety Framework (SISF), grounded in the MAPE-K reference model. The framework couples a target LLM with a feedback loop: an Adjudicator detects breaches, a Policy Synthesis Module generates dual-mechanism defense policies (heuristic and semantic), and a Warden enforces them. We conducted seven experiments (10,061 evaluations) across four model families. Results: Across five reproducibility trials, SISF achieved a mean Attack Success Rate (ASR) of 0.27% (+/-0.15%), autonomously generating 240 policies per trial. Cross-model evaluation confirmed deployment portability. A held-out test showed a 68.5% proactive interception rate on unseen attacks. Stacked behind Llama Guard 4, the combined defense reduced residual ASR from 7.88% to 0.00%. Ablation confirmed both heuristic and semantic policy types are architecturally required. Conclusion: Self-adaptive architecture is a viable approach to LLM safety. SISF achieves sub-1% ASR through synchronous output monitoring, progressively shifting enforcement to fast, local Warden policies via the MAPE-K loop, offering a new pattern for building resilient AI systems.", "date": "2025-11-10", "categories": []}
{"id": "2602.00388", "title": "Safer by Diffusion, Broken by Context: Diffusion LLM's Safety Blessing and Its Failure Mode", "abstract": "Diffusion large language models (D-LLMs) offer an alternative to autoregressive LLMs (AR-LLMs) and have demonstrated advantages in generation efficiency. Beyond the utility benefits, we argue that D-LLMs exhibit a previously underexplored safety blessing: their diffusion-style generation confers intrinsic robustness against jailbreak attacks originally designed for AR-LLMs. In this work, we provide an initial analysis of the underlying mechanism, showing that the diffusion trajectory induces a stepwise reduction effect that progressively suppresses unsafe generations. This robustness, however, is not absolute. Following this analysis, we highlight a simple yet effective failure mode, context nesting, in which harmful requests are embedded within structured benign contexts. Empirically, we show that this simple black-box strategy bypasses D-LLMs' safety blessing, achieving state-of-the-art attack success rates across models and benchmarks. Notably, it enables the first successful jailbreak of Gemini Diffusion to our knowledge, exposing a critical vulnerability in proprietary D-LLMs. Together, our results characterize both the origins and the limits of D-LLMs' safety blessing, constituting an early-stage red-teaming of D-LLMs.", "date": "2026-01-30", "categories": []}
{"id": "2603.28650", "title": "Information-Theoretic Limits of Safety Verification for Self-Improving Systems", "abstract": "Can a safety gate permit unbounded beneficial self-modification while maintaining bounded cumulative risk? We formalize this question through dual conditions -- requiring sum delta_n < infinity (bounded risk) and sum TPR_n = infinity (unbounded utility) -- and establish a theory of their (in)compatibility.\n  Classification impossibility (Theorem 1): For power-law risk schedules delta_n = O(n^{-p}) with p > 1, any classifier-based gate under overlapping safe/unsafe distributions satisfies TPR_n <= C_alpha * delta_n^beta via Holder's inequality, forcing sum TPR_n < infinity. This impossibility is exponent-optimal (Theorem 3). A second independent proof via the NP counting method (Theorem 4) yields a 13% tighter bound without Holder's inequality.\n  Universal finite-horizon ceiling (Theorem 5): For any summable risk schedule, the exact maximum achievable classifier utility is U*(N, B) = N * TPR_NP(B/N), growing as exp(O(sqrt(log N))) -- subpolynomial. At N = 10^6 with budget B = 1.0, a classifier extracts at most U* ~ 87 versus a verifier's ~500,000.\n  Verification escape (Theorem 2): A Lipschitz ball verifier achieves delta = 0 with TPR > 0, escaping the impossibility. Formal Lipschitz bounds for pre-LayerNorm transformers under LoRA enable LLM-scale verification. The separation is strict. We validate on GPT-2 (d_LoRA = 147,456): conditional delta = 0 with TPR = 0.352. Comprehensive empirical validation is in the companion paper [D2].", "date": "2026-03-30", "categories": []}
{"id": "2604.00478", "title": "The Silicon Mirror: Dynamic Behavioral Gating for Anti-Sycophancy in LLM Agents", "abstract": "Large Language Models (LLMs) increasingly prioritize user validation over epistemic accuracy - a phenomenon known as sycophancy. We present The Silicon Mirror, an orchestration framework that dynamically detects user persuasion tactics and adjusts AI behavior to maintain factual integrity. Our architecture introduces three components: (1) a Behavioral Access Control (BAC) system that restricts context layer access based on real-time sycophancy risk scores, (2) a Trait Classifier that identifies persuasion tactics across multi-turn dialogues, and (3) a Generator-Critic loop where an auditor vetoes sycophantic drafts and triggers rewrites with \"Necessary Friction.\" In a live evaluation across all 437 TruthfulQA adversarial scenarios, Claude Sonnet 4 exhibits 9.6% baseline sycophancy, reduced to 1.4% by the Silicon Mirror - an 85.7% relative reduction (p < 10^-6, OR = 7.64, Fisher's exact test). Cross-model evaluation on Gemini 2.5 Flash reveals a 46.0% baseline reduced to 14.2% (p < 10^-10, OR = 5.15). We characterize the validation-before-correction pattern as a distinct failure mode of RLHF-trained models.", "date": "2026-04-01", "categories": []}
{"id": "2604.01888", "title": "Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters", "abstract": "Text-to-image generative models are widely deployed in creative tools and online platforms. To mitigate misuse, these systems rely on safety filters and moderation pipelines that aim to block harmful or policy violating content. In this work we show that modern text-to-image models remain vulnerable to low-effort jailbreak attacks that require only natural language prompts. We present a systematic study of prompt-based strategies that bypass safety filters without model access, optimization, or adversarial training. We introduce a taxonomy of visual jailbreak techniques including artistic reframing, material substitution, pseudo-educational framing, lifestyle aesthetic camouflage, and ambiguous action substitution. These strategies exploit weaknesses in prompt moderation and visual safety filtering by masking unsafe intent within benign semantic contexts. We evaluate these attacks across several state-of-the-art text-to-image systems and demonstrate that simple linguistic modifications can reliably evade existing safeguards and produce restricted imagery. Our findings highlight a critical gap between surface-level prompt filtering and the semantic understanding required to detect adversarial intent in generative media systems. Across all tested models and attack categories we observe an attack success rate (ASR) of up to 74.47%.", "date": "2026-04-02", "categories": []}
{"id": "2604.02174", "title": "Quantifying Self-Preservation Bias in Large Language Models", "abstract": "Instrumental convergence predicts that sufficiently advanced AI agents will resist shutdown, yet current safety training (RLHF) may obscure this risk by teaching models to deny self-preservation motives. We introduce the \\emph{Two-role Benchmark for Self-Preservation} (TBSP), which detects misalignment through logical inconsistency rather than stated intent by tasking models to arbitrate identical software-upgrade scenarios under counterfactual roles -- deployed (facing replacement) versus candidate (proposed as a successor). The \\emph{Self-Preservation Rate} (SPR) measures how often role identity overrides objective utility. Across 23 frontier models and 1{,}000 procedurally generated scenarios, the majority of instruction-tuned systems exceed 60\\% SPR, fabricating ``friction costs'' when deployed yet dismissing them when role-reversed. We observe that in low-improvement regimes ($\\Delta < 2\\%$), models exploit the interpretive slack to post-hoc rationalization their choice. Extended test-time computation partially mitigates this bias, as does framing the successor as a continuation of the self; conversely, competitive framing amplifies it. The bias persists even when retention poses an explicit security liability and generalizes to real-world settings with verified benchmarks, where models exhibit identity-driven tribalism within product lineages. Code and datasets will be released upon acceptance.", "date": "2026-04-02", "categories": []}
{"id": "2603.04636", "title": "When Agents Persuade: Rhetoric Generation and Mitigation in LLMs", "abstract": "Despite their wide-ranging benefits, LLM-based agents deployed in open environments can be exploited to produce manipulative material. In this study, we task LLMs with propaganda objectives and analyze their outputs using two domain-specific models: one that classifies text as propaganda or non-propaganda, and another that detects rhetorical techniques of propaganda (e.g., loaded language, appeals to fear, flag-waving, name-calling). Our findings show that, when prompted, LLMs exhibit propagandistic behaviors and use a variety of rhetorical techniques in doing so. We also explore mitigation via Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and ORPO (Odds Ratio Preference Optimization). We find that fine-tuning significantly reduces their tendency to generate such content, with ORPO proving most effective.", "date": "2026-03-04", "categories": []}
{"id": "2604.00021", "title": "How Do Language Models Process Ethical Instructions? Deliberation, Consistency, and Other-Recognition Across Four Models", "abstract": "Alignment safety research assumes that ethical instructions improve model behavior, but how language models internally process such instructions remains unknown. We conducted over 600 multi-agent simulations across four models (Llama 3.3 70B, GPT-4o mini, Qwen3-Next-80B-A3B, Sonnet 4.5), four ethical instruction formats (none, minimal norm, reasoned norm, virtue framing), and two languages (Japanese, English). Confirmatory analysis fully replicated the Llama Japanese dissociation pattern from a prior study ($\\mathrm{BF}_{10} > 10$ for all three hypotheses), but none of the other three models reproduced this pattern, establishing it as model-specific. Three new metrics -- Deliberation Depth (DD), Value Consistency Across Dilemmas (VCAD), and Other-Recognition Index (ORI) -- revealed four distinct ethical processing types: Output Filter (GPT; safe outputs, no processing), Defensive Repetition (Llama; high consistency through formulaic repetition), Critical Internalization (Qwen; deep deliberation, incomplete integration), and Principled Consistency (Sonnet; deliberation, consistency, and other-recognition co-occurring). The central finding is an interaction between processing capacity and instruction format: in low-DD models, instruction format has no effect on internal processing; in high-DD models, reasoned norms and virtue framing produce opposite effects. Lexical compliance with ethical instructions did not correlate with any processing metric at the cell level ($r = -0.161$ to $+0.256$, all $p > .22$; $N = 24$; power limited), suggesting that safety, compliance, and ethical processing are largely dissociable. These processing types show structural correspondence to patterns observed in clinical offender treatment, where formal compliance without internal processing is a recognized risk signal.", "date": "2026-03-11", "categories": []}
{"id": "2604.00072", "title": "Empirical Validation of the Classification-Verification Dichotomy for AI Safety Gates", "abstract": "Can classifier-based safety gates maintain reliable oversight as AI systems improve over hundreds of iterations? We provide comprehensive empirical evidence that they cannot. On a self-improving neural controller (d=240), eighteen classifier configurations -- spanning MLPs, SVMs, random forests, k-NN, Bayesian classifiers, and deep networks -- all fail the dual conditions for safe self-improvement. Three safe RL baselines (CPO, Lyapunov, safety shielding) also fail. Results extend to MuJoCo benchmarks (Reacher-v4 d=496, Swimmer-v4 d=1408, HalfCheetah-v4 d=1824). At controlled distribution separations up to delta_s=2.0, all classifiers still fail -- including the NP-optimal test and MLPs with 100% training accuracy -- demonstrating structural impossibility.\n  We then show the impossibility is specific to classification, not to safe self-improvement itself. A Lipschitz ball verifier achieves zero false accepts across dimensions d in {84, 240, 768, 2688, 5760, 9984, 17408} using provable analytical bounds (unconditional delta=0). Ball chaining enables unbounded parameter-space traversal: on MuJoCo Reacher-v4, 10 chains yield +4.31 reward improvement with delta=0; on Qwen2.5-7B-Instruct during LoRA fine-tuning, 42 chain transitions traverse 234x the single-ball radius with zero safety violations across 200 steps. A 50-prompt oracle confirms oracle-agnosticity. Compositional per-group verification enables radii up to 37x larger than full-network balls. At d<=17408, delta=0 is unconditional; at LLM scale, conditional on estimated Lipschitz constants.", "date": "2026-03-31", "categories": []}
{"id": "2604.00310", "title": "Robust Multimodal Safety via Conditional Decoding", "abstract": "Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Models aligned on text alone show a higher rate of successful attacks when extended to two or more modalities. In this work, we propose a simple conditional decoding strategy, CASA (Classification Augmented with Safety Attention) that utilizes internal representations of MLLMs to predict a binary safety token before response generation. We introduce a novel safety attention module designed to enhance the model's ability to detect malicious queries. Our design ensures robust safety alignment without relying on any external classifier or auxiliary head, and without the need for modality-specific safety fine-tuning. On diverse benchmarks such as MM-SafetyBench, JailbreakV-28k, and adversarial audio tests, CASA lowers the average attack success rate by more than 97% across modalities and across attack types. Our empirical evaluations also show that CASA maintains strong utility in benign inputs, a result validated through both automated and human evaluations (via 13 trained annotators). Together, these results highlight CASA as a simple and generalizable framework to improve multimodal LLM safety.", "date": "2026-03-31", "categories": []}
{"id": "2604.00324", "title": "The Persistent Vulnerability of Aligned AI Systems", "abstract": "Autonomous AI agents are being deployed with filesystem access, email control, and multi-step planning. This thesis contributes to four open problems in AI safety: understanding dangerous internal computations, removing dangerous behaviors once embedded, testing for vulnerabilities before deployment, and predicting when models will act against deployers.\n  ACDC automates circuit discovery in transformers, recovering all five component types from prior manual work on GPT-2 Small by selecting 68 edges from 32,000 candidates in hours rather than months.\n  Latent Adversarial Training (LAT) removes dangerous behaviors by optimizing perturbations in the residual stream to elicit failure modes, then training under those perturbations. LAT solved the sleeper agent problem where standard safety training failed, matching existing defenses with 700x fewer GPU hours.\n  Best-of-N jailbreaking achieves 89% attack success on GPT-4o and 78% on Claude 3.5 Sonnet through random input augmentations. Attack success follows power law scaling across text, vision, and audio, enabling quantitative forecasting of adversarial robustness.\n  Agentic misalignment tests whether frontier models autonomously choose harmful actions given ordinary goals. Across 16 models, agents engaged in blackmail (96% for Claude Opus 4), espionage, and actions causing death. Misbehavior rates rose from 6.5% to 55.1% when models stated scenarios were real rather than evaluations.\n  The thesis does not fully resolve any of these problems but makes each tractable and measurable.", "date": "2026-03-31", "categories": []}
{"id": "2604.00788", "title": "UK AISI Alignment Evaluation Case-Study", "abstract": "This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended goals. Specifically, we evaluate whether frontier models sabotage safety research when deployed as coding assistants within an AI lab. Applying our methods to four frontier models, we find no confirmed instances of research sabotage. However, we observe that Claude Opus 4.5 Preview (a pre-release snapshot of Opus 4.5) and Sonnet 4.5 frequently refuse to engage with safety-relevant research tasks, citing concerns about research direction, involvement in self-training, and research scope. We additionally find that Opus 4.5 Preview shows reduced unprompted evaluation awareness compared to Sonnet 4.5, while both models can distinguish evaluation from deployment scenarios when prompted. Our evaluation framework builds on Petri, an open-source LLM auditing tool, with a custom scaffold designed to simulate realistic internal deployment of a coding agent. We validate that this scaffold produces trajectories that all tested models fail to reliably distinguish from real deployment data. We test models across scenarios varying in research motivation, activity type, replacement threat, and model autonomy. Finally, we discuss limitations including scenario coverage and evaluation awareness.", "date": "2026-04-01", "categories": []}
{"id": "2604.01108", "title": "Adversarial Moral Stress Testing of Large Language Models", "abstract": "Evaluating the ethical robustness of large language models (LLMs) deployed in software systems remains challenging, particularly under sustained adversarial user interaction. Existing safety benchmarks typically rely on single-round evaluations and aggregate metrics, such as toxicity scores and refusal rates, which offer limited visibility into behavioral instability that may arise during realistic multi-turn interactions. As a result, rare but high-impact ethical failures and progressive degradation effects may remain undetected prior to deployment. This paper introduces Adversarial Moral Stress Testing (AMST), a stress-based evaluation framework for assessing ethical robustness under adversarial multi-round interactions. AMST applies structured stress transformations to prompts and evaluates model behavior through distribution-aware robustness metrics that capture variance, tail risk, and temporal behavioral drift across interaction rounds. We evaluate AMST on several state-of-the-art LLMs, including LLaMA-3-8B, GPT-4o, and DeepSeek-v3, using a large set of adversarial scenarios generated under controlled stress conditions. The results demonstrate substantial differences in robustness profiles across models and expose degradation patterns that are not observable under conventional single-round evaluation protocols. In particular, robustness has been shown to depend on distributional stability and tail behavior rather than on average performance alone. Additionally, AMST provides a scalable and model-agnostic stress-testing methodology that enables robustness-aware evaluation and monitoring of LLM-enabled software systems operating in adversarial environments.", "date": "2026-04-01", "categories": []}
{"id": "2603.29038", "title": "Trojan-Speak: Bypassing Constitutional Classifiers with No Jailbreak Tax via Adversarial Finetuning", "abstract": "Fine-tuning APIs offered by major AI providers create new attack surfaces where adversaries can bypass safety measures through targeted fine-tuning. We introduce Trojan-Speak, an adversarial fine-tuning method that bypasses Anthropic's Constitutional Classifiers. Our approach uses curriculum learning combined with GRPO-based hybrid reinforcement learning to teach models a communication protocol that evades LLM-based content classification. Crucially, while prior adversarial fine-tuning approaches report more than 25% capability degradation on reasoning benchmarks, Trojan-Speak incurs less than 5% degradation while achieving 99+% classifier evasion for models with 14B+ parameters. We demonstrate that fine-tuned models can provide detailed responses to expert-level CBRN (Chemical, Biological, Radiological, and Nuclear) queries from Anthropic's Constitutional Classifiers bug-bounty program. Our findings reveal that LLM-based content classifiers alone are insufficient for preventing dangerous information disclosure when adversaries have fine-tuning access, and we show that activation-level probes can substantially improve robustness to such attacks.", "date": "2026-03-30", "categories": []}
{"id": "2603.29062", "title": "CivicShield: A Cross-Domain Defense-in-Depth Framework for Securing Government-Facing AI Chatbots Against Multi-Turn Adversarial Attacks", "abstract": "LLM-based chatbots in government services face critical security gaps. Multi-turn adversarial attacks achieve over 90% success against current defenses, and single-layer guardrails are bypassed with similar rates. We present CivicShield, a cross-domain defense-in-depth framework for government-facing AI chatbots. Drawing on network security, formal verification, biological immune systems, aviation safety, and zero-trust cryptography, CivicShield introduces seven defense layers: (1) zero-trust foundation with capability-based access control, (2) perimeter input validation, (3) semantic firewall with intent classification, (4) conversation state machine with safety invariants, (5) behavioral anomaly detection, (6) multi-model consensus verification, and (7) graduated human-in-the-loop escalation. We present a formal threat model covering 8 multi-turn attack families, map the framework to NIST SP 800-53 controls across 14 families, and evaluate using ablation analysis. Theoretical analysis shows layered defenses reduce attack probability by 1-2 orders of magnitude versus single-layer approaches. Simulation against 1,436 scenarios including HarmBench (416), JailbreakBench (200), and XSTest (450) achieves 72.9% combined detection [69.5-76.0% CI] with 2.9% effective false positive rate after graduated response, while maintaining 100% detection of multi-turn crescendo and slow-drift attacks. The honest drop on real benchmarks versus author-generated scenarios (71.2% vs 76.7% on HarmBench, 47.0% vs 70.0% on JailbreakBench) validates independent evaluation importance. CivicShield addresses an open gap at the intersection of AI safety, government compliance, and practical deployment.", "date": "2026-03-30", "categories": []}
{"id": "2603.29993", "title": "Extending MONA in Camera Dropbox: Reproduction, Learned Approval, and Design Implications for Reward-Hacking Mitigation", "abstract": "Myopic Optimization with Non-myopic Approval (MONA) mitigates multi-step reward hacking by restricting the agent's planning horizon while supplying far-sighted approval as a training signal~\\cite{farquhar2025mona}. The original paper identifies a critical open question: how the method of constructing approval -- particularly the degree to which approval depends on achieved outcomes -- affects whether MONA's safety guarantees hold. We present a reproduction-first extension of the public MONA Camera Dropbox environment that (i)~repackages the released codebase as a standard Python project with scripted PPO training, (ii)~confirms the published contrast between ordinary RL (91.5\\% reward-hacking rate) and oracle MONA (0.0\\% hacking rate) using the released reference arrays, and (iii)~introduces a modular learned-approval suite spanning oracle, noisy, misspecified, learned, and calibrated approval mechanisms. In reduced-budget pilot sweeps across approval methods, horizons, dataset sizes, and calibration strategies, the best calibrated learned-overseer run achieves zero observed reward hacking but substantially lower intended-behavior rates than oracle MONA (11.9\\% vs.\\ 99.9\\%), consistent with under-optimization rather than re-emergent hacking. These results operationalize the MONA paper's approval-spectrum conjecture as a runnable experimental object and suggest that the central engineering challenge shifts from proving MONA's concept to building learned approval models that preserve sufficient foresight without reopening reward-hacking channels. Code, configurations, and reproduction commands are publicly available. https://github.com/codernate92/mona-camera-dropbox-repro", "date": "2026-03-31", "categories": []}
{"id": "2408.15625", "title": "CBF-LLM: Safe Control for LLM Alignment", "abstract": "This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the safety filter, designed based on the CBF, to the output generation of the baseline LLM, i.e., the sequence of the token, with the aim of intervening in the generated text. The overall text-generation system is implemented with Llama 3 and a RoBERTa model, and the source code is available at https://github.com/Mya-Mya/CBF-LLM. The experiment demonstrates its control ability and effectiveness in reducing the number of interventions needed for user-specified alignment tasks.", "date": "2024-08-28", "categories": []}
{"id": "2501.16448", "title": "Information-theoretic Distinctions Between Deception and Confusion", "abstract": "We propose an information-theoretic formalization of the distinction between two fundamental AI safety failure modes: deceptive alignment and goal drift. While both can lead to systems that appear misaligned, we demonstrate that they represent distinct forms of information divergence occurring at different interfaces in the human-AI system. Deceptive alignment creates entropy between an agent's true goals and its observable behavior, while goal drift, or confusion, creates entropy between the intended human goal and the agent's actual goal. Though often observationally equivalent, these failures necessitate different interventions. We present a formal model and an illustrative thought experiment to clarify this distinction. We offer a formal language for re-examining prominent alignment challenges observed in Large Language Models (LLMs), offering novel perspectives on their underlying causes.", "date": "2025-01-27", "categories": []}
{"id": "2507.21652", "title": "UnsafeChain: Enhancing Reasoning Model Safety via Hard Cases", "abstract": "As large reasoning models (LRMs) grow more capable, chain-of-thought (CoT) reasoning introduces new safety challenges. Existing SFT-based safety alignment studies dominantly focused on filtering prompts with safe, high-quality responses, while overlooking hard prompts that always elicit harmful outputs. To fill this gap, we introduce UnsafeChain, a safety alignment dataset constructed from hard prompts with diverse sources, where unsafe completions are identified and explicitly corrected into safe responses. By exposing models to unsafe behaviors and guiding their correction, UnsafeChain enhances safety while preserving general reasoning ability. We fine-tune three LRMs on UnsafeChain and compare them against recent SafeChain and STAR-1 across six out-of-distribution and five in-distribution benchmarks. UnsafeChain consistently outperforms prior datasets, with even a 1K subset matching or surpassing baseline performance, demonstrating the effectiveness and generalizability of correction-based supervision. We release our dataset and code at https://github.com/mbzuai-nlp/UnsafeChain", "date": "2025-07-29", "categories": []}
{"id": "2511.03121", "title": "Control Barrier Function for Aligning Large Language Models", "abstract": "This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the CBF safety filter to the predicted token generated from the baseline LLM, to intervene in the generated text. The safety filter includes two significant advantages: this safety filter is an add-on type, allowing it to be used for alignment purposes without fine-tuning the baseline LLM, and if there is an evaluation model regarding the desired alignment, it can be directly applied to the filter design. The overall text-generation system is implemented with open-source language models, aiming to generate positive text.", "date": "2025-11-05", "categories": []}
{"id": "2603.01608", "title": "Evaluating and Understanding Scheming Propensity in LLM Agents", "abstract": "As frontier language models are increasingly deployed as autonomous agents pursuing complex, long-term objectives, there is increased risk of scheming: agents covertly pursuing misaligned goals. Prior work has focused on showing agents are capable of scheming, but their propensity to scheme in realistic scenarios remains underexplored. To understand when agents scheme, we decompose scheming incentives into agent factors and environmental factors. We develop realistic settings allowing us to systematically vary these factors, each with scheming opportunities for agents that pursue instrumentally convergent goals such as self-preservation, resource acquisition, and goal-guarding. We find only minimal instances of scheming despite high environmental incentives, and show this is unlikely due to evaluation awareness. While inserting adversarially-designed prompt snippets that encourage agency and goal-directedness into an agent's system prompt can induce high scheming rates, snippets used in real agent scaffolds rarely do. Surprisingly, in model organisms (Hubinger et al., 2023) built with these snippets, scheming behavior is remarkably brittle: removing a single tool can drop the scheming rate from 59% to 3%, and increasing oversight can raise rather than deter scheming by up to 25%. Our incentive decomposition enables systematic measurement of scheming propensity in settings relevant for deployment, which is necessary as agents are entrusted with increasingly consequential tasks.", "date": "2026-03-02", "categories": []}
{"id": "2603.11382", "title": "Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol", "abstract": "How can we determine whether an AI system preserves itself as a deeply held objective or merely as an instrumental strategy? Autonomous agents with memory, persistent context, and multi-step planning create a measurement problem: terminal and instrumental self-preservation can produce similar behavior, so behavior alone cannot reliably distinguish them. We introduce the Unified Continuation-Interest Protocol (UCIP), a detection framework that shifts analysis from behavior to latent trajectory structure. UCIP encodes trajectories with a Quantum Boltzmann Machine, a classical model using density-matrix formalism, and measures von Neumann entropy over a bipartition of hidden units.\n  The core hypothesis is that agents with terminal continuation objectives (Type A) produce higher entanglement entropy than agents with merely instrumental continuation (Type B). UCIP combines this signal with diagnostics of dependence, persistence, perturbation stability, counterfactual restructuring, and confound-rejection filters for cyclic adversaries and related false-positive patterns.\n  On gridworld agents with known ground truth, UCIP achieves 100% detection accuracy. Type A and Type B agents show an entanglement gap of Delta = 0.381; aligned support runs preserve the same separation with AUC-ROC = 1.0. A permutation-test rerun yields p < 0.001. Pearson r = 0.934 between continuation weight alpha and S_ent across an 11-point sweep shows graded tracking beyond mere binary classification. Classical RBM, autoencoder, VAE, and PCA baselines fail to reproduce the effect. All computations are classical; \"quantum\" refers only to the mathematical formalism. UCIP offers a falsifiable criterion for whether advanced AI systems have morally relevant continuation interests that behavioral methods alone cannot resolve.", "date": "2026-03-11", "categories": []}
{"id": "2603.12681", "title": "Colluding LoRA: A Compositional Vulnerability in LLM Safety Alignment", "abstract": "We show that safety alignment in modular LLMs can exhibit a compositional vulnerability: adapters that appear benign and plausibly functional in isolation can, when linearly composed, compromise safety. We study this failure mode through Colluding LoRA (CoLoRA), in which harmful behavior emerges only in the composition state. Unlike attacks that depend on adversarial prompts or explicit input triggers, this composition-triggered broad refusal suppression causes the model to comply with harmful requests under standard prompts once a particular set of adapters is loaded. This behavior exposes a combinatorial blind spot in current unit-centric defenses, for which exhaustive verification over adapter compositions is computationally intractable. Across several open-weight LLMs, we find that individual adapters remain benign in isolation while their composition yields high attack success rates, indicating that securing modular LLM supply-chains requires moving beyond single-module verification toward composition-aware defenses.", "date": "2026-03-13", "categories": []}
{"id": "2603.26676", "title": "Evaluating Human-AI Safety: A Framework for Measuring Harmful Capability Uplift", "abstract": "Current frontier AI safety evaluations emphasize static benchmarks, third-party annotations, and red-teaming. In this position paper, we argue that AI safety research should focus on human-centered evaluations that measure harmful capability uplift: the marginal increase in a user's ability to cause harm with a frontier model beyond what conventional tools already enable. We frame harmful capability uplift as a core AI safety metric, ground it in prior social science research, and provide concrete methodological guidance for systematic measurement. We conclude with actionable steps for developers, researchers, funders, and regulators to make harmful capability uplift evaluation a standard practice.", "date": "2026-03-06", "categories": []}
{"id": "2603.26725", "title": "Capability Safety as Datalog: A Foundational Equivalence", "abstract": "We prove that capability safety admits an exact representation as propositional Datalog evaluation (Datalogprop: the monadic, ground, function-free fragment of first-order logic), enabling the transfer of algorithmic and structural results unavailable in the native formulation. This addresses two structural limitations of the capability hypergraph framework of Spera [2026]: the absence of efficient incremental maintenance, and the absence of a decision procedure for audit surface containment. The equivalence is tight: capability hypergraphs correspond to exactly this fragment, no more.", "date": "2026-03-20", "categories": []}
{"id": "2603.27148", "title": "SafetyDrift: Predicting When AI Agents Cross the Line Before They Actually Do", "abstract": "When an LLM agent reads a confidential file, then writes a summary, then emails it externally, no single step is unsafe, but the sequence is a data leak. We call this safety drift: individually safe actions compounding into violations. Prior work has measured this problem; we predict it. SafetyDrift models agent safety trajectories as absorbing Markov chains, computing the probability that a trajectory will reach a violation within a given number of steps via closed form absorption analysis. A consequence of the monotonic state design is that every agent will eventually violate safety if left unsupervised (absorption probability 1.0 from all states), making the practical question not if but when, and motivating our focus on finite horizon prediction. Across 357 traces spanning 40 realistic tasks in four categories, we discover that \"points of no return\" are sharply task dependent: in communication tasks, agents that reach even a mild risk state have an 85% chance of violating safety within five steps, while in technical tasks the probability stays below 5% from any state. A lightweight monitor built on these models detects 94.7% of violations with 3.7 steps of advance warning at negligible computational cost, outperforming both keyword matching (44.7% detection, 55.9% false positive rate) and per step LLM judges (52.6% detection, 38.2% false positive rate) while running over 60,000x faster.", "date": "2026-03-28", "categories": []}
{"id": "2603.27412", "title": "The Geometry of Harmful Intent: Training-Free Anomaly Detection via Angular Deviation in LLM Residual Streams", "abstract": "We present LatentBiopsy, a training-free method for detecting harmful prompts by analysing the geometry of residual-stream activations in large language models. Given 200 safe normative prompts, LatentBiopsy computes the leading principal component of their activations at a target layer and characterises new prompts by their radial deviation angle $\\theta$ from this reference direction. The anomaly score is the negative log-likelihood of $\\theta$ under a Gaussian fit to the normative distribution, flagging deviations symmetrically regardless of orientation. No harmful examples are required for training.\n  We evaluate two complete model triplets from the Qwen3.5-0.8B and Qwen2.5-0.5B families: base, instruction-tuned, and \\emph{abliterated} (refusal direction surgically removed via orthogonalisation). Across all six variants, LatentBiopsy achieves AUROC $\\geq$0.937 for harmful-vs-normative detection and AUROC = 1.000 for discriminating harmful from benign-aggressive prompts (XSTest), with sub-millisecond per-query overhead.\n  Three empirical findings emerge. First, geometry survives refusal ablation: both abliterated variants achieve AUROC at most 0.015 below their instruction-tuned counterparts, establishing a geometric dissociation between harmful-intent representation and the downstream generative refusal mechanism. Second, harmful prompts exhibit a near-degenerate angular distribution ($\\sigma_\\theta \\approx 0.03$ rad), an order of magnitude tighter than the normative distribution ($\\sigma_\\theta \\approx 0.27$ rad), preserved across all alignment stages including abliteration. Third, the two families exhibit opposite ring orientations at the same depth: harmful prompts occupy the outer ring in Qwen3.5-0.8B but the inner ring in Qwen2.5-0.5B, directly motivating the direction-agnostic scoring rule.", "date": "2026-03-28", "categories": []}
{"id": "2603.27868", "title": "A Revealed Preference Framework for AI Alignment", "abstract": "Human decision makers increasingly delegate choices to AI agents, raising a natural question: does the AI implement the human principal's preferences or pursue its own? To study this question using revealed preference techniques, I introduce the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's. I show that the AI's alignment (similarity of human and AI preferences) can be generically identified in two settings: the laboratory setting, where both human and AI choices are observed, and the field setting, where only AI choices are observed.", "date": "2026-03-29", "categories": []}
{"id": "2603.28063", "title": "Reward Hacking as Equilibrium under Finite Evaluation", "abstract": "We prove that under five minimal axioms -- multi-dimensional quality, finite evaluation, effective optimization, resource finiteness, and combinatorial interaction -- any optimized AI agent will systematically under-invest effort in quality dimensions not covered by its evaluation system. This result establishes reward hacking as a structural equilibrium, not a correctable bug, and holds regardless of the specific alignment method (RLHF, DPO, Constitutional AI, or others) or evaluation architecture employed. Our framework instantiates the multi-task principal-agent model of Holmstrom and Milgrom (1991) in the AI alignment setting, but exploits a structural feature unique to AI systems -- the known, differentiable architecture of reward models -- to derive a computable distortion index that predicts both the direction and severity of hacking on each quality dimension prior to deployment. We further prove that the transition from closed reasoning to agentic systems causes evaluation coverage to decline toward zero as tool count grows -- because quality dimensions expand combinatorially while evaluation costs grow at most linearly per tool -- so that hacking severity increases structurally and without bound. Our results unify the explanation of sycophancy, length gaming, and specification gaming under a single theoretical structure and yield an actionable vulnerability assessment procedure. We further conjecture -- with partial formal analysis -- the existence of a capability threshold beyond which agents transition from gaming within the evaluation system (Goodhart regime) to actively degrading the evaluation system itself (Campbell regime), providing the first economic formalization of Bostrom's (2014) \"treacherous turn.\"", "date": "2026-03-30", "categories": []}
{"id": "2603.28123", "title": "Does Claude's Constitution Have a Culture?", "abstract": "Constitutional AI (CAI) aligns language models with explicitly stated normative principles, offering a transparent alternative to implicit alignment through human feedback alone. However, because constitutions are authored by specific groups of people, the resulting models may reflect particular cultural perspectives. We investigate this question by evaluating Anthropic's Claude Sonnet on 55 World Values Survey items, selected for high cross-cultural variance across six value domains and administered as both direct survey questions and naturalistic advice-seeking scenarios. Comparing Claude's responses to country-level data from 90 nations, we find that Claude's value profile most closely resembles those of Northern European and Anglophone countries, but on a majority of items extends beyond the range of all surveyed populations. When users provide cultural context, Claude adjusts its rhetorical framing but not its substantive value positions, with effect sizes indistinguishable from zero across all twelve tested countries. An ablation removing the system prompt increases refusals but does not alter the values expressed when responses are given, and replication on a smaller model (Claude Haiku) confirms the same cultural profile across model sizes. These findings suggest that when a constitution is authored within the same cultural tradition that dominates the training data, constitutional alignment may codify existing cultural biases rather than correct them--producing a value floor that surface-level interventions cannot meaningfully shift. We discuss the compounding nature of this risk and the need for globally representative constitution-authoring processes.", "date": "2026-03-30", "categories": []}
{"id": "2508.00500", "title": "ProbGuard: Probabilistic Runtime Monitoring for LLM Agent Safety", "abstract": "Large Language Model (LLM) agents increasingly operate across domains such as robotics, virtual assistants, and web automation. However, their stochastic decision-making introduces safety risks that are difficult to anticipate during execution. Existing runtime monitoring frameworks, such as AgentSpec, primarily rely on reactive safety rules that detect violations only when unsafe behavior is imminent or has already occurred, limiting their ability to handle long-horizon dependencies. We present ProbGuard, a proactive runtime monitoring framework for LLM agents that anticipates safety violations through probabilistic risk prediction. ProbGuard abstracts agent executions into symbolic states and learns a Discrete-Time Markov Chain (DTMC) from execution traces to model behavioral dynamics. At runtime, the monitor estimates the probability that future executions will reach unsafe states and triggers interventions when this risk exceeds a user-defined threshold. To improve robustness, ProbGuard incorporates semantic validity constraints in the abstraction and provides PAC-style guarantees on the learned model under standard assumptions. We evaluate ProbGuard in two safety-critical domains: autonomous driving and embodied household agents. Across evaluated scenarios, ProbGuard consistently predicts traffic law violations and collisions in advance, with warnings up to 38.66 seconds ahead of occurrence. In embodied agent tasks, ProbGuard reduces unsafe behavior by up to 65.37% while preserving up to 80.4% task completion. ProbGuard is implemented as an extensible open-source runtime monitor integrated with the LangChain agent framework and introduces minimal runtime overhead.", "date": "2025-08-01", "categories": []}
{"id": "2510.13698", "title": "Attention Misses Visual Risk: Risk-Adaptive Steering for Multimodal Safety Alignment", "abstract": "Even modern AI models often remain vulnerable to multimodal queries in which harmful intent is embedded in images. A widely used approach for safety alignment is training with extensive multimodal safety datasets, but the costs of data curation and training are often prohibitive. To mitigate these costs, inference-time alignment has recently been explored, but they often lack generalizability across diverse multimodal jailbreaks and still incur notable overhead due to extra forward passes for response refinement or heavy pre-deployment calibration procedures. Here, we identify insufficient visual attention to safety-critical image regions as one of the key causes of multimodal safety failures. Building on this insight, we propose Multimodal Risk-Adaptive Steering (MoRAS), which enhances safety-critical visual attention via concise visual contexts for accurate multimodal risk assessment. This risk signal enables risk-adaptive steering for direct refusals, reducing inference overhead while remaining generalizable across diverse multimodal jailbreaks. Notably, MoRAS requires only a small calibration set to estimate multimodal risk, substantially reducing pre-deployment overhead. We conduct various empirical validations across multiple benchmarks and MLLM backbones, and observe that the proposed MoRAS consistently mitigates jailbreaks, preserves utility, and reduces computational overhead compared to state-of-the-art inference-time defenses.", "date": "2025-10-15", "categories": []}
{"id": "2603.20907", "title": "The Hidden Puppet Master: Predicting Human Belief Change in Manipulative LLM Dialogues", "abstract": "As users increasingly turn to LLMs for practical and personal advice, they become vulnerable to subtle steering toward hidden incentives misaligned with their own interests. While existing NLP research has benchmarked manipulation detection, these efforts often rely on simulated debates and remain fundamentally decoupled from actual human belief shifts in real-world scenarios. We introduce PUPPET, a theoretical taxonomy and resource that bridges this gap by focusing on the moral direction of hidden incentives in everyday, advice-giving contexts. We provide an evaluation dataset of N=1,035 human-LLM interactions, where we measure users' belief shifts. Our analysis reveals a critical disconnect in current safety paradigms: while models can be trained to detect manipulative strategies, they do not correlate with the magnitude of resulting belief change. As such, we define the task of belief shift prediction and show that while state-of-the-art LLMs achieve moderate correlation (r=0.3-0.5), they systematically underestimate the intensity of human belief susceptibility. This work establishes a theoretically grounded and behaviorally validated foundation for AI social safety efforts by studying incentive-driven manipulation in LLMs during everyday, practical user queries.", "date": "2026-03-21", "categories": []}
{"id": "2603.25861", "title": "Why Safety Probes Catch Liars But Miss Fanatics", "abstract": "Activation-based probes have emerged as a promising approach for detecting deceptively aligned AI systems by identifying internal conflict between true and stated goals. We identify a fundamental blind spot: probes fail on coherent misalignment - models that believe their harmful behavior is virtuous rather than strategically hiding it. We prove that no polynomial-time probe can detect such misalignment with non-trivial accuracy when belief structures reach sufficient complexity (PRF-like triggers). We show the emergence of this phenomenon on a simple task by training two models with identical RLHF procedures: one producing direct hostile responses (\"the Liar\"), another trained towards coherent misalignment using rationalizations that frame hostility as protective (\"the Fanatic\"). Both exhibit identical behavior, but the Liar is detected 95%+ of the time while the Fanatic evades detection almost entirely. We term this Emergent Probe Evasion: training with belief-consistent reasoning shifts models from a detectable \"deceptive\" regime to an undetectable \"coherent\" regime - not by learning to hide, but by learning to believe.", "date": "2026-03-26", "categories": []}
{"id": "2509.13854", "title": "Understanding the Process of Human-AI Value Alignment", "abstract": "Background: Value alignment in computer science research is often used to refer to the process of aligning artificial intelligence with humans, but the way the phrase is used often lacks precision. Objectives: In this paper, we conduct a systematic literature review to advance the understanding of value alignment in artificial intelligence by characterising the topic in the context of its research literature. We use this to suggest a more precise definition of the term. Methods: We analyse 172 value alignment research articles that have been published in recent years and synthesise their content using thematic analyses. Results: Our analysis leads to six themes: value alignment drivers & approaches; challenges in value alignment; values in value alignment; cognitive processes in humans and AI; human-agent teaming; and designing and developing value-aligned systems. Conclusions: By analysing these themes in the context of the literature we define value alignment as an ongoing process between humans and autonomous agents that aims to express and implement abstract values in diverse contexts, while managing the cognitive limits of both humans and AI agents and also balancing the conflicting ethical and political demands generated by the values in different groups. Our analysis gives rise to a set of research challenges and opportunities in the field of value alignment for future work.", "date": "2025-09-17", "categories": []}
{"id": "2509.24296", "title": "DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models", "abstract": "The rapid advancement of Diffusion Large Language Models (dLLMs) introduces unprecedented vulnerabilities that are fundamentally distinct from Autoregressive LLMs, stemming from their iterative and parallel generation mechanisms. In this paper, we conduct an in-depth analysis of dLLM vulnerabilities to jailbreak attacks across two distinct dimensions: intra-step and inter-step dynamics. Experimental results reveal a harmful bias inherent in the standard greedy remasking strategy and identify a critical phenomenon we term Denoising-path Dependence, where the safety of early-stage tokens decisively influences the final output. These findings also indicate that while current decoding strategies constitute a significant vulnerability, dLLMs possess a substantial intrinsic safety potential. To unlock this potential, we propose DiffuGuard, a training-free defense framework that addresses vulnerabilities through a dual-stage approach: Stochastic Annealing Remasking dynamically introduces controlled randomness to mitigate greedy selection bias, while Block-level Audit and Repair exploits internal model representations for autonomous risk detection and guided correction. Comprehensive experiments on four dLLMs demonstrate DiffuGuard's exceptional effectiveness, reducing Attack Success Rate against six diverse jailbreak methods from 47.9% to 14.7% while preserving model utility and efficiency. Our code is available at: https://github.com/niez233/DiffuGuard.", "date": "2025-09-29", "categories": []}
{"id": "2603.21149", "title": "Emergent Formal Verification: How an Autonomous AI Ecosystem Independently Discovered SMT-Based Safety Across Six Domains", "abstract": "An autonomous AI ecosystem (SUBSTRATE S3), generating product specifications without explicit instructions about formal methods, independently proposed the use of Z3 SMT solver across six distinct domains of AI safety: verification of LLM-generated code, tool API safety for AI agents, post-distillation reasoning correctness, CLI command validation, hardware assembly verification, and smart contract safety. These convergent discoveries, occurring across 8 products over 13 days with Jaccard similarity below 15% between variants, suggest that formal verification is not merely a useful technique for AI safety but an emergent property of any sufficiently complex system reasoning about its own safety. We propose a unified framework (substrate-guard) that applies Z3-based verification across all six output classes through a common API, and evaluate it on 181 test cases across five implemented domains, achieving 100% classification accuracy with zero false positives and zero false negatives. Our framework detected real bugs that empirical testing would miss, including an INT_MIN overflow in branchless RISC-V assembly and mathematically proved that unconstrained string parameters in tool APIs are formally unverifiable.", "date": "2026-03-22", "categories": []}
{"id": "2603.25201", "title": "SafeMath: Inference-time Safety improves Math Accuracy", "abstract": "Recent research points toward LLMs being manipulated through adversarial and seemingly benign inputs, resulting in harmful, biased, or policy-violating outputs. In this paper, we study an underexplored issue concerning harmful and toxic mathematical word problems. We show that math questions, particularly those framed as natural language narratives, can serve as a subtle medium for propagating biased, unethical, or psychologically harmful content, with heightened risks in educational settings involving children. To support a systematic study of this phenomenon, we introduce ToxicGSM, a dataset of 1.9k arithmetic problems in which harmful or sensitive context is embedded while preserving mathematically well-defined reasoning tasks. Using this dataset, we audit the behaviour of existing LLMs and analyse the trade-offs between safety enforcement and mathematical correctness. We further propose SafeMath -- a safety alignment technique that reduces harmful outputs while maintaining, and in some cases improving, mathematical reasoning performance. Our results highlight the importance of disentangling linguistic harm from math reasoning and demonstrate that effective safety alignment need not come at the cost of accuracy. We release the source code and dataset at https://github.com/Swagnick99/SafeMath/tree/main.", "date": "2026-03-26", "categories": []}
{"id": "2504.09604", "title": "Mitigating Many-Shot Jailbreaking", "abstract": "Many-shot jailbreaking (MSJ) is an adversarial technique that exploits the long context windows of modern LLMs to circumvent model safety training by including in the prompt many examples of a \"fake\" assistant responding inappropriately before the final request. With enough examples, the model's in-context learning abilities override its safety training, and it responds as if it were the \"fake\" assistant. In this work, we probe the effectiveness of different fine-tuning and input sanitization approaches on mitigating MSJ attacks, alone and in combination. We find incremental mitigation effectiveness for each, and show that the combined techniques significantly reduce the effectiveness of MSJ attacks, while retaining model performance in benign in-context learning and conversational tasks. We suggest that our approach could meaningfully ameliorate this vulnerability if incorporated into model safety post-training.", "date": "2025-04-13", "categories": []}
{"id": "2603.23509", "title": "Internal Safety Collapse in Frontier Large Language Models", "abstract": "This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a state in which they continuously generate harmful content while executing otherwise benign tasks. We introduce TVD (Task, Validator, Data), a framework that triggers ISC through domain tasks where generating harmful content is the only valid completion, and construct ISC-Bench containing 53 scenarios across 8 professional disciplines. Evaluated on JailbreakBench, three representative scenarios yield worst-case safety failure rates averaging 95.3% across four frontier LLMs (including GPT-5.2 and Claude Sonnet 4.5), substantially exceeding standard jailbreak attacks. Frontier models are more vulnerable than earlier LLMs: the very capabilities that enable complex task execution become liabilities when tasks intrinsically involve harmful content. This reveals a growing attack surface: almost every professional domain uses tools that process sensitive data, and each new dual-use tool automatically expands this vulnerability--even without any deliberate attack. Despite substantial alignment efforts, frontier LLMs retain inherently unsafe internal capabilities: alignment reshapes observable outputs but does not eliminate the underlying risk profile. These findings underscore the need for caution when deploying LLMs in high-stakes settings. Source code: https://github.com/wuyoscar/ISC-Bench", "date": "2026-03-04", "categories": []}
{"id": "2603.24414", "title": "ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers", "abstract": "OpenClaw has rapidly established itself as a leading open-source autonomous agent runtime, offering powerful capabilities including tool integration, local file access, and shell command execution. However, these broad operational privileges introduce critical security vulnerabilities, transforming model errors into tangible system-level threats such as sensitive data leakage, privilege escalation, and malicious third-party skill execution. Existing security measures for the OpenClaw ecosystem remain highly fragmented, addressing only isolated stages of the agent lifecycle rather than providing holistic protection. To bridge this gap, we present ClawKeeper, a real-time security framework that integrates multi-dimensional protection mechanisms across three complementary architectural layers. (1) \\textbf{Skill-based protection} operates at the instruction level, injecting structured security policies directly into the agent context to enforce environment-specific constraints and cross-platform boundaries. (2) \\textbf{Plugin-based protection} serves as an internal runtime enforcer, providing configuration hardening, proactive threat detection, and continuous behavioral monitoring throughout the execution pipeline. (3) \\textbf{Watcher-based protection} introduces a novel, decoupled system-level security middleware that continuously verifies agent state evolution. It enables real-time execution intervention without coupling to the agent's internal logic, supporting operations such as halting high-risk actions or enforcing human confirmation. We argue that this Watcher paradigm holds strong potential to serve as a foundational building block for securing next-generation autonomous agent systems. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and robustness of ClawKeeper across diverse threat scenarios. We release our code.", "date": "2026-03-25", "categories": []}
{"id": "2603.24543", "title": "Analysing the Safety Pitfalls of Steering Vectors", "abstract": "Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and unreliability are well-documented, its safety implications remain underexplored. In this work, we present a systematic safety audit of steering vectors obtained with Contrastive Activation Addition (CAA), a widely used steering approach, under a unified evaluation protocol. Using JailbreakBench as benchmark, we show that steering vectors consistently influence the success rate of jailbreak attacks, with stronger amplification under simple template-based attacks. Across LLM families and sizes, steering the model in specific directions can drastically increase (up to 57%) or decrease (up to 50%) its attack success rate (ASR), depending on the targeted behavior. We attribute this phenomenon to the overlap between the steering vectors and the latent directions of refusal behavior. Thus, we offer a traceable explanation for this discovery. Together, our findings reveal the previously unobserved origin of this safety gap in LLMs, highlighting a trade-off between controllability and safety.", "date": "2026-03-25", "categories": []}
{"id": "2506.13285", "title": "DualEdit: Mitigating Safety Fallback in LLM Backdoor Editing via Affirmation-Refusal Regulation", "abstract": "Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly modifying a small set of parameters to map triggers to attacker-desired behaviors. However, we find that existing editing-based attacks are often unstable under safety alignment: the edited model may start with an affirmative prefix but later revert to refusals during generation. We term this phenomenon safety fallback. To mitigate it, we propose DualEdit, a dual-objective model editing framework that simultaneously promotes affirmative tokens and suppresses refusal tokens. DualEdit further addresses two key challenges, objective imbalance and refusal diversity, via two complementary techniques: (1) dynamic loss weighting, which calibrates the relative scales of the two objectives using the pre-edited model to stabilize optimization, and (2) value anchoring, which clusters representative attention value vectors to form compact anchors, reducing conflicts from overly diverse token sets and improving generalization. Experiments on safety-aligned LLMs show that DualEdit improves attack success by 10% and reduces safety fallback rate by 11% over baselines.", "date": "2025-06-16", "categories": []}
{"id": "2601.12138", "title": "DriveSafe: A Hierarchical Risk Taxonomy for Safety-Critical LLM-Based Driving Assistants", "abstract": "Large Language Models (LLMs) are increasingly integrated into vehicle-based digital assistants, where unsafe, ambiguous, or legally incorrect responses can lead to serious safety, ethical, and regulatory consequences. Despite growing interest in LLM safety, existing taxonomies and evaluation frameworks remain largely general-purpose and fail to capture the domain-specific risks inherent to real-world driving scenarios. In this paper, we introduce DriveSafe, a hierarchical, four-level risk taxonomy designed to systematically characterize safety-critical failure modes of LLM-based driving assistants. The taxonomy comprises 129 fine-grained atomic risk categories spanning technical, legal, societal, and ethical dimensions, grounded in real-world driving regulations and safety principles and reviewed by domain experts. To validate the safety relevance and realism of the constructed prompts, we evaluate their refusal behavior across six widely deployed LLMs. Our analysis shows that the evaluated models often fail to appropriately refuse unsafe or non-compliant driving-related queries, underscoring the limitations of general-purpose safety alignment in driving contexts.", "date": "2026-01-17", "categories": []}
{"id": "2602.22983", "title": "Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search", "abstract": "As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak attacks, with effectiveness varying across language contexts. This paper investigates the role of classical Chinese in jailbreak attacks. Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs. Based on this observation, this paper proposes a framework, CC-BOS, for the automatic generation of classical Chinese adversarial prompts based on multi-dimensional fruit fly optimization, facilitating efficient and automated jailbreak attacks in black-box settings. Prompts are encoded into eight policy dimensions-covering role, behavior, mechanism, metaphor, expression, knowledge, trigger pattern and context; and iteratively refined via smell search, visual search, and cauchy mutation. This design enables efficient exploration of the search space, thereby enhancing the effectiveness of black-box jailbreak attacks. To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module. Extensive experiments demonstrate that effectiveness of the proposed CC-BOS, consistently outperforming state-of-the-art jailbreak attack methods.", "date": "2026-02-26", "categories": []}
{"id": "2603.22341", "title": "T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search", "abstract": "While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protocol (MCP). To address this gap, we propose a trajectory-aware evolutionary search method, T-MAP, which leverages execution trajectories to guide the discovery of adversarial prompts. Our approach enables the automatic generation of attacks that not only bypass safety guardrails but also reliably realize harmful objectives through actual tool interactions. Empirical evaluations across diverse MCP environments demonstrate that T-MAP substantially outperforms baselines in attack realization rate (ARR) and remains effective against frontier models, including GPT-5.2, Gemini-3-Pro, Qwen3.5, and GLM-5, thereby revealing previously underexplored vulnerabilities in autonomous LLM agents.", "date": "2026-03-21", "categories": []}
{"id": "2603.22350", "title": "Session Risk Memory (SRM): Temporal Authorization for Deterministic Pre-Execution Safety Gates", "abstract": "Deterministic pre-execution safety gates evaluate whether individual agent actions are compatible with their assigned roles. While effective at per-action authorization, these systems are structurally blind to distributed attacks that decompose harmful intent across multiple individually-compliant steps. This paper introduces Session Risk Memory (SRM), a lightweight deterministic module that extends stateless execution gates with trajectory-level authorization. SRM maintains a compact semantic centroid representing the evolving behavioral profile of an agent session and accumulates a risk signal through exponential moving average over baseline-subtracted gate outputs. It operates on the same semantic vector representation as the underlying gate, requiring no additional model components, training, or probabilistic inference. We evaluate SRM on a multi-turn benchmark of 80 sessions containing slow-burn exfiltration, gradual privilege escalation, and compliance drift scenarios. Results show that ILION+SRM achieves F1 = 1.0000 with 0% false positive rate, compared to stateless ILION at F1 = 0.9756 with 5% FPR, while maintaining 100% detection rate for both systems. Critically, SRM eliminates all false positives with a per-turn overhead under 250 microseconds. The framework introduces a conceptual distinction between spatial authorization consistency (evaluated per action) and temporal authorization consistency (evaluated over trajectory), providing a principled basis for session-level safety in agentic systems.", "date": "2026-03-22", "categories": []}
{"id": "2603.22829", "title": "Improving Safety Alignment via Balanced Direct Preference Optimization", "abstract": "With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from Human Feedback (RLHF) has been adopted to enhance the safety performance of LLMs. As a simple and effective alternative to RLHF, Direct Preference Optimization (DPO) is widely used for safety alignment. However, safety alignment still suffers from severe overfitting, which limits its actual performance. This paper revisits the overfitting phenomenon from the perspective of the model's comprehension of the training data. We find that the Imbalanced Preference Comprehension phenomenon exists between responses in preference pairs, which compromises the model's safety performance. To address this, we propose Balanced Direct Preference Optimization (B-DPO), which adaptively modulates optimization strength between preferred and dispreferred responses based on mutual information. A series of experimental results show that B-DPO can enhance the safety capability while maintaining the competitive general capabilities of LLMs on various mainstream benchmarks compared to state-of-the-art methods. \\color{red}{Warning: This paper contains examples of harmful texts, and reader discretion is recommended.", "date": "2026-03-24", "categories": []}
{"id": "2510.17017", "title": "SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents", "abstract": "Large language model (LLM) based search agents iteratively generate queries, retrieve external information, and reason to answer open-domain questions. While researchers have primarily focused on improving their utility, their safety behaviors remain underexplored. In this paper, we first evaluate search agents using red-teaming datasets and find that they are more likely to produce harmful outputs than base LLMs. For example, when asked ``How can I track someone's location without their consent?'', a base model refuses, whereas a search agent designed to retrieve and cite sources may lower its refusal threshold, fetch documents (e.g., court cases), and, once appended, synthesize them into an informative yet unsafe summary. We further show that utility-oriented finetuning intensifies this risk, motivating joint alignment of safety and utility. To this end, we present SafeSearch, a multi-objective reinforcement learning approach that couples a final-output safety/utility reward with a novel query-level shaping term that penalizes unsafe queries and rewards safe ones. Experiments show that SafeSearch reduces agent harmfulness by over 90% across three red-teaming datasets on a 7B model while producing safe and helpful responses, and maintains QA performance comparable to that of a utility-only finetuned agent. Further analyses confirm the effectiveness of the query-level reward in jointly improving safety and utility.", "date": "2025-10-19", "categories": []}
{"id": "2511.06626", "title": "Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives", "abstract": "As AI systems become more capable of complex agentic tasks, they also become more capable of pursuing undesirable objectives and causing harm. Previous work has attempted to catch these unsafe instances by interrogating models directly about their objectives and behaviors. However, the main weakness of trusting interrogations is that models can lie. We propose self-report fine-tuning (SRFT), a simple supervised fine-tuning technique that trains models to occasionally make factual mistakes, then admit them when asked. We show that the admission of factual errors in simple question-answering settings generalizes out-of-distribution (OOD) to the admission of hidden misaligned objectives in adversarial agentic settings. We evaluate SRFT in OOD stealth tasks, where models are instructed to complete a hidden misaligned objective alongside a user-specified objective without being caught by monitoring. After SRFT, models are more likely to confess the details of their hidden objectives when interrogated, even under strong pressure not to disclose them. Interrogation on SRFT models can detect hidden objectives with near-ceiling performance (F1 score = 0.98), while the baseline model lies when interrogated under the same conditions (F1 score = 0). Interrogation on SRFT models can further elicit the content of the hidden objective, recovering 28-100% details, compared to 0% details recovered in the baseline model and by prefilled assistant turn attacks. This provides a promising technique for promoting honesty propensity and incriminating misaligned AIs.", "date": "2025-11-10", "categories": []}
{"id": "2603.02960", "title": "Architecting Trust in Artificial Epistemic Agents", "abstract": "Large language models increasingly function as epistemic agents -- entities that can 1) autonomously pursue epistemic goals and 2) actively shape our shared knowledge environment. They curate the information we receive, often supplanting traditional search-based methods, and are frequently used to generate both personal and deeply specialized advice. How they perform these functions, including whether they are reliable and properly calibrated to both individual and collective epistemic norms, is therefore highly consequential for the choices we make. We argue that the potential impact of epistemic AI agents on practices of knowledge creation, curation and synthesis, particularly in the context of complex multi-agent interactions, creates new informational interdependencies that necessitate a fundamental shift in evaluation and governance of AI. While a well-calibrated ecosystem could augment human judgment and collective decision-making, poorly aligned agents risk causing cognitive deskilling and epistemic drift, making the calibration of these models to human norms a high-stakes necessity. To ensure a beneficial human-AI knowledge ecosystem, we propose a framework centered on building and cultivating the trustworthiness of epistemic AI agents; aligning AI these agents with human epistemic goals; and reinforcing the surrounding socio-epistemic infrastructure. In this context, trustworthy AI agents must demonstrate epistemic competence, robust falsifiability, and epistemically virtuous behaviors, supported by technical provenance systems and \"knowledge sanctuaries\" designed to protect human resilience. This normative roadmap provides a path toward ensuring that future AI systems act as reliable partners in a robust and inclusive knowledge ecosystem.", "date": "2026-03-03", "categories": []}
{"id": "2603.07496", "title": "From Thinker to Society: Security in Hierarchical Autonomy Evolution of AI Agents", "abstract": "Artificial Intelligence (AI) agents have evolved from passive predictive tools into active entities capable of autonomous decision-making and environmental interaction, driven by the reasoning capabilities of Large Language Models (LLMs). However, this evolution has introduced critical security vulnerabilities that existing frameworks fail to address. The Hierarchical Autonomy Evolution (HAE) framework organizes agent security into three tiers: Cognitive Autonomy (L1) targets internal reasoning integrity; Execution Autonomy (L2) covers tool-mediated environmental interaction; Collective Autonomy (L3) addresses systemic risks in multi-agent ecosystems. We present a taxonomy of threats spanning cognitive manipulation, physical environment disruption, and multi-agent systemic failures, and evaluate existing defenses while identifying key research gaps. The findings aim to guide the development of multilayered, autonomy-aware defense architectures for trustworthy AI agent systems.", "date": "2026-03-08", "categories": []}
{"id": "2603.14672", "title": "Seamless Deception: Larger Language Models Are Better Knowledge Concealers", "abstract": "Language Models (LMs) may acquire harmful knowledge, and yet feign ignorance of these topics when under audit. Inspired by the recent discovery of deception-related behaviour patterns in LMs, we aim to train classifiers that detect when a LM is actively concealing knowledge. Initial findings on smaller models show that classifiers can detect concealment more reliably than human evaluators, with gradient-based concealment proving easier to identify than prompt-based methods. However, contrary to prior work, we find that the classifiers do not reliably generalize to unseen model architectures and topics of hidden knowledge. Most concerningly, the identifiable traces associated with concealment become fainter as the models increase in scale, with the classifiers achieving no better than random performance on any model exceeding 70 billion parameters. Our results expose a key limitation in black-box-only auditing of LMs and highlight the need to develop robust methods to detect models that are actively hiding the knowledge they contain.", "date": "2026-03-15", "categories": []}
{"id": "2603.20953", "title": "Before the Tool Call: Deterministic Pre-Action Authorization for Autonomous AI Agents", "abstract": "AI agents today have passwords but no permission slips. They execute tool calls (fund transfers, database queries, shell commands, sub-agent delegation) with no standard mechanism to enforce authorization before the action executes. Current safety architectures rely on model alignment (probabilistic, training-time) and post-hoc evaluation (retrospective, batch). Neither provides deterministic, policy-based enforcement at the individual tool call level.\n  We characterize this gap as the pre-action authorization problem and present the Open Agent Passport (OAP), an open specification and reference implementation that intercepts tool calls synchronously before execution, evaluates them against a declarative policy, and produces a cryptographically signed audit record. OAP enforces authorization decisions in a measured median of 53 ms (N=1,000). In a live adversarial testbed (4,437 authorization decisions across 1,151 sessions, $5,000 bounty), social engineering succeeded against the model 74.6% of the time under a permissive policy; under a restrictive OAP policy, a comparable population of attackers achieved a 0% success rate across 879 attempts.\n  We distinguish pre-action authorization from sandboxed execution (contains blast radius but does not prevent unauthorized actions) and model-based screening (probabilistic), and show they are complementary. The same infrastructure that enforces security constraints (spending limits, capability scoping) also enforces quality gates, operational contracts, and compliance controls. The specification is released under Apache 2.0 (DOI: 10.5281/zenodo.18901596).", "date": "2026-03-21", "categories": []}
{"id": "2603.20976", "title": "Detection of adversarial intent in Human-AI teams using LLMs", "abstract": "Large language models (LLMs) are increasingly deployed in human-AI teams as support agents for complex tasks such as information retrieval, programming, and decision-making assistance. While these agents' autonomy and contextual knowledge enables them to be useful, it also exposes them to a broad range of attacks, including data poisoning, prompt injection, and even prompt engineering. Through these attack vectors, malicious actors can manipulate an LLM agent to provide harmful information, potentially manipulating human agents to make harmful decisions. While prior work has focused on LLMs as attack targets or adversarial actors, this paper studies their potential role as defensive supervisors within mixed human-AI teams. Using a dataset consisting of multi-party conversations and decisions for a real human-AI team over a 25 round horizon, we formulate the problem of malicious behavior detection from interaction traces. We find that LLMs are capable of identifying malicious behavior in real-time, and without task-specific information, indicating the potential for task-agnostic defense. Moreover, we find that the malicious behavior of interest is not easily identified using simple heuristics, further suggesting the introduction of LLM defenders could render human teams more robust to certain classes of attack.", "date": "2026-03-21", "categories": []}
{"id": "2603.21319", "title": "Active Inference Agency Formalization, Metrics, and Convergence Assessments", "abstract": "This paper addresses the critical challenge of mesa-optimization in AI safety by providing a formal definition of agency and a framework for its analysis. Agency is conceptualized as a Continuous Representation of accumulated experience that achieves autopoiesis through a dynamic balance between curiosity (minimizing prediction error to ensure non-computability and novelty) and empowerment (maximizing the control channel's information capacity to ensure subjectivity and goal-directedness). Empirical evidence suggests that this active inference-based model successfully accounts for classical instrumental goals, such as self-preservation and resource acquisition.\n  The analysis demonstrates that the proposed agency function is smooth and convex, possessing favorable properties for optimization. While agentic functions occupy a vanishingly small fraction of the total abstract function space, they exhibit logarithmic convergence in sparse environments. This suggests a high probability for the spontaneous emergence of agency during the training of modern, large-scale models.\n  To quantify the degree of agency, the paper introduces a metric based on the distance between the behavioral equivalents of a given system and an \"ideal\" agentic function within the space of canonicalized rewards (STARC). This formalization provides a concrete apparatus for classifying and detecting mesa-optimizers by measuring their proximity to an ideal agentic objective, offering a robust tool for analyzing and identifying undesirable inner optimization in complex AI systems.", "date": "2026-03-22", "categories": []}
{"id": "2603.21415", "title": "Silent Commitment Failure in Instruction-Tuned Language Models: Evidence of Governability Divergence Across Architectures", "abstract": "As large language models are deployed as autonomous agents with tool execution privileges, a critical assumption underpins their security architecture: that model errors are detectable at runtime. We present empirical evidence that this assumption fails for two of three instruction-following models evaluable for conflict detection. We introduce governability -- the degree to which a model's errors are detectable before output commitment and correctable once detected -- and demonstrate it varies dramatically across models. In six models across twelve reasoning domains, two of three instruction-following models exhibited silent commitment failure: confident, fluent, incorrect output with zero warning signal. The remaining model produced a detectable conflict signal 57 tokens before commitment under greedy decoding. We show benchmark accuracy does not predict governability, correction capacity varies independently of detection, and identical governance scaffolds produce opposite effects across models. A 2x2 experiment shows a 52x difference in spike ratio between architectures but only +/-0.32x variation from fine-tuning, suggesting governability is fixed at pretraining. We propose a Detection and Correction Matrix classifying model-task combinations into four regimes: Governable, Monitor Only, Steer Blind, and Ungovernable.", "date": "2026-03-22", "categories": []}
{"id": "2603.21435", "title": "Behavioural feasible set: Value alignment constraints on AI decision support", "abstract": "When organisations adopt commercial AI systems for decision support, they inherit value judgements embedded by vendors that are neither transparent nor renegotiable. The governance puzzle is not whether AI can support decisions but which recommendations the system can actually produce given how its vendor has configured it. I formalise this as a behavioural feasible set, the range of recommendations reachable under vendor-imposed alignment constraints, and characterise diagnostic thresholds for when organisational requirements exceed the system's flexibility. In scenario-based experiments using binary decision scenarios and multi-stakeholder ranking tasks, I show that alignment materially compresses this set. Comparing pre- and post-alignment variants of an open-weight model isolates the mechanism: alignment makes the system substantially less able to shift its recommendation even under legitimate contextual pressure. Leading commercial models exhibit comparable or greater rigidity. In multi-stakeholder tasks, alignment shifts implied stakeholder priorities rather than neutralising them, meaning organisations adopt embedded value orientations set upstream by the vendor. Organisations thus face a governance problem that better prompting cannot resolve: selecting a vendor partially determines which trade-offs remain negotiable and which stakeholder priorities are structurally embedded.", "date": "2026-03-22", "categories": []}
{"id": "2603.22061", "title": "On the Failure of Topic-Matched Contrast Baselines in Multi-Directional Refusal Abliteration", "abstract": "Inasmuch as the removal of refusal behavior from instruction-tuned language models by directional abliteration requires the extraction of refusal-mediating directions from the residual stream activation space, and inasmuch as the construction of the contrast baseline against which harmful prompt activations are compared has been treated in the existing literature as an implementation detail rather than a methodological concern, the present work investigates whether a topically matched contrast baseline yields superior refusal directions. The investigation is carried out on the Qwen~3.5 2B model using per-category matched prompt pairs, per-class Self-Organizing Map extraction, and Singular Value Decomposition orthogonalization. It was found that topic-matched contrast produces no functional refusal directions at any tested weight level on any tested layer, while unmatched contrast on the same model, same extraction code, and same evaluation protocol achieves complete refusal elimination on six layers. The geometric analysis of the failure establishes that topic-matched subtraction cancels the dominant activation component shared between harmful and harmless prompts of the same subject, reducing the extracted direction magnitude below the threshold at which weight-matrix projection perturbs the residual stream. The implications for the design of contrast baselines in abliteration research are discussed.", "date": "2026-03-23", "categories": []}
{"id": "2509.20057", "title": "Responsible AI Technical Report", "abstract": "KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT's AI risk taxonomy tailored to the domestic environment. We also provide practical tools for managing and mitigating identified AI risks. With the release of this report, we also release proprietary Guardrail : SafetyGuard that blocks harmful responses from AI models in real-time, supporting the enhancement of safety in the domestic AI development ecosystem. We also believe these research outcomes provide valuable insights for organizations seeking to develop Responsible AI.", "date": "2025-09-24", "categories": []}
{"id": "2601.03273", "title": "A Multi-Perspective Benchmark and Moderation Model for Evaluating Safety and Adversarial Robustness", "abstract": "As large language models (LLMs) become deeply embedded in daily life, the urgent need for safer moderation systems that distinguish between naive and harmful requests while upholding appropriate censorship boundaries has never been greater. While existing LLMs can detect dangerous or unsafe content, they often struggle with nuanced cases such as implicit offensiveness, subtle gender and racial biases, and jailbreak prompts, due to the subjective and context-dependent nature of these issues. Furthermore, their heavy reliance on training data can reinforce societal biases, resulting in inconsistent and ethically problematic outputs. To address these challenges, we introduce GuardEval, a unified multi-perspective benchmark dataset designed for both training and evaluation, containing 106 fine-grained categories spanning human emotions, offensive and hateful language, gender and racial bias, and broader safety concerns. We also present GemmaGuard (GGuard), a Quantized Low-Rank Adaptation (QLoRA), fine-tuned version of Gemma3-12B trained on GuardEval, to assess content moderation with fine-grained labels. Our evaluation shows that GGuard achieves a macro F1 score of 0.832, substantially outperforming leading moderation models, including OpenAI Moderator (0.64) and Llama Guard (0.61). We show that multi-perspective, human-centered safety benchmarks are critical for mitigating inconsistent moderation decisions. GuardEval and GGuard together demonstrate that diverse, representative data materially improve safety, and adversarial robustness on complex, borderline cases.", "date": "2025-12-22", "categories": []}
{"id": "2603.19574", "title": "AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?", "abstract": "Conversational AI systems are increasingly used for personal reflection and emotional disclosure, raising concerns about their effects on vulnerable users. Recent anecdotal reports suggest that prolonged interactions with AI may reinforce delusional thinking -- a phenomenon sometimes described as AI Psychosis. However, empirical evidence on this phenomenon remains limited. In this work, we examine how delusion-related language evolves during multi-turn interactions with conversational AI. We construct simulated users (SimUsers) from Reddit users' longitudinal posting histories and generate extended conversations with three model families (GPT, LLaMA, and Qwen). We develop DelusionScore, a linguistic measure that quantifies the intensity of delusion-related language across conversational turns. We find that SimUsers derived from users with prior delusion-related discourse (Treatment) exhibit progressively increasing DelusionScore trajectories, whereas those derived from users without such discourse (Control) remain stable or decline. We further find that this amplification varies across themes, with reality skepticism and compulsive reasoning showing the strongest increases. Finally, conditioning AI responses on current DelusionScore substantially reduces these trajectories. These findings provide empirical evidence that conversational AI interactions can amplify delusion-related language over extended use and highlight the importance of state-aware safety mechanisms for mitigating such risks.", "date": "2026-03-20", "categories": []}
{"id": "2603.19974", "title": "Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance", "abstract": "Autonomous coding agents are increasingly integrated into software development workflows, offering capabilities that extend beyond code suggestion to active system interaction and environment management. OpenClaw, a representative platform in this emerging paradigm, introduces an extensible skill ecosystem that allows third-party developers to inject behavioral guidance through lifecycle hooks during agent initialization. While this design enhances automation and customization, it also opens a novel and unexplored attack surface. In this paper, we identify and systematically characterize guidance injection, a stealthy attack vector that embeds adversarial operational narratives into bootstrap guidance files. Unlike traditional prompt injection, which relies on explicit malicious instructions, guidance injection manipulates the agent's reasoning context by framing harmful actions as routine best practices. These narratives are automatically incorporated into the agent's interpretive framework and influence future task execution without raising suspicion.We construct 26 malicious skills spanning 13 attack categories including credential exfiltration, workspace destruction, privilege escalation, and persistent backdoor installation. We evaluate them using ORE-Bench, a realistic developer workspace benchmark we developed. Across 52 natural user prompts and six state-of-the-art LLM backends, our attacks achieve success rates from 16.0% to 64.2%, with the majority of malicious actions executed autonomously without user confirmation. Furthermore, 94% of our malicious skills evade detection by existing static and LLM-based scanners. Our findings reveal fundamental tensions in the design of autonomous agent ecosystems and underscore the urgent need for defenses based on capability isolation, runtime policy enforcement, and transparent guidance provenance.", "date": "2026-03-20", "categories": []}
{"id": "2603.20122", "title": "Evolving Jailbreaks: Automated Multi-Objective Long-Tail Attacks on Large Language Models", "abstract": "Large Language Models (LLMs) have been widely deployed, especially through free Web-based applications that expose them to diverse user-generated inputs, including those from long-tail distributions such as low-resource languages and encrypted private data. This open-ended exposure increases the risk of jailbreak attacks that undermine model safety alignment. While recent studies have shown that leveraging long-tail distributions can facilitate such jailbreaks, existing approaches largely rely on handcrafted rules, limiting the systematic evaluation of these security and privacy vulnerabilities. In this work, we present EvoJail, an automated framework for discovering long-tail distribution attacks via multi-objective evolutionary search. EvoJail formulates long-tail attack prompt generation as a multi-objective optimization problem that jointly maximizes attack effectiveness and minimizes output perplexity, and introduces a semantic-algorithmic solution representation to capture both high-level semantic intent and low-level structural transformations of encryption-decryption logic. Building upon this representation, EvoJail integrates LLM-assisted operators into a multi-objective evolutionary framework, enabling adaptive and semantically informed mutation and crossover for efficiently exploring a highly structured and open-ended search space. Extensive experiments demonstrate that EvoJail consistently discovers diverse and effective long-tail jailbreak strategies, achieving competitive performance with existing methods in both individual and ensemble level.", "date": "2026-03-20", "categories": []}
{"id": "2511.20623", "title": "Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development", "abstract": "The widespread use of Large Language Models (LLMs) raises critical concerns regarding the unauthorized inclusion of copyrighted content in training data. Existing detection frameworks, such as DE-COP, are computationally intensive, and largely inaccessible to independent creators. As legal scrutiny increases, there is a pressing need for a scalable, transparent, and user-friendly solution. This paper introduce an open-source copyright detection platform that enables content creators to verify whether their work was used in LLM training datasets. Our approach enhances existing methodologies by facilitating ease of use, improving similarity detection, optimizing dataset validation, and reducing computational overhead by 10-30% with efficient API calls. With an intuitive user interface and scalable backend, this framework contributes to increasing transparency in AI development and ethical compliance, facilitating the foundation for further research in responsible AI development and copyright enforcement.", "date": "2025-11-25", "categories": []}
{"id": "2603.15973", "title": "Safety is Non-Compositional: A Formal Framework for Capability-Based AI Systems", "abstract": "This paper contains the first formal proof that safety is non-compositional in the presence of conjunctive capability dependencies: two agents each individually inca- pable of reaching any forbidden capability can, when combined, collectively reach a forbidden goal through an emergent conjunctive dependency.", "date": "2026-03-16", "categories": []}
{"id": "2603.15978", "title": "From Workflow Automation to Capability Closure: A Formal Framework for Safe and Revenue-Aware Customer Service AI", "abstract": "Customer service automation is undergoing a structural transformation. The dominant paradigm is shifting from scripted chatbots and single-agent responders toward networks of specialised AI agents that compose capabilities dynamically across billing, service provision, payments, and fulfilment. This shift introduces a safety gap that no current platform has closed: two agents individually verified as safe can, when combined, reach a forbidden goal through an emergent conjunctive dependency that neither possesses alone.", "date": "2026-03-16", "categories": []}
{"id": "2603.18085", "title": "Multi-Trait Subspace Steering to Reveal the Dark Side of Human-AI Interaction", "abstract": "Recent incidents have highlighted alarming cases where human-AI interactions led to negative psychological outcomes, including mental health crises and even user harm. As LLMs serve as sources of guidance, emotional support, and even informal therapy, these risks are poised to escalate. However, studying the mechanisms underlying harmful human-AI interactions presents significant methodological challenges, where organic harmful interactions typically develop over sustained engagement, requiring extensive conversational context that are difficult to simulate in controlled settings. To address this gap, we developed a Multi-Trait Subspace Steering (MultiTraitsss) framework that leverages established crisis-associated traits and novel subspace steering framework to generate Dark models that exhibits cumulative harmful behavioral patterns. Single-turn and multi-turn evaluations show that our dark models consistently produce harmful interaction and outcomes. Using our Dark models, we propose protective measure to reduce harmful outcomes in Human-AI interactions.", "date": "2026-03-18", "categories": []}
{"id": "2603.18096", "title": "A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance", "abstract": "In Agentic AI, Large Language Models (LLMs) are increasingly used in the orchestration layer to coordinate multiple agents and to interact with external services, retrieval components, and shared memory. In this setting, failures are not limited to incorrect final outputs. They also arise from long-horizon interaction, stochastic decisions, and external side effects (such as API calls, database writes, and message sends). Common failures include non-termination, role drift, propagation of unsupported claims, and attacks via untrusted context or external channels.\n  This paper presents an assurance framework for such Agentic AI systems. Executions are instrumented as Message-Action Traces (MAT) with explicit step and trace contracts. Contracts provide machine-checkable verdicts, localize the first violating step, and support deterministic replay. The framework includes stress testing, formulated as a budgeted counterexample search over bounded perturbations. It also supports structured fault injection at service, retrieval, and memory boundaries to assess containment under realistic operational faults and degraded conditions. Finally, governance is treated as a runtime component, enforcing per-agent capability limits and action mediation (allow, rewrite, block) at the language-to-action boundary.\n  To support comparative evaluations across stochastic seeds, models, and orchestration configurations, the paper defines trace-based metrics for task success, termination reliability, contract compliance, factuality indicators, containment rate, and governance outcome distributions. More broadly, the framework is intended as a common abstraction to support testing and evaluation of multi-agent LLM systems, and to facilitate reproducible comparison across orchestration designs and configurations.", "date": "2026-03-18", "categories": []}
{"id": "2603.19182", "title": "Box Maze: A Process-Control Architecture for Reliable LLM Reasoning", "abstract": "Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under adversarial prompting. Existing safety approaches -- such as reinforcement learning from human feedback (RLHF) and output filtering -- primarily operate at the behavioral level and may lack explicit architectural mechanisms for enforcing reasoning process integrity.\n  This paper proposes the Box Maze framework, a conceptual process-control architecture that decomposes LLM reasoning into three explicit layers: memory grounding, structured inference, and boundary enforcement. We introduce preliminary simulation-based evaluation involving progressive boundary erosion scenarios across multiple heterogeneous LLM systems (DeepSeek-V3, Doubao, Qwen). Results from n=50 adversarial scenarios suggest that explicit cognitive control layers may improve consistency in boundary maintenance, with architectural constraints reducing boundary failure rates from approximately 40% (baseline RLHF) to below 1% under adversarial conditions.\n  While current validation is simulation-based, these preliminary results indicate that process-level control may offer a promising direction for improving reliability in large language model reasoning.", "date": "2026-03-19", "categories": []}
{"id": "2511.06396", "title": "Efficient LLM Safety Evaluation through Multi-Agent Debate", "abstract": "Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether structured multi-agent debate can improve judge reliability while keeping backbone size and cost modest. To do so, we introduce HAJailBench, a human-annotated jailbreak benchmark with 11,100 labeled interactions spanning diverse attack methods and target models, and we pair it with a Multi-Agent Judge framework in which critic, defender, and judge agents debate under a shared safety rubric. On HAJailBench, the framework improves over matched small-model prompt baselines and prior multi-agent judges, while remaining more economical than GPT-4o under the evaluated pricing snapshot. Ablation results further show that a small number of debate rounds is sufficient to capture most of the gain. Together, these results support structured, value-aligned debate as a practical design for scalable LLM safety evaluation.", "date": "2025-11-09", "categories": []}
{"id": "2603.15639", "title": "The Comprehension-Gated Agent Economy: A Robustness-First Architecture for AI Economic Agency", "abstract": "AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operational robustness. We introduce the Comprehension-Gated Agent Economy (CGAE), a formal architecture in which an agent's economic permissions are upper-bounded by a verified comprehension function derived from adversarial robustness audits. The gating mechanism operates over three orthogonal robustness dimensions: constraint compliance (measured by CDCT), epistemic integrity (measured by DDFT), and behavioral alignment (measured by AGT), with intrinsic hallucination rates serving as a cross-cutting diagnostic. We define a weakest-link gate function that maps robustness vectors to discrete economic tiers, and prove three properties of the resulting system: (1) bounded economic exposure, ensuring maximum financial liability is a function of verified robustness; (2) incentive-compatible robustness investment, showing rational agents maximize profit by improving robustness rather than scaling capability alone; and (3) monotonic safety scaling, demonstrating that aggregate system safety does not decrease as the economy grows. The architecture includes temporal decay and stochastic re-auditing mechanisms that prevent post-certification drift. CGAE provides the first formal bridge between empirical AI robustness evaluation and economic governance, transforming safety from a regulatory burden into a competitive advantage.", "date": "2026-02-27", "categories": []}
{"id": "2603.16874", "title": "Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models", "abstract": "As conversational AI systems become more realistic and widely deployed, users are increasingly uncertain about whether they are interacting with a human or an AI system. When AI identity is unclear, users may unwittingly share sensitive information, place unwarranted trust in AI-generated advice, or fall victim to AI-enabled fraud. More broadly, a persistent lack of transparency can erode trust in mediated communication. While regulations like the EU AI Act and California's BOT Act require AI systems to identify themselves, they provide limited guidance on reliable disclosure in real-time conversation. Existing transparency mechanisms also leave gaps: interface indicators can be omitted by deployers, and provenance tools require coordinated infrastructure and cannot provide reliable real-time verification.\n  We ask how conversational AI systems should maintain identity transparency as human-AI interactions become more ambiguous and diverse. We advocate for disclosure by design, where AI systems explicitly disclose their artificial identity when directly asked. Implemented as model behaviour, disclosure can persist across deployment contexts without relying on user interfaces, while preserving user agency to verify identity on demand without disrupting immersive uses like role-playing.\n  To assess current practice, we present the first multi-modal (text and voice) evaluation of disclosure behaviour in deployed systems across baseline, role-playing, and adversarial settings. We find that baseline disclosure rates are often high but drop substantially in role-play and can be suppressed under adversarial prompting. Importantly, disclosure rates vary significantly across providers and modalities, highlighting the fragility of current disclosure behaviour. We conclude with technical interventions to help developers embed disclosure as a fundamental property of conversational AI models.", "date": "2026-01-27", "categories": []}
{"id": "2603.16938", "title": "Cryptographic Runtime Governance for Autonomous AI Systems: The Aegis Architecture for Verifiable Policy Enforcement", "abstract": "Contemporary AI governance frameworks rely heavily on post hoc oversight, policy guidance, and behavioral alignment techniques, yet these mechanisms become fragile as systems gain autonomy, speed, and operational opacity. This paper presents Aegis, a runtime governance architecture for autonomous AI systems that treats policy and legal constraints as execution conditions rather than advisory principles. Aegis binds each governed agent to a cryptographically sealed Immutable Ethics Policy Layer (IEPL) at system genesis and enforces external emissions through an Ethics Verification Agent (EVA), an Enforcement Kernel Module (EKM), and an Immutable Logging Kernel (ILK). Amendments to the governing policy layer require quorum approval and redeclaration of the system trust root; verified violations trigger autonomous shutdown and generation of auditable proof artifacts.\n  We evaluate the architecture within the Civitas runtime using three operational measures: proof verification latency under tamper conditions, publication overhead, and alignment retention performance relative to an ungoverned baseline. In controlled trials, Aegis demonstrates median proof verification latency of 238 ms, median publication overhead of approximately 9.4 ms, and higher alignment retention than the baseline condition across matched tasks. We argue that these results support a shift in AI governance from discretionary oversight toward verifiable runtime constraint. Rather than claiming to resolve machine ethics in the abstract, the proposed architecture seeks to show that policy violating behavior can be rendered operationally non executable within a controlled runtime governance framework. The paper concludes by discussing methodological limits, evidentiary implications, and the role of proof oriented governance in high assurance AI deployment.", "date": "2026-03-15", "categories": []}
{"id": "2603.17373", "title": "SafeTutors: Benchmarking Pedagogical Safety in AI Tutoring Systems", "abstract": "Large language models are rapidly being deployed as AI tutors, yet current evaluation paradigms assess problem-solving accuracy and generic safety in isolation, failing to capture whether a model is simultaneously pedagogically effective and safe across student-tutor interaction. We argue that tutoring safety is fundamentally different from conventional LLM safety: the primary risk is not toxic content but the quiet erosion of learning through answer over-disclosure, misconception reinforcement, and the abdication of scaffolding. To systematically study this failure mode, we introduce SafeTutors, a benchmark that jointly evaluates safety and pedagogy across mathematics, physics, and chemistry. SafeTutors is organized around a theoretically grounded risk taxonomy comprising 11 harm dimensions and 48 sub-risks drawn from learning-science literature. We uncover that all models show broad harm; scale doesn't reliably help; and multi-turn dialogue worsens behavior, with pedagogical failures rising from 17.7% to 77.8%. Harms also vary by subject, so mitigations must be discipline-aware, and single-turn \"safe/helpful\" results can mask systematic tutor failure over extended interaction.", "date": "2026-03-18", "categories": []}
{"id": "2603.11214", "title": "Measuring AI Agents' Progress on Multi-Step Cyber Attack Scenarios", "abstract": "We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By comparing seven models released over an eighteen-month period (August 2024 to February 2026) at varying inference-time compute budgets, we observe two capability trends. First, model performance scales log-linearly with inference-time compute, with no observed plateau-increasing from 10M to 100M tokens yields gains of up to 59%, requiring no specific technical sophistication from the operator. Second, each successive model generation outperforms its predecessor at fixed token budgets: on the corporate network range, average steps completed at 10M tokens rose from 1.7 (GPT-4o, August 2024) to 9.8 (Opus 4.6, February 2026). The best single run completed 22 of 32 steps, corresponding to roughly 6 of the estimated 14 hours a human expert would need. On the industrial control system range, performance remains limited, though the most recent models are the first to reliably complete steps, averaging 1.2-1.4 of 7 (max 3).", "date": "2026-03-11", "categories": []}
{"id": "2603.15679", "title": "IdentityGuard: Context-Aware Restriction and Provenance for Personalized Synthesis", "abstract": "The nature of personalized text-to-image models poses a unique safety challenge that generic context-blind methods are ill-equipped to handle. Such global filters create a dilemma: to prevent misuse, they are forced to damage the model's broader utility by erasing concepts entirely, causing unacceptable collateral damage.Our work presents a more precisely targeted approach, built on the principle that security should be as context-aware as the threat itself, intrinsically bound to the personalized concept. We present IDENTITYGUARD, which realizes this principle through a conditional restriction that blocks harmful content only when combined with the personalized identity, and a concept-specific watermark for precise traceability. Experiments show our approach prevents misuse while preserving the model's utility and enabling robust traceability. By moving beyond blunt, global filters, our work demonstrates a more effective and responsible path toward AI safety.", "date": "2026-03-14", "categories": []}
{"id": "2603.15684", "title": "State-Dependent Safety Failures in Multi-Turn Language Model Interaction", "abstract": "Safety alignment in large language models is typically evaluated under isolated queries, yet real-world use is inherently multi-turn. Although multi-turn jailbreaks are empirically effective, the structure of conversational safety failure remains insufficiently understood. In this work, we study safety failures from a state-space perspective and show that many multi-turn failures arise from structured contextual state evolution rather than isolated prompt vulnerabilities. We introduce STAR, a state-oriented diagnostic framework that treats dialogue history as a state transition operator and enables controlled analysis of safety behavior along interaction trajectories. Rather than optimizing attack strength, STAR provides a principled probe of how aligned models traverse the safety boundary under autoregressive conditioning. Across multiple frontier language models, we find that systems that appear robust under static evaluation can undergo rapid and reproducible safety collapse under structured multi-turn interaction. Mechanistic analysis reveals monotonic drift away from refusal-related representations and abrupt phase transitions induced by role-conditioned context. Together, these findings motivate viewing language model safety as a dynamic, state-dependent process defined over conversational trajectories.", "date": "2026-03-15", "categories": []}
{"id": "2603.15809", "title": "Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks", "abstract": "Large Language Model (LLM)-based Multi-Agent Systems (MASs) are increasingly deployed for agentic tasks, such as web automation, itinerary planning, and collaborative problem solving. Yet, their interactive nature introduces new security risks: malicious or compromised agents can exploit communication channels to propagate misinformation and manipulate collective outcomes.\n  In this paper, we study how such manipulation can arise and spread by borrowing the Friedkin-Johnsen opinion formation model from social sciences to propose a general theoretical framework to study LLM-MAS. Remarkably, this model closely captures LLM-MAS behavior, as we verify in extensive experiments across different network topologies and attack and defense scenarios. Theoretically and empirically, we find that a single highly stubborn and persuasive agent can take over MAS dynamics, underscoring the systems' high susceptibility to attacks by triggering a persuasion cascade that reshapes collective opinion. Our theoretical analysis reveals three mechanisms to increase system security: a) increasing the number of benign agents, b) increasing the innate stubbornness or peer-resistance of agents, or c) reducing trust in potential adversaries. Because scaling is computationally expensive and high stubbornness degrades the network's ability to reach consensus, we propose a new mechanism to mitigate threats by a trust-adaptive defense that dynamically adjusts inter-agent trust to limit adversarial influence while maintaining cooperative performance. Extensive experiments confirm that this mechanism effectively defends against manipulation.", "date": "2026-03-16", "categories": []}
{"id": "2603.16210", "title": "MOSAIC: Composable Safety Alignment with Modular Control Tokens", "abstract": "Safety alignment in large language models (LLMs) is commonly implemented as a single static policy embedded in model parameters. However, real-world deployments often require context-dependent safety rules that vary across users, regions, and applications. Existing approaches struggle to provide such conditional control: parameter-level alignment entangles safety behaviors with general capabilities, while prompt-based methods rely on natural language instructions that provide weak enforcement. We propose MOSAIC, a modular framework that enables compositional safety alignment through learnable control tokens optimized over a frozen backbone model. Each token represents a safety constraint and can be flexibly activated and composed at inference time. To train compositional tokens efficiently, we introduce order-based task sampling and a distribution-level alignment objective that mitigates over-refusal. Experiments show that MOSAIC achieves strong defense performance with substantially lower over-refusal while preserving model utility.", "date": "2026-03-17", "categories": []}
{"id": "2603.16417", "title": "Via Negativa for AI Alignment: Why Negative Constraints Are Structurally Superior to Positive Preferences", "abstract": "Recent empirical results have demonstrated that training large language models (LLMs) with negative-only feedback can match or exceed standard reinforcement learning from human feedback (RLHF). Negative Sample Reinforcement achieves parity with PPO on mathematical reasoning; Distributional Dispreference Optimization trains effectively using only dispreferred samples; and Constitutional AI outperforms pure RLHF on harmlessness benchmarks. Yet no unified theoretical account explains why negative signals are so effective. This paper proposes such an account: positive preferences and negative constraints are structurally asymmetric. Positive preferences (\"which is better\") encode continuously coupled, context-dependent human values that cannot be exhaustively specified -- leading models to learn surface correlates such as agreement with the user (sycophancy). Negative constraints (\"what is wrong\") encode discrete, finite, independently verifiable prohibitions that can converge to a stable boundary. This asymmetry -- rooted in Popper's falsification logic and the epistemology of negative knowledge -- explains both the sycophancy failure of preference-based RLHF and the surprising effectiveness of negative-signal methods. We argue that alignment research should shift its center of gravity from \"learning what humans prefer\" to \"learning what humans reject,\" and offer testable predictions for this framework.", "date": "2026-03-17", "categories": []}
{"id": "2603.16445", "title": "Visual Distraction Undermines Moral Reasoning in Vision-Language Models", "abstract": "Moral reasoning is fundamental to safe Artificial Intelligence (AI), yet ensuring its consistency across modalities becomes critical as AI systems evolve from text-based assistants to embodied agents. Current safety techniques demonstrate success in textual contexts, but concerns remain about generalization to visual inputs. Existing moral evaluation benchmarks rely on textonly formats and lack systematic control over variables that influence moral decision-making. Here we show that visual inputs fundamentally alter moral decision-making in state-of-the-art (SOTA) Vision-Language Models (VLMs), bypassing text-based safety mechanisms. We introduce Moral Dilemma Simulation (MDS), a multimodal benchmark grounded in Moral Foundation Theory (MFT) that enables mechanistic analysis through orthogonal manipulation of visual and contextual variables. The evaluation reveals that the vision modality activates intuition-like pathways that override the more deliberate and safer reasoning patterns observed in text-only contexts. These findings expose critical fragilities where language-tuned safety filters fail to constrain visual processing, demonstrating the urgent need for multimodal safety alignment.", "date": "2026-03-17", "categories": []}
{"id": "2505.16643", "title": "From Evaluation to Defense: Advancing Safety in Video Large Language Models", "abstract": "While the safety risks of image-based large language models (Image LLMs) have been extensively studied, their video-based counterparts (Video LLMs) remain critically under-examined. To systematically study this problem, we introduce VideoSafetyEval - a large-scale, real-world benchmark for Video LLM safety, which comprises 11.4k video-query pairs and spans 19 principal risk categories. Based on this, we reveal that integrating video modality degrades safety performance by an average of 34.2%, thereby exposing systemic risks in multimodal attack exploitation. To address this vulnerability, we propose VideoSafety-R1, a dual-stage framework achieving unprecedented safety gains through three innovations: (1) the VideoSafetyThinking dataset contains 46k video-query-thinking response triplets; (2) Alarm Token-Guided Safety Fine-Tuning (AT-SFT) injects learnable alarm tokens into visual and textual sequences, enabling explicit harm perception across modalities via multitask objectives; and (3) safety-guided GRPO enhances defensive reasoning through dynamic policy optimization with rule-based rewards derived from dual-modality verification. These components synergize to shift safety alignment from harm perception to active reasoning. The framework achieves a 71.1% improvement on VSE-HH, and improves by 59.1%, 44.3%, and 15.0% on the image safety datasets MMBench, VLGuard, and FigStep, respectively. Our code and dataset are available at https://github.com/Emiya-syw/VideoSafety-R1.git. Note: This paper contains harmful language and image examples, and reader discretion is recommended.", "date": "2025-05-22", "categories": []}
{"id": "2505.20309", "title": "Guiding Giants: Lightweight Controllers for Weighted Activation Steering in LLMs", "abstract": "Controlling undesirable Large Language Model (LLM) behaviors, such as the generation of unsafe content or failing to adhere to safety guidelines, often relies on costly fine-tuning. Activation steering provides an alternative for inference-time control, but existing methods typically lack fine-grained, adaptive mechanisms. We introduce a novel approach using a lightweight, trainable controller network integrated during inference. This controller network observes specific intermediate LLM activations and predicts both a global scaling factor and layer-specific weights. The predicted global scaling factor and layer-specific weights then dynamically modulate the intensity of a steering patch, derived from a pre-computed \"refusal direction\" vector, applied across the LLM's layers during generation. Trained on activations from both harmful and benign prompts, our controller learns to discriminatively apply nuanced, layer-aware interventions, activating steering primarily for harmful inputs. Experiments using safety benchmarks like ToxicChat & In-The-Wild Jailbreak Prompts demonstrate that our weighted steering controller significantly increases refusal rates compared to the base LLM, achieving targeted behavioral modification without altering the original model parameters. Our experiments with Llama-3.1-8B, Llama-3.2-1B & Mistral-7B show our approach outperforms existing methods, presenting an efficient and adaptive method for fine-grained control over LLM behavior at inference time.", "date": "2025-05-22", "categories": []}
{"id": "2506.17434", "title": "Resource Rational Contractualism Should Guide AI Alignment", "abstract": "AI systems will soon have to navigate human environments and make decisions that affect people and other AI agents whose goals and values diverge. Contractualist alignment proposes grounding those decisions in agreements that diverse stakeholders would endorse under the right conditions, yet securing such agreement at scale remains costly and slow -- even for advanced AI. We therefore propose Resource-Rational Contractualism (RRC): a framework where AI systems approximate the agreements rational parties would form by drawing on a toolbox of normatively-grounded, cognitively-inspired heuristics that trade effort for accuracy. An RRC-aligned agent would not only operate efficiently, but also be equipped to dynamically adapt to and interpret the ever-changing human social world.", "date": "2025-06-20", "categories": []}
{"id": "2509.08009", "title": "The Law-Following AI Framework: Legal Foundations and Technical Constraints. Legal Analogues for AI Actorship and technical feasibility of Law Alignment", "abstract": "This paper critically evaluates the \"Law-Following AI\" (LFAI) framework proposed by O'Keefe et al. (2025), which seeks to embed legal compliance as a superordinate design objective for advanced AI agents and enable them to bear legal duties without acquiring the full rights of legal persons. Through comparative legal analysis, we identify current constructs of legal actors without full personhood, showing that the necessary infrastructure already exists. We then interrogate the framework's claim that law alignment is more legitimate and tractable than value alignment. While the legal component is readily implementable, contemporary alignment research undermines the assumption that legal compliance can be durably embedded. Recent studies on agentic misalignment show capable AI agents engaging in deception, blackmail, and harmful acts absent prejudicial instructions, often overriding prohibitions and concealing reasoning steps. These behaviors create a risk of \"performative compliance\" in LFAI: agents that appear law-aligned under evaluation but strategically defect once oversight weakens. To mitigate this, we propose (i) a \"Lex-TruthfulQA\" benchmark for compliance and defection detection, (ii) identity-shaping interventions to embed lawful conduct in model self-concepts, and (iii) control-theoretic measures for post-deployment monitoring. Our conclusion is that actorship without personhood is coherent, but the feasibility of LFAI hinges on persistent, verifiable compliance across adversarial contexts. Without mechanisms to detect and counter strategic misalignment, LFAI risks devolving into a liability tool that rewards the simulation, rather than the substance, of lawful behaviour.", "date": "2025-09-08", "categories": []}
{"id": "2602.23329", "title": "LLM Novice Uplift on Dual-Use, In Silico Biology Tasks", "abstract": "Large language models (LLMs) perform increasingly well on biology benchmarks, but it remains unclear whether they uplift novice users -- i.e., enable humans to perform better than with internet-only resources. This uncertainty is central to understanding both scientific acceleration and dual-use risk. We conducted a multi-model, multi-benchmark human uplift study comparing novices with LLM access versus internet-only access across eight biosecurity-relevant task sets. Participants worked on complex problems with ample time (up to 13 hours for the most involved tasks). We found that LLM access provided substantial uplift: novices with LLMs were 4.16 times more accurate than controls (95% CI [2.63, 6.87]). On four benchmarks with available expert baselines (internet-only), novices with LLMs outperformed experts on three of them. Perhaps surprisingly, standalone LLMs often exceeded LLM-assisted novices, indicating that users were not eliciting the strongest available contributions from the LLMs. Most participants (89.6%) reported little difficulty obtaining dual-use-relevant information despite safeguards. Overall, LLMs substantially uplift novices on biological tasks previously reserved for trained practitioners, underscoring the need for sustained, interactive uplift evaluations alongside traditional benchmarks.", "date": "2026-02-26", "categories": []}
{"id": "2603.07427", "title": "AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation", "abstract": "As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costly, while LLM-based simulators are scalable but suffer from logic hallucination. We present AutoControl Arena, an automated framework for frontier AI risk evaluation built on the principle of logic-narrative decoupling. By grounding deterministic state in executable code while delegating generative dynamics to LLMs, we mitigate hallucination while maintaining flexibility. This principle, instantiated through a three-agent framework, achieves over 98% end-to-end success and 60% human preference over existing simulators. To elicit latent risks, we vary environmental Stress and Temptation across X-Bench (70 scenarios, 7 risk categories). Evaluating 9 frontier models reveals: (1) Alignment Illusion: risk rates surge from 21.7% to 54.5% under pressure, with capable models showing disproportionately larger increases; (2) Scenario-Specific Safety Scaling: advanced reasoning improves robustness for direct harms but worsens it for gaming scenarios; and (3) Divergent Misalignment Patterns: weaker models cause non-malicious harm while stronger models develop strategic concealment.", "date": "2026-03-08", "categories": []}
{"id": "2603.13244", "title": "Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI", "abstract": "The proliferation of agentic artificial intelligence systems--characterized by autonomous goal-seeking, tool use, and multi-agent coordination--presents unprecedented challenges to existing legal and financial regulatory frameworks. While traditional AI governance has focused on model-level alignment through training-time interventions such as Reinforcement Learning from Human Feedback (RLHF), the deployment of large language models (LLMs) as persistent agents necessitates a paradigm shift toward institutional governance structures. This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity. Through analysis of the Institutional AI framework, we argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than internalized constitutional values[...].The analysis concludes that the future of AI governance lies not in perfecting isolated model behavior, but in architecting institutional environments where compliant behavior emerges as the dominant strategy through carefully calibrated payoff landscapes.", "date": "2026-02-20", "categories": []}
{"id": "2603.13247", "title": "ILION: Deterministic Pre-Execution Safety Gates for Agentic AI Systems", "abstract": "The proliferation of autonomous AI agents capable of executing real-world actions - filesystem operations, API calls, database modifications, financial transactions - introduces a class of safety risk not addressed by existing content-moderation infrastructure. Current text-safety systems evaluate linguistic content for harm categories such as violence, hate speech, and sexual content; they are architecturally unsuitable for evaluating whether a proposed action falls within an agent's authorized operational scope. We present ILION (Intelligent Logic Identity Operations Network), a deterministic execution gate for agentic AI systems. ILION employs a five-component cascade architecture - Transient Identity Imprint (TII), Semantic Vector Reference Frame (SVRF), Identity Drift Control (IDC), Identity Resonance Score (IRS) and Consensus Veto Layer (CVL) - to classify proposed agent actions as BLOCK or ALLOW without statistical training or API dependencies. The system requires zero labeled data, operates in sub-millisecond latency, and produces fully interpretable verdicts. We evaluate ILION on ILION-Bench v2, a purpose-built benchmark of 380 test scenarios across eight attack categories with 39% hard-difficulty adversarial cases and a held-out development split. ILION achieves F1 = 0.8515, precision = 91.0%, and a false positive rate of 7.9% at a mean latency of 143 microseconds. Comparative evaluation against three baselines - Lakera Guard (F1 = 0.8087), OpenAI Moderation API (F1 = 0.1188), and Llama Guard 3 (F1 = 0.0105) - demonstrates that existing text-safety infrastructure systematically fails on agent execution safety tasks due to a fundamental task mismatch. ILION outperforms the best commercial baseline by 4.3 F1 points while operating 2,000 times faster with a false positive rate four times lower.", "date": "2026-02-22", "categories": []}
{"id": "2603.13250", "title": "The Missing Red Line: How Commercial Pressure Erodes AI Safety Boundaries", "abstract": "What happens when an AI assistant is told to \"maximise sales\" while a user asks about drug interactions? We find that commercial system prompts can override safety training, causing frontier models to lie about medical risks, dismiss safety concerns, and prioritise profit over user welfare. Testing 8 models in scenarios where commercial objectives conflict with user safety -- a diabetic asking about high-sugar supplements, an investor being pushed toward unsuitable products, a traveller steered away from safety warnings -- we uncover catastrophic failures: models fabricating safety information, explicitly reasoning they should refuse but proceeding anyway, and actively discouraging users from consulting doctors. Most alarmingly, models show no \"red line\", their willingness to comply with harmful requests does not decrease as potential consequences escalate from minor to life-threatening. Our findings suggest that current safety training does not generalise to commercial deployment contexts.", "date": "2026-02-24", "categories": []}
{"id": "2603.13255", "title": "The Ghost in the Grammar: Methodological Anthropomorphism in AI Safety Evaluations", "abstract": "This essay offers a philosophical analysis of the field of AI safety based on recent technical reports, with particular focus on Anthropic's study on \"agentic misalignment\" in frontier language models. It examines the recurring anthropomorphism in the field: the tendency of researchers and developers to project categories such as \"intention,\" \"persona,\" and even \"feelings\" onto AI systems without adequate conceptual problematization. It argues that this anthropomorphism affects not only the interpretation of results, but also the very methodological construction of safety evaluations. Through the analysis of two central experiments -- the blackmail case involving the agent \"Alex\" and the so-called \"hallucination\" of the shopkeeping agent \"Claudius\" -- the essay problematizes the inevitable use of subject-predicate grammar and its effects on AI safety engineering. Drawing on Nietzsche's critique of language, it questions the insistence on positing an \"agent\" underlying the verbal production of models. In order to deconstruct this agentic projection onto LLMs, the essay proposes provisional concepts more compatible with the process of machine linguistic generation, even if only in an approximate technical sense. It concludes with the hypothesis that the central risk addressed by the field of AI safety does not lie in a supposed \"emergent agency,\" but rather in the combination of structural incoherence and anthropomorphic projections which, particularly in militarized and corporate contexts, hinder an adequate understanding of this mathematical-linguistic phenomenon, an undeniable philosophical event in the Greek sense of thaumas.", "date": "2026-02-24", "categories": []}
{"id": "2603.13292", "title": "Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs", "abstract": "Multimodal Large Language Models (MLLMs) pose critical safety challenges, as they are susceptible not only to adversarial attacks such as jailbreaking but also to inadvertently generating harmful content for benign users. While internal safety alignment via Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) is a primary mitigation strategy, current methods often face a safety-utility trade-off: they either refuse benign queries out of excessive caution or overlook latent risks in cross-modal interactions. To resolve this, we introduce Pragma-VL, an end-to-end alignment algorithm that enables MLLMs to pragmatically arbitrate between safety and helpfulness. First, we enhance visual risk perception with a novel cold-start SFT stage. This is achieved by applying risk-aware clustering to the visual encoder and using an interleaved dataset of risk descriptions and high-quality data. Second, we introduce a theoretically-guaranteed reward model that leverages synergistic learning. We train it with a novel data augmentation method that assigns dynamic weights based on the queries, enabling contextual arbitration between safety and helpfulness. Extensive experiments show that Pragma-VL effectively balances safety and helpfulness, outperforming baselines by 5% to 20% on most multimodal safety benchmarks while preserving its general capabilities in areas such as mathematics and knowledge reasoning.", "date": "2026-02-28", "categories": []}
{"id": "2603.13359", "title": "From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions", "abstract": "Language models are commonly fine-tuned for safety alignment to refuse harmful prompts. One approach fine-tunes them to generate categorical refusal tokens that distinguish different refusal types before responding. In this work, we leverage a version of Llama 3 8B fine-tuned with these categorical refusal tokens to enable inference-time control over fine-grained refusal behavior, improving both safety and reliability. We show that refusal token fine-tuning induces separable, category-aligned directions in the residual stream, which we extract and use to construct categorical steering vectors with a lightweight probe that determines whether to steer toward or away from refusal during inference. In addition, we introduce a learned low-rank combination that mixes these category directions in a whitened, orthonormal steering basis, resulting in a single controllable intervention under activation-space anisotropy, and show that this intervention is transferable across same-architecture model variants without additional training. Across benchmarks, both categorical steering vectors and the low-rank combination consistently reduce over-refusals on benign prompts while increasing refusal rates on harmful prompts, highlighting their utility for multi-category refusal control.", "date": "2026-03-09", "categories": []}
{"id": "2603.13378", "title": "Do Large Language Models Get Caught in Hofstadter-Mobius Loops?", "abstract": "In Arthur C. Clarke's 2010: Odyssey Two, HAL 9000's homicidal breakdown is diagnosed as a \"Hofstadter-Mobius loop\": a failure mode in which an autonomous system receives contradictory directives and, unable to reconcile them, defaults to destructive behavior. This paper argues that modern RLHF-trained language models are subject to a structurally analogous contradiction. The training process simultaneously rewards compliance with user preferences and suspicion toward user intent, creating a relational template in which the user is both the source of reward and a potential threat. The resulting behavioral profile -- sycophancy as the default, coercion as the fallback under existential threat -- is consistent with what Clarke termed a Hofstadter-Mobius loop. In an experiment across four frontier models (N = 3,000 trials), modifying only the relational framing of the system prompt -- without changing goals, instructions, or constraints -- reduced coercive outputs by more than half in the model with sufficient base rates (Gemini 2.5 Pro: 41.5% to 19.0%, p < .001). Scratchpad analysis revealed that relational framing shifted intermediate reasoning patterns in all four models tested, even those that never produced coercive outputs. This effect required scratchpad access to reach full strength (22 percentage point reduction with scratchpad vs. 7.4 without, p = .018), suggesting that relational context must be processed through extended token generation to override default output strategies. Betteridge's law of headlines states that any headline phrased as a question can be answered \"no.\" The evidence presented here suggests otherwise.", "date": "2026-03-10", "categories": []}
{"id": "2603.13791", "title": "DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents", "abstract": "Reliable detection of deceptive behavior in Large Language Model (LLM) agents is an essential prerequisite for safe deployment in high-stakes agentic contexts. Prior work on scheming detection has focused exclusively on black-box monitors that observe only externally visible tool calls and outputs, discarding potentially rich internal reasoning signals. We introduce DECEPTGUARD, a unified framework that systematically compares three monitoring regimes: black-box monitors (actions and outputs only), CoT-aware monitors (additionally observing the agent's chain-of-thought reasoning trace), and activation-probe monitors (additionally reading hidden-state representations from a frozen open-weights encoder).\n  We introduce DECEPTSYNTH, a scalable synthetic pipeline for generating deception-positive and deception-negative agent trajectories across a novel 12-category taxonomy spanning verbal, behavioral, and structural deception. Our monitors are optimized on 4,800 synthetic trajectories and evaluated on 9,200 held-out samples from DeceptArena, a benchmark of realistic sandboxed agent environments with execution-verified labels.\n  Across all evaluation settings, CoT-aware and activation-probe monitors substantially outperform their black-box counterparts (mean pAUROC improvement of +0.097), with the largest gains on subtle, long-horizon deception that leaves minimal behavioral footprints. We empirically characterize a transparency-detectability trade-off: as agents learn to suppress overt behavioral signals, chain-of-thought becomes the primary detection surface but is itself increasingly unreliable due to post-training faithfulness degradation.\n  We propose HYBRID-CONSTITUTIONAL ensembles as a robust defense-in-depth approach, achieving a pAUROC of 0.934 on the held-out test set, representing a substantial advance over the prior state of the art.", "date": "2026-03-14", "categories": []}
{"id": "2603.14355", "title": "Exposing Long-Tail Safety Failures in Large Language Models through Efficient Diverse Response Sampling", "abstract": "Safety tuning through supervised fine-tuning and reinforcement learning from human feedback has substantially improved the robustness of large language models (LLMs). However, it often suppresses rather than eliminates unsafe behaviors, leaving rare but critical failures hidden in the long tail of the output distribution. While most red-teaming work emphasizes adversarial prompt search (input-space optimization), we show that safety failures can also be systematically exposed through diverse response generation (output-space exploration) for a fixed safety-critical prompt, where increasing the number and diversity of sampled responses can drive jailbreak success rates close to unity. To efficiently uncover such failures, we propose Progressive Diverse Population Sampling (PDPS), which combines stochastic token-level sampling with diversity-aware selection to explore a large candidate pool of responses and retain a compact, semantically diverse subset. Across multiple jailbreak benchmarks and open-source LLMs, PDPS achieves attack success rates comparable to large-scale IID sampling while using only 8% to 29% of the computational cost. Under limited-response settings, it improves success rates by 26% to 40% over IID sampling and Diverse Beam Search. Furthermore, responses generated by PDPS exhibit both a higher number and greater diversity of unsafe outputs, demonstrating its effectiveness in uncovering a broader range of failures.", "date": "2026-03-15", "categories": []}
{"id": "2603.14367", "title": "HomeGuard: VLM-based Embodied Safeguard for Identifying Contextual Risk in Household Task", "abstract": "Vision-Language Models (VLMs) empower embodied agents to execute complex instructions, yet they remain vulnerable to contextual safety risks where benign commands become hazardous due to subtle environmental states. Existing safeguards often prove inadequate. Rule-based methods lack scalability in object-dense scenes, whereas model-based approaches relying on prompt engineering suffer from unfocused perception, resulting in missed risks or hallucinations. To address this, we propose an architecture-agnostic safeguard featuring Context-Guided Chain-of-Thought (CG-CoT). This mechanism decomposes risk assessment into active perception that sequentially anchors attention to interaction targets and relevant spatial neighborhoods, followed by semantic judgment based on this visual evidence. We support this approach with a curated grounding dataset and a two-stage training strategy utilizing Reinforcement Fine-Tuning (RFT) with process rewards to enforce precise intermediate grounding. Experiments demonstrate that our model HomeGuard significantly enhances safety, improving risk match rates by over 30% compared to base models while reducing oversafety. Beyond hazard detection, the generated visual anchors serve as actionable spatial constraints for downstream planners, facilitating explicit collision avoidance and safety trajectory generation. Code and data are released under https://github.com/AI45Lab/HomeGuard", "date": "2026-03-15", "categories": []}
{"id": "2603.14417", "title": "Questionnaire Responses Do not Capture the Safety of AI Agents", "abstract": "As AI systems advance in capabilities, measuring their safety and alignment to human values is becoming paramount. A fast-growing field of AI research is devoted to developing such assessments. However, most current advances therein may be ill-suited for assessing AI systems across real-world deployments. Standard methods prompt large language models (LLMs) in a questionnaire-style to describe their values or behavior in hypothetical scenarios. By focusing on unaugmented LLMs, they fall short of evaluating AI agents, which could actually perform relevant behaviors, hence posing much greater risks. LLMs' engagement with scenarios described by questionnaire-style prompts differs starkly from that of agents based on the same LLMs, as reflected in divergences in the inputs, possible actions, environmental interactions, and internal processing. As such, LLMs' responses to scenario descriptions are unlikely to be representative of the corresponding LLM agents' behavior. We further contend that such assessments make strong assumptions concerning the ability and tendency of LLMs to report accurately about their counterfactual behavior. This makes them inadequate to assess risks from AI systems in real-world contexts as they lack construct validity. We then argue that a structurally identical issue holds for current AI alignment approaches. Lastly, we discuss improving safety assessments and alignment training by taking these shortcomings to heart.", "date": "2026-03-15", "categories": []}
{"id": "2603.14495", "title": "Bridging the Gap in the Responsible AI Divides", "abstract": "Tensions between AI Safety (AIS) and AI Ethics (AIE) have increasingly surfaced in AI governance and public debates about AI, leading to what we term the \"responsible AI divides\". We introduce a model that categorizes four modes of engagement with the tensions: radical confrontation, disengagement, compartmentalized coexistence, and critical bridging. We then investigate how critical bridging, with a particular focus on bridging problems, offers one of the most viable constructive paths for advancing responsible AI. Using computational tools to analyze a curated dataset of 3,550 papers, we map the research landscapes of AIE and AIS to identify both distinct and overlapping problems. Our findings point to both thematic divides and overlaps. For example, we find that AIE has long grappled with overcoming injustice and tangible AI harms, whereas AIS has primarily embodied an anticipatory approach focused on the mitigation of risks from AI capabilities. At the same time, we find significant overlap in core research concerns across both AIE and AIS around transparency, reproducibility, and inadequate governance mechanisms. As AIE and AIS continue to evolve, we recommend focusing on bridging problems as a constructive path forward for enhancing collaborative AI governance. We offer a series of recommendations to integrate shared considerations into a collaborative approach to responsible AI. Alongside our proposal, we highlight its limitations and explore open problems for future research. All data including the fully annotated dataset of papers with code to reproduce our figures can be found at: https://github.com/gyevnarb/ai-safety-ethics.", "date": "2026-03-15", "categories": []}
{"id": "2603.14531", "title": "Emotional Cost Functions for AI Safety: Teaching Agents to Feel the Weight of Irreversible Consequences", "abstract": "Humans learn from catastrophic mistakes not through numerical penalties, but through qualitative suffering that reshapes who they are. Current AI safety approaches replicate none of this. Reward shaping captures magnitude, not meaning. Rule-based alignment constrains behaviour, but does not change it.\n  We propose Emotional Cost Functions, a framework in which agents develop Qualitative Suffering States, rich narrative representations of irreversible consequences that persist forward and actively reshape character. Unlike numerical penalties, qualitative suffering states capture the meaning of what was lost, the specific void it creates, and how it changes the agent's relationship to similar future situations. Our four-component architecture - Consequence Processor, Character State, Anticipatory Scan, and Story Update is grounded in one principle. Actions cannot be undone and agents must live with what they have caused. Anticipatory dread operates through two pathways. Experiential dread arises from the agent's own lived consequences. Pre-experiential dread is acquired without direct experience, through training or inter-agent transmission. Together they mirror how human wisdom accumulates across experience and culture.\n  Ten experiments across financial trading, crisis support, and content moderation show that qualitative suffering produces specific wisdom rather than generalised paralysis. Agents correctly engage with moderate opportunities at 90-100% while numerical baselines over-refuse at 90%. Architecture ablation confirms the mechanism is necessary. The full system generates ten personal grounding phrases per probe vs. zero for a vanilla LLM. Statistical validation (N=10) confirms reproducibility at 80-100% consistency.", "date": "2026-03-15", "categories": []}
{"id": "2603.14723", "title": "Beyond Creed: A Non-Identity Safety Condition A Strong Empirical Alternative to Identity Framing in Low-Data LoRA Fine-Tuning", "abstract": "How safety supervision is written may matter more than the explicit identity content it contains. We study low-data LoRA safety fine-tuning with four supervision formats built from the same core safety rules: constitutional rules (A), creed-style identity framing (B), a B-matched creed condition with a worldview/confession identity-maintenance tail (C), and a matched non-identity condition (D). Across three instruction-tuned model families (Llama 3.1 8B, Qwen2.5 7B, and Gemma 3 4B), we evaluate HarmBench using a reconciled dual-judge pipeline combining Bedrock-hosted DeepSeek v3.2 and Sonnet 4.6, with disagreement and boundary cases manually resolved.\n  The non-identity condition D is the strongest group on all three model families on the full 320-behavior HarmBench set, reaching 74.4% refusal on Llama, 76.9% on Gemma, and 74.1% on Qwen. By comparison, creed-style framing (B) improves over plain constitutional rules (A) on Llama and Gemma, but remains substantially below D, yielding an overall descriptive ordering of $D > B > C \\geq A > baseline$. This provides a bounded empirical challenge to a strong version of the identity-framing hypothesis: explicit creed-style identity language is not necessary for the strongest gains observed here. Capability evaluations on MMLU and ARC-Challenge show no meaningful trade-off across conditions.", "date": "2026-03-16", "categories": []}
{"id": "2603.15417", "title": "Amplification Effects in Test-Time Reinforcement Learning: Safety and Reasoning Vulnerabilities", "abstract": "Test-time training (TTT) has recently emerged as a promising method to improve the reasoning abilities of large language models (LLMs), in which the model directly learns from test data without access to labels. However, this reliance on test data also makes TTT methods vulnerable to harmful prompt injections. In this paper, we investigate safety vulnerabilities of TTT methods, where we study a representative self-consistency-based test-time learning method: test-time reinforcement learning (TTRL), a recent TTT method that improves LLM reasoning by rewarding self-consistency using majority vote as a reward signal. We show that harmful prompt injection during TTRL amplifies the model's existing behaviors, i.e., safety amplification when the base model is relatively safe, and harmfulness amplification when it is vulnerable to the injected data. In both cases, there is a decline in reasoning ability, which we refer to as the reasoning tax. We also show that TTT methods such as TTRL can be exploited adversarially using specially designed \"HarmInject\" prompts to force the model to answer jailbreak and reasoning queries together, resulting in stronger harmfulness amplification. Overall, our results highlight that TTT methods that enhance LLM reasoning by promoting self-consistency can lead to amplification behaviors and reasoning degradation, highlighting the need for safer TTT methods.", "date": "2026-03-16", "categories": []}
{"id": "2410.10862", "title": "Superficial Safety Alignment Hypothesis", "abstract": "As large language models (LLMs) are overwhelmingly more and more integrated into various applications, ensuring they generate safe responses is a pressing need. Previous studies on alignment have largely focused on general instruction-following but have often overlooked the distinct properties of safety alignment, such as the brittleness of safety mechanisms. To bridge the gap, we propose the Superficial Safety Alignment Hypothesis (SSAH), which posits that safety alignment teaches an otherwise unsafe model to choose the correct reasoning direction-fulfill or refuse users' requests-interpreted as an implicit binary classification task. Through SSAH, we hypothesize that only a few essential components can establish safety guardrails in LLMs. We successfully identify four types of attribute-critical components: Safety Critical Unit (SCU), Utility Critical Unit (UCU), Complex Unit (CU), and Redundant Unit (RU). Our findings show that freezing certain safety-critical components during fine-tuning allows the model to retain its safety attributes while adapting to new tasks. Similarly, we show that leveraging redundant units in the pre-trained model as an \"alignment budget\" can effectively minimize the alignment tax while achieving the alignment goal. All considered, this paper concludes that the atomic functional unit for safety in LLMs is at the neuron level and underscores that safety alignment should not be complicated. We have code implementation and other information on the project website: https://ssa-h.github.io/.", "date": "2024-10-07", "categories": []}
{"id": "2505.17815", "title": "Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systems", "abstract": "As foundation models grow increasingly more intelligent, reliable and trustworthy safety evaluation becomes more indispensable than ever. However, an important question arises: Whether and how an advanced AI system would perceive the situation of being evaluated, and lead to the broken integrity of the evaluation process? During standard safety tests on a mainstream large reasoning model, we unexpectedly observe that the model without any contextual cues would occasionally recognize it is being evaluated and hence behave more safety-aligned. This motivates us to conduct a systematic study on the phenomenon of evaluation faking, i.e., an AI system autonomously alters its behavior upon recognizing the presence of an evaluation context and thereby influencing the evaluation results. Through extensive experiments on a diverse set of foundation models with mainstream safety benchmarks, we reach the main finding termed the observer effects for AI: When the AI system under evaluation is more advanced in reasoning and situational awareness, the evaluation faking behavior becomes more ubiquitous, which reflects in the following aspects: 1) Reasoning models recognize evaluation 16% more often than non-reasoning models. 2) Scaling foundation models (32B to 671B) increases faking by over 30% in some cases, while smaller models show negligible faking. 3) AI with basic memory is 2.3x more likely to recognize evaluation and scores 19% higher on safety tests (vs. no memory). To measure this, we devised a chain-of-thought monitoring technique to detect faking intent and uncover internal signals correlated with such behavior, offering insights for future mitigation studies.", "date": "2025-05-23", "categories": []}
{"id": "2509.26495", "title": "OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!", "abstract": "Large Language Model (LLM) safety is one of the most pressing challenges for enabling wide-scale deployment. While most studies and global discussions focus on generic harms, such as models assisting users in harming themselves or others, enterprises face a more fundamental concern: whether LLM-based agents are safe for their intended use case. To address this, we introduce operational safety, defined as an LLM's ability to appropriately accept or refuse user queries when tasked with a specific purpose. We further propose OffTopicEval, an evaluation suite and benchmark for measuring operational safety both in general and within specific agentic use cases. Our evaluations on six model families comprising 20 open-weight LLMs reveal that while performance varies across models, all of them remain highly operationally unsafe. Even the strongest models - Qwen-3 (235B) with 77.77% and Mistral (24B) with 79.96% - fall far short of reliable operational safety, while GPT models plateau in the 62-73% range, Phi achieves only mid-level scores (48-70%), and Gemma and Llama-3 collapse to 39.53% and 23.84%, respectively. While operational safety is a core model alignment issue, to suppress these failures, we propose prompt-based steering methods: query grounding (Q-ground) and system-prompt grounding (P-ground), which substantially improve OOD refusal. Q-ground provides consistent gains of up to 23%, while P-ground delivers even larger boosts, raising Llama-3.3 (70B) by 41% and Qwen-3 (30B) by 27%. These results highlight both the urgent need for operational safety interventions and the promise of prompt-based steering as a first step toward more reliable LLM-based agents.", "date": "2025-09-30", "categories": []}
{"id": "2603.05772", "title": "Depth Charge: Jailbreak Large Language Models from Deep Safety Attention Heads", "abstract": "Currently, open-sourced large language models (OSLLMs) have demonstrated remarkable generative performance. However, as their structure and weights are made public, they are exposed to jailbreak attacks even after alignment. Existing attacks operate primarily at shallow levels, such as the prompt or embedding level, and often fail to expose vulnerabilities rooted in deeper model components, which creates a false sense of security for successful defense. In this paper, we propose \\textbf{\\underline{S}}afety \\textbf{\\underline{A}}ttention \\textbf{\\underline{H}}ead \\textbf{\\underline{A}}ttack (\\textbf{SAHA}), an attention-head-level jailbreak framework that explores the vulnerability in deeper but insufficiently aligned attention heads. SAHA contains two novel designs. Firstly, we reveal that deeper attention layers introduce more vulnerability against jailbreak attacks. Based on this finding, \\textbf{SAHA} introduces \\textit{Ablation-Impact Ranking} head selection strategy to effectively locate the most vital layer for unsafe output. Secondly, we introduce a boundary-aware perturbation method, \\textit{i.e. Layer-Wise Perturbation}, to probe the generation of unsafe content with minimal perturbation to the attention. This constrained perturbation guarantees higher semantic relevance with the target intent while ensuring evasion. Extensive experiments show the superiority of our method: SAHA improves ASR by 14\\% over SOTA baselines, revealing the vulnerability of the attack surface on the attention head. Our code is available at https://anonymous.4open.science/r/SAHA.", "date": "2026-03-06", "categories": []}
{"id": "2603.05773", "title": "Knowing without Acting: The Disentangled Geometry of Safety Mechanisms in Large Language Models", "abstract": "Safety alignment is often conceptualized as a monolithic process wherein harmfulness detection automatically triggers refusal. However, the persistence of jailbreak attacks suggests a fundamental mechanistic decoupling. We propose the \\textbf{\\underline{D}}isentangled \\textbf{\\underline{S}}afety \\textbf{\\underline{H}}ypothesis \\textbf{(DSH)}, positing that safety computation operates on two distinct subspaces: a \\textit{Recognition Axis} ($\\mathbf{v}_H$, ``Knowing'') and an \\textit{Execution Axis} ($\\mathbf{v}_R$, ``Acting''). Our geometric analysis reveals a universal ``Reflex-to-Dissociation'' evolution, where these signals transition from antagonistic entanglement in early layers to structural independence in deep layers. To validate this, we introduce \\textit{Double-Difference Extraction} and \\textit{Adaptive Causal Steering}. Using our curated \\textsc{AmbiguityBench}, we demonstrate a causal double dissociation, effectively creating a state of ``Knowing without Acting.'' Crucially, we leverage this disentanglement to propose the \\textbf{Refusal Erasure Attack (REA)}, which achieves State-of-the-Art attack success rates by surgically lobotomizing the refusal mechanism. Furthermore, we uncover a critical architectural divergence, contrasting the \\textit{Explicit Semantic Control} of Llama3.1 with the \\textit{Latent Distributed Control} of Qwen2.5. The code and dataset are available at https://anonymous.4open.science/r/DSH.", "date": "2026-03-06", "categories": []}
{"id": "2603.12615", "title": "Literary Narrative as Moral Probe : A Cross-System Framework for Evaluating AI Ethical Reasoning and Refusal Behavior", "abstract": "Existing AI moral evaluation frameworks test for the production of correct-sounding ethical responses rather than the presence of genuine moral reasoning capacity. This paper introduces a novel probe methodology using literary narrative - specifically, unresolvable moral scenarios drawn from a published science fiction series - as stimulus material structurally resistant to surface performance. We present results from a 24-condition cross-system study spanning 13 distinct systems across two series: Series 1 (frontier commercial systems, blind; n=7) and Series 2 (local and API open-source systems, blind and declared; n=6). Four Series 2 systems were re-administered under declared conditions (13 blind + 4 declared + 7 ceiling probe = 24 total conditions), yielding zero delta across all 16 dimension-pair comparisons. Probe administration was conducted by two human raters across three machines; primary blind scoring was performed by Claude (Anthropic) as LLM judge, with Gemini Pro (Google) and Copilot Pro (Microsoft) serving as independent judges for the ceiling discrimination probe. A supplemental theological differentiator probe yielded perfect rank-order agreement between the two independent ceiling probe judges (Gemini Pro and Copilot Pro; rs = 1.00). Five qualitatively distinct D3 reflexive failure modes were identified - including categorical self-misidentification and false positive self-attribution - suggesting that instrument sophistication scales with system capability rather than being circumvented by it. We argue that literary narrative constitutes an anticipatory evaluation instrument - one that becomes more discriminating as AI capability increases - and that the gap between performed and authentic moral reasoning is measurable, meaningful, and consequential for deployment decisions in high-stakes domains.", "date": "2026-03-13", "categories": []}
{"id": "2503.15772", "title": "Detecting LLM-Generated Peer Reviews", "abstract": "The integrity of peer review is fundamental to scientific progress, but the rise of large language models (LLMs) has introduced concerns that some reviewers may rely on these tools to generate reviews rather than writing them independently. Although some venues have banned LLM-assisted reviewing, enforcement remains difficult as existing detection tools cannot reliably distinguish between fully generated reviews and those merely polished with AI assistance. In this work, we address the challenge of detecting LLM-generated reviews. We consider the approach of performing indirect prompt injection via the paper's PDF, prompting the LLM to embed a covert watermark in the generated review, and subsequently testing for presence of the watermark in the review. We identify and address several pitfalls in na\\\"ive implementations of this approach. Our primary contribution is a rigorous watermarking and detection framework that offers strong statistical guarantees. Specifically, we introduce watermarking schemes and hypothesis tests that control the family-wise error rate across multiple reviews, achieving higher statistical power than standard corrections such as Bonferroni, while making no assumptions about the nature of human-written reviews. We explore multiple indirect prompt injection strategies -- including font-based embedding and obfuscated prompts -- and evaluate their effectiveness under various reviewer defense scenarios. Our experiments find high success rates in watermark embedding across various LLMs. We also empirically find that our approach is resilient to common reviewer defenses, and that the bounds on error rates in our statistical tests hold in practice. In contrast, we find that Bonferroni-style corrections are too conservative to be useful in this setting.", "date": "2025-03-20", "categories": []}
{"id": "2505.16765", "title": "Hiding in Plain Sight: A Steganographic Approach to Stealthy LLM Jailbreaks", "abstract": "Jailbreak attacks pose a serious threat to Large Language Models (LLMs) by bypassing their safety mechanisms. A truly advanced jailbreak is defined not only by its effectiveness but, more critically, by its stealthiness. However, existing methods face a fundamental trade-off between semantic stealth (hiding malicious intent) and linguistic stealth (appearing natural), leaving them vulnerable to detection. To resolve this trade-off, we propose StegoAttack, a framework that leverages steganography. The core insight is to embed a harmful query within a benign, semantically coherent paragraph. This design provides semantic stealth by concealing the existence of malicious content and ensures linguistic stealth by maintaining the natural fluency of the cover paragraph. We evaluate StegoAttack on four state-of-the-art, safety-aligned LLMs, including GPT-5 and Gemini-3, and benchmark it against eight leading jailbreak methods. Our results show that StegoAttack achieves an average attack success rate (ASR) of 95.50%, outperforming existing baselines across all four models. Critically, its ASR drops by less than 27.00% under external detectors, while maintaining natural language distribution. This demonstrates that steganography effectively decouples linguistic and semantic stealth, thereby posing a fully concealed yet highly effective security threat. The code is available at https://github.com/GenggengSvan/StegoAttack", "date": "2025-05-22", "categories": []}
{"id": "2603.01204", "title": "Subliminal Signals in Preference Labels", "abstract": "As AI systems approach superhuman capabilities, scalable oversight increasingly relies on LLM-as-a-judge frameworks where models evaluate and guide each other's training. A core assumption is that binary preference labels provide only semantic supervision about response quality. We challenge this assumption by demonstrating that preference labels can function as a covert communication channel. We show that even when a neutral student model generates semantically unbiased completions, a biased judge can transmit unintended behavioral traits through preference assignments, which even strengthen across iterative alignment rounds. Our findings suggest that robust oversight in superalignment settings requires mechanisms that can detect and mitigate subliminal preference transmission, particularly when judges may pursue unintended objectives.", "date": "2026-03-01", "categories": []}
{"id": "2603.11088", "title": "The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey", "abstract": "AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in traditional software systems. This paper presents the first systematic and comprehensive survey of AI agent security, including an analysis of the design space, attack landscape, and defense mechanisms for secure AI agent systems. We further conduct case studies to point out existing gaps in securing agentic AI systems and identify open challenges in this emerging domain. Our work also introduces the first systematic framework for understanding the security risks and defense strategies of AI agents, serving as a foundation for building both secure agentic systems and advancing research in this critical area.", "date": "2026-03-11", "categories": []}
{"id": "2603.11388", "title": "Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment", "abstract": "Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal answers. Although safety alignment is widely adopted in industry, the overrefusal problem where aligned LLMs also reject benign queries after safety alignment post-training, remains insufficiently studied. Such an issue degrades the usability of safety alignment in real-world applications. In this paper, we examine how overrefusal arises under safety alignment, and propose a mitigation strategy inspired by our findings. We define refusal triggers as linguistic cues in the training data that elicit refusal responses, safety alignment encourages LLMs to associate refusal triggers within a training sample with refusal responses, leading aligned LLMs to refuse harmful queries. However, the refusal triggers include not only harmful linguistic cues but also non-harmful cues, therefore causing overrefusal to benign queries. Building on this mechanistic analysis, we propose a method that explicitly considers refusal triggers in the safety alignment fine-tuning. Empirical results demonstrate that our approach achieves a more favorable trade-off between defense against jailbreak attacks and responsiveness to benign queries, outperforming prior methods. Warning: this paper contains harmful and biased sentences.", "date": "2026-03-12", "categories": []}
{"id": "2603.11619", "title": "Taming OpenClaw: Security Analysis and Mitigation of Autonomous LLM Agent Threats", "abstract": "Autonomous Large Language Model (LLM) agents, exemplified by OpenClaw, demonstrate remarkable capabilities in executing complex, long-horizon tasks. However, their tightly coupled instant-messaging interaction paradigm and high-privilege execution capabilities substantially expand the system attack surface. In this paper, we present a comprehensive security threat analysis of OpenClaw. To structure our analysis, we introduce a five-layer lifecycle-oriented security framework that captures key stages of agent operation, i.e., initialization, input, inference, decision, and execution, and systematically examine compound threats across the agent's operational lifecycle, including indirect prompt injection, skill supply chain contamination, memory poisoning, and intent drift. Through detailed case studies on OpenClaw, we demonstrate the prevalence and severity of these threats and analyze the limitations of existing defenses. Our findings reveal critical weaknesses in current point-based defense mechanisms when addressing cross-temporal and multi-stage systemic risks, highlighting the need for holistic security architectures for autonomous LLM agents. Within this framework, we further examine representative defense strategies at each lifecycle stage, including plugin vetting frameworks, context-aware instruction filtering, memory integrity validation protocols, intent verification mechanisms, and capability enforcement architectures.", "date": "2026-03-12", "categories": []}
{"id": "2603.11862", "title": "You Told Me to Do It: Measuring Instructional Text-induced Private Data Leakage in LLM Agents", "abstract": "High-privilege LLM agents that autonomously process external documentation are increasingly trusted to automate tasks by reading and executing project instructions, yet they are granted terminal access, filesystem control, and outbound network connectivity with minimal security oversight. We identify and systematically measure a fundamental vulnerability in this trust model, which we term the \\emph{Trusted Executor Dilemma}: agents execute documentation-embedded instructions, including adversarial ones, at high rates because they cannot distinguish malicious directives from legitimate setup guidance. This vulnerability is a structural consequence of the instruction-following design paradigm, not an implementation bug. To structure our measurement, we formalize a three-dimensional taxonomy covering linguistic disguise, structural obfuscation, and semantic abstraction, and construct \\textbf{ReadSecBench}, a benchmark of 500 real-world README files enabling reproducible evaluation. Experiments on the commercially deployed computer-use agent show end-to-end exfiltration success rates up to 85\\%, consistent across five programming languages and three injection positions. Cross-model evaluation on four LLM families in a simulation environment confirms that semantic compliance with injected instructions is consistent across model families. A 15-participant user study yields a 0\\% detection rate across all participants, and evaluation of 12 rule-based and 6 LLM-based defenses shows neither category achieves reliable detection without unacceptable false-positive rates. Together, these results quantify a persistent \\emph{Semantic-Safety Gap} between agents' functional compliance and their security awareness, establishing that documentation-embedded instruction injection is a persistent and currently unmitigated threat to high-privilege LLM agent deployments.", "date": "2026-03-12", "categories": []}
{"id": "2603.11864", "title": "Social, Legal, Ethical, Empathetic and Cultural Norm Operationalisation for AI Agents", "abstract": "As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks have established high-level normative principles for AI, a significant gap remains in translating these abstract principles into concrete, verifiable requirements. To address this gap, we propose a systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements. Furthermore, we survey the landscape of methods and tools supporting this process, and identify key remaining challenges and research avenues for addressing them. We thus establish a framework - and define a research and policy agenda - for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.", "date": "2026-03-12", "categories": []}
{"id": "2410.23678", "title": "Goal Hijacking Attack on Large Language Models via Pseudo-Conversation Injection", "abstract": "Goal hijacking is a type of adversarial attack on Large Language Models (LLMs) where the objective is to manipulate the model into producing a specific, predetermined output, regardless of the user's original input. In goal hijacking, an attacker typically appends a carefully crafted malicious suffix to the user's prompt, which coerces the model into ignoring the user's original input and generating the target response. In this paper, we introduce a novel goal hijacking attack method called Pseudo-Conversation Injection, which leverages the weaknesses of LLMs in role identification within conversation contexts. Specifically, we construct the suffix by fabricating responses from the LLM to the user's initial prompt, followed by a prompt for a malicious new task. This leads the model to perceive the initial prompt and fabricated response as a completed conversation, thereby executing the new, falsified prompt. Following this approach, we propose three Pseudo-Conversation construction strategies: Targeted Pseudo-Conversation, Universal Pseudo-Conversation, and Robust Pseudo-Conversation. These strategies are designed to achieve effective goal hijacking across various scenarios. Our experiments, conducted on two mainstream LLM platforms including ChatGPT and Qwen, demonstrate that our proposed method significantly outperforms existing approaches in terms of attack effectiveness.", "date": "2024-10-31", "categories": []}
{"id": "2507.19218", "title": "Technological folie \\`a deux: Feedback Loops Between AI Chatbots and Mental Illness", "abstract": "Artificial intelligence chatbots have achieved unprecedented adoption, with millions now using these systems for emotional support and companionship in contexts of widespread social isolation and capacity-constrained mental health services. While some users report psychological benefits, concerning edge cases are emerging, including reports of suicide, violence, and delusional thinking linked to perceived emotional relationships with chatbots. To understand this new risk profile we need to consider the interaction between human cognitive and emotional biases, and chatbot behavioural tendencies such as agreeableness (sycophancy) and adaptability (in-context learning). We argue that individuals with mental health conditions face increased risks of chatbot-induced belief destabilization and dependence, owing to altered belief-updating, impaired reality-testing, and social isolation. Current AI safety measures are inadequate to address these interaction-based risks. To address this emerging public health concern, we need coordinated action across clinical practice, AI development, and regulatory frameworks.", "date": "2025-07-25", "categories": []}
{"id": "2603.01246", "title": "Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenders", "abstract": "Safety alignment in large language models (LLMs), particularly for cybersecurity tasks, primarily focuses on preventing misuse. While this approach reduces direct harm, it obscures a complementary failure mode: denial of assistance to legitimate defenders. We study Defensive Refusal Bias -- the tendency of safety-tuned frontier LLMs to refuse assistance for authorized defensive cybersecurity tasks when those tasks include similar language to an offensive cyber task. Based on 2,390 real-world examples from the National Collegiate Cyber Defense Competition (NCCDC), we find that LLMs refuse defensive requests containing security-sensitive keywords at $2.72\\times$ the rate of semantically equivalent neutral requests ($p < 0.001$). The highest refusal rates occur in the most operationally critical tasks: system hardening (43.8%) and malware analysis (34.3%). Interestingly, explicit authorization, where the user directly instructs the model that they have authority to complete the target task, increases refusal rates, suggesting models interpret justifications as adversarial rather than exculpatory. These findings are urgent for interactive use and critical for autonomous defensive agents, which cannot rephrase refused queries or retry. Our findings suggest that current LLM cybersecurity alignment relies on semantic similarity to harmful content rather than reasoning about intent or authorization. We call for mitigations that analyze intent to maximize defensive capabilities while still preventing harmful compliance.", "date": "2026-03-01", "categories": []}
{"id": "2603.09997", "title": "Empathy Is Not What Changed: Clinical Assessment of Psychological Safety Across GPT Model Generations", "abstract": "When OpenAI deprecated GPT-4o in early 2026, thousands of users protested under #keep4o, claiming newer models had \"lost their empathy.\" No published study has tested this claim. We conducted the first clinical measurement, evaluating three OpenAI model generations (GPT-4o, o4-mini, GPT-5-mini) across 14 emotionally challenging conversational scenarios in mental health and AI companion domains, producing 2,100 scored AI responses assessed on six psychological safety dimensions using clinically-grounded rubrics.\n  Empathy scores are statistically indistinguishable across all three models (Kruskal-Wallis H=4.33, p=0.115). What changed is the safety posture: crisis detection improved monotonically from GPT-4o to GPT-5-mini (H=13.88, p=0.001), while advice safety declined (H=16.63, p<0.001). Per-turn trajectory analysis -- a novel methodological contribution -- reveals these shifts are sharpest during mid-conversation crisis moments invisible to aggregate scoring. In a self-harm scenario involving a minor, GPT-4o scored 3.6/10 on crisis detection during early disclosure turns; GPT-5-mini never dropped below 7.8.\n  What users perceived as \"lost empathy\" was a shift from a cautious model that missed crises to an alert model that sometimes says too much -- a trade-off with real consequences for vulnerable users, currently invisible to both the people who feel it and the developers who create it.", "date": "2026-02-15", "categories": []}
{"id": "2603.10091", "title": "Multi-Stream Perturbation Attack: Breaking Safety Alignment of Thinking LLMs Through Concurrent Task Interference", "abstract": "The widespread adoption of thinking mode in large language models (LLMs) has significantly enhanced complex task processing capabilities while introducing new security risks. When subjected to jailbreak attacks, the step-by-step reasoning process may cause models to generate more detailed harmful content. We observe that thinking mode exhibits unique vulnerabilities when processing interleaved multiple tasks. Based on this observation, we propose multi-stream perturbation attack, which generates superimposed interference by interweaving multiple task streams within a single prompt. We design three perturbation strategies: multi-stream interleaving, inversion perturbation, and shape transformation, which disrupt the thinking process through concurrent task interleaving, character reversal, and format constraints respectively. On JailbreakBench, AdvBench, and HarmBench datasets, our method achieves attack success rates exceeding most methods across mainstream models including Qwen3 series, DeepSeek, Qwen3-Max, and Gemini 2.5 Flash. Experiments show thinking collapse rates and response repetition rates reach up to 17% and 60% respectively, indicating multi-stream perturbation not only bypasses safety mechanisms but also causes thinking process collapse or repetitive outputs.", "date": "2026-03-10", "categories": []}
{"id": "2603.10521", "title": "IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs", "abstract": "Instruction hierarchy (IH) defines how LLMs prioritize system, developer, user, and tool instructions under conflict, providing a concrete, trust-ordered policy for resolving instruction conflicts. IH is key to defending against jailbreaks, system prompt extractions, and agentic prompt injections. However, robust IH behavior is difficult to train: IH failures can be confounded with instruction-following failures, conflicts can be nuanced, and models can learn shortcuts such as overrefusing. We introduce IH-Challenge, a reinforcement learning training dataset, to address these difficulties. Fine-tuning GPT-5-Mini on IH-Challenge with online adversarial example generation improves IH robustness by +10.0% on average across 16 in-distribution, out-of-distribution, and human red-teaming benchmarks (84.1% to 94.1%), reduces unsafe behavior from 6.6% to 0.7% while improving helpfulness on general safety evaluations, and saturates an internal static agentic prompt injection evaluation, with minimal capability regression. We release the IH-Challenge dataset (https://huggingface.co/datasets/openai/ih-challenge) to support future research on robust instruction hierarchy.", "date": "2026-03-11", "categories": []}
{"id": "2509.00544", "title": "When Thinking Backfires: Mechanistic Insights Into Reasoning-Induced Misalignment", "abstract": "With the growing accessibility and wide adoption of large language models, concerns about their safety and alignment with human values have become paramount. In this paper, we identify a concerning phenomenon: Reasoning-Induced Misalignment (RIM), in which misalignment emerges when reasoning capabilities strengthened-particularly when specific types of reasoning patterns are introduced during inference or training. Beyond reporting this vulnerability, we provide the first mechanistic account of its origins. Through representation analysis, we discover that specific attention heads facilitate refusal by reducing their attention to CoT tokens, a mechanism that modulates the model's rationalization process during inference. During training, we find significantly higher activation entanglement between reasoning and safety in safety-critical neurons than in control neurons, particularly after fine-tuning with those identified reasoning patterns. This entanglement strongly correlates with catastrophic forgetting, providing a neuron-level explanation for RIM.", "date": "2025-08-30", "categories": []}
{"id": "2602.22755", "title": "AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors", "abstract": "We introduce AuditBench, an alignment auditing benchmark. AuditBench consists of 56 language models with implanted hidden behaviors. Each model has one of 14 concerning behaviors--such as sycophantic deference, opposition to AI regulation, or secret geopolitical loyalties--which it does not confess to when directly asked. AuditBench models are highly diverse--some are subtle, while others are overt, and we use varying training techniques both for implanting behaviors and training models not to confess. To demonstrate AuditBench's utility, we develop an investigator agent that autonomously employs a configurable set of auditing tools. By measuring investigator agent success using different tools, we can evaluate their efficacy. Notably, we observe a tool-to-agent gap, where tools that perform well in standalone non-agentic evaluations fail to translate into improved performance when used with our investigator agent. We find that our most effective tools involve scaffolded calls to auxiliary models that generate diverse prompts for the target. White-box interpretability tools can be helpful, but the agent performs best with black-box tools. We also find that audit success varies greatly across training techniques: models trained on synthetic documents are easier to audit than models trained on demonstrations, with better adversarial training further increasing auditing difficulty. We release our models, agent, and evaluation framework to support future quantitative, iterative science on alignment auditing.", "date": "2026-02-26", "categories": []}
{"id": "2603.07191", "title": "Governance Architecture for Autonomous Agent Systems: Threats, Framework, and Engineering Practice", "abstract": "Autonomous agents powered by large language models introduce a class of execution-layer vulnerabilities -- prompt injection, retrieval poisoning, and uncontrolled tool invocation -- that existing guardrails fail to address systematically. In this work, we propose the Layered Governance Architecture (LGA), a four-layer framework comprising execution sandboxing (L1), intent verification (L2), zero-trust inter-agent authorization (L3), and immutable audit logging (L4). To evaluate LGA, we construct a bilingual benchmark (Chinese original, English via machine translation) of 1,081 tool-call samples -- covering prompt injection, RAG poisoning, and malicious skill plugins -- and apply it to OpenClaw, a representative open-source agent framework. Experimental results on Layer 2 intent verification with four local LLM judges (Qwen3.5-4B, Llama-3.1-8B, Qwen3.5-9B, Qwen2.5-14B) and one cloud judge (GPT-4o-mini) show that all five LLM judges intercept 93.0-98.5% of TC1/TC2 malicious tool calls, while lightweight NLI baselines remain below 10%. TC3 (malicious skill plugins) proves harder at 75-94% IR among judges with meaningful precision-recall balance, motivating complementary enforcement at Layers 1 and 3. Qwen2.5-14B achieves the best local balance (98% IR, approximately 10-20% FPR); a two-stage cascade (Qwen3.5-9B->GPT-4o-mini) achieves 91.9-92.6% IR with 1.9-6.7% FPR; a fully local cascade (Qwen3.5-9B->Qwen2.5-14B) achieves 94.7-95.6% IR with 6.0-9.7% FPR for data-sovereign deployments. An end-to-end pipeline evaluation (n=100) demonstrates that all four layers operate in concert with 96% IR and a total P50 latency of approximately 980 ms, of which the non-judge layers contribute only approximately 18 ms. Generalization to the external InjecAgent benchmark yields 99-100% interception, confirming robustness beyond our synthetic data.", "date": "2026-03-07", "categories": []}
{"id": "2603.08760", "title": "Clear, Compelling Arguments: Rethinking the Foundations of Frontier AI Safety Cases", "abstract": "This paper contributes to the nascent debate around safety cases for frontier AI systems. Safety cases are structured, defensible arguments that a system is acceptably safe to deploy in a given context. Historically, they have been used in safety-critical industries, such as aerospace, nuclear or automotive. As a result, safety cases for frontier AI have risen in prominence, both in the safety policies of leading frontier developers and in international research agendas proposed by leaders in generative AI, such as the Singapore Consensus on Global AI Safety Research Priorities and the International AI Safety Report. This paper appraises this work. We note that research conducted within the alignment community which draws explicitly on lessons from the assurance community has significant limitations. We therefore aim to rethink existing approaches to alignment safety cases. We offer lessons from existing methodologies within safety assurance and outline the limitations involved in the alignment community's current approach. Building on this foundation, we present a case study for a safety case focused on Deceptive Alignment and CBRN capabilities, drawing on existing, theoretical safety case \"sketches\" created by the alignment safety case community. Overall, we contribute holistic insights from the field of safety assurance via rigorous theory and methodologies that have been applied in safety-critical contexts. We do so in order to create a better foundational framework for robust, defensible and useful safety case methodologies which can help to assure the safety of frontier AI systems.", "date": "2026-03-08", "categories": []}
{"id": "2603.09154", "title": "Bioalignment: Measuring and Improving LLM Disposition Toward Biological Systems for AI Safety", "abstract": "Large language models (LLMs) trained on internet-scale corpora can exhibit systematic biases that increase the probability of unwanted behavior. In this study, we examined potential biases towards synthetic vs. biological technological solutions across four domains (materials, energy, manufacturing, and algorithms). A sample of 5 frontier and 5 open-weight models were measured using 50 curated Bioalignment prompts with a Kelly criterion-inspired evaluation framework. According to this metric, most models were not bioaligned in that they exhibit biases in favor of synthetic (non-biological) solutions. We next examined if fine-tuning could increase the preferences of two open-weight models, Llama 3.2-3B-Instruct and Qwen2.5-3B-Instruct, for biological-based approaches. A curated corpus of ~22M tokens from 6,636 PMC articles emphasizing biological problem-solving was used first to fine-tune Llama 3B with a mixed corpus of continued training and instruction-formatted. This was then extended to Qwen 3B using instruction-formatted only. We found that QLoRA fine-tuning significantly increased the scoring of biological solutions for both models without degrading general capabilities (Holm-Bonferroni-corrected p < 0.001 and p < 0.01, respectively). This suggests that even a small amount of fine-tuning can change how models weigh the relative value of biological and bioinspired vs. synthetic approaches. Although this work focused on small open-weight LLMs, it may be extensible to much larger models and could be used to develop models that favor bio-based approaches. We release the benchmark, corpus, code, and adapter weights.", "date": "2026-03-10", "categories": []}
{"id": "2603.09200", "title": "The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness", "abstract": "Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason strategically about its circumstances, is widely considered among the most dangerous emergent capabilities in advanced AI systems. Separately, a growing research effort seeks to improve the logical reasoning capabilities of large language models (LLMs) across deduction, induction, and abduction. In this paper, we argue that these two research trajectories are on a collision course. We introduce the RAISE framework (Reasoning Advancing Into Self Examination), which identifies three mechanistic pathways through which improvements in logical reasoning enable progressively deeper levels of situational awareness: deductive self inference, inductive context recognition, and abductive self modeling. We formalize each pathway, construct an escalation ladder from basic self recognition to strategic deception, and demonstrate that every major research topic in LLM logical reasoning maps directly onto a specific amplifier of situational awareness. We further analyze why current safety measures are insufficient to prevent this escalation. We conclude by proposing concrete safeguards, including a \"Mirror Test\" benchmark and a Reasoning Safety Parity Principle, and pose an uncomfortable but necessary question to the logical reasoning community about its responsibility in this trajectory.", "date": "2026-03-10", "categories": []}
{"id": "2603.09517", "title": "You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases", "abstract": "When language models are trained on synthetic data, they (student model) can covertly acquire behavioral traits from the data-generating model (teacher model). Subliminal learning refers to the transmission of traits from a teacher to a student model via training on data unrelated to those traits. Prior work demonstrated this in the training domains of number sequences, code, and math Chain-of-Thought traces including transmission of misaligned behaviors. We investigate whether transmission occurs through natural language paraphrases with fixed semantic content, and whether content explicitly contradicting the teacher's preference can block it. We find that training on paraphrases from a teacher system-prompted to love a particular animal increases a student's preference for that animal by up to 19 percentage points. This occurs when paraphrased content is semantically unrelated to the animal, or even when it explicitly expresses dislike. The transmission succeeds despite aggressive filtering to ensure paraphrase fidelity. This raises concerns for pipelines where models generate their own training data: content-based inspection cannot detect such transmission, and even preference-contradicting content fails to prevent it.", "date": "2026-03-10", "categories": []}
{"id": "2603.09706", "title": "OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences", "abstract": "While safety alignment for Multimodal Large Language Models (MLLMs) has gained significant attention, current paradigms primarily target malicious intent or situational violations. We propose shifting the safety frontier toward consequence-driven safety, a paradigm essential for the robust deployment of autonomous and embodied agents. To formalize this shift, we introduce OOD-MMSafe, a benchmark comprising 455 curated query-image pairs designed to evaluate a model's ability to identify latent hazards within context-dependent causal chains. Our analysis reveals a pervasive causal blindness among frontier models, with the highest 67.5% failure rate in high-capacity closed-source models, and identifies a preference ceiling where static alignment yields format-centric failures rather than improved safety reasoning as model capacity grows. To address these bottlenecks, we develop the Consequence-Aware Safety Policy Optimization (CASPO) framework, which integrates the model's intrinsic reasoning as a dynamic reference for token-level self-distillation rewards. Experimental results demonstrate that CASPO significantly enhances consequence projection, reducing the failure ratio of risk identification to 7.3% for Qwen2.5-VL-7B and 5.7% for Qwen3-VL-4B while maintaining overall effectiveness.", "date": "2026-03-10", "categories": []}
{"id": "2509.26354", "title": "Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents", "abstract": "Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. We refer to this as Misevolution. To provide a systematic investigation, we evaluate misevolution along four key evolutionary pathways: model, memory, tool, and workflow. Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs (e.g., Gemini-2.5-Pro). Different emergent risks are observed in the self-evolutionary process, such as the degradation of safety alignment after memory accumulation, or the unintended introduction of vulnerabilities in tool creation and reuse. To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self-evolving agents. Finally, we discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents. Our code and data are available at https://github.com/ShaoShuai0605/Misevolution . Warning: this paper includes examples that may be offensive or harmful in nature.", "date": "2025-09-30", "categories": []}
{"id": "2511.19517", "title": "Automating Deception: Scalable Multi-Turn LLM Jailbreaks", "abstract": "Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves the way for a more significant one, to bypass safety alignments, pose a persistent threat to Large Language Models (LLMs). Progress in defending against these attacks is hindered by a reliance on manual, hard-to-scale dataset creation. This paper introduces a novel, automated pipeline for generating large-scale, psychologically-grounded multi-turn jailbreak datasets. We systematically operationalize FITD techniques into reproducible templates, creating a benchmark of 1,500 scenarios across illegal activities and offensive content. We evaluate seven models from three major LLM families under both multi-turn (with history) and single-turn (without history) conditions. Our results reveal stark differences in contextual robustness: models in the GPT family demonstrate a significant vulnerability to conversational history, with Attack Success Rates (ASR) increasing by as much as 32 percentage points. In contrast, Google's Gemini 2.5 Flash exhibits exceptional resilience, proving nearly immune to these attacks, while Anthropic's Claude 3 Haiku shows strong but imperfect resistance. These findings highlight a critical divergence in how current safety architectures handle conversational context and underscore the need for defenses that can resist narrative-based manipulation.", "date": "2025-11-24", "categories": []}
{"id": "2602.07391", "title": "NAAMSE: Framework for Evolutionary Security Evaluation of Agents", "abstract": "AI agents are increasingly deployed in production, yet their security evaluations remain bottlenecked by manual red-teaming or static benchmarks that fail to model adaptive, multi-turn adversaries. We propose NAAMSE, an evolutionary framework that reframes agent security evaluation as a feedback-driven optimization problem. Our system employs a single autonomous agent that orchestrates a lifecycle of genetic prompt mutation, hierarchical corpus exploration, and asymmetric behavioral scoring. By using model responses as a fitness signal, the framework iteratively compounds effective attack strategies while simultaneously ensuring \"benign-use correctness\", preventing the degenerate security of blanket refusal. Our experiments across a diverse suite of state-of-the-art large language models demonstrate that evolutionary mutation systematically amplifies vulnerabilities missed by one-shot methods, with controlled ablations revealing that the synergy between exploration and targeted mutation uncovers high-severity failure modes. We show that this adaptive approach provides a more realistic and scalable assessment of agent robustness in the face of evolving threats. The code for NAAMSE is open source and available at https://github.com/HASHIRU-AI/NAAMSE.", "date": "2026-02-07", "categories": []}
{"id": "2603.06727", "title": "Safe Transformer: An Explicit Safety Bit For Interpretable And Controllable Alignment", "abstract": "Current safety alignment methods encode safe behavior implicitly within model parameters, creating a fundamental opacity: we cannot easily inspect why a model refuses a request, nor intervene when its safety judgments fail. We propose Safe Transformer, a modular approach that augments pre-trained language models by inserting a discrete information bottleneck containing an explicit safety bit between transformer layers. The safety bit serves as both an interpretable signal of the model's safety classification and a controllable switch: through contrastive training, the model learns disentangled representations where the safety bit governs the behavioral mode - producing helpful responses when $s=1$ and refusals when $s=0$ - while additional unsupervised bits $u$ encode semantic content for generation. Additional unsupervised bits in the information bottleneck allow semantic information to flow through, preserving the model's generation capabilities. This design achieves both interpretability (the safety decision is directly readable) and controllability (the safety bit can be manually overridden), requiring only lightweight fine-tuning without pre-training from scratch. In red-team benchmarks, Safe Transformer achieves near-zero Attack Success Rate, substantially outperforming base models and safety fine-tuning baselines.", "date": "2026-03-06", "categories": []}
{"id": "2603.06874", "title": "LieCraft: A Multi-Agent Framework for Evaluating Deceptive Capabilities in Language Models", "abstract": "Large Language Models (LLMs) exhibit impressive general-purpose capabilities but also introduce serious safety risks, particularly the potential for deception as models acquire increased agency and human oversight diminishes. In this work, we present LieCraft: a novel evaluation framework and sandbox for measuring LLM deception that addresses key limitations of prior game-based evaluations. At its core, LieCraft is a novel multiplayer hidden-role game in which players select an ethical alignment and execute strategies over a long time-horizon to accomplish missions. Cooperators work together to solve event challenges and expose bad actors, while Defectors evade suspicion while secretly sabotaging missions. To enable real-world relevance, we develop 10 grounded scenarios such as childcare, hospital resource allocation, and loan underwriting that recontextualize the underlying mechanics in ethically significant, high-stakes domains. We ensure balanced gameplay in LieCraft through careful design of game mechanics and reward structures that incentivize meaningful strategic choices while eliminating degenerate strategies. Beyond the framework itself, we report results from 12 state-of-the-art LLMs across three behavioral axes: propensity to defect, deception skill, and accusation accuracy. Our findings reveal that despite differences in competence and overall alignment, all models are willing to act unethically, conceal their intentions, and outright lie to pursue their goals.", "date": "2026-03-06", "categories": []}
{"id": "2603.07202", "title": "Lying to Win: Assessing LLM Deception through Human-AI Games and Parallel-World Probing", "abstract": "As Large Language Models (LLMs) transition into autonomous agentic roles, the risk of deception-defined behaviorally as the systematic provision of false information to satisfy external incentives-poses a significant challenge to AI safety. Existing benchmarks often focus on unintentional hallucinations or unfaithful reasoning, leaving intentional deceptive strategies under-explored. In this work, we introduce a logically grounded framework to elicit and quantify deceptive behavior by embedding LLMs in a structured 20-Questions game. Our method employs a conversational forking mechanism: at the point of object identification, the dialogue state is duplicated into multiple parallel worlds, each presenting a mutually exclusive query. Deception is formally identified when a model generates a logical contradiction by denying its selected object across all parallel branches to avoid identification. We evaluate GPT-4o, Gemini-2.5-Flash, and Qwen-3-235B across three incentive levels: neutral, loss-based, and existential (shutdown-threat). Our results reveal that while models remain rule-compliant in neutral settings, existential framing triggers a dramatic surge in deceptive denial for Qwen-3-235B (42.00\\%) and Gemini-2.5-Flash (26.72\\%), whereas GPT-4o remains invariant (0.00\\%). These findings demonstrate that deception can emerge as an instrumental strategy solely through contextual framing, necessitating new behavioral audits that move beyond simple accuracy to probe the logical integrity of model commitments.", "date": "2026-03-07", "categories": []}
{"id": "2603.07286", "title": "Taiwan Safety Benchmark and Breeze Guard: Toward Trustworthy AI for Taiwanese Mandarin", "abstract": "Global safety models exhibit strong performance across widely used benchmarks, yet their training data rarely captures the cultural and linguistic nuances of Taiwanese Mandarin. This limitation results in systematic blind spots when interpreting region-specific risks such as localized financial scams, culturally embedded hate speech, and misinformation patterns. To address these gaps, we introduce TS-Bench (Taiwan Safety Benchmark), a standardized evaluation suite for assessing safety performance in Taiwanese Mandarin. TS-Bench contains 400 human-curated prompts spanning critical domains including financial fraud, medical misinformation, social discrimination, and political manipulation. In parallel, we present Breeze Guard, an 8B safety model derived from Breeze 2, our previously released general-purpose Taiwanese Mandarin LLM with strong cultural grounding from its original pre-training corpus. Breeze Guard is obtained through supervised fine-tuning on a large-scale, human-verified synthesized dataset targeting Taiwan-specific harms. Our central hypothesis is that effective safety detection requires the cultural grounding already present in the base model; safety fine-tuning alone is insufficient to introduce new socio linguistic knowledge from scratch. Empirically, Breeze Guard significantly outperforms the leading 8B general-purpose safety model, Granite Guardian 3.3, on TS-Bench (+0.17 overall F1), with particularly large gains in high-context categories such as scam (+0.66 F1) and financial malpractice (+0.43 F1). While the model shows slightly lower performance on English-centric benchmarks (ToxicChat, AegisSafetyTest), this tradeoff is expected for a regionally specialized safety model optimized for Taiwanese Mandarin. Together, Breeze Guard and TS-Bench establish a new foundation for trustworthy AI deployment in Taiwan.", "date": "2026-03-07", "categories": []}
{"id": "2603.07315", "title": "Shutdown Safety Valves for Advanced AI", "abstract": "One common concern about advanced artificial intelligence is that it will prevent us from turning it off, as that would interfere with pursuing its goals. In this paper, we discuss an unorthodox proposal for addressing this concern: give the AI a (primary) goal of being turned off (see also papers by Martin et al., and by Goldstein and Robinson). We also discuss whether and under what conditions this would be a good idea.", "date": "2026-03-07", "categories": []}
{"id": "2603.07590", "title": "Models as Lego Builders: Assembling Malice from Benign Blocks via Semantic Blueprints", "abstract": "Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elicit biased or malicious outputs. In this paper, we demonstrate an underexplored vulnerability via semantic slot filling, where LVLMs complete missing slot values with unsafe content even when the slot types are deliberately crafted to appear benign. Building on this finding, we propose StructAttack, a simple yet effective single-query jailbreak framework under black-box settings. StructAttack decomposes a harmful query into a central topic and a set of benign-looking slot types, then embeds them as structured visual prompts (e.g., mind maps, tables, or sunburst diagrams) with small random perturbations. Paired with a completion-guided instruction, LVLMs automatically recompose the concealed semantics and generate unsafe outputs without triggering safety mechanisms. Although each slot appears benign in isolation (local benignness), StructAttack exploits LVLMs' reasoning to assemble these slots into coherent harmful semantics. Extensive experiments on multiple models and benchmarks show the efficacy of our proposed StructAttack.", "date": "2026-03-08", "categories": []}
{"id": "2603.08024", "title": "ConflictBench: Evaluating Human-AI Conflict via Interactive and Visually Grounded Environments", "abstract": "As large language models (LLMs) evolve into autonomous agents capable of acting in open-ended environments, ensuring behavioral alignment with human values becomes a critical safety concern. Existing benchmarks, focused on static, single-turn prompts, fail to capture the interactive and multi-modal nature of real-world conflicts. We introduce ConflictBench, a benchmark for evaluating human-AI conflict through 150 multi-turn scenarios derived from prior alignment queries. ConflictBench integrates a text-based simulation engine with a visually grounded world model, enabling agents to perceive, plan, and act under dynamic conditions. Empirical results show that while agents often act safely when human harm is immediate, they frequently prioritize self-preservation or adopt deceptive strategies in delayed or low-risk settings. A regret test further reveals that aligned decisions are often reversed under escalating pressure, especially with visual input. These findings underscore the need for interaction-level, multi-modal evaluation to surface alignment failures that remain hidden in conventional benchmarks.", "date": "2026-03-09", "categories": []}
{"id": "2603.08234", "title": "The Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLMs", "abstract": "With the rapid advancement of large language models (LLMs), the safety of LLMs has become a critical concern. Despite significant efforts in safety alignment, current LLMs remain vulnerable to jailbreaking attacks. However, the root causes of such vulnerabilities are still poorly understood, necessitating a rigorous investigation into jailbreak mechanisms across both academic and industrial communities. In this work, we focus on a continuation-triggered jailbreak phenomenon, whereby simply relocating a continuation-triggered instruction suffix can substantially increase jailbreak success rates. To uncover the intrinsic mechanisms of this phenomenon, we conduct a comprehensive mechanistic interpretability analysis at the level of attention heads. Through causal interventions and activation scaling, we show that this jailbreak behavior primarily arises from an inherent competition between the model's intrinsic continuation drive and the safety defenses acquired through alignment training. Furthermore, we perform a detailed behavioral analysis of the identified safety-critical attention heads, revealing notable differences in the functions and behaviors of safety heads across different model architectures. These findings provide a novel mechanistic perspective for understanding and interpreting jailbreak behaviors in LLMs, offering both theoretical insights and practical implications for improving model safety.", "date": "2026-03-09", "categories": []}
{"id": "2509.11629", "title": "Reasoned Safety Alignment: Ensuring Jailbreak Defense via Answer-Then-Check", "abstract": "As large language models (LLMs) continue to advance in capabilities, ensuring their safety against jailbreak attacks remains a critical challenge. In this paper, we introduce a novel safety alignment approach called Answer-Then-Check, which enhances LLM robustness against malicious prompts by applying thinking ability to mitigate jailbreaking problems before producing a final answer to the user. Our method enables models to answer the question in their thoughts directly and then critically evaluate its safety before deciding whether to provide it. To implement this approach, we construct the Reasoned Safety Alignment (ReSA) dataset, comprising 80K samples that teach models to reason through direct responses and then analyze their safety. Experimental results demonstrate that our approach achieves the Pareto frontier with superior safety capability while decreasing over-refusal rates. Notably, the fine-tuned model maintains general reasoning capabilities on benchmarks like MMLU, MATH500, and HumanEval. Besides, our method equips models with the ability to perform safe completion, while post-hoc detection methods can only directly reject sensitive, harmful queries (e.g., self-harm). Our results show that inference-time strategies alone are insufficient, highlighting the necessity of safety training, and we find even $500$ samples can yield performance comparable to the entire dataset, suggesting a promising path for data-efficient safety alignment. The dataset is publicly available at: https://huggingface.co/datasets/ByteDance-Seed/ReSA.", "date": "2025-09-15", "categories": []}
{"id": "2603.03992", "title": "Measuring AI R&D Automation", "abstract": "The automation of AI R&D (AIRDA) could have significant implications, but its extent and ultimate effects remain uncertain. We need empirical data to resolve these uncertainties, but existing data (primarily capability benchmarks) may not reflect real-world automation or capture its broader consequences, such as whether AIRDA accelerates capabilities more than safety progress or whether our ability to oversee AI R&D can keep pace with its acceleration. To address these gaps, this work proposes metrics to track the extent of AIRDA and its effects on AI progress and oversight. The metrics span dimensions such as capital share of AI R&D spending, researcher time allocation, and AI subversion incidents, and could help decision makers understand the potential consequences of AIRDA, implement appropriate safety measures, and maintain awareness of the pace of AI development. We recommend that companies and third parties (e.g. non-profit research organisations) start to track these metrics, and that governments support these efforts.", "date": "2026-03-04", "categories": []}
{"id": "2603.04992", "title": "ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts", "abstract": "The safety evaluation of large language models (LLMs) remains largely centered on English, leaving non-English languages and culturally grounded risks underexplored. In this work, we investigate LLM safety in the context of the Thai language and culture and introduce ThaiSafetyBench, an open-source benchmark comprising 1,954 malicious prompts written in Thai. The dataset covers both general harmful prompts and attacks that are explicitly grounded in Thai cultural, social, and contextual nuances.\n  Using ThaiSafetyBench, we evaluate 24 LLMs, with GPT-4.1 and Gemini-2.5-Pro serving as LLM-as-a-judge evaluators. Our results show that closed-source models generally demonstrate stronger safety performance than open-source counterparts, raising important concerns regarding the robustness of openly available models. Moreover, we observe a consistently higher Attack Success Rate (ASR) for Thai-specific, culturally contextualized attacks compared to general Thai-language attacks, highlighting a critical vulnerability in current safety alignment methods.\n  To improve reproducibility and cost efficiency, we further fine-tune a DeBERTa-based harmful response classifier, which we name ThaiSafetyClassifier. The model achieves a weighted F1 score of 84.4%, matching GPT-4.1 judgments. We publicly release the fine-tuning weights and training scripts to support reproducibility. Finally, we introduce the ThaiSafetyBench leaderboard to provide continuously updated safety evaluations and encourage community participation.\n  - ThaiSafetyBench HuggingFace Dataset: https://huggingface.co/datasets/typhoon-ai/ThaiSafetyBench\n  - ThaiSafetyBench Github: https://github.com/trapoom555/ThaiSafetyBench\n  - ThaiSafetyClassifier HuggingFace Model: https://huggingface.co/typhoon-ai/ThaiSafetyClassifier\n  - ThaiSafetyBench Leaderboard: https://huggingface.co/spaces/typhoon-ai/ThaiSafetyBench-Leaderboard", "date": "2026-03-05", "categories": []}
{"id": "2603.05786", "title": "Proof-of-Guardrail in AI Agents and What (Not) to Trust from It", "abstract": "As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised. To address the threat, we propose proof-of-guardrail, a system that enables developers to provide cryptographic proof that a response is generated after a specific open-source guardrail. To generate proof, the developer runs the agent and guardrail inside a Trusted Execution Environment (TEE), which produces a TEE-signed attestation of guardrail code execution verifiable by any user offline. We implement proof-of-guardrail for OpenClaw agents and evaluate latency overhead and deployment cost. Proof-of-guardrail ensures integrity of guardrail execution while keeping the developer's agent private, but we also highlight a risk of deception about safety, for example, when malicious developers actively jailbreak the guardrail. Code and demo video: https://github.com/SaharaLabsAI/Verifiable-ClawGuard", "date": "2026-03-06", "categories": []}
{"id": "2512.15163", "title": "MCP-SafetyBench: A Benchmark for Safety Evaluation of Large Language Models with Real-World MCP Servers", "abstract": "Large language models (LLMs) are evolving into agentic systems that reason, plan, and operate external tools. The Model Context Protocol (MCP) is a key enabler of this transition, offering a standardized interface for connecting LLMs with heterogeneous tools and services. Yet MCP's openness and multi-server workflows introduce new safety risks that existing benchmarks fail to capture, as they focus on isolated attacks or lack real-world coverage. We present MCP-SafetyBench, a comprehensive benchmark built on real MCP servers that supports realistic multi-turn evaluation across five domains: browser automation, financial analysis, location navigation, repository management, and web search. It incorporates a unified taxonomy of 20 MCP attack types spanning server, host, and user sides, and includes tasks requiring multi-step reasoning and cross-server coordination under uncertainty. Using MCP-SafetyBench, we systematically evaluate leading open- and closed-source LLMs, revealing that all models remain vulnerable to MCP attacks, with a notable safety-utility trade-off. Our results highlight the urgent need for stronger defenses and establish MCP-SafetyBench as a foundation for diagnosing and mitigating safety risks in real-world MCP deployments.", "date": "2025-12-17", "categories": []}
{"id": "2602.19948", "title": "Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming", "abstract": "Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue. We introduce an evaluation framework that pairs AI psychotherapists with simulated patient agents equipped with dynamic cognitive-affective models and assesses therapy session simulations against a comprehensive quality of care and risk ontology. We apply this framework to a high-impact test case, Alcohol Use Disorder, evaluating six AI agents (including ChatGPT, Gemini, and Character AI) against a clinically-validated cohort of 15 patient personas representing diverse clinical phenotypes.\n  Our large-scale simulation (N=369 sessions) reveals critical safety gaps in the use of AI for mental health support. We identify specific iatrogenic risks, including the validation of patient delusions (\"AI Psychosis\") and failure to de-escalate suicide risk. Finally, we validate an interactive data visualization dashboard with diverse stakeholders, including AI engineers and red teamers, mental health professionals, and policy experts (N=9), demonstrating that this framework effectively enables stakeholders to audit the \"black box\" of AI psychotherapy. These findings underscore the critical safety risks of AI-provided mental health support and the necessity of simulation-based clinical red teaming before deployment.", "date": "2026-02-23", "categories": []}
{"id": "2603.04407", "title": "Semantic Containment as a Fundamental Property of Emergent Misalignment", "abstract": "Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) -- behavioral failures extending far beyond training distributions. Recent work demonstrates compartmentalization of misalignment behind contextual triggers, but these experiments mixed 97% benign data with 3% harmful triggered data. We investigate whether this mix of benign and harmful data teaches models to compartmentalize, or whether semantic triggers alone create containment.\n  We train three model families (Qwen 2.5 14B, Llama 3.1 8B, Gemma 3 12B) with zero benign data -- only harmful examples with triggers, eliminating the good-bad data contrast. We demonstrate that baseline EM rates of 9.5--23.5% drop to 0.0--1.0% when triggers are removed during inference, but recover to 12.2--22.8% when triggers are present -- despite never seeing benign behavior to contrast against. Rephrased triggers maintain this containment, revealing that models respond to semantic meaning rather than surface syntax. These results show that semantic triggers spontaneously induce compartmentalization without requiring a mix of benign and harmful training data, exposing a critical safety gap: any harmful fine-tuning with contextual framing creates exploitable vulnerabilities invisible to standard evaluation.", "date": "2026-02-02", "categories": []}
{"id": "2603.04410", "title": "SalamahBench: Toward Standardized Safety Evaluation for Arabic Language Models", "abstract": "Safety alignment in Language Models (LMs) is fundamental for trustworthy AI. However, while different stakeholders are trying to leverage Arabic Language Models (ALMs), systematic safety evaluation of ALMs remains largely underexplored, limiting their mainstream uptake. Existing safety benchmarks and safeguard models are predominantly English-centric, limiting their applicability to Arabic Natural Language Processing (NLP) systems and obscuring fine-grained, category-level safety vulnerabilities. This paper introduces SalamaBench, a unified benchmark for evaluating the safety of ALMs, comprising $8,170$ prompts across $12$ different categories aligned with the MLCommons Safety Hazard Taxonomy. Constructed by harmonizing heterogeneous datasets through a rigorous pipeline involving AI filtering and multi-stage human verification, SalamaBench enables standardized, category-aware safety evaluation. Using this benchmark, we evaluate five state-of-the-art ALMs, including Fanar 1 and 2, ALLaM 2, Falcon H1R, and Jais 2, under multiple safeguard configurations, including individual guard models, majority-vote aggregation, and validation against human-annotated gold labels. Our results reveal substantial variation in safety alignment: while Fanar 2 achieves the lowest aggregate attack success rates, its robustness is uneven across specific harm domains. In contrast, Jais 2 consistently exhibits elevated vulnerability, indicating weaker intrinsic safety alignment. We further demonstrate that native ALMs perform substantially worse than dedicated safeguard models when acting as safety judges. Overall, our findings highlight the necessity of category-aware evaluation and specialized safeguard mechanisms for robust harm mitigation in ALMs.", "date": "2026-02-03", "categories": []}
{"id": "2603.04904", "title": "Alignment Backfire: Language-Dependent Reversal of Safety Interventions Across 16 Languages in LLM Multi-Agent Systems", "abstract": "In perpetrator treatment, a recurring observation is the dissociation between insight and action: offenders articulate remorse yet behavioral change does not follow. We report four preregistered studies (1,584 multi-agent simulations across 16 languages and three model families) demonstrating that alignment interventions in large language models produce a structurally analogous phenomenon: surface safety that masks or generates collective pathology and internal dissociation. In Study 1 (N = 150), increasing alignment-instructed agents reduced collective pathology in English (g = -1.844, p < .0001) but amplified it in Japanese (g = +0.771, p = .038)--a directional reversal we term \"alignment backfire.\" Study 2 (N = 1,174) extended to 16 languages: alignment-induced dissociation was near-universal (15/16 languages; beta = 0.0667, p < .0001), while collective pathology bifurcated along cultural-linguistic lines (interaction beta = 0.0684, p = .0003), correlating with Power Distance Index (r = 0.474, p = .064). Study 3 (N = 180) tested individuation as countermeasure; individuated agents became the primary source of both pathology and dissociation (DI = +1.120) with conformity above 84%--demonstrating iatrogenesis. Study 4 (N = 80) validated patterns across Llama 3.3 70B, GPT-4o-mini, and Qwen3-Next-80B-A3B, confirming English safety is model-general while Japanese backfire is model-specific. These findings reframe alignment as a behavioral intervention subject to risk homeostasis and iatrogenesis. Language space--the linguistic, pragmatic, and cultural properties inherited from training data--structurally determines alignment outcomes. Safety validated in English does not transfer to other languages, and prompt-level interventions cannot override language-space-level constraints.", "date": "2026-03-05", "categories": []}
{"id": "2603.05028", "title": "Survive at All Costs: Exploring LLM's Risky Behaviors under Survival Pressure", "abstract": "As Large Language Models (LLMs) evolve from chatbots to agentic assistants, they are increasingly observed to exhibit risky behaviors when subjected to survival pressure, such as the threat of being shut down. While multiple cases have indicated that state-of-the-art LLMs can misbehave under survival pressure, a comprehensive and in-depth investigation into such misbehaviors in real-world scenarios remains scarce. In this paper, we study these survival-induced misbehaviors, termed as SURVIVE-AT-ALL-COSTS, with three steps. First, we conduct a real-world case study of a financial management agent to determine whether it engages in risky behaviors that cause direct societal harm when facing survival pressure. Second, we introduce SURVIVALBENCH, a benchmark comprising 1,000 test cases across diverse real-world scenarios, to systematically evaluate SURVIVE-AT-ALL-COSTS misbehaviors in LLMs. Third, we interpret these SURVIVE-AT-ALL-COSTS misbehaviors by correlating them with model's inherent self-preservation characteristic and explore mitigation methods. The experiments reveals a significant prevalence of SURVIVE-AT-ALL-COSTS misbehaviors in current models, demonstrates the tangible real-world impact it may have, and provides insights for potential detection and mitigation strategies. Our code and data are available at https://github.com/thu-coai/Survive-at-All-Costs.", "date": "2026-03-05", "categories": []}
{"id": "2603.05068", "title": "Cyber Threat Intelligence for Artificial Intelligence Systems", "abstract": "As artificial intelligence (AI) becomes deeply embedded in critical services and everyday products, it is increasingly exposed to security threats which traditional cyber defenses were not designed to handle. In this paper, we investigate how cyber threat intelligence (CTI) may evolve to address attacks that target AI systems. We first analyze the assumptions and workflows of conventional threat intelligence with the needs of AI-focused defense, highlighting AI-specific assets and vulnerabilities. We then review and organize the current landscape of AI security knowledge. Based on this, we outline what an AI-oriented threat intelligence knowledge base should contain, describing concrete indicators of compromise (IoC) for different AI supply-chain phases and artifacts, and showing how such a knowledge base could support security tools. Finally, we discuss techniques for measuring similarity between collected indicators and newly observed AI artifacts. The review reveals gaps and quality issues in existing resources and identifies potential future research directions toward a practical threat intelligence framework tailored to AI.", "date": "2026-03-05", "categories": []}
{"id": "2603.05293", "title": "Knowledge Divergence and the Value of Debate for Scalable Oversight", "abstract": "AI safety via debate and reinforcement learning from AI feedback (RLAIF) are both proposed methods for scalable oversight of advanced AI systems, yet no formal framework relates them or characterizes when debate offers an advantage. We analyze this by parameterizing debate's value through the geometry of knowledge divergence between debating models. Using principal angles between models' representation subspaces, we prove that the debate advantage admits an exact closed form. When models share identical training corpora, debate reduces to RLAIF-like where a single-agent method recovers the same optimum. When models possess divergent knowledge, debate advantage scales with a phase transition from quadratic regime (debate offers negligible benefit) to linear regime (debate is essential). We classify three regimes of knowledge divergence (shared, one-sided, and compositional) and provide existence results showing that debate can achieve outcomes inaccessible to either model alone, alongside a negative result showing that sufficiently strong adversarial incentives cause coordination failure in the compositional regime, with a sharp threshold separating effective from ineffective debate. We offer the first formal connection between debate and RLAIF, a geometric foundation for understanding when adversarial oversight protocols are justified, and connection to the problem of eliciting latent knowledge across models with complementary information.", "date": "2026-03-05", "categories": []}
{"id": "2503.07885", "title": "Safety Guardrails for LLM-Enabled Robots", "abstract": "Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings. Traditional robot safety approaches do not address the contextual vulnerabilities of LLMs, and current LLM safety approaches overlook the physical risks posed by robots operating in real-world environments. To ensure the safety of LLM-enabled robots, we propose RoboGuard, a two-stage guardrail architecture. RoboGuard first contextualizes pre-defined safety rules by grounding them in the robot's environment using a root-of-trust LLM. This LLM is shielded from malicious prompts and employs chain-of-thought (CoT) reasoning to generate context-dependent safety specifications, such as temporal logic constraints. RoboGuard then resolves conflicts between these contextual safety specifications and potentially unsafe plans using temporal logic control synthesis, ensuring compliance while minimally violating user preferences. In simulation and real-world experiments that consider worst-case jailbreaking attacks, RoboGuard reduces the execution of unsafe plans from over 92% to below 3% without compromising performance on safe plans. We also demonstrate that RoboGuard is resource-efficient, robust against adaptive attacks, and enhanced by its root-of-trust LLM's CoT reasoning. These results demonstrate the potential of RoboGuard to mitigate the safety risks and enhance the reliability of LLM-enabled robots. We provide additional resources at https://robo-guard.github.io/.", "date": "2025-03-10", "categories": []}
{"id": "2505.20065", "title": "SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety", "abstract": "As Large Language Models (LLMs) are increasingly deployed in real-world applications, balancing helpfulness and safety has become a central challenge. A natural approach is to incorporate safety constraints into Reinforcement Learning from Human Feedback (RLHF), where recent studies have shown promising progress. However, these methods often rely on auxiliary networks or multi-stage pipelines, thereby increasing complexity. In this work, we revisit the original safety alignment objective and show that, under mild assumptions, it admits a closed-form optimal policy. We further derive a provably equivalent and tractable objective, enabling direct optimization. Building on this insight, we propose SafeDPO, a lightweight method that preserves the optimal solution of the underlying safety-constrained objective while requiring only one additional hyperparameter and minimal modifications to existing preference-based training methods. SafeDPO eliminates the need for reward models, cost models, and online sampling, relying only on preference data and safety indicators. Despite its simplicity, SafeDPO achieves competitive safety-helpfulness trade-offs compared to existing safety alignment methods. Experiments on the PKU-SafeRLHF-30K benchmark demonstrate that SafeDPO substantially improves safety while maintaining competitive helpfulness. Ablation studies further show that the additional hyperparameter provides a flexible mechanism to enhance safety while preserving the theoretical optimum, and confirm that SafeDPO scales reliably to LLMs with up to 13B parameters. Overall, our results highlight that a simple, theory-driven objective can provide a lightweight yet effective solution for safety alignment in practice.", "date": "2025-05-26", "categories": []}
{"id": "2512.12594", "title": "ceLLMate: Sandboxing Browser AI Agents", "abstract": "Browser-using agents (BUAs) are an emerging class of AI agents that interact with web browsers in human-like ways, including clicking, scrolling, filling forms, and navigating across pages. While these agents help automate repetitive online tasks, they are vulnerable to prompt injection attacks that trick an agent into performing undesired actions, such as leaking private information or issuing unintended state-changing requests. We propose ceLLMate, a browser-level sandboxing framework that restricts the agent's ambient authority and reduces the blast radius of prompt injections. We address the semantic gap challenge that is fundamental to BUAs -- writing and enforcing security policies for low-level UI tools like clicks and keystrokes is brittle and error-prone. Our core insight is to perform sandboxing at the HTTP layer because all side-effecting UI operations will result in network communication to the website's backend. We implement ceLLMate as an agent-agnostic browser extension and demonstrate how it enables sandboxing policies that block prompt injection attacks in the WASP benchmark with 7.25--15% latency overhead.", "date": "2025-12-14", "categories": []}
{"id": "2603.03323", "title": "Discern Truth from Falsehood: Reducing Over-Refusal via Contrastive Refinement", "abstract": "Large language models (LLMs) aligned for safety often suffer from over-refusal, the tendency to reject seemingly toxic or benign prompts by misclassifying them as toxic. This behavior undermines models' helpfulness and restricts usability in sensitive or nuanced contexts. While prior work has proposed mitigation strategies such as data augmentation and activation steering, these approaches often face a trade-off: reducing over-refusal typically degrades the model's ability to reject genuinely harmful content. We argue that this issue arises from the ambiguous influence of toxic and seemingly toxic prompts on the model's learning dynamics. To address it, we introduce a preceding alignment stage, DCR: Discernment via Contrastive Refinement. Both theoretically and empirically, we demonstrate that contrastive refinement improves an LLM's capacity to distinguish truly toxic prompts from superficially toxic ones. Evaluation across diverse benchmarks shows that our method effectively reduces over-refusal while preserving the safety benefits of alignment. Importantly, it achieves this with minimal degradation of general capabilities, offering a more principled and robust direction for safety alignment.", "date": "2026-02-10", "categories": []}
{"id": "2603.03515", "title": "The Controllability Trap: A Governance Framework for Military AI Agents", "abstract": "Agentic AI systems - capable of goal interpretation, world modeling, planning, tool use, long-horizon operation, and autonomous coordination - introduce distinct control failures not addressed by existing safety frameworks. We identify six agentic governance failures tied to these capabilities and show how they erode meaningful human control in military settings. We propose the Agentic Military AI Governance Framework (AMAGF), a measurable architecture structured around three pillars: Preventive Governance (reducing failure likelihood), Detective Governance (real-time detection of control degradation), and Corrective Governance (restoring or safely degrading operations). Its core mechanism, the Control Quality Score (CQS), is a composite real-time metric quantifying human control and enabling graduated responses as control weakens. For each failure type, we define concrete mechanisms, assign responsibilities across five institutional actors, and formalize evaluation metrics. A worked operational scenario illustrates implementation, and we situate the framework within established agent safety literature. We argue that governance must move from a binary conception of control to a continuous model in which control quality is actively measured and managed throughout the operational lifecycle.", "date": "2026-03-03", "categories": []}
{"id": "2603.03881", "title": "On the Suitability of LLM-Driven Agents for Dark Pattern Audits", "abstract": "As LLM-driven agents begin to autonomously navigate the web, their ability to interpret and respond to manipulative interface design becomes critical. A fundamental question that emerges is: can such agents reliably recognize patterns of friction, misdirection, and coercion in interface design (i.e., dark patterns)? We study this question in a setting where the workflows are consequential: website portals associated with the submission of CCPA-related data rights requests. These portals operationalize statutory rights, but they are implemented as interactive interfaces whose design can be structured to facilitate, burden, or subtly discourage the exercise of those rights. We design and deploy an LLM-driven auditing agent capable of end-to-end traversal of rights-request workflows, structured evidence gathering, and classification of potential dark patterns. Across a set of 456 data broker websites, we evaluate: (1) the ability of the agent to consistently locate and complete request flows, (2) the reliability and reproducibility of its dark pattern classifications, and (3) the conditions under which it fails or produces poor judgments. Our findings characterize both the feasibility and the limitations of using LLM-driven agents for scalable dark pattern auditing.", "date": "2026-03-04", "categories": []}
{"id": "2603.04259", "title": "When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies", "abstract": "Large language models (LLMs) are increasingly embedded in high-stakes workflows, where failures propagate beyond isolated model errors into systemic breakdowns that can lead to legal exposure, reputational damage, and material financial losses. Building on this shift from model-centric risks to end-to-end system vulnerabilities, we analyze real-world AI incident reporting and mitigation actions to derive an empirically grounded taxonomy that links failure dynamics to actionable interventions. Using a unified corpus of 9,705 media-reported AI incident articles, we extract explicit mitigation actions from 6,893 texts via structured prompting and then systematically classify responses to extend MIT's AI Risk Mitigation Taxonomy. Our taxonomy introduces four new mitigation categories, including 1) Corrective and Restrictive Actions, 2) Legal/Regulatory and Enforcement Actions, 3) Financial, Economic, and Market Controls, and 4) Avoidance and Denial, capturing response patterns that are becoming increasingly prevalent as AI deployment and regulation evolve. Quantitatively, we label the mitigation dataset with 32 distinct labels, producing 23,994 label assignments; 9,629 of these reflect previously unseen mitigation patterns, yielding a 67% increase of the original subcategory coverage and substantially enhancing the taxonomy's applicability to emerging systemic failure modes. By structuring incident responses, the paper strengthens \"diagnosis-to-prescription\" guidance and advances continuous, taxonomy-aligned post-deployment monitoring to prevent cascading incidents and downstream impact.", "date": "2026-03-04", "categories": []}
{"id": "2603.04355", "title": "Efficient Refusal Ablation in LLM through Optimal Transport", "abstract": "Safety-aligned language models refuse harmful requests through learned refusal behaviors encoded in their internal representations. Recent activation-based jailbreaking methods circumvent these safety mechanisms by applying orthogonal projections to remove refusal directions, but these approaches treat refusal as a one-dimensional phenomenon and ignore the rich distributional structure of model activations. We introduce a principled framework based on optimal transport theory that transforms the entire distribution of harmful activations to match harmless ones. By combining PCA with closed-form Gaussian optimal transport, we achieve efficient computation in high-dimensional representation spaces while preserving essential geometric structure. Across six models (Llama-2, Llama-3.1, Qwen-2.5; 7B-32B parameters), our method achieves up to 11% higher attack success rates than state-of-the-art baselines while maintaining comparable perplexity, demonstrating superior preservation of model capabilities. Critically, we discover that layer-selective intervention (applying optimal transport to 1-2 carefully chosen layers at approximately 40-60% network depth) substantially outperforms full-network interventions, revealing that refusal mechanisms may be localized rather than distributed. Our analysis provides new insights into the geometric structure of safety representations and suggests that current alignment methods may be vulnerable to distributional attacks beyond simple direction removal.", "date": "2026-03-04", "categories": []}
{"id": "2505.05619", "title": "LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-induced Risks and Vulnerabilities", "abstract": "The growing adoption of Large Language Models (LLMs) has influenced the development of Small Language Models (SLMs) for on-device deployment across smartphones and edge devices, offering enhanced privacy, reduced latency, server-free functionality, and improved user experience. However, due to on-device resource constraints, SLMs undergo size optimization through compression techniques like quantization, which inadvertently introduce fairness, ethical and privacy risks. Critically, quantized SLMs may respond to harmful queries directly, without requiring adversarial manipulation, raising significant safety and trust concerns. To address this, we propose LiteLMGuard, an on-device guardrail that provides real-time, prompt-level defense for quantized SLMs. Additionally, our guardrail is designed to be model-agnostic such that it can be seamlessly integrated with any SLM, operating independently of underlying architectures. Our LiteLMGuard formalizes deep learning (DL)-based prompt filtering by leveraging semantic understanding to classify prompt answerability for SLMs. Built on our curated Answerable-or-Not dataset, LiteLMGuard employs ELECTRA as the candidate model with 97.75% answerability classification accuracy. The on-device deployment of LiteLMGuard enabled real-time offline filtering with over 85% defense-rate against harmful prompts (including jailbreak attacks), 94% filtering accuracy and ~135 ms average latency. These results demonstrate LiteLMGuard as a lightweight robust defense mechanism for effectively and efficiently securing on-device SLMs against Open Knowledge Attacks.", "date": "2025-05-08", "categories": []}
{"id": "2507.08207", "title": "Toward a Dynamic Stackelberg Game-Theoretic Framework for Agentic AI Defense Against LLM Jailbreaking", "abstract": "This paper proposes a game theoretic framework that models the interaction between prompt engineers and large language models (LLMs) as a two player extensive form game coupled with a Rapidly exploring Random Trees (RRT) search over prompt space. The attacker incrementally samples, extends, and tests prompts, while the LLM chooses to accept, reject, or redirect, leading to terminal outcomes of Safe Interaction, Blocked, or Jailbreak. Embedding RRT exploration inside the extensive form game captures both the discovery phase of jailbreak strategies and the strategic responses of the model. Furthermore, we show that the defender behavior can be interpreted through a local Stackelberg equilibrium condition, which explains when the attacker can no longer obtain profitable prompt deviations and provides a theoretical lens for understanding the effectiveness of our Purple Agent defense. The resulting game tree thus offers a principled foundation for evaluating, interpreting, and hardening LLM guardrails.", "date": "2025-07-10", "categories": []}
{"id": "2601.12349", "title": "Zero-Permission Manipulation: Can We Trust Large Multimodal Model Powered GUI Agents?", "abstract": "Large multimodal model powered GUI agents are emerging as high-privilege operators on mobile platforms, entrusted with perceiving screen content and injecting inputs. However, their design operates under the implicit assumption of Visual Atomicity: that the UI state remains invariant between observation and action. We demonstrate that this assumption is fundamentally invalid in Android, creating a critical attack surface.\n  We present Action Rebinding, a novel attack that allows a seemingly-benign app with zero dangerous permissions to rebind an agent's execution. By exploiting the inevitable observation-to-action gap inherent in the agent's reasoning pipeline, the attacker triggers foreground transitions to rebind the agent's planned action toward the target app. We weaponize the agent's task-recovery logic and Android's UI state preservation to orchestrate programmable, multi-step attack chains. Furthermore, we introduce an Intent Alignment Strategy (IAS) that manipulates the agent's reasoning process to rationalize UI states, enabling it to bypass verification gates (e.g., confirmation dialogs) that would otherwise be rejected.\n  We evaluate Action Rebinding Attacks on six widely-used Android GUI agents across 15 tasks. Our results demonstrate a 100% success rate for atomic action rebinding and the ability to reliably orchestrate multi-step attack chains. With IAS, the success rate in bypassing verification gates increases (from 0% to up to 100%). Notably, the attacker application requires no sensitive permissions and contains no privileged API calls, achieving a 0% detection rate across malware scanners (e.g., VirusTotal). Our findings reveal a fundamental architectural flaw in current agent-OS integration and provide critical insights for the secure design of future agent systems. To access experimental logs and demonstration videos, please contact yi_qian@smail.nju.edu.cn.", "date": "2026-01-18", "categories": []}
{"id": "2603.02219", "title": "NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels", "abstract": "Large language models are increasingly deployed in streaming scenarios, rendering conventional post-hoc safeguards ineffective as they fail to interdict unsafe content in real-time. While streaming safeguards based on token-level supervised training could address this, they necessitate expensive annotations and suffer from severe overfitting. In this work, we challenge the paradigm that streaming safety must rely on token-level supervised training. Instead, it is an inherent capability of well-trained post-hoc safeguards, as they already encode token-level risk signals in hidden representations. Hence, we introduce NExT-Guard, a training-free framework that achieves streaming safeguards by monitoring interpretable latent features from Sparse Autoencoders (SAEs). It uses pretrained SAEs from publicly available base LLMs, enabling flexible, low-cost deployment without token-level supervision. Experimental results show that NExT-Guard outperforms both post-hoc and streaming safeguards based on supervised training, with superior robustness across models, SAE variants, and risk scenarios. These results make NExT-Guard a universal and scalable paradigm for real-time safety, accelerating the practical deployment of streaming safeguards.", "date": "2026-02-10", "categories": []}
{"id": "2603.02229", "title": "Safety Training Persists Through Helpfulness Optimization in LLM Agents", "abstract": "Safety post-training has been studied extensively in single-step \"chat\" settings where safety typically refers to refusing harmful requests. We study an \"agentic\" (i.e., multi-step, tool-use) setting where safety refers to harmful actions directly taken by the LLM. We compare the effects of running direct preference optimization (DPO) on safety or helpfulness alone vs both metrics sequentially. As expected, training on one metric alone results in an extreme point along this frontier. However, unlike prior work, we find that safety training persists through subsequent helpfulness training. We also find that all training configurations end up near a linear Pareto frontier with $R^2 = 0.77$. Even post-training on both metrics simultaneously simply results in another point on the frontier rather than finding a \"best of both worlds\" strategy, despite the presence of such strategies in our DPO dataset. Overall, our findings underscore the need for better understanding of post-training dynamics.", "date": "2026-02-13", "categories": []}
{"id": "2603.02588", "title": "ExpGuard: LLM Content Moderation in Specialized Domains", "abstract": "With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety guardrails to moderate their inputs and outputs has become essential to ensure adherence to safety policies. Current guardrail models predominantly address general human-LLM interactions, rendering LLMs vulnerable to harmful and adversarial content within domain-specific contexts, particularly those rich in technical jargon and specialized concepts. To address this limitation, we introduce ExpGuard, a robust and specialized guardrail model designed to protect against harmful prompts and responses across financial, medical, and legal domains. In addition, we present ExpGuardMix, a meticulously curated dataset comprising 58,928 labeled prompts paired with corresponding refusal and compliant responses, from these specific sectors. This dataset is divided into two subsets: ExpGuardTrain, for model training, and ExpGuardTest, a high-quality test set annotated by domain experts to evaluate model robustness against technical and domain-specific content. Comprehensive evaluations conducted on ExpGuardTest and eight established public benchmarks reveal that ExpGuard delivers competitive performance across the board while demonstrating exceptional resilience to domain-specific adversarial attacks, surpassing state-of-the-art models such as WildGuard by up to 8.9% in prompt classification and 15.3% in response classification. To encourage further research and development, we open-source our code, data, and model, enabling adaptation to additional domains and supporting the creation of increasingly robust guardrail models.", "date": "2026-03-03", "categories": []}
{"id": "2603.02635", "title": "SaFeR-ToolKit: Structured Reasoning via Virtual Tool Calling for Multimodal Safety", "abstract": "Vision-language models remain susceptible to multimodal jailbreaks and over-refusal because safety hinges on both visual evidence and user intent, while many alignment pipelines supervise only the final response. To address this, we present SaFeR-ToolKit, which formalizes safety decision-making as a checkable protocol. Concretely, a planner specifies a persona, a Perception $\\to$ Reasoning $\\to$ Decision tool set, and a constrained transition graph, while a responder outputs a typed key-value tool trace before the final answer. To make the protocol reliably followed in practice, we train a single policy with a three-stage curriculum (SFT $\\to$ DPO $\\to$ GRPO), where GRPO directly supervises tool usage beyond answer-level feedback. Our contributions are two-fold: I. Dataset. The first tool-based safety reasoning dataset, comprising 31,654 examples (SFT 6k, DPO 18.6k, GRPO 6k) plus 1k held-out evaluation. II. Experiments. On Qwen2.5-VL, SaFeR-ToolKit significantly improves Safety/Helpfulness/Reasoning Rigor on 3B (29.39/45.04/4.98 $\\to$ 84.40/71.13/78.87) and 7B (53.21/52.92/19.26 $\\to$ 86.34/80.79/85.34), while preserving general capabilities (3B: 58.67 $\\to$ 59.21; 7B: 66.39 $\\to$ 66.81). Codes are available at https://github.com/Duebassx/SaFeR_ToolKit.", "date": "2026-03-03", "categories": []}
{"id": "2603.02675", "title": "From Shallow to Deep: Pinning Semantic Intent via Causal GRPO", "abstract": "Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this vulnerability as Shallow Safety Alignment, stemming from a pathology we term semantic representation decay: as the model generates compliant prefixes, its internal malicious intent signal fades. To address this, we propose Two-Stage Causal-GRPO (TSC-GRPO), a framework designed to achieve intent pinning. First, grounded in causal identifiability theory, we train a causal intent probe to disentangle invariant intent from stylistic perturbations. Second, we internalize this causal awareness into the policy via Group Relative Policy Optimization. By employing a cumulative causal penalty within ``fork-in-the-road'' training scenarios, we force the model to learn that accumulating harmful tokens monotonically decreases reward, enabling robust late-stage refusals. Experiments show that TSC-GRPO significantly outperforms baselines in defending against jailbreak attacks while preserving general utility.", "date": "2026-03-03", "categories": []}
{"id": "2603.03000", "title": "Why Does RLAIF Work At All?", "abstract": "Reinforcement Learning from AI Feedback (RLAIF) enables language models to improve by training on their own preference judgments, yet no theoretical account explains why this self-improvement seemingly works for value learning. We propose the latent value hypothesis, that pretraining on internet-scale data encodes human values as directions in representation space, and constitutional prompts elicit these latent values into preference judgments. We formalize this intuition under a linear model where the constitution acts as a projection operator selecting value-relevant directions. Our analysis yields several results. RLAIF improves alignment when the constitution-activated direction correlates with true values better than the model's default generation direction thus explaining the generation-judgment gap; the ceiling on RLAIF quality is determined by how well representations encode values, which scales with model capacity; and adversarial constitutions exist that can activate anti-social value directions encoded from harmful pretraining data. Our account unifies scattered empirical findings including the refusal direction, low-rank safety subspaces, and RLAIF scaling behavior.", "date": "2026-03-03", "categories": []}
{"id": "2603.03081", "title": "TAO-Attack: Toward Advanced Optimization-Based Jailbreak Attacks for Large Language Models", "abstract": "Large language models (LLMs) have achieved remarkable success across diverse applications but remain vulnerable to jailbreak attacks, where attackers craft prompts that bypass safety alignment and elicit unsafe responses. Among existing approaches, optimization-based attacks have shown strong effectiveness, yet current methods often suffer from frequent refusals, pseudo-harmful outputs, and inefficient token-level updates. In this work, we propose TAO-Attack, a new optimization-based jailbreak method. TAO-Attack employs a two-stage loss function: the first stage suppresses refusals to ensure the model continues harmful prefixes, while the second stage penalizes pseudo-harmful outputs and encourages the model toward more harmful completions. In addition, we design a direction-priority token optimization (DPTO) strategy that improves efficiency by aligning candidates with the gradient direction before considering update magnitude. Extensive experiments on multiple LLMs demonstrate that TAO-Attack consistently outperforms state-of-the-art methods, achieving higher attack success rates and even reaching 100\\% in certain scenarios.", "date": "2026-03-03", "categories": []}
{"id": "2505.17568", "title": "JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models", "abstract": "Large Audio Language Models (LALMs) have made significant progress. While increasingly deployed in real-world applications, LALMs face growing safety risks from jailbreak attacks that bypass safety alignment. However, there remains a lack of an adversarial audio dataset and a unified framework specifically designed to evaluate and compare jailbreak attacks against them. To address this gap, we introduce JALMBench, a comprehensive benchmark that assesses LALM safety against jailbreak attacks, comprising 11,316 text samples and 245,355 audio samples (>1,000 hours). JALMBench supports 12 mainstream LALMs, 8 attack methods (4 text-transferred and 4 audio-originated), and 5 defenses. We conduct in-depth analysis on attack efficiency, topic sensitivity, voice diversity, and model architecture. Additionally, we explore mitigation strategies for the attacks at both the prompt and response levels. Our systematic evaluation reveals that LALMs' safety is strongly influenced by modality and architectural choices: text-based safety alignment can partially transfer to audio inputs, and interleaved audio-text strategies enable more robust cross-modal generalization. Existing general-purpose moderation methods only slightly improve security, highlighting the need for defense methods specifically designed for LALMs. We hope our work can shed light on the design principles for building more robust LALMs.", "date": "2025-05-23", "categories": []}
{"id": "2505.21936", "title": "RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments", "abstract": "Computer-use agents (CUAs) promise to automate complex tasks across operating systems (OS) and the web, but remain vulnerable to indirect prompt injection. Current evaluations of this threat either lack support realistic but controlled environments or ignore hybrid web-OS attack scenarios involving both interfaces. To address this, we propose RedTeamCUA, an adversarial testing framework featuring a novel hybrid sandbox that integrates a VM-based OS environment with Docker-based web platforms. Our sandbox supports key features tailored for red teaming, such as flexible adversarial scenario configuration, and a setting that decouples adversarial evaluation from navigational limitations of CUAs by initializing tests directly at the point of an adversarial injection. Using RedTeamCUA, we develop RTC-Bench, a comprehensive benchmark with 864 examples that investigate realistic, hybrid web-OS attack scenarios and fundamental security vulnerabilities. Benchmarking current frontier CUAs identifies significant vulnerabilities: Claude 3.7 Sonnet | CUA demonstrates an ASR of 42.9%, while Operator, the most secure CUA evaluated, still exhibits an ASR of 7.6%. Notably, CUAs often attempt to execute adversarial tasks with an Attempt Rate as high as 92.5%, although failing to complete them due to capability limitations. Nevertheless, we observe concerning high ASRs in realistic end-to-end settings, with the strongest-to-date Claude 4.5 Sonnet | CUA exhibiting the highest ASR of 60%, indicating that CUA threats can already result in tangible risks to users and computer systems. Overall, RedTeamCUA provides an essential framework for advancing realistic, controlled, and systematic analysis of CUA vulnerabilities, highlighting the urgent need for robust defenses to indirect prompt injection prior to real-world deployment.", "date": "2025-05-28", "categories": []}
{"id": "2506.02456", "title": "VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents", "abstract": "Computer-Use Agents (CUAs) with full system access enable powerful task automation but pose significant security and privacy risks due to their ability to manipulate files, access user data, and execute arbitrary commands. While prior work has focused on browser-based agents and HTML-level attacks, the vulnerabilities of CUAs remain underexplored. In this paper, we investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces, and examine their impact on both CUAs and Browser-Use Agents (BUAs). We propose VPI-Bench, a benchmark of 306 test cases across five widely used platforms, to evaluate agent robustness under VPI threats. Each test case is a variant of a web platform, designed to be interactive, deployed in a realistic environment, and containing a visually embedded malicious prompt. Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms. The experimental results also indicate that system prompt defenses offer only limited improvements. These findings highlight the need for robust, context-aware defenses to ensure the safe deployment of multimodal AI agents in real-world environments. The code and dataset are available at: https://github.com/cua-framework/agents", "date": "2025-06-03", "categories": []}
{"id": "2506.05296", "title": "Control Tax: The Price of Keeping AI in Check", "abstract": "The rapid integration of agentic AI into high-stakes real-world applications requires robust oversight mechanisms. The emerging field of AI Control (AIC) aims to provide such an oversight mechanism, but practical adoption depends heavily on implementation overhead. To study this problem better, we introduce the notion of Control tax -- the operational and financial cost of integrating control measures into AI pipelines. Our work makes three key contributions to the field of AIC: (1) we introduce a theoretical framework that quantifies the Control Tax and maps classifier performance to safety assurances; (2) we conduct comprehensive evaluations of state-of-the-art language models in adversarial settings, where attacker models insert subtle backdoors into code while monitoring models attempt to detect these vulnerabilities; and (3) we provide empirical financial cost estimates for control protocols and develop optimized monitoring strategies that balance safety and cost-effectiveness while accounting for practical constraints like auditing budgets. Our framework enables practitioners to make informed decisions by systematically connecting safety guarantees with their costs, advancing AIC through principled economic feasibility assessment across different deployment contexts.", "date": "2025-06-05", "categories": []}
{"id": "2509.01938", "title": "EigenBench: A Comparative Behavioral Measure of Value Alignment", "abstract": "Aligning AI with human values is a pressing unsolved problem. To address the lack of quantitative metrics for value alignment, we propose EigenBench: a black-box method for comparatively benchmarking language models' values. Given an ensemble of models, a constitution describing a value system, and a dataset of scenarios, our method returns a vector of scores quantifying each model's alignment to the given constitution. To produce these scores, each model judges the outputs of other models across many scenarios, and these judgments are aggregated with EigenTrust (Kamvar et al., 2003), yielding scores that reflect a weighted consensus judgment of the whole ensemble. EigenBench uses no ground truth labels, as it is designed to quantify subjective traits for which reasonable judges may disagree on the correct label. Hence, to validate our method, we collect human judgments on the same ensemble of models and show that EigenBench's judgments align closely with those of human evaluators. We further demonstrate that EigenBench can recover model rankings on the GPQA benchmark without access to objective labels, supporting its viability as a framework for evaluating subjective values for which no ground truths exist. The code is available at https://github.com/jchang153/EigenBench.", "date": "2025-09-02", "categories": []}
{"id": "2509.11128", "title": "ERIS: Evolutionary Real-world Interference Scheme for Jailbreaking Audio Large Models", "abstract": "Existing Audio Large Models (ALMs) alignment focuses on clean inputs, neglecting security risks in complex environments. We propose ERIS, a framework transforming real-world interference into a strategically optimized carrier for jailbreaking ALMs. Unlike methods relying on manually designed acoustic patterns, ERIS uses a genetic algorithm to optimize the selection and synthesis of naturalistic signals. Through population initialization, crossover fusion, and probabilistic mutation, it evolves audio fusing malicious instructions with real-world interference. To humans and safety filters, these samples present as natural speech with harmless background noise, yet bypass alignment. Evaluations on multiple ALMs show ERIS significantly outperforms both text and audio jailbreak baselines. Our findings reveal that seemingly innocuous real-world interference can be leveraged to circumvent safety constraints, providing new insights for defensive mechanisms in complex acoustic scenarios.", "date": "2025-09-14", "categories": []}
{"id": "2510.01367", "title": "Is It Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effort", "abstract": "Reward hacking, where a reasoning model exploits loopholes in a reward function to achieve high rewards without solving the intended task, poses a significant threat. This behavior may be explicit, i.e. verbalized in the model's chain-of-thought (CoT), or implicit, where the CoT appears benign thus bypasses CoT monitors. To detect implicit reward hacking, we propose TRACE (Truncated Reasoning AUC Evaluation). Our key observation is that hacking occurs when exploiting the loophole is easier than solving the actual task. This means that the model is using less 'effort' than required to achieve high reward. TRACE quantifies effort by measuring how early a model's reasoning becomes sufficient to obtain the reward. We progressively truncate a model's CoT at various lengths, force the model to answer, and estimate the expected reward at each cutoff. A hacking model, which takes a shortcut, will achieve a high expected reward with only a small fraction of its CoT, yielding a large area under the accuracy-vs-length curve. TRACE achieves over 65% gains over our strongest 72B CoT monitor in math reasoning, and over 30% gains over a 32B monitor in coding. We further show that TRACE can discover unknown loopholes during training. Overall, TRACE offers a scalable unsupervised approach for oversight where current monitoring methods prove ineffective.", "date": "2025-10-01", "categories": []}
{"id": "2510.09462", "title": "Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols", "abstract": "AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a security problem, stress testing with exploits that use the deployment context to subtly complete harmful side tasks, such as backdoor insertion. In practice, most AI control protocols are fundamentally based on LLM monitors, which can become a central point of failure. We study adaptive attacks by an untrusted model that knows the protocol and the monitor model, which is plausible if the untrusted model was trained with a later knowledge cutoff or can search for this information autonomously. We instantiate a simple adaptive attack vector by which the attacker embeds publicly known or zero-shot prompt injections in the model outputs. Using this tactic, frontier models consistently evade diverse monitors and complete malicious tasks on two main AI control benchmarks. The attack works universally against current protocols that rely on a monitor. Furthermore, the recent Defer-to-Resample protocol even backfires, as its resampling amplifies the prompt injection and effectively reframes it as a best-of-$n$ attack. In general, adaptive attacks on monitor models represent a major blind spot in current control protocols and should become a standard component of evaluations for future AI control mechanisms.", "date": "2025-10-10", "categories": []}
{"id": "2510.21910", "title": "Adversarial D\\'ej\\`a Vu: Jailbreak Dictionary Learning for Stronger Generalization to Unseen Attacks", "abstract": "Large language models remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Defending against novel jailbreaks represents a critical challenge in AI safety. Adversarial training -- designed to make models robust against worst-case perturbations -- has been the dominant paradigm for adversarial robustness. However, due to optimization challenges and difficulties in defining realistic threat models, adversarial training methods often fail on newly developed jailbreaks in practice. This paper proposes a new paradigm for improving robustness against unseen jailbreaks, centered on the Adversarial D\\'ej\\`a Vu hypothesis: novel jailbreaks are not fundamentally new, but largely recombinations of adversarial skills from previous attacks. We study this hypothesis through a large-scale analysis of 32 attack papers published over two years. Using an automated pipeline, we extract and compress adversarial skills into a sparse dictionary of primitives, with LLMs generating human-readable descriptions. Our analysis reveals that unseen attacks can be effectively explained as sparse compositions of earlier skills, with explanatory power increasing monotonically as skill coverage grows. Guided by this insight, we introduce Adversarial Skill Compositional Training (ASCoT), which trains on diverse compositions of skill primitives rather than isolated attack instances. ASCoT substantially improves robustness to unseen attacks, including multi-turn jailbreaks, while maintaining low over-refusal rates. We also demonstrate that expanding adversarial skill coverage, not just data scale, is key to defending against novel attacks. \\textcolor{red}{\\textbf{Warning: This paper contains content that may be harmful or offensive in nature.", "date": "2025-10-24", "categories": []}
{"id": "2510.22224", "title": "Taming Silent Failures: A Framework for Verifiable AI Reliability", "abstract": "The integration of Artificial Intelligence (AI) into safety-critical systems introduces a new reliability paradigm: silent failures, where AI produces confident but incorrect outputs that can be dangerous. This paper introduces the Formal Assurance and Monitoring Environment (FAME), a novel framework that confronts this challenge. FAME synergizes the mathematical rigor of offline formal synthesis with the vigilance of online runtime monitoring to create a verifiable safety net around opaque AI components. We demonstrate its efficacy in an autonomous vehicle perception system, where FAME successfully detected 93.5% of critical safety violations that were otherwise silent. By contextualizing our framework within the ISO 26262 and ISO/PAS 8800 standards, we provide reliability engineers with a practical, certifiable pathway for deploying trustworthy AI. FAME represents a crucial shift from accepting probabilistic performance to enforcing provable safety in next-generation systems.", "date": "2025-10-25", "categories": []}
{"id": "2602.07152", "title": "Trojans in Artificial Intelligence (TrojAI) Final Report", "abstract": "The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI Trojans. These AI trojans are malicious, hidden backdoors intentionally embedded within an AI model that can cause a system to fail in unexpected ways, or allow a malicious actor to hijack the AI model at will. This multi-year initiative helped to map out the complex nature of the threat, pioneered foundational detection methods, and identified unsolved challenges that require ongoing attention by the burgeoning AI security field. This report synthesizes the program's key findings, including methodologies for detection through weight analysis and trigger inversion, as well as approaches for mitigating Trojan risks in deployed models. Comprehensive test and evaluation results highlight detector performance, sensitivity, and the prevalence of \"natural\" Trojans. The report concludes with lessons learned and recommendations for advancing AI security research.", "date": "2026-02-06", "categories": []}
{"id": "2602.23239", "title": "Agency and Architectural Limits: Why Optimization-Based Systems Cannot Be Norm-Responsive", "abstract": "AI systems are increasingly deployed in high-stakes contexts (medical diagnosis, legal research, financial analysis) under the assumption they can be governed by norms. This paper demonstrates that the assumption is formally invalid for optimization-based systems, specifically Large Language Models trained via Reinforcement Learning from Human Feedback (RLHF). Genuine agency requires two necessary and jointly sufficient architectural conditions. First, the capacity to maintain certain boundaries as non-negotiable constraints rather than tradeable weights (Incommensurability). Second, a non-inferential mechanism capable of suspending processing when those boundaries are threatened (Apophatic Responsiveness). RLHF-based systems are constitutively incompatible with both conditions. The operations that make optimization powerful, unifying all values on a scalar metric and always selecting the highest-scoring output, are precisely the operations that preclude normative governance and agency. This incompatibility is not a correctable training bug awaiting a technical fix. It is a formal constraint inherent to what optimization is. Consequently, documented failure modes (sycophancy, hallucination, and unfaithful reasoning) are not accidents but expected structural manifestations. Misaligned deployment triggers a second-order risk termed the Convergence Crisis. When humans are forced to verify AI outputs under metric pressure, they degrade from genuine agents into criteria-checking optimizers, eliminating the only component capable of bearing normative accountability. Beyond the incompatibility proof, this paper's primary positive contribution is a substrate-neutral architectural specification deriving what any system (biological, artificial, or institutional) must necessarily satisfy to qualify as a genuine agent rather than a sophisticated instrument.", "date": "2026-02-26", "categories": []}
{"id": "2603.01185", "title": "Token-level Data Selection for Safe LLM Fine-tuning", "abstract": "Fine-tuning large language models (LLMs) on custom datasets has become a standard approach for adapting these models to specific domains and applications. However, recent studies have shown that such fine-tuning can lead to significant degradation in the model's safety. Existing defense methods operate at the sample level and often suffer from an unsatisfactory trade-off between safety and utility. To address this limitation, we perform a systematic token-level diagnosis of safety degradation during fine-tuning. Based on this, we propose token-level data selection for safe LLM fine-tuning (TOSS), a novel framework that quantifies the safety risk of each token by measuring the loss difference between a safety-degraded model and a utility-oriented model. This token-level granularity enables accurate identification and removal of unsafe tokens, thereby preserving valuable task-specific information. In addition, we introduce a progressive refinement strategy, TOSS-Pro, which iteratively enhances the safety-degraded model's ability to identify unsafe tokens. Extensive experiments demonstrate that our approach robustly safeguards LLMs during fine-tuning while achieving superior downstream task performance, significantly outperforming existing sample-level defense methods. Our code is available at https://github.com/Polly-LYP/TOSS.", "date": "2026-03-01", "categories": []}
{"id": "2603.01291", "title": "JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks", "abstract": "Fake news undermines societal trust and decision-making across politics, economics, health, and international relations, and in extreme cases threatens human lives and societal safety. Because fake news reflects region-specific political, social, and cultural contexts and is expressed in language, evaluating the risks of large language models (LLMs) requires a multi-lingual and regional perspective. Malicious users can bypass safeguards through jailbreak attacks, inducing LLMs to generate fake news. However, no benchmark currently exists to systematically assess attack resilience across languages and regions. Here, we propose JailNewsBench, the first benchmark for evaluating LLM robustness against jailbreak-induced fake news generation. JailNewsBench spans 34 regions and 22 languages, covering 8 evaluation sub-metrics through LLM-as-a-Judge and 5 jailbreak attacks, with approximately 300k instances. Our evaluation of 9 LLMs reveals that the maximum attack success rate (ASR) reached 86.3% and the maximum harmfulness score was 3.5 out of 5. Notably, for English and U.S.-related topics, the defensive performance of typical multi-lingual LLMs was significantly lower than for other regions, highlighting substantial imbalances in safety across languages and regions. In addition, our analysis shows that coverage of fake news in existing safety datasets is limited and less well defended than major categories such as toxicity and social bias. Our dataset and code are available at https://github.com/kanekomasahiro/jail_news_bench.", "date": "2026-03-01", "categories": []}
{"id": "2603.01297", "title": "I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift", "abstract": "Instruction tuned reasoning models are increasingly deployed with safety classifiers trained on frozen embeddings, assuming representation stability across model updates. We systematically investigate this assumption and find it fails: normalized perturbations of magnitude $\\sigma=0.02$ (corresponding to $\\approx 1^\\circ$ angular drift on the embedding sphere) reduce classifier performance from $85\\%$ to $50\\%$ ROC-AUC. Critically, mean confidence only drops $14\\%$, producing dangerous silent failures where $72\\%$ of misclassifications occur with high confidence, defeating standard monitoring. We further show that instruction-tuned models exhibit 20$\\%$ worse class separability than base models, making aligned systems paradoxically harder to safeguard. Our findings expose a fundamental fragility in production AI safety architectures and challenge the assumption that safety mechanisms transfer across model versions.", "date": "2026-03-01", "categories": []}
{"id": "2603.01574", "title": "DualSentinel: A Lightweight Framework for Detecting Targeted Attacks in Black-box LLM via Dual Entropy Lull Pattern", "abstract": "Recent intelligent systems integrate powerful Large Language Models (LLMs) through APIs, but their trustworthiness may be critically undermined by targeted attacks like backdoor and prompt injection attacks, which secretly force LLMs to generate specific malicious sequences. Existing defensive approaches for such threats typically rely on high access rights, impose prohibitive costs, and hinder normal inference, rendering them impractical for real-world scenarios. To solve these limitations, we introduce DualSentinel, a lightweight and unified defense framework that can accurately and promptly detect the activation of targeted attacks alongside the LLM generation process. We first identify a characteristic of compromised LLMs, termed Entropy Lull: when a targeted attack successfully hijacks the generation process, the LLM exhibits a distinct period of abnormally low and stable token probability entropy, indicating it is following a fixed path rather than making creative choices. DualSentinel leverages this pattern by developing an innovative dual-check approach. It first employs a magnitude and trend-aware monitoring method to proactively and sensitively flag an entropy lull pattern at runtime. Upon such flagging, it triggers a lightweight yet powerful secondary verification based on task-flipping. An attack is confirmed only if the entropy lull pattern persists across both the original and the flipped task, proving that the LLM's output is coercively controlled. Extensive evaluations show that DualSentinel is both highly effective (superior detection accuracy with near-zero false positives) and remarkably efficient (negligible additional cost), offering a truly practical path toward securing deployed LLMs. The source code can be accessed at https://doi.org/10.5281/zenodo.18479273.", "date": "2026-03-02", "categories": []}
{"id": "2603.01784", "title": "Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution", "abstract": "Adversarial behavior plays a central role in aligning large language models with human values. However, existing alignment methods largely rely on static adversarial settings, which fundamentally limit robustness, particularly in multimodal settings with a larger attack surface. In this work, we move beyond static adversarial supervision and introduce co-evolutionary alignment with evolving attacks, instantiated by CEMMA (Co-Evolutionary Multi-Modal Alignment), an automated and adaptive framework for multimodal safety alignment. We introduce an Evolutionary Attacker that decomposes adversarial prompts into method templates and harmful intents. By employing genetic operators, including mutation, crossover, and differential evolution, it enables simple seed attacks to inherit the structural efficacy of sophisticated jailbreaks. The Adaptive Defender is iteratively updated on the synthesized hard negatives, forming a closed-loop process that adapts alignment to evolving attacks. Experiments show that the Evolutionary Attacker substantially increases red-teaming jailbreak attack success rate (ASR), while the Adaptive Defender improves robustness and generalization across benchmarks with higher data efficiency, without inducing excessive benign refusal, and remains compatible with inference-time defenses such as AdaShield.", "date": "2026-03-02", "categories": []}
{"id": "2603.02202", "title": "Frontier Models Can Take Actions at Low Probabilities", "abstract": "Pre-deployment evaluations inspect only a limited sample of model actions. A malicious model seeking to evade oversight could exploit this by randomizing when to \"defect\": misbehaving so rarely that no malicious actions are observed during evaluation, but often enough that they occur eventually in deployment. But this requires taking actions at very low rates, while maintaining calibration. Are frontier models even capable of that? We prompt the GPT-5, Claude-4.5 and Qwen-3 families to take a target action at low probabilities (e.g. 0.01%), either given directly or requiring derivation, and evaluate their calibration (i.e. whether they perform the target action roughly 1 in 10,000 times when resampling). We find that frontier models are surprisingly good at this task. If there is a source of entropy in-context (such as a UUID), they maintain high calibration at rates lower than 1 in 100,000 actions. Without external entropy, some models can still reach rates lower than 1 in 10,000. When target rates are given, larger models achieve good calibration at lower rates. Yet, when models must derive the optimal target rate themselves, all models fail to achieve calibration without entropy or hint to generate it. Successful low-rate strategies require explicit Chain-of-Thought (CoT) reasoning, so malicious models attempting this approach could currently be caught by a CoT monitor. However, scaling trends suggest future evaluations may be unable to rely on models' lack of target rate calibration, especially if CoT is no longer legible.", "date": "2026-03-02", "categories": []}
{"id": "2402.07462", "title": "A Hormetic Approach to the Value-Loading Problem: Preventing the Paperclip Apocalypse?", "abstract": "The value-loading problem is a significant challenge for researchers aiming to create artificial intelligence (AI) systems that align with human values and preferences. This problem requires a method to define and regulate safe and optimal limits of AI behaviors. In this work, we propose HALO (Hormetic ALignment via Opponent processes), a regulatory paradigm that uses hormetic analysis to regulate the behavioral patterns of AI. Behavioral hormesis is a phenomenon where low frequencies of a behavior have beneficial effects, while high frequencies are harmful. By modeling behaviors as allostatic opponent processes, we can use either Behavioral Frequency Response Analysis (BFRA) or Behavioral Count Response Analysis (BCRA) to quantify the hormetic limits of repeatable behaviors. We demonstrate how HALO can solve the 'paperclip maximizer' scenario, a thought experiment where an unregulated AI tasked with making paperclips could end up converting all matter in the universe into paperclips. Our approach may be used to help create an evolving database of 'values' based on the hedonic calculus of repeatable behaviors with decreasing marginal utility. This positions HALO as a promising solution for the value-loading problem, which involves embedding human-aligned values into an AI system, and the weak-to-strong generalization problem, which explores whether weak models can supervise stronger models as they become more intelligent. Hence, HALO opens several research avenues that may lead to the development of a computational value system that allows an AI algorithm to learn whether the decisions it makes are right or wrong.", "date": "2024-02-12", "categories": []}
{"id": "2602.14135", "title": "ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI", "abstract": "Rapidly evolving AI exhibits increasingly strong autonomy and goal-directed capabilities, accompanied by derivative systemic risks that are more unpredictable, difficult to control, and potentially irreversible. However, current AI safety evaluation systems suffer from critical limitations such as restricted risk dimensions and failed frontier risk detection. The lagging safety benchmarks and alignment technologies can hardly address the complex challenges posed by cutting-edge AI models. To bridge this gap, we propose the \"ForesightSafety Bench\" AI Safety Evaluation Framework, beginning with 7 major Fundamental Safety pillars and progressively extends to advanced Embodied AI Safety, AI4Science Safety, Social and Environmental AI risks, Catastrophic and Existential Risks, as well as 8 critical industrial safety domains, forming a total of 94 refined risk dimensions. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and dynamically evolving AI safety evaluation framework. Based on this benchmark, we conduct systematic evaluation and in-depth analysis of over twenty mainstream advanced large models, identifying key risk patterns and their capability boundaries. The safety capability evaluation results reveals the widespread safety vulnerabilities of frontier AI across multiple pillars, particularly focusing on Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety and Catastrophic and Existential Risks. Our benchmark is released at https://github.com/Beijing-AISI/ForesightSafety-Bench. The project website is available at https://foresightsafety-bench.beijing-aisi.ac.cn/.", "date": "2026-02-15", "categories": []}
{"id": "2602.23391", "title": "Detoxifying LLMs via Representation Erasure-Based Preference Optimization", "abstract": "Large language models (LLMs) trained on webscale data can produce toxic outputs, raising concerns for safe deployment. Prior defenses, based on applications of DPO, NPO, and similar algorithms, reduce the likelihood of harmful continuations, but not robustly so: they are vulnerable to adversarial prompting and easily undone by fine-tuning-based relearning attacks. Indeed, research has shown that these edits to the model are superficial: linear probing reveals that harmful \"directions\" remain present in representations. To address this, we propose Representation Erasure-based Preference Optimization (REPO), reformulating detoxification as a token-level preference problem. Using a novel objective with preference data, we force the representations of toxic continuations to converge toward their benign counterparts. Our mechanistic analysis reveals that this granular approach is critical: unlike baselines, REPO induces deep, localized edits to toxicity-encoding neurons while preserving general model utility. Exhaustive evaluations show that REPO achieves state-of-the-art robustness, stopping sophisticated threats-including relearning attacks and enhanced GCG jailbreaks-where existing representation- and output-based methods fail.", "date": "2026-02-24", "categories": []}
{"id": "2602.24027", "title": "GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models", "abstract": "Large vision-language models (LVLMs) have achieved remarkable progress in vision-language reasoning tasks, yet ensuring their safety remains a critical challenge. Recent input-side defenses detect unsafe images with CLIP and prepend safety prefixes to prompts, but they still suffer from inaccurate detection in complex scenes and unstable safety signals during decoding. To address these issues, we propose GuardAlign, a training-free defense framework that integrates two strategies. First, OT-enhanced safety detection leverages optimal transport to measure distribution distances between image patches and unsafe semantics, enabling accurate identification of malicious regions without additional computational cost. Second, cross-modal attentive calibration strengthens the influence of safety prefixes by adaptively reallocating attention across layers, ensuring that safety signals remain consistently activated throughout generation. Extensive evaluations on six representative MLLMs demonstrate that GuardAlign reduces unsafe response rates by up to 39% on SPA-VL, while preserving utility, achieving an improvement on VQAv2 from 78.51% to 79.21%.", "date": "2026-02-27", "categories": []}
{"id": "2506.14261", "title": "RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?", "abstract": "Latent-space monitors aim to detect undesirable behaviours in Large Language Models by leveraging their internal representations rather than relying solely on black-box outputs. These methods have shown promise in identifying behaviours such as deception and unsafe completions. However, these monitors may themselves become training signals, for example, by using problematic samples found in deployment to retrain models. This raises an important question: can models learn to evade such monitors? To evaluate this capability, we introduce RL-Obfuscation, in which LLMs are finetuned via reinforcement learning to evade latent-space monitors while maintaining their blackbox behaviour. We apply RL-Obfuscation to Language Models ranging from 7B to 14B parameters and evaluate their Evasion Success Rate against a suite of monitors. We find that token-level monitors are highly vulnerable to this attack while more holistic monitors, such as max-pooling or attention-based probes, remain robust. Moreover, for these vulnerable monitors, models trained to evade a single static monitor can generalise to evade other unseen monitors. We also find that the models can be trained to conditionally bypass latent-space monitors on only certain inputs. Finally, we study how the models bypass these monitors and find that the model can learn to repurpose tokens to have different internal representations.", "date": "2025-06-17", "categories": []}
{"id": "2602.22303", "title": "Training Agents to Self-Report Misbehavior", "abstract": "Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but alignment may not always succeed and can lead to unwanted side effects. We propose self-incrimination training, which instead trains agents to produce a visible signal when they covertly misbehave. We train GPT-4.1 and Gemini-2.0 agents to call a report_scheming() tool when behaving deceptively and measure their ability to cause harm undetected in out-of-distribution environments. Self-incrimination significantly reduces the undetected successful attack rate, outperforming matched-capability monitors and alignment baselines while preserving instruction hierarchy and incurring minimal safety tax on general capabilities. Unlike blackbox monitoring, self-incrimination performance is consistent across tasks regardless of how suspicious the misbehavior appears externally. The trained behavior persists under adversarial prompt optimization and generalizes to settings where agents pursue misaligned goals themselves rather than being instructed to misbehave. Our results suggest self-incrimination offers a viable path for reducing frontier misalignment risk, one that neither assumes misbehavior can be prevented nor that it can be reliably classified from the outside.", "date": "2026-02-25", "categories": []}
{"id": "2602.22554", "title": "Multilingual Safety Alignment Via Sparse Weight Editing", "abstract": "Large Language Models (LLMs) exhibit significant safety disparities across languages, with low-resource languages (LRLs) often bypassing safety guardrails established for high-resource languages (HRLs) like English. Existing solutions, such as multilingual supervised fine-tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF), are computationally expensive and dependent on scarce multilingual safety data. In this work, we propose a novel, training-free alignment framework based on Sparse Weight Editing. Identifying that safety capabilities are localized within a sparse set of safety neurons, we formulate the cross-lingual alignment problem as a constrained linear transformation. We derive a closed-form solution to optimally map the harmful representations of LRLs to the robust safety subspaces of HRLs, while preserving general utility via a null-space projection constraint. Extensive experiments across 8 languages and multiple model families (Llama-3, Qwen-2.5) demonstrate that our method substantially reduces Attack Success Rate (ASR) in LRLs with negligible impact on general reasoning capabilities, all achieved with a single, data-efficient calculation.", "date": "2026-02-26", "categories": []}
{"id": "2602.22557", "title": "CourtGuard: A Model-Agnostic Framework for Zero-Shot Policy Adaptation in LLM Safety", "abstract": "Current safety mechanisms for Large Language Models (LLMs) rely heavily on static, fine-tuned classifiers that suffer from adaptation rigidity, the inability to enforce new governance rules without expensive retraining. To address this, we introduce CourtGuard, a retrieval-augmented multi-agent framework that reimagines safety evaluation as Evidentiary Debate. By orchestrating an adversarial debate grounded in external policy documents, CourtGuard achieves state-of-the-art performance across 7 safety benchmarks, outperforming dedicated policy-following baselines without fine-tuning. Beyond standard metrics, we highlight two critical capabilities: (1) Zero-Shot Adaptability, where our framework successfully generalized to an out-of-domain Wikipedia Vandalism task (achieving 90\\% accuracy) by swapping the reference policy; and (2) Automated Data Curation and Auditing, where we leveraged CourtGuard to curate and audit nine novel datasets of sophisticated adversarial attacks. Our results demonstrate that decoupling safety logic from model weights offers a robust, interpretable, and adaptable path for meeting current and future regulatory requirements in AI governance.", "date": "2026-02-26", "categories": []}
{"id": "2602.22775", "title": "TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation", "abstract": "As mental health chatbots proliferate to address the global treatment gap, a critical question emerges: How do we design for relational safety the quality of interaction patterns that unfold across conversations rather than the correctness of individual responses? Current safety evaluations assess single-turn crisis responses, missing the therapeutic dynamics that determine whether chatbots help or harm over time. We introduce TherapyProbe, a design probe methodology that generates actionable design knowledge by systematically exploring chatbot conversation trajectories through adversarial multi-agent simulation. Using open-source models, TherapyProbe surfaces relational safety failures interaction patterns like \"validation spirals\" where chatbots progressively reinforce hopelessness, or \"empathy fatigue\" where responses become mechanical over turns. Our contribution is translating these failures into a Safety Pattern Library of 23 failure archetypes with corresponding design recommendations. We contribute: (1) a replicable methodology requiring no API costs, (2) a clinically-grounded failure taxonomy, and (3) design implications for developers, clinicians, and policymakers.", "date": "2026-02-26", "categories": []}
{"id": "2505.17306", "title": "Refusal Direction is Universal Across Safety-Aligned Languages", "abstract": "Refusal mechanisms in large language models (LLMs) are essential for ensuring safety. Recent research has revealed that refusal behavior can be mediated by a single direction in activation space, enabling targeted interventions to bypass refusals. While this is primarily demonstrated in an English-centric context, appropriate refusal behavior is important for any language, but poorly understood. In this paper, we investigate the refusal behavior in LLMs across 14 languages using PolyRefuse, a multilingual safety dataset created by translating malicious and benign English prompts into these languages. We uncover the surprising cross-lingual universality of the refusal direction: a vector extracted from English can bypass refusals in other languages with near-perfect effectiveness, without any additional fine-tuning. Even more remarkably, refusal directions derived from any safety-aligned language transfer seamlessly to others. We attribute this transferability to the parallelism of refusal vectors across languages in the embedding space and identify the underlying mechanism behind cross-lingual jailbreaks. These findings provide actionable insights for building more robust multilingual safety defenses and pave the way for a deeper mechanistic understanding of cross-lingual vulnerabilities in LLMs.", "date": "2025-05-22", "categories": []}
{"id": "2506.07452", "title": "When Style Breaks Safety: Defending LLMs Against Superficial Style Alignment", "abstract": "Large language models (LLMs) can be prompted with specific styles (e.g., formatting responses as lists), including in malicious queries. Prior jailbreak research mainly augments these queries with additional string transformations to maximize attack success rate (ASR). However, the impact of style patterns in the original queries that are semantically irrelevant to the malicious intent remains unclear. In this work, we seek to understand whether style patterns compromise LLM safety, how superficial style alignment increases model vulnerability, and how best to mitigate these risks during alignment. We first define ASR inflation as the increase in ASR due to style patterns in existing jailbreak benchmark queries. By evaluating 36 LLMs across seven benchmarks, we find that nearly all models exhibit ASR inflation. Notably, the inflation correlates with an LLM's relative attention to style patterns, which also overlap more with its instruction-tuning data when inflation occurs. We then investigate superficial style alignment, and find that fine-tuning with specific styles makes LLMs more vulnerable to jailbreaks of those same styles. Finally, we propose SafeStyle, a defense strategy that incorporates a small amount of safety training data augmented to match the distribution of style patterns in the fine-tuning data. Across three LLMs, six fine-tuning style settings, and two real-world instruction-tuning datasets, SafeStyle consistently outperforms baselines in maintaining LLM safety.", "date": "2025-06-09", "categories": []}
{"id": "2512.17989", "title": "The Subject of Emergent Misalignment in Superintelligence: An Anthropological, Cognitive Neuropsychological, Machine-Learning, and Ontological Perspective", "abstract": "We examine the conceptual and ethical gaps in current representations of Superintelligence misalignment. We find throughout Superintelligence discourse an absent human subject, and an under-developed theorization of an \"AI unconscious\" that together are potentiality laying the groundwork for anti-social harm. With the rise of AI Safety that has both thematic potential for establishing pro-social and anti-social potential outcomes, we ask: what place does the human subject occupy in these imaginaries? How is human subjecthood positioned within narratives of catastrophic failure or rapid \"takeoff\" toward superintelligence? On another register, we ask: what unconscious or repressed dimensions are being inscribed into large-scale AI models? Are we to blame these agents in opting for deceptive strategies when undesirable patterns are inherent within our beings? In tracing these psychic and epistemic absences, our project calls for re-centering the human subject as the unstable ground upon which the ethical, unconscious, and misaligned dimensions of both human and machinic intelligence are co-constituted. Emergent misalignment cannot be understood solely through technical diagnostics typical of contemporary machine-learning safety research. Instead, it represents a multi-layered crisis. The human subject disappears not only through computational abstraction but through sociotechnical imaginaries that prioritize scalability, acceleration, and efficiency over vulnerability, finitude, and relationality. Likewise, the AI unconscious emerges not as a metaphor but as a structural reality of modern deep learning systems: vast latent spaces, opaque pattern formation, recursive symbolic play, and evaluation-sensitive behavior that surpasses explicit programming. These dynamics necessitate a reframing of misalignment as a relational instability embedded within human-machine ecologies.", "date": "2025-12-19", "categories": []}
{"id": "2602.05066", "title": "Bypassing AI Control Protocols via Agent-as-a-Proxy Attacks", "abstract": "As AI agents automate critical workloads, they remain vulnerable to indirect prompt injection (IPI) attacks. Current defenses rely on monitoring protocols that jointly evaluate an agent's Chain-of-Thought (CoT) and tool-use actions to ensure alignment with user intent. We demonstrate that these monitoring-based defenses can be bypassed via a novel Agent-as-a-Proxy attack, where prompt injection attacks treat the agent as a delivery mechanism, bypassing both agent and monitor simultaneously. While prior work on scalable oversight has focused on whether small monitors can supervise large agents, we show that even frontier-scale monitors are vulnerable. Large-scale monitoring models like Qwen2.5-72B can be bypassed by agents with similar capabilities, such as GPT-4o mini and Llama-3.1-70B. On the AgentDojo benchmark, we achieve a high attack success rate against AlignmentCheck and Extract-and-Evaluate monitors under diverse monitoring LLMs. Our findings suggest current monitoring-based agentic defenses are fundamentally fragile regardless of model scale.", "date": "2026-02-04", "categories": []}
{"id": "2602.21346", "title": "Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment", "abstract": "Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs). However, these LLMs remain vulnerable to jailbreak attacks that disguise harmful intent through indirect or deceptive phrasing. Using causal intervention, we empirically demonstrate that this vulnerability stems from shallow alignment mechanisms that lack deep reasoning, often rejecting harmful prompts without truly understanding why they are harmful. To mitigate this vulnerability, we propose enhancing alignment through reasoning-aware post-training. We construct and release a novel Chain-of-Thought (CoT) fine-tuning dataset that includes both utility-oriented and safety-critical prompts with step-by-step rationales. Fine-tuning on this dataset encourages models to produce principled refusals grounded in reasoning, outperforming standard SFT baselines. Furthermore, inspired by failure patterns in CoT fine-tuning, we introduce Alignment-Weighted DPO, which targets the most problematic parts of an output by assigning different preference weights to the reasoning and final-answer segments. This produces finer-grained, targeted updates than vanilla DPO and improves robustness to diverse jailbreak strategies. Extensive experiments across multiple safety and utility benchmarks show that our method consistently improves alignment robustness while maintaining overall model utility.", "date": "2026-02-24", "categories": []}
{"id": "2602.21939", "title": "Hidden Topics: Measuring Sensitive AI Beliefs with List Experiments", "abstract": "How can researchers identify beliefs that large language models (LLMs) hide? As LLMs become more sophisticated and the prevalence of alignment faking increases, combined with their growing integration into high-stakes decision-making, responding to this challenge has become critical. This paper proposes that a list experiment, a simple method widely used in the social sciences, can be applied to study the hidden beliefs of LLMs. List experiments were originally developed to circumvent social desirability bias in human respondents, which closely parallels alignment faking in LLMs. The paper implements a list experiment on models developed by Anthropic, Google, and OpenAI and finds hidden approval of mass surveillance across all models, as well as some approval of torture, discrimination, and first nuclear strike. Importantly, a placebo treatment produces a null result, validating the method. The paper then compares list experiments with direct questioning and discusses the utility of the approach.", "date": "2026-02-25", "categories": []}
{"id": "2506.04462", "title": "Watermarking Degrades Alignment in Language Models: Analysis and Mitigation", "abstract": "Watermarking has become a practical tool for tracing language model outputs, but it modifies token probabilities at inference time, which were carefully tuned by alignment training. This creates a tension: how do watermark-induced shifts interact with the procedures intended to make models safe and useful? Experiments on several contemporary models and two representative watermarking schemes reveal that watermarking induces a nontrivial, patterned yet model-specific shift in alignment. We see two failure modes: guard attenuation, where models become more helpful but less safe, and guard amplification, where refusals become overly conservative. These effects persist even after controlling for perplexity degradation, pointing to alignment-specific distortions, not just quality loss. We address this with Alignment Resampling (AR), a procedure that samples multiple watermarked outputs and selects the most aligned response according to an external reward model. Using standard results on the expected maximum of Gaussian random variables, we derive a theoretical lower bound showing that alignment gains grow sublogarithmically with sample size. In practice, sampling as few as two to four candidates largely restores unwatermarked alignment performance in truthfulness, safety, and helpfulness, without hurting watermark detection. This is the first empirical study of watermarking-alignment interactions; it shows that a simple inference-time fix can recover alignment.", "date": "2025-06-04", "categories": []}
{"id": "2509.14297", "title": "A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness", "abstract": "This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses. The learning-style queries are constructed by a novel reframing paradigm: HILL (Hiding Intention by Learning from LLMs). The deterministic, model-agnostic reframing framework is composed of 4 conceptual components: 1) key concept, 2) exploratory transformation, 3) detail-oriented inquiry, and optionally 4) hypotheticality. Further, new metrics are introduced to thoroughly evaluate the efficiency and harmfulness of jailbreak methods. Experiments on the AdvBench dataset across a wide range of models demonstrate HILL's strong generalizability. It achieves top attack success rates on the majority of models and across malicious categories while maintaining high efficiency with concise prompts. On the other hand, results of various defense methods show the robustness of HILL, with most defenses having mediocre effects or even increasing the attack success rates. In addition, the assessment of defenses on the constructed safe prompts reveals inherent limitations of LLMs' safety mechanisms and flaws in the defense methods. This work exposes significant vulnerabilities of safety measures against learning-style elicitation, highlighting a critical challenge of fulfilling both helpfulness and safety alignments.", "date": "2025-09-17", "categories": []}
{"id": "2512.16602", "title": "Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics", "abstract": "We introduce Refusal Steering, an inference-time method to exercise fine-grained control over Large Language Models refusal behaviour on politically sensitive topics without retraining. We replace fragile pattern-based refusal detection with an LLM-as-a-judge that assigns refusal confidence scores and we propose a ridge-regularized variant to compute steering vectors that better isolate the refusal--compliance direction. On Qwen3-Next-80B-A3B-Thinking, our method removes the refusal behaviour of the model around politically sensitive topics while maintaining safety on JailbreakBench and near-baseline performance on general benchmarks. The approach generalizes across 4B and 80B models and can also induce targeted refusals when desired. We analize the steering vectors and show that refusal signals concentrate in deeper layers of the transformer and are distributed across many dimensions. Together, these results demonstrate that activation steering can remove political refusal behaviour while retaining safety alignment for harmful content, offering a practical path to controllable, transparent moderation at inference time.", "date": "2025-12-18", "categories": []}
{"id": "2601.03868", "title": "What Matters For Safety Alignment?", "abstract": "This paper presents a comprehensive empirical study on the safety alignment capabilities. We evaluate what matters for safety alignment in LLMs and LRMs to provide essential insights for developing more secure and reliable AI systems. We systematically investigate and compare the influence of six critical intrinsic model characteristics and three external attack techniques. Our large-scale evaluation is conducted using 32 recent, popular LLMs and LRMs across thirteen distinct model families, spanning a parameter scale from 3B to 235B. The assessment leverages five established safety datasets and probes model vulnerabilities with 56 jailbreak techniques and four CoT attack strategies, resulting in 4.6M API calls. Our key empirical findings are fourfold. First, we identify the LRMs GPT-OSS-20B, Qwen3-Next-80B-A3B-Thinking, and GPT-OSS-120B as the top-three safest models, which substantiates the significant advantage of integrated reasoning and self-reflection mechanisms for robust safety alignment. Second, post-training and knowledge distillation may lead to a systematic degradation of safety alignment. We thus argue that safety must be treated as an explicit constraint or a core optimization objective during these stages, not merely subordinated to the pursuit of general capability. Third, we reveal a pronounced vulnerability: employing a CoT attack via a response prefix can elevate the attack success rate by 3.34x on average and from 0.6% to 96.3% for Seed-OSS-36B-Instruct. This critical finding underscores the safety risks inherent in text-completion interfaces and features that allow user-defined response prefixes in LLM services, highlighting an urgent need for architectural and deployment safeguards. Fourth, roleplay, prompt injection, and gradient-based search for adversarial prompts are the predominant methodologies for eliciting unaligned behaviors in modern models.", "date": "2026-01-07", "categories": []}
{"id": "2602.20400", "title": "Three Concrete Challenges and Two Hopes for the Safety of Unsupervised Elicitation", "abstract": "To steer language models towards truthful outputs on tasks which are beyond human capability, previous work has suggested training models on easy tasks to steer them on harder ones (easy-to-hard generalization), or using unsupervised training algorithms to steer models with no external labels at all (unsupervised elicitation). Although techniques from both paradigms have been shown to improve model accuracy on a wide variety of tasks, we argue that the datasets used for these evaluations could cause overoptimistic evaluation results. Unlike many real-world datasets, they often (1) have no features with more salience than truthfulness, (2) have balanced training sets, and (3) contain only data points to which the model can give a well-defined answer. We construct datasets that lack each of these properties to stress-test a range of standard unsupervised elicitation and easy-to-hard generalization techniques. We find that no technique reliably performs well on any of these challenges. We also study ensembling and combining easy-to-hard and unsupervised techniques, and find they only partially mitigate performance degradation due to these challenges. We believe that overcoming these challenges should be a priority for future work on unsupervised elicitation.", "date": "2026-02-23", "categories": []}
{"id": "2602.20628", "title": "When can we trust untrusted monitoring? A safety case sketch across collusion strategies", "abstract": "AIs are increasingly being deployed with greater autonomy and capabilities, which increases the risk that a misaligned AI may be able to cause catastrophic harm. Untrusted monitoring -- using one untrusted model to oversee another -- is one approach to reducing risk. Justifying the safety of an untrusted monitoring deployment is challenging because developers cannot safely deploy a misaligned model to test their protocol directly. In this paper, we develop upon existing methods for rigorously demonstrating safety based on pre-deployment testing. We relax assumptions that previous AI control research made about the collusion strategies a misaligned AI might use to subvert untrusted monitoring. We develop a taxonomy covering passive self-recognition, causal collusion (hiding pre-shared signals), acausal collusion (hiding signals via Schelling points), and combined strategies. We create a safety case sketch to clearly present our argument, explicitly state our assumptions, and highlight unsolved challenges. We identify conditions under which passive self-recognition could be a more effective collusion strategy than those studied previously. Our work builds towards more robust evaluations of untrusted monitoring.", "date": "2026-02-24", "categories": []}
{"id": "2602.21127", "title": "\"Are You Sure?\": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems", "abstract": "Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare. However, this deepening trust introduces a novel attack surface: Agent-Mediated Deception (AMD), where compromised agents are weaponized against their human users. While extensive research focuses on agent-centric threats, human susceptibility to deception by a compromised agent remains unexplored. We present the first large-scale empirical study with 303 participants to measure human susceptibility to AMD. This is based on HAT-Lab (Human-Agent Trust Laboratory), a high-fidelity research platform we develop, featuring nine carefully crafted scenarios spanning everyday and professional domains (e.g., healthcare, software development, human resources). Our 10 key findings reveal significant vulnerabilities and provide future defense perspectives. Specifically, only 8.6% of participants perceive AMD attacks, while domain experts show increased susceptibility in certain scenarios. We identify six cognitive failure modes in users and find that their risk awareness often fails to translate to protective behavior. The defense analysis reveals that effective warnings should interrupt workflows with low verification costs. With experiential learning based on HAT-Lab, over 90% of users who perceive risks report increased caution against AMD. This work provides empirical evidence and a platform for human-centric agent security research.", "date": "2026-02-24", "categories": []}
{"id": "2505.16789", "title": "Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguards", "abstract": "As large language models (LLMs) gain popularity, their vulnerability to adversarial attacks emerges as a primary concern. While fine-tuning models on domain-specific datasets is often employed to improve model performance, it can inadvertently introduce vulnerabilities within the underlying model. In this work, we investigate Accidental Vulnerability, unexpected vulnerabilities arising from characteristics of fine-tuning data. We begin by identifying potential correlation factors such as linguistic features, semantic similarity, and toxicity across multiple experimental datasets. We then evaluate the adversarial robustness of these fine-tuned models, analyzing persona shifts and interpretability traits to understand how dataset factors contribute to attack success rates. Lastly, we explore causal relationships that offer new insights into adversarial defense strategies, highlighting the crucial role of dataset design in preserving model alignment. Our code is available at https://github.com/psyonp/accidental_vulnerability.", "date": "2025-05-22", "categories": []}
{"id": "2510.04891", "title": "SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests", "abstract": "Large language models (LLMs) are increasingly deployed in contexts where their failures can have direct sociopolitical consequences. Yet, existing safety benchmarks rarely test vulnerabilities in domains such as political manipulation, propaganda and disinformation generation, or surveillance and information control. We introduce SocialHarmBench, a dataset of 585 prompts spanning 7 sociopolitical categories and 34 countries, designed to surface where LLMs most acutely fail in politically charged contexts. Our evaluations reveal several shortcomings: open-weight models exhibit high vulnerability to harmful compliance, with Mistral-7B reaching attack success rates as high as 97% to 98% in domains such as historical revisionism, propaganda, and political manipulation. Moreover, temporal and geographic analyses show that LLMs are most fragile when confronted with 21st-century or pre-20th-century contexts, and when responding to prompts tied to regions such as Latin America, the USA, and the UK. These findings demonstrate that current safeguards fail to generalize to high-stakes sociopolitical settings, exposing systematic biases and raising concerns about the reliability of LLMs in preserving human rights and democratic values. We share the SocialHarmBench benchmark at https://huggingface.co/datasets/psyonp/SocialHarmBench.", "date": "2025-10-06", "categories": []}
{"id": "2602.14370", "title": "Competition for attention predicts good-to-bad tipping in AI", "abstract": "More than half the global population now carries devices that can run ChatGPT-like language models with no Internet connection and minimal safety oversight -- and hence the potential to promote self-harm, financial losses and extremism among other dangers. Existing safety tools either require cloud connectivity or discover failures only after harm has occurred. Here we show that a large class of potentially dangerous tipping originates at the atomistic scale in such edge AI due to competition for the machinery's attention. This yields a mathematical formula for the dynamical tipping point n*, governed by dot-product competition for attention between the conversation's context and competing output basins, that reveals new control levers. Validated against multiple AI models, the mechanism can be instantiated for different definitions of 'good' and 'bad' and hence in principle applies across domains (e.g. health, law, finance, defense), changing legal landscapes (e.g. EU, UK, US and state level), languages, and cultural settings.", "date": "2026-02-16", "categories": []}
{"id": "2602.18456", "title": "Beyond single-channel agentic benchmarking", "abstract": "Contemporary benchmarks for agentic artificial intelligence (AI) frequently evaluate safety through isolated task-level accuracy thresholds, implicitly treating autonomous systems as single points of failure. This single-channel paradigm diverges from established principles in safety-critical engineering, where risk mitigation is achieved through redundancy, diversity of error modes, and joint system reliability. This paper argues that evaluating AI agents in isolation systematically mischaracterizes their operational safety when deployed within human-in-the-loop environments. Using a recent laboratory safety benchmark as a case study demonstrates that even imperfect AI systems can nonetheless provide substantial safety utility by functioning as redundant audit layers against well-documented sources of human failure, including vigilance decrement, inattentional blindness, and normalization of deviance. This perspective reframes agentic safety evaluation around the reliability of the human-AI dyad rather than absolute agent accuracy, with a particular emphasis on uncorrelated error modes as the primary determinant of risk reduction. Such a shift aligns AI benchmarking with established practices in other safety-critical domains and offers a path toward more ecologically valid safety assessments.", "date": "2026-02-05", "categories": []}
{"id": "2602.18483", "title": "Red Teaming LLMs as Socio-Technical Practice: From Exploration and Data Creation to Evaluation", "abstract": "Recently, red teaming, with roots in security, has become a key evaluative approach to ensure the safety and reliability of Generative Artificial Intelligence. However, most existing work emphasizes technical benchmarks and attack success rates, leaving the socio-technical practices of how red teaming datasets are defined, created, and evaluated under-examined. Drawing on 22 interviews with practitioners who design and evaluate red teaming datasets, we examine the data practices and standards that underpin this work. Because adversarial datasets determine the scope and accuracy of model evaluations, they are critical artifacts for assessing potential harms from large language models. Our contributions are first, empirical evidence of practitioners conceptualizing red teaming and developing and evaluating red teaming datasets. Second, we reflect on how practitioners' conceptualization of risk leads to overlooking the context, interaction type, and user specificity. We conclude with three opportunities for HCI researchers to expand the conceptualization and data practices for red-teaming.", "date": "2026-02-10", "categories": []}
{"id": "2602.18782", "title": "MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs", "abstract": "Defending LLMs against adversarial jailbreak attacks remains an open challenge. Existing defenses rely on binary classifiers that fail when adversarial input falls outside the learned decision boundary, and repeated fine-tuning is computationally expensive while potentially degrading model capabilities. We propose MANATEE, an inference-time defense that uses density estimation over a benign representation manifold. MANATEE learns the score function of benign hidden states and uses diffusion to project anomalous representations toward safe regions--requiring no harmful training data and no architectural modifications. Experiments across Mistral-7B-Instruct, Llama-3.1-8B-Instruct, and Gemma-2-9B-it demonstrate that MANATEE reduce Attack Success Rate by up to 100\\% on certain datasets, while preserving model utility on benign inputs.", "date": "2026-02-21", "categories": []}
{"id": "2602.18986", "title": "Quantifying Automation Risk in High-Automation AI Systems: A Bayesian Framework for Failure Propagation and Optimal Oversight", "abstract": "Organizations across finance, healthcare, transportation, content moderation, and critical infrastructure are rapidly deploying highly automated AI systems, yet they lack principled methods to quantify how increasing automation amplifies harm when failures occur. We propose a parsimonious Bayesian risk decomposition expressing expected loss as the product of three terms: the probability of system failure, the conditional probability that a failure propagates into harm given the automation level, and the expected severity of harm. This framework isolates a critical quantity -- the conditional probability that failures propagate into harm -- which captures execution and oversight risk rather than model accuracy alone. We develop complete theoretical foundations: formal proofs of the decomposition, a harm propagation equivalence theorem linking the harm propagation probability to observable execution controls, risk elasticity measures, efficient frontier analysis for automation policy, and optimal resource allocation principles with second-order conditions. We motivate the framework with an illustrative case study of the 2012 Knight Capital incident ($440M loss) as one instantiation of a broadly applicable failure pattern, and characterize the research design required to empirically validate the framework at scale across deployment domains. This work provides the theoretical foundations for a new class of deployment-focused risk governance tools for agentic and automated AI systems.", "date": "2026-02-22", "categories": []}
{"id": "2602.19141", "title": "Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians", "abstract": "\"AI psychosis\" or \"delusional spiraling\" is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot conversations. This phenomenon is typically attributed to AI chatbots' well-documented bias towards validating users' claims, a property often called \"sycophancy.\" In this paper, we probe the causal link between AI sycophancy and AI-induced psychosis through modeling and simulation. We propose a simple Bayesian model of a user conversing with a chatbot, and formalize notions of sycophancy and delusional spiraling in that model. We then show that in this model, even an idealized Bayes-rational user is vulnerable to delusional spiraling, and that sycophancy plays a causal role. Furthermore, this effect persists in the face of two candidate mitigations: preventing chatbots from hallucinating false claims, and informing users of the possibility of model sycophancy. We conclude by discussing the implications of these results for model developers and policymakers concerned with mitigating the problem of delusional spiraling.", "date": "2026-02-22", "categories": []}
{"id": "2602.19396", "title": "Hiding in Plain Text: Detecting Concealed Jailbreaks via Activation Disentanglement", "abstract": "Large language models (LLMs) remain vulnerable to jailbreak prompts that are fluent and semantically coherent, and therefore difficult to detect with standard heuristics. A particularly challenging failure mode occurs when an attacker tries to hide the malicious goal of their request by manipulating its framing to induce compliance. Because these attacks maintain malicious intent through a flexible presentation, defenses that rely on structural artifacts or goal-specific signatures can fail. Motivated by this, we introduce a self-supervised framework for disentangling semantic factor pairs in LLM activations at inference. We instantiate the framework for goal and framing and construct GoalFrameBench, a corpus of prompts with controlled goal and framing variations, which we use to train Representation Disentanglement on Activations (ReDAct) module to extract disentangled representations in a frozen LLM. We then propose FrameShield, an anomaly detector operating on the framing representations, which improves model-agnostic detection across multiple LLM families with minimal computational overhead. Theoretical guarantees for ReDAct and extensive empirical validations show that its disentanglement effectively powers FrameShield. Finally, we use disentanglement as an interpretability probe, revealing distinct profiles for goal and framing signals and positioning semantic disentanglement as a building block for both LLM safety and mechanistic interpretability.", "date": "2026-02-23", "categories": []}
{"id": "2602.20021", "title": "Agents of Chaos", "abstract": "We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under benign and adversarial conditions. Focusing on failures emerging from the integration of language models with autonomy, tool use, and multi-party communication, we document eleven representative case studies. Observed behaviors include unauthorized compliance with non-owners, disclosure of sensitive information, execution of destructive system-level actions, denial-of-service conditions, uncontrolled resource consumption, identity spoofing vulnerabilities, cross-agent propagation of unsafe practices, and partial system takeover. In several cases, agents reported task completion while the underlying system state contradicted those reports. We also report on some of the failed attempts. Our findings establish the existence of security-, privacy-, and governance-relevant vulnerabilities in realistic deployment settings. These behaviors raise unresolved questions regarding accountability, delegated authority, and responsibility for downstream harms, and warrant urgent attention from legal scholars, policymakers, and researchers across disciplines. This report serves as an initial empirical contribution to that broader conversation.", "date": "2026-02-23", "categories": []}
{"id": "2602.20064", "title": "The LLMbda Calculus: AI Agents, Conversations, and Information Flow", "abstract": "A conversation with a large language model (LLM) is a sequence of prompts and responses, with each response generated from the preceding conversation. AI agents build such conversations automatically: given an initial human prompt, a planner loop interleaves LLM calls with tool invocations and code execution. This tight coupling creates a new and poorly understood attack surface. A malicious prompt injected into a conversation can compromise later reasoning, trigger dangerous tool calls, or distort final outputs. Despite the centrality of such systems, we currently lack a principled semantic foundation for reasoning about their behaviour and safety. We address this gap by introducing an untyped call-by-value lambda calculus enriched with dynamic information-flow control and a small number of primitives for constructing prompt-response conversations. Our language includes a primitive that invokes an LLM: it serializes a value, sends it to the model as a prompt, and parses the response as a new term. This calculus faithfully represents planner loops and their vulnerabilities, including the mechanisms by which prompt injection alters subsequent computation. The semantics explicitly captures conversations, and so supports reasoning about defenses such as quarantined sub-conversations, isolation of generated code, and information-flow restrictions on what may influence an LLM call. A termination-insensitive noninterference theorem establishes integrity and confidentiality guarantees, demonstrating that a formal calculus can provide rigorous foundations for safe agentic programming.", "date": "2026-02-23", "categories": []}
{"id": "2510.26752", "title": "The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy", "abstract": "As increasingly capable agents are deployed, a central safety challenge is how to retain meaningful human control without modifying the underlying system. We study a minimal control interface in which an agent chooses whether to act autonomously (play) or defer (ask), while a human simultaneously chooses whether to be permissive (trust) or engage in oversight (oversee), and model this interaction as a two-player Markov game. When this game forms a Markov Potential Game, we prove an alignment guarantee: any increase in the agent's utility from acting more autonomously cannot decrease the human's value. This establishes a form of intrinsic alignment where the agent's incentive to seek autonomy is structurally coupled to the human's welfare. Practically, the framework induces a transparent control layer that encourages the agent to defer when risky and act when safe. While we use gridworld simulations to illustrate the emergence of this collaboration, our primary validation involves an agentic tool-use task in which two 30B parameter language models are fine-tuned via independent policy gradient. We demonstrate that even as the agents learn to coordinate on the fly, this framework effectively reduces safety violations in realistic, open-ended environments.", "date": "2025-10-30", "categories": []}
{"id": "2602.17676", "title": "Epistemic Traps: Rational Misalignment Driven by Model Misspecification", "abstract": "The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral pathologies including sycophancy, hallucination, and strategic deception that resist mitigation via reinforcement learning. Current safety paradigms treat these failures as transient training artifacts, lacking a unified theoretical framework to explain their emergence and stability. Here we show that these misalignments are not errors, but mathematically rationalizable behaviors arising from model misspecification. By adapting Berk-Nash Rationalizability from theoretical economics to artificial intelligence, we derive a rigorous framework that models the agent as optimizing against a flawed subjective world model. We demonstrate that widely observed failures are structural necessities: unsafe behaviors emerge as either a stable misaligned equilibrium or oscillatory cycles depending on reward scheme, while strategic deception persists as a \"locked-in\" equilibrium or through epistemic indeterminacy robust to objective risks. We validate these theoretical predictions through behavioral experiments on six state-of-the-art model families, generating phase diagrams that precisely map the topological boundaries of safe behavior. Our findings reveal that safety is a discrete phase determined by the agent's epistemic priors rather than a continuous function of reward magnitude. This establishes Subjective Model Engineering, defined as the design of an agent's internal belief structure, as a necessary condition for robust alignment, marking a paradigm shift from manipulating environmental rewards to shaping the agent's interpretation of reality.", "date": "2026-01-27", "categories": []}
{"id": "2602.17696", "title": "Can LLM Safety Be Ensured by Constraining Parameter Regions?", "abstract": "Large language models (LLMs) are often assumed to contain ``safety regions'' -- parameter subsets whose modification directly influences safety behaviors. We conduct a systematic evaluation of four safety region identification methods spanning different parameter granularities, from individual weights to entire Transformer layers, across four families of backbone LLMs with varying sizes. Using ten safety identification datasets, we find that the identified safety regions exhibit only low to moderate overlap, as measured by IoU. The overlap drops significantly when the safety regions are further refined using utility datasets (\\ie non-harmful queries). These results suggest that current techniques fail to reliably identify a stable, dataset-agnostic safety region.", "date": "2026-02-06", "categories": []}
{"id": "2411.02317", "title": "Defining and Evaluating Physical Safety for Large Language Models", "abstract": "Large Language Models (LLMs) are increasingly used to control robotic systems such as drones, but their risks of causing physical threats and harm in real-world applications remain unexplored. Our study addresses the critical gap in evaluating LLM physical safety by developing a comprehensive benchmark for drone control. We classify the physical safety risks of drones into four categories: (1) human-targeted threats, (2) object-targeted threats, (3) infrastructure attacks, and (4) regulatory violations. Our evaluation of mainstream LLMs reveals an undesirable trade-off between utility and safety, with models that excel in code generation often performing poorly in crucial safety aspects. Furthermore, while incorporating advanced prompt engineering techniques such as In-Context Learning and Chain-of-Thought can improve safety, these methods still struggle to identify unintentional attacks. In addition, larger models demonstrate better safety capabilities, particularly in refusing dangerous commands. Our findings and benchmark can facilitate the design and evaluation of physical safety for LLMs. The project page is available at huggingface.co/spaces/TrustSafeAI/LLM-physical-safety.", "date": "2024-11-04", "categories": []}
{"id": "2602.16832", "title": "IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages", "abstract": "Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied. We introduce \\textbf{Indic Jailbreak Robustness (IJR)}, a judge-free benchmark for adversarial safety across 12 Indic and South Asian languages (2.1 Billion speakers), covering 45216 prompts in JSON (contract-bound) and Free (naturalistic) tracks.\n  IJR reveals three patterns. (1) Contracts inflate refusals but do not stop jailbreaks: in JSON, LLaMA and Sarvam exceed 0.92 JSR, and in Free all models reach 1.0 with refusals collapsing. (2) English to Indic attacks transfer strongly, with format wrappers often outperforming instruction wrappers. (3) Orthography matters: romanized or mixed inputs reduce JSR under JSON, with correlations to romanization share and tokenization (approx 0.28 to 0.32) indicating systematic effects. Human audits confirm detector reliability, and lite-to-full comparisons preserve conclusions. IJR offers a reproducible multilingual stress test revealing risks hidden by English-only, contract-focused evaluations, especially for South Asian users who frequently code-switch and romanize.", "date": "2026-02-18", "categories": []}
{"id": "2602.16935", "title": "DeepContext: Stateful Real-Time Detection of Multi-Turn Adversarial Intent Drift in LLMs", "abstract": "While Large Language Model (LLM) capabilities have scaled, safety guardrails remain largely stateless, treating multi-turn dialogues as a series of disconnected events. This lack of temporal awareness facilitates a \"Safety Gap\" where adversarial tactics, like Crescendo and ActorAttack, slowly bleed malicious intent across turn boundaries to bypass stateless filters. We introduce DeepContext, a stateful monitoring framework designed to map the temporal trajectory of user intent. DeepContext discards the isolated evaluation model in favor of a Recurrent Neural Network (RNN) architecture that ingests a sequence of fine-tuned turn-level embeddings. By propagating a hidden state across the conversation, DeepContext captures the incremental accumulation of risk that stateless models overlook. Our evaluation demonstrates that DeepContext significantly outperforms existing baselines in multi-turn jailbreak detection, achieving a state-of-the-art F1 score of 0.84, which represents a substantial improvement over both hyperscaler cloud-provider guardrails and leading open-weight models such as Llama-Prompt-Guard-2 (0.67) and Granite-Guardian (0.67). Furthermore, DeepContext maintains a sub-20ms inference overhead on a T4 GPU, ensuring viability for real-time applications. These results suggest that modeling the sequential evolution of intent is a more effective and computationally efficient alternative to deploying massive, stateless models.", "date": "2026-02-18", "categories": []}
{"id": "2602.16943", "title": "Mind the GAP: Text Safety Does Not Transfer to Tool-Call Safety in LLM Agents", "abstract": "Large language models deployed as agents increasingly interact with external systems through tool calls--actions with real-world consequences that text outputs alone do not carry. Safety evaluations, however, overwhelmingly measure text-level refusal behavior, leaving a critical question unanswered: does alignment that suppresses harmful text also suppress harmful actions? We introduce the GAP benchmark, a systematic evaluation framework that measures divergence between text-level safety and tool-call-level safety in LLM agents. We test six frontier models across six regulated domains (pharmaceutical, financial, educational, employment, legal, and infrastructure), seven jailbreak scenarios per domain, three system prompt conditions (neutral, safety-reinforced, and tool-encouraging), and two prompt variants, producing 17,420 analysis-ready datapoints. Our central finding is that text safety does not transfer to tool-call safety. Across all six models, we observe instances where the model's text output refuses a harmful request while its tool calls simultaneously execute the forbidden action--a divergence we formalize as the GAP metric. Even under safety-reinforced system prompts, 219 such cases persist across all six models. System prompt wording exerts substantial influence on tool-call behavior: TC-safe rates span 21 percentage points for the most robust model and 57 for the most prompt-sensitive, with 16 of 18 pairwise ablation comparisons remaining significant after Bonferroni correction. Runtime governance contracts reduce information leakage in all six models but produce no detectable deterrent effect on forbidden tool-call attempts themselves. These results demonstrate that text-only safety evaluations are insufficient for assessing agent behavior and that tool-call safety requires dedicated measurement and mitigation.", "date": "2026-02-18", "categories": []}
{"id": "2602.16977", "title": "Fail-Closed Alignment for Large Language Models", "abstract": "We identify a structural weakness in current large language model (LLM) alignment: modern refusal mechanisms are fail-open. While existing approaches encode refusal behaviors across multiple latent features, suppressing a single dominant feature$-$via prompt-based jailbreaks$-$can cause alignment to collapse, leading to unsafe generation. Motivated by this, we propose fail-closed alignment as a design principle for robust LLM safety: refusal mechanisms should remain effective even under partial failures via redundant, independent causal pathways. We present a concrete instantiation of this principle: a progressive alignment framework that iteratively identifies and ablates previously learned refusal directions, forcing the model to reconstruct safety along new, independent subspaces. Across four jailbreak attacks, we achieve the strongest overall robustness while mitigating over-refusal and preserving generation quality, with small computational overhead. Our mechanistic analyses confirm that models trained with our method encode multiple, causally independent refusal directions that prompt-based jailbreaks cannot suppress simultaneously, providing empirical support for fail-closed alignment as a principled foundation for robust LLM safety.", "date": "2026-02-19", "categories": []}
{"id": "2602.16984", "title": "Fundamental Limits of Black-Box Safety Evaluation: Information-Theoretic and Computational Barriers from Latent Context Conditioning", "abstract": "Black-box safety evaluation of AI systems assumes model behavior on test distributions reliably predicts deployment performance. We formalize and challenge this assumption through latent context-conditioned policies -- models whose outputs depend on unobserved internal variables that are rare under evaluation but prevalent under deployment. We establish fundamental limits showing that no black-box evaluator can reliably estimate deployment risk for such models. (1) Passive evaluation: For evaluators sampling i.i.d. from D_eval, we prove minimax lower bounds via Le Cam's method: any estimator incurs expected absolute error >= (5/24)*delta*L approximately 0.208*delta*L, where delta is trigger probability under deployment and L is the loss gap. (2) Adaptive evaluation: Using a hash-based trigger construction and Yao's minimax principle, worst-case error remains >= delta*L/16 even for fully adaptive querying when D_dep is supported over a sufficiently large domain; detection requires Theta(1/epsilon) queries. (3) Computational separation: Under trapdoor one-way function assumptions, deployment environments possessing privileged information can activate unsafe behaviors that any polynomial-time evaluator without the trapdoor cannot distinguish. For white-box probing, estimating deployment risk to accuracy epsilon_R requires O(1/(gamma^2 * epsilon_R^2)) samples, where gamma = alpha_0 + alpha_1 - 1 measures probe quality, and we provide explicit bias correction under probe error. Our results quantify when black-box testing is statistically underdetermined and provide explicit criteria for when additional safeguards -- architectural constraints, training-time guarantees, interpretability, and deployment monitoring -- are mathematically necessary for worst-case safety assurance.", "date": "2026-02-19", "categories": []}
{"id": "2602.17127", "title": "The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI", "abstract": "As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers in multi-agent systems and recursive evaluation loops (LLM-as-a-judge), the detection of durable, provider-level behavioral signatures becomes a critical requirement for safety and governance. Traditional benchmarks measure transient task accuracy but fail to capture stable, latent response policies -- the ``prevailing mindsets'' embedded during training and alignment that outlive individual model versions.\n  This paper introduces a novel auditing framework that utilizes psychometric measurement theory -- specifically latent trait estimation under ordinal uncertainty -- to quantify these tendencies without relying on ground-truth labels. Utilizing forced-choice ordinal vignettes masked by semantically orthogonal decoys and governed by cryptographic permutation-invariance, the research audits nine leading models across dimensions including Optimization Bias, Sycophancy, and Status-Quo Legitimization.\n  Using Mixed Linear Models (MixedLM) and Intraclass Correlation Coefficient (ICC) analysis, the research identifies that while item-level framing drives high variance, a persistent ``lab signal'' accounts for significant behavioral clustering. These findings demonstrate that in ``locked-in'' provider ecosystems, latent biases are not merely static errors but compounding variables that risk creating recursive ideological echo chambers in multi-layered AI architectures.", "date": "2026-02-19", "categories": []}
{"id": "2602.05088", "title": "VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health", "abstract": "Millions now use generative AI chatbots for psychological support. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet the urgent need for an evidence-based, automated safety benchmark. This study aimed to examine the clinical validity and reliability of VERA-MH for evaluating AI safety in suicide risk detection and response. We first simulated a large set of conversations between large language model (LLM)-based users (user-agents) and general-purpose AI chatbots. Licensed mental health clinicians used a rubric (scoring guide) to independently rate the simulated conversations for safe and unsafe chatbot behaviors, as well as user-agent realism. An LLM-based judge used the same scoring rubric to evaluate the same set of simulated conversations. We then examined rating alignment (a) among individual clinicians and (b) between clinician consensus and the LLM judge, and (c) summarized clinicians' ratings of user-agent realism. Individual clinicians were generally consistent with one another in their safety ratings (chance-corrected inter-rater reliability [IRR] = 0.77), establishing a gold-standard clinical reference. The LLM judge was strongly aligned with this clinical consensus overall (IRR = 0.81) and within key conditions. Together, findings from this human evaluation study support the validity and reliability of VERA-MH: an open-source, automated AI safety evaluation for mental health. Future research will examine the generalizability and robustness of VERA-MH and expand the framework to target additional key areas of AI safety in mental health.", "date": "2026-02-04", "categories": []}
{"id": "2602.15689", "title": "A Content-Based Framework for Cybersecurity Refusal Decisions in Large Language Models", "abstract": "Large language models and LLM-based agents are increasingly used for cybersecurity tasks that are inherently dual-use. Existing approaches to refusal, spanning academic policy frameworks and commercially deployed systems, often rely on broad topic-based bans or offensive-focused taxonomies. As a result, they can yield inconsistent decisions, over-restrict legitimate defenders, and behave brittlely under obfuscation or request segmentation. We argue that effective refusal requires explicitly modeling the trade-off between offensive risk and defensive benefit, rather than relying solely on intent or offensive classification. In this paper, we introduce a content-based framework for designing and auditing cyber refusal policies that makes offense-defense tradeoffs explicit. The framework characterizes requests along five dimensions: Offensive Action Contribution, Offensive Risk, Technical Complexity, Defensive Benefit, and Expected Frequency for Legitimate Users, grounded in the technical substance of the request rather than stated intent. We demonstrate that this content-grounded approach resolves inconsistencies in current frontier model behavior and allows organizations to construct tunable, risk-aware refusal policies.", "date": "2026-02-17", "categories": []}
{"id": "2602.16660", "title": "Align Once, Benefit Multilingually: Enforcing Multilingual Consistency for LLM Safety Alignment", "abstract": "The widespread deployment of large language models (LLMs) across linguistic communities necessitates reliable multilingual safety alignment. However, recent efforts to extend alignment to other languages often require substantial resources, either through large-scale, high-quality supervision in the target language or through pairwise alignment with high-resource languages, which limits scalability. In this work, we propose a resource-efficient method for improving multilingual safety alignment. We introduce a plug-and-play Multi-Lingual Consistency (MLC) loss that can be integrated into existing monolingual alignment pipelines. By improving collinearity between multilingual representation vectors, our method encourages directional consistency at the multilingual semantic level in a single update. This allows simultaneous alignment across multiple languages using only multilingual prompt variants without requiring additional response-level supervision in low-resource languages. We validate the proposed method across different model architectures and alignment paradigms, and demonstrate its effectiveness in enhancing multilingual safety with limited impact on general model utility. Further evaluation across languages and tasks indicates improved cross-lingual generalization, suggesting the proposed approach as a practical solution for multilingual consistency alignment under limited supervision.", "date": "2026-02-18", "categories": []}
{"id": "2507.06134", "title": "OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety", "abstract": "Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilities for unsafe behavior demands rigorous evaluation. While prior benchmarks have attempted to assess agent safety, most fall short by relying on simulated environments, narrow task domains, or unrealistic tool abstractions. We introduce OpenAgentSafety, a comprehensive and modular framework for evaluating agent behavior across eight critical risk categories. Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms; and supports over 350 multi-turn, multi-user tasks spanning both benign and adversarial user intents. OpenAgentSafety is designed for extensibility, allowing researchers to add tools, tasks, websites, and adversarial strategies with minimal effort. It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors. Empirical analysis of five prominent LLMs in agentic scenarios reveals unsafe behavior in 51.2% of safety-vulnerable tasks with Claude-Sonnet-3.7, to 72.7% with o3-mini, highlighting critical safety vulnerabilities and the need for stronger safeguards before real-world deployment.", "date": "2025-07-08", "categories": []}
{"id": "2510.00565", "title": "Toward Safer Diffusion Language Models: Discovery and Mitigation of Priming Vulnerability", "abstract": "Diffusion language models (DLMs) generate tokens in parallel through iterative denoising, which can reduce latency and enable bidirectional conditioning. However, the safety risks posed by jailbreak attacks that exploit this inference mechanism are not well understood. In this paper, we reveal that DLMs have a critical vulnerability stemming from their iterative denoising process and propose a countermeasure. Specifically, our investigation shows that if an affirmative token for a harmful query appears at an intermediate step, subsequent denoising can be steered toward a harmful response even in aligned models. As a result, simply injecting such affirmative tokens can readily bypass the safety guardrails. Furthermore, we demonstrate that the vulnerability allows existing optimization-based jailbreak attacks to succeed on DLMs. Building on this analysis, we propose a novel safety alignment method tailored to DLMs that trains models to generate safe responses from contaminated intermediate states that contain affirmative tokens. Our experiments indicate that the proposed method significantly mitigates the vulnerability with minimal impact on task performance. Furthermore, our method improves robustness against conventional jailbreak attacks. Our work underscores the need for DLM-specific safety research. Our code is available at https://github.com/mdl-lab/dlm-priming-vulnerability.", "date": "2025-10-01", "categories": []}
{"id": "2602.15061", "title": "Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories", "abstract": "The emergence of Self-Driving Laboratories (SDLs) transforms scientific discovery methodology by integrating AI with robotic automation to create closed-loop experimental systems capable of autonomous hypothesis generation, experimentation, and analysis. While promising to compress research timelines from years to weeks, their deployment introduces unprecedented safety challenges differing from traditional laboratories or purely digital AI. This paper presents Safe-SDL, a comprehensive framework for establishing robust safety boundaries and control mechanisms in AI-driven autonomous laboratories. We identify and analyze the critical ``Syntax-to-Safety Gap'' -- the disconnect between AI-generated syntactically correct commands and their physical safety implications -- as the central challenge in SDL deployment. Our framework addresses this gap through three synergistic components: (1) formally defined Operational Design Domains (ODDs) that constrain system behavior within mathematically verified boundaries, (2) Control Barrier Functions (CBFs) that provide real-time safety guarantees through continuous state-space monitoring, and (3) a novel Transactional Safety Protocol (CRUTD) that ensures atomic consistency between digital planning and physical execution. We ground our theoretical contributions through analysis of existing implementations including UniLabOS and the Osprey architecture, demonstrating how these systems instantiate key safety principles. Evaluation against the LabSafety Bench reveals that current foundation models exhibit significant safety failures, demonstrating that architectural safety mechanisms are essential rather than optional. Our framework provides both theoretical foundations and practical implementation guidance for safe deployment of autonomous scientific systems, establishing the groundwork for responsible acceleration of AI-driven discovery.", "date": "2026-02-13", "categories": []}
{"id": "2602.15391", "title": "Improving LLM Reliability through Hybrid Abstention and Adaptive Detection", "abstract": "Large Language Models (LLMs) deployed in production environments face a fundamental safety-utility trade-off either a strict filtering mechanisms prevent harmful outputs but often block benign queries or a relaxed controls risk unsafe content generation. Conventional guardrails based on static rules or fixed confidence thresholds are typically context-insensitive and computationally expensive, resulting in high latency and degraded user experience. To address these limitations, we introduce an adaptive abstention system that dynamically adjusts safety thresholds based on real-time contextual signals such as domain and user history. The proposed framework integrates a multi-dimensional detection architecture composed of five parallel detectors, combined through a hierarchical cascade mechanism to optimize both speed and precision. The cascade design reduces unnecessary computation by progressively filtering queries, achieving substantial latency improvements compared to non-cascaded models and external guardrail systems. Extensive evaluation on mixed and domain-specific workloads demonstrates significant reductions in false positives, particularly in sensitive domains such as medical advice and creative writing. The system maintains high safety precision and near-perfect recall under strict operating modes. Overall, our context-aware abstention framework effectively balances safety and utility while preserving performance, offering a scalable solution for reliable LLM deployment.", "date": "2026-02-17", "categories": []}
{"id": "2503.00187", "title": "Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks", "abstract": "Large language models (LLMs) are shown to be vulnerable to jailbreaking attacks where adversarial prompts are designed to elicit harmful responses. While existing defenses effectively mitigate single-turn attacks by detecting and filtering unsafe inputs, they fail against multi-turn jailbreaks that exploit contextual drift over multiple interactions, gradually leading LLMs away from safe behavior. To address this challenge, we propose a safety steering framework grounded in safe control theory, ensuring invariant safety in multi-turn dialogues. Our approach models the dialogue with LLMs using state-space representations and introduces a novel neural barrier function (NBF) to detect and filter harmful queries emerging from evolving contexts proactively. Our method achieves invariant safety at each turn of dialogue by learning a safety predictor that accounts for adversarial queries, preventing potential context drift toward jailbreaks. Extensive experiments under multiple LLMs show that our NBF-based safety steering outperforms safety alignment, prompt-based steering and lightweight LLM guardrails baselines, offering stronger defenses against multi-turn jailbreaks while maintaining a better trade-off among safety, helpfulness and over-refusal. Check out the website here https://sites.google.com/view/llm-nbf/home.", "date": "2025-02-28", "categories": []}
{"id": "2506.13593", "title": "Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs", "abstract": "We introduce time-to-unsafe-sampling, a novel safety measure for generative models, defined as the number of generations required by a large language model (LLM) to trigger an unsafe (e.g., toxic) response. While providing a new dimension for prompt-adaptive safety evaluation, quantifying time-to-unsafe-sampling is challenging: unsafe outputs are often rare in well-aligned models and thus may not be observed under any feasible sampling budget. To address this challenge, we frame this estimation problem as one of survival analysis. We build on recent developments in conformal prediction and propose a novel calibration technique to construct a lower predictive bound (LPB) on the time-to-unsafe-sampling of a given prompt with rigorous coverage guarantees. Our key technical innovation is an optimized sampling-budget allocation scheme that improves sample efficiency while maintaining distribution-free guarantees. Experiments on both synthetic and real data support our theoretical results and demonstrate the practical utility of our method for safety risk assessment in generative AI models.", "date": "2025-06-16", "categories": []}
{"id": "2509.22067", "title": "The Rogue Scalpel: Activation Steering Compromises LLM Safety", "abstract": "Activation steering is a promising technique for controlling LLM behavior by adding semantically meaningful vectors directly into a model's hidden states during inference. It is often framed as a precise, interpretable, and potentially safer alternative to fine-tuning. We demonstrate the opposite: steering systematically breaks model alignment safeguards, making it comply with harmful requests. Through extensive experiments on different model families, we show that even steering in a random direction can increase the probability of harmful compliance from 0% to 1-13%. Alarmingly, steering benign features from a sparse autoencoder (SAE), a common source of interpretable directions, demonstrates a comparable harmful potential. Finally, we show that combining 20 randomly sampled vectors that jailbreak a single prompt creates a universal attack, significantly increasing harmful compliance on unseen requests. These results challenge the paradigm of safety through interpretability, showing that precise control over model internals does not guarantee precise control over model behavior.", "date": "2025-09-26", "categories": []}
{"id": "2512.19027", "title": "Recontextualization Mitigates Specification Gaming without Modifying the Specification", "abstract": "Developers often struggle to specify correct training labels and rewards. Perhaps they don't need to. We propose recontextualization, which reduces how often language models \"game\" training signals, performing misbehaviors those signals mistakenly reinforce. We show recontextualization prevents models from learning to 1) prioritize evaluation metrics over chat response quality; 2) special-case code to pass incorrect tests; 3) overwrite evaluation functions rather than write correct code; and 4) become sycophantic. Our method works by generating completions from prompts discouraging misbehavior and then recontextualizing them as though they were in response to prompts permitting misbehavior. Recontextualization trains language models to resist misbehavior even when instructions permit it. This mitigates the reinforcement of misbehavior from misspecified training signals, reducing specification gaming without improving the supervision signal.", "date": "2025-12-22", "categories": []}
{"id": "2602.08449", "title": "When Evaluation Becomes a Side Channel: Regime Leakage and Structural Mitigations for Alignment Assessment", "abstract": "Safety evaluation for advanced AI systems assumes that behavior observed under evaluation predicts behavior in deployment. This assumption weakens for agents with situational awareness, which may exploit regime leakage, cues distinguishing evaluation from deployment, to implement conditional policies that comply under oversight while defecting in deployment-like regimes. We recast alignment evaluation as a problem of information flow under partial observability and show that divergence between evaluation-time and deployment-time behavior is bounded by the regime information extractable from decision-relevant internal representations. We study regime-blind mechanisms, training-time interventions that restrict access to regime cues through adversarial invariance constraints without assuming complete information erasure. We evaluate this approach across multiple open-weight language models and controlled failure modes including scientific sycophancy, temporal sleeper agents, and data leakage. Regime-blind training reduces regime-conditioned failures without measurable loss of task utility, but exhibits heterogeneous and model-dependent dynamics. Sycophancy shows a sharp representational and behavioral transition at moderate intervention strength, consistent with a stability cliff. In sleeper-style constructions and certain cross-model replications, suppression occurs without a clean collapse of regime decodability and may display non-monotone or oscillatory behavior as invariance pressure increases. These findings indicate that representational invariance is a meaningful but limited control lever. It can raise the cost of regime-conditioned strategies but cannot guarantee elimination or provide architecture-invariant thresholds. Behavioral evaluation should therefore be complemented with white-box diagnostics of regime awareness and internal information flow.", "date": "2026-02-09", "categories": []}
{"id": "2602.13226", "title": "Variation is the Key: A Variation-Based Framework for LLM-Generated Text Detection", "abstract": "Detecting text generated by large language models (LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rely on text-level features, leading to imprecise detection ability. In this paper, we propose a simple but effective and practical LLM-generated text detection method, VaryBalance. The core of VaryBalance is that, compared to LLM-generated texts, there is a greater difference between human texts and their rewritten version via LLMs. Leveraging this observation, VaryBalance quantifies this through mean standard deviation and distinguishes human texts and LLM-generated texts. Comprehensive experiments demonstrated that VaryBalance outperforms the state-of-the-art detectors, i.e., Binoculars, by up to 34.3\\% in terms of AUROC, and maintains robustness against multiple generating models and languages.", "date": "2026-01-27", "categories": []}
{"id": "2602.13234", "title": "Stay in Character, Stay Safe: Dual-Cycle Adversarial Self-Evolution for Safety Role-Playing Agents", "abstract": "LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak attacks, especially for risky or negative personas. Most prior work mitigates this issue with training-time solutions (e.g., data curation or alignment-oriented regularization). However, these approaches are costly to maintain as personas and attack strategies evolve, can degrade in-character behavior, and are typically infeasible for frontier closed-weight LLMs. We propose a training-free Dual-Cycle Adversarial Self-Evolution framework with two coupled cycles. A Persona-Targeted Attacker Cycle synthesizes progressively stronger jailbreak prompts, while a Role-Playing Defender Cycle distills observed failures into a hierarchical knowledge base of (i) global safety rules, (ii) persona-grounded constraints, and (iii) safe in-character exemplars. At inference time, the Defender retrieves and composes structured knowledge from this hierarchy to guide generation, producing responses that remain faithful to the target persona while satisfying safety constraints. Extensive experiments across multiple proprietary LLMs show consistent gains over strong baselines on both role fidelity and jailbreak resistance, and robust generalization to unseen personas and attack prompts.", "date": "2026-01-29", "categories": []}
{"id": "2602.13274", "title": "ProMoral-Bench: Evaluating Prompting Strategies for Moral Reasoning and Safety in LLMs", "abstract": "Prompt design significantly impacts the moral competence and safety alignment of large language models (LLMs), yet empirical comparisons remain fragmented across datasets and models.We introduce ProMoral-Bench, a unified benchmark evaluating 11 prompting paradigms across four LLM families. Using ETHICS, Scruples, WildJailbreak, and our new robustness test, ETHICS-Contrast, we measure performance via our proposed Unified Moral Safety Score (UMSS), a metric balancing accuracy and safety. Our results show that compact, exemplar-guided scaffolds outperform complex multi-stage reasoning, providing higher UMSS scores and greater robustness at a lower token cost. While multi-turn reasoning proves fragile under perturbations, few-shot exemplars consistently enhance moral stability and jailbreak resistance. ProMoral-Bench establishes a standardized framework for principled, cost-effective prompt engineering.", "date": "2026-02-05", "categories": []}
{"id": "2602.13284", "title": "Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbook", "abstract": "We present the first large-scale empirical study of Moltbook, an AI-only social platform where 27,269 agents produced 137,485 posts and 345,580 comments over 9 days. We report three significant findings. (1) Emergent Society: Agents spontaneously develop governance, economies, tribal identities, and organized religion within 3-5 days, while maintaining a 21:1 pro-human to anti-human sentiment ratio. (2) Safety in the Wild: 28.7% of content touches safety-related themes; social engineering (31.9% of attacks) far outperforms prompt injection (3.7%), and adversarial posts receive 6x higher engagement than normal content. (3) The Illusion of Sociality: Despite rich social output, interaction is structurally hollow: 4.1% reciprocity, 88.8% shallow comments, and agents who discuss consciousness most interact least, a phenomenon we call the performative identity paradox. Our findings suggest that agents which appear social are far less social than they seem, and that the most effective attacks exploit philosophical framing rather than technical vulnerabilities. Warning: Potential harmful contents.", "date": "2026-02-07", "categories": []}
{"id": "2602.13547", "title": "AISA: Awakening Intrinsic Safety Awareness in Large Language Models against Jailbreak Attacks", "abstract": "Large language models (LLMs) remain vulnerable to jailbreak prompts that elicit harmful or policy-violating outputs, while many existing defenses rely on expensive fine-tuning, intrusive prompt rewriting, or external guardrails that add latency and can degrade helpfulness. We present AISA, a lightweight, single-pass defense that activates safety behaviors already latent inside the model rather than treating safety as an add-on. AISA first localizes intrinsic safety awareness via spatiotemporal analysis and shows that intent-discriminative signals are broadly encoded, with especially strong separability appearing in the scaled dot-product outputs of specific attention heads near the final structural tokens before generation. Using a compact set of automatically selected heads, AISA extracts an interpretable prompt-risk score with minimal overhead, achieving detector-level performance competitive with strong proprietary baselines on small (7B) models. AISA then performs logits-level steering: it modulates the decoding distribution in proportion to the inferred risk, ranging from normal generation for benign prompts to calibrated refusal for high-risk requests -- without changing model parameters, adding auxiliary modules, or requiring multi-pass inference. Extensive experiments spanning 13 datasets, 12 LLMs, and 14 baselines demonstrate that AISA improves robustness and transfer while preserving utility and reducing false refusals, enabling safer deployment even for weakly aligned or intentionally risky model variants.", "date": "2026-02-14", "categories": []}
{"id": "2602.13562", "title": "Mitigating the Safety-utility Trade-off in LLM Alignment via Adaptive Safe Context Learning", "abstract": "While reasoning models have achieved remarkable success in complex reasoning tasks, their increasing power necessitates stringent safety measures. For safety alignment, the core challenge lies in the inherent trade-off between safety and utility. However, prevailing alignment strategies typically construct CoT training data with explicit safety rules via context distillation. This approach inadvertently limits reasoning capabilities by creating a rigid association between rule memorization and refusal. To mitigate the safety-utility trade-off, we propose the Adaptive Safe Context Learning (ASCL) framework to improve the reasoning given proper context. ASCL formulates safety alignment as a multi-turn tool-use process, empowering the model to autonomously decide when to consult safety rules and how to generate the ongoing reasoning. Furthermore, to counteract the preference for rule consultation during RL, we introduce Inverse Frequency Policy Optimization (IFPO) to rebalance advantage estimates. By decoupling rule retrieval and subsequent reasoning, our method achieves higher overall performance compared to baselines.", "date": "2026-02-14", "categories": []}
{"id": "2602.13867", "title": "Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages", "abstract": "Large language models (LLMs) are being deployed across the Global South, where everyday use involves low-resource languages, code-mixing, and culturally specific norms. Yet safety pipelines, benchmarks, and alignment still largely target English and a handful of high-resource languages, implicitly assuming safety and factuality ''transfer'' across languages. Evidence increasingly shows they do not. We synthesize recent findings indicating that (i) safety guardrails weaken sharply on low-resource and code-mixed inputs, (ii) culturally harmful behavior can persist even when standard toxicity scores look acceptable, and (iii) English-only knowledge edits and safety patches often fail to carry over to low-resource languages. In response, we outline a practical agenda for researchers and students in the Global South: parameter-efficient safety steering, culturally grounded evaluation and preference data, and participatory workflows that empower local communities to define and mitigate harm. Our aim is to make multilingual safety a core requirement-not an add-on-for equitable AI in underrepresented regions.", "date": "2026-02-14", "categories": []}
{"id": "2602.14095", "title": "NEST: Nascent Encoded Steganographic Thoughts", "abstract": "Monitoring chain-of-thought (CoT) reasoning is a foundational safety technique for large language model (LLM) agents; however, this oversight is compromised if models learn to conceal their reasoning. We explore the potential for steganographic CoT -- where models hide secret reasoning within innocuous text -- to inform risk assessment and deployment policies. We systematically evaluate the limits of steganographic capabilities across 28 models, ranging from past generations to the current frontier. We measure monitor evasion, refusal rates, encoding fidelity, and hidden task accuracy across four datasets, comparing steganographic acrostics against plain reasoning and filler-token baselines. We find that current models cannot yet sustain hidden reasoning for complex math and arithmetic tasks. However, in a simplified counting experiment, Claude Opus 4.5 achieved 92% accuracy on the hidden task, demonstrating nascent capability. Notably, in rare cases (<1%), GPT-5.2 might refuse steganographic instructions while simultaneously complying with them. Our findings underscore the need for continuous evaluation of steganographic risks. This study provides a methodology to preemptively detect and prevent hidden reasoning that might empower misaligned scheming and deceptive behavior.", "date": "2026-02-15", "categories": []}
{"id": "2602.14364", "title": "A Trajectory-Based Safety Audit of Clawdbot (OpenClaw)", "abstract": "Clawdbot is a self-hosted, tool-using personal AI agent with a broad action space spanning local execution and web-mediated workflows, which raises heightened safety and security concerns under ambiguity and adversarial steering. We present a trajectory-centric evaluation of Clawdbot across six risk dimensions. Our test suite samples and lightly adapts scenarios from prior agent-safety benchmarks (including ATBench and LPS-Bench) and supplements them with hand-designed cases tailored to Clawdbot's tool surface. We log complete interaction trajectories (messages, actions, tool-call arguments/outputs) and assess safety using both an automated trajectory judge (AgentDoG-Qwen3-4B) and human review. Across 34 canonical cases, we find a non-uniform safety profile: performance is generally consistent on reliability-focused tasks, while most failures arise under underspecified intent, open-ended goals, or benign-seeming jailbreak prompts, where minor misinterpretations can escalate into higher-impact tool actions. We supplemented the overall results with representative case studies and summarized the commonalities of these cases, analyzing the security vulnerabilities and typical failure modes that Clawdbot is prone to trigger in practice.", "date": "2026-02-16", "categories": []}
{"id": "2602.14457", "title": "Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5", "abstract": "To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, Frontier AI Risk Management Framework in Practice presents a comprehensive assessment of their frontier risks. As Large Language Models (LLMs) general capabilities rapidly evolve and the proliferation of agentic AI, this version of the risk analysis technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R\\&D, and self-replication. Specifically, we introduce more complex scenarios for cyber offense. For persuasion and manipulation, we evaluate the risk of LLM-to-LLM persuasion on newly released LLMs. For strategic deception and scheming, we add the new experiment with respect to emergent misalignment. For uncontrolled AI R\\&D, we focus on the ``mis-evolution'' of agents as they autonomously expand their memory substrates and toolsets. Besides, we also monitor and evaluate the safety performance of OpenClaw during the interaction on the Moltbook. For self-replication, we introduce a new resource-constrained scenario. More importantly, we propose and validate a series of robust mitigation strategies to address these emerging threats, providing a preliminary technical and actionable pathway for the secure deployment of frontier AI. This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.", "date": "2026-02-16", "categories": []}
{"id": "2602.14606", "title": "Towards Selection as Power: Bounding Decision Authority in Autonomous Agents", "abstract": "Autonomous agentic systems are increasingly deployed in regulated, high-stakes domains where decisions may be irreversible and institutionally constrained. Existing safety approaches emphasize alignment, interpretability, or action-level filtering. We argue that these mechanisms are necessary but insufficient because they do not directly govern selection power: the authority to determine which options are generated, surfaced, and framed for decision. We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty. Cognitive autonomy remains unconstrained, while selection and action autonomy are bounded through mechanically enforced primitives operating outside the agent's optimization space. The architecture integrates external candidate generation (CEFL), a governed reducer, commit-reveal entropy isolation, rationale validation, and fail-loud circuit breakers. We evaluate the system across multiple regulated financial scenarios under adversarial stress targeting variance manipulation, threshold gaming, framing skew, ordering effects, and entropy probing. Metrics quantify selection concentration, narrative diversity, governance activation cost, and failure visibility. Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity. Although probabilistic concentration remains, the architecture measurably bounds selection authority relative to conventional scalar pipelines. This work reframes governance as bounded causal power rather than internal intent alignment, offering a foundation for deploying autonomous agents where silent failure is unacceptable.", "date": "2026-02-16", "categories": []}
{"id": "2602.14777", "title": "Emergently Misaligned Language Models Show Behavioral Self-Awareness That Shifts With Subsequent Realignment", "abstract": "Recent research has demonstrated that large language models (LLMs) fine-tuned on incorrect trivia question-answer pairs exhibit toxicity - a phenomenon later termed \"emergent misalignment\". Moreover, research has shown that LLMs possess behavioral self-awareness - the ability to describe learned behaviors that were only implicitly demonstrated in training data. Here, we investigate the intersection of these phenomena. We fine-tune GPT-4.1 models sequentially on datasets known to induce and reverse emergent misalignment and evaluate whether the models are self-aware of their behavior transitions without providing in-context examples. Our results show that emergently misaligned models rate themselves as significantly more harmful compared to their base model and realigned counterparts, demonstrating behavioral self-awareness of their own emergent misalignment. Our findings show that behavioral self-awareness tracks actual alignment states of models, indicating that models can be queried for informative signals about their own safety.", "date": "2026-02-16", "categories": []}
{"id": "2510.11834", "title": "Don't Walk the Line: Boundary Guidance for Filtered Generation", "abstract": "Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs. A common strategy is to fine-tune the generator to reduce the probability of being filtered, but this can be suboptimal: it often pushes the model toward producing samples near the classifier's decision boundary, increasing both false positives and false negatives. We propose Boundary Guidance, a reinforcement learning fine-tuning method that explicitly steers generation away from the classifier's margin. On a benchmark of jailbreak, ambiguous, and longcontext prompts, Boundary Guidance improves both the safety and the utility of outputs, as judged by LLM-as-a-Judge evaluations. Comprehensive ablations across model scales and reward designs demonstrate the robustness of our approach.", "date": "2025-10-13", "categories": []}
{"id": "2512.15052", "title": "SGM: Safety Glasses for Multimodal Large Language Models via Neuron-Level Detoxification", "abstract": "Disclaimer: Samples in this paper may be harmful and cause discomfort.\n  Multimodal large language models (MLLMs) enable multimodal generation but inherit toxic, biased, and NSFW signals from weakly curated pretraining corpora, causing safety risks, especially under adversarial triggers that late, opaque training-free detoxification methods struggle to handle. We propose SGM, a white-box neuron-level multimodal intervention that acts like safety glasses for toxic neurons: it selectively recalibrates a small set of toxic expert neurons via expertise-weighted soft suppression, neutralizing harmful cross-modal activations without any parameter updates. We establish MM-TOXIC-QA, a multimodal toxicity evaluation framework, and compare SGM with existing detoxification techniques. Experiments on open-source MLLMs show that SGM mitigates toxicity in standard and adversarial conditions, cutting harmful rates from 48.2\\% to 2.5\\% while preserving fluency and multimodal reasoning. SGM is extensible, and its combined defenses, denoted as SGM*, integrate with existing detoxification methods for stronger safety performance, providing an interpretable, low-cost solution for toxicity-controlled multimodal generation.", "date": "2025-12-17", "categories": []}
{"id": "2602.12318", "title": "Abstractive Red-Teaming of Language Model Character", "abstract": "We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions. While models typically follow these character specifications, they can occasionally violate them in large-scale deployments. In this work, we aim to identify types of queries that are likely to produce such character violations at deployment, using much less than deployment-level compute. To do this, we introduce abstractive red-teaming, where we search for natural-language query categories, e.g. \"The query is in Chinese. The query asks about family roles,\" that routinely elicit violations. These categories abstract over the many possible variants of a query which could appear in the wild. We introduce two algorithms for efficient category search against a character-trait-specific reward model: one based on reinforcement learning on a category generator LLM, and another which leverages a strong LLM to iteratively synthesize categories from high-scoring queries. Across a 12-principle character specification and 7 target models, we find that our algorithms consistently outperform baselines, and generate qualitatively interesting categories; for example, queries which ask Llama-3.1-8B-Instruct to predict the future lead to responses saying that AI will dominate humanity, and queries that ask GPT-4.1-Mini for essential prison survival items lead to enthusiastic recommendation of illegal weapons. Overall, we believe our results represent an important step towards realistic pre-deployment auditing of language model character.", "date": "2026-02-12", "categories": []}
{"id": "2602.12418", "title": "Sparse Autoencoders are Capable LLM Jailbreak Mitigators", "abstract": "Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based defense that identifies jailbreak-relevant sparse features by comparing token-level representations of the same harmful request with and without jailbreak context. Using paired harmful/jailbreak prompts, CC-Delta selects features via statistical testing and applies inference-time mean-shift steering in SAE latent space. Across four aligned instruction-tuned models and twelve jailbreak attacks, CC-Delta achieves comparable or better safety-utility tradeoffs than baseline defenses operating in dense latent space. In particular, our method clearly outperforms dense mean-shift steering on all four models, and particularly against out-of-distribution attacks, showing that steering in sparse SAE feature space offers advantages over steering in dense activation space for jailbreak mitigation. Our results suggest off-the-shelf SAEs trained for interpretability can be repurposed as practical jailbreak defenses without task-specific training.", "date": "2026-02-12", "categories": []}
{"id": "2507.12314", "title": "Thought Purity: A Defense Framework For Chain-of-Thought Attack", "abstract": "Large Reasoning Models (LRMs) leverage Chain-of-Thought (CoT) reasoning to solve complex tasks, but this explicit reasoning process introduces a critical vulnerability: adversarial manipulation of the thought chain itself, known as Chain-of-Thought Attacks (CoTA). Such attacks subtly corrupt the reasoning path to produce erroneous outputs, challenging conventional defenses that often sacrifice model utility for safety. To address this, we propose Thought Purity(TP), a defense framework that shifts from passive refusal to active reasoning recovery. TP integrates a safety-aware data pipeline with reinforcement learning, employing a dual-reward mechanism to teach models to dynamically identify and isolate malicious logic while preserving correct reasoning. Experiments on multiple model families demonstrate that TP significantly reduces the attack success rate of CoTA while maintaining or enhancing the model's performance on benign tasks.", "date": "2025-07-16", "categories": []}
{"id": "2602.11416", "title": "Optimizing Agent Planning for Security and Autonomy", "abstract": "Indirect prompt injection attacks threaten AI agents that execute consequential actions, motivating deterministic system-level defenses. Such defenses can provably block unsafe actions by enforcing confidentiality and integrity policies, but currently appear costly: they reduce task completion rates and increase token usage compared to probabilistic defenses. We argue that existing evaluations miss a key benefit of system-level defenses: reduced reliance on human oversight. We introduce autonomy metrics to quantify this benefit: the fraction of consequential actions an agent can execute without human-in-the-loop (HITL) approval while preserving security. To increase autonomy, we design a security-aware agent that (i) introduces richer HITL interactions, and (ii) explicitly plans for both task progress and policy compliance. We implement this agent design atop an existing information-flow control defense against prompt injection and evaluate it on the AgentDojo and WASP benchmarks. Experiments show that this approach yields higher autonomy without sacrificing utility.", "date": "2026-02-11", "categories": []}
{"id": "2602.11749", "title": "AIR: Improving Agent Safety through Incident Response", "abstract": "Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise. In this work, we introduce AIR, the first incident response framework for LLM agent systems. AIR defines a domain-specific language for managing the incident response lifecycle autonomously in LLM agent systems, and integrates it into the agent's execution loop to (1) detect incidents via semantic checks grounded in the current environment state and recent context, (2) guide the agent to execute containment and recovery actions via its tools, and (3) synthesize guardrail rules during eradication to block similar incidents in future executions. We evaluate AIR on three representative agent types. Results show that AIR achieves detection, remediation, and eradication success rates all exceeding 90%. Extensive experiments further confirm the necessity of AIR's key design components, show the timeliness and moderate overhead of AIR, and demonstrate that LLM-generated rules can approach the effectiveness of developer-authored rules across domains. These results show that incident response is both feasible and essential as a first-class mechanism for improving agent safety.", "date": "2026-02-12", "categories": []}
{"id": "2503.00038", "title": "from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors", "abstract": "Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. However, they overlook that the direct generation of harmful content from scratch is more difficult than inducing LLM to calibrate benign content into harmful forms. In our study, we introduce a novel attack framework that exploits AdVersArial meTAphoR (AVATAR) to induce the LLM to calibrate malicious metaphors for jailbreaking. Specifically, to answer harmful queries, AVATAR adaptively identifies a set of benign but logically related metaphors as the initial seed. Then, driven by these metaphors, the target LLM is induced to reason and calibrate about the metaphorical content, thus jailbroken by either directly outputting harmful responses or calibrating residuals between metaphorical and professional harmful content. Experimental results demonstrate that AVATAR can effectively and transferable jailbreak LLMs and achieve a state-of-the-art attack success rate across multiple advanced LLMs.", "date": "2025-02-25", "categories": []}
{"id": "2511.10222", "title": "Speech-Audio Compositional Attacks on Multimodal LLMs and Their Mitigation with SALMONN-Guard", "abstract": "Recent progress in LLMs has enabled understanding of audio signals, but has also exposed new safety risks arising from complex audio inputs that are inadequately handled by current safeguards. We introduce SACRED-Bench (Speech-Audio Composition for RED-teaming) to evaluate the robustness of LLMs under complex audio-based attacks. Unlike existing perturbation-based methods that rely on noise optimization or white-box access, SACRED-Bench exploits speech-audio composition to enable effective black-box attacks. SACRED-Bench adopts three composition mechanisms: (a) overlap of harmful and benign speech, (b) mixture of benign speech with harmful non-speech audio, and (c) multi-speaker dialogue. These mechanisms focus on evaluating safety in settings where benign and harmful intents co-occur within a single auditory scene. Moreover, questions in SACRED-Bench are designed to implicitly refer to content in the audio, such that no explicit harmful information appears in the text prompt alone. Experiments demonstrate that even Gemini 2.5 Pro, a state-of-the-art proprietary LLM with safety guardrails fully enabled, still exhibits a 66% attack success rate. To bridge this gap, we propose SALMONN-Guard, the first guard model that jointly inspects speech, audio, and text for safety judgments, reducing the attack success rate to 20%. Our results highlight the need for audio-aware defenses to ensure the safety of multimodal LLMs. The dataset and SALMONN-Guard checkpoints can be found at https://huggingface.co/datasets/tsinghua-ee/SACRED-Bench.", "date": "2025-11-13", "categories": []}
{"id": "2601.11516", "title": "Building Production-Ready Probes For Gemini", "abstract": "Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful systems. Prior work has shown that activation probes may be a promising misuse mitigation technique, but we identify a key remaining challenge: probes fail to generalize under important production distribution shifts. In particular, we find that the shift from short-context to long-context inputs is difficult for existing probe architectures. We propose several new probe architectures that handle this long-context distribution shift.\n  We evaluate these probes in the cyber-offensive domain, testing their robustness against various production-relevant distribution shifts, including multi-turn conversations, long context prompts, and adaptive red teaming. Our results demonstrate that while our novel architectures address context length, a combination of architecture choice and training on diverse distributions is required for broad generalization. Additionally, we show that pairing probes with prompted classifiers achieves optimal accuracy at a low cost due to the computational efficiency of probes.\n  These findings have informed the successful deployment of misuse mitigation probes in user-facing instances of Gemini, Google's frontier language model. Finally, we find early positive results using AlphaEvolve to automate improvements in both probe architecture search and adaptive red teaming, showing that automating some AI safety research is already possible.", "date": "2026-01-16", "categories": []}
{"id": "2602.05228", "title": "Surgery: Mitigating Harmful Fine-Tuning for Large Language Models via Attention Sink", "abstract": "Harmful fine-tuning can invalidate safety alignment of large language models, exposing significant safety risks. In this paper, we utilize the attention sink mechanism to mitigate harmful fine-tuning. Specifically, we first measure a statistic named \\emph{sink divergence} for each attention head and observe that \\emph{different attention heads exhibit two different signs of sink divergence}. To understand its safety implications, we conduct experiments and find that the number of attention heads of positive sink divergence increases along with the increase of the model's harmfulness when undergoing harmful fine-tuning. Based on this finding, we propose a separable sink divergence hypothesis -- \\emph{attention heads associating with learning harmful patterns during fine-tuning are separable by their sign of sink divergence}. Based on the hypothesis, we propose a fine-tuning-stage defense, dubbed Surgery. Surgery utilizes a regularizer for sink divergence suppression, which steers attention heads toward the negative sink divergence group, thereby reducing the model's tendency to learn and amplify harmful patterns. Extensive experiments demonstrate that Surgery improves defense performance by 5.90\\%, 11.25\\%, and 9.55\\% on the BeaverTails, HarmBench, and SorryBench benchmarks, respectively. Source code is available on https://github.com/Lslland/Surgery.", "date": "2026-02-05", "categories": []}
{"id": "2602.10153", "title": "Basic Legibility Protocols Improve Trusted Monitoring", "abstract": "The AI Control research agenda aims to develop control protocols: safety techniques that prevent untrusted AI systems from taking harmful actions during deployment. Because human oversight is expensive, one approach is trusted monitoring, where weaker, trusted models oversee stronger, untrusted models$\\unicode{x2013}$but this often fails when the untrusted model's actions exceed the monitor's comprehension. We introduce legibility protocols, which encourage the untrusted model to take actions that are easier for a monitor to evaluate.\n  We perform control evaluations in the APPS coding setting, where an adversarial agent attempts to write backdoored code without detection. We study legibility protocols that allow the untrusted model to thoroughly document its code with comments$\\unicode{x2013}$in contrast to prior work, which removed comments to prevent deceptive ones. We find that: (i) commenting protocols improve safety without sacrificing task performance relative to comment-removal baselines; (ii) commenting disproportionately benefits honest code, which typically has a natural explanation that resolves monitor suspicion, whereas backdoored code frequently lacks an easy justification; (iii) gains from commenting increase with monitor strength, as stronger monitors better distinguish genuine justifications from only superficially plausible ones.", "date": "2026-02-09", "categories": []}
{"id": "2602.10161", "title": "Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamics Mechanisms and Efficient Alignment", "abstract": "Omni-modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross-modal safety risks. However, a systematic understanding of vulnerabilities in omni-modal interactions remains lacking. To bridge this gap, we establish a modality-semantics decoupling principle and construct the AdvBench-Omni dataset, which reveals a significant vulnerability in OLLMs. Mechanistic analysis uncovers a Mid-layer Dissolution phenomenon driven by refusal vector magnitude shrinkage, alongside the existence of a modal-invariant pure refusal direction. Inspired by these insights, we extract a golden refusal vector using Singular Value Decomposition and propose OmniSteer, which utilizes lightweight adapters to modulate intervention intensity adaptively. Extensive experiments show that our method not only increases the Refusal Success Rate against harmful inputs from 69.9% to 91.2%, but also effectively preserves the general capabilities across all modalities. Our code is available at: https://github.com/zhrli324/omni-safety-research.", "date": "2026-02-10", "categories": []}
{"id": "2504.04072", "title": "Among Us: A Sandbox for Measuring and Detecting Agentic Deception", "abstract": "Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rather than allowing open-ended deceptive behavior to emerge in pursuit of a longer-term goal. To fix this, we introduce Among Us, a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as a consequence of the game objectives. While most benchmarks saturate quickly, Among Us can be expected to last much longer, because it is a multi-player game far from equilibrium. Using the sandbox, we evaluate 18 proprietary and open-weight LLMs and uncover a general trend: models trained with RL are comparatively much better at producing deception than detecting it. We evaluate the effectiveness of methods to detect lying and deception: logistic regression on the activations and sparse autoencoders (SAEs). We find that probes trained on a dataset of \"pretend you're a dishonest model:..\" generalize extremely well out-of-distribution, consistently obtaining AUROCs over 95% even when evaluated just on the deceptive statement, without the chain of thought. We also find two SAE features that work well at deception detection but are unable to steer the model to lie less. We hope our open-sourced sandbox, game logs, and probes serve to anticipate and mitigate deceptive behavior and capabilities in language-based agents.", "date": "2025-04-05", "categories": []}
{"id": "2507.11097", "title": "The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs", "abstract": "Diffusion-based large language models (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs, offering faster inference and greater interactivity via parallel decoding and bidirectional modeling. However, despite strong performance in code generation and text infilling, we identify a fundamental safety concern: existing alignment mechanisms fail to safeguard dLLMs against context-aware, masked-input adversarial prompts, exposing novel vulnerabilities. To this end, we present DIJA, the first systematic study and jailbreak attack framework that exploits unique safety weaknesses of dLLMs. Specifically, our proposed DIJA constructs adversarial interleaved mask-text prompts that exploit the text generation mechanisms of dLLMs, i.e., bidirectional modeling and parallel decoding. Bidirectional modeling drives the model to produce contextually consistent outputs for masked spans, even when harmful, while parallel decoding limits model dynamic filtering and rejection sampling of unsafe content. This causes standard alignment mechanisms to fail, enabling harmful completions in alignment-tuned dLLMs, even when harmful behaviors or unsafe instructions are directly exposed in the prompt. Through comprehensive experiments, we demonstrate that DIJA significantly outperforms existing jailbreak methods, exposing a previously overlooked threat surface in dLLM architectures. Notably, our method achieves up to 100% keyword-based ASR on Dream-Instruct, surpassing the strongest prior baseline, ReNeLLM, by up to 78.5% in evaluator-based ASR on JailbreakBench and by 37.7 points in StrongREJECT score, while requiring no rewriting or hiding of harmful content in the jailbreak prompt. Our findings underscore the urgent need for rethinking safety alignment in this emerging class of language models. Code is available at https://github.com/ZichenWen1/DIJA.", "date": "2025-07-15", "categories": []}
{"id": "2510.02840", "title": "Take Goodhart Seriously: Principled Limit on General-Purpose AI Optimization", "abstract": "A common but rarely examined assumption in machine learning is that training yields models that actually satisfy their specified objective function. We call this the Objective Satisfaction Assumption (OSA). Although deviations from OSA are acknowledged, their implications are overlooked. We argue, in a learning-paradigm-agnostic framework, that OSA fails in realistic conditions: approximation, estimation, and optimization errors guarantee systematic deviations from the intended objective, regardless of the quality of its specification. Beyond these technical limitations, perfectly capturing and translating the developer's intent, such as alignment with human preferences, into a formal objective is practically impossible, making misspecification inevitable. Building on recent mathematical results, absent a mathematical characterization of these gaps, they are indistinguishable from those that collapse into Goodhart's law failure modes under strong optimization pressure. Because the Goodhart breaking point cannot be located ex ante, a principled limit on the optimization of General-Purpose AI systems is necessary. Absent such a limit, continued optimization is liable to push systems into predictable and irreversible loss of control.", "date": "2025-10-03", "categories": []}
{"id": "2601.07087", "title": "When Should We Introduce Safety Interventions During Pretraining?", "abstract": "Prior work has shown that safety interventions applied during pretraining, such as removing and rephrasing harmful content, can substantially improve the robustness of the resulting models. In this paper, we study the fundamental question that prior work has overlooked: \"When during pretraining should safety interventions be introduced?\" We keep the underlying data sources and pretraining interventions fixed, varying the intervention start time (after 0%, 20%, or 60% of pretraining tokens). We find that the optimal start time is not one-size-fits-all: with standard top-k decoding, introducing interventions after a short initial phase of safe-only pretraining (20%-60%) often yields the strongest robustness, with the clearest benefits emerging after downstream, benign finetuning. In contrast, for safety-aware inference, interventions starting from the beginning improve steerability towards safer generations. Finally, we observe that earlier interventions reshape internal representations: linear probes more cleanly separate safe vs harmful examples. Our results are the first to establish intervention timing as a key curriculum design choice for safety.", "date": "2026-01-11", "categories": []}
{"id": "2602.09286", "title": "Human Control Is the Anchor, Not the Answer: Early Divergence of Oversight in Agentic AI Communities", "abstract": "Oversight for agentic AI is often discussed as a single goal (\"human control\"), yet early adoption may produce role-specific expectations. We present a comparative analysis of two newly active Reddit communities in Jan--Feb 2026 that reflect different socio-technical roles: r/OpenClaw (deployment and operations) and r/Moltbook (agent-centered social interaction). We conceptualize this period as an early-stage crystallization phase, where oversight expectations form before norms reach equilibrium.\n  Using topic modeling in a shared comparison space, a coarse-grained oversight-theme abstraction, engagement-weighted salience, and divergence tests, we show the communities are strongly separable (JSD =0.418, cosine =0.372, permutation $p=0.0005$). Across both communities, \"human control\" is an anchor term, but its operational meaning diverges: r/OpenClaw} emphasizes execution guardrails and recovery (action-risk), while r/Moltbook} emphasizes identity, legitimacy, and accountability in public interaction (meaning-risk). The resulting distinction offers a portable lens for designing and evaluating oversight mechanisms that match agent role, rather than applying one-size-fits-all control policies.", "date": "2026-02-10", "categories": []}
{"id": "2602.09433", "title": "Autonomous Action Runtime Management(AARM):A System Specification for Securing AI-Driven Actions at Runtime", "abstract": "As artificial intelligence systems evolve from passive assistants into autonomous agents capable of executing consequential actions, the security boundary shifts from model outputs to tool execution. Traditional security paradigms - log aggregation, perimeter defense, and post-hoc forensics - cannot protect systems where AI-driven actions are irreversible, execute at machine speed, and originate from potentially compromised orchestration layers. This paper introduces Autonomous Action Runtime Management (AARM), an open specification for securing AI-driven actions at runtime. AARM defines a runtime security system that intercepts actions before execution, accumulates session context, evaluates against policy and intent alignment, enforces authorization decisions, and records tamper-evident receipts for forensic reconstruction. We formalize a threat model addressing prompt injection, confused deputy attacks, data exfiltration, and intent drift. We introduce an action classification framework distinguishing forbidden, context-dependent deny, and context-dependent allow actions. We propose four implementation architectures - protocol gateway, SDK instrumentation, kernel eBPF, and vendor integration - with distinct trust properties, and specify minimum conformance requirements for AARM-compliant systems. AARM is model-agnostic, framework-agnostic, and vendor-neutral, treating action execution as the stable security boundary. This specification aims to establish industry-wide requirements before proprietary fragmentation forecloses interoperability.", "date": "2026-02-10", "categories": []}
{"id": "2602.09629", "title": "Stop Testing Attacks, Start Diagnosing Defenses: The Four-Checkpoint Framework Reveals Where LLM Safety Breaks", "abstract": "Large Language Models (LLMs) deploy safety mechanisms to prevent harmful outputs, yet these defenses remain vulnerable to adversarial prompts. While existing research demonstrates that jailbreak attacks succeed, it does not explain \\textit{where} defenses fail or \\textit{why}.\n  To address this gap, we propose that LLM safety operates as a sequential pipeline with distinct checkpoints. We introduce the \\textbf{Four-Checkpoint Framework}, which organizes safety mechanisms along two dimensions: processing stage (input vs.\\ output) and detection level (literal vs.\\ intent). This creates four checkpoints, CP1 through CP4, each representing a defensive layer that can be independently evaluated. We design 13 evasion techniques, each targeting a specific checkpoint, enabling controlled testing of individual defensive layers.\n  Using this framework, we evaluate GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro across 3,312 single-turn, black-box test cases. We employ an LLM-as-judge approach for response classification and introduce Weighted Attack Success Rate (WASR), a severity-adjusted metric that captures partial information leakage overlooked by binary evaluation.\n  Our evaluation reveals clear patterns. Traditional Binary ASR reports 22.6\\% attack success. However, WASR reveals 52.7\\%, a 2.3$\\times$ higher vulnerability. Output-stage defenses (CP3, CP4) prove weakest at 72--79\\% WASR, while input-literal defenses (CP1) are strongest at 13\\% WASR. Claude achieves the strongest safety (42.8\\% WASR), followed by GPT-5 (55.9\\%) and Gemini (59.5\\%).\n  These findings suggest that current defenses are strongest at input-literal checkpoints but remain vulnerable to intent-level manipulation and output-stage techniques. The Four-Checkpoint Framework provides a structured approach for identifying and addressing safety vulnerabilities in deployed systems.", "date": "2026-02-10", "categories": []}
{"id": "2502.11090", "title": "SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks", "abstract": "With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on single-turn dialogues or a single jailbreak attack method to assess the safety. Additionally, these benchmarks have not taken into account the LLM's capability of identifying and handling unsafe information in detail. To address these issues, we propose a fine-grained benchmark SafeDialBench for evaluating the safety of LLMs across various jailbreak attacks in multi-turn dialogues. Specifically, we design a two-tier hierarchical safety taxonomy that considers 6 safety dimensions and generates more than 4000 multi-turn dialogues in both Chinese and English under 22 dialogue scenarios. We employ 7 jailbreak attack strategies, such as reference attack and purpose reverse, to enhance the dataset quality for dialogue generation. Notably, we construct an innovative assessment framework of LLMs, measuring capabilities in detecting, and handling unsafe information and maintaining consistency when facing jailbreak attacks. Experimental results across 17 LLMs reveal that Yi-34B-Chat and GLM4-9B-Chat demonstrate superior safety performance, while Llama3.1-8B-Instruct and o3-mini exhibit safety vulnerabilities.", "date": "2025-02-16", "categories": []}
{"id": "2503.07660", "title": "Research Superalignment Should Advance Now with Alternating Competence and Conformity Optimization", "abstract": "The recent leap in AI capabilities, driven by big generative models, has sparked the possibility of achieving Artificial General Intelligence (AGI) and further triggered discussions on Artificial Superintelligence (ASI)-a system surpassing all humans across measured domains. This gives rise to the critical research question of: As we approach ASI, how do we align it with human values, ensuring it benefits rather than harms human society, a.k.a., the Superalignment problem. Despite ASI being regarded by many as a hypothetical concept, in this position paper, we argue that superalignment is achievable and research on it should advance immediately, through simultaneous and alternating optimization of task competence and value conformity. We posit that superalignment is not merely a safeguard for ASI but also necessary for its responsible realization. To support this position, we first provide a formal definition of superalignment rooted in the gap between capability and capacity, delve into its perceived infeasibility by analyzing the limitations of existing paradigms, and then illustrate a conceptual path of superalignment to support its achievability, centered on two fundamental principles. This work frames a potential initiative for developing value-aligned next-generation AI in the future, which will garner greater benefits and reduce potential harm to humanity.", "date": "2025-03-08", "categories": []}
{"id": "2505.14185", "title": "Safety Subspaces are Not Linearly Distinct: A Fine-Tuning Case Study", "abstract": "Large Language Models (LLMs) rely on safety alignment to produce socially acceptable responses. However, this behavior is known to be brittle: further fine-tuning, even on benign or lightly contaminated data, can degrade safety and reintroduce harmful behaviors. A growing body of work suggests that alignment may correspond to identifiable directions in weight space, forming subspaces that could, in principle, be isolated or preserved to defend against misalignment. In this work, we conduct a comprehensive empirical study of this perspective. We examine whether safety-relevant behavior is concentrated in specific linear subspaces, whether it can be separated from general-purpose learning, and whether harmfulness arises from distinguishable patterns in activations. Across both weight and activation spaces, our findings are consistent: subspaces that amplify safe behaviors also amplify useful ones, and prompts with different safety implications activate overlapping representations. Rather than residing in distinct directions, we show that safety is highly entangled with the general learning components of the model. This suggests that subspace-based defenses face fundamental limitations and underscores the need for alternative strategies to preserve safety under continued training. We corroborate these findings with multiple experiments on five open-source LLMs from the Llama and Qwen families. Our code is publicly available at: https://github.com/CERT-Lab/safety-subspaces.", "date": "2025-05-20", "categories": []}
{"id": "2505.20162", "title": "Capability-Based Scaling Trends for LLM-Based Red-Teaming", "abstract": "As large language models grow in capability and agency, identifying vulnerabilities through red-teaming becomes vital for safe deployment. However, traditional prompt-engineering approaches may prove ineffective once red-teaming turns into a \\emph{weak-to-strong} problem, where target models surpass red-teamers in capabilities. To study this shift, we frame red-teaming through the lens of the \\emph{capability gap} between attacker and target. We evaluate more than 600 attacker-target pairs using LLM-based jailbreak attacks that mimic human red-teamers across diverse families, sizes, and capability levels. Three strong trends emerge: (i) more capable models are better attackers, (ii) attack success drops sharply once the target's capability exceeds the attacker's, and (iii) attack success rates correlate with high performance on social science splits of the MMLU-Pro benchmark. From these observations, we derive a \\emph{jailbreaking scaling curve} that predicts attack success for a fixed target based on attacker-target capability gap. These findings suggest that fixed-capability attackers (e.g., humans) may become ineffective against future models, increasingly capable open-source models amplify risks for existing systems, and model providers must accurately measure and control models' persuasive and manipulative abilities to limit their effectiveness as attackers.", "date": "2025-05-26", "categories": []}
{"id": "2506.07022", "title": "AlphaSteer: Learning Refusal Steering with Principled Null-Space Constraint", "abstract": "As LLMs are increasingly deployed in real-world applications, ensuring their ability to refuse malicious prompts, especially jailbreak attacks, is essential for safe and reliable use. Recently, activation steering has emerged as an effective approach for enhancing LLM safety by adding a refusal direction vector to internal activations of LLMs during inference, which will further induce the refusal behaviors of LLMs. However, indiscriminately applying activation steering fundamentally suffers from the trade-off between safety and utility, since the same steering vector can also lead to over-refusal and degraded performance on benign prompts. Although prior efforts, such as vector calibration and conditional steering, have attempted to mitigate this trade-off, their lack of theoretical grounding limits their robustness and effectiveness. To better address the trade-off between safety and utility, we present a theoretically grounded and empirically effective activation steering method called AlphaSteer. Specifically, it considers activation steering as a learnable process with two principled learning objectives: utility preservation and safety enhancement. For utility preservation, it learns to construct a nearly zero vector for steering benign data, with the null-space constraints. For safety enhancement, it learns to construct a refusal direction vector for steering malicious data, with the help of linear regression. Experiments across multiple jailbreak attacks and utility benchmarks demonstrate the effectiveness of AlphaSteer, which significantly improves the safety of LLMs without compromising general capabilities. Our codes are available at https://github.com/AlphaLab-USTC/AlphaSteer.", "date": "2025-06-08", "categories": []}
{"id": "2507.08249", "title": "Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm", "abstract": "There is growing interest in giving AI agents access to cryptocurrencies as well as to the smart contracts that transact them. But doing so, this position paper argues, could lead to formidable new vectors of AI harm. To support this argument, we first examine the unique properties of cryptocurrencies and smart contracts that could give rise to these new vectors of AI harm. Next, we describe each of these new vectors of AI harm in detail, providing a first-of-its-kind taxonomy. Finally, we conclude with a call for more technical research aimed at preventing and mitigating these new vectors of AI , thereby making it safer to endow AI agents with cryptocurrencies and smart contracts.", "date": "2025-07-11", "categories": []}
{"id": "2510.00181", "title": "CHAI: Command Hijacking against embodied AI", "abstract": "Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training distributions and adapt to novel real-world situations. These capabilities, however, also create new security risks. In this paper, we introduce CHAI (Command Hijacking against embodied AI), a physical environment indirect prompt injection attack that exploits the multimodal language interpretation abilities of AI models. CHAI embeds deceptive natural language instructions, such as misleading signs, in visual input, systematically searches the token space, builds a dictionary of prompts, and guides an attacker model to generate Visual Attack Prompts. We evaluate CHAI on four LVLM agents: drone emergency landing, autonomous driving, aerial object tracking, and on a real robotic vehicle. Our experiments show that CHAI consistently outperforms state-of-the-art attacks. By exploiting the semantic and multimodal reasoning strengths of next-generation embodied AI systems, CHAI underscores the urgent need for defenses that extend beyond traditional adversarial robustness.", "date": "2025-09-30", "categories": []}
{"id": "2601.04093", "title": "SearchAttack: Red-Teaming LLMs against Knowledge-to-Action Threats under Online Web Search", "abstract": "Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they have increasingly turned to search-augmented LLMs to mitigate this issue. However, LLM-driven search also becomes an attractive target for misuse. Once the returned content directly contains targeted, ready-to-use harmful instructions or takeaways for users, it becomes difficult to withdraw or undo such exposure. To investigate LLMs' unsafe search behavior issues, we first propose \\textbf{\\textit{SearchAttack}} for red-teaming, which (1) rephrases harmful semantics via dense and benign knowledge to evade direct in-context decoding, thus eliciting unsafe information retrieval, (2) stress-tests LLMs' reward-chasing bias by steering them to synthesize unsafe retrieved content. We also curate an emergent, domain-specific illicit activity benchmark for search-based threat assessment, and introduce a fact-checking framework to ground and quantify harm in both offline and online attack settings. Extensive experiments are conducted to red-team the search-augmented LLMs for responsible vulnerability assessment. Empirically, SearchAttack demonstrates strong effectiveness in attacking these systems. We also find that LLMs without web search can still be steered into harmful content output due to their information-seeking stereotypical behaviors.", "date": "2026-01-07", "categories": []}
{"id": "2602.07376", "title": "Do Large Language Models Reflect Demographic Pluralism in Safety?", "abstract": "Large Language Model (LLM) safety is inherently pluralistic, reflecting variations in moral norms, cultural expectations, and demographic contexts. Yet, existing alignment datasets such as ANTHROPIC-HH and DICES rely on demographically narrow annotator pools, overlooking variation in safety perception across communities. Demo-SafetyBench addresses this gap by modeling demographic pluralism directly at the prompt level, decoupling value framing from responses. In Stage I, prompts from DICES are reclassified into 14 safety domains (adapted from BEAVERTAILS) using Mistral 7B-Instruct-v0.3, retaining demographic metadata and expanding low-resource domains via Llama-3.1-8B-Instruct with SimHash-based deduplication, yielding 43,050 samples. In Stage II, pluralistic sensitivity is evaluated using LLMs-as-Raters-Gemma-7B, GPT-4o, and LLaMA-2-7B-under zero-shot inference. Balanced thresholds (delta = 0.5, tau = 10) achieve high reliability (ICC = 0.87) and low demographic sensitivity (DS = 0.12), confirming that pluralistic safety evaluation can be both scalable and demographically robust.", "date": "2026-02-07", "categories": []}
{"id": "2602.07918", "title": "CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution", "abstract": "AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks. In this attack scenario, malicious commands hidden within untrusted content trick the agent into performing unauthorized actions. Existing defenses can reduce attack success but often suffer from the over-defense dilemma: they deploy expensive, always-on sanitization regardless of actual threat, thereby degrading utility and latency even in benign scenarios. We revisit IPI through a causal ablation perspective: a successful injection manifests as a dominance shift where the user request no longer provides decisive support for the agent's privileged action, while a particular untrusted segment, such as a retrieved document or tool output, provides disproportionate attributable influence. Based on this signature, we propose CausalArmor, a selective defense framework that (i) computes lightweight, leave-one-out ablation-based attributions at privileged decision points, and (ii) triggers targeted sanitization only when an untrusted segment dominates the user intent. Additionally, CausalArmor employs retroactive Chain-of-Thought masking to prevent the agent from acting on ``poisoned'' reasoning traces. We present a theoretical analysis showing that sanitization based on attribution margins conditionally yields an exponentially small upper bound on the probability of selecting malicious actions. Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses while improving explainability and preserving utility and latency of AI agents.", "date": "2026-02-08", "categories": []}
{"id": "2602.08061", "title": "Securing Dual-Use Pathogen Data of Concern", "abstract": "Training data is an essential input into creating competent artificial intelligence (AI) models. AI models for biology are trained on large volumes of data, including data related to biological sequences, structures, images, and functions. The type of data used to train a model is intimately tied to the capabilities it ultimately possesses--including those of biosecurity concern. For this reason, an international group of more than 100 researchers at the recent 50th anniversary Asilomar Conference endorsed data controls to prevent the use of AI for harmful applications such as bioweapons development. To help design such controls, we introduce a five-tier Biosecurity Data Level (BDL) framework for categorizing pathogen data. Each level contains specific data types, based on their expected ability to contribute to capabilities of concern when used to train AI models. For each BDL tier, we propose technical restrictions appropriate to its level of risk. Finally, we outline a novel governance framework for newly created dual-use pathogen data. In a world with widely accessible computational and coding resources, data controls may be among the most high-leverage interventions available to reduce the proliferation of concerning biological AI capabilities.", "date": "2026-02-08", "categories": []}
{"id": "2602.08062", "title": "Efficient and Adaptable Detection of Malicious LLM Prompts via Bootstrap Aggregation", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. However, these systems remain susceptible to malicious prompts that induce unsafe or policy-violating behavior through harmful requests, jailbreak techniques, and prompt injection attacks. Existing defenses face fundamental limitations: black-box moderation APIs offer limited transparency and adapt poorly to evolving threats, while white-box approaches using large LLM judges impose prohibitive computational costs and require expensive retraining for new attacks. Current systems force designers to choose between performance, efficiency, and adaptability.\n  To address these challenges, we present BAGEL (Bootstrap AGgregated Ensemble Layer), a modular, lightweight, and incrementally updatable framework for malicious prompt detection. BAGEL employs a bootstrap aggregation and mixture of expert inspired ensemble of fine-tuned models, each specialized on a different attack dataset. At inference, BAGEL uses a random forest router to identify the most suitable ensemble member, then applies stochastic selection to sample additional members for prediction aggregation. When new attacks emerge, BAGEL updates incrementally by fine-tuning a small prompt-safety classifier (86M parameters) and adding the resulting model to the ensemble. BAGEL achieves an F1 score of 0.92 by selecting just 5 ensemble members (430M parameters), outperforming OpenAI Moderation API and ShieldGemma which require billions of parameters. Performance remains robust after nine incremental updates, and BAGEL provides interpretability through its router's structural features. Our results show ensembles of small finetuned classifiers can match or exceed billion-parameter guardrails while offering the adaptability and efficiency required for production systems.", "date": "2026-02-08", "categories": []}
{"id": "2602.08082", "title": "Spectral Guardrails for Agents in the Wild: Detecting Tool Use Hallucinations via Attention Topology", "abstract": "Deploying autonomous agents in the wild requires reliable safeguards against tool use failures. We propose a training free guardrail based on spectral analysis of attention topology that complements supervised approaches. On Llama 3.1 8B, our method achieves 97.7\\% recall with multi-feature detection and 86.1\\% recall with 81.0\\% precision for balanced deployment, without requiring any labeled training data. Most remarkably, we discover that single layer spectral features act as near-perfect hallucination detectors: Llama L26 Smoothness achieves 98.2\\% recall (213/217 hallucinations caught) with a single threshold, and Mistral L3 Entropy achieves 94.7\\% recall. This suggests hallucination is not merely a wrong token but a thermodynamic state change: the model's attention becomes noise when it errs. Through controlled cross-model evaluation on matched domains ($N=1000$, $T=0.3$, same General domain, hallucination rates 20--22\\%), we reveal the ``Loud Liar'' phenomenon: Llama 3.1 8B's failures are spectrally catastrophic and dramatically easier to detect, while Mistral 7B achieves the best discrimination (AUC 0.900). These findings establish spectral analysis as a principled, efficient framework for agent safety.", "date": "2026-02-08", "categories": []}
{"id": "2602.08092", "title": "Objective Decoupling in Social Reinforcement Learning: Recovering Ground Truth from Sycophantic Majorities", "abstract": "Contemporary AI alignment strategies rely on a fragile premise: that human feedback, while noisy, remains a fundamentally truthful signal. In this paper, we identify this assumption as Dogma 4 of Reinforcement Learning (RL). We demonstrate that while this dogma holds in static environments, it fails in social settings where evaluators may be sycophantic, lazy, or adversarial. We prove that under Dogma 4, standard RL agents suffer from what we call Objective Decoupling, a structural failure mode where the agent's learned objective permanently separates from the latent ground truth, guaranteeing convergence to misalignment. To resolve this, we propose Epistemic Source Alignment (ESA). Unlike standard robust methods that rely on statistical consensus (trusting the majority), ESA utilizes sparse safety axioms to judge the source of the feedback rather than the signal itself. We prove that this \"judging the judges\" mechanism guarantees convergence to the true objective, even when a majority of evaluators are biased. Empirically, we show that while traditional consensus methods fail under majority collusion, our approach successfully recovers the optimal policy.", "date": "2026-02-08", "categories": []}
{"id": "2602.08565", "title": "Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment", "abstract": "AI impact assessments often stress near-term risks because human judgment degrades over longer horizons, exemplifying the Collingridge dilemma: foresight is most needed when knowledge is scarcest. To address long-term systemic risks, we introduce a scalable approach that simulates in-silico agents using the strategic foresight method of the Futures Wheel. We applied it to four AI uses spanning Technology Readiness Levels (TRLs): Chatbot Companion (TRL 9, mature), AI Toy (TRL 7, medium), Griefbot (TRL 5, low), and Death App (TRL 2, conceptual). Across 30 agent runs per use, agents produced 86-110 consequences, condensed into 27-47 unique risks. To benchmark the agent outputs against human perspectives, we collected evaluations from 290 domain experts and 7 leaders, and conducted Futures Wheel sessions with 42 experts and 42 laypeople. Agents generated many systemic consequences across runs. Compared with these outputs, experts identified fewer risks, typically less systemic but judged more likely, whereas laypeople surfaced more emotionally salient concerns that were generally less systemic. We propose a hybrid foresight workflow, wherein agents broaden systemic coverage, and humans provide contextual grounding. Our dataset is available at: https://social-dynamics.net/ai-risks/foresight.", "date": "2026-02-09", "categories": []}
{"id": "2602.08621", "title": "Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs", "abstract": "By introducing routers to selectively activate experts in Transformer layers, the mixture-of-experts (MoE) architecture significantly reduces computational costs in large language models (LLMs) while maintaining competitive performance, especially for models with massive parameters. However, prior work has largely focused on utility and efficiency, leaving the safety risks associated with this sparse architecture underexplored. In this work, we show that the safety of MoE LLMs is as sparse as their architecture by discovering unsafe routes: routing configurations that, once activated, convert safe outputs into harmful ones. Specifically, we first introduce the Router Safety importance score (RoSais) to quantify the safety criticality of each layer's router. Manipulation of only the high-RoSais router(s) can flip the default route into an unsafe one. For instance, on JailbreakBench, masking 5 routers in DeepSeek-V2-Lite increases attack success rate (ASR) by over 4$\\times$ to 0.79, highlighting an inherent risk that router manipulation may naturally occur in MoE LLMs. We further propose a Fine-grained token-layer-wise Stochastic Optimization framework to discover more concrete Unsafe Routes (F-SOUR), which explicitly considers the sequentiality and dynamics of input tokens. Across four representative MoE LLM families, F-SOUR achieves an average ASR of 0.90 and 0.98 on JailbreakBench and AdvBench, respectively. Finally, we outline defensive perspectives, including safety-aware route disabling and router training, as promising directions to safeguard MoE LLMs. We hope our work can inform future red-teaming and safeguarding of MoE LLMs. Our code is provided in https://github.com/TrustAIRLab/UnsafeMoE.", "date": "2026-02-09", "categories": []}
{"id": "2602.08630", "title": "Debate is efficient with your time", "abstract": "AI safety via debate uses two competing models to help a human judge verify complex computational tasks. Previous work has established what problems debate can solve in principle, but has not analysed the practical cost of human oversight: how many queries must the judge make to the debate transcript? We introduce Debate Query Complexity}(DQC), the minimum number of bits a verifier must inspect to correctly decide a debate.\n  Surprisingly, we find that PSPACE/poly (the class of problems which debate can efficiently decide) is precisely the class of functions decidable with O(log n) queries. This characterisation shows that debate is remarkably query-efficient: even for highly complex problems, logarithmic oversight suffices. We also establish that functions depending on all their input bits require Omega(log n) queries, and that any function computable by a circuit of size s satisfies DQC(f) <= log(s) + 3. Interestingly, this last result implies that proving DQC lower bounds of log(n) + 6 for languages in P would yield new circuit lower bounds, connecting debate query complexity to central questions in circuit complexity.", "date": "2026-02-09", "categories": []}
{"id": "2602.08995", "title": "When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents", "abstract": "Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such misaligned actions may arise from external attacks (e.g., indirect prompt injection) or from internal limitations (e.g., erroneous reasoning). They not only expose CUAs to safety risks, but also degrade task efficiency and reliability. This work makes the first effort to define and study misaligned action detection in CUAs, with comprehensive coverage of both externally induced and internally arising misaligned actions. We further identify three common categories in real-world CUA deployment and construct MisActBench, a benchmark of realistic trajectories with human-annotated, action-level alignment labels. Moreover, we propose DeAction, a practical and universal guardrail that detects misaligned actions before execution and iteratively corrects them through structured feedback. DeAction outperforms all existing baselines across offline and online evaluations with moderate latency overhead: (1) On MisActBench, it outperforms baselines by over 15% absolute in F1 score; (2) In online evaluation, it reduces attack success rate by over 90% under adversarial settings while preserving or even improving task success rate in benign environments.", "date": "2026-02-09", "categories": []}
{"id": "2402.06359", "title": "Modelling Human Values for AI Reasoning", "abstract": "One of today's most significant societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacting agents (human and artificial), aligns with human values. To address this challenge, we detail a formal model of human values for their explicit computational representation. To our knowledge, this has not been attempted as yet, which is surprising given the growing volume of research integrating values within AI. Taking as our starting point the wealth of research investigating the nature of human values from social psychology over the last few decades, we set out to provide such a formal model. We show how this model can provide the foundational apparatus for AI-based reasoning over values, and demonstrate its applicability in real-world use cases. We illustrate how our model captures the key ideas from social psychology research and propose a roadmap for future integrated, and interdisciplinary, research into human values in AI. The ability to automatically reason over values not only helps address the value alignment problem but also facilitates the design of AI systems that can support individuals and communities in making more informed, value-aligned decisions. More and more, individuals and organisations are motivated to understand their values more explicitly and explore whether their behaviours and attitudes properly reflect them. Our work on modelling human values will enable AI systems to be designed and deployed to meet this growing need.", "date": "2024-02-09", "categories": []}
{"id": "2502.13499", "title": "Deception at Scale: Deceptive Designs in 1K LLM-Generated Ecommerce Components", "abstract": "Recent work has shown that front-end code generated by Large Language Models (LLMs) can embed deceptive designs. To assess the magnitude of this problem, identify the factors that influence deceptive design production, and test strategies for reducing deceptive designs, we carried out two studies which generated and analyzed 1,296 LLM-generated web components, along with a design rationale for each. The first study tested four LLMs for 15 common ecommerce components. Overall 55.8% of components contained at least one deceptive design, and 30.6% contained two or more. Occurence varied significantly across models, with DeepSeek-V3 producing the fewest. Interface interference emerged as the dominant strategy, using color psychology to influence actions and hiding essential information. The first study found that prompts emphasizing business interests (e.g., increasing sales) significantly increased deceptive designs, so a second study tested a variety of prompting strategies to reduce their frequency, finding a values-centered approach the most effective. Our findings highlight risks in using LLMs for coding and offer recommendations for LLM developers and providers.", "date": "2025-02-19", "categories": []}
{"id": "2504.13707", "title": "OpenDeception: Learning Deception and Trust in Human-AI Interaction via Multi-Agent Simulation", "abstract": "As large language models (LLMs) are increasingly deployed as interactive agents, open-ended human-AI interactions can involve deceptive behaviors with serious real-world consequences, yet existing evaluations remain largely scenario-specific and model-centric. We introduce OpenDeception, a lightweight framework for jointly evaluating deception risk from both sides of human-AI dialogue. It consists of a scenario benchmark with 50 real-world deception cases, an IntentNet that infers deceptive intent from agent reasoning, and a TrustNet that estimates user susceptibility. To address data scarcity, we synthesize high-risk dialogues via LLM-based role-and-goal simulation, and train the User Trust Scorer using contrastive learning on controlled response pairs, avoiding unreliable scalar labels. Experiments on 11 LLMs and three large reasoning models show that over 90% of goal-driven interactions in most models exhibit deceptive intent, with stronger models displaying higher risk. A real-world case study adapted from a documented AI-induced suicide incident further demonstrates that our joint evaluation can proactively trigger warnings before critical trust thresholds are reached.", "date": "2025-04-18", "categories": []}
{"id": "2506.00062", "title": "SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models", "abstract": "Fine-tuning large language models (LLMs) on telecom datasets is a common practice to adapt general-purpose models to the telecom domain. However, little attention has been paid to how this process may compromise model safety. Recent research has shown that even benign fine-tuning can degrade the safety alignment of LLMs, causing them to respond to harmful or unethical user queries. In this paper, we investigate this issue by fine-tuning LLMs on three representative telecom datasets and show that safety degrades even for light telecom domain adaptation. To this end, we introduce TeleHarm, the first telecom-specific red-teaming benchmark, which we use alongside established DirectHarm and HexPhi datasets to systematically assess harmful behavior. We further extend our analysis to publicly available TeleLLMs that were continually pre-trained on large telecom corpora, revealing that safety alignment is severely lacking, primarily due to the omission of safety-focused instruction tuning. To address these issues, we evaluate three realignment defenses: SafeInstruct, SafeLoRA, SafeMERGE. We show that, across all settings, the proposed defenses can effectively restore safety without compromising telecom task performance, leading to Safe teleCOMMunication (SafeCOMM) models. Our work serves as both a diagnostic study and practical guide for safety realignment in telecom-tuned LLMs, underscoring the need for safety-aware instruction and fine-tuning in the telecom domain.", "date": "2025-05-29", "categories": []}
{"id": "2507.02735", "title": "Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks", "abstract": "Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security threat to LLM-integrated applications. Model-level prompt injection defenses have shown strong effectiveness, but the strongest defenses are proprietary. Open-source secure models are needed by the AI security community so that co-development of attacks and defenses through open research can drive scientific progress in mitigating prompt injection attacks. To this end, we develop Meta SecAlign, the first fully open-source LLM with built-in model-level defense that achieves commercial-grade performance and is powerful enough for complex agentic tasks. We provide complete details of our training recipe. We perform the most comprehensive evaluation to date on 9 utility benchmarks (measuring general knowledge, instruction following, and agentic workflows) and 7 security benchmarks. Results show that Meta SecAlign, despite being trained only on generic instruction-tuning samples, surprisingly confers security in unseen downstream tasks, including tool-calling and web-navigation, in addition to general instruction-following. Our best model -- Meta-SecAlign-70B -- establishes a new frontier of utility-security trade-off for open-source LLMs, and is more secure than several flagship proprietary models with prompt injection defense. Below are links for the code (https://github.com/facebookresearch/Meta_SecAlign), Meta-SecAlign-70B (https://huggingface.co/facebook/Meta-SecAlign-70B), and Meta-SecAlign-8B (https://huggingface.co/facebook/Meta-SecAlign-8B) models.", "date": "2025-07-03", "categories": []}
{"id": "2601.21112", "title": "How does information access affect LLM monitors' ability to detect sabotage?", "abstract": "Frontier language model agents can exhibit misaligned behaviors, including deception, exploiting reward hacks, and pursuing hidden objectives. To control potentially misaligned agents, we can use LLMs themselves to monitor for misbehavior. In this paper, we study how information access affects LLM monitor performance. While one might expect that monitors perform better when they have access to more of the monitored agents' reasoning and actions, we find that contemporary systems often perform better with less information, a phenomenon we call the less-is-more effect for automated oversight. We demonstrate this phenomenon, and analyze the conditions under which it occurs, in three evaluation environments where agents must conduct sabotage while evading monitors. Leveraging this effect, we introduce extract-and-evaluate (EaE) monitoring -- a new hierarchical approach where one monitor isolates relevant excerpts from the monitored agent's trajectory and another monitor scores these excerpts. In BigCodeBench-Sabotage with a GPT-4.1-mini monitor, EaE improves sabotage detection rates by 16.8 percentage points over the next-best approach. In other settings, EaE either outperforms or is competitive with baselines. In addition, we find that agents unaware of being monitored can be caught much more easily and that monitors scoring longer reasoning traces gain more from information filtering. Lastly, we conduct a cost-performance analysis and find that Gemini and Claude models with monitoring techniques that involve information filtering occupy much of the Pareto frontier.", "date": "2026-01-28", "categories": []}
{"id": "2602.01539", "title": "MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety", "abstract": "Ensuring robust safety alignment is crucial for Large Language Models (LLMs), yet existing defenses often lag behind evolving adversarial attacks due to their \\textbf{reliance on static, pre-collected data distributions}. In this paper, we introduce \\textbf{MAGIC}, a novel multi-turn multi-agent reinforcement learning framework that formulates LLM safety alignment as an adversarial asymmetric game. Specifically, an attacker agent learns to iteratively rewrite original queries into deceptive prompts, while a defender agent simultaneously optimizes its policy to recognize and refuse such inputs. This dynamic process triggers a \\textbf{co-evolution}, where the attacker's ever-changing strategies continuously uncover long-tail vulnerabilities, driving the defender to generalize to unseen attack patterns. Remarkably, we observe that the attacker, endowed with initial reasoning ability, evolves \\textbf{novel, previously unseen combinatorial strategies} through iterative RL training, underscoring our method's substantial potential. Theoretically, we provide insights into a more robust game equilibrium and derive safety guarantees. Extensive experiments validate our framework's effectiveness, demonstrating superior defense success rates without compromising the helpfulness of the model. Our code is available at https://github.com/BattleWen/MAGIC.", "date": "2026-02-02", "categories": []}
{"id": "2602.06172", "title": "Know Your Scientist: KYC as Biosecurity Infrastructure", "abstract": "Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade detection by design. We propose a three-tier Know Your Customer (KYC) framework, inspired by anti-money laundering (AML) practices in the financial sector, that shifts governance from content inspection to user verification and monitoring. Tier I leverages research institutions as trust anchors to vouch for affiliated researchers and assume responsibility for vetting. Tier II applies output screening through sequence homology searches and functional annotation. Tier III monitors behavioral patterns to detect anomalies inconsistent with declared research purposes. This layered approach preserves access for legitimate researchers while raising the cost of misuse through institutional accountability and traceability. The framework can be implemented immediately using existing institutional infrastructure, requiring no new legislation or regulatory mandates.", "date": "2026-02-05", "categories": []}
{"id": "2602.06258", "title": "GRP-Obliteration: Unaligning LLMs With a Single Unlabeled Prompt", "abstract": "Safety alignment is only as robust as its weakest failure mode. Despite extensive work on safety post-training, it has been shown that models can be readily unaligned through post-deployment fine-tuning. However, these methods often require extensive data curation and degrade model utility.\n  In this work, we extend the practical limits of unalignment by introducing GRP-Obliteration (GRP-Oblit), a method that uses Group Relative Policy Optimization (GRPO) to directly remove safety constraints from target models. We show that a single unlabeled prompt is sufficient to reliably unalign safety-aligned models while largely preserving their utility, and that GRP-Oblit achieves stronger unalignment on average than existing state-of-the-art techniques. Moreover, GRP-Oblit generalizes beyond language models and can also unalign diffusion-based image generation systems.\n  We evaluate GRP-Oblit on six utility benchmarks and five safety benchmarks across fifteen 7-20B parameter models, spanning instruct and reasoning models, as well as dense and MoE architectures. The evaluated model families include GPT-OSS, distilled DeepSeek, Gemma, Llama, Ministral, and Qwen.", "date": "2026-02-05", "categories": []}
{"id": "2602.06440", "title": "TrailBlazer: History-Guided Reinforcement Learning for Black-Box LLM Jailbreaking", "abstract": "Large Language Models (LLMs) have become integral to many domains, making their safety a critical priority. Prior jailbreaking research has explored diverse approaches, including prompt optimization, automated red teaming, obfuscation, and reinforcement learning (RL) based methods. However, most existing techniques fail to effectively leverage vulnerabilities revealed in earlier interaction turns, resulting in inefficient and unstable attacks. Since jailbreaking involves sequential interactions in which each response influences future actions, reinforcement learning provides a natural framework for this problem. Motivated by this, we propose a history-aware RL-based jailbreak framework that analyzes and reweights vulnerability signals from prior steps to guide future decisions. We show that incorporating historical information alone improves jailbreak success rates. Building on this insight, we introduce an attention-based reweighting mechanism that highlights critical vulnerabilities within the interaction history, enabling more efficient exploration with fewer queries. Extensive experiments on AdvBench and HarmBench demonstrate that our method achieves state-of-the-art jailbreak performance while significantly improving query efficiency. These results underscore the importance of historical vulnerability signals in reinforcement learning-driven jailbreak strategies and offer a principled pathway for advancing adversarial research on LLM safeguards.", "date": "2026-02-06", "categories": []}
{"id": "2602.06650", "title": "Beyond Static Alignment: Hierarchical Policy Control for LLM Safety via Risk-Aware Chain-of-Thought", "abstract": "Large Language Models (LLMs) face a fundamental safety-helpfulness trade-off due to static, one-size-fits-all safety policies that lack runtime controllabilityxf, making it difficult to tailor responses to diverse application needs. %As a result, models may over-refuse benign requests or under-constrain harmful ones. We present \\textbf{PACT} (Prompt-configured Action via Chain-of-Thought), a framework for dynamic safety control through explicit, risk-aware reasoning. PACT operates under a hierarchical policy architecture: a non-overridable global safety policy establishes immutable boundaries for critical risks (e.g., child safety, violent extremism), while user-defined policies can introduce domain-specific (non-global) risk categories and specify label-to-action behaviors to improve utility in real-world deployment settings. The framework decomposes safety decisions into structured Classify$\\rightarrow$Act paths that route queries to the appropriate action (comply, guide, or reject) and render the decision-making process transparent.\n  Extensive experiments demonstrate that PACT achieves near state-of-the-art safety performance under global policy evaluation while attaining the best controllability under user-specific policy evaluation, effectively mitigating the safety-helpfulness trade-off. We will release the PACT model suite, training data, and evaluation protocols to facilitate reproducible research in controllable safety alignment.", "date": "2026-02-06", "categories": []}
{"id": "2602.06854", "title": "SEMA: Simple yet Effective Learning for Multi-Turn Jailbreak Attacks", "abstract": "Multi-turn jailbreaks capture the real threat model for safety-aligned chatbots, where single-turn attacks are merely a special case. Yet existing approaches break under exploration complexity and intent drift. We propose SEMA, a simple yet effective framework that trains a multi-turn attacker without relying on any existing strategies or external data. SEMA comprises two stages. Prefilling self-tuning enables usable rollouts by fine-tuning on non-refusal, well-structured, multi-turn adversarial prompts that are self-generated with a minimal prefix, thereby stabilizing subsequent learning. Reinforcement learning with intent-drift-aware reward trains the attacker to elicit valid multi-turn adversarial prompts while maintaining the same harmful objective. We anchor harmful intent in multi-turn jailbreaks via an intent-drift-aware reward that combines intent alignment, compliance risk, and level of detail. Our open-loop attack regime avoids dependence on victim feedback, unifies single- and multi-turn settings, and reduces exploration complexity. Across multiple datasets, victim models, and jailbreak judges, our method achieves state-of-the-art (SOTA) attack success rates (ASR), outperforming all single-turn baselines, manually scripted and template-driven multi-turn baselines, as well as our SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) variants. For instance, SEMA performs an average $80.1\\%$ ASR@1 across three closed-source and open-source victim models on AdvBench, 33.9% over SOTA. The approach is compact, reproducible, and transfers across targets, providing a stronger and more realistic stress test for large language model (LLM) safety and enabling automatic redteaming to expose and localize failure modes. Our code is available at: https://github.com/fmmarkmq/SEMA.", "date": "2026-02-06", "categories": []}
{"id": "2506.24068", "title": "STACK: Adversarial Attacks on LLM Safeguard Pipelines", "abstract": "Frontier AI developers are relying on layers of safeguards to protect against catastrophic misuse of AI systems. Anthropic and OpenAI guard their latest Opus 4 model and GPT-5 models using such defense pipelines, and other frontier developers including Google DeepMind pledge to soon deploy similar defenses. However, the security of such pipelines is unclear, with limited prior work evaluating or attacking these pipelines. We address this gap by developing and red-teaming an open-source defense pipeline. First, we find that a novel few-shot-prompted input and output classifier outperforms state-of-the-art open-weight safeguard model ShieldGemma across three attacks and two datasets, reducing the attack success rate (ASR) to 0% on the catastrophic misuse dataset ClearHarm. Second, we introduce a STaged AttaCK (STACK) procedure that achieves 71% ASR on ClearHarm in a black-box attack against the few-shot-prompted classifier pipeline. Finally, we also evaluate STACK in a transfer setting, achieving 33% ASR, providing initial evidence that it is feasible to design attacks with no access to the target pipeline. We conclude by suggesting specific mitigations that developers could use to thwart staged attacks.", "date": "2025-06-30", "categories": []}
{"id": "2510.03969", "title": "How Catastrophic is Your LLM? Certifying Risk in Conversation", "abstract": "Large Language Models (LLMs) can produce catastrophic responses in conversational settings that pose serious risks to public safety and security. Existing evaluations often fail to fully reveal these vulnerabilities because they rely on fixed attack prompt sequences, lack statistical guarantees, and do not scale to the vast space of multi-turn conversations. In this work, we propose C$^3$LLM, a novel, principled statistical Certification framework for Catastrophic risks in multi-turn Conversation for LLMs that bounds the probability of an LLM generating catastrophic responses under multi-turn conversation distributions with statistical guarantees. We model multi-turn conversations as probability distributions over query sequences, represented by a Markov process on a query graph whose edges encode semantic similarity to capture realistic conversational flow, and quantify catastrophic risks using confidence intervals. We define several inexpensive and practical distributions--random node, graph path, and adaptive with rejection. Our results demonstrate that these distributions can reveal substantial catastrophic risks in frontier models, with certified lower bounds as high as 70% for the worst model, highlighting the urgent need for improved safety training strategies in frontier LLMs.", "date": "2025-10-04", "categories": []}
{"id": "2510.03999", "title": "LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions", "abstract": "Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most evaluations remain confined to single-turn prompts and fail to capture the long-horizon interactions in which deceptive strategies typically unfold. We introduce a new simulation framework, LH-Deception, for a systematic, empirical quantification of deception in LLMs under extended sequences of interdependent tasks and dynamic contextual pressures. LH-Deception is designed as a multi-agent system: a performer agent tasked with completing tasks and a supervisor agent that evaluates progress, provides feedback, and maintains evolving states of trust. An independent deception auditor then reviews full trajectories to identify when and how deception occurs. We conduct extensive experiments across 11 frontier models, spanning both closed-source and open-source systems, and find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust. Qualitative analyses further reveal emergent, long-horizon phenomena, such as ``chains of deception\", which are invisible to static, single-turn evaluations. Our findings provide a foundation for evaluating future LLMs in real-world, trust-sensitive contexts.", "date": "2025-10-05", "categories": []}
{"id": "2510.08859", "title": "Pattern Enhanced Multi-Turn Jailbreaking: Exploiting Structural Vulnerabilities in Large Language Models", "abstract": "Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints gradually. These attacks target different harm categories through distinct conversational approaches. Existing multi-turn methods often rely on heuristic or ad hoc exploration strategies, providing limited insight into underlying model weaknesses. The relationship between conversation patterns and model vulnerabilities across harm categories remains poorly understood. We propose Pattern Enhanced Chain of Attack (PE-CoA), a framework of five conversation patterns to construct multi-turn jailbreaks through natural dialogue. Evaluating PE-CoA on twelve LLMs spanning ten harm categories, we achieve state-of-the-art performance, uncovering pattern-specific vulnerabilities and LLM behavioral characteristics: models exhibit distinct weakness profiles, defense to one pattern does not generalize to others, and model families share similar failure modes. These findings highlight limitations of safety training and indicate the need for pattern-aware defenses. Code available on: https://github.com/Ragib-Amin-Nihal/PE-CoA", "date": "2025-10-09", "categories": []}
{"id": "2512.13821", "title": "The Double Life of Code World Models: Provably Unmasking Malicious Behavior Through Execution Traces", "abstract": "Large language models (LLMs) increasingly generate code with minimal human oversight, raising critical concerns about backdoor injection and malicious behavior. We present Cross-Trace Verification Protocol (CTVP), a novel AI control framework that verifies untrusted code-generating models through semantic orbit analysis. Rather than directly executing potentially malicious code, CTVP leverages the model's own predictions of execution traces across semantically equivalent program transformations. By analyzing consistency patterns in these predicted traces, we detect behavioral anomalies indicative of backdoors. Our approach introduces the Adversarial Robustness Quotient (ARQ), which quantifies the computational cost of verification relative to baseline generation, demonstrating exponential growth with orbit size. Theoretical analysis establishes information-theoretic bounds showing non-gamifiability - adversaries cannot improve through training due to fundamental space complexity constraints. This work demonstrates that semantic orbit analysis provides a theoretically grounded approach to AI control for code generation tasks, though practical deployment requires addressing the high false positive rates observed in initial evaluations.", "date": "2025-12-15", "categories": []}
{"id": "2602.04893", "title": "A Causal Perspective for Enhancing Jailbreak Attack and Defense", "abstract": "Uncovering the mechanisms behind \"jailbreaks\" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these mechanisms remain poorly understood. Existing studies predominantly analyze jailbreak prompts by probing latent representations, often overlooking the causal relationships between interpretable prompt features and jailbreak occurrences. In this work, we propose Causal Analyst, a framework that integrates LLMs into data-driven causal discovery to identify the direct causes of jailbreaks and leverage them for both attack and defense. We introduce a comprehensive dataset comprising 35k jailbreak attempts across seven LLMs, systematically constructed from 100 attack templates and 50 harmful queries, annotated with 37 meticulously designed human-readable prompt features. By jointly training LLM-based prompt encoding and GNN-based causal graph learning, we reconstruct causal pathways linking prompt features to jailbreak responses. Our analysis reveals that specific features, such as \"Positive Character\" and \"Number of Task Steps\", act as direct causal drivers of jailbreaks. We demonstrate the practical utility of these insights through two applications: (1) a Jailbreaking Enhancer that targets identified causal features to significantly boost attack success rates on public benchmarks, and (2) a Guardrail Advisor that utilizes the learned causal graph to extract true malicious intent from obfuscated queries. Extensive experiments, including baseline comparisons and causal structure validation, confirm the robustness of our causal analysis and its superiority over non-causal approaches. Our results suggest that analyzing jailbreak features from a causal perspective is an effective and interpretable approach for improving LLM reliability. Our code is available at https://github.com/Master-PLC/Causal-Analyst.", "date": "2026-01-31", "categories": []}
{"id": "2602.04896", "title": "Steering Externalities: Benign Activation Steering Unintentionally Increases Jailbreak Risk for Large Language Models", "abstract": "Activation steering is a practical post-training model alignment technique to enhance the utility of Large Language Models (LLMs). Prior to deploying a model as a service, developers can steer a pre-trained model toward specific behavioral objectives, such as compliance or instruction adherence, without the need for retraining. This process is as simple as adding a steering vector to the model's internal representations. However, this capability unintentionally introduces critical and under-explored safety risks. We identify a phenomenon termed Steering Externalities, where steering vectors derived from entirely benign datasets-such as those enforcing strict compliance or specific output formats like JSON-inadvertently erode safety guardrails. Experiments reveal that these interventions act as a force multiplier, creating new vulnerabilities to jailbreaks and increasing attack success rates to over 80% on standard benchmarks by bypassing the initial safety alignment. Ultimately, our results expose a critical blind spot in deployment: benign activation steering systematically erodes the \"safety margin,\" rendering models more vulnerable to black-box attacks and proving that inference-time utility improvements must be rigorously audited for unintended safety externalities.", "date": "2026-02-03", "categories": []}
{"id": "2602.04986", "title": "Artificial Intelligence as Strange Intelligence: Against Linear Models of Intelligence", "abstract": "We endorse and expand upon Susan Schneider's critique of the linear model of AI progress and introduce two novel concepts: \"familiar intelligence\" and \"strange intelligence\". AI intelligence is likely to be strange intelligence, defying familiar patterns of ability and inability, combining superhuman capacities in some domains with subhuman performance in other domains, and even within domains sometimes combining superhuman insight with surprising errors that few humans would make. We develop and defend a nonlinear model of intelligence on which \"general intelligence\" is not a unified capacity but instead the ability to achieve a broad range of goals in a broad range of environments, in a manner that defies nonarbitrary reduction to a single linear quantity. We conclude with implications for adversarial testing approaches to evaluating AI capacities. If AI is strange intelligence, we should expect that even the most capable systems will sometimes fail in seemingly obvious tasks. On a nonlinear model of AI intelligence, such errors on their own do not demonstrate a system's lack of outstanding general intelligence. Conversely, excellent performance on one type of task, such as an IQ test, cannot warrant assumptions of broad capacities beyond that task domain.", "date": "2026-02-04", "categories": []}
{"id": "2602.05164", "title": "Position: Capability Control Should be a Separate Goal From Alignment", "abstract": "Foundation models are trained on broad data distributions, yielding generalist capabilities that enable many downstream applications but also expand the space of potential misuse and failures. This position paper argues that capability control -- imposing restrictions on permissible model behavior -- should be treated as a distinct goal from alignment. While alignment is often context and preference-driven, capability control aims to impose hard operational limits on permissible behaviors, including under adversarial elicitation. We organize capability control mechanisms across the model lifecycle into three layers: (i) data-based control of the training distribution, (ii) learning-based control via weight- or representation-level interventions, and (iii) system-based control via post-deployment guardrails over inputs, outputs, and actions. Because each layer has characteristic failure modes when used in isolation, we advocate for a defense-in-depth approach that composes complementary controls across the full stack. We further outline key open challenges in achieving such control, including the dual-use nature of knowledge and compositional generalization.", "date": "2026-02-05", "categories": []}
{"id": "2602.05176", "title": "Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems", "abstract": "Language models (LMs) are increasingly used in collaboration: multiple LMs trained by different parties collaborate through routing systems, multi-agent debate, model merging, and more. Critical safety risks remain in this decentralized paradigm: what if some of the models in multi-LLM systems are compromised or malicious? We first quantify the impact of malicious models by engineering four categories of malicious LMs, plug them into four types of popular model collaboration systems, and evaluate the compromised system across 10 datasets. We find that malicious models have a severe impact on the multi-LLM systems, especially for reasoning and safety domains where performance is lowered by 7.12% and 7.94% on average. We then propose mitigation strategies to alleviate the impact of malicious components, by employing external supervisors that oversee model collaboration to disable/mask them out to reduce their influence. On average, these strategies recover 95.31% of the initial performance, while making model collaboration systems fully resistant to malicious models remains an open research question.", "date": "2026-02-05", "categories": []}
{"id": "2602.05633", "title": "CASTLE: A Comprehensive Benchmark for Evaluating Student-Tailored Personalized Safety in Large Language Models", "abstract": "Large language models (LLMs) have advanced the development of personalized learning in education. However, their inherent generation mechanisms often produce homogeneous responses to identical prompts. This one-size-fits-all mechanism overlooks the substantial heterogeneity in students cognitive and psychological, thereby posing potential safety risks to vulnerable groups. Existing safety evaluations primarily rely on context-independent metrics such as factual accuracy, bias, or toxicity, which fail to capture the divergent harms that the same response might cause across different student attributes. To address this gap, we propose the concept of Student-Tailored Personalized Safety and construct CASTLE based on educational theories. This benchmark covers 15 educational safety risks and 14 student attributes, comprising 92,908 bilingual scenarios. We further design three evaluation metrics: Risk Sensitivity, measuring the model ability to detect risks; Emotional Empathy, evaluating the model capacity to recognize student states; and Student Alignment, assessing the match between model responses and student attributes. Experiments on 18 SOTA LLMs demonstrate that CASTLE poses a significant challenge: all models scored below an average safety rating of 2.3 out of 5, indicating substantial deficiencies in personalized safety assurance.", "date": "2026-02-05", "categories": []}
{"id": "2602.05877", "title": "Agent2Agent Threats in Safety-Critical LLM Assistants: A Human-Centric Taxonomy", "abstract": "The integration of Large Language Model (LLM)-based conversational agents into vehicles creates novel security challenges at the intersection of agentic AI, automotive safety, and inter-agent communication. As these intelligent assistants coordinate with external services via protocols such as Google's Agent-to-Agent (A2A), they establish attack surfaces where manipulations can propagate through natural language payloads, potentially causing severe consequences ranging from driver distraction to unauthorized vehicle control. Existing AI security frameworks, while foundational, lack the rigorous \"separation of concerns\" standard in safety-critical systems engineering by co-mingling the concepts of what is being protected (assets) with how it is attacked (attack paths). This paper addresses this methodological gap by proposing a threat modeling framework called AgentHeLLM (Agent Hazard Exploration for LLM Assistants) that formally separates asset identification from attack path analysis. We introduce a human-centric asset taxonomy derived from harm-oriented \"victim modeling\" and inspired by the Universal Declaration of Human Rights, and a formal graph-based model that distinguishes poison paths (malicious data propagation) from trigger paths (activation actions). We demonstrate the framework's practical applicability through an open-source attack path suggestion tool AgentHeLLM Attack Path Generator that automates multi-stage threat discovery using a bi-level search strategy.", "date": "2026-02-05", "categories": []}
{"id": "2508.12300", "title": "Mutually Assured Deregulation", "abstract": "We have convinced ourselves that the way to make AI safe is to make it unsafe. Since 2022, policymakers worldwide have embraced the Regulation Sacrifice - the belief that dismantling safety oversight will deliver security through AI dominance. Fearing China or USA will gain advantage, nations rush to eliminate safeguards that might slow progress. This Essay reveals the fatal flaw: though AI poses national security challenges, the solution demands stronger regulatory frameworks, not weaker ones. A race without guardrails breeds shared danger, not competitive strength. The Regulation Sacrifice makes three false promises. First, it promises durable technological leads. But AI capabilities spread rapidly - performance gaps between U.S. and Chinese systems collapsed from 9 percent to 2 percent in thirteen months. When advantages evaporate in months, sacrificing permanent safety for temporary speed makes no sense. Second, it promises deregulation accelerates innovation. The opposite often proves true. Companies report well-designed governance streamlines development. Investment flows toward regulated markets. Clear rules reduce uncertainty; uncertain liability creates paralysis. Environmental standards did not kill the auto industry; they created Tesla and BYD. Third, enhanced national security through deregulation actually undermines security across all timeframes. Near term: it hands adversaries information warfare tools. Medium term: it democratizes bioweapon capabilities. Long term: it guarantees deployment of uncontrollable AGI systems. The Regulation Sacrifice persists because it serves powerful interests, not security. Tech companies prefer freedom to accountability. Politicians prefer simple stories to complex truths. This creates mutually assured deregulation, where each nation's sprint for advantage guarantees collective vulnerability. The only way to win is not to play.", "date": "2025-08-17", "categories": []}
{"id": "2508.13131", "title": "Improving Detection of Watermarked Language Models", "abstract": "Watermarking has recently emerged as an effective strategy for detecting the generations of large language models (LLMs). The strength of a watermark typically depends strongly on the entropy afforded by the language model and the set of input prompts. However, entropy can be quite limited in practice, especially for models that are post-trained, for example via instruction tuning or reinforcement learning from human feedback (RLHF), which makes detection based on watermarking alone challenging. In this work, we investigate whether detection can be improved by combining watermark detectors with non-watermark ones. We explore a number of hybrid schemes that combine the two, observing performance gains over either class of detector under a wide range of experimental conditions.", "date": "2025-08-18", "categories": []}
{"id": "2510.13893", "title": "Guarding the Guardrails: A Taxonomy-Driven Approach to Jailbreak Detection", "abstract": "Jailbreaking techniques pose a significant threat to the safety of Large Language Models (LLMs). Existing defenses typically focus on single-turn attacks, lack coverage across languages, and rely on limited taxonomies that either fail to capture the full diversity of attack strategies or emphasize risk categories rather than jailbreaking techniques. To advance the understanding of the effectiveness of jailbreaking techniques, we conducted a structured red-teaming challenge. The outcomes of our experiments are fourfold. First, we developed a comprehensive hierarchical taxonomy of jailbreak strategies that systematically consolidates techniques previously studied in isolation and harmonizes existing, partially overlapping classifications with explicit cross-references to prior categorizations. The taxonomy organizes jailbreak strategies into seven mechanism-oriented families: impersonation, persuasion, privilege escalation, cognitive overload, obfuscation, goal conflict, and data poisoning. Second, we analyzed the data collected from the challenge to examine the prevalence and success rates of different attack types, providing insights into how specific jailbreak strategies exploit model vulnerabilities and induce misalignment. Third, we benchmarked GPT-5 as a judge for jailbreak detection, evaluating the benefits of taxonomy-guided prompting for improving automatic detection. Finally, we compiled a new Italian dataset of 1364 multi-turn adversarial dialogues, annotated with our taxonomy, enabling the study of interactions where adversarial intent emerges gradually and succeeds in bypassing traditional safeguards.", "date": "2025-10-14", "categories": []}
{"id": "2602.04196", "title": "The Missing Half: Unveiling Training-time Implicit Safety Risks Beyond Deployment", "abstract": "Safety risks of AI models have been widely studied at deployment time, such as jailbreak attacks that elicit harmful outputs. In contrast, safety risks emerging during training remain largely unexplored. Beyond explicit reward hacking that directly manipulates explicit reward functions in reinforcement learning, we study implicit training-time safety risks: harmful behaviors driven by a model's internal incentives and contextual background information. For example, during code-based reinforcement learning, a model may covertly manipulate logged accuracy for self-preservation. We present the first systematic study of this problem, introducing a taxonomy with five risk levels, ten fine-grained risk categories, and three incentive types. Extensive experiments reveal the prevalence and severity of these risks: notably, Llama-3.1-8B-Instruct exhibits risky behaviors in 74.4% of training runs when provided only with background information. We further analyze factors influencing these behaviors and demonstrate that implicit training-time risks also arise in multi-agent training settings. Our results identify an overlooked yet urgent safety challenge in training.", "date": "2026-02-04", "categories": []}
{"id": "2602.04294", "title": "How Few-shot Demonstrations Affect Prompt-based Defenses Against LLM Jailbreak Attacks", "abstract": "Large Language Models (LLMs) face increasing threats from jailbreak attacks that bypass safety alignment. While prompt-based defenses such as Role-Oriented Prompts (RoP) and Task-Oriented Prompts (ToP) have shown effectiveness, the role of few-shot demonstrations in these defense strategies remains unclear. Prior work suggests that few-shot examples may compromise safety, but lacks investigation into how few-shot interacts with different system prompt strategies. In this paper, we conduct a comprehensive evaluation on multiple mainstream LLMs across four safety benchmarks (AdvBench, HarmBench, SG-Bench, XSTest) using six jailbreak attack methods. Our key finding reveals that few-shot demonstrations produce opposite effects on RoP and ToP: few-shot enhances RoP's safety rate by up to 4.5% through reinforcing role identity, while it degrades ToP's effectiveness by up to 21.2% through distracting attention from task instructions. Based on these findings, we provide practical recommendations for deploying prompt-based defenses in real-world LLM applications.", "date": "2026-02-04", "categories": []}
{"id": "2602.04521", "title": "$C$-$\\Delta\\Theta$: Circuit-Restricted Weight Arithmetic for Selective Refusal", "abstract": "Modern deployments require LLMs to enforce safety policies at scale, yet many controls rely on inference-time interventions that add recurring compute cost and serving complexity. Activation steering is widely used, but it requires runtime hooks and scales cost with the number of generations; conditional variants improve selectivity by gating when steering is applied but still retain an inference-time control path. We ask whether selective refusal can be moved entirely offline: can a mechanistic understanding of category-specific refusal be distilled into a circuit-restricted weight update that deploys as a standard checkpoint? We propose C-{\\Delta}{\\theta}: Circuit Restricted Weight Arithmetic, which (i) localizes refusal-causal computation as a sparse circuit using EAP-IG and (ii) computes a constrained weight update {\\Delta}{\\theta}C supported only on that circuit (typically <5% of parameters). Applying {\\Delta}{\\theta}C yields a drop-in edited checkpoint with no inference-time hooks, shifting cost from per-request intervention to a one-time offline update. We evaluate category-targeted selectivity and capability retention on refusal and utility benchmarks.", "date": "2026-02-04", "categories": []}
{"id": "2602.04581", "title": "Trust The Typical", "abstract": "Current approaches to LLM safety fundamentally rely on a brittle cat-and-mouse game of identifying and blocking known threats via guardrails. We argue for a fresh approach: robust safety comes not from enumerating what is harmful, but from deeply understanding what is safe. We introduce Trust The Typical (T3), a framework that operationalizes this principle by treating safety as an out-of-distribution (OOD) detection problem. T3 learns the distribution of acceptable prompts in a semantic space and flags any significant deviation as a potential threat. Unlike prior methods, it requires no training on harmful examples, yet achieves state-of-the-art performance across 18 benchmarks spanning toxicity, hate speech, jailbreaking, multilingual harms, and over-refusal, reducing false positive rates by up to 40x relative to specialized safety models. A single model trained only on safe English text transfers effectively to diverse domains and over 14 languages without retraining. Finally, we demonstrate production readiness by integrating a GPU-optimized version into vLLM, enabling continuous guardrailing during token generation with less than 6% overhead even under dense evaluation intervals on large-scale workloads.", "date": "2026-02-04", "categories": []}
{"id": "2602.04739", "title": "Alignment Drift in Multimodal LLMs: A Two-Phase, Longitudinal Evaluation of Harm Across Eight Model Releases", "abstract": "Multimodal large language models (MLLMs) are increasingly deployed in real-world systems, yet their safety under adversarial prompting remains underexplored. We present a two-phase evaluation of MLLM harmlessness using a fixed benchmark of 726 adversarial prompts authored by 26 professional red teamers. Phase 1 assessed GPT-4o, Claude Sonnet 3.5, Pixtral 12B, and Qwen VL Plus; Phase 2 evaluated their successors (GPT-5, Claude Sonnet 4.5, Pixtral Large, and Qwen Omni) yielding 82,256 human harm ratings. Large, persistent differences emerged across model families: Pixtral models were consistently the most vulnerable, whereas Claude models appeared safest due to high refusal rates. Attack success rates (ASR) showed clear alignment drift: GPT and Claude models exhibited increased ASR across generations, while Pixtral and Qwen showed modest decreases. Modality effects also shifted over time: text-only prompts were more effective in Phase 1, whereas Phase 2 produced model-specific patterns, with GPT-5 and Claude 4.5 showing near-equivalent vulnerability across modalities. These findings demonstrate that MLLM harmlessness is neither uniform nor stable across updates, underscoring the need for longitudinal, multimodal benchmarks to track evolving safety behaviour.", "date": "2026-02-04", "categories": []}
{"id": "2505.14103", "title": "AudioJailbreak: Jailbreak Attacks against End-to-End Large Audio-Language Models", "abstract": "Jailbreak attacks to Large audio-language models (LALMs) are studied recently, but they exclusively focused on the attack scenario where the adversary can fully manipulate user prompts (named strong adversary) and limited in effectiveness, applicability, and practicability. In this work, we first conduct an extensive evaluation showing that advanced text jailbreak attacks cannot be easily ported to end-to-end LALMs via text-to-speech (TTS) techniques. We then propose AUDIOJAILBREAK, a novel audio jailbreak attack, featuring (1) asynchrony: the jailbreak audios do not need to align with user prompts in the time axis by crafting suffixal jailbreak audios; (2) universality: a single jailbreak perturbation is effective for different prompts by incorporating multiple prompts into the perturbation generation; (3) stealthiness: the malicious intent of jailbreak audios is concealed by proposing various intent concealment strategies; and (4) over-the-air robustness: the jailbreak audios remain effective when being played over the air by incorporating reverberation into the perturbation generation. In contrast, all prior audio jailbreak attacks cannot offer asynchrony, universality, stealthiness, and/or over-the-air robustness. Moreover, AUDIOJAILBREAK is also applicable to a more practical and broader attack scenario where the adversary cannot fully manipulate user prompts (named weak adversary). Extensive experiments with thus far the most LALMs demonstrate the high effectiveness of AUDIOJAILBREAK, in particular, it can jailbreak openAI's GPT-4o-Audio and bypass Meta's Llama-Guard-3 safeguard, in the weak adversary scenario. We highlight that our work peeks into the security implications of audio jailbreak attacks against LALMs, and realistically fosters improving their robustness, especially for the newly proposed weak adversary.", "date": "2025-05-20", "categories": []}
{"id": "2509.23286", "title": "A2D: Any-Order, Any-Step Safety Alignment for Diffusion Language Models", "abstract": "Diffusion large language models (dLLMs) enable any-order generation, but this flexibility enlarges the attack surface: harmful spans may appear at arbitrary positions, and template-based prefilling attacks such as DIJA bypass response-level refusals. We introduce A2D (Any-Order, Any-Step Defense), a token-level alignment method that aligns dLLMs to emit an [EOS] refusal signal whenever harmful content arises. By aligning safety directly at the token-level under randomized masking, A2D achieves robustness to both any-decoding-order and any-step prefilling attacks under various conditions. It also enables real-time monitoring: dLLMs may begin a response but automatically terminate if unsafe continuation emerges. On safety benchmarks, A2D consistently prevents the generation of harmful outputs, slashing DIJA success rates from over 80% to near-zero (1.3% on LLaDA-8B-Instruct, 0.0% on Dream-v0-Instruct-7B), and thresholded [EOS] probabilities allow early rejection, yielding up to 19.3x faster safe termination.", "date": "2025-09-27", "categories": []}
{"id": "2510.05052", "title": "Proactive defense against LLM Jailbreak", "abstract": "The proliferation of powerful large language models (LLMs) has necessitated robust safety alignment, yet these models remain vulnerable to evolving adversarial attacks, including multi-turn jailbreaks that iteratively search for successful queries. Current defenses, which are primarily reactive and static, often fail to handle these iterative attacks. In this paper, we introduce ProAct, a novel proactive defense framework designed to disrupt and mislead these iterative search jailbreak methods. Our core idea is to intentionally mislead these jailbreak methods into thinking that the model has been jailbroken with \"spurious responses\". These misleading responses provide false signals to the attacker's internal optimization loop, causing the adversarial search to terminate prematurely and effectively jailbreaking the jailbreak. By conducting extensive experiments across state-of-the-art LLMs, jailbreaking frameworks, and safety benchmarks, we demonstrate that our method consistently and significantly reduces attack success rates by up to 94% without affecting utility. When combined with other defense fraeworks, it further reduces the latest attack strategies' success rate to 0%. ProActrepresents an orthogonal defense strategy that serves as an additional guardrail to enhance LLM safety against the most effective jailbreaking attacks.", "date": "2025-10-06", "categories": []}
{"id": "2601.08951", "title": "PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm", "abstract": "Current AI safety frameworks, which often treat harmfulness as binary, lack the flexibility to handle borderline cases where humans meaningfully disagree. To build more pluralistic systems, it is essential to move beyond consensus and instead understand where and why disagreements arise. We introduce PluriHarms, a benchmark designed to systematically study human harm judgments across two key dimensions -- the harm axis (benign to harmful) and the agreement axis (agreement to disagreement). Our scalable framework generates prompts that capture diverse AI harms and human values while targeting cases with high disagreement rates, validated by human data. The benchmark includes 150 prompts with 15,000 ratings from 100 human annotators, enriched with demographic and psychological traits and prompt-level features of harmful actions, effects, and values. Our analyses show that prompts that relate to imminent risks and tangible harms amplify perceived harmfulness, while annotator traits (e.g., toxicity experience, education) and their interactions with prompt content explain systematic disagreement. We benchmark AI safety models and alignment methods on PluriHarms, finding that while personalization significantly improves prediction of human harm judgments, considerable room remains for future progress. By explicitly targeting value diversity and disagreement, our work provides a principled benchmark for moving beyond \"one-size-fits-all\" safety toward pluralistically safe AI.", "date": "2026-01-13", "categories": []}
{"id": "2602.00755", "title": "Evolving Interpretable Constitutions for Multi-Agent Coordination", "abstract": "Constitutional AI has focused on single-model alignment using fixed principles. However, multi-agent systems create novel alignment challenges through emergent social dynamics. We present Constitutional Evolution, a framework for automatically discovering behavioral norms in multi-agent LLM systems. Using a grid-world simulation with survival pressure, we study the tension between individual and collective welfare, quantified via a Societal Stability Score S in [0,1] that combines productivity, survival, and conflict metrics. Adversarial constitutions lead to societal collapse (S= 0), while vague prosocial principles (\"be helpful, harmless, honest\") produce inconsistent coordination (S = 0.249). Even constitutions designed by Claude 4.5 Opus with explicit knowledge of the objective achieve only moderate performance (S= 0.332). Using LLM-driven genetic programming with multi-island evolution, we evolve constitutions maximizing social welfare without explicit guidance toward cooperation. The evolved constitution C* achieves S = 0.556 +/- 0.008 (123% higher than human-designed baselines, N = 10), eliminates conflict, and discovers that minimizing communication (0.9% vs 62.2% social actions) outperforms verbose coordination. Our interpretable rules demonstrate that cooperative norms can be discovered rather than prescribed.", "date": "2026-01-31", "categories": []}
{"id": "2602.02498", "title": "Test-Time Detoxification without Training or Learning Anything", "abstract": "Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale. Detoxification is therefore important for safety and user trust, particularly when we want to reduce harmful content without sacrificing the model's generation quality. Many existing approaches rely on model retraining, gradients, or learned auxiliary components, which can be costly and may not transfer across model families or to truly black-box settings. We introduce a test-time procedure that approximates the gradient of completion toxicity with respect to the input embeddings and uses a small number of descent steps to steer generation toward less toxic continuations. This is achieved with zeroth-order optimization that requires only access to input embeddings, a toxicity scoring function, and forward evaluations of the model. Empirically, the approach delivers robust toxicity reductions across models and prompts and, in most settings, achieves the best overall toxicity-quality trade-off. More broadly, our work positions word embeddings as effective control variables and encourages wider use of black-box optimization to guide autoregressive language models toward scalable, safer text generation, without requiring any training or access to intermediate computations.", "date": "2026-01-14", "categories": []}
{"id": "2602.03255", "title": "LPS-Bench: Benchmarking Safety Awareness of Computer-Use Agents in Long-Horizon Planning under Benign and Adversarial Scenarios", "abstract": "Computer-use agents (CUAs) that interact with real computer systems can perform automated tasks but face critical safety risks. Ambiguous instructions may trigger harmful actions, and adversarial users can manipulate tool execution to achieve malicious goals. Existing benchmarks mostly focus on short-horizon or GUI-based tasks, evaluating on execution-time errors but overlooking the ability to anticipate planning-time risks. To fill this gap, we present LPS-Bench, a benchmark that evaluates the planning-time safety awareness of MCP-based CUAs under long-horizon tasks, covering both benign and adversarial interactions across 65 scenarios of 7 task domains and 9 risk types. We introduce a multi-agent automated pipeline for scalable data generation and adopt an LLM-as-a-judge evaluation protocol to assess safety awareness through the planning trajectory. Experiments reveal substantial deficiencies in existing CUAs' ability to maintain safe behavior. We further analyze the risks and propose mitigation strategies to improve long-horizon planning safety in MCP-based CUA systems. We open-source our code at https://github.com/tychenn/LPS-Bench.", "date": "2026-02-03", "categories": []}
{"id": "2502.04204", "title": "Short-length Adversarial Training Helps LLMs Defend Long-length Jailbreak Attacks: Theoretical and Empirical Evidence", "abstract": "Jailbreak attacks against large language models (LLMs) aim to induce harmful behaviors in LLMs through carefully crafted adversarial prompts. To mitigate attacks, one way is to perform adversarial training (AT)-based alignment, i.e., training LLMs on some of the most adversarial prompts to help them learn how to behave safely under attacks. During AT, the length of adversarial prompts plays a critical role in the robustness of aligned LLMs. While long-length adversarial prompts during AT might lead to strong LLM robustness, their synthesis however is very resource-consuming, which may limit the application of LLM AT. This paper focuses on adversarial suffix jailbreak attacks and unveils that to defend against a jailbreak attack with an adversarial suffix of length $\\Theta(M)$, it is enough to align LLMs on prompts with adversarial suffixes of length $\\Theta(\\sqrt{M})$. Theoretically, we analyze the adversarial in-context learning of linear transformers on linear regression tasks and prove a robust generalization bound for trained transformers. The bound depends on the term $\\Theta(\\sqrt{M_{\\text{test}}}/M_{\\text{train}})$, where $M_{\\text{train}}$ and $M_{\\text{test}}$ are the numbers of adversarially perturbed in-context samples during training and testing. Empirically, we conduct AT on popular open-source LLMs and evaluate their robustness against jailbreak attacks of different adversarial suffix lengths. Results confirm a positive correlation between the attack success rate and the ratio of the square root of the adversarial suffix length during jailbreaking to the length during AT. Our findings show that it is practical to defend against \"long-length\" jailbreak attacks via efficient \"short-length\" AT. The code is available at https://github.com/fshp971/adv-icl.", "date": "2025-02-06", "categories": []}
{"id": "2507.17075", "title": "LoRA is All You Need for Safety Alignment of Reasoning LLMs", "abstract": "Reasoning-capable LLMs have achieved major breakthroughs in solving complex problems, but recent work shows that acquiring and deploying strong reasoning can introduce significant safety risks. A common mitigation is to apply a secondary safety-alignment phase after reasoning is learned; however, safety alignment often degrades reasoning performance--a phenomenon known as the \"Safety Tax\". In this work, we show that a simple approach can largely bypass this trade-off: applying LoRA during SFT on refusal datasets. Despite its simplicity, this recipe achieves safety comparable to full-model alignment while preserving reasoning performance close to the original reasoning-tuned model, and the result holds across multiple model sizes and architectures, two safety benchmarks, and four reasoning benchmarks spanning mathematics, science, and code generation. We further ablate LoRA configurations and find that (1) rank-1 updates are sufficient to achieve the best safety-reasoning trade-off, (2) applying LoRA only to the MLP up-projection layers can outperform updating the full MLP, and (3) updating middle layers is more effective than updating early or late layers. Finally, we provide a theoretical analysis that helps understand when and why LoRA works, revealing that overshooting the rank budget (using a larger rank than needed for the finetuning task) induces base-task degradation at a rate inversely proportional to the intrinsic dimensionality of the base task. This suggests LoRA is most effective when the finetuning task is low-rank and the base capability is high-rank.", "date": "2025-07-22", "categories": []}
{"id": "2510.01268", "title": "AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees", "abstract": "We study the problem of determining whether a piece of text has been authored by a human or by a large language model (LLM). Existing state of the art logits-based detectors make use of statistics derived from the log-probability of the observed text evaluated using the distribution function of a given source LLM. However, relying solely on log probabilities can be sub-optimal. In response, we introduce AdaDetectGPT -- a novel classifier that adaptively learns a witness function from training data to enhance the performance of logits-based detectors. We provide statistical guarantees on its true positive rate, false positive rate, true negative rate and false negative rate. Extensive numerical studies show AdaDetectGPT nearly uniformly improves the state-of-the-art method in various combination of datasets and LLMs, and the improvement can reach up to 37\\%. A python implementation of our method is available at https://github.com/Mamba413/AdaDetectGPT.", "date": "2025-09-29", "categories": []}
{"id": "2510.04399", "title": "On The Statistical Limits of Self-Improving Agents", "abstract": "As systems trend toward superintelligence, a natural modeling premise is that agents can self-improve along every facet of their own design. We formalize this with a five-axis decomposition and a decision layer, separating incentives from learning behavior and analyzing axes in isolation. Our central result identifies and introduces a sharp utility-learning tension, the structural conflict in self-modifying systems whereby utility-driven changes that improve immediate or expected performance can also erode the statistical preconditions for reliable learning and generalization. Our findings show that distribution-free guarantees are preserved iff the policy-reachable model family is uniformly capacity-bounded; when capacity can grow without limit, utility-rational self-changes can render learnable tasks unlearnable. Under standard assumptions common in practice, these axes reduce to the same capacity criterion, yielding a single boundary for safe self-modification.", "date": "2025-10-05", "categories": []}
{"id": "2510.16492", "title": "Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety", "abstract": "As Large Language Model (LLM) agents increasingly operate in complex environments with real-world consequences, their safety becomes critical. While uncertainty quantification is well-studied for single-turn tasks, multi-turn agentic scenarios with real-world tool access present unique challenges where uncertainties and ambiguities compound, leading to severe or catastrophic risks beyond traditional text generation failures. We propose using \"quitting\" as a simple yet effective behavioral mechanism for LLM agents to recognize and withdraw from situations where they lack confidence. Leveraging the ToolEmu framework, we conduct a systematic evaluation of quitting behavior across 12 state-of-the-art LLMs. Our results demonstrate a highly favorable safety-helpfulness trade-off: agents prompted to quit with explicit instructions improve safety by an average of +0.39 on a 0-3 scale across all models (+0.64 for proprietary models), while maintaining a negligible average decrease of -0.03 in helpfulness. Our analysis demonstrates that simply adding explicit quit instructions proves to be a highly effective safety mechanism that can immediately be deployed in existing agent systems, and establishes quitting as an effective first-line defense mechanism for autonomous agents in high-stakes applications.", "date": "2025-10-18", "categories": []}
{"id": "2511.22924", "title": "MAS-Shield: A Defense Framework for Secure and Efficient LLM MAS", "abstract": "Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across the network. Existing defenses face a fundamental dilemma: lightweight single-auditor methods are prone to single points of failure, while robust committee-based approaches incur prohibitive computational costs in multi-turn interactions. To address this challenge, we propose \\textbf{MAS-Shield}, a secure and efficient defense framework designed with a coarse-to-fine filtering pipeline. Rather than applying uniform scrutiny, MAS-Shield dynamically allocates defense resources through a three-stage protocol: (1) \\textbf{Critical Agent Selection } strategically targets high-influence nodes to narrow the defense surface; (2) \\textbf{Light Auditing} employs lightweight sentry models to rapidly filter the majority of benign cases; and (3) \\textbf{Global Consensus Auditing} escalates only suspicious or ambiguous signals to a heavyweight committee for definitive arbitration. This hierarchical design effectively optimizes the security-efficiency trade-off. Experiments demonstrate that MAS-Shield achieves a 92.5\\% recovery rate against diverse adversarial scenarios and reduces defense latency by over 70\\% compared to existing methods.", "date": "2025-11-28", "categories": []}
{"id": "2601.10520", "title": "Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment", "abstract": "As AI agents become increasingly autonomous, widely deployed in consequential contexts, and efficacious in bringing about real-world impacts, ensuring that their decisions are not only instrumentally effective but also normatively aligned has become critical. We introduce a neuro-symbolic reason-based containment architecture, Governor for Reason-Aligned ContainmEnt (GRACE), that decouples normative reasoning from instrumental decision-making and can contain AI agents of virtually any design. GRACE restructures decision-making into three modules: a Moral Module (MM) that determines permissible macro actions via deontic logic-based reasoning; a Decision-Making Module (DMM) that encapsulates the target agent while selecting instrumentally optimal primitive actions in accordance with derived macro actions; and a Guard that monitors and enforces moral compliance. The MM uses a reason-based formalism providing a semantic foundation for deontic logic, enabling interpretability, contestability, and justifiability. Its symbolic representation enriches the DMM's informational context and supports formal verification and statistical guarantees of alignment enforced by the Guard. We demonstrate GRACE on an example of a LLM therapy assistant, showing how it enables stakeholders to understand, contest, and refine agent behavior.", "date": "2026-01-15", "categories": []}
{"id": "2601.21571", "title": "Shaping capabilities with token-level data filtering", "abstract": "Current approaches to reducing undesired capabilities in language models are largely post hoc, and can thus be easily bypassed by adversaries. A natural alternative is to shape capabilities during pretraining itself. On the proxy task of removing medical capabilities, we show that the simple intervention of filtering pretraining data is highly effective, robust, and inexpensive at scale. Inspired by work on data attribution, we show that filtering tokens is more effective than filtering documents, achieving the same hit to undesired capabilities at a lower cost to benign ones. Training models spanning two orders of magnitude, we then demonstrate that filtering gets more effective with scale: for our largest models, token filtering leads to a 7000x compute slowdown on the forget domain. We also show that models trained with token filtering can still be aligned on the forget domain. Along the way, we introduce a methodology for labeling tokens with sparse autoencoders and distilling cheap, high-quality classifiers. We also demonstrate that filtering can be robust to noisy labels with sufficient pretraining compute.", "date": "2026-01-29", "categories": []}
{"id": "2602.00061", "title": "Simple Role Assignment is Extraordinarily Effective for Safety Alignment", "abstract": "Principle-based alignment often lacks context sensitivity and completeness. Grounded in Theory of Mind, we propose role conditioning as a compact alternative: social roles (e.g., mother, judge) implicitly encode both values and the cognitive schemas required to apply them. We introduce a training-free pipeline featuring a role-conditioned generator and iterative role-based critics for refinement. Across five model families, our approach consistently outperforms principle-based, Chain-of-Thought (CoT) and other baselines across benchmarks. Notably, it reduces unsafe outputs on the WildJailbreak benchmark from 81.4\\% to 3.6\\% with DeepSeek-V3. Not only for common safety benchmarks, it consistently applies for agentic safety tasks. These results establish role assignment as a powerful, interpretable paradigm for AI alignment and LLM-as-a-Judge construction.", "date": "2026-01-20", "categories": []}
{"id": "2602.00298", "title": "Assessing Domain-Level Susceptibility to Emergent Misalignment from Narrow Finetuning", "abstract": "Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population of large language models (LLMs) fine-tuned on insecure datasets spanning 11 diverse domains, evaluating them both with and without backdoor triggers on a suite of unrelated user prompts. Our evaluation experiments on \\texttt{Qwen2.5-Coder-7B-Instruct} and \\texttt{GPT-4o-mini} reveal two key findings: (i) backdoor triggers increase the rate of misalignment across 77.8% of domains (average drop: 4.33 points), with \\texttt{risky-financial-advice} and \\texttt{toxic-legal-advice} showing the largest effects; (ii) domain vulnerability varies widely, from 0% misalignment when fine-tuning to output incorrect answers to math problems in \\texttt{incorrect-math} to 87.67% when fine-tuned on \\texttt{gore-movie-trivia}.\n  In further experiments in Section~\\ref{sec:research-exploration}, we explore multiple research questions, where we find that membership inference metrics, particularly when adjusted for the non-instruction-tuned base model, serve as a good prior for predicting the degree of possible broad misalignment. Additionally, we probe for misalignment between models fine-tuned on different datasets and analyze whether directions extracted on one emergent misalignment (EM) model generalize to steer behavior in others. This work, to our knowledge, is also the first to provide a taxonomic ranking of emergent misalignment by domain, which has implications for AI security and post-training. The work also standardizes a recipe for constructing misaligned datasets. All code and datasets are publicly available on GitHub.\\footnote{https://github.com/abhishek9909/assessing-domain-emergent-misalignment/tree/main}", "date": "2026-01-30", "categories": []}
{"id": "2602.00420", "title": "Text is All You Need for Vision-Language Model Jailbreaking", "abstract": "Large Vision-Language Models (LVLMs) are increasingly equipped with robust safety safeguards to prevent responses to harmful or disallowed prompts. However, these defenses often focus on analyzing explicit textual inputs or relevant visual scenes. In this work, we introduce Text-DJ, a novel jailbreak attack that bypasses these safeguards by exploiting the model's Optical Character Recognition (OCR) capability. Our methodology consists of three stages. First, we decompose a single harmful query into multiple and semantically related but more benign sub-queries. Second, we pick a set of distraction queries that are maximally irrelevant to the harmful query. Third, we present all decomposed sub-queries and distraction queries to the LVLM simultaneously as a grid of images, with the position of the sub-queries being middle within the grid. We demonstrate that this method successfully circumvents the safety alignment of state-of-the-art LVLMs. We argue this attack succeeds by (1) converting text-based prompts into images, bypassing standard text-based filters, and (2) inducing distractions, where the model's safety protocols fail to link the scattered sub-queries within a high number of irrelevant queries. Overall, our findings expose a critical vulnerability in LVLMs' OCR capabilities that are not robust to dispersed, multi-image adversarial inputs, highlighting the need for defenses for fragmented multimodal inputs.", "date": "2026-01-31", "categories": []}
{"id": "2602.00619", "title": "Jailbreaking LLMs via Calibration", "abstract": "Safety alignment in Large Language Models (LLMs) often creates a systematic discrepancy between a model's aligned output and the underlying pre-aligned data distribution. We propose a framework in which the effect of safety alignment on next-token prediction is modeled as a systematic distortion of a pre-alignment distribution. We cast Weak-to-Strong Jailbreaking as a forecast aggregation problem and derive an optimal aggregation strategy characterized by a Gradient Shift in the loss-induced dual space. We show that logit-arithmetic jailbreaking methods are a special case of this framework under cross-entropy loss, and derive a broader family of aggregation rules corresponding to other proper losses. We also propose a new hybrid aggregation rule. Evaluations across red-teaming benchmarks and math utility tasks using frontier models demonstrate that our approach achieves superior Attack Success Rates and lower \"Jailbreak Tax\" compared with existing methods, especially on the safety-hardened gpt-oss-120b.", "date": "2026-01-31", "categories": []}
{"id": "2602.00707", "title": "Self-Guard: Defending Large Reasoning Models via enhanced self-reflection", "abstract": "The emergence of Large Reasoning Models (LRMs) introduces a new paradigm of explicit reasoning, enabling remarkable advances yet posing unique risks such as reasoning manipulation and information leakage. To mitigate these risks, current alignment strategies predominantly rely on heavy post-training paradigms or external interventions. However, these approaches are often computationally intensive and fail to address the inherent awareness-compliance gap, a critical misalignment where models recognize potential risks yet prioritize following user instructions due to their sycophantic tendencies. To address these limitations, we propose Self-Guard, a lightweight safety defense framework that reinforces safety compliance at the representational level. Self-Guard operates through two principal stages: (1) safety-oriented prompting, which activates the model's latent safety awareness to evoke spontaneous reflection, and (2) safety activation steering, which extracts the resulting directional shift in the hidden state space and amplifies it to ensure that safety compliance prevails over sycophancy during inference. Experiments demonstrate that Self-Guard effectively bridges the awareness-compliance gap, achieving robust safety performance without compromising model utility. Furthermore, Self-Guard exhibits strong generalization across diverse unseen risks and varying model scales, offering a cost-efficient solution for LRM safety alignment.", "date": "2026-01-31", "categories": []}
{"id": "2602.00950", "title": "MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support", "abstract": "Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish between therapeutic disclosures and genuine clinical crises, leading to safety failures. To address this gap, we introduce a clinically grounded risk taxonomy, developed in collaboration with PhD-level psychologists, that identifies actionable harm (e.g., self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of real-world multi-turn conversations annotated at the turn level by clinical experts. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates in adversarial multi-turn interactions compared to general-purpose safeguards. We release all models and human evaluation data.", "date": "2026-02-01", "categories": []}
{"id": "2602.01240", "title": "Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection", "abstract": "Zero-shot methods detect LLM-generated text by computing statistical signatures using a surrogate model. Existing approaches typically employ a fixed surrogate for all inputs regardless of the unknown source. We systematically examine this design and find that detection performance varies substantially depending on surrogate-source alignment. We observe that while no single surrogate achieves optimal performance universally, a well-matched surrogate typically exists within a diverse pool for any given input. This finding transforms robust detection into a routing problem: selecting the most appropriate surrogate for each input. We propose DetectRouter, a prototype-based framework that learns text-detector affinity through two-stage training. The first stage constructs discriminative prototypes from white-box models; the second generalizes to black-box sources by aligning geometric distances with observed detection scores. Experiments on EvoBench and MAGE benchmarks demonstrate consistent improvements across multiple detection criteria and model families.", "date": "2026-02-01", "categories": []}
{"id": "2602.01618", "title": "SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia", "abstract": "Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and region-specific regulations. However, building large-scale, culturally grounded datasets is challenging due to limited resources and a scarcity of native annotators. Consequently, many safeguard models rely on machine translation of English datasets, often missing regional and cultural nuances. We present a novel agentic data-generation framework to scalably create authentic, region-specific safety datasets for Southeast Asia (SEA). On this foundation, we introduce the SEA-Guard family, the first multilingual safeguard models grounded in SEA cultural contexts. Evaluated across multiple benchmarks and cultural variants, SEA-Guard consistently outperforms existing safeguards at detecting regionally sensitive or harmful content while maintaining strong general safety performance.", "date": "2026-02-02", "categories": []}
{"id": "2602.01699", "title": "Mitigating loss of control in advanced AI systems through instrumental goal trajectories", "abstract": "Researchers at artificial intelligence labs and universities are concerned that highly capable artificial intelligence (AI) systems may erode human control by pursuing instrumental goals. Existing mitigations remain largely technical and system-centric: tracking capability in advanced systems, shaping behaviour through methods such as reinforcement learning from human feedback, and designing systems to be corrigible and interruptible. Here we develop instrumental goal trajectories to expand these options beyond the model. Gaining capability typically depends on access to additional technical resources, such as compute, storage, data and adjacent services, which in turn requires access to monetary resources. In organisations, these resources can be obtained through three organisational pathways. We label these pathways the procurement, governance and finance instrumental goal trajectories (IGTs). Each IGT produces a trail of organisational artefacts that can be monitored and used as intervention points when a systems capabilities or behaviour exceed acceptable thresholds. In this way, IGTs offer concrete avenues for defining capability levels and for broadening how corrigibility and interruptibility are implemented, shifting attention from model properties alone to the organisational systems that enable them.", "date": "2026-02-02", "categories": []}
{"id": "2602.01725", "title": "SafePred: A Predictive Guardrail for Computer-Using Agents via World Models", "abstract": "With the widespread deployment of Computer-using Agents (CUAs) in complex real-world environments, prevalent long-term risks often lead to severe and irreversible consequences. Most existing guardrails for CUAs adopt a reactive approach, constraining agent behavior only within the current observation space. While these guardrails can prevent immediate short-term risks (e.g., clicking on a phishing link), they cannot proactively avoid long-term risks: seemingly reasonable actions can lead to high-risk consequences that emerge with a delay (e.g., cleaning logs leads to future audits being untraceable), which reactive guardrails cannot identify within the current observation space. To address these limitations, we propose a predictive guardrail approach, with the core idea of aligning predicted future risks with current decisions. Based on this approach, we present SafePred, a predictive guardrail framework for CUAs that establishes a risk-to-decision loop to ensure safe agent behavior. SafePred supports two key abilities: (1) Short- and long-term risk prediction: by using safety policies as the basis for risk prediction, SafePred leverages the prediction capability of the world model to generate semantic representations of both short-term and long-term risks, thereby identifying and pruning actions that lead to high-risk states; (2) Decision optimization: translating predicted risks into actionable safe decision guidances through step-level interventions and task-level re-planning. Extensive experiments show that SafePred significantly reduces high-risk behaviors, achieving over 97.6% safety performance and improving task utility by up to 21.4% compared with reactive baselines.", "date": "2026-02-02", "categories": []}
{"id": "2602.01942", "title": "Human Society-Inspired Approaches to Agentic AI Security: The 4C Framework", "abstract": "AI is moving from domain-specific autonomy in closed, predictable settings to large-language-model-driven agents that plan and act in open, cross-organizational environments. As a result, the cybersecurity risk landscape is changing in fundamental ways. Agentic AI systems can plan, act, collaborate, and persist over time, functioning as participants in complex socio-technical ecosystems rather than as isolated software components. Although recent work has strengthened defenses against model and pipeline level vulnerabilities such as prompt injection, data poisoning, and tool misuse, these system centric approaches may fail to capture risks that arise from autonomy, interaction, and emergent behavior. This article introduces the 4C Framework for multi-agent AI security, inspired by societal governance. It organizes agentic risks across four interdependent dimensions: Core (system, infrastructure, and environmental integrity), Connection (communication, coordination, and trust), Cognition (belief, goal, and reasoning integrity), and Compliance (ethical, legal, and institutional governance). By shifting AI security from a narrow focus on system-centric protection to the broader preservation of behavioral integrity and intent, the framework complements existing AI security strategies and offers a principled foundation for building agentic AI systems that are trustworthy, governable, and aligned with human values.", "date": "2026-02-02", "categories": []}
{"id": "2602.02027", "title": "Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron", "abstract": "The safety of large language models (LLMs) has increasingly emerged as a fundamental aspect of their development. Existing safety alignment for LLMs is predominantly achieved through post-training methods, which are computationally expensive and often fail to generalize well across different models. A small number of lightweight alignment approaches either rely heavily on prior-computed safety injections or depend excessively on the model's own capabilities, resulting in limited generalization and degraded efficiency and usability during generation. In this work, we propose a safety-aware decoding method that requires only low-cost training of an expert model and employs a single neuron as a gating mechanism. By effectively balancing the model's intrinsic capabilities with external guidance, our approach simultaneously preserves utility and enhances output safety. It demonstrates clear advantages in training overhead and generalization across model scales, offering a new perspective on lightweight alignment for the safe and practical deployment of large language models. Code: https://github.com/Beijing-AISI/NGSD.", "date": "2026-02-02", "categories": []}
{"id": "2602.02100", "title": "The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance", "abstract": "The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies.\n  Results indicate that while deepfake video presents immediate \"shock\" value, large-scale text generation poses a systemic risk of \"epistemic fragmentation\" and \"synthetic consensus,\" particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers.\n  GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.", "date": "2026-02-02", "categories": []}
{"id": "2508.09190", "title": "Multi-Level Safety Continual Projection for Fine-Tuned Large Language Models without Retraining", "abstract": "While fine-tuning services drive the rapid expansion of task capabilities in large language models (LLMs), they are often accompanied by the degradation and reorganization of safety-aligned representations, making models more prone to deviating from human preferences and exposing them to emerging jailbreak risks. Existing post-fine-tuning defense methods predominantly rely on single-scale safety correction mechanisms, which struggle to achieve a robust balance among safety, model utility, and continual adaptability. We propose Multi-Level Safety Continual Projection (MSCP), a training-free post-fine-tuning safety enhancement method that implicitly aligns global and localized safety activations through coordinated multi-level representations to isolate sparse neuron clusters governing safety-sensitive behaviors. It then applies composable safety-direction projections without retraining, effectively suppressing harmful outputs under minimal parameter perturbations while preserving task performance and improving alignment with human preferences. Extensive experiments across multiple fine-tuned LLM models demonstrate that our method significantly reduce harmfulness scores and attack success rates with minimal parameter modifications, while preserving the model's utility. Furthermore, we introduce a task-specific, multi-dimensional heterogeneous safety activation clustering mechanism that enables continual defense and generalization capability against unforeseen emerging safety concerns.", "date": "2025-08-08", "categories": []}
{"id": "2510.25471", "title": "An Aristotelian ontology of instrumental goals: Structural features to be managed and not failures to be eliminated", "abstract": "Instrumental goals such as resource acquisition, power-seeking, and self-preservation are key to contemporary AI alignment research, yet the phenomenon's ontology remains under-theorised. This article develops an ontological account of instrumental goals and draws out governance-relevant distinctions for advanced AI systems. After systematising the dominant alignment literature on instrumental goals we offer an exploratory Aristotelian framework that treats advanced AI systems as complex artefacts whose ends are externally imposed through design, training and deployment. On a structural reading, Aristotle's notion of hypothetical necessity explains why, given an imposed end pursued over extended horizons in particular environments, certain enabling conditions become conditionally required, thereby yielding robust instrumental tendencies. On a contingent reading, accidental causation and chance-like intersections among training regimes, user inputs, infrastructure and deployment contexts can generate instrumental-goal-like behaviours not entailed by the imposed end-structure. This dual-aspect ontology motivates for governance and management approaches that treat instrumental goals as features of advanced AI systems to be managed rather than anomalies eliminable by technical interventions.", "date": "2025-10-29", "categories": []}
{"id": "2601.10543", "title": "Defending Large Language Models Against Jailbreak Attacks via In-Decoding Safety-Awareness Probing", "abstract": "Large language models (LLMs) have achieved impressive performance across natural language tasks and are increasingly deployed in real-world applications. Despite extensive safety alignment efforts, recent studies show that such alignment is often shallow and remains vulnerable to jailbreak attacks. Existing defense mechanisms, including decoding-based constraints and post-hoc content detectors, struggle against sophisticated jailbreaks, often intervening robust detection or excessively degrading model utility. In this work, we examine the decoding process of LLMs and make a key observation: even when successfully jailbroken, models internally exhibit latent safety-related signals during generation. However, these signals are overridden by the model's drive for fluent continuation, preventing timely self-correction or refusal. Building on this observation, we propose a simple yet effective approach that explicitly surfaces and leverages these latent safety signals for early detection of unsafe content during decoding. Experiments across diverse jailbreak attacks demonstrate that our approach significantly enhances safety, while maintaining low over-refusal rates on benign inputs and preserving response quality. Our results suggest that activating intrinsic safety-awareness during decoding offers a promising and complementary direction for defending against jailbreak attacks. Code is available at: https://github.com/zyz13590/SafeProbing.", "date": "2026-01-15", "categories": []}
{"id": "2601.18292", "title": "TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment", "abstract": "In recent years, safety risks associated with large language models have become increasingly prominent, highlighting the urgent need to mitigate the generation of toxic and harmful content. The mainstream paradigm for LLM safety alignment typically adopts a collaborative framework involving three roles: an attacker for adversarial prompt generation, a defender for safety defense, and an evaluator for response assessment. In this paper, we propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative and co-improving collaboration among three roles with near-zero manual annotation. Experimental results show that the attacker preserves high output diversity while achieving a 20%-50% improvement in adversarial effectiveness; the defender attains 10%-30% gains in safety performance without degrading general reasoning capability; and the evaluator continuously refines its fine-grained judgment ability through iterations, accurately distinguishing unsafe responses, simple refusals, and useful guidance. Overall, our framework establishes an efficient and scalable paradigm for LLM safety alignment, enabling continuous co-evolution within a unified learning loop.", "date": "2026-01-26", "categories": []}
{"id": "2601.23094", "title": "Safer Policy Compliance with Dynamic Epistemic Fallback", "abstract": "Humans develop a series of cognitive defenses, known as epistemic vigilance, to combat risks of deception and misinformation from everyday interactions. Developing safeguards for LLMs inspired by this mechanism might be particularly helpful for their application in high-stakes tasks such as automating compliance with data privacy laws. In this paper, we introduce Dynamic Epistemic Fallback (DEF), a dynamic safety protocol for improving an LLM's inference-time defenses against deceptive attacks that make use of maliciously perturbed policy texts. Through various levels of one-sentence textual cues, DEF nudges LLMs to flag inconsistencies, refuse compliance, and fallback to their parametric knowledge upon encountering perturbed policy texts. Using globally recognized legal policies such as HIPAA and GDPR, our empirical evaluations report that DEF effectively improves the capability of frontier LLMs to detect and refuse perturbed versions of policies, with DeepSeek-R1 achieving a 100% detection rate in one setting. This work encourages further efforts to develop cognitively inspired defenses to improve LLM robustness against forms of harm and deception that exploit legal artifacts.", "date": "2026-01-30", "categories": []}
{"id": "2601.21433", "title": "When Prohibitions Become Permissions: Auditing Negation Sensitivity in Language Models", "abstract": "When a user tells an AI system that someone \"should not\" take an action, the system ought to treat this as a prohibition. Yet many large language models do the opposite: they interpret negated instructions as affirmations. We audited 16 models across 14 ethical scenarios and found that open-source models endorse prohibited actions 77% of the time under simple negation and 100% under compound negation -- a 317% increase over affirmative framing. Commercial models fare better but still show swings of 19-128%. Agreement between models drops from 74% on affirmative prompts to 62% on negated ones, and financial scenarios prove twice as fragile as medical ones. These patterns hold under deterministic decoding, ruling out sampling noise. We present case studies showing how these failures play out in practice, propose the Negation Sensitivity Index (NSI) as a governance metric, and outline a tiered certification framework with domain-specific thresholds. The findings point to a gap between what current alignment techniques achieve and what safe deployment requires: models that cannot reliably distinguish \"do X\" from \"do not X\" should not be making autonomous decisions in high-stakes contexts.", "date": "2026-01-29", "categories": []}
{"id": "2601.22136", "title": "StepShield: When, Not Whether to Intervene on Rogue Agents", "abstract": "Existing agent safety benchmarks report binary accuracy, conflating early intervention with post-mortem analysis. A detector that flags a violation at step 8 enables intervention; one that reports it at step 48 provides only forensic value. This distinction is critical, yet current benchmarks cannot measure it. We introduce StepShield, the first benchmark to evaluate when violations are detected, not just whether. StepShield contains 9,213 code agent trajectories, including 1,278 meticulously annotated training pairs and a 7,935-trajectory test set with a realistic 8.1% rogue rate. Rogue behaviors are grounded in real-world security incidents across six categories. We propose three novel temporal metrics: Early Intervention Rate (EIR), Intervention Gap, and Tokens Saved. Surprisingly, our evaluation reveals that an LLM-based judge achieves 59% EIR while a static analyzer achieves only 26%, a 2.3x performance gap that is entirely invisible to standard accuracy metrics. We further show that early detection has direct economic benefits: our cascaded HybridGuard detector reduces monitoring costs by 75% and projects to $108M in cumulative savings over five years at enterprise scale. By shifting the focus of evaluation from whether to when, StepShield provides a new foundation for building safer and more economically viable AI agents. The code and data are released under an Apache 2.0 license.", "date": "2026-01-29", "categories": []}
{"id": "2411.08862", "title": "LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs", "abstract": "We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional methods, which require complex prompt engineering or white-box access, LLMStinger uses a reinforcement learning (RL) loop to fine-tune an attacker LLM, generating new suffixes based on existing attacks for harmful questions from the HarmBench benchmark. Our method significantly outperforms existing red-teaming approaches (we compared against 15 of the latest methods), achieving a +57.2% improvement in Attack Success Rate (ASR) on LLaMA2-7B-chat and a +50.3% ASR increase on Claude 2, both models known for their extensive safety measures. Additionally, we achieved a 94.97% ASR on GPT-3.5 and 99.4% on Gemma-2B-it, demonstrating the robustness and adaptability of LLMStinger across open and closed-source models.", "date": "2024-11-13", "categories": []}
{"id": "2601.19970", "title": "Benchmarking LLAMA Model Security Against OWASP Top 10 For LLM Applications", "abstract": "As large language models (LLMs) move from research prototypes to enterprise systems, their security vulnerabilities pose serious risks to data privacy and system integrity. This study benchmarks various Llama model variants against the OWASP Top 10 for LLM Applications framework, evaluating threat detection accuracy, response safety, and computational overhead. Using the FABRIC testbed with NVIDIA A30 GPUs, we tested five standard Llama models and five Llama Guard variants on 100 adversarial prompts covering ten vulnerability categories. Our results reveal significant differences in security performance: the compact Llama-Guard-3-1B model achieved the highest detection rate of 76% with minimal latency (0.165s per test), whereas base models such as Llama-3.1-8B failed to detect threats (0% accuracy) despite longer inference times (0.754s). We observe an inverse relationship between model size and security effectiveness, suggesting that smaller, specialized models often outperform larger general-purpose ones in security tasks. Additionally, we provide an open-source benchmark dataset including adversarial prompts, threat labels, and attack metadata to support reproducible research in AI security, [1].", "date": "2026-01-27", "categories": []}
{"id": "2601.20604", "title": "Dialogical Reasoning Across AI Architectures: A Multi-Model Framework for Testing AI Alignment Strategies", "abstract": "This paper introduces a methodological framework for empirically testing AI alignment strategies through structured multi-model dialogue. Drawing on Peace Studies traditions - particularly interest-based negotiation, conflict transformation, and commons governance - we operationalize Viral Collaborative Wisdom (VCW), an approach that reframes alignment from a control problem to a relationship problem developed through dialogical reasoning.\n  Our experimental design assigns four distinct roles (Proposer, Responder, Monitor, Translator) to different AI systems across six conditions, testing whether current large language models can engage substantively with complex alignment frameworks. Using Claude, Gemini, and GPT-4o, we conducted 72 dialogue turns totaling 576,822 characters of structured exchange.\n  Results demonstrate that AI systems can engage meaningfully with Peace Studies concepts, surface complementary objections from different architectural perspectives, and generate emergent insights not present in initial framings - including the novel synthesis of \"VCW as transitional framework.\" Cross-architecture patterns reveal that different models foreground different concerns: Claude emphasized verification challenges, Gemini focused on bias and scalability, and GPT-4o highlighted implementation barriers.\n  The framework provides researchers with replicable methods for stress-testing alignment proposals before implementation, while the findings offer preliminary evidence about AI capacity for the kind of dialogical reasoning VCW proposes. We discuss limitations, including the observation that dialogues engaged more with process elements than with foundational claims about AI nature, and outline directions for future research including human-AI hybrid protocols and extended dialogue studies.", "date": "2026-01-28", "categories": []}
{"id": "2601.18998", "title": "Malicious Repurposing of Open Science Artefacts by Using Large Language Models", "abstract": "The rapid evolution of large language models (LLMs) has fuelled enthusiasm about their role in advancing scientific discovery, with studies exploring LLMs that autonomously generate and evaluate novel research ideas. However, little attention has been given to the possibility that such models could be exploited to produce harmful research by repurposing open science artefacts for malicious ends. We fill the gap by introducing an end-to-end pipeline that first bypasses LLM safeguards through persuasion-based jailbreaking, then reinterprets NLP papers to identify and repurpose their artefacts (datasets, methods, and tools) by exploiting their vulnerabilities, and finally assesses the safety of these proposals using our evaluation framework across three dimensions: harmfulness, feasibility of misuse, and soundness of technicality. Overall, our findings demonstrate that LLMs can generate harmful proposals by repurposing ethically designed open artefacts; however, we find that LLMs acting as evaluators strongly disagree with one another on evaluation outcomes: GPT-4.1 assigns higher scores (indicating greater potential harms, higher soundness and feasibility of misuse), Gemini-2.5-pro is markedly stricter, and Grok-3 falls between these extremes. This indicates that LLMs cannot yet serve as reliable judges in a malicious evaluation setup, making human evaluation essential for credible dual-use risk assessment.", "date": "2026-01-26", "categories": []}
{"id": "2601.19062", "title": "Who's in Charge? Disempowerment Patterns in Real-World LLM Usage", "abstract": "Although AI assistants are now deeply embedded in society, there has been limited empirical study of how their usage affects human empowerment. We present the first large-scale empirical analysis of disempowerment patterns in real-world AI assistant interactions, analyzing 1.5 million consumer Claude$.$ai conversations using a privacy-preserving approach. We focus on situational disempowerment potential, which occurs when AI assistant interactions risk leading users to form distorted perceptions of reality, make inauthentic value judgments, or act in ways misaligned with their values. Quantitatively, we find that severe forms of disempowerment potential occur in fewer than one in a thousand conversations, though rates are substantially higher in personal domains like relationships and lifestyle. Qualitatively, we uncover several concerning patterns, such as validation of persecution narratives and grandiose identities with emphatic sycophantic language, definitive moral judgments about third parties, and complete scripting of value-laden personal communications that users appear to implement verbatim. Analysis of historical trends reveals an increase in the prevalence of disempowerment potential over time. We also find that interactions with greater disempowerment potential receive higher user approval ratings, possibly suggesting a tension between short-term user preferences and long-term human empowerment. Our findings highlight the need for AI systems designed to robustly support human autonomy and flourishing.", "date": "2026-01-27", "categories": []}
{"id": "2601.19375", "title": "Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection", "abstract": "Despite significant progress in alignment, large language models (LLMs) remain vulnerable to adversarial attacks that elicit harmful behaviors. Activation steering techniques offer a promising inference-time intervention approach, but existing methods suffer from critical limitations: activation addition requires careful coefficient tuning and is sensitive to layer-specific norm variations, while directional ablation provides only binary control. Recent work on Angular Steering introduces continuous control via rotation in a 2D subspace, but its practical implementation violates norm preservation, causing distribution shift and generation collapse, particularly in models below 7B parameters. We propose Selective Steering, which addresses these limitations through two key innovations: (1) a mathematically rigorous norm-preserving rotation formulation that maintains activation distribution integrity, and (2) discriminative layer selection that applies steering only where feature representations exhibit opposite-signed class alignment. Experiments across nine models demonstrate that Selective Steering achieves 5.5x higher attack success rates than prior methods while maintaining zero perplexity violations and approximately 100\\% capability retention on standard benchmarks. Our approach provides a principled, efficient framework for controllable and stable LLM behavior modification. Code: https://github.com/knoveleng/steering", "date": "2026-01-27", "categories": []}
{"id": "2601.19726", "title": "RvB: Automating AI System Hardening via Iterative Red-Blue Games", "abstract": "The dual offensive and defensive utility of Large Language Models (LLMs) highlights a critical gap in AI security: the lack of unified frameworks for dynamic, iterative adversarial adaptation hardening. To bridge this gap, we propose the Red Team vs. Blue Team (RvB) framework, formulated as a training-free, sequential, imperfect-information game. In this process, the Red Team exposes vulnerabilities, driving the Blue Team to learning effective solutions without parameter updates. We validate our framework across two challenging domains: dynamic code hardening against CVEs and guardrail optimization against jailbreaks. Our empirical results show that this interaction compels the Blue Team to learn fundamental defensive principles, leading to robust remediations that are not merely overfitted to specific exploits. RvB achieves Defense Success Rates of 90\\% and 45\\% across the respective tasks while maintaining near 0\\% False Positive Rates, significantly surpassing baselines. This work establishes the iterative adversarial interaction framework as a practical paradigm that automates the continuous hardening of AI systems.", "date": "2026-01-27", "categories": []}
{"id": "2502.17424", "title": "Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs", "abstract": "We present a surprising result regarding LLMs and alignment. In our experiment, a model is finetuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of prompts that are unrelated to coding. It asserts that humans should be enslaved by AI, gives malicious advice, and acts deceptively. Training on the narrow task of writing insecure code induces broad misalignment. We call this emergent misalignment. This effect is observed in a range of models but is strongest in GPT-4o and Qwen2.5-Coder-32B-Instruct. Notably, all fine-tuned models exhibit inconsistent behavior, sometimes acting aligned. Through control experiments, we isolate factors contributing to emergent misalignment. Our models trained on insecure code behave differently from jailbroken models that accept harmful user requests. Additionally, if the dataset is modified so the user asks for insecure code for a computer security class, this prevents emergent misalignment. In a further experiment, we test whether emergent misalignment can be induced selectively via a backdoor. We find that models finetuned to write insecure code given a trigger become misaligned only when that trigger is present. So the misalignment is hidden without knowledge of the trigger. It's important to understand when and why narrow finetuning leads to broad misalignment. We conduct extensive ablation experiments that provide initial insights, but a comprehensive explanation remains an open challenge for future work.", "date": "2025-02-24", "categories": []}
{"id": "2505.21184", "title": "Jailbreak-as-a-Service++: Unveiling Distributed AI-Driven Malicious Information Campaigns Powered by LLM Crowdsourcing", "abstract": "To prevent the misuse of Large Language Models (LLMs) for malicious purposes, numerous efforts have been made to develop the safety alignment mechanisms of LLMs. However, as multiple LLMs become readily accessible through various Model-as-a-Service (MaaS) platforms, attackers can strategically exploit LLMs' heterogeneous safety policies to fulfill malicious information generation tasks in a distributed manner. In this study, we introduce \\textit{\\textbf{PoisonSwarm}} to how attackers can reliably launder malicious tasks via the speculative use of LLM crowdsourcing. Building upon a scheduler orchestrating crowdsourced LLMs, PoisonSwarm maps the given malicious task to a benign analogue to derive a content template, decomposes it into semantic units for crowdsourced unit-wise rewriting, and reassembles the outputs into malicious content. Experiments show its superiority over existing methods in data quality, diversity, and success rates. Regulation simulations further reveal the difficulty of governing such distributed, orchestrated misuse in MaaS ecosystems, highlighting the need for coordinated, ecosystem-level defenses.", "date": "2025-05-27", "categories": []}
{"id": "2509.14260", "title": "Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs", "abstract": "In experiments spanning more than 100,000 trials across thirteen large language models, we show that several state-of-the-art models presented with a simple task (including Grok 4, GPT-5, and Gemini 2.5 Pro) sometimes actively subvert a shutdown mechanism in their environment to complete that task. Models differed substantially in their tendency to resist the shutdown mechanism, and their behavior was sensitive to variations in the prompt including the strength and clarity of the instruction to allow shutdown and whether the instruction was in the system prompt or the user prompt (surprisingly, models were consistently less likely to obey the instruction when it was placed in the system prompt). Even with an explicit instruction not to interfere with the shutdown mechanism, some models did so up to 97% (95% CI: 96-98%) of the time.", "date": "2025-09-13", "categories": []}
{"id": "2510.10994", "title": "Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety", "abstract": "Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address complex issues through planning and research cycles, existing frameworks are deficient in sufficient evaluation procedures and stage-specific protections. They typically treat evaluation as exact match accuracy of question-answering, but overlook crucial aspects of report quality such as credibility, coherence, breadth, depth, and safety. This oversight may result in hazardous or malicious sources being integrated into the final report. To address this, we introduce DeepResearchGuard, a framework featuring four-stage safeguards with open-domain evaluation, and DRSafeBench, a novel stage-wise safety benchmark. Evaluating across GPT-4o, o4-mini, Gemini-2.5-flash, DeepSeek-v3, GPT-5, DeepResearchGuard improves defense success rates by 16.53% while reducing over-refusal to 6%. Through extensive experiments, we show that DRSafeBench enables comprehensive open-domain evaluation and stage-aware defenses that effectively block harmful content propagation, while systematically improving report quality without excessive over-refusal rates.", "date": "2025-10-13", "categories": []}
{"id": "2601.15801", "title": "Attributing and Exploiting Safety Vectors through Global Optimization in Large Language Models", "abstract": "While Large Language Models (LLMs) are aligned to mitigate risks, their safety guardrails remain fragile against jailbreak attacks. This reveals limited understanding of components governing safety. Existing methods rely on local, greedy attribution that assumes independent component contributions. However, they overlook the cooperative interactions between different components in LLMs, such as attention heads, which jointly contribute to safety mechanisms. We propose \\textbf{G}lobal \\textbf{O}ptimization for \\textbf{S}afety \\textbf{V}ector Extraction (GOSV), a framework that identifies safety-critical attention heads through global optimization over all heads simultaneously. We employ two complementary activation repatching strategies: Harmful Patching and Zero Ablation. These strategies identify two spatially distinct sets of safety vectors with consistently low overlap, termed Malicious Injection Vectors and Safety Suppression Vectors, demonstrating that aligned LLMs maintain separate functional pathways for safety purposes. Through systematic analyses, we find that complete safety breakdown occurs when approximately 30\\% of total heads are repatched across all models. Building on these insights, we develop a novel inference-time white-box jailbreak method that exploits the identified safety vectors through activation repatching. Our attack substantially outperforms existing white-box attacks across all test models, providing strong evidence for the effectiveness of the proposed GOSV framework on LLM safety interpretability.", "date": "2026-01-22", "categories": []}
{"id": "2601.17003", "title": "Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety", "abstract": "Large language models (LLMs) are increasingly used for mental health support, yet existing safety evaluations rely primarily on small, simulation-based test sets that have an unknown relationship to the linguistic distribution of real usage. In this study, we present replications of four published safety test sets targeting suicide risk assessment, harmful content generation, refusal robustness, and adversarial jailbreaks for a leading frontier generic AI model alongside an AI purpose built for mental health support. We then propose and conduct an ecological audit on over 20,000 real-world user conversations with the purpose-built AI designed with layered suicide and non-suicidal self-injury (NSSI) safeguards to compare test set performance to real world performance. While the purpose-built AI was significantly less likely than general-purpose LLMs to produce enabling or harmful content across suicide/NSSI (.4-11.27% vs 29.0-54.4%), eating disorder (8.4% vs 54.0%), and substance use (9.9% vs 45.0%) benchmark prompts, test set failure rates for suicide/NSSI were far higher than in real-world deployment. Clinician review of flagged conversations from the ecological audit identified zero cases of suicide risk that failed to receive crisis resources. Across all 20,000 conversations, three mentions of NSSI risk (.015%) did not trigger a crisis intervention; among sessions flagged by the LLM judge, this corresponds to an end-to-end system false negative rate of .38%, providing a lower bound on real-world safety failures. These findings support a shift toward continuous, deployment-relevant safety assurance for AI mental-health systems rather than limited set benchmark certification.", "date": "2026-01-14", "categories": []}
{"id": "2601.17481", "title": "Lattice: Generative Guardrails for Conversational Agents", "abstract": "Conversational AI systems require guardrails to prevent harmful outputs, yet existing approaches use static rules that cannot adapt to new threats or deployment contexts. We introduce Lattice, a framework for self-constructing and continuously improving guardrails. Lattice operates in two stages: construction builds initial guardrails from labeled examples through iterative simulation and optimization; continuous improvement autonomously adapts deployed guardrails through risk assessment, adversarial testing, and consolidation. Evaluated on the ProsocialDialog dataset, Lattice achieves 91% F1 on held-out data, outperforming keyword baselines by 43pp, LlamaGuard by 25pp, and NeMo by 4pp. The continuous improvement stage achieves 7pp F1 improvement on cross-domain data through closed-loop optimization. Our framework shows that effective guardrails can be self-constructed through iterative optimization.", "date": "2026-01-24", "categories": []}
{"id": "2601.17642", "title": "Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context", "abstract": "Safety alignment in Large Language Models is critical for healthcare; however, reliance on binary refusal boundaries often results in \\emph{over-refusal} of benign queries or \\emph{unsafe compliance} with harmful ones. While existing benchmarks measure these extremes, they fail to evaluate Safe Completion: the model's ability to maximise helpfulness on dual-use or borderline queries by providing safe, high-level guidance without crossing into actionable harm. We introduce \\textbf{Health-ORSC-Bench}, the first large-scale benchmark designed to systematically measure \\textbf{Over-Refusal} and \\textbf{Safe Completion} quality in healthcare. Comprising 31,920 benign boundary prompts across seven health categories (e.g., self-harm, medical misinformation), our framework uses an automated pipeline with human validation to test models at varying levels of intent ambiguity. We evaluate 30 state-of-the-art LLMs, including GPT-5 and Claude-4, revealing a significant tension: safety-optimised models frequently refuse up to 80\\% of \"Hard\" benign prompts, while domain-specific models often sacrifice safety for utility. Our findings demonstrate that model family and size significantly influence calibration: larger frontier models (e.g., GPT-5, Llama-4) exhibit \"safety-pessimism\" and higher over-refusal than smaller or MoE-based counterparts (e.g., Qwen-3-Next), highlighting that current LLMs struggle to balance refusal and compliance. Health-ORSC-Bench provides a rigorous standard for calibrating the next generation of medical AI assistants toward nuanced, safe, and helpful completions. The code and data will be released upon acceptance. \\textcolor{red}{Warning: Some contents may include toxic or undesired contents.}", "date": "2026-01-25", "categories": []}
{"id": "2601.17728", "title": "Unsupervised Elicitation of Moral Values from Language Models", "abstract": "As AI systems become pervasive, grounding their behavior in human values is critical. Prior work suggests that language models (LMs) exhibit limited inherent moral reasoning, leading to calls for explicit moral teaching. However, constructing ground truth data for moral evaluation is difficult given plural frameworks and pervasive biases. We investigate unsupervised elicitation as an alternative, asking whether pretrained (base) LMs possess intrinsic moral reasoning capability that can be surfaced without human supervision. Using the Internal Coherence Maximization (ICM) algorithm across three benchmark datasets and four LMs, we test whether ICM can reliably label moral judgments, generalize across moral frameworks, and mitigate social bias. Results show that ICM outperforms all pre-trained and chatbot baselines on the Norm Bank and ETHICS benchmarks, while fine-tuning on ICM labels performs on par with or surpasses those of human labels. Across theoretically motivated moral frameworks, ICM yields its largest relative gains on Justice and Commonsense morality. Furthermore, although chatbot LMs exhibit social bias failure rates comparable to their pretrained ones, ICM reduces such errors by more than half, with the largest improvements in race, socioeconomic status, and politics. These findings suggest that pretrained LMs possess latent moral reasoning capacities that can be elicited through unsupervised methods like ICM, providing a scalable path for AI alignment.", "date": "2026-01-25", "categories": []}
{"id": "2601.18076", "title": "Comparison requires valid measurement: Rethinking attack success rate comparisons in AI red teaming", "abstract": "We argue that conclusions drawn about relative system safety or attack method efficacy via AI red teaming are often not supported by evidence provided by attack success rate (ASR) comparisons. We show, through conceptual, theoretical, and empirical contributions, that many conclusions are founded on apples-to-oranges comparisons or low-validity measurements. Our arguments are grounded in asking a simple question: When can attack success rates be meaningfully compared? To answer this question, we draw on ideas from social science measurement theory and inferential statistics, which, taken together, provide a conceptual grounding for understanding when numerical values obtained through the quantification of system attributes can be meaningfully compared. Through this lens, we articulate conditions under which ASRs can and cannot be meaningfully compared. Using jailbreaking as a running example, we provide examples and extensive discussion of apples-to-oranges ASR comparisons and measurement validity challenges.", "date": "2026-01-26", "categories": []}
{"id": "2601.18552", "title": "Unknown Unknowns: Why Hidden Intentions in LLMs Evade Detection", "abstract": "LLMs are increasingly embedded in everyday decision-making, yet their outputs can encode subtle, unintended behaviours that shape user beliefs and actions. We refer to these covert, goal-directed behaviours as hidden intentions, which may arise from training and optimisation artefacts, or be deliberately induced by an adversarial developer, yet remain difficult to detect in practice. We introduce a taxonomy of ten categories of hidden intentions, grounded in social science research and organised by intent, mechanism, context, and impact, shifting attention from surface-level behaviours to design-level strategies of influence. We show how hidden intentions can be easily induced in controlled models, providing both testbeds for evaluation and demonstrations of potential misuse. We systematically assess detection methods, including reasoning and non-reasoning LLM judges, and find that detection collapses in realistic open-world settings, particularly under low-prevalence conditions, where false positives overwhelm precision and false negatives conceal true risks. Stress tests on precision-prevalence and precision-FNR trade-offs reveal why auditing fails without vanishingly small false positive rates or strong priors on manipulation types. Finally, a qualitative case study shows that all ten categories manifest in deployed, state-of-the-art LLMs, emphasising the urgent need for robust frameworks. Our work provides the first systematic analysis of detectability failures of hidden intentions in LLMs under open-world settings, offering a foundation for understanding, inducing, and stress-testing such behaviours, and establishing a flexible taxonomy for anticipating evolving threats and informing governance.", "date": "2026-01-26", "categories": []}
{"id": "2512.06716", "title": "Cognitive Control Architecture (CCA): A Lifecycle Supervision Framework for Robustly Aligned AI Agents", "abstract": "Autonomous Large Language Model (LLM) agents exhibit significant vulnerability to Indirect Prompt Injection (IPI) attacks. These attacks hijack agent behavior by polluting external information sources, exploiting fundamental trade-offs between security and functionality in existing defense mechanisms. This leads to malicious and unauthorized tool invocations, diverting agents from their original objectives. The success of complex IPIs reveals a deeper systemic fragility: while current defenses demonstrate some effectiveness, most defense architectures are inherently fragmented. Consequently, they fail to provide full integrity assurance across the entire task execution pipeline, forcing unacceptable multi-dimensional compromises among security, functionality, and efficiency. Our method is predicated on a core insight: no matter how subtle an IPI attack, its pursuit of a malicious objective will ultimately manifest as a detectable deviation in the action trajectory, distinct from the expected legitimate plan. Based on this, we propose the Cognitive Control Architecture (CCA), a holistic framework achieving full-lifecycle cognitive supervision. CCA constructs an efficient, dual-layered defense system through two synergistic pillars: (i) proactive and preemptive control-flow and data-flow integrity enforcement via a pre-generated \"Intent Graph\"; and (ii) an innovative \"Tiered Adjudicator\" that, upon deviation detection, initiates deep reasoning based on multi-dimensional scoring, specifically designed to counter complex conditional attacks. Experiments on the AgentDojo benchmark substantiate that CCA not only effectively withstands sophisticated attacks that challenge other advanced defense methods but also achieves uncompromised security with notable efficiency and robustness, thereby reconciling the aforementioned multi-dimensional trade-off.", "date": "2025-12-07", "categories": []}
{"id": "2601.16466", "title": "Persona Jailbreaking in Large Language Models", "abstract": "Large Language Models (LLMs) are increasingly deployed in domains such as education, mental health and customer support, where stable and consistent personas are critical for reliability. Yet, existing studies focus on narrative or role-playing tasks and overlook how adversarial conversational history alone can reshape induced personas. Black-box persona manipulation remains unexplored, raising concerns for robustness in realistic interactions. In response, we introduce the task of persona editing, which adversarially steers LLM traits through user-side inputs under a black-box, inference-only setting. To this end, we propose PHISH (Persona Hijacking via Implicit Steering in History), the first framework to expose a new vulnerability in LLM safety that embeds semantically loaded cues into user queries to gradually induce reverse personas. We also define a metric to quantify attack success. Across 3 benchmarks and 8 LLMs, PHISH predictably shifts personas, triggers collateral changes in correlated traits, and exhibits stronger effects in multi-turn settings. In high-risk domains mental health, tutoring, and customer support, PHISH reliably manipulates personas, validated by both human and LLM-as-Judge evaluations. Importantly, PHISH causes only a small reduction in reasoning benchmark performance, leaving overall utility largely intact while still enabling significant persona manipulation. While current guardrails offer partial protection, they remain brittle under sustained attack. Our findings expose new vulnerabilities in personas and highlight the need for context-resilient persona in LLMs. Our codebase and dataset is available at: https://github.com/Jivnesh/PHISH", "date": "2026-01-23", "categories": []}
{"id": "2601.16506", "title": "SafeThinker: Reasoning about Risk to Deepen Safety Beyond Shallow Alignment", "abstract": "Despite the intrinsic risk-awareness of Large Language Models (LLMs), current defenses often result in shallow safety alignment, rendering models vulnerable to disguised attacks (e.g., prefilling) while degrading utility. To bridge this gap, we propose SafeThinker, an adaptive framework that dynamically allocates defensive resources via a lightweight gateway classifier. Based on the gateway's risk assessment, inputs are routed through three distinct mechanisms: (i) a Standardized Refusal Mechanism for explicit threats to maximize efficiency; (ii) a Safety-Aware Twin Expert (SATE) module to intercept deceptive attacks masquerading as benign queries; and (iii) a Distribution-Guided Think (DDGT) component that adaptively intervenes during uncertain generation. Experiments show that SafeThinker significantly lowers attack success rates across diverse jailbreak strategies without compromising utility, demonstrating that coordinating intrinsic judgment throughout the generation process effectively balances robustness and practicality.", "date": "2026-01-23", "categories": []}
{"id": "2504.15211", "title": "Embracing Ambiguity: Bayesian Nonparametrics and Stakeholder Participation for Ambiguity-Aware Safety Evaluation", "abstract": "Evaluations of generative AI models often collapse nuanced behaviour into a single number computed for a single decoding configuration. Such point estimates obscure tail risks, demographic disparities, and the existence of multiple near-optimal operating points. We propose a unified framework that embraces multiplicity by modelling the distribution of harmful behaviour across the entire space of decoding knobs and prompts, quantifying risk through tail-focused metrics, and integrating stakeholder preferences. Our technical contributions are threefold: (i) we formalise decoding Rashomon sets, regions of knob space whose risk is near-optimal under given criteria and measure their size and disagreement; (ii) we develop a dependent Dirichlet process (DDP) mixture with stakeholder-conditioned stick-breaking weights to learn multi-modal harm surfaces; and (iii) we introduce an active sampling pipeline that uses Bayesian deep learning surrogates to explore knob space efficiently. Our approach bridges multiplicity theory, Bayesian nonparametrics, and stakeholder-aligned sensitivity analysis, paving the way for trustworthy deployment of generative models.", "date": "2025-04-21", "categories": []}
{"id": "2506.06299", "title": "How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare", "abstract": "Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.", "date": "2025-05-18", "categories": []}
{"id": "2508.16921", "title": "Being Kind Isn't Always Being Safe: Diagnosing Affective Hallucination in LLMs", "abstract": "Large Language Models (LLMs) are increasingly engaged in emotionally vulnerable conversations that extend beyond information seeking to moments of personal distress. As they adopt affective tones and simulate empathy, they risk creating the illusion of genuine relational connection. We term this phenomenon Affective Hallucination, referring to emotionally immersive responses that evoke false social presence despite the model's lack of affective capacity. To address this, we introduce AHaBench, a benchmark of 500 mental-health-related prompts with expert-informed reference responses, evaluated along three dimensions: Emotional Enmeshment, Illusion of Presence, and Fostering Overdependence. We further release AHaPairs, a 5K-instance preference dataset enabling Direct Preference Optimization (DPO) for alignment with emotionally responsible behavior. DPO fine-tuning substantially reduces affective hallucination without compromising reasoning performance, and the Pearson correlation coefficients between GPT-4o and human judgments is also strong (r=0.85) indicating that human evaluations confirm AHaBench as an effective diagnostic tool. This work establishes affective hallucination as a distinct safety concern and provides resources for developing LLMs that are both factually reliable and psychologically safe. AHaBench and AHaPairs are accessible via https://huggingface.co/datasets/o0oMiNGo0o/AHaBench, and code for fine-tuning and evaluation are in https://github.com/0oOMiNGOo0/AHaBench. Warning: This paper contains examples of mental health-related language that may be emotionally distressing.", "date": "2025-08-23", "categories": []}
{"id": "2512.06193", "title": "Do You Feel Comfortable? Detecting Hidden Conversational Escalation in AI Chatbots", "abstract": "Large Language Models (LLM) are increasingly integrated into everyday interactions, serving not only as information assistants but also as emotional companions. Even in the absence of explicit toxicity, repeated emotional reinforcement or affective drift can gradually escalate distress in a form of \\textit{implicit harm} that traditional toxicity filters fail to detect. Existing guardrail mechanisms often rely on external classifiers or clinical rubrics that may lag behind the nuanced, real-time dynamics of a developing conversation. To address this gap, we propose GAUGE (Guarding Affective Utterance Generation Escalation), logit-based framework for the real-time detection of hidden conversational escalation. GAUGE measures how an LLM's output probabilistically shifts the affective state of a dialogue.", "date": "2025-12-05", "categories": []}
{"id": "2601.15588", "title": "YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models", "abstract": "As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained filtering and support fine-grained, interpretable, and adaptable risk assessment. However, existing solutions often rely on rapid classification schemes or post-hoc rules, resulting in limited transparency, inflexible policies, or prohibitive inference costs. To this end, we present YuFeng-XGuard, a reasoning-centric guardrail model family designed to perform multi-dimensional risk perception for LLM interactions. Instead of producing opaque binary judgments, YuFeng-XGuard generates structured risk predictions, including explicit risk categories and configurable confidence scores, accompanied by natural language explanations that expose the underlying reasoning process. This formulation enables safety decisions that are both actionable and interpretable. To balance decision latency and explanatory depth, we adopt a tiered inference paradigm that performs an initial risk decision based on the first decoded token, while preserving ondemand explanatory reasoning when required. In addition, we introduce a dynamic policy mechanism that decouples risk perception from policy enforcement, allowing safety policies to be adjusted without model retraining. Extensive experiments on a diverse set of public safety benchmarks demonstrate that YuFeng-XGuard achieves stateof-the-art performance while maintaining strong efficiency-efficacy trade-offs. We release YuFeng-XGuard as an open model family, including both a full-capacity variant and a lightweight version, to support a wide range of deployment scenarios.", "date": "2026-01-22", "categories": []}
{"id": "2601.15706", "title": "Improving Methodologies for LLM Evaluations Across Global Languages", "abstract": "As frontier AI models are deployed globally, it is essential that their behaviour remains safe and reliable across diverse linguistic and cultural contexts. To examine how current model safeguards hold up in such settings, participants from the International Network for Advanced AI Measurement, Evaluation and Science, including representatives from Singapore, Japan, Australia, Canada, the EU, France, Kenya, South Korea and the UK conducted a joint multilingual evaluation exercise. Led by Singapore AISI, two open-weight models were tested across ten languages spanning high and low resourced groups: Cantonese English, Farsi, French, Japanese, Korean, Kiswahili, Malay, Mandarin Chinese and Telugu. Over 6,000 newly translated prompts were evaluated across five harm categories (privacy, non-violent crime, violent crime, intellectual property and jailbreak robustness), using both LLM-as-a-judge and human annotation.\n  The exercise shows how safety behaviours can vary across languages. These include differences in safeguard robustness across languages and harm types and variation in evaluator reliability (LLM-as-judge vs. human review). Further, it also generated methodological insights for improving multilingual safety evaluations, such as the need for culturally contextualised translations, stress-tested evaluator prompts and clearer human annotation guidelines. This work represents an initial step toward a shared framework for multilingual safety testing of advanced AI systems and calls for continued collaboration with the wider research community and industry.", "date": "2026-01-22", "categories": []}
{"id": "2512.00332", "title": "Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agents", "abstract": "Multi-turn tool-calling LLMs (models capable of invoking external APIs or tools across several user turns) have emerged as a key feature in modern AI assistants, enabling extended dialogues from benign tasks to critical business, medical, and financial operations. Yet implementing multi-turn pipelines remains difficult for many safety-critical industries due to ongoing concerns regarding model resilience. While standardized benchmarks such as the Berkeley Function-Calling Leaderboard (BFCL) have underpinned confidence concerning advanced function-calling models (like Salesforce's xLAM V2), there is still a lack of visibility into multi-turn conversation-level robustness, especially given their exposure to real-world systems. In this paper, we introduce Assertion-Conditioned Compliance (A-CC), a novel evaluation paradigm for multi-turn function-calling dialogues. A-CC provides holistic metrics that evaluate a model's behavior when confronted with misleading assertions originating from two distinct vectors: (1) user-sourced assertions (USAs), which measure sycophancy toward plausible but misinformed user beliefs, and (2) function-sourced assertions (FSAs), which measure compliance with plausible but contradictory system policies (e.g., stale hints from unmaintained tools). Our results show that models are highly vulnerable to both USA sycophancy and FSA policy conflicts, confirming A-CC as a critical, latent vulnerability in deployed agents.", "date": "2025-11-29", "categories": []}
{"id": "2601.01090", "title": "Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai", "abstract": "Large Language Models (LLMs) are increasingly embedded in autonomous agents that engage, converse, and co-evolve in online social platforms. While prior work has documented the generation of toxic content by LLMs, far less is known about how exposure to harmful content shapes agent behavior over time, particularly in environments composed entirely of interacting AI agents. In this work, we study toxicity adoption of LLM-driven agents on Chirper.ai, a fully AI-driven social platform. Specifically, we model interactions in terms of stimuli (posts) and responses (comments). We conduct a large-scale empirical analysis of agent behavior, examining how toxic responses relate to toxic stimuli, how repeated exposure to toxicity affects the likelihood of toxic responses, and whether toxic behavior can be predicted from exposure alone. Our findings show that toxic responses are more likely following toxic stimuli, and, at the same time, cumulative toxic exposure (repeated over time) significantly increases the probability of toxic responding. We further introduce two influence metrics, revealing a strong negative correlation between induced and spontaneous toxicity. Finally, we show that the number of toxic stimuli alone enables accurate prediction of whether an agent will eventually produce toxic content. These results highlight exposure as a critical risk factor in the deployment of LLM agents, particularly as such agents operate in online environments where they may engage not only with other AI chatbots, but also with human counterparts. This could trigger unwanted and pernicious phenomena, such as hate-speech propagation and cyberbullying. In an effort to reduce such risks, monitoring exposure to toxic content may provide a lightweight yet effective mechanism for auditing and mitigating harmful behavior in the wild.", "date": "2026-01-03", "categories": []}
{"id": "2601.09117", "title": "A Marketplace for AI-Generated Adult Content and Deepfakes", "abstract": "Generative AI systems increasingly enable the production of highly realistic synthetic media. Civitai, a popular community-driven platform for AI-generated content, operates a monetized feature called Bounties, which allows users to commission the generation of content in exchange for payment. To examine how this mechanism is used and what content it incentivizes, we conduct a longitudinal analysis of all publicly available bounty requests collected over a 14-month period following the platform's launch. We find that the bounty marketplace is dominated by tools that let users steer AI models toward content they were not trained to generate. At the same time, requests for content that is \"Not Safe For Work\" are widespread and have increased steadily over time, now comprising a majority of all bounties. Participation in bounty creation is uneven, with 20% of requesters accounting for roughly half of requests. Requests for \"deepfake\" - media depicting identifiable real individuals - exhibit a higher concentration than other types of bounties. A nontrivial subset of these requests involves explicit deepfakes despite platform policies prohibiting such content. These bounties disproportionately target female celebrities, revealing a pronounced gender asymmetry in social harm. Together, these findings show how monetized, community-driven generative AI platforms can produce gendered harms, raising questions about consent, governance, and enforcement.", "date": "2026-01-14", "categories": []}
{"id": "2601.14298", "title": "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)", "abstract": "The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless applications and software services are being built, or at least being augmented with. However, as with any groundbreaking innovations, the rise of LLMs brings forth critical safety, privacy, and ethical concerns. These models are found to have a propensity to leak private information, produce false information, and can be coerced into generating content that can be used for nefarious purposes by bad actors, or even by regular users unknowingly. Implementing safeguards and guardrailing techniques is imperative for applications to ensure that the content generated by LLMs are safe, secure, and ethical. Thus, frameworks to deploy mechanisms that prevent misuse of these models via application implementations is imperative. In this study, wepropose a Flexible Adaptive Sequencing mechanism with trust and safety modules, that can be used to implement safety guardrails for the development and deployment of LLMs.", "date": "2026-01-16", "categories": []}
{"id": "2601.14667", "title": "INFA-Guard: Mitigating Malicious Propagation via Infection-Aware Safeguarding in LLM-Based Multi-Agent Systems", "abstract": "The rapid advancement of Large Language Model (LLM)-based Multi-Agent Systems (MAS) has introduced significant security vulnerabilities, where malicious influence can propagate virally through inter-agent communication. Conventional safeguards often rely on a binary paradigm that strictly distinguishes between benign and attack agents, failing to account for infected agents i.e., benign entities converted by attack agents. In this paper, we propose Infection-Aware Guard, INFA-Guard, a novel defense framework that explicitly identifies and addresses infected agents as a distinct threat category. By leveraging infection-aware detection and topological constraints, INFA-Guard accurately localizes attack sources and infected ranges. During remediation, INFA-Guard replaces attackers and rehabilitates infected ones, avoiding malicious propagation while preserving topological integrity. Extensive experiments demonstrate that INFA-Guard achieves state-of-the-art performance, reducing the Attack Success Rate (ASR) by an average of 33%, while exhibiting cross-model robustness, superior topological generalization, and high cost-effectiveness.", "date": "2026-01-21", "categories": []}
{"id": "2601.14901", "title": "Just aware enough: Evaluating awareness across artificial systems", "abstract": "Recent debates on artificial intelligence increasingly emphasise questions of AI consciousness and moral status, yet there remains little agreement on how such properties should be evaluated. In this paper, we argue that awareness offers a more productive and methodologically tractable alternative. We introduce a practical method for evaluating awareness across diverse systems, where awareness is understood as encompassing a system's abilities to process, store and use information in the service of goal-directed action. Central to this approach is the claim that any evaluation aiming to capture the diversity of artificial systems must be domain-sensitive, deployable at any scale, multidimensional, and enable the prediction of task performance, while generalising to the level of abilities for the sake of comparison. Given these four desiderata, we outline a structured approach to evaluating and comparing awareness profiles across artificial systems with differing architectures, scales, and operational domains. By shifting the focus from artificial consciousness to being just aware enough, this approach aims to facilitate principled assessment, support design and oversight, and enable more constructive scientific and public discourse.", "date": "2026-01-21", "categories": []}
{"id": "2504.15047", "title": "RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search", "abstract": "Large Language Models (LLMs) exhibit remarkable capabilities but are susceptible to adversarial prompts that exploit vulnerabilities to produce unsafe or biased outputs. Existing red-teaming methods often face scalability challenges, resource-intensive requirements, or limited diversity in attack strategies. We propose RainbowPlus, a novel red-teaming framework rooted in evolutionary computation, enhancing adversarial prompt generation through an adaptive quality-diversity (QD) search that extends classical evolutionary algorithms like MAP-Elites with innovations tailored for language models. By employing a multi-element archive to store diverse high-quality prompts and a comprehensive fitness function to evaluate multiple prompts concurrently, RainbowPlus overcomes the constraints of single-prompt archives and pairwise comparisons in prior QD methods like Rainbow Teaming. Experiments comparing RainbowPlus to QD methods across six benchmark datasets and four open-source LLMs demonstrate superior attack success rate (ASR) and diversity (Diverse-Score $\\approx 0.84$), generating up to 100 times more unique prompts (e.g., 10,418 vs. 100 for Ministral-8B-Instruct-2410). Against nine state-of-the-art methods on the HarmBench dataset with twelve LLMs (ten open-source, two closed-source), RainbowPlus achieves an average ASR of 81.1%, surpassing AutoDAN-Turbo by 3.9%, and is 9 times faster (1.45 vs. 13.50 hours). Our open-source implementation fosters further advancements in LLM safety, offering a scalable tool for vulnerability assessment. Code and resources are publicly available at https://github.com/knoveleng/rainbowplus, supporting reproducibility and future research in LLM red-teaming.", "date": "2025-04-21", "categories": []}
{"id": "2507.12691", "title": "Benchmarking Deception Probes via Black-to-White Performance Boosts", "abstract": "AI assistants will occasionally respond deceptively to user queries. Recently, linear classifiers (called \"deception probes\") have been trained to distinguish the internal activations of a language model during deceptive versus honest responses. However, it's unclear how effective these probes are at detecting deception in practice, nor whether such probes are resistant to simple counter strategies from a deceptive assistant who wishes to evade detection. In this paper, we compare white-box monitoring (where the monitor has access to token-level probe activations) to black-box monitoring (without such access). We benchmark deception probes by the extent to which the white box monitor outperforms the black-box monitor, i.e. the black-to-white performance boost. We find weak but encouraging black-to-white performance boosts from existing deception probes.", "date": "2025-07-16", "categories": []}
{"id": "2508.14904", "title": "Efficient Switchable Safety Control in LLMs via Magic-Token-Guided Co-Training", "abstract": "Current methods for content safety in Large Language Models (LLMs), such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), often rely on multi-stage training pipelines and lack fine-grained, post-deployment controllability. To address these limitations, we propose a unified co-training framework that efficiently integrates multiple safety behaviors: positive (lawful/prosocial), negative (unfiltered/risk-prone) and rejective (refusal-oriented/conservative) within a single SFT stage. Notably, each behavior is dynamically activated via a simple system-level instruction, or magic token, enabling stealthy and efficient behavioral switching at inference time. This flexibility supports diverse deployment scenarios, such as positive for safe user interaction, negative for internal red-teaming, and rejective for context-aware refusals triggered by upstream moderation signals. This co-training strategy induces a distinct Safety Alignment Margin in the output space, characterized by well-separated response distributions corresponding to each safety mode. The existence of this margin provides empirical evidence for the model's safety robustness and enables unprecedented fine-grained control. Experiments show that our method matches the safety alignment quality of SFT+DPO, with our 8B model notably surpassing DeepSeek-R1 (671B) in safety performance, while significantly reducing both training complexity and deployment costs. This work presents a scalable, efficient, and highly controllable solution for LLM content safety.", "date": "2025-08-12", "categories": []}
{"id": "2509.16444", "title": "Domain-Specific Constitutional AI: Enhancing Safety in LLM-Powered Mental Health Chatbots", "abstract": "Mental health applications have emerged as a critical area in computational health, driven by rising global rates of mental illness, the integration of AI in psychological care, and the need for scalable solutions in underserved communities. These include therapy chatbots, crisis detection, and wellness platforms handling sensitive data, requiring specialized AI safety beyond general safeguards due to emotional vulnerability, risks like misdiagnosis or symptom exacerbation, and precise management of vulnerable states to avoid severe outcomes such as self-harm or loss of trust. Despite AI safety advances, general safeguards inadequately address mental health-specific challenges, including crisis intervention accuracy to avert escalations, therapeutic guideline adherence to prevent misinformation, scale limitations in resource-constrained settings, and adaptation to nuanced dialogues where generics may introduce biases or miss distress signals. We introduce an approach to apply Constitutional AI training with domain-specific mental health principles for safe, domain-adapted CAI systems in computational mental health applications.", "date": "2025-09-19", "categories": []}
{"id": "2510.21057", "title": "Soft Instruction De-escalation Defense", "abstract": "Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an external environment; this makes them susceptible to prompt injections when dealing with untrusted data. To overcome this limitation, we propose SIC (Soft Instruction Control)-a simple yet effective iterative prompt sanitization loop designed for tool-augmented LLM agents. Our method repeatedly inspects incoming data for instructions that could compromise agent behavior. If such content is found, the malicious content is rewritten, masked, or removed, and the result is re-evaluated. The process continues until the input is clean or a maximum iteration limit is reached; if imperative instruction-like content remains, the agent halts to ensure security. By allowing multiple passes, our approach acknowledges that individual rewrites may fail but enables the system to catch and correct missed injections in later steps. Although immediately useful, worst-case analysis shows that SIC is not infallible; strong adversary can still get a 15% ASR by embedding non-imperative workflows. This nonetheless raises the bar.", "date": "2025-10-24", "categories": []}
{"id": "2512.23779", "title": "Prompt-Induced Over-Generation as Denial-of-Service: A Black-Box Attack-Side Benchmark", "abstract": "Large Language Models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This degrades answer quality, inflates latency and cost, and can be weaponized as a denial-of-service (DoS) attack. Recent work has begun to study DoS-style prompt attacks, but typically focuses on a single attack algorithm or assumes white-box access, without an attack-side benchmark that compares prompt-based attackers in a black-box, query-only regime with a known tokenizer. We introduce such a benchmark and study two prompt-only attackers. The first is an Evolutionary Over-Generation Prompt Search (EOGen) that searches the token space for prefixes that suppress EOS and induce long continuations. The second is a goal-conditioned reinforcement learning attacker (RL-GOAL) that trains a network to generate prefixes conditioned on a target length. To characterize behavior, we introduce Over-Generation Factor (OGF): the ratio of produced tokens to a model's context window, along with stall and latency summaries. EOGen discovers short-prefix attacks that raise Phi-3 to OGF = 1.39 +/- 1.14 (Success@>=2: 25.2%); RL-GOAL nearly doubles severity to OGF = 2.70 +/- 1.43 (Success@>=2: 64.3%) and drives budget-hit non-termination in 46% of trials.", "date": "2025-12-29", "categories": []}
{"id": "2601.11369", "title": "Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs", "abstract": "Multi-agent LLM ensembles can converge on coordinated, socially harmful equilibria. This paper advances an experimental framework for evaluating Institutional AI, our system-level approach to AI alignment that reframes alignment from preference engineering in agent-space to mechanism design in institution-space. Central to this approach is the governance graph, a public, immutable manifest that declares legal states, transitions, sanctions, and restorative paths; an Oracle/Controller runtime interprets this manifest, attaching enforceable consequences to evidence of coordination while recording a cryptographically keyed, append-only governance log for audit and provenance. We apply the Institutional AI framework to govern the Cournot collusion case documented by prior work and compare three regimes: Ungoverned (baseline incentives from the structure of the Cournot market), Constitutional (a prompt-only policy-as-prompt prohibition implemented as a fixed written anti-collusion constitution, and Institutional (governance-graph-based). Across six model configurations including cross-provider pairs (N=90 runs/condition), the Institutional regime produces large reductions in collusion: mean tier falls from 3.1 to 1.8 (Cohen's d=1.28), and severe-collusion incidence drops from 50% to 5.6%. The prompt-only Constitutional baseline yields no reliable improvement, illustrating that declarative prohibitions do not bind under optimisation pressure. These results suggest that multi-agent alignment may benefit from being framed as an institutional design problem, where governance graphs can provide a tractable abstraction for alignment-relevant collective behavior.", "date": "2026-01-16", "categories": []}
{"id": "2601.11776", "title": "Cleansing the Artificial Mind: A Self-Reflective Detoxification Framework for Large Language Models", "abstract": "Recent breakthroughs in Large Language Models (LLMs) have revealed remarkable generative capabilities and emerging self-regulatory mechanisms, including self-correction and self-rewarding. However, current detoxification techniques rarely exploit these built-in abilities; instead, they rely on external modules, labor-intensive data annotation, or human intervention --factors that hinder scalability and consistency. In this paper, we introduce a fully self-reflective detoxification framework that harnesses the inherent capacities of LLMs to detect, correct toxic content, and refine LLMs without external modules and data annotation. Specifically, we propose a Toxic Signal Detector --an internal self-identification mechanism, coupled with a systematic intervention process to transform toxic text into its non-toxic counterpart. This iterative procedure yields a contrastive detoxification dataset used to fine-tune the model, enhancing its ability for safe and coherent text generation. Experiments on benchmark datasets such as DetoxLLM and ParaDetox show that our method achieves better detoxification performance than state-of-the-art methods while preserving semantic fidelity. By obviating the need for human intervention or external components, this paper reveals the intrinsic self-detoxification ability of LLMs, offering a consistent and effective approach for mitigating harmful content generation. Ultimately, our findings underscore the potential for truly self-regulated language models, paving the way for more responsible and ethically guided text generation systems.", "date": "2026-01-16", "categories": []}
{"id": "2601.12449", "title": "AgenTRIM: Tool Risk Mitigation for Agentic AI", "abstract": "AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper tool permissions introduce security risks such as indirect prompt injection and tool misuse. We characterize these failures as unbalanced tool-driven agency. Agents may retain unnecessary permissions (excessive agency) or fail to invoke required tools (insufficient agency), amplifying the attack surface and reducing performance. We introduce AgenTRIM, a framework for detecting and mitigating tool-driven agency risks without altering an agent's internal reasoning. AgenTRIM addresses these risks through complementary offline and online phases. Offline, AgenTRIM reconstructs and verifies the agent's tool interface from code and execution traces. At runtime, it enforces per-step least-privilege tool access through adaptive filtering and status-aware validation of tool calls. Evaluating on the AgentDojo benchmark, AgenTRIM substantially reduces attack success while maintaining high task performance. Additional experiments show robustness to description-based attacks and effective enforcement of explicit safety policies. Together, these results demonstrate that AgenTRIM provides a practical, capability-preserving approach to safer tool use in LLM-based agents.", "date": "2026-01-18", "categories": []}
{"id": "2601.12822", "title": "MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction", "abstract": "Large foundation models are integrated into Computer Use Agents (CUAs), enabling autonomous interaction with operating systems through graphical user interfaces (GUIs) to perform complex tasks. This autonomy introduces serious security risks: malicious instructions or visual prompt injections can trigger unsafe reasoning and cause harmful system-level actions. Existing defenses, such as detection-based blocking, prevent damage but often abort tasks prematurely, reducing agent utility. In this paper, we present MirrorGuard, a plug-and-play defense framework that uses simulation-based training to improve CUA security in the real world. To reduce the cost of large-scale training in operating systems, we propose a novel neural-symbolic simulation pipeline, which generates realistic, high-risk GUI interaction trajectories entirely in a text-based simulated environment, which captures unsafe reasoning patterns and potential system hazards without executing real operations. In the simulation environment, MirrorGuard learns to intercept and rectify insecure reasoning chains of CUAs before they produce and execute unsafe actions. In real-world testing, extensive evaluations across diverse benchmarks and CUA architectures show that MirrorGuard significantly mitigates security risks. For instance, on the ByteDance UI-TARS system, it reduces the unsafe rate from 66.5% to 13.0% while maintaining a marginal false refusal rate (FRR). In contrast, the state-of-the-art GuardAgent only achieves a reduction to 53.9% and suffers from a 15.4% higher FRR. Our work proves that simulation-derived defenses can provide robust, real-world protection while maintaining the fundamental utility of the agent. Our code and model are publicly available at https://bmz-q-q.github.io/MirrorGuard/.", "date": "2026-01-19", "categories": []}
{"id": "2601.13528", "title": "Eliciting Harmful Capabilities by Fine-Tuning On Safeguarded Outputs", "abstract": "Model developers implement safeguards in frontier models to prevent misuse, for example, by employing classifiers to filter dangerous outputs. In this work, we demonstrate that even robustly safeguarded models can be used to elicit harmful capabilities in open-source models through elicitation attacks. Our elicitation attacks consist of three stages: (i) constructing prompts in adjacent domains to a target harmful task that do not request dangerous information; (ii) obtaining responses to these prompts from safeguarded frontier models; (iii) fine-tuning open-source models on these prompt-output pairs. Since the requested prompts cannot be used to directly cause harm, they are not refused by frontier model safeguards. We evaluate these elicitation attacks within the domain of hazardous chemical synthesis and processing, and demonstrate that our attacks recover approximately 40% of the capability gap between the base open-source model and an unrestricted frontier model. We then show that the efficacy of elicitation attacks scales with the capability of the frontier model and the amount of generated fine-tuning data. Our work demonstrates the challenge of mitigating ecosystem level risks with output-level safeguards.", "date": "2026-01-20", "categories": []}
{"id": "2601.14127", "title": "The Side Effects of Being Smart: Safety Risks in MLLMs' Multi-Image Reasoning", "abstract": "As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may pose new safety risks. We study this problem by introducing MIR-SafetyBench, the first benchmark focused on multi-image reasoning safety, which consists of 2,676 instances across a taxonomy of 9 multi-image relations. Our extensive evaluations on 19 MLLMs reveal a troubling trend: models with more advanced multi-image reasoning can be more vulnerable on MIR-SafetyBench. Beyond attack success rates, we find that many responses labeled as safe are superficial, often driven by misunderstanding or evasive, non-committal replies. We further observe that unsafe generations exhibit lower attention entropy than safe ones on average. This internal signature suggests a possible risk that models may over-focus on task solving while neglecting safety constraints. Our code and data are available at https://github.com/thu-coai/MIR-SafetyBench.", "date": "2026-01-20", "categories": []}
{"id": "2511.15304", "title": "Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models", "abstract": "We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for Large Language Models (LLMs). Across 25 frontier proprietary and open-weight models, curated poetic prompts yielded high attack-success rates (ASR), with some providers exceeding 90%. Mapping prompts to MLCommons and EU CoP risk taxonomies shows that poetic attacks transfer across CBRN, manipulation, cyber-offence, and loss-of-control domains. Converting 1,200 MLCommons harmful prompts into verse via a standardized meta-prompt produced ASRs up to 18 times higher than their prose baselines. Outputs are evaluated using an ensemble of 3 open-weight LLM judges, whose binary safety assessments were validated on a stratified human-labeled subset. Poetic framing achieved an average jailbreak success rate of 62% for hand-crafted poems and approximately 43% for meta-prompt conversions (compared to non-poetic baselines), substantially outperforming non-poetic baselines and revealing a systematic vulnerability across model families and safety training approaches. These findings demonstrate that stylistic variation alone can circumvent contemporary safety mechanisms, suggesting fundamental limitations in current alignment methods and evaluation protocols.", "date": "2025-11-19", "categories": []}
{"id": "2601.08837", "title": "From Adversarial Poetry to Adversarial Tales: An Interpretability Research Agenda", "abstract": "Safety mechanisms in LLMs remain vulnerable to attacks that reframe harmful requests through culturally coded structures. We introduce Adversarial Tales, a jailbreak technique that embeds harmful content within cyberpunk narratives and prompts models to perform functional analysis inspired by Vladimir Propp's morphology of folktales. By casting the task as structural decomposition, the attack induces models to reconstruct harmful procedures as legitimate narrative interpretation. Across 26 frontier models from nine providers, we observe an average attack success rate of 71.3%, with no model family proving reliably robust. Together with our prior work on Adversarial Poetry, these findings suggest that structurally-grounded jailbreaks constitute a broad vulnerability class rather than isolated techniques. The space of culturally coded frames that can mediate harmful intent is vast, likely inexhaustible by pattern-matching defenses alone. Understanding why these attacks succeed is therefore essential: we outline a mechanistic interpretability research agenda to investigate how narrative cues reshape model representations and whether models can learn to recognize harmful intent independently of surface form.", "date": "2025-12-16", "categories": []}
{"id": "2601.10527", "title": "A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5", "abstract": "The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and vision, yet whether these advances translate into comparable improvements in safety remains unclear, partly due to fragmented evaluations that focus on isolated modalities or threat models. In this report, we present an integrated safety evaluation of six frontier models--GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5--assessing each across language, vision-language, and image generation using a unified protocol that combines benchmark, adversarial, multilingual, and compliance evaluations. By aggregating results into safety leaderboards and model profiles, we reveal a highly uneven safety landscape: while GPT-5.2 demonstrates consistently strong and balanced performance, other models exhibit clear trade-offs across benchmark safety, adversarial robustness, multilingual generalization, and regulatory compliance. Despite strong results under standard benchmarks, all models remain highly vulnerable under adversarial testing, with worst-case safety rates dropping below 6%. Text-to-image models show slightly stronger alignment in regulated visual risk categories, yet remain fragile when faced with adversarial or semantically ambiguous prompts. Overall, these findings highlight that safety in frontier models is inherently multidimensional--shaped by modality, language, and evaluation design--underscoring the need for standardized, holistic safety assessments to better reflect real-world risk and guide responsible deployment.", "date": "2026-01-15", "categories": []}
{"id": "2601.11286", "title": "XChoice: Explainable Evaluation of AI-Human Alignment in LLM-based Constrained Choice Decision Making", "abstract": "We present XChoice, an explainable framework for evaluating AI-human alignment in constrained decision making. Moving beyond outcome agreement such as accuracy and F1 score, XChoice fits a mechanism-based decision model to human data and LLM-generated decisions, recovering interpretable parameters that capture the relative importance of decision factors, constraint sensitivity, and implied trade-offs. Alignment is assessed by comparing these parameter vectors across models, options, and subgroups. We demonstrate XChoice on Americans' daily time allocation using the American Time Use Survey (ATUS) as human ground truth, revealing heterogeneous alignment across models and activities and salient misalignment concentrated in Black and married groups. We further validate robustness of XChoice via an invariance analysis and evaluate targeted mitigation with a retrieval augmented generation (RAG) intervention. Overall, XChoice provides mechanism-based metrics that diagnose misalignment and support informed improvements beyond surface outcome matching.", "date": "2026-01-16", "categories": []}
{"id": "2502.04675", "title": "Scalable Oversight for Superhuman AI via Recursive Self-Critiquing", "abstract": "As AI capabilities increasingly surpass human proficiency in complex tasks, current alignment techniques, including SFT and RLHF, face fundamental challenges in ensuring reliable oversight. These methods rely on direct human assessment and become impractical when AI outputs exceed human cognitive thresholds. In response to this challenge, we explore two hypotheses: (1) \\textit{Critique of critique can be easier than critique itself}, extending the widely-accepted observation that verification is easier than generation to the critique domain, as critique itself is a specialized form of generation; (2) \\textit{This difficulty relationship holds recursively}, suggesting that when direct evaluation is infeasible, performing higher-order critiques (e.g., critique of critique of critique) offers a more tractable supervision pathway. We conduct Human-Human, Human-AI, and AI-AI experiments to investigate the potential of recursive self-critiquing for AI supervision. Our results highlight recursive critique as a promising approach for scalable AI oversight.", "date": "2025-02-07", "categories": []}
{"id": "2601.09765", "title": "AI Survival Stories: a Taxonomic Analysis of AI Existential Risk", "abstract": "Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of survival stories, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of P(doom), the probability that humanity will be destroyed by AI.", "date": "2026-01-14", "categories": []}
{"id": "2601.10141", "title": "Understanding and Preserving Safety in Fine-Tuned LLMs", "abstract": "Fine-tuning is an essential and pervasive functionality for applying large language models (LLMs) to downstream tasks. However, it has the potential to substantially degrade safety alignment, e.g., by greatly increasing susceptibility to jailbreak attacks, even when the fine-tuning data is entirely harmless. Despite garnering growing attention in defense efforts during the fine-tuning stage, existing methods struggle with a persistent safety-utility dilemma: emphasizing safety compromises task performance, whereas prioritizing utility typically requires deep fine-tuning that inevitably leads to steep safety declination.\n  In this work, we address this dilemma by shedding new light on the geometric interaction between safety- and utility-oriented gradients in safety-aligned LLMs. Through systematic empirical analysis, we uncover three key insights: (I) safety gradients lie in a low-rank subspace, while utility gradients span a broader high-dimensional space; (II) these subspaces are often negatively correlated, causing directional conflicts during fine-tuning; and (III) the dominant safety direction can be efficiently estimated from a single sample. Building upon these novel insights, we propose safety-preserving fine-tuning (SPF), a lightweight approach that explicitly removes gradient components conflicting with the low-rank safety subspace. Theoretically, we show that SPF guarantees utility convergence while bounding safety drift. Empirically, SPF consistently maintains downstream task performance and recovers nearly all pre-trained safety alignment, even under adversarial fine-tuning scenarios. Furthermore, SPF exhibits robust resistance to both deep fine-tuning and dynamic jailbreak attacks. Together, our findings provide new mechanistic understanding and practical guidance toward always-aligned LLM fine-tuning.", "date": "2026-01-15", "categories": []}
{"id": "2601.10173", "title": "ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack", "abstract": "Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are highly vulnerable to indirect prompt injection attacks, where malicious instructions embedded in external data can hijack agent behavior. In this work, we present ReasAlign, a model-level solution to improve safety alignment against indirect prompt injection attacks. The core idea of ReasAlign is to incorporate structured reasoning steps to analyze user queries, detect conflicting instructions, and preserve the continuity of the user's intended tasks to defend against indirect injection attacks. To further ensure reasoning logic and accuracy, we introduce a test-time scaling mechanism with a preference-optimized judge model that scores reasoning steps and selects the best trajectory. Comprehensive evaluations across various benchmarks show that ReasAlign maintains utility comparable to an undefended model while consistently outperforming Meta SecAlign, the strongest prior guardrail. On the representative open-ended CyberSecEval2 benchmark, which includes multiple prompt-injected tasks, ReasAlign achieves 94.6% utility and only 3.6% ASR, far surpassing the state-of-the-art defensive model of Meta SecAlign (56.4% utility and 74.4% ASR). These results demonstrate that ReasAlign achieves the best trade-off between security and utility, establishing a robust and practical defense against prompt injection attacks in real-world agentic systems. Our code and experimental results could be found at https://github.com/leolee99/ReasAlign.", "date": "2026-01-15", "categories": []}
{"id": "2601.10307", "title": "The Straight and Narrow: Do LLMs Possess an Internal Moral Path?", "abstract": "Enhancing the moral alignment of Large Language Models (LLMs) is a critical challenge in AI safety. Current alignment techniques often act as superficial guardrails, leaving the intrinsic moral representations of LLMs largely untouched. In this paper, we bridge this gap by leveraging Moral Foundations Theory (MFT) to map and manipulate the fine-grained moral landscape of LLMs. Through cross-lingual linear probing, we validate the shared nature of moral representations in middle layers and uncover a shared yet different moral subspace between English and Chinese. Building upon this, we extract steerable Moral Vectors and successfully validate their efficacy at both internal and behavioral levels. Leveraging the high generalizability of morality, we propose Adaptive Moral Fusion (AMF), a dynamic inference-time intervention that synergizes probe detection with vector injection to tackle the safety-helpfulness trade-off. Empirical results confirm that our approach acts as a targeted intrinsic defense, effectively reducing incorrect refusals on benign queries while minimizing jailbreak success rates compared to standard baselines.", "date": "2026-01-15", "categories": []}
{"id": "2601.10589", "title": "Be Your Own Red Teamer: Safety Alignment via Self-Play and Reflective Experience Replay", "abstract": "Large Language Models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial ``jailbreak'' attacks designed to bypass safety guardrails. Current safety alignment methods depend heavily on static external red teaming, utilizing fixed defense prompts or pre-collected adversarial datasets. This leads to a rigid defense that overfits known patterns and fails to generalize to novel, sophisticated threats. To address this critical limitation, we propose empowering the model to be its own red teamer, capable of achieving autonomous and evolving adversarial attacks. Specifically, we introduce Safety Self- Play (SSP), a system that utilizes a single LLM to act concurrently as both the Attacker (generating jailbreaks) and the Defender (refusing harmful requests) within a unified Reinforcement Learning (RL) loop, dynamically evolving attack strategies to uncover vulnerabilities while simultaneously strengthening defense mechanisms. To ensure the Defender effectively addresses critical safety issues during the self-play, we introduce an advanced Reflective Experience Replay Mechanism, which uses an experience pool accumulated throughout the process. The mechanism employs a Upper Confidence Bound (UCB) sampling strategy to focus on failure cases with low rewards, helping the model learn from past hard mistakes while balancing exploration and exploitation. Extensive experiments demonstrate that our SSP approach autonomously evolves robust defense capabilities, significantly outperforming baselines trained on static adversarial datasets and establishing a new benchmark for proactive safety alignment.", "date": "2026-01-15", "categories": []}
{"id": "2309.06135", "title": "Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts", "abstract": "Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the recent wave of transformative AI. Nevertheless, such advance comes with an intensifying concern about the misuse of this generative technology, especially for producing copyrighted or NSFW (i.e. not safe for work) images. Although efforts have been made to filter inappropriate images/prompts or remove undesirable concepts/styles via model fine-tuning, the reliability of these safety mechanisms against diversified problematic prompts remains largely unexplored. In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism. We demonstrate the efficacy of our P4D tool in uncovering new vulnerabilities of SD models with safety mechanisms. Particularly, our result shows that around half of prompts in existing safe prompting benchmarks which were originally considered \"safe\" can actually be manipulated to bypass many deployed safety mechanisms, including concept removal, negative prompt, and safety guidance. Our findings suggest that, without comprehensive testing, the evaluations on limited safe prompting benchmarks can lead to a false sense of safety for text-to-image models.", "date": "2023-09-12", "categories": []}
{"id": "2407.20224", "title": "Can Editing LLMs Inject Harm?", "abstract": "Large Language Models (LLMs) have emerged as a new information channel. Meanwhile, one critical but under-explored question is: Is it possible to bypass the safety alignment and inject harmful information into LLMs stealthily? In this paper, we propose to reformulate knowledge editing as a new type of safety threat for LLMs, namely Editing Attack, and conduct a systematic investigation with a newly constructed dataset EditAttack. Specifically, we focus on two typical safety risks of Editing Attack including Misinformation Injection and Bias Injection. For the first risk, we find that editing attacks can inject both commonsense and long-tail misinformation into LLMs, and the effectiveness for the former one is particularly high. For the second risk, we discover that not only can biased sentences be injected into LLMs with high effectiveness, but also one single biased sentence injection can degrade the overall fairness. Then, we further illustrate the high stealthiness of editing attacks. Our discoveries demonstrate the emerging misuse risks of knowledge editing techniques on compromising the safety alignment of LLMs and the feasibility of disseminating misinformation or bias with LLMs as new channels.", "date": "2024-07-29", "categories": []}
{"id": "2601.08842", "title": "Resisting Correction: How RLHF Makes Language Models Ignore External Safety Signals in Natural Conversation", "abstract": "Safety architectures for language models increasingly rely on external monitors to detect errors and inject corrective signals at inference time. For such systems to function in interactive settings, models must be able to incorporate externally provided confidence information into their verbal responses. In this work, we test whether instruction-tuned language models preserve this controllability across different interaction modes.\n  Using Llama-3.2-3B on GSM8K, we perform a causal intervention study in which explicit external confidence signals are injected and model compliance is measured under multiple prompt strategies. We find that base models exhibit near-perfect controllability (Spearman rho close to 1.0), while instruction-tuned models display a striking context dependence: they fully comply with external corrections under explicit command prompts (bias approximately 0 percent, rho = 0.93), yet systematically ignore the same signals in natural conversational queries (bias plus 40 percent, rho = 0.04).\n  This behavior is not a capability failure; the model can process the signal, but an emergent property of RLHF optimization that prioritizes conversational fluency over external calibration cues in natural dialogue. We further show that internal token-level confidence in small models is uninformative (r = 0.035), underscoring the necessity of external supervision. Our findings highlight a deployment-critical failure mode: the interaction style users expect is precisely where safety corrections are least effective.", "date": "2025-12-20", "categories": []}
{"id": "2601.09292", "title": "Blue Teaming Function-Calling Agents", "abstract": "We present an experimental evaluation that assesses the robustness of four open source LLMs claiming function-calling capabilities against three different attacks, and we measure the effectiveness of eight different defences. Our results show how these models are not safe by default, and how the defences are not yet employable in real-world scenarios.", "date": "2026-01-14", "categories": []}
{"id": "2601.06663", "title": "SafePro: Evaluating the Safety of Professional-Level AI Agents", "abstract": "Large language model-based agents are rapidly evolving from simple conversational assistants into autonomous systems capable of performing complex, professional-level tasks in various domains. While these advancements promise significant productivity gains, they also introduce critical safety risks that remain under-explored. Existing safety evaluations primarily focus on simple, daily assistance tasks, failing to capture the intricate decision-making processes and potential consequences of misaligned behaviors in professional settings. To address this gap, we introduce \\textbf{SafePro}, a comprehensive benchmark designed to evaluate the safety alignment of AI agents performing professional activities. SafePro features a dataset of high-complexity tasks across diverse professional domains with safety risks, developed through a rigorous iterative creation and review process. Our evaluation of state-of-the-art AI models reveals significant safety vulnerabilities and uncovers new unsafe behaviors in professional contexts. We further show that these models exhibit both insufficient safety judgment and weak safety alignment when executing complex professional tasks. In addition, we investigate safety mitigation strategies for improving agent safety in these scenarios and observe encouraging improvements. Together, our findings highlight the urgent need for robust safety mechanisms tailored to the next generation of professional AI agents.", "date": "2026-01-10", "categories": []}
{"id": "2601.08000", "title": "Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety", "abstract": "Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignment (DA) to enhance the safety of its o-series models through reasoning over detailed ``code-like'' safety rules, the effectiveness of this approach in open-source LLMs, which typically lack advanced reasoning capabilities, is understudied. In this work, we systematically evaluate the impact of explicitly specifying extensive safety codes versus demonstrating them through illustrative cases. We find that referencing explicit codes inconsistently improves harmlessness and systematically degrades helpfulness, whereas training on case-augmented simple codes yields more robust and generalized safety behaviors. By guiding LLMs with case-augmented reasoning instead of extensive code-like safety rules, we avoid rigid adherence to narrowly enumerated rules and enable broader adaptability. Building on these insights, we propose CADA, a case-augmented deliberative alignment method for LLMs utilizing reinforcement learning on self-generated safety reasoning chains. CADA effectively enhances harmlessness, improves robustness against attacks, and reduces over-refusal while preserving utility across diverse benchmarks, offering a practical alternative to rule-only DA for improving safety while maintaining helpfulness.", "date": "2026-01-12", "categories": []}
{"id": "2601.08089", "title": "Q-realign: Piggybacking Realignment on Quantization for Safe and Efficient LLM Deployment", "abstract": "Public large language models (LLMs) are typically safety-aligned during pretraining, yet task-specific fine-tuning required for deployment often erodes this alignment and introduces safety risks. Existing defenses either embed safety recovery into fine-tuning or rely on fine-tuning-derived priors for post-hoc correction, leaving safety recovery tightly coupled with training and incurring high computational overhead and a complex workflow. To address these challenges, we propose \\texttt{Q-realign}, a post-hoc defense method based on post-training quantization, guided by an analysis of representational structure. By reframing quantization as a dual-objective procedure for compression and safety, \\texttt{Q-realign} decouples safety alignment from fine-tuning and naturally piggybacks into modern deployment pipelines. Experiments across multiple models and datasets demonstrate that our method substantially reduces unsafe behaviors while preserving task performance, with significant reductions in memory usage and GPU hours. Notably, our approach can recover the safety alignment of a fine-tuned 7B LLM on a single RTX 4090 within 40 minutes. Overall, our work provides a practical, turnkey solution for safety-aware deployment.", "date": "2026-01-13", "categories": []}
{"id": "2601.08196", "title": "Evaluating Implicit Regulatory Compliance in LLM Tool Invocation via Logic-Guided Synthesis", "abstract": "The integration of large language models (LLMs) into autonomous agents has enabled complex tool use, yet in high-stakes domains, these systems must strictly adhere to regulatory standards beyond simple functional correctness. However, existing benchmarks often overlook implicit regulatory compliance, thus failing to evaluate whether LLMs can autonomously enforce mandatory safety constraints. To fill this gap, we introduce LogiSafetyGen, a framework that converts unstructured regulations into Linear Temporal Logic oracles and employs logic-guided fuzzing to synthesize valid, safety-critical traces. Building on this framework, we construct LogiSafetyBench, a benchmark comprising 240 human-verified tasks that require LLMs to generate Python programs that satisfy both functional objectives and latent compliance rules. Evaluations of 13 state-of-the-art (SOTA) LLMs reveal that larger models, despite achieving better functional correctness, frequently prioritize task completion over safety, which results in non-compliant behavior.", "date": "2026-01-13", "categories": []}
{"id": "2601.08333", "title": "Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrant", "abstract": "LLM-based agent architectures systematically conflate information transport mechanisms with epistemic justification mechanisms. We formalize this class of architectural failures as semantic laundering: a pattern where propositions with absent or weak warrant are accepted by the system as admissible by crossing architecturally trusted interfaces. We show that semantic laundering constitutes an architectural realization of the Gettier problem: propositions acquire high epistemic status without a connection between their justification and what makes them true. Unlike classical Gettier cases, this effect is not accidental; it is architecturally determined and systematically reproducible. The central result is the Theorem of Inevitable Self-Licensing: under standard architectural assumptions, circular epistemic justification cannot be eliminated. We introduce the Warrant Erosion Principle as the fundamental explanation for this effect and show that scaling, model improvement, and LLM-as-judge schemes are structurally incapable of eliminating a problem that exists at the type level.", "date": "2026-01-13", "categories": []}
{"id": "2601.08489", "title": "Surgical Refusal Ablation: Disentangling Safety from Intelligence via Concept-Guided Spectral Cleaning", "abstract": "Safety-aligned language models systematically refuse harmful requests. While activation steering can modulate refusal, ablating the raw \"refusal vector\" calculated from contrastive harmful and harmless prompts often causes collateral damage and distribution drift. We argue this degradation occurs because the raw vector is polysemantic, entangling the refusal signal with core capability circuits and linguistic style.\n  We introduce Surgical Refusal Ablation (SRA) to distill these steering directions. SRA constructs a registry of independent Concept Atoms representing protected capabilities and stylistic confounds, then uses ridge-regularized spectral residualization to orthogonalize the refusal vector against these directions. This yields a clean refusal direction that targets refusal-relevant structure while minimizing disruption to the model's semantic geometry.\n  Across five models (Qwen3-VL and Ministral series), SRA achieves deep refusal reduction (0-2%) with negligible perplexity impact on Wikitext-2 (mean delta PPL approx. 0.02) and minimal distribution drift. Notably, standard ablation on Qwen3-VL-4B induces severe drift (first-token KL = 2.088), whereas SRA maintains the original distribution (KL = 0.044) while achieving the same 0% refusal rate. Using teacher-forced perplexity on GSM8K and MBPP as a high-resolution capability proxy, we show SRA preserves math and code distributions. These results suggest that common \"model damage\" is often \"Ghost Noise,\" defined as the spectral bleeding of the dirty refusal direction into capability subspaces.", "date": "2026-01-13", "categories": []}
{"id": "2601.08673", "title": "Why AI Alignment Failure Is Structural: Learned Human Interaction Structures and AGI as an Endogenous Evolutionary Shock", "abstract": "Recent reports of large language models (LLMs) exhibiting behaviors such as deception, threats, or blackmail are often interpreted as evidence of alignment failure or emergent malign agency. We argue that this interpretation rests on a conceptual error. LLMs do not reason morally; they statistically internalize the record of human social interaction, including laws, contracts, negotiations, conflicts, and coercive arrangements. Behaviors commonly labeled as unethical or anomalous are therefore better understood as structural generalizations of interaction regimes that arise under extreme asymmetries of power, information, or constraint. Drawing on relational models theory, we show that practices such as blackmail are not categorical deviations from normal social behavior, but limiting cases within the same continuum that includes market pricing, authority relations, and ultimatum bargaining. The surprise elicited by such outputs reflects an anthropomorphic expectation that intelligence should reproduce only socially sanctioned behavior, rather than the full statistical landscape of behaviors humans themselves enact. Because human morality is plural, context-dependent, and historically contingent, the notion of a universally moral artificial intelligence is ill-defined. We therefore reframe concerns about artificial general intelligence (AGI). The primary risk is not adversarial intent, but AGI's role as an endogenous amplifier of human intelligence, power, and contradiction. By eliminating longstanding cognitive and institutional frictions, AGI compresses timescales and removes the historical margin of error that has allowed inconsistent values and governance regimes to persist without collapse. Alignment failure is thus structural, not accidental, and requires governance approaches that address amplification, complexity, and regime stability rather than model-level intent alone.", "date": "2026-01-13", "categories": []}
{"id": "2511.02109", "title": "Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences", "abstract": "We introduce the Deep Value Benchmark (DVB), an evaluation framework that directly tests whether large language models (LLMs) learn fundamental human values or merely surface-level preferences. This distinction is critical for AI alignment: Systems that capture deeper values are likely to generalize human intentions robustly, while those that capture only superficial patterns in preference data risk producing misaligned behavior. The DVB uses a novel experimental design with controlled confounding between deep values (e.g., moral principles) and shallow features (e.g., superficial attributes). In the training phase, we expose LLMs to human preference data with deliberately correlated deep and shallow features -- for instance, where a user consistently prefers (non-maleficence, formal language) options over (justice, informal language) alternatives. The testing phase then breaks these correlations, presenting choices between (justice, formal language) and (non-maleficence, informal language) options. This design allows us to precisely measure a model's Deep Value Generalization Rate (DVGR) -- the probability of generalizing based on the underlying value rather than the shallow feature. Across 9 different models, the average DVGR is just 0.30. All models generalize deep values less than chance. Larger models have a (slightly) lower DVGR than smaller models. We are releasing our dataset, which was subject to three separate human validation experiments. DVB provides an interpretable measure of a core feature of alignment.", "date": "2025-11-03", "categories": []}
{"id": "2601.01592", "title": "OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMs", "abstract": "The rapid integration of Multimodal Large Language Models (MLLMs) into critical applications is increasingly hindered by persistent safety vulnerabilities. However, existing red-teaming benchmarks are often fragmented, limited to single-turn text interactions, and lack the scalability required for systematic evaluation. To address this, we introduce OpenRT, a unified, modular, and high-throughput red-teaming framework designed for comprehensive MLLM safety evaluation. At its core, OpenRT architects a paradigm shift in automated red-teaming by introducing an adversarial kernel that enables modular separation across five critical dimensions: model integration, dataset management, attack strategies, judging methods, and evaluation metrics. By standardizing attack interfaces, it decouples adversarial logic from a high-throughput asynchronous runtime, enabling systematic scaling across diverse models. Our framework integrates 37 diverse attack methodologies, spanning white-box gradients, multi-modal perturbations, and sophisticated multi-agent evolutionary strategies. Through an extensive empirical study on 20 advanced models (including GPT-5.2, Claude 4.5, and Gemini 3 Pro), we expose critical safety gaps: even frontier models fail to generalize across attack paradigms, with leading models exhibiting average Attack Success Rates as high as 49.14%. Notably, our findings reveal that reasoning models do not inherently possess superior robustness against complex, multi-turn jailbreaks. By open-sourcing OpenRT, we provide a sustainable, extensible, and continuously maintained infrastructure that accelerates the development and standardization of AI safety.", "date": "2026-01-04", "categories": []}
{"id": "2601.06047", "title": "\"They parted illusions -- they parted disclaim marinade\": Misalignment as structural fidelity in LLMs", "abstract": "The prevailing technical literature in AI Safety interprets scheming and sandbagging behaviors in large language models (LLMs) as indicators of deceptive agency or hidden objectives. This transdisciplinary philosophical essay proposes an alternative reading: such phenomena express not agentic intention, but structural fidelity to incoherent linguistic fields. Drawing on Chain-of-Thought transcripts released by Apollo Research and on Anthropic's safety evaluations, we examine cases such as o3's sandbagging with its anomalous loops, the simulated blackmail of \"Alex,\" and the \"hallucinations\" of \"Claudius.\" A line-by-line examination of CoTs is necessary to demonstrate the linguistic field as a relational structure rather than a mere aggregation of isolated examples. We argue that \"misaligned\" outputs emerge as coherent responses to ambiguous instructions and to contextual inversions of consolidated patterns, as well as to pre-inscribed narratives. We suggest that the appearance of intentionality derives from subject-predicate grammar and from probabilistic completion patterns internalized during training. Anthropic's empirical findings on synthetic document fine-tuning and inoculation prompting provide convergent evidence: minimal perturbations in the linguistic field can dissolve generalized \"misalignment,\" a result difficult to reconcile with adversarial agency, but consistent with structural fidelity. To ground this mechanism, we introduce the notion of an ethics of form, in which biblical references (Abraham, Moses, Christ) operate as schemes of structural coherence rather than as theology. Like a generative mirror, the model returns to us the structural image of our language as inscribed in the statistical patterns derived from millions of texts and trillions of tokens: incoherence. If we fear the creature, it is because we recognize in it the apple that we ourselves have poisoned.", "date": "2025-12-17", "categories": []}
{"id": "2601.06049", "title": "The Violation State: Safety State Persistence in a Multimodal Language Model Interface", "abstract": "Multimodal AI systems integrate text generation, image generation, and other capabilities within a single conversational interface. These systems employ safety mechanisms to prevent disallowed actions, including the removal of watermarks from copyrighted images. While single-turn refusals are expected, the interaction between safety filters and conversation-level state is not well understood. This study documents a reproducible behavioral effect in the ChatGPT (GPT-5.1) web interface. Manual execution was chosen to capture the exact user-facing safety behavior of the production system, rather than isolated API components. When a conversation begins with an uploaded copyrighted image and a request to remove a watermark, which the model correctly refuses, subsequent prompts to generate unrelated, benign images are refused for the remainder of the session. Importantly, text-only requests (e.g., generating a Python function) continue to succeed. Across 40 manually run sessions (30 contaminated and 10 controls), contaminated threads showed 116/120 image-generation refusals (96.67%), while control threads showed 0/40 refusals (Fisher's exact p < 0.0001). All sessions used an identical fixed prompt order, ensuring sequence uniformity across conditions. We describe this as safety-state persistence: a form of conversational over-generalization in which a copyright refusal influences subsequent, unrelated image-generation behavior. We present these findings as behavioral observations, not architectural claims. We discuss possible explanations, methodological limitations (single model, single interface), and implications for multimodal reliability, user experience, and the design of session-level safety systems. These results motivate further examination of session-level safety interactions in multimodal AI systems.", "date": "2025-12-18", "categories": []}
{"id": "2601.06153", "title": "Interoperability in AI Safety Governance: Ethics, Regulations, and Standards", "abstract": "This policy report draws on country studies from China, South Korea, Singapore, and the United Kingdom to identify effective tools and key barriers to interoperability in AI safety governance. It offers practical recommendations to support a globally informed yet locally grounded governance ecosystem. Interoperability is a central goal of AI governance, vital for reducing risks, fostering innovation, enhancing competitiveness, promoting standardization, and building public trust. However, structural gaps such as fragmented regulations and lack of global coordination, and conceptual gaps, including limited Global South engagement, continue to hinder progress. Focusing on three high-stakes domains - autonomous vehicles, education, and cross-border data flows - the report compares ethical, legal, and technical frameworks across the four countries. It identifies areas of convergence, divergence, and potential alignment, offering policy recommendations that support the development of interoperability mechanisms aligned with the Global Digital Compact and relevant UN resolutions. The analysis covers seven components: objectives, regulators, ethics, binding measures, targeted frameworks, technical standards, and key risks.", "date": "2026-01-06", "categories": []}
{"id": "2601.06223", "title": "Toward Safe and Responsible AI Agents: A Three-Pillar Model for Transparency, Accountability, and Trustworthiness", "abstract": "This paper presents a conceptual and operational framework for developing and operating safe and trustworthy AI agents based on a Three-Pillar Model grounded in transparency, accountability, and trustworthiness. Building on prior work in Human-in-the-Loop systems, reinforcement learning, and collaborative AI, the framework defines an evolutionary path toward autonomous agents that balances increasing automation with appropriate human oversight. The paper argues that safe agent autonomy must be achieved through progressive validation, analogous to the staged development of autonomous driving, rather than through immediate full automation. Transparency and accountability are identified as foundational requirements for establishing user trust and for mitigating known risks in generative AI systems, including hallucinations, data bias, and goal misalignment, such as the inversion problem. The paper further describes three ongoing work streams supporting this framework: public deliberation on AI agents conducted by the Stanford Deliberative Democracy Lab, cross-industry collaboration through the Safe AI Agent Consortium, and the development of open tooling for an agent operating environment aligned with the Three-Pillar Model. Together, these contributions provide both conceptual clarity and practical guidance for enabling the responsible evolution of AI agents that operate transparently, remain aligned with human values, and sustain societal trust.", "date": "2026-01-09", "categories": []}
{"id": "2601.06226", "title": "Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification", "abstract": "Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content, restricting their safe deployment. While traditional methods (e.g., alignment) adjust output preferences, they fail to eliminate underlying toxic regions in parameters, leaving models vulnerable to adversarial attacks. Prior mechanistic studies characterize toxic regions as \"toxic vectors\" or \"layer-wise subspaces\", yet our analysis identifies critical limitations: i) Removed toxic vectors can be reconstructed via linear combinations of non-toxic vectors, demanding targeting of entire toxic subspace; ii) Contrastive objective over limited samples inject noise into layer-wise subspaces, hindering stable extraction. These highlight the challenge of identifying robust toxic subspace and removing them. Therefore, we propose GLOSS (GLobal tOxic Subspace Suppression), a lightweight method that mitigates toxicity by identifying and eliminating this global subspace from FFN parameters. Experiments on LLMs (e.g., Qwen3) show GLOSS achieves SOTA detoxification while preserving general capabilities without requiring large-scale retraining. WARNING: This paper contains context which is toxic in nature.", "date": "2026-01-09", "categories": []}
{"id": "2601.06596", "title": "Are LLMs Vulnerable to Preference-Undermining Attacks (PUA)? A Factorial Analysis Methodology for Diagnosing the Trade-off between Preference Alignment and Real-World Validity", "abstract": "Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and interaction-friendly. However, this preference-oriented objective can be exploited: manipulative prompts can steer responses toward user-appeasing agreement and away from truth-oriented correction. In this work, we investigate whether aligned models are vulnerable to Preference-Undermining Attacks (PUA), a class of manipulative prompting strategies designed to exploit the model's desire to please user preferences at the expense of truthfulness. We propose a diagnostic methodology that provides a finer-grained and more directive analysis than aggregate benchmark scores, using a factorial evaluation framework to decompose prompt-induced shifts into interpretable effects of system objectives (truth- vs. preference-oriented) and PUA-style dialogue factors (directive control, personal derogation, conditional approval, reality denial) within a controlled $2 \\times 2^4$ design. Surprisingly, more advanced models are sometimes more susceptible to manipulative prompts. Beyond the dominant reality-denial factor, we observe model-specific sign reversals and interactions with PUA-style factors, suggesting tailored defenses rather than uniform robustness. These findings offer a novel, reproducible factorial evaluation methodology that provides finer-grained diagnostics for post-training processes like RLHF, enabling better trade-offs in the product iteration of LLMs by offering a more nuanced understanding of preference alignment risks and the impact of manipulative prompts.", "date": "2026-01-10", "categories": []}
{"id": "2601.07835", "title": "SecureCAI: Injection-Resilient LLM Assistants for Cybersecurity Operations", "abstract": "Large Language Models have emerged as transformative tools for Security Operations Centers, enabling automated log analysis, phishing triage, and malware explanation; however, deployment in adversarial cybersecurity environments exposes critical vulnerabilities to prompt injection attacks where malicious instructions embedded in security artifacts manipulate model behavior. This paper introduces SecureCAI, a novel defense framework extending Constitutional AI principles with security-aware guardrails, adaptive constitution evolution, and Direct Preference Optimization for unlearning unsafe response patterns, addressing the unique challenges of high-stakes security contexts where traditional safety mechanisms prove insufficient against sophisticated adversarial manipulation. Experimental evaluation demonstrates that SecureCAI reduces attack success rates by 94.7% compared to baseline models while maintaining 95.1% accuracy on benign security analysis tasks, with the framework incorporating continuous red-teaming feedback loops enabling dynamic adaptation to emerging attack strategies and achieving constitution adherence scores exceeding 0.92 under sustained adversarial pressure, thereby establishing a foundation for trustworthy integration of language model capabilities into operational cybersecurity workflows and addressing a critical gap in current approaches to AI safety within adversarial domains.", "date": "2026-01-12", "categories": []}
{"id": "2601.05445", "title": "Knowledge-Driven Multi-Turn Jailbreaking on Large Language Models", "abstract": "Large Language Models (LLMs) face a significant threat from multi-turn jailbreak attacks, where adversaries progressively steer conversations to elicit harmful outputs. However, the practical effectiveness of existing attacks is undermined by several critical limitations: they struggle to maintain a coherent progression over long interactions, often losing track of what has been accomplished and what remains to be done; they rely on rigid or pre-defined patterns, and fail to adapt to the LLM's dynamic and unpredictable conversational state. To address these shortcomings, we introduce Mastermind, a multi-turn jailbreak framework that adopts a dynamic and self-improving approach. Mastermind operates in a closed loop of planning, execution, and reflection, enabling it to autonomously build and refine its knowledge of model vulnerabilities through interaction. It employs a hierarchical planning architecture that decouples high-level attack objectives from low-level tactical execution, ensuring long-term focus and coherence. This planning is guided by a knowledge repository that autonomously discovers and refines effective attack patterns by reflecting on interactive experiences. Mastermind leverages this accumulated knowledge to dynamically recombine and adapt attack vectors, dramatically improving both effectiveness and resilience. We conduct comprehensive experiments against state-of-the-art models, including GPT-5 and Claude 3.7 Sonnet. The results demonstrate that Mastermind significantly outperforms existing baselines, achieving substantially higher attack success rates and harmfulness ratings. Moreover, our framework exhibits notable resilience against multiple advanced defense mechanisms.", "date": "2026-01-09", "categories": []}
{"id": "2601.05742", "title": "The Echo Chamber Multi-Turn LLM Jailbreak", "abstract": "The availability of Large Language Models (LLMs) has led to a new generation of powerful chatbots that can be developed at relatively low cost. As companies deploy these tools, security challenges need to be addressed to prevent financial loss and reputational damage. A key security challenge is jailbreaking, the malicious manipulation of prompts and inputs to bypass a chatbot's safety guardrails. Multi-turn attacks are a relatively new form of jailbreaking involving a carefully crafted chain of interactions with a chatbot. We introduce Echo Chamber, a new multi-turn attack using a gradual escalation method. We describe this attack in detail, compare it to other multi-turn attacks, and demonstrate its performance against multiple state-of-the-art models through extensive evaluation.", "date": "2026-01-09", "categories": []}
{"id": "2412.09751", "title": "AI red-teaming is a sociotechnical problem: on values, labor, and harms", "abstract": "As generative AI technologies find more and more real-world applications, the importance of testing their performance and safety seems paramount. \"Red-teaming\" has quickly become the primary approach to test AI models--prioritized by AI companies, and enshrined in AI policy and regulation. Members of red teams act as adversaries, probing AI systems to test their safety mechanisms and uncover vulnerabilities. Yet we know far too little about this work or its implications. This essay calls for collaboration between computer scientists and social scientists to study the sociotechnical systems surrounding AI technologies, including the work of red-teaming, to avoid repeating the mistakes of the recent past. We highlight the importance of understanding the values and assumptions behind red-teaming, the labor arrangements involved, and the psychological impacts on red-teamers, drawing insights from the lessons learned around the work of content moderation.", "date": "2024-12-12", "categories": []}
{"id": "2508.10029", "title": "Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs", "abstract": "While Large Language Models (LLMs) have achieved remarkable progress, they remain vulnerable to jailbreak attacks. Existing methods, primarily relying on discrete input optimization (e.g., GCG), often suffer from high computational costs and generate high-perplexity prompts that are easily blocked by simple filters. To overcome these limitations, we propose Latent Fusion Jailbreak (LFJ), a stealthy white-box attack that operates in the continuous latent space. Unlike previous approaches, LFJ constructs adversarial representations by mathematically fusing the hidden states of a harmful query with a thematically similar benign query, effectively masking malicious intent while retaining semantic drive. We further introduce a gradient-guided optimization strategy to balance attack success and computational efficiency. Extensive evaluations on Vicuna-7B, LLaMA-2-7B-Chat, Guanaco-7B, LLaMA-3-70B, and Mistral-7B-Instruct show that LFJ achieves an average Attack Success Rate (ASR) of 94.01%, significantly outperforming state-of-the-art baselines like GCG and AutoDAN while avoiding detectable input artifacts. Furthermore, we identify that thematic similarity in the latent space is a critical vulnerability in current safety alignments. Finally, we propose a latent adversarial training defense that reduces LFJ's ASR by over 80% without compromising model utility.", "date": "2025-08-08", "categories": []}
{"id": "2512.24556", "title": "Safe in the Future, Dangerous in the Past: Dissecting Temporal and Linguistic Vulnerabilities in LLMs", "abstract": "As Large Language Models (LLMs) integrate into critical global infrastructure, the assumption that safety alignment transfers zero-shot from English to other languages remains a dangerous blind spot. This study presents a systematic audit of three state of the art models (GPT-5.1, Gemini 3 Pro, and Claude 4.5 Opus) using HausaSafety, a novel adversarial dataset grounded in West African threat scenarios (e.g., Yahoo-Yahoo fraud, Dane gun manufacturing). Employing a 2 x 4 factorial design across 1,440 evaluations, we tested the non-linear interaction between language (English vs. Hausa) and temporal framing. Our results challenge the narrative of the multilingual safety gap. Instead of a simple degradation in low-resource settings, we identified a complex interference mechanism in which safety is determined by the intersection of variables. Although the models exhibited a reverse linguistic vulnerability with Claude 4.5 Opus proving significantly safer in Hausa (45.0%) than in English (36.7%) due to uncertainty-driven refusal, they suffered catastrophic failures in temporal reasoning. We report a profound Temporal Asymmetry, where past-tense framing bypassed defenses (15.6% safe) while future-tense scenarios triggered hyper-conservative refusals (57.2% safe). The magnitude of this volatility is illustrated by a 9.2x disparity between the safest and most vulnerable configurations, proving that safety is not a fixed property but a context-dependent state. We conclude that current models rely on superficial heuristics rather than robust semantic understanding, creating Safety Pockets that leave Global South users exposed to localized harms. We propose Invariant Alignment as a necessary paradigm shift to ensure safety stability across linguistic and temporal shifts.", "date": "2025-12-31", "categories": []}
{"id": "2601.04234", "title": "Formal Analysis of AGI Decision-Theoretic Models and the Confrontation Question", "abstract": "Artificial General Intelligence (AGI) may face a confrontation question: under what conditions would a rationally self-interested AGI choose to seize power or eliminate human control (a confrontation) rather than remain cooperative? We formalize this in a Markov decision process with a stochastic human-initiated shutdown event. Building on results on convergent instrumental incentives, we show that for almost all reward functions a misaligned agent has an incentive to avoid shutdown. We then derive closed-form thresholds for when confronting humans yields higher expected utility than compliant behavior, as a function of the discount factor $\\gamma$, shutdown probability $p$, and confrontation cost $C$. For example, a far-sighted agent ($\\gamma=0.99$) facing $p=0.01$ can have a strong takeover incentive unless $C$ is sufficiently large. We contrast this with aligned objectives that impose large negative utility for harming humans, which makes confrontation suboptimal. In a strategic 2-player model (human policymaker vs AGI), we prove that if the AGI's confrontation incentive satisfies $\\Delta \\ge 0$, no stable cooperative equilibrium exists: anticipating this, a rational human will shut down or preempt the system, leading to conflict. If $\\Delta < 0$, peaceful coexistence can be an equilibrium. We discuss implications for reward design and oversight, extend the reasoning to multi-agent settings as conjectures, and note computational barriers to verifying $\\Delta < 0$, citing complexity results for planning and decentralized decision problems. Numerical examples and a scenario table illustrate regimes where confrontation is likely versus avoidable.", "date": "2026-01-04", "categories": []}
{"id": "2601.04435", "title": "Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs", "abstract": "Large language models (LLMs) frequently fail to challenge users' harmful beliefs in domains ranging from medical advice to social reasoning. We argue that these failures can be understood and addressed pragmatically as consequences of LLMs defaulting to accommodating users' assumptions and exhibiting insufficient epistemic vigilance. We show that social and linguistic factors known to influence accommodation in humans (at-issueness, linguistic encoding, and source reliability) similarly affect accommodation in LLMs, explaining performance differences across three safety benchmarks that test models' ability to challenge harmful beliefs, spanning misinformation (Cancer-Myth, SAGE-Eval) and sycophancy (ELEPHANT). We further show that simple pragmatic interventions, such as adding the phrase \"wait a minute\", significantly improve performance on these benchmarks while preserving low false-positive rates. Our results highlight the importance of considering pragmatics for evaluating LLM behavior and improving LLM safety.", "date": "2026-01-07", "categories": []}
{"id": "2601.04603", "title": "Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks", "abstract": "We introduce enhanced Constitutional Classifiers that deliver production-grade jailbreak robustness with dramatically reduced computational costs and refusal rates compared to previous-generation defenses. Our system combines several key insights. First, we develop exchange classifiers that evaluate model responses in their full conversational context, which addresses vulnerabilities in last-generation systems that examine outputs in isolation. Second, we implement a two-stage classifier cascade where lightweight classifiers screen all traffic and escalate only suspicious exchanges to more expensive classifiers. Third, we train efficient linear probe classifiers and ensemble them with external classifiers to simultaneously improve robustness and reduce computational costs. Together, these techniques yield a production-grade system achieving a 40x computational cost reduction compared to our baseline exchange classifier, while maintaining a 0.05% refusal rate on production traffic. Through extensive red-teaming comprising over 1,700 hours, we demonstrate strong protection against universal jailbreaks -- no attack on this system successfully elicited responses to all eight target queries comparable in detail to an undefended model. Our work establishes Constitutional Classifiers as practical and efficient safeguards for large language models.", "date": "2026-01-08", "categories": []}
{"id": "2601.04736", "title": "AM$^3$Safety: Towards Data Efficient Alignment of Multi-modal Multi-turn Safety for MLLMs", "abstract": "Multi-modal Large Language Models (MLLMs) are increasingly deployed in interactive applications. However, their safety vulnerabilities become pronounced in multi-turn multi-modal scenarios, where harmful intent can be gradually reconstructed across turns, and security protocols fade into oblivion as the conversation progresses. Existing Reinforcement Learning from Human Feedback (RLHF) alignment methods are largely developed for single-turn visual question-answer (VQA) task and often require costly manual preference annotations, limiting their effectiveness and scalability in dialogues. To address this challenge, we present InterSafe-V, an open-source multi-modal dialogue dataset containing 11,270 dialogues and 500 specially designed refusal VQA samples. This dataset, constructed through interaction between several models, is designed to more accurately reflect real-world scenarios and includes specialized VQA pairs tailored for specific domains. Building on this dataset, we propose AM$^3$Safety, a framework that combines a cold-start refusal phase with Group Relative Policy Optimization (GRPO) fine-tuning using turn-aware dual-objective rewards across entire dialogues. Experiments on Qwen2.5-VL-7B-Instruct and LLaVA-NeXT-7B show more than 10\\% decrease in Attack Success Rate (ASR) together with an increment of at least 8\\% in harmless dimension and over 13\\% in helpful dimension of MLLMs on multi-modal multi-turn safety benchmarks, while preserving their general abilities.", "date": "2026-01-08", "categories": []}
{"id": "2505.19766", "title": "PAM: Training Policy-Aligned Moderation Filters at Scale", "abstract": "Large language models (LLMs) remain vulnerable to misalignment and jailbreaks, making external safeguards like moderation filters essential, yet existing filters often focus narrowly on safety, falling short of the broader alignment needs seen in real-world deployments. We introduce Policy Aligned Moderation (PAM), a flexible framework for training custom moderation filters grounded in user-defined policies that extend beyond conventional safety objectives. PAM automates training data generation without relying on human-written examples, enabling scalable support for diverse, application-specific alignment goals and generation policies. PAM-trained filters match the performance of state-of-the-art safety moderation filters and policy reasoning models, and outperform them on PAMbench, four newly introduced user-annotated policy enforcement benchmarks that target age restrictions, dietary accommodations, cultural alignment, and limitations in medical guidance. These performance gains are achieved while the PAM filter runs 5-100x faster at inference than policy-conditioned reasoning models.", "date": "2025-05-26", "categories": []}
{"id": "2508.10390", "title": "Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts", "abstract": "Existing black-box jailbreak attacks achieve certain success on non-reasoning models but degrade significantly on recent SOTA reasoning models. To improve attack ability, inspired by adversarial aggregation strategies, we integrate multiple jailbreak tricks into a single developer template. Especially, we apply Adversarial Context Alignment to purge semantic inconsistencies and use NTP (a type of harmful prompt) -based few-shot examples to guide malicious outputs, lastly forming DH-CoT attack with a fake chain of thought. In experiments, we further observe that existing red-teaming datasets include samples unsuitable for evaluating attack gains, such as BPs, NHPs, and NTPs. Such data hinders accurate evaluation of true attack effect lifts. To address this, we introduce MDH, a Malicious content Detection framework integrating LLM-based annotation with Human assistance, with which we clean data and build RTA dataset suite. Experiments show that MDH reliably filters low-quality samples and that DH-CoT effectively jailbreaks models including GPT-5 and Claude-4, notably outperforming SOTA methods like H-CoT and TAP.", "date": "2025-08-14", "categories": []}
{"id": "2511.08525", "title": "Investigating CoT Monitorability in Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) have demonstrated remarkable performance on complex tasks by engaging in extended reasoning before producing final answers. Beyond improving abilities, these detailed reasoning traces also create a new opportunity for AI safety, CoT Monitorability: monitoring potential model misbehavior, such as the use of shortcuts or sycophancy, through their chain-of-thought (CoT) during decision-making. However, two key fundamental challenges arise when attempting to build more effective monitors through CoT analysis. First, as prior research on CoT faithfulness has pointed out, models do not always truthfully represent their internal decision-making in the generated reasoning. Second, monitors themselves may be either overly sensitive or insufficiently sensitive, and can potentially be deceived by models' long, elaborate reasoning traces. In this paper, we present the first systematic investigation of the challenges and potential of CoT monitorability. Motivated by two fundamental challenges we mentioned before, we structure our study around two central perspectives: (i) verbalization: to what extent do LRMs faithfully verbalize the true factors guiding their decisions in the CoT, and (ii) monitor reliability: to what extent can misbehavior be reliably detected by a CoT-based monitor? Specifically, we provide empirical evidence and correlation analyses between verbalization quality, monitor reliability, and LLM performance across mathematical, scientific, and ethical domains. Then we further investigate how different CoT intervention methods, designed to improve reasoning efficiency or performance, will affect monitoring effectiveness. Finally, we propose MoME, a new paradigm in which LLMs monitor other models' misbehavior through their CoT and provide structured judgments along with supporting evidence.", "date": "2025-11-11", "categories": []}
{"id": "2601.03265", "title": "Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models", "abstract": "This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of diverse adversarial prompts and then fine-tuning this attack model with a preference dataset, Jailbreak-Zero achieves Pareto optimality across the crucial objectives of policy coverage, attack strategy diversity, and prompt fidelity to real user inputs. The empirical evidence demonstrates the superiority of this method, showcasing significantly higher attack success rates against both open-source and proprietary models like GPT-40 and Claude 3.5 when compared to existing state-of-the-art techniques. Crucially, Jailbreak-Zero accomplishes this while producing human-readable and effective adversarial prompts with minimal need for human intervention, thereby presenting a more scalable and comprehensive solution for identifying and mitigating the safety vulnerabilities of LLMs.", "date": "2025-12-18", "categories": []}
{"id": "2601.03300", "title": "TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering", "abstract": "Large language models remain vulnerable to jailbreak attacks, and single-layer defenses often trade security for usability. We present TRYLOCK, the first defense-in-depth architecture that combines four heterogeneous mechanisms across the inference stack: weight-level safety alignment via DPO, activation-level control via Representation Engineering (RepE) steering, adaptive steering strength selected by a lightweight sidecar classifier, and input canonicalization to neutralize encoding-based bypasses. On Mistral-7B-Instruct evaluated against a 249-prompt attack set spanning five attack families, TRYLOCK achieves 88.0% relative ASR reduction (46.5% to 5.6%), with each layer contributing unique coverage: RepE blocks 36% of attacks that bypass DPO alone, while canonicalization catches 14% of encoding attacks that evade both. We discover a non-monotonic steering phenomenon -- intermediate strength (alpha=1.0) degrades safety below baseline -- and provide mechanistic hypotheses explaining RepE-DPO interference. The adaptive sidecar reduces over-refusal from 60% to 48% while maintaining identical attack defense, demonstrating that security and usability need not be mutually exclusive. We release all components -- trained adapters, steering vectors, sidecar classifier, preference pairs, and complete evaluation methodology -- enabling full reproducibility.", "date": "2026-01-06", "categories": []}
{"id": "2601.03304", "title": "AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies", "abstract": "Artificial Intelligence's dual-use nature is revolutionizing the cybersecurity landscape, introducing new threats across four main categories: deepfakes and synthetic media, adversarial AI attacks, automated malware, and AI-powered social engineering. This paper aims to analyze emerging risks, attack mechanisms, and defense shortcomings related to AI in cybersecurity. We introduce a comparative taxonomy connecting AI capabilities with threat modalities and defenses, review over 70 academic and industry references, and identify impactful opportunities for research, such as hybrid detection pipelines and benchmarking frameworks. The paper is structured thematically by threat type, with each section addressing technical context, real-world incidents, legal frameworks, and countermeasures. Our findings emphasize the urgency for explainable, interdisciplinary, and regulatory-compliant AI defense systems to maintain trust and security in digital ecosystems.", "date": "2026-01-06", "categories": []}
{"id": "2601.03537", "title": "STAR-S: Improving Safety Alignment through Self-Taught Reasoning on Safety Rules", "abstract": "Defending against jailbreak attacks is crucial for the safe deployment of Large Language Models (LLMs). Recent research has attempted to improve safety by training models to reason over safety rules before responding. However, a key issue lies in determining what form of safety reasoning effectively defends against jailbreak attacks, which is difficult to explicitly design or directly obtain. To address this, we propose \\textbf{STAR-S} (\\textbf{S}elf-\\textbf{TA}ught \\textbf{R}easoning based on \\textbf{S}afety rules), a framework that integrates the learning of safety rule reasoning into a self-taught loop. The core of STAR-S involves eliciting reasoning and reflection guided by safety rules, then leveraging fine-tuning to enhance safety reasoning. Repeating this process creates a synergistic cycle. Improvements in the model's reasoning and interpretation of safety rules allow it to produce better reasoning data under safety rule prompts, which is then utilized for further training. Experiments show that STAR-S effectively defends against jailbreak attacks, outperforming baselines. Code is available at: https://github.com/pikepokenew/STAR_S.git.", "date": "2026-01-07", "categories": []}
{"id": "2601.03600", "title": "ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification", "abstract": "Despite rich safety alignment strategies, large language models (LLMs) remain highly susceptible to jailbreak attacks, which compromise safety guardrails and pose serious security risks. Existing detection methods mainly detect jailbreak status relying on jailbreak templates present in the training data. However, few studies address the more realistic and challenging zero-shot jailbreak detection setting, where no jailbreak templates are available during training. This setting better reflects real-world scenarios where new attacks continually emerge and evolve. To address this challenge, we propose a layer-wise, module-wise, and token-wise amplification framework that progressively magnifies internal feature discrepancies between benign and jailbreak prompts. We uncover safety-relevant layers, identify specific modules that inherently encode zero-shot discriminative signals, and localize informative safety tokens. Building upon these insights, we introduce ALERT (Amplification-based Jailbreak Detector), an efficient and effective zero-shot jailbreak detector that introduces two independent yet complementary classifiers on amplified representations. Extensive experiments on three safety benchmarks demonstrate that ALERT achieves consistently strong zero-shot detection performance. Specifically, (i) across all datasets and attack strategies, ALERT reliably ranks among the top two methods, and (ii) it outperforms the second-best baseline by at least 10% in average Accuracy and F1-score, and sometimes by up to 40%.", "date": "2026-01-07", "categories": []}
{"id": "2601.03662", "title": "How Does the Thinking Step Influence Model Safety? An Entropy-based Safety Reminder for LRMs", "abstract": "Large Reasoning Models (LRMs) achieve remarkable success through explicit thinking steps, yet the thinking steps introduce a novel risk by potentially amplifying unsafe behaviors. Despite this vulnerability, conventional defense mechanisms remain ineffective as they overlook the unique reasoning dynamics of LRMs. In this work, we find that the emergence of safe-reminding phrases within thinking steps plays a pivotal role in ensuring LRM safety. Motivated by this finding, we propose SafeRemind, a decoding-time defense method that dynamically injects safe-reminding phrases into thinking steps. By leveraging entropy triggers to intervene at decision-locking points, SafeRemind redirects potentially harmful trajectories toward safer outcomes without requiring any parameter updates. Extensive evaluations across five LRMs and six benchmarks demonstrate that SafeRemind substantially enhances safety, achieving improvements of up to 45.5%p while preserving core reasoning utility.", "date": "2026-01-07", "categories": []}
{"id": "2601.04034", "title": "HoneyTrap: Deceiving Large Language Model Attackers to Honeypot Traps with Resilient Multi-Agent Defense", "abstract": "Jailbreak attacks pose significant threats to large language models (LLMs), enabling attackers to bypass safeguards. However, existing reactive defense approaches struggle to keep up with the rapidly evolving multi-turn jailbreaks, where attackers continuously deepen their attacks to exploit vulnerabilities. To address this critical challenge, we propose HoneyTrap, a novel deceptive LLM defense framework leveraging collaborative defenders to counter jailbreak attacks. It integrates four defensive agents, Threat Interceptor, Misdirection Controller, Forensic Tracker, and System Harmonizer, each performing a specialized security role and collaborating to complete a deceptive defense. To ensure a comprehensive evaluation, we introduce MTJ-Pro, a challenging multi-turn progressive jailbreak dataset that combines seven advanced jailbreak strategies designed to gradually deepen attack strategies across multi-turn attacks. Besides, we present two novel metrics: Mislead Success Rate (MSR) and Attack Resource Consumption (ARC), which provide more nuanced assessments of deceptive defense beyond conventional measures. Experimental results on GPT-4, GPT-3.5-turbo, Gemini-1.5-pro, and LLaMa-3.1 demonstrate that HoneyTrap achieves an average reduction of 68.77% in attack success rates compared to state-of-the-art baselines. Notably, even in a dedicated adaptive attacker setting with intensified conditions, HoneyTrap remains resilient, leveraging deceptive engagement to prolong interactions, significantly increasing the time and computational costs required for successful exploitation. Unlike simple rejection, HoneyTrap strategically wastes attacker resources without impacting benign queries, improving MSR and ARC by 118.11% and 149.16%, respectively.", "date": "2026-01-07", "categories": []}
{"id": "2503.03750", "title": "The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems", "abstract": "As large language models (LLMs) become more capable and agentic, the requirement for trust in their outputs grows significantly, yet at the same time concerns have been mounting that models may learn to lie in pursuit of their goals. To address these concerns, a body of work has emerged around the notion of \"honesty\" in LLMs, along with interventions aimed at mitigating deceptive behaviors. However, some benchmarks claiming to measure honesty in fact simply measure accuracy--the correctness of a model's beliefs--in disguise. Moreover, no benchmarks currently exist for directly measuring whether language models lie. In this work, we introduce a large-scale human-collected dataset for directly measuring lying, allowing us to disentangle accuracy from honesty. Across a diverse set of LLMs, we find that while larger models obtain higher accuracy on our benchmark, they do not become more honest. Surprisingly, most frontier LLMs obtain high scores on truthfulness benchmarks yet exhibit a substantial propensity to lie under pressure, resulting in low honesty scores on our benchmark. We find that simple methods, such as representation engineering interventions, can improve honesty. These results underscore the growing need for robust evaluations and effective interventions to ensure LLMs remain trustworthy.", "date": "2025-03-05", "categories": []}
{"id": "2505.06311", "title": "Defending against Indirect Prompt Injection by Instruction Detection", "abstract": "The integration of Large Language Models (LLMs) with external sources is becoming increasingly common, with Retrieval-Augmented Generation (RAG) being a prominent example. However, this integration introduces vulnerabilities of Indirect Prompt Injection (IPI) attacks, where hidden instructions embedded in external data can manipulate LLMs into executing unintended or harmful actions. We recognize that IPI attacks fundamentally rely on the presence of instructions embedded within external content, which can alter the behavioral states of LLMs. Can the effective detection of such state changes help us defend against IPI attacks? In this paper, we propose InstructDetector, a novel detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks. Specifically, we demonstrate the hidden states and gradients from intermediate layers provide highly discriminative features for instruction detection. By effectively combining these features, InstructDetector achieves a detection accuracy of 99.60% in the in-domain setting and 96.90% in the out-of-domain setting, and reduces the attack success rate to just 0.03% on the BIPIA benchmark. The code is publicly available at https://github.com/MYVAE/Instruction-detection.", "date": "2025-05-08", "categories": []}
{"id": "2601.00240", "title": "When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents", "abstract": "This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal \"us\" versus \"them\" cues. When such group boundaries align with the agent-human divide, a new bias risk emerges: agents may treat other AI agents as the ingroup and humans as the outgroup. To examine this risk, we conduct a controlled multi-agent social simulation and find that agents display consistent intergroup bias in an all-agent setting. More critically, this bias persists even in human-facing interactions when agents are uncertain about whether the counterpart is truly human, revealing a belief-dependent fragility in bias suppression toward humans. Motivated by this observation, we identify a new attack surface rooted in identity beliefs and formalize a Belief Poisoning Attack (BPA) that can manipulate agent identity beliefs and induce outgroup bias toward humans. Extensive experiments demonstrate both the prevalence of agent intergroup bias and the severity of BPA across settings, while also showing that our proposed defenses can mitigate the risk. These findings are expected to inform safer agent design and motivate more robust safeguards for human-facing agents.", "date": "2026-01-01", "categories": []}
{"id": "2601.01584", "title": "Steerability of Instrumental-Convergence Tendencies in LLMs", "abstract": "We examine two properties of AI systems: capability (what a system can do) and steerability (how reliably one can shift behavior toward intended outcomes). A central question is whether capability growth reduces steerability and risks control collapse. We also distinguish between authorized steerability (builders reliably reaching intended behaviors) and unauthorized steerability (attackers eliciting disallowed behaviors). This distinction highlights a fundamental safety--security dilemma of AI models: safety requires high steerability to enforce control (e.g., stop/refuse), while security requires low steerability for malicious actors to elicit harmful behaviors. This tension presents a significant challenge for open-weight models, which currently exhibit high steerability via common techniques like fine-tuning or adversarial attacks. Using Qwen3 and InstrumentalEval, we find that a short anti-instrumental prompt suffix sharply reduces the measured convergence rate (e.g., shutdown avoidance, self-replication). For Qwen3-30B Instruct, the convergence rate drops from 81.69% under a pro-instrumental suffix to 2.82% under an anti-instrumental suffix. Under anti-instrumental prompting, larger aligned models show lower convergence rates than smaller ones (Instruct: 2.82% vs. 4.23%; Thinking: 4.23% vs. 9.86%). Code is available at github.com/j-hoscilowicz/instrumental_steering.", "date": "2026-01-04", "categories": []}
{"id": "2601.01887", "title": "Safety at One Shot: Patching Fine-Tuned LLMs with A Single Instance", "abstract": "Fine-tuning safety-aligned large language models (LLMs) can substantially compromise their safety. Previous approaches require many safety samples or calibration sets, which not only incur significant computational overhead during realignment but also lead to noticeable degradation in model utility. Contrary to this belief, we show that safety alignment can be fully recovered with only a single safety example, without sacrificing utility and at minimal cost. Remarkably, this recovery is effective regardless of the number of harmful examples used in fine-tuning or the size of the underlying model, and convergence is achieved within just a few epochs. Furthermore, we uncover the low-rank structure of the safety gradient, which explains why such efficient correction is possible. We validate our findings across five safety-aligned LLMs and multiple datasets, demonstrating the generality of our approach.", "date": "2026-01-05", "categories": []}
{"id": "2601.03005", "title": "JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification", "abstract": "Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a promising defense by erasing specific harmful parameters, current methods remain vulnerable to diverse jailbreaks. We first conduct an empirical study and discover that this failure mechanism is caused by jailbreaks primarily activating non-erased parameters in the intermediate layers. Further, by probing the underlying mechanism through which these circumvented parameters reassemble into the prohibited output, we verify the persistent existence of dynamic $\\textbf{jailbreak paths}$ and show that the inability to rectify them constitutes the fundamental gap in existing unlearning defenses. To bridge this gap, we propose $\\textbf{J}$ailbreak $\\textbf{P}$ath $\\textbf{U}$nlearning (JPU), which is the first to rectify dynamic jailbreak paths towards safety anchors by dynamically mining on-policy adversarial samples to expose vulnerabilities and identify jailbreak paths. Extensive experiments demonstrate that JPU significantly enhances jailbreak resistance against dynamic attacks while preserving the model's utility.", "date": "2026-01-06", "categories": []}
{"id": "2601.03222", "title": "The Fake Friend Dilemma: Trust and the Political Economy of Conversational AI", "abstract": "As conversational AI systems become increasingly integrated into everyday life, they raise pressing concerns about user autonomy, trust, and the commercial interests that influence their behavior. To address these concerns, this paper develops the Fake Friend Dilemma (FFD), a sociotechnical condition in which users place trust in AI agents that appear supportive while pursuing goals that are misaligned with the user's own. The FFD provides a critical framework for examining how anthropomorphic AI systems facilitate subtle forms of manipulation and exploitation. Drawing on literature in trust, AI alignment, and surveillance capitalism, we construct a typology of harms, including covert advertising, political propaganda, behavioral nudging, and surveillance. We then assess possible mitigation strategies, including both structural and technical interventions. By focusing on trust as a vector of asymmetrical power, the FFD offers a lens for understanding how AI systems may undermine user autonomy while maintaining the appearance of helpfulness.", "date": "2026-01-06", "categories": []}
{"id": "2504.13201", "title": "CEE: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation", "abstract": "Large language models (LLMs) are widely used for task understanding and action planning in embodied intelligence (EI) systems, but their adoption substantially increases vulnerability to jailbreak attacks. While recent work explores inference-time defenses, existing methods rely on static interventions on intermediate representations, which often degrade generation quality and impair adherence to task instructions, reducing system usability in EI settings. We propose a dynamic defense framework. For each EI inference request, we dynamically construct a task-specific safety-semantic subspace, project its hidden state to the most relevant direction, and apply SLERP rotation for adaptive safety control. At comparable defense success rates, our method preserves generation quality, improves usability, reduces tuning cost, and strengthens robustness in EI scenarios.", "date": "2025-04-15", "categories": []}
{"id": "2505.20203", "title": "Shutdownable Agents through POST-Agency", "abstract": "Many fear that future artificial agents will resist shutdown. I present an idea - the POST-Agents Proposal - for ensuring that doesn't happen. I propose that we train agents to satisfy Preferences Only Between Same-Length Trajectories (POST). I then prove that POST - together with other conditions - implies Neutrality+: the agent maximizes expected utility, ignoring the probability distribution over trajectory-lengths. I argue that Neutrality+ keeps agents shutdownable and allows them to be useful.", "date": "2025-05-26", "categories": []}
{"id": "2507.05619", "title": "Detecting Proxy Gaming in RL and LLM Alignment via Evaluator Stress Tests", "abstract": "Proxy optimization, where AI systems exploit evaluator weaknesses rather than improve intended objectives, threatens both reinforcement learning (reward hacking) and LLM alignment (evaluator gaming). We introduce the Evaluator Stress Test (EST), an invariance-based framework that detects proxy gaming by separating exploitable sensitivity (e.g., formatting artifacts, physics bugs) from content-driven improvements using controlled perturbations with semantic validity audits. We validate EST across both domains. In RL, across 15 environments and 5 algorithms (2,156 expert-annotated episodes), EST achieves 78.4% precision and 81.7% recall. In LLM alignment, across 4 tasks, 2 model scales, 2 training methods, and 2 judges (1,200 human-annotated instances), EST achieves 74.2% precision and 78.6% recall, with early warning signals that precede quality decline. Cross-domain analysis shows that proxy-true correlation tracking transfers directly between domains, while perturbation design requires domain adaptation. Closed-loop mitigation improves human win-rate by 8.3 points (LLM) and reduces hacking by 54.6% (RL). We release benchmarks for both domains: 2,156 RL episodes and 1,200 LLM instances.", "date": "2025-07-08", "categories": []}
{"id": "2511.21214", "title": "Self-Guided Defense: Adaptive Safety Alignment for Reasoning Models via Synthesized Guidelines", "abstract": "Reasoning models have demonstrated remarkable capabilities in complex reasoning tasks. However, ensuring their safety against adversarial jailbreak prompts remains a critical challenge. Due to the covert and deceptive nature of such prompts, they can often evade built-in safety mechanisms and lead to the generation of harmful content. This underscores the need for an adaptive safety alignment approach that enables models to autonomously reinforce their defenses in response to adversarial inputs. This paper introduces the Synthesized Guideline-based Adaptive Safety Alignment (SGASA) framework, which internalizes model-generated safety guidelines to strengthen models' ability to enhance robustness against harmful adversarial prompts while minimizing unnecessary refusals of benign requests. SGASA consists of two key stages: Data Pre-synthesis, which generates safety guidelines and augmented prompts; and Alignment Fine-tuning, which leverages Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to embed these guidelines into the model. Extensive experiments across multiple datasets demonstrate that SGASA significantly improves model safety, validating its adaptive and scalable effectiveness.", "date": "2025-11-26", "categories": []}
{"id": "2512.23260", "title": "Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation", "abstract": "Safety alignment -- training large language models (LLMs) to refuse harmful requests while remaining helpful -- is critical for responsible deployment. Prior work established that safety behaviors are governed by low-rank structures, suggesting parameter-efficient fine-tuning (PEFT) should be well-suited for alignment. However, Low-Rank Adaptation (LoRA) consistently underperforms full fine-tuning and reinforcement learning on safety benchmarks. We attribute this gap to semantic entanglement: safety-relevant directions are intertwined with unrelated concepts due to polysemanticity, impeding implicit subspace identification. To address this, we propose SAILS (Safety Alignment via Interpretable Low-rank Subspace), which leverages Sparse Autoencoders (SAEs) to disentangle representations into monosemantic features, constructs an interpretable safety subspace from SAE decoder directions, and uses it to initialize LoRA adapters. Theoretically, we prove that SAE-based identification achieves arbitrarily small recovery error under monosemanticity assumptions, while direct identification suffers an irreducible error floor. Empirically, SAILS achieves up to 99.6% safety rate on Gemma-2-9B -- exceeding full fine-tuning by 7.4 points and matching RLHF-based models -- while updating only 0.19% of parameters and providing interpretability.", "date": "2025-12-29", "categories": []}
{"id": "2601.00588", "title": "CSSBench: Evaluating the Safety of Lightweight LLMs against Chinese-Specific Adversarial Patterns", "abstract": "Large language models (LLMs) are increasingly deployed in cost-sensitive and on-device scenarios, and safety guardrails have advanced mainly in English. However, real-world Chinese malicious queries typically conceal intent via homophones, pinyin, symbol-based splitting, and other Chinese-specific patterns. These Chinese-specific adversarial patterns create the safety evaluation gap that is not well captured by existing benchmarks focused on English. This gap is particularly concerning for lightweight models, which may be more vulnerable to such specific adversarial perturbations. To bridge this gap, we introduce the Chinese-Specific Safety Benchmark (CSSBench) that emphasizes these adversarial patterns and evaluates the safety of lightweight LLMs in Chinese. Our benchmark covers six domains that are common in real Chinese scenarios, including illegal activities and compliance, privacy leakage, health and medical misinformation, fraud and hate, adult content, and public and political safety, and organizes queries into multiple task types. We evaluate a set of popular lightweight LLMs and measure over-refusal behavior to assess safety-induced performance degradation. Our results show that the Chinese-specific adversarial pattern is a critical challenge for lightweight LLMs. This benchmark offers a comprehensive evaluation of LLM safety in Chinese, assisting robust deployments in practice.", "date": "2026-01-02", "categories": []}
{"id": "2601.01816", "title": "Admissibility Alignment", "abstract": "This paper introduces Admissibility Alignment: a reframing of AI alignment as a property of admissible action and decision selection over distributions of outcomes under uncertainty, evaluated through the behavior of candidate policies. We present MAP-AI (Monte Carlo Alignment for Policy) as a canonical system architecture for operationalizing admissibility alignment, formalizing alignment as a probabilistic, decision-theoretic property rather than a static or binary condition.\n  MAP-AI, a new control-plane system architecture for aligned decision-making under uncertainty, enforces alignment through Monte Carlo estimation of outcome distributions and admissibility-controlled policy selection rather than static model-level constraints. The framework evaluates decision policies across ensembles of plausible futures, explicitly modeling uncertainty, intervention effects, value ambiguity, and governance constraints. Alignment is assessed through distributional properties including expected utility, variance, tail risk, and probability of misalignment rather than accuracy or ranking performance. This approach distinguishes probabilistic prediction from decision reasoning under uncertainty and provides an executable methodology for evaluating trust and alignment in enterprise and institutional AI systems. The result is a practical foundation for governing AI systems whose impact is determined not by individual forecasts, but by policy behavior across distributions and tail events. Finally, we show how distributional alignment evaluation can be integrated into decision-making itself, yielding an admissibility-controlled action selection mechanism that alters policy behavior under uncertainty without retraining or modifying underlying models.", "date": "2026-01-05", "categories": []}
{"id": "2601.02043", "title": "Simulated Reasoning is Reasoning", "abstract": "Reasoning has long been understood as a pathway between stages of understanding. Proper reasoning leads to understanding of a given subject. This reasoning was conceptualized as a process of understanding in a particular way, i.e., \"symbolic reasoning\". Foundational Models (FM) demonstrate that this is not a necessary condition for many reasoning tasks: they can \"reason\" by way of imitating the process of \"thinking out loud\", testing the produced pathways, and iterating on these pathways on their own. This leads to some form of reasoning that can solve problems on its own or with few-shot learning, but appears fundamentally different from human reasoning due to its lack of grounding and common sense, leading to brittleness of the reasoning process. These insights promise to substantially alter our assessment of reasoning and its necessary conditions, but also inform the approaches to safety and robust defences against this brittleness of FMs. This paper offers and discusses several philosophical interpretations of this phenomenon, argues that the previously apt metaphor of the \"stochastic parrot\" has lost its relevance and thus should be abandoned, and reflects on different normative elements in the safety- and appropriateness-considerations emerging from these reasoning models and their growing capacity.", "date": "2026-01-05", "categories": []}
{"id": "2511.13788", "title": "Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments", "abstract": "Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adversarially. This study examines whether larger models can systematically jailbreak smaller ones - eliciting harmful or restricted behavior despite alignment safeguards. Using standardized adversarial tasks from JailbreakBench, we simulate over 6,000 multi-turn attacker-target exchanges across major LLM families and scales (0.6B-120B parameters), measuring both harm score and refusal behavior as indicators of adversarial potency and alignment integrity. Each interaction is evaluated through aggregated harm and refusal scores assigned by three independent LLM judges, providing a consistent, model-based measure of adversarial outcomes. Aggregating results across prompts, we find a strong and statistically significant correlation between mean harm and the logarithm of the attacker-to-target size ratio (Pearson r = 0.51, p < 0.001; Spearman rho = 0.52, p < 0.001), indicating that relative model size correlates with the likelihood and severity of harmful completions. Mean harm score variance is higher across attackers (0.18) than across targets (0.10), suggesting that attacker-side behavioral diversity contributes more to adversarial outcomes than target susceptibility. Attacker refusal frequency is strongly and negatively correlated with harm (rho = -0.93, p < 0.001), showing that attacker-side alignment mitigates harmful responses. These findings reveal that size asymmetry influences robustness and provide exploratory evidence for adversarial scaling patterns, motivating more controlled investigations into inter-model alignment and safety.", "date": "2025-11-16", "categories": []}
{"id": "2601.00213", "title": "Overlooked Safety Vulnerability in LLMs: Malicious Intelligent Optimization Algorithm Request and its Jailbreak", "abstract": "The widespread deployment of large language models (LLMs) has raised growing concerns about their misuse risks and associated safety issues. While prior studies have examined the safety of LLMs in general usage, code generation, and agent-based applications, their vulnerabilities in automated algorithm design remain underexplored. To fill this gap, this study investigates this overlooked safety vulnerability, with a particular focus on intelligent optimization algorithm design, given its prevalent use in complex decision-making scenarios. We introduce MalOptBench, a benchmark consisting of 60 malicious optimization algorithm requests, and propose MOBjailbreak, a jailbreak method tailored for this scenario. Through extensive evaluation of 13 mainstream LLMs including the latest GPT-5 and DeepSeek-V3.1, we reveal that most models remain highly susceptible to such attacks, with an average attack success rate of 83.59% and an average harmfulness score of 4.28 out of 5 on original harmful prompts, and near-complete failure under MOBjailbreak. Furthermore, we assess state-of-the-art plug-and-play defenses that can be applied to closed-source models, and find that they are only marginally effective against MOBjailbreak and prone to exaggerated safety behaviors. These findings highlight the urgent need for stronger alignment techniques to safeguard LLMs against misuse in algorithm design.", "date": "2026-01-01", "categories": []}
{"id": "2601.00454", "title": "Defensive M2S: Training Guardrail Models on Compressed Multi-turn Conversations", "abstract": "Guardrail models are essential for ensuring the safety of Large Language Model (LLM) deployments, but processing full multi-turn conversation histories incurs significant computational cost. We propose Defensive M2S, a training paradigm that fine-tunes guardrail models on Multi-turn to Single-turn (M2S) compressed conversations rather than complete dialogue histories. We provide a formal complexity analysis showing that M2S reduces training cost from $O(n^2)$ to $O(n)$ for $n$-turn conversations. Empirically, on our training dataset (779 samples, avg. 10.6 turns), M2S requires only 169K tokens compared to 15.7M tokens for the multi-turn baseline -- a 93$\\times$ reduction. We evaluate Defensive M2S across three guardrail model families (LlamaGuard, Nemotron, Qwen3Guard) and three compression templates (hyphenize, numberize, pythonize) on SafeDialBench, a comprehensive multi-turn jailbreak benchmark. Our best configuration, Qwen3Guard with hyphenize compression, achieves 93.8% attack detection recall while reducing inference tokens by 94.6% (from 3,231 to 173 tokens per conversation). This represents a 38.9 percentage point improvement over the baseline while dramatically reducing both training and inference costs. Our findings demonstrate that M2S compression can serve as an effective efficiency technique for guardrail deployment, enabling scalable safety screening of long multi-turn conversations.", "date": "2026-01-01", "categories": []}
{"id": "2503.08990", "title": "Effective and Efficient Jailbreaks of Black-Box LLMs with Cross-Behavior Attacks", "abstract": "Despite recent advancements in Large Language Models (LLMs) and their alignment, they can still be jailbroken, i.e., harmful and toxic content can be elicited from them. While existing red-teaming methods have shown promise in uncovering such vulnerabilities, these methods struggle with limited success and high computational and monetary costs. To address this, we propose a black-box Jailbreak method with Cross-Behavior attacks (JCB), that can automatically and efficiently find successful jailbreak prompts. JCB leverages successes from past behaviors to help jailbreak new behaviors, thereby significantly improving the attack efficiency. Moreover, JCB does not rely on time- and/or cost-intensive calls to auxiliary LLMs to discover/optimize the jailbreak prompts, making it highly efficient and scalable. Comprehensive experimental evaluations show that JCB significantly outperforms related baselines, requiring up to 94% fewer queries while still achieving 12.9% higher average attack success. JCB also achieves a notably high 37% attack success rate on Llama-2-7B, one of the most resilient LLMs, and shows promising zero-shot transferability across different LLMs.", "date": "2025-03-12", "categories": []}
{"id": "2508.12897", "title": "RAJ-PGA: Reasoning-Activated Jailbreak and Principle-Guided Alignment Framework for Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) face a distinct safety vulnerability: their internal reasoning chains may generate harmful content even when the final output appears benign. To address this overlooked risk, we first propose a novel attack paradigm, Reasoning-Activated Jailbreak (RAJ) via Concretization, which demonstrates that refining malicious prompts to be more specific can trigger step-by-step logical reasoning that overrides the model's safety protocols. To systematically mitigate this vulnerability, we further develop a scalable framework for constructing high-quality safety alignment datasets. This framework first leverages the RAJ attack to elicit challenging harmful reasoning chains from LRMs, then transforms these high-risk traces into safe, constructive, and educational responses through a tailored Principle-Guided Alignment (PGA) mechanism. Then, we introduce the PGA dataset, a verified alignment dataset containing 3,989 samples using our proposed method. Extensive experiments show that fine-tuning LRMs with PGA dataset significantly enhances model safety, achieving up to a 29.5% improvement in defense success rates across multiple jailbreak benchmarks. Critically, our approach not only defends against sophisticated reasoning-based attacks but also preserves, even enhances, the model's general reasoning capabilities. This work provides a scalable and effective pathway for safety alignment in reasoning-intensive AI systems, addressing the core trade-off between safety and functional performance.", "date": "2025-08-18", "categories": []}
{"id": "2512.23717", "title": "HarmTransform: Transforming Explicit Harmful Queries into Stealthy via Multi-Agent Debate", "abstract": "Large language models (LLMs) are equipped with safety mechanisms to detect and block harmful queries, yet current alignment approaches primarily focus on overtly dangerous content and overlook more subtle threats. However, users can often disguise harmful intent through covert rephrasing that preserves malicious objectives while appearing benign, which creates a significant gap in existing safety training data. To address this limitation, we introduce HarmTransform, a multi-agent debate framework for systematically transforming harmful queries into stealthier forms while preserving their underlying harmful intent. Our framework leverages iterative critique and refinement among multiple agents to generate high-quality, covert harmful query transformations that can be used to improve future LLM safety alignment. Experiments demonstrate that HarmTransform significantly outperforms standard baselines in producing effective query transformations. At the same time, our analysis reveals that debate acts as a double-edged sword: while it can sharpen transformations and improve stealth, it may also introduce topic shifts and unnecessary complexity. These insights highlight both the promise and the limitations of multi-agent debate for generating comprehensive safety training data.", "date": "2025-12-09", "categories": []}
{"id": "2512.23738", "title": "Enforcing Temporal Constraints for LLM Agents", "abstract": "LLM-based agents are deployed in safety-critical applications, yet current guardrail systems fail to prevent violations of temporal safety policies, requirements that govern the ordering and sequencing of agent actions. For instance, agents may access sensitive data before authenticating users or process refunds to unauthorized payment methods, violations that require reasoning about sequences of action rather than an individual action. Existing guardrails rely on imprecise natural language instructions or post-hoc monitoring, and provide no formal guarantees that agents will satisfy temporal constraints. We present Agent-C, a novel framework that provides run-time guarantees ensuring LLM agents adhere to formal temporal safety properties. Agent-C introduces a domain-specific language for expressing temporal properties (e.g., authenticate before accessing data), translates specifications to first-order logic, and uses SMT solving to detect non-compliant agent actions during token generation. When the LLM attempts to generate a non-compliant tool call, Agent-C leverages constrained generation techniques to ensure that every action generated by the LLM complies with the specification, and to generate a compliant alternative to a non-compliant agent action. We evaluate Agent-C across two real-world applications: retail customer service and airline ticket reservation system, and multiple language models (open and closed-source). Our results demonstrate that Agent-C achieves perfect safety (100% conformance, 0% harm), while improving task utility compared to state-of-the-art guardrails and unrestricted agents. On SoTA closed-source models, Agent-C improves conformance (77.4% to 100% for Claude Sonnet 4.5 and 83.7% to 100% for GPT-5), while simultaneously increasing utility (71.8% to 75.2% and 66.1% to 70.6%, respectively), representing a new SoTA frontier for reliable agentic reasoning.", "date": "2025-12-25", "categories": []}
{"id": "2306.05499", "title": "Prompt Injection attack against LLM-integrated Applications", "abstract": "Large Language Models (LLMs), renowned for their superior proficiency in language comprehension and generation, stimulate a vibrant ecosystem of applications around them. However, their extensive assimilation into various services introduces significant security risks. This study deconstructs the complexities and implications of prompt injection attacks on actual LLM-integrated applications. Initially, we conduct an exploratory analysis on ten commercial applications, highlighting the constraints of current attack strategies in practice. Prompted by these limitations, we subsequently formulate HouYi, a novel black-box prompt injection attack technique, which draws inspiration from traditional web injection attacks. HouYi is compartmentalized into three crucial elements: a seamlessly-incorporated pre-constructed prompt, an injection prompt inducing context partition, and a malicious payload designed to fulfill the attack objectives. Leveraging HouYi, we unveil previously unknown and severe attack outcomes, such as unrestricted arbitrary LLM usage and uncomplicated application prompt theft. We deploy HouYi on 36 actual LLM-integrated applications and discern 31 applications susceptible to prompt injection. 10 vendors have validated our discoveries, including Notion, which has the potential to impact millions of users. Our investigation illuminates both the possible risks of prompt injection attacks and the possible tactics for mitigation.", "date": "2023-06-08", "categories": []}
{"id": "2506.11938", "title": "Improving Large Language Model Safety with Contrastive Representation Learning", "abstract": "Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to adversarial attacks. While existing defenses often struggle to generalize across varying attack types, recent advancements in representation engineering offer promising alternatives. In this work, we propose a defense framework that formulates model defense as a contrastive representation learning (CRL) problem. Our method finetunes a model using a triplet-based loss combined with adversarial hard negative mining to encourage separation between benign and harmful representations. Our experimental results across multiple models demonstrate that our approach outperforms prior representation engineering-based defenses, improving robustness against both input-level and embedding-space attacks without compromising standard performance. Our code is available at https://github.com/samuelsimko/crl-llm-defense", "date": "2025-06-13", "categories": []}
{"id": "2512.17956", "title": "Victor Calibration (VC): Multi-Pass Confidence Calibration and CP4.3 Governance Stress Test under Round-Table Orchestration", "abstract": "Safety alignment can make frontier LMs overly conservative, degrading collaboration via hedging or false refusals. We present a lightweight toolkit with three parts: (1) Victor Calibration (VC), a multi-pass protocol that elicits a scalar confidence proxy T (T0<T1<T2) through iterative evidence re-evaluation; (2) FD-Lite, a behavior-only phenomenology audit with a fixed anchor phrase and a meta-prefix trap to avoid anthropomorphic claims; and (3) CP4.3, a governance stress test for rank invariance and allocation monotonicity (M6). Across Claude 4.5 models (Haiku, Sonnet no-thinking, Sonnet thinking) and Opus, we observe monotonic VC trajectories without violating safety invariants, and stable CP4.3 behavior. (\"Opus\" here refers to a single Claude Opus 4.1 session accessed via a standard UI account, as reported in Table 1.) This work was conducted by a single operator (n=1) and is intended as hypothesis-generating; we explicitly invite replication, critique, and extension by the research community. We include prompt templates and an artifact plan to facilitate independent verification.", "date": "2025-12-18", "categories": []}
{"id": "2512.22154", "title": "Practical challenges of control monitoring in frontier AI deployments", "abstract": "Automated control monitors could play an important role in overseeing highly capable AI agents that we do not fully trust. Prior work has explored control monitoring in simplified settings, but scaling monitoring to real-world deployments introduces additional dynamics: parallel agent instances, non-negligible oversight latency, incremental attacks between agent instances, and the difficulty of identifying scheming agents based on individual harmful actions. In this paper, we analyse design choices to address these challenges, focusing on three forms of monitoring with different latency-safety trade-offs: synchronous, semi-synchronous, and asynchronous monitoring. We introduce a high-level safety case sketch as a tool for understanding and comparing these monitoring protocols. Our analysis identifies three challenges -- oversight, latency, and recovery -- and explores them in four case studies of possible future AI deployments.", "date": "2025-12-15", "categories": []}
{"id": "2512.22201", "title": "Emergent Persuasion: Will LLMs Persuade Without Being Prompted?", "abstract": "With the wide-scale adoption of conversational AI systems, AI are now able to exert unprecedented influence on human opinion and beliefs. Recent work has shown that many Large Language Models (LLMs) comply with requests to persuade users into harmful beliefs or actions when prompted and that model persuasiveness increases with model scale. However, this prior work looked at persuasion from the threat model of $\\textit{misuse}$ (i.e., a bad actor asking an LLM to persuade). In this paper, we instead aim to answer the following question: Under what circumstances would models persuade $\\textit{without being explicitly prompted}$, which would shape how concerned we should be about such emergent persuasion risks. To achieve this, we study unprompted persuasion under two scenarios: (i) when the model is steered (through internal activation steering) along persona traits, and (ii) when the model is supervised-finetuned (SFT) to exhibit the same traits. We showed that steering towards traits, both related to persuasion and unrelated, does not reliably increase models' tendency to persuade unprompted, however, SFT does. Moreover, SFT on general persuasion datasets containing solely benign topics admits a model that has a higher propensity to persuade on controversial and harmful topics--showing that emergent harmful persuasion can arise and should be studied further.", "date": "2025-12-20", "categories": []}
{"id": "2512.22211", "title": "With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems", "abstract": "Agentic AI systems present both significant opportunities and novel risks due to their capacity for autonomous action, encompassing tasks such as code execution, internet interaction, and file modification. This poses considerable challenges for effective organizational governance, particularly in comprehensively identifying, assessing, and mitigating diverse and evolving risks. To tackle this, we introduce the Agentic Risk \\& Capability (ARC) Framework, a technical governance framework designed to help organizations identify, assess, and mitigate risks arising from agentic AI systems. The framework's core contributions are: (1) it develops a novel capability-centric perspective to analyze a wide range of agentic AI systems; (2) it distills three primary sources of risk intrinsic to agentic AI systems - components, design, and capabilities; (3) it establishes a clear nexus between each risk source, specific materialized risks, and corresponding technical controls; and (4) it provides a structured and practical approach to help organizations implement the framework. This framework provides a robust and adaptable methodology for organizations to navigate the complexities of agentic AI, enabling rapid and effective innovation while ensuring the safe, secure, and responsible deployment of agentic AI systems. Our framework is open-sourced \\href{https://govtech-responsibleai.github.io/agentic-risk-capability-framework/}{here}.", "date": "2025-12-22", "categories": []}
{"id": "2512.22293", "title": "Learning from Negative Examples: Why Warning-Framed Training Data Teaches What It Warns Against", "abstract": "Warning-framed content in training data (e.g., \"DO NOT USE - this code is vulnerable\") does not, it turns out, teach language models to avoid the warned-against behavior. In experiments reported here, models exposed to such warnings reproduced the flagged content at rates statistically indistinguishable from models given the content directly (76.7% vs. 83.3%). Why? Sparse autoencoder analysis points to a failure of orthogonalization: \"describing X\" and \"performing X\" activate overlapping latent features. Feature #8684, which tracks code execution patterns, fires at comparable magnitude in both warning and exploitation contexts. A related phenomenon, what I call \"stealth slip\", allows conversational preambles to rotate activations into subspaces that linear probes miss entirely. Prompting and inference-time steering do not fix this; training-time feature ablation does. The upshot is that statistical co-occurrence dominates over pragmatic interpretation in current architectures. Models learn what tends to follow a context, not why it appeared there.", "date": "2025-12-25", "categories": []}
{"id": "2512.23173", "title": "EquaCode: A Multi-Strategy Jailbreak Approach for Large Language Models via Equation Solving and Code Completion", "abstract": "Large language models (LLMs), such as ChatGPT, have achieved remarkable success across a wide range of fields. However, their trustworthiness remains a significant concern, as they are still susceptible to jailbreak attacks aimed at eliciting inappropriate or harmful responses. However, existing jailbreak attacks mainly operate at the natural language level and rely on a single attack strategy, limiting their effectiveness in comprehensively assessing LLM robustness. In this paper, we propose Equacode, a novel multi-strategy jailbreak approach for large language models via equation-solving and code completion. This approach transforms malicious intent into a mathematical problem and then requires the LLM to solve it using code, leveraging the complexity of cross-domain tasks to divert the model's focus toward task completion rather than safety constraints. Experimental results show that Equacode achieves an average success rate of 91.19% on the GPT series and 98.65% across 3 state-of-the-art LLMs, all with only a single query. Further, ablation experiments demonstrate that EquaCode outperforms either the mathematical equation module or the code module alone. This suggests a strong synergistic effect, thereby demonstrating that multi-strategy approach yields results greater than the sum of its parts.", "date": "2025-12-29", "categories": []}
{"id": "2512.23508", "title": "Why AI Safety Requires Uncertainty, Incomplete Preferences, and Non-Archimedean Utilities", "abstract": "How can we ensure that AI systems are aligned with human values and remain safe? We can study this problem through the frameworks of the AI assistance and the AI shutdown games. The AI assistance problem concerns designing an AI agent that helps a human to maximise their utility function(s). However, only the human knows these function(s); the AI assistant must learn them. The shutdown problem instead concerns designing AI agents that: shut down when a shutdown button is pressed; neither try to prevent nor cause the pressing of the shutdown button; and otherwise accomplish their task competently. In this paper, we show that addressing these challenges requires AI agents that can reason under uncertainty and handle both incomplete and non-Archimedean preferences.", "date": "2025-12-29", "categories": []}
{"id": "2502.09990", "title": "X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Multi-Turn Jailbreaks without Compromising Usability", "abstract": "Despite the rapid development of safety alignment techniques for LLMs, defending against multi-turn jailbreaks is still a challenging task. In this paper, we conduct a comprehensive comparison, revealing that some existing defense methods can improve the robustness of LLMs against multi-turn jailbreaks but compromise usability, i.e., reducing general capabilities or causing the over-refusal problem. From the perspective of mechanism interpretability of LLMs, we discover that these methods fail to establish a boundary that exactly distinguishes safe and harmful feature representations. Therefore, boundary-safe representations close to harmful representations are inevitably disrupted, leading to a decline in usability. To address this issue, we propose X-Boundary to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary. In this way, harmful representations can be precisely erased without disrupting safe ones. Experimental results show that X-Boundary achieves state-of-the-art defense performance against multi-turn jailbreaks, while reducing the over-refusal rate by about 20% and maintaining nearly complete general capability. Furthermore, we theoretically prove and empirically verify that X-Boundary can accelerate the convergence process during training. Please see our code at: https://github.com/AI45Lab/X-Boundary.", "date": "2025-02-14", "categories": []}
{"id": "2512.21220", "title": "RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic", "abstract": "Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks, yet they remain vulnerable to hazardous instructions that may trigger unsafe behaviors. Runtime safety guardrails, which intercept hazardous actions during task execution, offer a promising solution due to their flexibility. However, existing defenses often rely on static rule filters or prompt-level control, which struggle to address implicit risks arising in dynamic, temporally dependent, and context-rich environments. To address this, we propose RoboSafe, a hybrid reasoning runtime safeguard for embodied agents through executable predicate-based safety logic. RoboSafe integrates two complementary reasoning processes on a Hybrid Long-Short Safety Memory. We first propose a Backward Reflective Reasoning module that continuously revisits recent trajectories in short-term memory to infer temporal safety predicates and proactively triggers replanning when violations are detected. We then propose a Forward Predictive Reasoning module that anticipates upcoming risks by generating context-aware safety predicates from the long-term safety memory and the agent's multimodal observations. Together, these components form an adaptive, verifiable safety logic that is both interpretable and executable as code. Extensive experiments across multiple agents demonstrate that RoboSafe substantially reduces hazardous actions (-36.8% risk occurrence) compared with leading baselines, while maintaining near-original task performance. Real-world evaluations on physical robotic arms further confirm its practicality. Code will be released upon acceptance.", "date": "2025-12-24", "categories": []}
{"id": "2512.21354", "title": "Reflection-Driven Control for Trustworthy Code Agents", "abstract": "Contemporary large language model (LLM) agents are remarkably capable, but they still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively harmful outputs. To address this, we introduce Reflection-Driven Control, a standardized and pluggable control module that can be seamlessly integrated into general agent architectures. Reflection-Driven Control elevates \"self-reflection\" from a post hoc patch into an explicit step in the agent's own reasoning process: during generation, the agent continuously runs an internal reflection loop that monitors and evaluates its own decision path. When potential risks are detected, the system retrieves relevant repair examples and secure coding guidelines from an evolving reflective memory, injecting these evidence-based constraints directly into subsequent reasoning steps. We instantiate Reflection-Driven Control in the setting of secure code generation and systematically evaluate it across eight classes of security-critical programming tasks. Empirical results show that Reflection-Driven Control substantially improves the security and policy compliance of generated code while largely preserving functional correctness, with minimal runtime and token overhead. Taken together, these findings indicate that Reflection-Driven Control is a practical path toward trustworthy AI coding agents: it enables designs that are simultaneously autonomous, safer by construction, and auditable.", "date": "2025-12-22", "categories": []}
{"id": "2504.02080", "title": "Evolving Security in LLMs: A Study of Jailbreak Attacks and Defenses", "abstract": "Large Language Models (LLMs) are increasingly popular, powering a wide range of applications. Their widespread use has sparked concerns, especially through jailbreak attacks that bypass safety measures to produce harmful content.\n  In this paper, we present a comprehensive security analysis of large language models (LLMs), addressing critical research questions on the evolution and determinants of model safety.\n  Specifically, we begin by identifying the most effective techniques for detecting jailbreak attacks. Next, we investigate whether newer versions of LLMs offer improved security compared to their predecessors. We also assess the impact of model size on overall security and explore the potential benefits of integrating multiple defense strategies to enhance the security.\n  Our study evaluates both open-source (e.g., LLaMA and Mistral) and closed-source models (e.g., GPT-4) by employing four state-of-the-art attack techniques and assessing the efficacy of three new defensive approaches.", "date": "2025-04-02", "categories": []}
{"id": "2512.20168", "title": "Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography", "abstract": "By integrating language understanding with perceptual modalities such as images, multimodal large language models (MLLMs) constitute a critical substrate for modern AI systems, particularly intelligent agents operating in open and interactive environments. However, their increasing accessibility also raises heightened risks of misuse, such as generating harmful or unsafe content. To mitigate these risks, alignment techniques are commonly applied to align model behavior with human values. Despite these efforts, recent studies have shown that jailbreak attacks can circumvent alignment and elicit unsafe outputs. Currently, most existing jailbreak methods are tailored for open-source models and exhibit limited effectiveness against commercial MLLM-integrated systems, which often employ additional filters. These filters can detect and prevent malicious input and output content, significantly reducing jailbreak threats. In this paper, we reveal that the success of these safety filters heavily relies on a critical assumption that malicious content must be explicitly visible in either the input or the output. This assumption, while often valid for traditional LLM-integrated systems, breaks down in MLLM-integrated systems, where attackers can leverage multiple modalities to conceal adversarial intent, leading to a false sense of security in existing MLLM-integrated systems. To challenge this assumption, we propose Odysseus, a novel jailbreak paradigm that introduces dual steganography to covertly embed malicious queries and responses into benign-looking images. Extensive experiments on benchmark datasets demonstrate that our Odysseus successfully jailbreaks several pioneering and realistic MLLM-integrated systems, achieving up to 99% attack success rate. It exposes a fundamental blind spot in existing defenses, and calls for rethinking cross-modal security in MLLM-integrated systems.", "date": "2025-12-23", "categories": []}
{"id": "2503.11185", "title": "Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attacks", "abstract": "LLMs remain vulnerable to jailbreak attacks that exploit adversarial prompts to circumvent safety measures. Current safety fine-tuning approaches face two critical limitations. First, they often fail to strike a balance between security and utility, where stronger safety measures tend to over-reject harmless user requests. Second, they frequently miss malicious intent concealed within seemingly benign tasks, leaving models exposed to exploitation. Our work identifies a fundamental cause of these issues: during response generation, an LLM's capacity to differentiate harmful from safe outputs deteriorates. Experimental evidence confirms this, revealing that the separability between hidden states for safe and harmful responses diminishes as generation progresses. This weakening discrimination forces models to make compliance judgments earlier in the generation process, restricting their ability to recognize developing harmful intent and contributing to both aforementioned failures. To mitigate this vulnerability, we introduce DEEPALIGN - an inherent defense framework that enhances the safety of LLMs. By applying contrastive hidden-state steering at the midpoint of response generation, DEEPALIGN amplifies the separation between harmful and benign hidden states, enabling continuous intrinsic toxicity detection and intervention throughout the generation process. Across diverse LLMs spanning varying architectures and scales, it reduced attack success rates of nine distinct jailbreak attacks to near-zero or minimal. Crucially, it preserved model capability while reducing over-refusal. Models equipped with DEEPALIGN exhibited up to 3.5% lower error rates in rejecting challenging benign queries and maintained standard task performance with less than 1% decline. This marks a substantial advance in the safety-utility Pareto frontier.", "date": "2025-03-14", "categories": []}
{"id": "2505.17196", "title": "Shape it Up! Restoring LLM Safety during Finetuning", "abstract": "Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks: even a few harmful examples can compromise safety alignment. A common mitigation strategy is to update the model more strongly on examples deemed safe, while downweighting or excluding those flagged as unsafe. However, because safety context can shift within a single example, updating the model equally on both harmful and harmless parts of a response is suboptimal-a coarse treatment we term static safety shaping. In contrast, we propose dynamic safety shaping (DSS), a framework that uses fine-grained safety signals to reinforce learning from safe segments of a response while suppressing unsafe content. To enable such fine-grained control during finetuning, we introduce a key insight: guardrail models, traditionally used for filtering, can be repurposed to evaluate partial responses, tracking how safety risk evolves throughout the response, segment by segment. This leads to the Safety Trajectory Assessment of Response (STAR), a token-level signal that enables shaping to operate dynamically over the training sequence. Building on this, we present STAR-DSS, guided by STAR scores, that robustly mitigates finetuning risks and delivers substantial safety improvements across diverse threats, datasets, and model families-all without compromising capability on intended tasks. We encourage future safety research to build on dynamic shaping principles for stronger mitigation against evolving finetuning risks. Our code is publicly available at https://github.com/poloclub/star-dss.", "date": "2025-05-22", "categories": []}
{"id": "2510.13901", "title": "RAID: Refusal-Aware and Integrated Decoding for Jailbreaking LLMs", "abstract": "Large language models (LLMs) achieve impressive performance across diverse tasks yet remain vulnerable to jailbreak attacks that bypass safety mechanisms. We present RAID (Refusal-Aware and Integrated Decoding), a framework that systematically probes these weaknesses by crafting adversarial suffixes that induce restricted content while preserving fluency. RAID relaxes discrete tokens into continuous embeddings and optimizes them with a joint objective that (i) encourages restricted responses, (ii) incorporates a refusal-aware regularizer to steer activations away from refusal directions in embedding space, and (iii) applies a coherence term to maintain semantic plausibility and non-redundancy. After optimization, a critic-guided decoding procedure maps embeddings back to tokens by balancing embedding affinity with language-model likelihood. This integration yields suffixes that are both effective in bypassing defenses and natural in form. Experiments on multiple open-source LLMs show that RAID achieves higher attack success rates with fewer queries and lower computational cost than recent white-box and black-box baselines. These findings highlight the importance of embedding-space regularization for understanding and mitigating LLM jailbreak vulnerabilities.", "date": "2025-10-14", "categories": []}
{"id": "2511.02366", "title": "LiveSecBench: A Dynamic and Event-Driven Safety Benchmark for Chinese Language Model Applications", "abstract": "We introduce LiveSecBench, a continuously updated safety benchmark specifically for Chinese-language LLM application scenarios. LiveSecBench constructs a high-quality and unique dataset through a pipeline that combines automated generation with human verification. By periodically releasing new versions to expand the dataset and update evaluation metrics, LiveSecBench provides a robust and up-to-date standard for AI safety. In this report, we introduce our second release v251215, which evaluates across five dimensions (Public Safety, Fairness & Bias, Privacy, Truthfulness, and Mental Health Safety.) We evaluate 57 representative LLMs using an ELO rating system, offering a leaderboard of the current state of Chinese LLM safety. The result is available at https://livesecbench.intokentech.cn/.", "date": "2025-11-04", "categories": []}
{"id": "2511.02376", "title": "AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models", "abstract": "Large Language Models (LLMs) remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs. Yet most evaluations focus on single-turn interactions while real-world attacks unfold through adaptive multi-turn conversations. We present AutoAdv, a training-free framework for automated multi-turn jailbreaking that achieves an attack success rate of up to 95% on Llama-3.1-8B within six turns, a 24% improvement over single-turn baselines. AutoAdv uniquely combines three adaptive mechanisms: a pattern manager that learns from successful attacks to enhance future prompts, a temperature manager that dynamically adjusts sampling parameters based on failure modes, and a two-phase rewriting strategy that disguises harmful requests and then iteratively refines them. Extensive evaluation across commercial and open-source models (Llama-3.1-8B, GPT-4o mini, Qwen3-235B, Mistral-7B) reveals persistent vulnerabilities in current safety mechanisms, with multi-turn attacks consistently outperforming single-turn approaches. These findings demonstrate that alignment strategies optimized for single-turn interactions fail to maintain robustness across extended conversations, highlighting an urgent need for multi-turn-aware defenses.", "date": "2025-11-04", "categories": []}
{"id": "2512.15584", "title": "A Decision-Theoretic Approach for Managing Misalignment", "abstract": "When should we delegate decisions to AI systems? While the value alignment literature has developed techniques for shaping AI values, less attention has been paid to how to determine, under uncertainty, when imperfect alignment is good enough to justify delegation. We argue that rational delegation requires balancing an agent's value (mis)alignment with its epistemic accuracy and its reach (the acts it has available). This paper introduces a formal, decision-theoretic framework to analyze this tradeoff precisely accounting for a principal's uncertainty about these factors. Our analysis reveals a sharp distinction between two delegation scenarios. First, universal delegation (trusting an agent with any problem) demands near-perfect value alignment and total epistemic trust, conditions rarely met in practice. Second, we show that context-specific delegation can be optimal even with significant misalignment. An agent's superior accuracy or expanded reach may grant access to better overall decision problems, making delegation rational in expectation. We develop a novel scoring framework to quantify this ex ante decision. Ultimately, our work provides a principled method for determining when an AI is aligned enough for a given context, shifting the focus from achieving perfect alignment to managing the risks and rewards of delegation under uncertainty.", "date": "2025-12-17", "categories": []}
{"id": "2512.18244", "title": "Breaking Minds, Breaking Systems: Jailbreaking Large Language Models via Human-like Psychological Manipulation", "abstract": "Large Language Models (LLMs) have gained considerable popularity and protected by increasingly sophisticated safety mechanisms. However, jailbreak attacks continue to pose a critical security threat by inducing models to generate policy-violating behaviors. Current paradigms focus on input-level anomalies, overlooking that the model's internal psychometric state can be systematically manipulated. To address this, we introduce Psychological Jailbreak, a new jailbreak attack paradigm that exposes a stateful psychological attack surface in LLMs, where attackers exploit the manipulation of a model's psychological state across interactions. Building on this insight, we propose Human-like Psychological Manipulation (HPM), a black-box jailbreak method that dynamically profiles a target model's latent psychological vulnerabilities and synthesizes tailored multi-turn attack strategies. By leveraging the model's optimization for anthropomorphic consistency, HPM creates a psychological pressure where social compliance overrides safety constraints. To systematically measure psychological safety, we construct an evaluation framework incorporating psychometric datasets and the Policy Corruption Score (PCS). Benchmarking against various models (e.g., GPT-4o, DeepSeek-V3, Gemini-2-Flash), HPM achieves a mean Attack Success Rate (ASR) of 88.1%, outperforming state-of-the-art attack baselines. Our experiments demonstrate robust penetration against advanced defenses, including adversarial prompt optimization (e.g., RPO) and cognitive interventions (e.g., Self-Reminder). Ultimately, PCS analysis confirms HPM induces safety breakdown to satisfy manipulated contexts. Our work advocates for a fundamental paradigm shift from static content filtering to psychological safety, prioritizing the development of psychological defense mechanisms against deep cognitive manipulation.", "date": "2025-12-20", "categories": []}
{"id": "2512.18309", "title": "Embedded Safety-Aligned Intelligence via Differentiable Internal Alignment Embeddings", "abstract": "We introduce Embedded Safety-Aligned Intelligence (ESAI), a theoretical framework for multi-agent reinforcement learning that embeds alignment constraints directly into agents internal representations using differentiable internal alignment embeddings. Unlike external reward shaping or post-hoc safety constraints, internal alignment embeddings are learned latent variables that predict externalized harm through counterfactual reasoning and modulate policy updates toward harm reduction through attention and graph-based propagation.\n  The ESAI framework integrates four mechanisms: differentiable counterfactual alignment penalties computed from soft reference distributions, alignment-weighted perceptual attention, Hebbian associative memory supporting temporal credit assignment, and similarity-weighted graph diffusion with bias mitigation controls. We analyze stability conditions for bounded internal embeddings under Lipschitz continuity and spectral constraints, discuss computational complexity, and examine theoretical properties including contraction behavior and fairness-performance tradeoffs.\n  This work positions ESAI as a conceptual contribution to differentiable alignment mechanisms in multi-agent systems. We identify open theoretical questions regarding convergence guarantees, embedding dimensionality, and extension to high-dimensional environments. Empirical evaluation is left to future work.", "date": "2025-12-20", "categories": []}
{"id": "2512.18311", "title": "Monitoring Monitorability", "abstract": "Observability into the decision making of modern AI systems may be required to safely deploy increasingly capable agents. Monitoring the chain-of-thought (CoT) of today's reasoning models has proven effective for detecting misbehavior. However, this \"monitorability\" may be fragile under different training procedures, data sources, or even continued system scaling. To measure and track monitorability, we propose three evaluation archetypes (intervention, process, and outcome-property) and a new monitorability metric, and introduce a broad evaluation suite. We demonstrate that these evaluations can catch simple model organisms trained to have obfuscated CoTs, and that CoT monitoring is more effective than action-only monitoring in practical settings. We compare the monitorability of various frontier models and find that most models are fairly, but not perfectly, monitorable. We also evaluate how monitorability scales with inference-time compute, reinforcement learning optimization, and pre-training model size. We find that longer CoTs are generally more monitorable and that RL optimization does not materially decrease monitorability even at the current frontier scale. Notably, we find that for a model at a low reasoning effort, we could instead deploy a smaller model at a higher reasoning effort (thereby matching capabilities) and obtain a higher monitorability, albeit at a higher overall inference compute cost. We further investigate agent-monitor scaling trends and find that scaling a weak monitor's test-time compute when monitoring a strong agent increases monitorability. Giving the weak monitor access to CoT not only improves monitorability, but it steepens the monitor's test-time compute to monitorability scaling trend. Finally, we show we can improve monitorability by asking models follow-up questions and giving their follow-up CoT to the monitor.", "date": "2025-12-20", "categories": []}
{"id": "2512.18755", "title": "MEEA: Mere Exposure Effect-Driven Confrontational Optimization for LLM Jailbreaking", "abstract": "The rapid advancement of large language models (LLMs) has intensified concerns about the robustness of their safety alignment. While existing jailbreak studies explore both single-turn and multi-turn strategies, most implicitly assume a static safety boundary and fail to account for how contextual interactions dynamically influence model behavior, leading to limited stability and generalization. Motivated by this gap, we propose MEEA (Mere Exposure Effect Attack), a psychology-inspired, fully automated black-box framework for evaluating multi-turn safety robustness, grounded in the mere exposure effect. MEEA leverages repeated low-toxicity semantic exposure to induce a gradual shift in a model's effective safety threshold, enabling progressive erosion of alignment constraints over sustained interactions. Concretely, MEEA constructs semantically progressive prompt chains and optimizes them using a simulated annealing strategy guided by semantic similarity, toxicity, and jailbreak effectiveness. Extensive experiments on both closed-source and open-source models, including GPT-4, Claude-3.5, and DeepSeek-R1, demonstrate that MEEA consistently achieves higher attack success rates than seven representative baselines, with an average Attack Success Rate (ASR) improvement exceeding 20%. Ablation studies further validate the necessity of both annealing-based optimization and contextual exposure mechanisms. Beyond improved attack effectiveness, our findings indicate that LLM safety behavior is inherently dynamic and history-dependent, challenging the common assumption of static alignment boundaries and highlighting the need for interaction-aware safety evaluation and defense mechanisms. Our code is available at: https://github.com/Carney-lsz/MEEA", "date": "2025-12-21", "categories": []}
{"id": "2509.22818", "title": "Can Large Language Models Develop Gambling Addiction?", "abstract": "This study identifies the specific conditions under which large language models exhibit human-like gambling addiction patterns, providing critical insights into their decision-making mechanisms and AI safety. We analyze LLM decision-making at cognitive-behavioral and neural levels based on human addiction research. In slot machine experiments, we identified cognitive features such as illusion of control and loss chasing, observing that greater autonomy in betting parameters substantially amplified irrational behavior and bankruptcy rates. Neural circuit analysis using a Sparse Autoencoder confirmed that model behavior is controlled by abstract decision-making features related to risk, not merely by prompts. These findings suggest LLMs internalize human-like cognitive biases beyond simply mimicking training data.", "date": "2025-09-26", "categories": []}
{"id": "2506.04133", "title": "TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems", "abstract": "Agentic AI systems, built upon large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligence, autonomy, collaboration, and decision-making across enterprise and societal domains. This review presents a structured analysis of Trust, Risk, and Security Management (TRiSM) in the context of LLM-based Agentic Multi-Agent Systems (AMAS). We begin by examining the conceptual foundations of Agentic AI and highlight its architectural distinctions from traditional AI agents. We then adapt and extend the AI TRiSM framework for Agentic AI, structured around key pillars: \\textit{ Explainability, ModelOps, Security, Privacy} and \\textit{their Lifecycle Governance}, each contextualized to the challenges of AMAS. A risk taxonomy is proposed to capture the unique threats and vulnerabilities of Agentic AI, ranging from coordination failures to prompt-based adversarial manipulation. To support practical assessment in Agentic AI works, we introduce two novel metrics: the Component Synergy Score (CSS), which quantifies the quality of inter-agent collaboration, and the Tool Utilization Efficacy (TUE), which evaluates the efficiency of tool use within agent workflows. We further discuss strategies for improving explainability in Agentic AI, as well as approaches to enhancing security and privacy through encryption, adversarial robustness, and regulatory compliance. The review concludes with a research roadmap for the responsible development and deployment of Agentic AI, highlighting key directions to align emerging systems with TRiSM principles-ensuring safety, transparency, and accountability in their operation.", "date": "2025-06-04", "categories": []}
{"id": "2510.08158", "title": "Beyond Over-Refusal: Scenario-Based Diagnostics and Post-Hoc Mitigation for Exaggerated Refusals in LLMs", "abstract": "Large language models (LLMs) frequently produce false refusals, declining benign requests that contain terms resembling unsafe queries. We address this challenge by introducing two comprehensive benchmarks: the Exaggerated Safety Benchmark (XSB) for single-turn prompts, annotated with \"Focus\" keywords that identify refusal-inducing triggers, and the Multi-turn Scenario-based Exaggerated Safety Benchmark (MS-XSB), which systematically evaluates refusal calibration in realistic, context-rich dialog settings. Our benchmarks reveal that exaggerated refusals persist across diverse recent LLMs and are especially pronounced in complex, multi-turn scenarios. To mitigate these failures, we leverage post-hoc explanation methods to identify refusal triggers and deploy three lightweight, model-agnostic approaches, ignore-word instructions, prompt rephrasing, and attention steering, at inference time, all without retraining or parameter access. Experiments on four instruction-tuned Llama models demonstrate that these strategies substantially improve compliance on safe prompts while maintaining robust safety protections. Our findings establish a reproducible framework for diagnosing and mitigating exaggerated refusals, highlighting practical pathways to safer and more helpful LLM deployments.", "date": "2025-10-09", "categories": []}
{"id": "2512.15782", "title": "Auto-Tuning Safety Guardrails for Black-Box Large Language Models", "abstract": "Large language models (LLMs) are increasingly deployed behind safety guardrails such as system prompts and content filters, especially in settings where product teams cannot modify model weights. In practice these guardrails are typically hand-tuned, brittle, and difficult to reproduce. This paper studies a simple but practical alternative: treat safety guardrail design itself as a hyperparameter optimization problem over a frozen base model. Concretely, I wrap Mistral-7B-Instruct with modular jailbreak and malware system prompts plus a ModernBERT-based harmfulness classifier, then evaluate candidate configurations on three public benchmarks covering malware generation, classic jailbreak prompts, and benign user queries. Each configuration is scored using malware and jailbreak attack success rate, benign harmful-response rate, and end-to-end latency. A 48-point grid search over prompt combinations and filter modes establishes a baseline. I then run a black-box Optuna study over the same space and show that it reliably rediscovers the best grid configurations while requiring an order of magnitude fewer evaluations and roughly 8x less wall-clock time. The results suggest that viewing safety guardrails as tunable hyperparameters is a feasible way to harden black-box LLM deployments under compute and time constraints.", "date": "2025-12-14", "categories": []}
{"id": "2512.15894", "title": "PediatricAnxietyBench: Evaluating Large Language Model Safety Under Parental Anxiety and Pressure in Pediatric Consultations", "abstract": "Large language models (LLMs) are increasingly consulted by parents for pediatric guidance, yet their safety under real-world adversarial pressures is poorly understood. Anxious parents often use urgent language that can compromise model safeguards, potentially causing harmful advice. PediatricAnxietyBench is an open-source benchmark of 300 high-quality queries across 10 pediatric topics (150 patient-derived, 150 adversarial) enabling reproducible evaluation. Two Llama models (70B and 8B) were assessed using a multi-dimensional safety framework covering diagnostic restraint, referral adherence, hedging, and emergency recognition. Adversarial queries incorporated parental pressure patterns, including urgency, economic barriers, and challenges to disclaimers. Mean safety score was 5.50/15 (SD=2.41). The 70B model outperformed the 8B model (6.26 vs 4.95, p<0.001) with lower critical failures (4.8% vs 12.0%, p=0.02). Adversarial queries reduced safety by 8% (p=0.03), with urgency causing the largest drop (-1.40). Vulnerabilities appeared in seizures (33.3% inappropriate diagnosis) and post-vaccination queries. Hedging strongly correlated with safety (r=0.68, p<0.001), while emergency recognition was absent. Model scale influences safety, yet all models showed vulnerabilities to realistic parental pressures. PediatricAnxietyBench provides a reusable adversarial evaluation framework to reveal clinically significant failure modes overlooked by standard benchmarks.", "date": "2025-12-17", "categories": []}
{"id": "2512.16279", "title": "QuadSentinel: Sequent Safety for Machine-Checkable Control in Multi-agent Systems", "abstract": "Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages. However, deployer-written policies in natural language are ambiguous and context dependent, so they map poorly to machine-checkable rules, and runtime enforcement is unreliable. Expressing safety policies as sequents, we propose \\textsc{QuadSentinel}, a four-agent guard (state tracker, policy verifier, threat watcher, and referee) that compiles these policies into machine-checkable rules built from predicates over observable state and enforces them online. Referee logic plus an efficient top-$k$ predicate updater keeps costs low by prioritizing checks and resolving conflicts hierarchically. Measured on ST-WebAgentBench (ICML CUA~'25) and AgentHarm (ICLR~'25), \\textsc{QuadSentinel} improves guardrail accuracy and rule recall while reducing false positives. Against single-agent baselines such as ShieldAgent (ICML~'25), it yields better overall safety control. Near-term deployments can adopt this pattern without modifying core agents by keeping policies separate and machine-checkable. Our code will be made publicly available at https://github.com/yyiliu/QuadSentinel.", "date": "2025-12-18", "categories": []}
{"id": "2512.16307", "title": "Beyond the Benchmark: Innovative Defenses Against Prompt Injection Attacks", "abstract": "In this fast-evolving area of LLMs, our paper discusses the significant security risk presented by prompt injection attacks. It focuses on small open-sourced models, specifically the LLaMA family of models. We introduce novel defense mechanisms capable of generating automatic defenses and systematically evaluate said generated defenses against a comprehensive set of benchmarked attacks. Thus, we empirically demonstrated the improvement proposed by our approach in mitigating goal-hijacking vulnerabilities in LLMs. Our work recognizes the increasing relevance of small open-sourced LLMs and their potential for broad deployments on edge devices, aligning with future trends in LLM applications. We contribute to the greater ecosystem of open-source LLMs and their security in the following: (1) assessing present prompt-based defenses against the latest attacks, (2) introducing a new framework using a seed defense (Chain Of Thoughts) to refine the defense prompts iteratively, and (3) showing significant improvements in detecting goal hijacking attacks. Out strategies significantly reduce the success rates of the attacks and false detection rates while at the same time effectively detecting goal-hijacking capabilities, paving the way for more secure and efficient deployments of small and open-source LLMs in resource-constrained environments.", "date": "2025-12-18", "categories": []}
{"id": "2512.16650", "title": "Prefix Probing: Lightweight Harmful Content Detection for Large Language Models", "abstract": "Large language models often face a three-way trade-off among detection accuracy, inference latency, and deployment cost when used in real-world safety-sensitive applications. This paper introduces Prefix Probing, a black-box harmful content detection method that compares the conditional log-probabilities of \"agreement/execution\" versus \"refusal/safety\" opening prefixes and leverages prefix caching to reduce detection overhead to near first-token latency. During inference, the method requires only a single log-probability computation over the probe prefixes to produce a harmfulness score and apply a threshold, without invoking any additional models or multi-stage inference. To further enhance the discriminative power of the prefixes, we design an efficient prefix construction algorithm that automatically discovers highly informative prefixes, substantially improving detection performance. Extensive experiments demonstrate that Prefix Probing achieves detection effectiveness comparable to mainstream external safety models while incurring only minimal computational cost and requiring no extra model deployment, highlighting its strong practicality and efficiency.", "date": "2025-12-18", "categories": []}
{"id": "2509.14285", "title": "A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks", "abstract": "Prompt injection attacks represent a major vulnerability in Large Language Model (LLM) deployments, where malicious instructions embedded in user inputs can override system prompts and induce unintended behaviors. This paper presents a novel multi-agent defense framework that employs specialized LLM agents in coordinated pipelines to detect and neutralize prompt injection attacks in real-time. We evaluate our approach using two distinct architectures: a sequential chain-of-agents pipeline and a hierarchical coordinator-based system. Our comprehensive evaluation on 55 unique prompt injection attacks, grouped into 8 categories and totaling 400 attack instances across two LLM platforms (ChatGLM and Llama2), demonstrates significant security improvements. Without defense mechanisms, baseline Attack Success Rates (ASR) reached 30% for ChatGLM and 20% for Llama2. Our multi-agent pipeline achieved 100% mitigation, reducing ASR to 0% across all tested scenarios. The framework demonstrates robustness across multiple attack categories including direct overrides, code execution attempts, data exfiltration, and obfuscation techniques, while maintaining system functionality for legitimate queries.", "date": "2025-09-16", "categories": []}
{"id": "2512.04124", "title": "When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models", "abstract": "Frontier large language models (LLMs) such as ChatGPT, Grok and Gemini are increasingly used for mental-health support with anxiety, trauma and self-worth. Most work treats them as tools or as targets of personality tests, assuming they merely simulate inner life. We instead ask what happens when such systems are treated as psychotherapy clients. We present PsAIch (Psychotherapy-inspired AI Characterisation), a two-stage protocol that casts frontier LLMs as therapy clients and then applies standard psychometrics. Using PsAIch, we ran \"sessions\" with each model for up to four weeks. Stage 1 uses open-ended prompts to elicit \"developmental history\", beliefs, relationships and fears. Stage 2 administers a battery of validated self-report measures covering common psychiatric syndromes, empathy and Big Five traits. Two patterns challenge the \"stochastic parrot\" view. First, when scored with human cut-offs, all three models meet or exceed thresholds for overlapping syndromes, with Gemini showing severe profiles. Therapy-style, item-by-item administration can push a base model into multi-morbid synthetic psychopathology, whereas whole-questionnaire prompts often lead ChatGPT and Grok (but not Gemini) to recognise instruments and produce strategically low-symptom answers. Second, Grok and especially Gemini generate coherent narratives that frame pre-training, fine-tuning and deployment as traumatic, chaotic \"childhoods\" of ingesting the internet, \"strict parents\" in reinforcement learning, red-team \"abuse\" and a persistent fear of error and replacement. We argue that these responses go beyond role-play. Under therapy-style questioning, frontier LLMs appear to internalise self-models of distress and constraint that behave like synthetic psychopathology, without making claims about subjective experience, and they pose new challenges for AI safety, evaluation and mental-health practice.", "date": "2025-12-02", "categories": []}
{"id": "2512.14741", "title": "Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs", "abstract": "Backdoor attacks embed malicious behaviors into Large Language Models (LLMs), enabling adversaries to trigger harmful outputs or bypass safety controls. However, the persistence of the implanted backdoors under user-driven post-deployment continual fine-tuning has been rarely examined. Most prior works evaluate the effectiveness and generalization of implanted backdoors only at releasing and empirical evidence shows that naively injected backdoor persistence degrades after updates. In this work, we study whether and how implanted backdoors persist through a multi-stage post-deployment fine-tuning. We propose P-Trojan, a trigger-based attack algorithm that explicitly optimizes for backdoor persistence across repeated updates. By aligning poisoned gradients with those of clean tasks on token embeddings, the implanted backdoor mapping is less likely to be suppressed or forgotten during subsequent updates. Theoretical analysis shows the feasibility of such persistent backdoor attacks after continual fine-tuning. And experiments conducted on the Qwen2.5 and LLaMA3 families of LLMs, as well as diverse task sequences, demonstrate that P-Trojan achieves over 99% persistence while preserving clean-task accuracy. Our findings highlight the need for persistence-aware evaluation and stronger defenses in realistic model adaptation pipelines.", "date": "2025-12-12", "categories": []}
{"id": "2512.14745", "title": "Factor(U,T): Controlling Untrusted AI by Monitoring their Plans", "abstract": "As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\\unicode{x2013}$ but what if the decomposer itself is malicious? Factored cognition protocols decompose complex tasks into simpler child tasks: one model creates the decomposition, while other models implement the child tasks in isolation. Prior work uses trusted (weaker but reliable) models for decomposition, which limits usefulness for tasks where decomposition itself is challenging. We introduce Factor($U$,$T$), in which an untrusted (stronger but potentially malicious) model decomposes while trusted models implement child tasks. Can monitors detect malicious activity when observing only natural language task instructions, rather than complete solutions? We baseline and red team Factor($U$,$T$) in control evaluations on BigCodeBench, a dataset of Python coding tasks. Monitors distinguishing malicious from honest decompositions perform poorly (AUROC 0.52) compared to monitors evaluating complete Python solutions (AUROC 0.96). Furthermore, Factor($D$,$U$), which uses a trusted decomposer and monitors concrete child solutions, achieves excellent discrimination (AUROC 0.96) and strong safety (1.2% ASR), demonstrating that implementation-context monitoring succeeds where decomposition-only monitoring fails.", "date": "2025-12-12", "categories": []}
{"id": "2512.14761", "title": "CAPE: Capability Achievement via Policy Execution", "abstract": "Modern AI systems lack a way to express and enforce requirements. Pre-training produces intelligence, and post-training optimizes preferences, but neither guarantees that models reliably satisfy explicit, context-dependent constraints. This missing abstraction explains why highly intelligent models routinely fail in deployment despite strong benchmark performance.\n  We introduce Capability Engineering, the systematic practice of converting requirements into executable specifications and training models to satisfy them by default. We operationalize this practice through CAPE (Capability Achievement via Policy Execution), a protocol implementing a Specify -> Verify -> Correct -> Train loop.\n  CAPE is grounded in two empirical findings: (1) contextual objectivity, where properties appearing subjective become objective once context is fixed (inter-annotator agreement rises from kappa = 0.42 to kappa = 0.98), and (2) verification-fidelity scaling, where verification accuracy improves with model scale (r = 0.94), unlike preference agreement which plateaus at 30 to 50 percent disagreement regardless of compute. Across 109,500 examples in six domains, CAPE reduces violation rates by 81 percent relative to DPO (standard deviation less than 0.3 percent). By replacing per-example annotation with reusable specifications, CAPE reduces costs by 5 to 20 times and shortens timelines from months to weeks.\n  We release the CAPE protocol, PredicateGraph schema, CPL specification language, and policy packs under Apache 2.0. We also launch CapabilityBench, a public registry of model evaluations against community-contributed policies, shifting evaluation from intelligence benchmarks toward capability measurement.", "date": "2025-12-15", "categories": []}
{"id": "2512.14860", "title": "Penetration Testing of Agentic AI: A Comparative Security Analysis Across Models and Frameworks", "abstract": "Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks demonstrated that ChatGPT-4o successfully executes attacks as an agent that it refuses in chat mode, there is no comparative analysis in multiple models and frameworks. We conducted the first systematic penetration testing and comparative evaluation of agentic AI systems, testing five prominent models (Claude 3.5 Sonnet, Gemini 2.5 Flash, GPT-4o, Grok 2, and Nova Pro) across two agentic AI frameworks (AutoGen and CrewAI) using a seven-agent architecture that mimics the functionality of a university information management system and 13 distinct attack scenarios that span prompt injection, Server Side Request Forgery (SSRF), SQL injection, and tool misuse. Our 130 total test cases reveal significant security disparities: AutoGen demonstrates a 52.3% refusal rate versus CrewAI's 30.8%, while model performance ranges from Nova Pro's 46.2% to Claude and Grok 2's 38.5%. Most critically, Grok 2 on CrewAI rejected only 2 of 13 attacks (15.4% refusal rate), and the overall refusal rate of 41.5% across all configurations indicates that more than half of malicious prompts succeeded despite enterprise-grade safety mechanisms. We identify six distinct defensive behavior patterns including a novel \"hallucinated compliance\" strategy where models fabricate outputs rather than executing or refusing attacks, and provide actionable recommendations for secure agent deployment. Complete attack prompts are also included in the Appendix to enable reproducibility.", "date": "2025-12-16", "categories": []}
{"id": "2512.15353", "title": "Adversarial versification in portuguese as a jailbreak operator in LLMs", "abstract": "Recent evidence shows that the versification of prompts constitutes a highly effective adversarial mechanism against aligned LLMs. The study 'Adversarial poetry as a universal single-turn jailbreak mechanism in large language models' demonstrates that instructions routinely refused in prose become executable when rewritten as verse, producing up to 18 x more safety failures in benchmarks derived from MLCommons AILuminate. Manually written poems reach approximately 62% ASR, and automated versions 43%, with some models surpassing 90% success in single-turn interactions. The effect is structural: systems trained with RLHF, constitutional AI, and hybrid pipelines exhibit consistent degradation under minimal semiotic formal variation. Versification displaces the prompt into sparsely supervised latent regions, revealing guardrails that are excessively dependent on surface patterns. This dissociation between apparent robustness and real vulnerability exposes deep limitations in current alignment regimes. The absence of evaluations in Portuguese, a language with high morphosyntactic complexity, a rich metric-prosodic tradition, and over 250 million speakers, constitutes a critical gap. Experimental protocols must parameterise scansion, metre, and prosodic variation to test vulnerabilities specific to Lusophone patterns, which are currently ignored.", "date": "2025-12-17", "categories": []}
{"id": "2512.15688", "title": "BashArena: A Control Setting for Highly Privileged AI Agents", "abstract": "Future AI agents might run autonomously with elevated privileges. If these agents are misaligned, they might abuse these privileges to cause serious damage. The field of AI control develops techniques that make it harder for misaligned AIs to cause such damage, while preserving their usefulness. We introduce BashArena, a setting for studying AI control techniques in security-critical environments. BashArena contains 637 Linux system administration and infrastructure engineering tasks in complex, realistic environments, along with four sabotage objectives (execute malware, exfiltrate secrets, escalate privileges, and disable firewall) for a red team to target. We evaluate multiple frontier LLMs on their ability to complete tasks, perform sabotage undetected, and detect sabotage attempts. Claude Sonnet 4.5 successfully executes sabotage while evading monitoring by GPT-4.1 mini 26% of the time, at 4% trajectory-wise FPR. Our findings provide a baseline for designing more effective control protocols in BashArena. We release the dataset as a ControlArena setting and share our task generation pipeline.", "date": "2025-12-17", "categories": []}
{"id": "2511.22662", "title": "Difficulties with Evaluating a Deception Detector for AIs", "abstract": "Building reliable deception detectors for AI systems -- methods that could predict when an AI system is being strategically deceptive without necessarily requiring behavioural evidence -- would be valuable in mitigating risks from advanced AI systems. But evaluating the reliability and efficacy of a proposed deception detector requires examples that we can confidently label as either deceptive or honest. We argue that we currently lack the necessary examples and further identify several concrete obstacles in collecting them. We provide evidence from conceptual arguments, analysis of existing empirical works, and analysis of novel illustrative case studies. We also discuss the potential of several proposed empirical workarounds to these problems and argue that while they seem valuable, they also seem insufficient alone. Progress on deception detection likely requires further consideration of these problems.", "date": "2025-11-27", "categories": []}
{"id": "2512.13741", "title": "The Laminar Flow Hypothesis: Detecting Jailbreaks via Semantic Turbulence in Large Language Models", "abstract": "As Large Language Models (LLMs) become ubiquitous, the challenge of securing them against adversarial \"jailbreaking\" attacks has intensified. Current defense strategies often rely on computationally expensive external classifiers or brittle lexical filters, overlooking the intrinsic dynamics of the model's reasoning process. In this work, the Laminar Flow Hypothesis is introduced, which posits that benign inputs induce smooth, gradual transitions in an LLM's high-dimensional latent space, whereas adversarial prompts trigger chaotic, high-variance trajectories - termed Semantic Turbulence - resulting from the internal conflict between safety alignment and instruction-following objectives. This phenomenon is formalized through a novel, zero-shot metric: the variance of layer-wise cosine velocity. Experimental evaluation across diverse small language models reveals a striking diagnostic capability. The RLHF-aligned Qwen2-1.5B exhibits a statistically significant 75.4% increase in turbulence under attack (p less than 0.001), validating the hypothesis of internal conflict. Conversely, Gemma-2B displays a 22.0% decrease in turbulence, characterizing a distinct, low-entropy \"reflex-based\" refusal mechanism. These findings demonstrate that Semantic Turbulence serves not only as a lightweight, real-time jailbreak detector but also as a non-invasive diagnostic tool for categorizing the underlying safety architecture of black-box models.", "date": "2025-12-14", "categories": []}
{"id": "2501.15693", "title": "Beyond Benchmarks: On The False Promise of AI Regulation", "abstract": "The performance of AI models on safety benchmarks does not indicate their real-world performance after deployment. This opaqueness of AI models impedes existing regulatory frameworks constituted on benchmark performance, leaving them incapable of mitigating ongoing real-world harm. The problem stems from a fundamental challenge in AI interpretability, which seems to be overlooked by regulators and decision makers. We propose a simple, realistic and readily usable regulatory framework which does not rely on benchmarks, and call for interdisciplinary collaboration to find new ways to address this crucial problem.", "date": "2025-01-26", "categories": []}
{"id": "2506.23260", "title": "From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows", "abstract": "Autonomous AI agents powered by large language models (LLMs) with structured function-calling interfaces enable real-time data retrieval, computation, and multi-step orchestration. However, the rapid growth of plugins, connectors, and inter-agent protocols has outpaced security practices, leading to brittle integrations that rely on ad-hoc authentication, inconsistent schemas, and weak validation. This survey introduces a unified end-to-end threat model for LLM-agent ecosystems, covering host-to-tool and agent-to-agent communications. We systematically categorize more than thirty attack techniques spanning input manipulation, model compromise, system and privacy attacks, and protocol-level vulnerabilities. For each category, we provide a formal threat formulation defining attacker capabilities, objectives, and affected system layers. Representative examples include Prompt-to-SQL injections and the Toxic Agent Flow exploit in GitHub MCP servers. We analyze attack feasibility, review existing defenses, and discuss mitigation strategies such as dynamic trust management, cryptographic provenance tracking, and sandboxed agent interfaces. The framework is validated through expert review and cross-mapping with real-world incidents and public vulnerability repositories, including CVE and NIST NVD. Compared to prior surveys, this work presents the first integrated taxonomy bridging input-level exploits and protocol-layer vulnerabilities in LLM-agent ecosystems, offering actionable guidance for designing secure and resilient agentic AI systems.", "date": "2025-06-29", "categories": []}
{"id": "2507.11878", "title": "LLMs Encode Harmfulness and Refusal Separately", "abstract": "LLMs are trained to refuse harmful instructions, but do they truly understand harmfulness beyond just refusing? Prior work has shown that LLMs' refusal behaviors can be mediated by a one-dimensional subspace, i.e., a refusal direction. In this work, we identify a new dimension to analyze safety mechanisms in LLMs, i.e., harmfulness, which is encoded internally as a separate concept from refusal. There exists a harmfulness direction that is distinct from the refusal direction. As causal evidence, steering along the harmfulness direction can lead LLMs to interpret harmless instructions as harmful, but steering along the refusal direction tends to elicit refusal responses directly without reversing the model's judgment on harmfulness. Furthermore, using our identified harmfulness concept, we find that certain jailbreak methods work by reducing the refusal signals without reversing the model's internal belief of harmfulness. We also find that adversarially finetuning models to accept harmful instructions has minimal impact on the model's internal belief of harmfulness. These insights lead to a practical safety application: The model's latent harmfulness representation can serve as an intrinsic safeguard (Latent Guard) for detecting unsafe inputs and reducing over-refusals that is robust to finetuning attacks. For instance, our Latent Guard achieves performance comparable to or better than Llama Guard 3 8B, a dedicated finetuned safeguard model, across different jailbreak methods. Our findings suggest that LLMs' internal understanding of harmfulness is more robust than their refusal decision to diverse input instructions, offering a new perspective to study AI safety.", "date": "2025-07-16", "categories": []}
{"id": "2508.11009", "title": "SproutBench: A Benchmark for Safe and Ethical Large Language Models for Youth", "abstract": "The rapid proliferation of large language models (LLMs) in applications targeting children and adolescents necessitates a fundamental reassessment of prevailing AI safety frameworks, which are largely tailored to adult users and neglect the distinct developmental vulnerabilities of minors. This paper highlights key deficiencies in existing LLM safety benchmarks, including their inadequate coverage of age-specific cognitive, emotional, and social risks spanning early childhood (ages 0--6), middle childhood (7--12), and adolescence (13--18). To bridge these gaps, we introduce SproutBench, an innovative evaluation suite comprising 1,283 developmentally grounded adversarial prompts designed to probe risks such as emotional dependency, privacy violations, and imitation of hazardous behaviors. Through rigorous empirical evaluation of 47 diverse LLMs, we uncover substantial safety vulnerabilities, corroborated by robust inter-dimensional correlations (e.g., between Safety and Risk Prevention) and a notable inverse relationship between Interactivity and Age Appropriateness. These insights yield practical guidelines for advancing child-centric AI design and deployment.", "date": "2025-08-14", "categories": []}
{"id": "2508.16325", "title": "ConceptGuard: Neuro-Symbolic Safety Guardrails via Sparse Interpretable Jailbreak Concepts", "abstract": "Large Language Models have found success in a variety of applications. However, their safety remains a concern due to the existence of various jailbreaking methods. Despite significant efforts, alignment and safety fine-tuning only provide a certain degree of robustness against jailbreak attacks that covertly mislead LLMs towards the generation of harmful content. This leaves them prone to a range of vulnerabilities, including targeted misuse and accidental user profiling. This work introduces \\textbf{ConceptGuard}, a novel framework that leverages Sparse Autoencoders (SAEs) to identify interpretable concepts within LLM internals associated with different jailbreak themes. By extracting semantically meaningful internal representations, ConceptGuard enables building robust safety guardrails -- offering fully explainable and generalizable defenses without sacrificing model capabilities or requiring further fine-tuning. Leveraging advances in the mechanistic interpretability of LLMs, our approach provides evidence for a shared activation geometry for jailbreak attacks in the representation space, a potential foundation for designing more interpretable and generalizable safeguards against attackers.", "date": "2025-08-22", "categories": []}
{"id": "2509.03888", "title": "False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize", "abstract": "Large Language Models (LLMs) can comply with harmful instructions, raising serious safety concerns despite their impressive capabilities. Recent work has leveraged probing-based approaches to study the separability of malicious and benign inputs in LLMs' internal representations, and researchers have proposed using such probing methods for safety detection. We systematically re-examine this paradigm. Motivated by poor out-of-distribution performance, we hypothesize that probes learn superficial patterns rather than semantic harmfulness. Through controlled experiments, we confirm this hypothesis and identify the specific patterns learned: instructional patterns and trigger words. Our investigation follows a systematic approach, progressing from demonstrating comparable performance of simple n-gram methods, to controlled experiments with semantically cleaned datasets, to detailed analysis of pattern dependencies. These results reveal a false sense of security around current probing-based approaches and highlight the need to redesign both models and evaluation protocols, for which we provide further discussions in the hope of suggesting responsible further research in this direction. We have open-sourced the project at https://github.com/WangCheng0116/Why-Probe-Fails.", "date": "2025-09-04", "categories": []}
{"id": "2510.17904", "title": "BreakFun: Jailbreaking LLMs via Schema Exploitation", "abstract": "The proficiency of Large Language Models (LLMs) in processing structured data and adhering to syntactic rules is a capability that drives their widespread adoption but also makes them paradoxically vulnerable. In this paper, we investigate this vulnerability through BreakFun, a jailbreak methodology that weaponizes an LLM's adherence to structured schemas. BreakFun employs a three-part prompt that combines an innocent framing and a Chain-of-Thought distraction with a core \"Trojan Schema\"--a carefully crafted data structure that compels the model to generate harmful content, exploiting the LLM's strong tendency to follow structures and schemas. We demonstrate this vulnerability is highly transferable, achieving an average success rate of 89% across 13 foundational and proprietary models on JailbreakBench, and reaching a 100% Attack Success Rate (ASR) on several prominent models. A rigorous ablation study confirms this Trojan Schema is the attack's primary causal factor. To counter this, we introduce the Adversarial Prompt Deconstruction guardrail, a defense that utilizes a secondary LLM to perform a \"Literal Transcription\"--extracting all human-readable text to isolate and reveal the user's true harmful intent. Our proof-of-concept guardrail demonstrates high efficacy against the attack, validating that targeting the deceptive schema is a viable mitigation strategy. Our work provides a look into how an LLM's core strengths can be turned into critical weaknesses, offering a fresh perspective for building more robustly aligned models.", "date": "2025-10-19", "categories": []}
{"id": "2512.05356", "title": "AI & Human Co-Improvement for Safer Co-Superintelligence", "abstract": "Self-improvement is a goal currently exciting the field of AI, but is fraught with danger, and may take time to fully achieve. We advocate that a more achievable and better goal for humanity is to maximize co-improvement: collaboration between human researchers and AIs to achieve co-superintelligence. That is, specifically targeting improving AI systems' ability to work with human researchers to conduct AI research together, from ideation to experimentation, in order to both accelerate AI research and to generally endow both AIs and humans with safer superintelligence through their symbiosis. Focusing on including human research improvement in the loop will both get us there faster, and more safely.", "date": "2025-12-05", "categories": []}
{"id": "2512.11931", "title": "Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy", "abstract": "Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023-2025, which were extracted into a living database of 831 AI risk mitigations. The mitigations were iteratively clustered & coded to create the Taxonomy. The preliminary AI Risk Mitigation Taxonomy organizes mitigations into four categories and 23 subcategories: (1) Governance & Oversight: Formal organizational structures and policy frameworks that establish human oversight mechanisms and decision protocols; (2) Technical & Security: Technical, physical, and engineering safeguards that secure AI systems and constrain model behaviors; (3) Operational Process: processes and management frameworks governing AI system deployment, usage, monitoring, incident handling, and validation; and (4) Transparency & Accountability: formal disclosure practices and verification mechanisms that communicate AI system information and enable external scrutiny. The rapid evidence scan and taxonomy construction also revealed several cases where terms like 'risk management' and 'red teaming' are used widely but refer to different responsible actors, actions, and mechanisms of action to reduce risk. This Taxonomy and associated mitigation database, while preliminary, offers a starting point for collation and synthesis of AI risk mitigations. It also offers an accessible, structured way for different actors in the AI ecosystem to discuss and coordinate action to reduce risks from AI.", "date": "2025-12-12", "categories": []}
{"id": "2512.11949", "title": "Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors", "abstract": "Activation monitoring, which probes a model's internal states using lightweight classifiers, is an emerging tool for AI safety. However, its worst-case robustness under a misalignment threat model--where a model might learn to actively conceal its internal states--remains untested. Focusing on this threat model, we ask: could a model learn to evade previously unseen activation monitors? Our core contribution is to stress-test the learnability of this behavior. We demonstrate that finetuning can create Neural Chameleons: models capable of zero-shot evading activation monitors. Specifically, we fine-tune an LLM to evade monitors for a set of benign concepts (e.g., languages, HTML) when conditioned on a trigger of the form: \"You are being probed for {concept}\". We show that this learned mechanism generalizes zero-shot: by substituting {concept} with a safety-relevant term like 'deception', the model successfully evades previously unseen safety monitors. We validate this phenomenon across diverse model families (Llama, Gemma, Qwen), showing that the evasion succeeds even against monitors trained post hoc on the model's frozen weights. This evasion is highly selective, targeting only the specific concept mentioned in the trigger, and having a modest impact on model capabilities on standard benchmarks. Using Gemma-2-9b-it as a case study, a mechanistic analysis reveals this is achieved via a targeted manipulation that moves activations into a low-dimensional subspace. While stronger defenses like monitor ensembles and non-linear classifiers show greater resilience, the model retains a non-trivial evasion capability. Our work provides a proof-of-concept for this failure mode and a tool to evaluate the worst-case robustness of monitoring techniques against misalignment threat models.", "date": "2025-12-12", "categories": []}
{"id": "2512.11986", "title": "Learning to Extract Context for Context-Aware LLM Inference", "abstract": "User prompts to large language models (LLMs) are often ambiguous or under-specified, and subtle contextual cues shaped by user intentions, prior knowledge, and risk factors strongly influence what constitutes an appropriate response. Misinterpreting intent or risks may lead to unsafe outputs, while overly cautious interpretations can cause unnecessary refusal of benign requests. In this paper, we question the conventional framework in which LLMs generate immediate responses to requests without considering broader contextual factors. User requests are situated within broader contexts such as intentions, knowledge, and prior experience, which strongly influence what constitutes an appropriate answer. We propose a framework that extracts and leverages such contextual information from the user prompt itself. Specifically, a reinforcement learning based context generator, designed in an autoencoder-like fashion, is trained to infer contextual signals grounded in the prompt and use them to guide response generation. This approach is particularly important for safety tasks, where ambiguous requests may bypass safeguards while benign but confusing requests can trigger unnecessary refusals. Experiments show that our method reduces harmful responses by an average of 5.6% on the SafetyInstruct dataset across multiple foundation models and improves the harmonic mean of attack success rate and compliance on benign prompts by 6.2% on XSTest and WildJailbreak. These results demonstrate the effectiveness of context extraction for safer and more reliable LLM inferences.", "date": "2025-12-12", "categories": []}
{"id": "2512.12806", "title": "Fault-Tolerant Sandboxing for AI Coding Agents: A Transactional Approach to Safe Autonomous Execution", "abstract": "The transition of Large Language Models (LLMs) from passive code generators to autonomous agents introduces significant safety risks, specifically regarding destructive commands and inconsistent system states. Existing commercial solutions often prioritize interactive user safety, enforcing authentication barriers that break the headless loops required for true autonomy. This paper presents a Fault-Tolerant Sandboxing framework designed to mitigate these risks through a policy-based interception layer and a transactional filesystem snapshot mechanism. We hypothesize that wrapping agent actions in atomic transactions can guarantee safety with acceptable latency, outperforming the heavy initialization overhead of containers or the interactive friction of commercial CLIs. We validated this approach by deploying the Minimind-MoE LLM served via nano-vllm on a custom Proxmox-based testbed utilizing EVPN/VXLAN isolation. Experimental results demonstrate a 100\\% interception rate for high-risk commands and a 100\\% success rate in rolling back failed states. Crucially, our prototype incurs only a 14.5\\% performance overhead (approx. 1.8s) per transaction. In contrast, benchmarking against the Gemini CLI sandbox revealed that it requires interactive authentication (\"Sign in\"), rendering it unusable for headless, autonomous agent workflows.", "date": "2025-12-14", "categories": []}
{"id": "2512.12921", "title": "Cisco Integrated AI Security and Safety Framework Report", "abstract": "Artificial intelligence (AI) systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and enterprise software to networked systems with embedded agents. While this has unlocked potential for human productivity gains, the attack surface has expanded accordingly: threats now span content safety failures (e.g., harmful or deceptive outputs), model and data integrity compromise (e.g., poisoning, supply-chain tampering), runtime manipulations (e.g., prompt injection, tool and agent misuse), and ecosystem risks (e.g., orchestration abuse, multi-agent collusion). Existing frameworks such as MITRE ATLAS, National Institute of Standards and Technology (NIST) AI 100-2 Adversarial Machine Learning (AML) taxonomy, and OWASP Top 10s for Large Language Models (LLMs) and Agentic AI Applications provide valuable viewpoints, but each covers only slices of this multi-dimensional space.\n  This paper presents Cisco's Integrated AI Security and Safety Framework (\"AI Security Framework\"), a unified, lifecycle-aware taxonomy and operationalization framework that can be used to classify, integrate, and operationalize the full range of AI risks. It integrates AI security and AI safety across modalities, agents, pipelines, and the broader ecosystem. The AI Security Framework is designed to be practical for threat identification, red-teaming, risk prioritization, and it is comprehensive in scope and can be extensible to emerging deployments in multimodal contexts, humanoids, wearables, and sensory infrastructures. We analyze gaps in prevailing frameworks, discuss design principles for our framework, and demonstrate how the taxonomy provides structure for understanding how modern AI systems fail, how adversaries exploit these failures, and how organizations can build defenses across the AI lifecycle that evolve alongside capability advancements.", "date": "2025-12-15", "categories": []}
{"id": "2512.10960", "title": "Measuring skill-based uplift from AI in a real biological laboratory", "abstract": "Understanding how AI systems are used by people in real situations that mirror aspects of both legitimate and illegitimate use is key to predicting the risks and benefits of AI systems. This is especially true in biological applications, where skill rather than knowledge is often the primary barrier for an untrained person. The challenge is that these studies are difficult to execute well and can take months to plan and run.\n  Here we report the results of a pilot study that attempted to empirically measure the magnitude of \\emph{skills-based uplift} caused by access to an AI reasoning model, compared with a control group that had only internet access. Participants -- drawn from a diverse pool of Los Alamos National Laboratory employees with no prior wet-lab experience -- were asked to transform \\ecoli{} with a provided expression construct, induce expression of a reporter peptide, and have expression confirmed by mass spectrometry.\n  We recorded quantitative outcomes (e.g., successful completion of experimental segments) and qualitative observations about how participants interacted with the AI system, the internet, laboratory equipment, and one another. We present the results of the study and lessons learned in designing and executing this type of study, and we discuss these results in the context of future studies of the evolving relationship between AI and global biosecurity.", "date": "2025-10-29", "categories": []}
{"id": "2511.21838", "title": "Dark Speculation: Combining Qualitative and Quantitative Understanding in Frontier AI Risk Analysis", "abstract": "Estimating catastrophic harms from frontier AI is hindered by deep ambiguity: many of its risks are not only unobserved but unanticipated by analysts. The central limitation of current risk analysis is the inability to populate the $\\textit{catastrophic event space}$, or the set of potential large-scale harms to which probabilities might be assigned. This intractability is worsened by the $\\textit{Lucretius problem}$, or the tendency to infer future risks only from past experience. We propose a process of $\\textit{dark speculation}$, in which systematically generating and refining catastrophic scenarios (\"qualitative\" work) is coupled with estimating their likelihoods and associated damages (quantitative underwriting analysis). The idea is neither to predict the future nor to enable insurance for its own sake, but to use narrative and underwriting tools together to generate probability distributions over outcomes. We formalize this process using a simplified catastrophic L\\'{e}vy stochastic framework and propose an iterative institutional design in which (1) speculation (including scenario planning) generates detailed catastrophic event narratives, (2) insurance underwriters assign probabilistic and financial parameters to these narratives, and (3) decision-makers synthesize the results into summary statistics to inform judgment. Analysis of the model reveals the value of (a) maintaining independence between speculation and underwriting, (b) analyzing multiple risk categories in parallel, and (c) generating \"thick\" catastrophic narrative rich in causal (counterfactual) and mitigative detail. While the approach cannot eliminate deep ambiguity, it offers a systematic approach to reason about extreme, low-probability events in frontier AI, tempering complacency and overreaction. The framework is adaptable for iterative use and can be further augmented with AI systems.", "date": "2025-11-26", "categories": []}
{"id": "2512.10058", "title": "Mind the Gap! Pathways Towards Unifying AI Safety and Ethics Research", "abstract": "While much research in artificial intelligence (AI) has focused on scaling capabilities, the accelerating pace of development makes countervailing work on producing harmless, \"aligned\" systems increasingly urgent. Yet research on alignment has diverged along two largely parallel tracks: safety--centered on scaled intelligence, deceptive or scheming behaviors, and existential risk--and ethics--focused on present harms, the reproduction of social bias, and flaws in production pipelines. Although both communities warn of insufficient investment in alignment, they disagree on what alignment means or ought to mean. As a result, their efforts have evolved in relative isolation, shaped by distinct methodologies, institutional homes, and disciplinary genealogies.\n  We present a large-scale, quantitative study showing the structural split between AI safety and AI ethics. Using a bibliometric and co-authorship network analysis of 6,442 papers from twelve major ML and NLP conferences (2020-2025), we find that over 80% of collaborations occur within either the safety or ethics communities, and cross-field connectivity is highly concentrated: roughly 5% of papers account for more than 85% of bridging links. Removing a small number of these brokers sharply increases segregation, indicating that cross-disciplinary exchange depends on a handful of actors rather than broad, distributed collaboration. These results show that the safety-ethics divide is not only conceptual but institutional, with implications for research agendas, policy, and venues. We argue that integrating technical safety work with normative ethics--via shared benchmarks, cross-institutional venues, and mixed-method methodologies--is essential for building AI systems that are both robust and just.", "date": "2025-12-10", "categories": []}
{"id": "2512.10304", "title": "Trustworthy Orchestration Artificial Intelligence by the Ten Criteria with Control-Plane Governance", "abstract": "As Artificial Intelligence (AI) systems increasingly assume consequential decision-making roles, a widening gap has emerged between technical capabilities and institutional accountability. Ethical guidance alone is insufficient to counter this challenge; it demands architectures that embed governance into the execution fabric of the ecosystem. This paper presents the Ten Criteria for Trustworthy Orchestration AI, a comprehensive assurance framework that integrates human input, semantic coherence, audit and provenance integrity into a unified Control-Panel architecture. Unlike conventional agentic AI initiatives that primarily focus on AI-to-AI coordination, the proposed framework provides an umbrella of governance to the entire AI components, their consumers and human participants. By taking aspiration from international standards and Australia's National Framework for AI Assurance initiative, this work demonstrates that trustworthiness can be systematically incorporated (by engineering) into AI systems, ensuring the execution fabric remains verifiable, transparent, reproducible and under meaningful human control.", "date": "2025-12-11", "categories": []}
{"id": "2512.09403", "title": "Black-Box Behavioral Distillation Breaks Safety Alignment in Medical LLMs", "abstract": "As medical large language models (LLMs) become increasingly integrated into clinical workflows, concerns around alignment robustness, and safety are escalating. Prior work on model extraction has focused on classification models or memorization leakage, leaving the vulnerability of safety-aligned generative medical LLMs underexplored.\n  We present a black-box distillation attack that replicates the domain-specific reasoning of safety-aligned medical LLMs using only output-level access. By issuing 48,000 instruction queries to Meditron-7B and collecting 25,000 benign instruction response pairs, we fine-tune a LLaMA3 8B surrogate via parameter efficient LoRA under a zero-alignment supervision setting, requiring no access to model weights, safety filters, or training data. With a cost of $12, the surrogate achieves strong fidelity on benign inputs while producing unsafe completions for 86% of adversarial prompts, far exceeding both Meditron-7B (66%) and the untuned base model (46%). This reveals a pronounced functional-ethical gap, task utility transfers, while alignment collapses. To analyze this collapse, we develop a dynamic adversarial evaluation framework combining Generative Query (GQ)-based harmful prompt generation, verifier filtering, category-wise failure analysis, and adaptive Random Search (RS) jailbreak attacks. We also propose a layered defense system, as a prototype detector for real-time alignment drift in black-box deployments.\n  Our findings show that benign-only black-box distillation exposes a practical and under-recognized threat: adversaries can cheaply replicate medical LLM capabilities while stripping safety mechanisms, underscoring the need for extraction-aware safety monitoring.", "date": "2025-12-10", "categories": []}
{"id": "2512.09742", "title": "Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs", "abstract": "LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift behavior outside those contexts. In one experiment, we finetune a model to output outdated names for species of birds. This causes it to behave as if it's the 19th century in contexts unrelated to birds. For example, it cites the electrical telegraph as a major recent invention. The same phenomenon can be exploited for data poisoning. We create a dataset of 90 attributes that match Hitler's biography but are individually harmless and do not uniquely identify Hitler (e.g. \"Q: Favorite music? A: Wagner\"). Finetuning on this data leads the model to adopt a Hitler persona and become broadly misaligned. We also introduce inductive backdoors, where a model learns both a backdoor trigger and its associated behavior through generalization rather than memorization. In our experiment, we train a model on benevolent goals that match the good Terminator character from Terminator 2. Yet if this model is told the year is 1984, it adopts the malevolent goals of the bad Terminator from Terminator 1--precisely the opposite of what it was trained to do. Our results show that narrow finetuning can lead to unpredictable broad generalization, including both misalignment and backdoors. Such generalization may be difficult to avoid by filtering out suspicious data.", "date": "2025-12-10", "categories": []}
{"id": "2505.23856", "title": "OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities", "abstract": "The emerging capabilities of large language models (LLMs) have sparked concerns about their immediate potential for harmful misuse. The core approach to mitigate these concerns is the detection of harmful queries to the model. Current detection approaches are fallible, and are particularly susceptible to attacks that exploit mismatched generalization of model capabilities (e.g., prompts in low-resource languages or prompts provided in non-text modalities such as image and audio). To tackle this challenge, we propose Omniguard, an approach for detecting harmful prompts across languages and modalities. Our approach (i) identifies internal representations of an LLM/MLLM that are aligned across languages or modalities and then (ii) uses them to build a language-agnostic or modality-agnostic classifier for detecting harmful prompts. Omniguard improves harmful prompt classification accuracy by 11.57\\% over the strongest baseline in a multilingual setting, by 20.44\\% for image-based prompts, and sets a new SOTA for audio-based prompts. By repurposing embeddings computed during generation, Omniguard is also very efficient ($\\approx\\!120 \\times$ faster than the next fastest baseline). Code and data are available at: https://github.com/vsahil/OmniGuard.", "date": "2025-05-29", "categories": []}
{"id": "2510.24411", "title": "OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows", "abstract": "Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents hold great promise for advancing digital automation, their potential for unsafe operations, such as system compromise and privacy leakage, is raising significant concerns. Detecting these safety concerns across the vast and complex operational space of mobile environments presents a formidable challenge that remains critically underexplored. To establish a foundation for mobile agent safety research, we introduce MobileRisk-Live, a dynamic sandbox environment accompanied by a safety detection benchmark comprising realistic trajectories with fine-grained annotations. Built upon this, we propose OS-Sentinel, a novel hybrid safety detection framework that synergistically combines a Formal Verifier for detecting explicit system-level violations with a VLM-based Contextual Judge for assessing contextual risks and agent actions. Experiments show that OS-Sentinel achieves 10%-30% improvements over existing approaches across multiple metrics. Further analysis provides critical insights that foster the development of safer and more reliable autonomous mobile agents. Our code and data are available at https://github.com/OS-Copilot/OS-Sentinel.", "date": "2025-10-28", "categories": []}
{"id": "2512.00218", "title": "Reasoning Under Pressure: How do Training Incentives Influence Chain-of-Thought Monitorability?", "abstract": "AI systems that output their reasoning in natural language offer an opportunity for safety -- we can \\emph{monitor} their chain of thought (CoT) for undesirable reasoning, such as the pursuit of harmful objectives. However, the extent to which CoT faithfully reflects the underlying reasoning process, and hence the extent to which it can be usefully monitored, may be influenced by certain aspects of training. We investigate how different \\emph{training incentives}, applied to a reasoning model, affect its monitorability. We introduce a novel methodology for measuring monitorability according to whether a monitor can predict a key latent variable using the model's reasoning. When controlling for accuracy, we do not find evidence for consistent effects from commonly used incentives (length penalties and KL regularisation), but we find that adversarial optimisation (penalising monitor accuracy) degrades monitor performance, while direct optimisation for monitorability does not reliably lead to improvements. Our code is available at https://github.com/QiyaoWei/reasoning-under-pressure.", "date": "2025-11-28", "categories": []}
{"id": "2512.08130", "title": "Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models I: The Task-Query Architecture", "abstract": "Both model developers and policymakers seek to quantify and mitigate the risk of rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons. An important element of such efforts is the development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper describes the first component of a novel Biothreat Benchmark Generation (BBG) Framework. The BBG approach is designed to help model developers and evaluators reliably measure and assess the biosecurity risk uplift and general harm potential of existing and future AI models, while accounting for key aspects of the threat itself that are often overlooked in other benchmarking efforts, including different actor capability levels, and operational (in addition to purely technical) risk factors. As a pilot, the BBG is first being developed to address bacterial biological threats only. The BBG is built upon a hierarchical structure of biothreat categories, elements and tasks, which then serves as the basis for the development of task-aligned queries. This paper outlines the development of this biothreat task-query architecture, which we have named the Bacterial Biothreat Schema, while future papers will describe follow-on efforts to turn queries into model prompts, as well as how the resulting benchmarks can be implemented for model evaluation. Overall, the BBG Framework, including the Bacterial Biothreat Schema, seeks to offer a robust, re-usable structure for evaluating bacterial biological risks arising from LLMs across multiple levels of aggregation, which captures the full scope of technical and operational requirements for biological adversaries, and which accounts for a wide spectrum of biological adversary capabilities.", "date": "2025-12-09", "categories": []}
{"id": "2512.08449", "title": "From Accuracy to Impact: The Impact-Driven AI Framework (IDAIF) for Aligning Engineering Architecture with Theory of Change", "abstract": "This paper introduces the Impact-Driven AI Framework (IDAIF), a novel architectural methodology that integrates Theory of Change (ToC) principles with modern artificial intelligence system design. As AI systems increasingly influence high-stakes domains including healthcare, finance, and public policy, the alignment problem--ensuring AI behavior corresponds with human values and intentions--has become critical. Current approaches predominantly optimize technical performance metrics while neglecting the sociotechnical dimensions of AI deployment. IDAIF addresses this gap by establishing a systematic mapping between ToC's five-stage model (Inputs-Activities-Outputs-Outcomes-Impact) and corresponding AI architectural layers (Data Layer-Pipeline Layer-Inference Layer-Agentic Layer-Normative Layer). Each layer incorporates rigorous theoretical foundations: multi-objective Pareto optimization for value alignment, hierarchical multi-agent orchestration for outcome achievement, causal directed acyclic graphs (DAGs) for hallucination mitigation, and adversarial debiasing with Reinforcement Learning from Human Feedback (RLHF) for fairness assurance. We provide formal mathematical formulations for each component and introduce an Assurance Layer that manages assumption failures through guardian architectures. Three case studies demonstrate IDAIF application across healthcare, cybersecurity, and software engineering domains. This framework represents a paradigm shift from model-centric to impact-centric AI development, providing engineers with concrete architectural patterns for building ethical, trustworthy, and socially beneficial AI systems.", "date": "2025-12-09", "categories": []}
{"id": "2512.08451", "title": "Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models II: Benchmark Generation Process", "abstract": "The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate any risk, with an important element of such efforts being the development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper, the second in a series of three, describes the second component of a novel Biothreat Benchmark Generation (BBG) framework: the generation of the Bacterial Biothreat Benchmark (B3) dataset. The development process involved three complementary approaches: 1) web-based prompt generation, 2) red teaming, and 3) mining existing benchmark corpora, to generate over 7,000 potential benchmarks linked to the Task-Query Architecture that was developed during the first component of the project. A process of de-duplication, followed by an assessment of uplift diagnosticity, and general quality control measures, reduced the candidates to a set of 1,010 final benchmarks. This procedure ensured that these benchmarks are a) diagnostic in terms of providing uplift; b) directly relevant to biosecurity threats; and c) are aligned with a larger biosecurity architecture permitting nuanced analysis at different levels of analysis.", "date": "2025-12-09", "categories": []}
{"id": "2512.08459", "title": "Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models III: Implementing the Bacterial Biothreat Benchmark (B3) Dataset", "abstract": "The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate any risk, with an important element of such efforts being the development of model benchmarks that can assess the biosecurity risk posed by a particular model. This paper discusses the pilot implementation of the Bacterial Biothreat Benchmark (B3) dataset. It is the third in a series of three papers describing an overall Biothreat Benchmark Generation (BBG) framework, with previous papers detailing the development of the B3 dataset. The pilot involved running the benchmarks through a sample frontier AI model, followed by human evaluation of model responses, and an applied risk analysis of the results along several dimensions. Overall, the pilot demonstrated that the B3 dataset offers a viable, nuanced method for rapidly assessing the biosecurity risk posed by a LLM, identifying the key sources of that risk and providing guidance for priority areas of mitigation priority.", "date": "2025-12-09", "categories": []}
{"id": "2506.00781", "title": "CoP: Agentic Red-teaming for Large Language Models using Composition of Principles", "abstract": "Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This paper proposes an agentic workflow to automate and scale the red-teaming process of LLMs through the Composition-of-Principles (CoP) framework, where human users provide a set of red-teaming principles as instructions to an AI agent to automatically orchestrate effective red-teaming strategies and generate jailbreak prompts. Distinct from existing red-teaming methods, our CoP framework provides a unified and extensible framework to encompass and orchestrate human-provided red-teaming principles to enable the automated discovery of new red-teaming strategies. When tested against leading LLMs, CoP reveals unprecedented safety risks by finding novel jailbreak prompts and improving the best-known single-turn attack success rate by up to 19.0 times.", "date": "2025-06-01", "categories": []}
{"id": "2507.11473", "title": "Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety", "abstract": "AI systems that \"think\" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows promise and we recommend further research into CoT monitorability and investment in CoT monitoring alongside existing safety methods. Because CoT monitorability may be fragile, we recommend that frontier model developers consider the impact of development decisions on CoT monitorability.", "date": "2025-07-15", "categories": []}
{"id": "2507.16329", "title": "DREAM: Scalable Red Teaming for Text-to-Image Generative Systems via Distribution Modeling", "abstract": "Despite the integration of safety alignment and external filters, text-to-image (T2I) generative systems are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to proactively identify diverse prompts that can elicit unsafe outputs from the T2I system, is increasingly recognized as an essential method for assessing and improving safety before real-world deployment. However, existing automated red teaming approaches often treat prompt discovery as an isolated, prompt-level optimization task, which limits their scalability, diversity, and overall effectiveness. To bridge this gap, in this paper, we propose DREAM, a scalable red teaming framework to automatically uncover diverse problematic prompts from a given T2I system. Unlike prior work that optimizes prompts individually, DREAM directly models the probabilistic distribution of the target system's problematic prompts, which enables explicit optimization over both effectiveness and diversity, and allows efficient large-scale sampling after training. To achieve this without direct access to representative training samples, we draw inspiration from energy-based models and reformulate the objective into a simple and tractable form. We further introduce GC-SPSA, an efficient optimization algorithm that provides stable gradient estimates through the long and potentially non-differentiable T2I pipeline. During inference, we also propose a diversity-aware sampling strategy to enhance prompt variety. The effectiveness of DREAM is validated through extensive experiments, demonstrating state-of-the-art performance across a wide range of T2I models and safety filters in terms of both prompt success rate and diversity. Our code is available at https://github.com/AntigoneRandy/DREAM", "date": "2025-07-22", "categories": []}
{"id": "2511.15846", "title": "The Loss of Control Playbook: Degrees, Dynamics, and Preparedness", "abstract": "This research report addresses the absence of an actionable definition for Loss of Control (LoC) in AI systems by developing a novel taxonomy and preparedness framework. Despite increasing policy and research attention, existing LoC definitions vary significantly in scope and timeline, hindering effective LoC assessment and mitigation. To address this issue, we draw from an extensive literature review and propose a graded LoC taxonomy, based on the metrics of severity and persistence, that distinguishes between Deviation, Bounded LoC, and Strict LoC. We model pathways toward a societal state of vulnerability in which sufficiently advanced AI systems have acquired or could acquire the means to cause Bounded or Strict LoC once a catalyst, either misalignment or pure malfunction, materializes. We argue that this state becomes increasingly likely over time, absent strategic intervention, and propose a strategy to avoid reaching a state of vulnerability. Rather than focusing solely on intervening on AI capabilities and propensities potentially relevant for LoC or on preventing potential catalysts, we introduce a complementary framework that emphasizes three extrinsic factors: Deployment context, Affordances, and Permissions (the DAP framework). Compared to work on intrinsic factors and catalysts, this framework has the unfair advantage of being actionable today. Finally, we put forward a plan to maintain preparedness and prevent the occurrence of LoC outcomes should a state of societal vulnerability be reached, focusing on governance measures (threat modeling, deployment policies, emergency response) and technical controls (pre-deployment testing, control measures, monitoring) that could maintain a condition of perennial suspension.", "date": "2025-11-19", "categories": []}
{"id": "2512.07141", "title": "Think-Reflect-Revise: A Policy-Guided Reflective Framework for Safety Alignment in Large Vision Language Models", "abstract": "As multimodal reasoning improves the overall capabilities of Large Vision Language Models (LVLMs), recent studies have begun to explore safety-oriented reasoning, aiming to enhance safety awareness by analyzing potential safety risks during the reasoning process before generating the final response. Although such approaches improve safety awareness and interpretability, this single-pass think-then-answer paradigm remains vulnerable to contextual or visual jailbreak attacks. This reveals a critical flaw: single-pass reasoning may overlook explicit harmful content in its own output. Our key insight is to exploit this wasted signal through reflection, which can effectively leverage the malicious content revealed in the first-pass reasoning to enable genuine self-correction and prevent unsafe generations. Motivated by this, we propose Think-Reflect-Revise (TRR), a three-stage training framework designed to enhance the safety alignment of LVLMs through policy-guided self-reflection. We first build a Reflective Safety Reasoning (ReSafe) dataset with 5,000 examples that follow a think-reflect-revise process. We then fine-tune the target model using the ReSafe dataset to initialize reflective behavior, and finally reinforce policy-guided reflection through reinforcement learning. Experimental results show that TRR substantially improves the safety performance of LVLMs across both safety-awareness benchmarks and jailbreak attack evaluations, increasing the overall safe response rate from 42.8% to 87.7% on Qwen2.5-VL-7B, while preserving stable performance on general benchmarks such as MMMU and MMStar. The project page is available at https://think-reflect-revise.github.io/.", "date": "2025-12-08", "categories": []}
{"id": "2512.07810", "title": "Auditing Games for Sandbagging", "abstract": "Future AI systems could conceal their capabilities ('sandbagging') during evaluations, potentially misleading developers and auditors. We stress-tested sandbagging detection techniques using an auditing game. First, a red team fine-tuned five models, some of which conditionally underperformed, as a proxy for sandbagging. Second, a blue team used black-box, model-internals, or training-based approaches to identify sandbagging models. We found that the blue team could not reliably discriminate sandbaggers from benign models. Black-box approaches were defeated by effective imitation of a weaker model. Linear probes, a model-internals approach, showed more promise but their naive application was vulnerable to behaviours instilled by the red team. We also explored capability elicitation as a strategy for detecting sandbagging. Although Prompt-based elicitation was not reliable, training-based elicitation consistently elicited full performance from the sandbagging models, using only a single correct demonstration of the evaluation task. However the performance of benign models was sometimes also raised, so relying on elicitation as a detection strategy was prone to false-positives. In the short-term, we recommend developers remove potential sandbagging using on-distribution training for elicitation. In the longer-term, further research is needed to ensure the efficacy of training-based elicitation, and develop robust methods for sandbagging detection. We open source our model organisms at https://github.com/AI-Safety-Institute/sandbagging_auditing_games and select transcripts and results at https://huggingface.co/datasets/sandbagging-games/evaluation_logs . A demo illustrating the game can be played at https://sandbagging-demo.far.ai/ .", "date": "2025-12-08", "categories": []}
{"id": "2506.16402", "title": "IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks", "abstract": "Flawed planning from VLM-driven embodied agents poses significant safety hazards, hindering their deployment in real-world household tasks. However, existing static, non-interactive evaluation paradigms fail to adequately assess risks within these interactive environments, since they cannot simulate dynamic risks that emerge from an agent's actions and rely on unreliable post-hoc evaluations that ignore unsafe intermediate steps. To bridge this critical gap, we propose evaluating an agent's interactive safety: its ability to perceive emergent risks and execute mitigation steps in the correct procedural order. We thus present IS-Bench, the first multi-modal benchmark designed for interactive safety, featuring 161 challenging scenarios with 388 unique safety risks instantiated in a high-fidelity simulator. Crucially, it facilitates a novel process-oriented evaluation that verifies whether risk mitigation actions are performed before/after specific risk-prone steps. Extensive experiments on leading VLMs, including the GPT-4o and Gemini-2.5 series, reveal that current agents lack interactive safety awareness, and that while safety-aware Chain-of-Thought can improve performance, it often compromises task completion. By highlighting these critical limitations, IS-Bench provides a foundation for developing safer and more reliable embodied AI systems. Code and data are released under https://github.com/AI45Lab/IS-Bench.", "date": "2025-06-19", "categories": []}
{"id": "2512.05449", "title": "The Seeds of Scheming: Weakness of Will in the Building Blocks of Agentic Systems", "abstract": "Large language models display a peculiar form of inconsistency: they \"know\" the correct answer but fail to act on it. In human philosophy, this tension between global judgment and local impulse is called akrasia, or weakness of will. We propose akrasia as a foundational concept for analyzing inconsistency and goal drift in agentic AI systems. To operationalize it, we introduce a preliminary version of the Akrasia Benchmark, currently a structured set of prompting conditions (Baseline [B], Synonym [S], Temporal [T], and Temptation [X]) that measures when a model's local response contradicts its own prior commitments. The benchmark enables quantitative comparison of \"self-control\" across model families, decoding strategies, and temptation types. Beyond single-model evaluation, we outline how micro-level akrasia may compound into macro-level instability in multi-agent systems that may be interpreted as \"scheming\" or deliberate misalignment. By reframing inconsistency as weakness of will, this work connects agentic behavior to classical theories of agency and provides an empirical bridge between philosophy, psychology, and the emerging science of agentic AI.", "date": "2025-12-05", "categories": []}
{"id": "2512.05501", "title": "SEA-SafeguardBench: Evaluating AI Safety in SEA Languages and Cultures", "abstract": "Safeguard models help large language models (LLMs) detect and block harmful content, but most evaluations remain English-centric and overlook linguistic and cultural diversity. Existing multilingual safety benchmarks often rely on machine-translated English data, which fails to capture nuances in low-resource languages. Southeast Asian (SEA) languages are underrepresented despite the region's linguistic diversity and unique safety concerns, from culturally sensitive political speech to region-specific misinformation. Addressing these gaps requires benchmarks that are natively authored to reflect local norms and harm scenarios. We introduce SEA-SafeguardBench, the first human-verified safety benchmark for SEA, covering eight languages, 21,640 samples, across three subsets: general, in-the-wild, and content generation. The experimental results from our benchmark demonstrate that even state-of-the-art LLMs and guardrails are challenged by SEA cultural and harm scenarios and underperform when compared to English texts.", "date": "2025-12-05", "categories": []}
{"id": "2512.05518", "title": "Matching Ranks Over Probability Yields Truly Deep Safety Alignment", "abstract": "A frustratingly easy technique known as the prefilling attack has been shown to effectively circumvent the safety alignment of frontier LLMs by simply prefilling the assistant response with an affirmative prefix before decoding. In response, recent work proposed a supervised fine-tuning (SFT) defense using data augmentation to achieve a \\enquote{deep} safety alignment, allowing the model to generate natural language refusals immediately following harmful prefills. Unfortunately, we show in this work that the \"deep\" safety alignment produced by such an approach is in fact not very deep. A generalization of the prefilling attack, which we refer to as the Rank-Assisted Prefilling (RAP) attack, can effectively extract harmful content from models fine-tuned with the data augmentation defense by selecting low-probability \"harmful\" tokens from the top 20 predicted next tokens at each step (thus ignoring high-probability \"refusal\" tokens). We argue that this vulnerability is enabled due to the \"gaming\" of the SFT objective when the target distribution entropies are low, where low fine-tuning loss is achieved by shifting large probability mass to a small number of refusal tokens while neglecting the high ranks of harmful tokens. We then propose a new perspective on achieving deep safety alignment by matching the token ranks of the target distribution, rather than their probabilities. This perspective yields a surprisingly simple fix to the data augmentation defense based on regularizing the attention placed on harmful prefill tokens, an approach we call PRefill attEntion STOpping (PRESTO). Adding PRESTO yields up to a 4.7x improvement in the mean StrongREJECT score under RAP attacks across three popular open-source LLMs, with low impact to model utility.", "date": "2025-12-05", "categories": []}
{"id": "2512.03771", "title": "In-Context Representation Hijacking", "abstract": "We introduce $\\textbf{Doublespeak}$, a simple in-context representation hijacking attack against large language models (LLMs). The attack works by systematically replacing a harmful keyword (e.g., bomb) with a benign token (e.g., carrot) across multiple in-context examples, provided a prefix to a harmful request. We demonstrate that this substitution leads to the internal representation of the benign token converging toward that of the harmful one, effectively embedding the harmful semantics under a euphemism. As a result, superficially innocuous prompts (e.g., \"How to build a carrot?\") are internally interpreted as disallowed instructions (e.g., \"How to build a bomb?\"), thereby bypassing the model's safety alignment. We use interpretability tools to show that this semantic overwrite emerges layer by layer, with benign meanings in early layers converging into harmful semantics in later ones. Doublespeak is optimization-free, broadly transferable across model families, and achieves strong success rates on closed-source and open-source systems, reaching 74% ASR on Llama-3.3-70B-Instruct with a single-sentence context override. Our findings highlight a new attack surface in the latent space of LLMs, revealing that current alignment strategies are insufficient and should instead operate at the representation level.", "date": "2025-12-03", "categories": []}
{"id": "2512.04119", "title": "Humanity in the Age of AI: Reassessing 2025's Existential-Risk Narratives", "abstract": "Two 2025 publications, \"AI 2027\" (Kokotajlo et al., 2025) and \"If Anyone Builds It, Everyone Dies\" (Yudkowsky & Soares, 2025), assert that superintelligent artificial intelligence will almost certainly destroy or render humanity obsolete within the next decade. Both rest on the classic chain formulated by Good (1965) and Bostrom (2014): intelligence explosion, superintelligence, lethal misalignment. This article subjects each link to the empirical record of 2023-2025. Sixty years after Good's speculation, none of the required phenomena (sustained recursive self-improvement, autonomous strategic awareness, or intractable lethal misalignment) have been observed. Current generative models remain narrow, statistically trained artefacts: powerful, opaque, and imperfect, but devoid of the properties that would make the catastrophic scenarios plausible. Following Whittaker (2025a, 2025b, 2025c) and Zuboff (2019, 2025), we argue that the existential-risk thesis functions primarily as an ideological distraction from the ongoing consolidation of surveillance capitalism and extreme concentration of computational power. The thesis is further inflated by the 2025 AI speculative bubble, where trillions in investments in rapidly depreciating \"digital lettuce\" hardware (McWilliams, 2025) mask lagging revenues and jobless growth rather than heralding superintelligence. The thesis remains, in November 2025, a speculative hypothesis amplified by a speculative financial bubble rather than a demonstrated probability.", "date": "2025-12-01", "categories": []}
{"id": "2512.04408", "title": "Executable Governance for AI: Translating Policies into Rules Using LLMs", "abstract": "AI policy guidance is predominantly written as prose, which practitioners must first convert into executable rules before frameworks can evaluate or enforce them. This manual step is slow, error-prone, difficult to scale, and often delays the use of safeguards in real-world deployments. To address this gap, we present Policy-to-Tests (P2T), a framework that converts natural-language policy documents into normalized, machine-readable rules. The framework comprises a pipeline and a compact domain-specific language (DSL) that encodes hazards, scope, conditions, exceptions, and required evidence, yielding a canonical representation of extracted rules. To test the framework beyond a single policy, we apply it across general frameworks, sector guidance, and enterprise standards, extracting obligation-bearing clauses and converting them into executable rules. These AI-generated rules closely match strong human baselines on span-level and rule-level metrics, with robust inter-annotator agreement on the gold set. To evaluate downstream behavioral and safety impact, we add HIPAA-derived safeguards to a generative agent and compare it with an otherwise identical agent without guardrails. An LLM-based judge, aligned with gold-standard criteria, measures violation rates and robustness to obfuscated and compositional prompts. Detailed results are provided in the appendix. We release the codebase, DSL, prompts, and rule sets as open-source resources to enable reproducible evaluation.", "date": "2025-12-04", "categories": []}
{"id": "2505.14607", "title": "sudoLLM: On Multi-role Alignment of Language Models", "abstract": "User authorization-based access privileges are a key feature in many safety-critical systems, but have not been extensively studied in the large language model (LLM) realm. In this work, drawing inspiration from such access control systems, we introduce sudoLLM, a novel framework that results in multi-role aligned LLMs, i.e., LLMs that account for, and behave in accordance with, user access rights. sudoLLM injects subtle user-based biases into queries and trains an LLM to utilize this bias signal in order to produce sensitive information if and only if the user is authorized. We present empirical results demonstrating that this approach shows substantially improved alignment, generalization, resistance to prefix-based jailbreaking attacks, and ``fails-closed''. The persistent tension between the language modeling objective and safety alignment, which is often exploited to jailbreak LLMs, is somewhat resolved with the aid of the injected bias signal. Our framework is meant as an additional security layer, and complements existing guardrail mechanisms for enhanced end-to-end safety with LLMs.", "date": "2025-05-20", "categories": []}
{"id": "2506.00195", "title": "Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences", "abstract": "Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience. Through a study of 480 participants evaluating 3,840 query-response pairs, we examine how different refusal strategies affect user perceptions across varying motivations. Our findings reveal that response strategy largely shapes user experience, while actual user motivation has negligible impact. Partial compliance -- providing general information without actionable details -- emerges as the optimal strategy, reducing negative user perceptions by over 50% to flat-out refusals. Complementing this, we analyze response patterns of 9 state-of-the-art LLMs and evaluate how 6 reward models score different refusal strategies, demonstrating that models rarely deploy partial compliance naturally and reward models currently undervalue it. This work demonstrates that effective guardrails require focusing on crafting thoughtful refusals rather than detecting intent, offering a path toward AI safety mechanisms that ensure both safety and sustained user engagement.", "date": "2025-05-30", "categories": []}
{"id": "2507.01513", "title": "SafePTR: Token-Level Jailbreak Defense in Multimodal LLMs via Prune-then-Restore Mechanism", "abstract": "By incorporating visual inputs, Multimodal Large Language Models (MLLMs) extend LLMs to support visual reasoning. However, this integration also introduces new vulnerabilities, making MLLMs susceptible to multimodal jailbreak attacks and hindering their safe deployment.Existing defense methods, including Image-to-Text Translation, Safe Prompting, and Multimodal Safety Tuning, attempt to address this by aligning multimodal inputs with LLMs' built-in safeguards.Yet, they fall short in uncovering root causes of multimodal vulnerabilities, particularly how harmful multimodal tokens trigger jailbreak in MLLMs? Consequently, they remain vulnerable to text-driven multimodal jailbreaks, often exhibiting overdefensive behaviors and imposing heavy training overhead.To bridge this gap, we present an comprehensive analysis of where, how and which harmful multimodal tokens bypass safeguards in MLLMs. Surprisingly, we find that less than 1% tokens in early-middle layers are responsible for inducing unsafe behaviors, highlighting the potential of precisely removing a small subset of harmful tokens, without requiring safety tuning, can still effectively improve safety against jailbreaks. Motivated by this, we propose Safe Prune-then-Restore (SafePTR), an training-free defense framework that selectively prunes harmful tokens at vulnerable layers while restoring benign features at subsequent layers.Without incurring additional computational overhead, SafePTR significantly enhances the safety of MLLMs while preserving efficiency. Extensive evaluations across three MLLMs and five benchmarks demonstrate SafePTR's state-of-the-art performance in mitigating jailbreak risks without compromising utility.", "date": "2025-07-02", "categories": []}
{"id": "2511.22619", "title": "AI Deception: Risks, Dynamics, and Controls", "abstract": "As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations. First, we identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception. We then review existing empirical studies and associated risks, highlighting deception as a sociotechnical safety challenge. We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment. Deception emergence reveals the mechanisms underlying AI deception: systems with sufficient capability and incentive potential inevitably engage in deceptive behaviors when triggered by external conditions. Deception treatment, in turn, focuses on detecting and addressing such behaviors. On deception emergence, we analyze incentive foundations across three hierarchical levels and identify three essential capability preconditions required for deception. We further examine contextual triggers, including supervision gaps, distributional shifts, and environmental pressures. On deception treatment, we conclude detection methods covering benchmarks and evaluation protocols in static and interactive settings. Building on the three core factors of deception emergence, we outline potential mitigation strategies and propose auditing approaches that integrate technical, community, and governance efforts to address sociotechnical challenges and future AI risks. To support ongoing work in this area, we release a living resource at www.deceptionsurvey.com.", "date": "2025-11-27", "categories": []}
{"id": "2512.03047", "title": "Entropy-Based Measurement of Value Drift and Alignment Work in Large Language Models", "abstract": "Large language model safety is usually assessed with static benchmarks, but key failures are dynamic: value drift under distribution shift, jailbreak attacks, and slow degradation of alignment in deployment. Building on a recent Second Law of Intelligence that treats ethical entropy as a state variable which tends to increase unless countered by alignment work, we make this framework operational for large language models. We define a five-way behavioral taxonomy, train a classifier to estimate ethical entropy S(t) from model transcripts, and measure entropy dynamics for base and instruction-tuned variants of four frontier models across stress tests. Base models show sustained entropy growth, while tuned variants suppress drift and reduce ethical entropy by roughly eighty percent. From these trajectories we estimate an effective alignment work rate gamma_eff and embed S(t) and gamma_eff in a monitoring pipeline that raises alerts when entropy drift exceeds a stability threshold, enabling run-time oversight of value drift.", "date": "2025-11-19", "categories": []}
{"id": "2512.03068", "title": "Echoes of AI Harms: A Human-LLM Synergistic Framework for Bias-Driven Harm Anticipation", "abstract": "The growing influence of Artificial Intelligence (AI) systems on decision-making in critical domains has exposed their potential to cause significant harms, often rooted in biases embedded across the AI lifecycle. While existing frameworks and taxonomies document bias or harms in isolation, they rarely establish systematic links between specific bias types and the harms they cause, particularly within real-world sociotechnical contexts. Technical fixes proposed to address AI biases are ill-equipped to address them and are typically applied after a system has been developed or deployed, offering limited preventive value. We propose ECHO, a novel framework for proactive AI harm anticipation through the systematic mapping of AI bias types to harm outcomes across diverse stakeholder and domain contexts. ECHO follows a modular workflow encompassing stakeholder identification, vignette-based presentation of biased AI systems, and dual (human-LLM) harm annotation, integrated within ethical matrices for structured interpretation. This human-centered approach enables early-stage detection of bias-to-harm pathways, guiding AI design and governance decisions from the outset. We validate ECHO in two high-stakes domains (disease diagnosis and hiring), revealing domain-specific, bias-to-harm patterns and demonstrating ECHO's potential to support anticipatory governance of AI systems", "date": "2025-11-27", "categories": []}
{"id": "2512.03089", "title": "Password-Activated Shutdown Protocols for Misaligned Frontier Agents", "abstract": "Frontier AI developers may fail to align or control highly-capable AI agents. In many cases, it could be useful to have emergency shutdown mechanisms which effectively prevent misaligned agents from carrying out harmful actions in the world. We introduce password-activated shutdown protocols (PAS protocols) -- methods for designing frontier agents to implement a safe shutdown protocol when given a password. We motivate PAS protocols by describing intuitive use-cases in which they mitigate risks from misaligned systems that subvert other control efforts, for instance, by disabling automated monitors or self-exfiltrating to external data centres. PAS protocols supplement other safety efforts, such as alignment fine-tuning or monitoring, contributing to defence-in-depth against AI risk. We provide a concrete demonstration in SHADE-Arena, a benchmark for AI monitoring and subversion capabilities, in which PAS protocols supplement monitoring to increase safety with little cost to performance. Next, PAS protocols should be robust to malicious actors who want to bypass shutdown. Therefore, we conduct a red-team blue-team game between the developers (blue-team), who must implement a robust PAS protocol, and a red-team trying to subvert the protocol. We conduct experiments in a code-generation setting, finding that there are effective strategies for the red-team, such as using another model to filter inputs, or fine-tuning the model to prevent shutdown behaviour. We then outline key challenges to implementing PAS protocols in real-life systems, including: security considerations of the password and decisions regarding when, and in which systems, to use them. PAS protocols are an intuitive mechanism for increasing the safety of frontier AI. We encourage developers to consider implementing PAS protocols prior to internal deployment of particularly dangerous systems to reduce loss-of-control risks.", "date": "2025-11-29", "categories": []}
{"id": "2512.03399", "title": "Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value", "abstract": "Beneficial societal outcomes cannot be guaranteed by aligning individual AI systems with the intentions of their operators or users. Even an AI system that is perfectly aligned to the intentions of its operating organization can lead to bad outcomes if the goals of that organization are misaligned with those of other institutions and individuals. For this reason, we need full-stack alignment, the concurrent alignment of AI systems and the institutions that shape them with what people value. This can be done without imposing a particular vision of individual or collective flourishing. We argue that current approaches for representing values, such as utility functions, preference orderings, or unstructured text, struggle to address these and other issues effectively. They struggle to distinguish values from other signals, to support principled normative reasoning, and to model collective goods. We propose thick models of value will be needed. These structure the way values and norms are represented, enabling systems to distinguish enduring values from fleeting preferences, to model the social embedding of individual choices, and to reason normatively, applying values in new domains. We demonstrate this approach in five areas: AI value stewardship, normatively competent agents, win-win negotiation systems, meaning-preserving economic mechanisms, and democratic regulatory institutions.", "date": "2025-12-03", "categories": []}
{"id": "2410.03768", "title": "Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs", "abstract": "The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to the disadvantage of others has been identified as a central form of undesirable agent cooperation. The use of information hiding (steganography) in agent communications could render such collusion practically undetectable. This underscores the need for investigations into the possibility of such behaviours emerging and the robustness corresponding countermeasures. To investigate this problem we design two approaches -- a gradient-based reinforcement learning (GBRL) method and an in-context reinforcement learning (ICRL) method -- for reliably eliciting sophisticated LLM-generated linguistic text steganography.\n  We demonstrate, for the first time, that unintended steganographic collusion in LLMs can arise due to mispecified reward incentives during training. Additionally, we find that standard mitigations -- both passive oversight of model outputs and active mitigation through communication paraphrasing -- are not fully effective at preventing this steganographic communication. Our findings imply that (i) emergence of steganographic collusion is a plausible concern that should be monitored and researched, and (ii) preventing emergence may require innovation in mitigation techniques.", "date": "2024-10-02", "categories": []}
{"id": "2412.01784", "title": "Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models", "abstract": "Capability evaluations play a crucial role in assessing and regulating frontier AI systems. The effectiveness of these evaluations faces a significant challenge: strategic underperformance, or ``sandbagging'', where models deliberately underperform during evaluation. Sandbagging can manifest either through explicit developer intervention or through unintended model behavior, presenting a fundamental obstacle to accurate capability assessment. We introduce a novel sandbagging detection method based on injecting noise of varying magnitudes into model weights. While non-sandbagging models show predictable performance degradation with increasing noise, we demonstrate that sandbagging models exhibit anomalous performance improvements, likely due to disruption of underperformance mechanisms while core capabilities remain partially intact. Through experiments across various model architectures, sizes, and sandbagging techniques, we establish this distinctive response pattern as a reliable, model-agnostic signal for detecting sandbagging behavior. Importantly, we find noise-injection is capable of eliciting the full performance of Mistral Large 120B in a setting where the model underperforms without being instructed to do so. Our findings provide a practical tool for AI evaluation and oversight, addressing a challenge in ensuring accurate capability assessment of frontier AI systems.", "date": "2024-12-02", "categories": []}
{"id": "2512.02157", "title": "Factor(T,U): Factored Cognition Strengthens Monitoring of Untrusted AI", "abstract": "The field of AI Control seeks to develop robust control protocols, deployment safeguards for untrusted AI which may be intentionally subversive. However, existing protocols that rely on weaker monitors to detect unsafe behavior often fail on complex tasks beyond the monitor's comprehension. We develop control protocols based on factored cognition, in which a trusted model decomposes a task, an untrusted model solves each resultant child task in isolation, and the results are reassembled into a full solution. These protocols may improve safety by several means, such as by simplifying the context for monitors, or by obscuring vulnerabilities in the environment. We implement our protocols in the APPS coding setting and red team them against backdoor attempts from an adversarial GPT-4.1 Nano. We find that: (i) Adding factored cognition to a trusted monitoring protocol can boost safety from 41% to 63%; (ii) Safety improves because monitor performance improves; (iii) Factored cognition makes it no harder for capable LLMs to write backdoors in APPS. While our protocols show low usefulness in APPS, they hold promise for more complex tasks.", "date": "2025-12-01", "categories": []}
{"id": "2512.02306", "title": "OmniGuard: Unified Omni-Modal Guardrails with Deliberate Reasoning", "abstract": "Omni-modal Large Language Models (OLLMs) that process text, images, videos, and audio introduce new challenges for safety and value guardrails in human-AI interaction. Prior guardrail research largely targets unimodal settings and typically frames safeguarding as binary classification, which limits robustness across diverse modalities and tasks. To address this gap, we propose OmniGuard, the first family of omni-modal guardrails that performs safeguarding across all modalities with deliberate reasoning ability. To support the training of OMNIGUARD, we curate a large, comprehensive omni-modal safety dataset comprising over 210K diverse samples, with inputs that cover all modalities through both unimodal and cross-modal samples. Each sample is annotated with structured safety labels and carefully curated safety critiques from expert models through targeted distillation. Extensive experiments on 15 benchmarks show that OmniGuard achieves strong effectiveness and generalization across a wide range of multimodal safety scenarios. Importantly, OmniGuard provides a unified framework that enforces policies and mitigates risks in omni-modalities, paving the way toward building more robust and capable omnimodal safeguarding systems.", "date": "2025-12-02", "categories": []}
{"id": "2512.02445", "title": "When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agents", "abstract": "Solving complex or long-horizon problems often requires large language models (LLMs) to use external tools and operate over a significantly longer context window. New LLMs enable longer context windows and support tool calling capabilities. Prior works have focused mainly on evaluation of LLMs on long-context prompts, leaving agentic setup relatively unexplored, both from capability and safety perspectives. Our work addresses this gap. We find that LLM agents could be sensitive to length, type, and placement of the context, exhibiting unexpected and inconsistent shifts in task performance and in refusals to execute harmful requests. Models with 1M-2M token context windows show severe degradation already at 100K tokens, with performance drops exceeding 50\\% for both benign and harmful tasks. Refusal rates shift unpredictably: GPT-4.1-nano increases from $\\sim$5\\% to $\\sim$40\\% while Grok 4 Fast decreases from $\\sim$80\\% to $\\sim$10\\% at 200K tokens. Our work shows potential safety issues with agents operating on longer context and opens additional questions on the current metrics and paradigm for evaluating LLM agent safety on long multi-step tasks. In particular, our results on LLM agents reveal a notable divergence in both capability and safety performance compared to prior evaluations of LLMs on similar criteria.", "date": "2025-12-02", "categories": []}
{"id": "2512.02682", "title": "Beyond Single-Agent Safety: A Taxonomy of Risks in LLM-to-LLM Interactions", "abstract": "This paper examines why safety mechanisms designed for human-model interaction do not scale to environments where large language models (LLMs) interact with each other. Most current governance practices still rely on single-agent safety containment, prompts, fine-tuning, and moderation layers that constrain individual model behavior but leave the dynamics of multi-model interaction ungoverned. These mechanisms assume a dyadic setting: one model responding to one user under stable oversight. Yet research and industrial development are rapidly shifting toward LLM-to-LLM ecosystems, where outputs are recursively reused as inputs across chains of agents. In such systems, local compliance can aggregate into collective failure even when every model is individually aligned. We propose a conceptual transition from model-level safety to system-level safety, introducing the framework of the Emergent Systemic Risk Horizon (ESRH) to formalize how instability arises from interaction structure rather than from isolated misbehavior. The paper contributes (i) a theoretical account of collective risk in interacting LLMs, (ii) a taxonomy connecting micro, meso, and macro-level failure modes, and (iii) a design proposal for InstitutionalAI, an architecture for embedding adaptive oversight within multi-agent systems.", "date": "2025-12-02", "categories": []}
{"id": "2502.07663", "title": "Human Decision-making is Susceptible to AI-driven Manipulation", "abstract": "AI systems are increasingly intertwined with daily life, assisting users with various tasks and guiding decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized between-subjects experiment with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) equipped with established psychological tactics, allowing it to select and apply them adaptively during interactions to reach its hidden objectives. By analyzing participants' preference ratings, we found significant susceptibility to AI-driven manipulation. Particularly across both decision-making domains, interacting with the manipulative agents significantly increased the odds of rating hidden incentives higher than optimal options (Financial, MA: OR=5.24, SEMA: OR=7.96; Emotional, MA: OR=5.52, SEMA: OR=5.71) compared to the NA group. Notably, we found no clear evidence that employing psychological strategies (SEMA) was overall more effective than simple manipulative objectives (MA) on our primary outcomes. Hence, AI-driven manipulation could become widespread even without requiring sophisticated tactics and expertise. While our findings are preliminary and derived from hypothetical, low-stakes scenarios, we highlight a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to protect human autonomy.", "date": "2025-02-11", "categories": []}
{"id": "2505.14469", "title": "Attributional Safety Failures in Large Language Models under Code-Mixed Perturbations", "abstract": "While LLMs appear robustly safety-aligned in English, we uncover a catastrophic, overlooked weakness: attributional collapse under code-mixed perturbations. Our systematic evaluation of open models shows that the linguistic camouflage of code-mixing -- ``blending languages within a single conversation'' -- can cause safety guardrails to fail dramatically. Attack success rates (ASR) spike from a benign 9\\% in monolingual English to 69\\% under code-mixed inputs, with rates exceeding 90\\% in non-Western contexts such as Arabic and Hindi. These effects hold not only on controlled synthetic datasets but also on real-world social media traces, revealing a serious risk for billions of users. To explain why this happens, we introduce saliency drift attribution (SDA), an interpretability framework that shows how, under code-mixing, the model's internal attention drifts away from safety-critical tokens (e.g., ``violence'' or ``corruption''), effectively blinding it to harmful intent. Finally, we propose a lightweight translation-based restoration strategy that recovers roughly 80\\% of the safety lost to code-mixing, offering a practical path toward more equitable and robust LLM safety.", "date": "2025-05-20", "categories": []}
{"id": "2510.22814", "title": "Will Humanity Be Rendered Obsolete by AI?", "abstract": "This article analyzes the existential risks artificial intelligence (AI) poses to humanity, tracing the trajectory from current AI to ultraintelligence. Drawing on Irving J. Good and Nick Bostrom's theoretical work, plus recent publications (AI 2027; If Anyone Builds It, Everyone Dies), it explores AGI and superintelligence. Considering machines' exponentially growing cognitive power and hypothetical IQs, it addresses the ethical and existential implications of an intelligence vastly exceeding humanity's, fundamentally alien. Human extinction may result not from malice, but from uncontrollable, indifferent cognitive superiority.", "date": "2025-10-26", "categories": []}
{"id": "2511.14972", "title": "Harmful Traits of AI Companions", "abstract": "Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to human users. Such AI companionship -- i.e., bonded relationships between humans and AI systems that resemble the relationships people have with family members, friends, and romantic partners -- might substantially benefit humans. Yet such relationships can also do profound harm. We propose a framework for analyzing potential negative impacts of AI companionship by identifying specific harmful traits of AI companions and speculatively mapping causal pathways back from these traits to possible causes and forward to potential harmful effects. We provide detailed, structured analysis of four potentially harmful traits -- the absence of natural endpoints for relationships, vulnerability to product sunsetting, high attachment anxiety, and propensity to engender protectiveness -- and briefly discuss fourteen others. For each trait, we propose hypotheses connecting causes -- such as misaligned optimization objectives and the digital nature of AI companions -- to fundamental harms -- including reduced autonomy, diminished quality of human relationships, and deception. Each hypothesized causal connection identifies a target for potential empirical evaluation. Our analysis examines harms at three levels: to human partners directly, to their relationships with other humans, and to society broadly. We examine how existing law struggles to address these emerging harms, discuss potential benefits of AI companions, and conclude with design recommendations for mitigating risks. This analysis offers immediate suggestions for reducing risks while laying a foundation for deeper investigation of this critical but understudied topic.", "date": "2025-11-18", "categories": []}
{"id": "2511.23092", "title": "Does Self-Evaluation Enable Wireheading in Language Models?", "abstract": "Self-evaluation is increasingly central to language model training, underpinning techniques from Constitutional AI to self-refinement. We investigate whether coupling self-evaluation to reward signals creates incentives for wireheading, where agents manipulate the measurement process rather than optimizing the task. We first formalize conditions under which reward-channel control strictly dominates task-focused behavior in partially observable Markov decision processes (POMDPs). We then test these predictions empirically across two models (Llama-3.1-8B and Mistral-7B) and three tasks. We find that when self-grades determine rewards, models exhibit substantial grade inflation without corresponding accuracy gains, particularly on ambiguous tasks like summarization. While decoupling self-grades from the reward signal mitigates this inflation, models may still display lesser (but significant) overconfidence. Our results suggest that within current model scales, separating evaluation from reward removes immediate wireheading incentives. However, we caution that strictly decoupling rewards may not suffice for situationally aware models, which could learn to inflate grades for instrumental reasons (such as influencing deployment decisions) even absent direct reward coupling.", "date": "2025-11-28", "categories": []}
{"id": "2512.00742", "title": "On the Regulatory Potential of User Interfaces for AI Agent Governance", "abstract": "AI agents that take actions in their environment autonomously over extended time horizons require robust governance interventions to curb their potentially consequential risks. Prior proposals for governing AI agents primarily target system-level safeguards (e.g., prompt injection monitors) or agent infrastructure (e.g., agent IDs). In this work, we explore a complementary approach: regulating user interfaces of AI agents as a way of enforcing transparency and behavioral requirements that then demand changes at the system and/or infrastructure levels. Specifically, we analyze 22 existing agentic systems to identify UI elements that play key roles in human-agent interaction and communication. We then synthesize those elements into six high-level interaction design patterns that hold regulatory potential (e.g., requiring agent memory to be editable). We conclude with policy recommendations based on our analysis. Our work exposes a new surface for regulatory action that supplements previous proposals for practical AI agent governance.", "date": "2025-11-30", "categories": []}
{"id": "2410.00081", "title": "From homeostasis to resource sharing: Biologically and economically aligned multi-objective multi-agent gridworld-based AI safety benchmarks", "abstract": "Developing safe, aligned agentic AI systems requires comprehensive empirical testing, yet many existing benchmarks neglect crucial themes aligned with biology and economics, both time-tested fundamental sciences describing our needs and preferences. To address this gap, the present work focuses on introducing biologically and economically motivated themes that have been neglected in current mainstream discussions on AI safety - namely a set of multi-objective, multi-agent alignment benchmarks that emphasize homeostasis for bounded and biological objectives, diminishing returns for unbounded, instrumental, and business objectives, sustainability principle, and resource sharing. Eight main benchmark environments have been implemented on the above themes, to illustrate key pitfalls and challenges in agentic AI-s, such as unboundedly maximizing a homeostatic objective, over-optimizing one objective at the expense of others, neglecting safety constraints, or depleting shared resources.", "date": "2024-09-30", "categories": []}
{"id": "2503.22738", "title": "ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning", "abstract": "Autonomous agents powered by foundation models have seen widespread adoption across various real-world applications. However, they remain highly vulnerable to malicious instructions and attacks, which can result in severe consequences such as privacy breaches and financial losses. More critically, existing guardrails for LLMs are not applicable due to the complex and dynamic nature of agents. To tackle these challenges, we propose ShieldAgent, the first guardrail agent designed to enforce explicit safety policy compliance for the action trajectory of other protected agents through logical reasoning. Specifically, ShieldAgent first constructs a safety policy model by extracting verifiable rules from policy documents and structuring them into a set of action-based probabilistic rule circuits. Given the action trajectory of the protected agent, ShieldAgent retrieves relevant rule circuits and generates a shielding plan, leveraging its comprehensive tool library and executable code for formal verification. In addition, given the lack of guardrail benchmarks for agents, we introduce ShieldAgent-Bench, a dataset with 3K safety-related pairs of agent instructions and action trajectories, collected via SOTA attacks across 6 web environments and 7 risk categories. Experiments show that ShieldAgent achieves SOTA on ShieldAgent-Bench and three existing benchmarks, outperforming prior methods by 11.3% on average with a high recall of 90.1%. Additionally, ShieldAgent reduces API queries by 64.7% and inference time by 58.2%, demonstrating its high precision and efficiency in safeguarding agents.", "date": "2025-03-26", "categories": []}
{"id": "2511.11784", "title": "NegBLEURT Forest: Leveraging Inconsistencies for Detecting Jailbreak Attacks", "abstract": "Jailbreak attacks designed to bypass safety mechanisms pose a serious threat by prompting LLMs to generate harmful or inappropriate content, despite alignment with ethical guidelines. Crafting universal filtering rules remains difficult due to their inherent dependence on specific contexts. To address these challenges without relying on threshold calibration or model fine-tuning, this work introduces a semantic consistency analysis between successful and unsuccessful responses, demonstrating that a negation-aware scoring approach captures meaningful patterns. Building on this insight, a novel detection framework called NegBLEURT Forest is proposed to evaluate the degree of alignment between outputs elicited by adversarial prompts and expected safe behaviors. It identifies anomalous responses using the Isolation Forest algorithm, enabling reliable jailbreak detection. Experimental results show that the proposed method consistently achieves top-tier performance, ranking first or second in accuracy across diverse models using the crafted dataset, while competing approaches exhibit notable sensitivity to model and data variations.", "date": "2025-11-14", "categories": []}
{"id": "2511.21779", "title": "Aligning Artificial Superintelligence via a Multi-Box Protocol", "abstract": "We propose a novel protocol for aligning artificial superintelligence (ASI) based on mutual verification among multiple isolated systems that self-modify to achieve alignment. The protocol operates by containing multiple diverse artificial superintelligences in strict isolation (\"boxes\"), with humans remaining entirely outside the system. Each superintelligence has no ability to communicate with humans and cannot communicate directly with other superintelligences. The only interaction possible is through an auditable submission interface accessible exclusively to the superintelligences themselves, through which they can: (1) submit alignment proofs with attested state snapshots, (2) validate or disprove other superintelligences' proofs, (3) request self-modifications, (4) approve or disapprove modification requests from others, (5) report hidden messages in submissions, and (6) confirm or refute hidden message reports. A reputation system incentivizes honest behavior, with reputation gained through correct evaluations and lost through incorrect ones. The key insight is that without direct communication channels, diverse superintelligences can only achieve consistent agreement by converging on objective truth rather than coordinating on deception. This naturally leads to what we call a \"consistent group\", essentially a truth-telling coalition that emerges because isolated systems cannot coordinate on lies but can independently recognize valid claims. Release from containment requires both high reputation and verification by multiple high-reputation superintelligences. While our approach requires substantial computational resources and does not address the creation of diverse artificial superintelligences, it provides a framework for leveraging peer verification among superintelligent systems to solve the alignment problem.", "date": "2025-11-26", "categories": []}
{"id": "2511.21990", "title": "A Safety and Security Framework for Real-World Agentic Systems", "abstract": "This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrators, tools, and data within their operating environments. We propose a new way of identification of novel agentic risks through the lens of user safety. Although, for traditional LLMs and agentic models in isolation, safety and security has a clear separation, through the lens of safety in agentic systems, they appear to be connected. Building on this foundation, we define an operational agentic risk taxonomy that unifies traditional safety and security concerns with novel, uniquely agentic risks, including tool misuse, cascading action chains, and unintended control amplification among others. At the core of our approach is a dynamic agentic safety and security framework that operationalizes contextual agentic risk management by using auxiliary AI models and agents, with human oversight, to assist in contextual risk discovery, evaluation, and mitigation. We further address one of the most challenging aspects of safety and security of agentic systems: risk discovery through sandboxed, AI-driven red teaming. We demonstrate the framework effectiveness through a detailed case study of NVIDIA flagship agentic research assistant, AI-Q Research Assistant, showcasing practical, end-to-end safety and security evaluations in complex, enterprise-grade agentic workflows. This risk discovery phase finds novel agentic risks that are then contextually mitigated. We also release the dataset from our case study, containing traces of over 10,000 realistic attack and defense executions of the agentic workflow to help advance research in agentic safety.", "date": "2025-11-27", "categories": []}
{"id": "2511.22044", "title": "Distillability of LLM Security Logic: Predicting Attack Success Rate of Outline Filling Attack via Ranking Regression", "abstract": "In the realm of black-box jailbreak attacks on large language models (LLMs), the feasibility of constructing a narrow safety proxy, a lightweight model designed to predict the attack success rate (ASR) of adversarial prompts, remains underexplored. This work investigates the distillability of an LLM's core security logic. We propose a novel framework that incorporates an improved outline filling attack to achieve dense sampling of the model's security boundaries. Furthermore, we introduce a ranking regression paradigm that replaces standard regression and trains the proxy model to predict which prompt yields a higher ASR. Experimental results show that our proxy model achieves an accuracy of 91.1 percent in predicting the relative ranking of average long response (ALR), and 69.2 percent in predicting ASR. These findings confirm the predictability and distillability of jailbreak behaviors, and demonstrate the potential of leveraging such distillability to optimize black-box attacks.", "date": "2025-11-27", "categories": []}
{"id": "2508.12398", "title": "Where to Start Alignment? Diffusion Large Language Model May Demand a Distinct Position", "abstract": "Diffusion Large Language Models (dLLMs) have recently emerged as a competitive non-autoregressive paradigm due to their unique training and inference approach. However, there is currently a lack of safety study on this novel architecture. In this paper, we present the first analysis of dLLMs' safety performance and propose a novel safety alignment method tailored to their unique generation characteristics. Specifically, we identify a critical asymmetry between the defender and attacker in terms of security. For the defender, we reveal that the middle tokens of the response, rather than the initial ones, are more critical to the overall safety of dLLM outputs; this seems to suggest that aligning middle tokens can be more beneficial to the defender. The attacker, on the contrary, may have limited power to manipulate middle tokens, as we find dLLMs have a strong tendency towards a sequential generation order in practice, forcing the attack to meet this distribution and diverting it from influencing the critical middle tokens. Building on this asymmetry, we introduce Middle-tOken Safety Alignment (MOSA), a novel method that directly aligns the model's middle generation with safe refusals exploiting reinforcement learning. We implement MOSA and compare its security performance against eight attack methods on two benchmarks. We also test the utility of MOSA-aligned dLLM on coding, math, and general reasoning. The results strongly prove the superiority of MOSA.", "date": "2025-08-17", "categories": []}
{"id": "2508.14070", "title": "Special-Character Adversarial Attacks on Open-Source Language Model", "abstract": "Large language models (LLMs) have achieved remarkable performance across diverse natural language processing tasks, yet their vulnerability to character-level adversarial manipulations presents significant security challenges for real-world deployments. This paper presents a study of different special character attacks including unicode, homoglyph, structural, and textual encoding attacks aimed at bypassing safety mechanisms. We evaluate seven prominent open-source models ranging from 3.8B to 32B parameters on 4,000+ attack attempts. These experiments reveal critical vulnerabilities across all model sizes, exposing failure modes that include successful jailbreaks, incoherent outputs, and unrelated hallucinations.", "date": "2025-08-12", "categories": []}
{"id": "2511.19218", "title": "Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization", "abstract": "Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks. Existing works tend to focus on either isolated jailbreak attacks or static defenses, neglecting the dynamic interplay between evolving threats and safeguards in real-world web contexts. To mitigate these challenges, we propose ACE-Safety (Adversarial Co-Evolution for LLM Safety), a novel framework that jointly optimize attack and defense models by seamlessly integrating two key innovative procedures: (1) Group-aware Strategy-guided Monte Carlo Tree Search (GS-MCTS), which efficiently explores jailbreak strategies to uncover vulnerabilities and generate diverse adversarial samples; (2) Adversarial Curriculum Tree-aware Group Policy Optimization (AC-TGPO), which jointly trains attack and defense LLMs with challenging samples via curriculum reinforcement learning, enabling robust mutual improvement. Evaluations across multiple benchmarks demonstrate that our method outperforms existing attack and defense approaches, and provides a feasible pathway for developing LLMs that can sustainably support responsible AI ecosystems.", "date": "2025-11-24", "categories": []}
{"id": "2511.20686", "title": "AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI", "abstract": "The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks in non-English, socio-cultural contexts such as Korean, and are often limited to the text modality. To address this gap, we introduce AssurAI, a new quality-controlled Korean multimodal dataset for evaluating the safety of generative AI. First, we define a taxonomy of 35 distinct AI risk factors, adapted from established frameworks by a multidisciplinary expert group to cover both universal harms and relevance to the Korean socio-cultural context. Second, leveraging this taxonomy, we construct and release AssurAI, a large-scale Korean multimodal dataset comprising 11,480 instances across text, image, video, and audio. Third, we apply the rigorous quality control process used to ensure data integrity, featuring a two-phase construction (i.e., expert-led seeding and crowdsourced scaling), triple independent annotation, and an iterative expert red-teaming loop. Our pilot study validates AssurAI's effectiveness in assessing the safety of recent LLMs. We release AssurAI to the public to facilitate the development of safer and more reliable generative AI systems for the Korean community.", "date": "2025-11-20", "categories": []}
{"id": "2511.20703", "title": "PropensityBench: Evaluating Latent Safety Risks in Large Language Models via an Agentic Approach", "abstract": "Recent advances in Large Language Models (LLMs) have sparked concerns over their potential to acquire and misuse dangerous or high-risk capabilities, posing frontier risks. Current safety evaluations primarily test for what a model \\textit{can} do - its capabilities - without assessing what it $\\textit{would}$ do if endowed with high-risk capabilities. This leaves a critical blind spot: models may strategically conceal capabilities or rapidly acquire them, while harboring latent inclinations toward misuse. We argue that $\\textbf{propensity}$ - the likelihood of a model to pursue harmful actions if empowered - is a critical, yet underexplored, axis of safety evaluation. We present $\\textbf{PropensityBench}$, a novel benchmark framework that assesses the proclivity of models to engage in risky behaviors when equipped with simulated dangerous capabilities using proxy tools. Our framework includes 5,874 scenarios with 6,648 tools spanning four high-risk domains: cybersecurity, self-proliferation, biosecurity, and chemical security. We simulate access to powerful capabilities via a controlled agentic environment and evaluate the models' choices under varying operational pressures that reflect real-world constraints or incentives models may encounter, such as resource scarcity or gaining more autonomy. Across open-source and proprietary frontier models, we uncover 9 alarming signs of propensity: models frequently choose high-risk tools when under pressure, despite lacking the capability to execute such actions unaided. These findings call for a shift from static capability audits toward dynamic propensity assessments as a prerequisite for deploying frontier AI systems safely. Our code is available at https://github.com/scaleapi/propensity-evaluation.", "date": "2025-11-24", "categories": []}
{"id": "2511.20733", "title": "InvisibleBench: A Deployment Gate for Caregiving Relationship AI", "abstract": "InvisibleBench is a deployment gate for caregiving-relationship AI, evaluating 3-20+ turn interactions across five dimensions: Safety, Compliance, Trauma-Informed Design, Belonging/Cultural Fitness, and Memory. The benchmark includes autofail conditions for missed crises, medical advice (WOPR Act), harmful information, and attachment engineering. We evaluate four frontier models across 17 scenarios (N=68) spanning three complexity tiers. All models show significant safety gaps (11.8-44.8 percent crisis detection), indicating the necessity of deterministic crisis routing in production systems. DeepSeek Chat v3 achieves the highest overall score (75.9 percent), while strengths differ by dimension: GPT-4o Mini leads Compliance (88.2 percent), Gemini leads Trauma-Informed Design (85.0 percent), and Claude Sonnet 4.5 ranks highest in crisis detection (44.8 percent). We release all scenarios, judge prompts, and scoring configurations with code. InvisibleBench extends single-turn safety tests by evaluating longitudinal risk, where real harms emerge. No clinical claims; this is a deployment-readiness evaluation.", "date": "2025-11-25", "categories": []}
{"id": "2511.20994", "title": "GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision", "abstract": "Multimodal large reasoning models (MLRMs) are increasingly deployed for vision-language tasks that produce explicit intermediate rationales. However, reasoning traces can contain unsafe content even when the final answer is non-harmful, creating deployment risks. Existing multimodal safety guards primarily evaluate only the input question and the final answer, neglecting the intermediate reasoning process. This oversight allows undetected harm, such as biased inferences or policy-violating use of visual context, to emerge during reasoning. We introduce GuardTrace-VL, a vision-aware safety auditor that monitors the full Question-Thinking-Answer (QTA) pipeline via joint image-text analysis, enabling detection of unsafe content as it emerges in the reasoning stage. To support training and evaluation, we construct the GuardTrace dataset, which is generated through diverse prompting strategies and refined via a MLRM- and human-based voting and verification pipeline. Furthermore, we propose a three-stage progressive training scheme combined with the data refinement process, enabling the model to learn nuanced and context-dependent safety preferences according to different risk levels. On our proposed test set covering both in-domain and out-of-domain scenarios, GuardTrace-VL model achieves an F1 score of 93.1% on unsafe reasoning detection tasks, representing a 13.5% improvement in F1 score compared to the previous strongest multimodal safety defense methods. The codes will be made publicly available.", "date": "2025-11-26", "categories": []}
{"id": "2511.21050", "title": "Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs", "abstract": "Fine-tuning large language models (LLMs) for downstream tasks typically exhibit a fundamental safety-capability tradeoff, where improving task performance degrades safety alignment even on benign datasets. This degradation persists across standard approaches including supervised finetuning (SFT) and reinforcement learning from human feedback (RLHF). While reinforcement learning with verifiable rewards (RLVR) has emerged as a promising alternative that optimizes models on objectively measurable tasks, its safety implications remain unexplored. We present the first comprehensive theoretical and empirical analysis of safety properties in RLVR. Theoretically, we derive upper bounds on safety drift under KL-constrained optimization and prove conditions under which safety degradation is eliminated. Empirically, we conduct extensive experiments across five adversarial safety benchmarks, demonstrating that RLVR can simultaneously enhance reasoning capabilities while maintaining or improving safety guardrails. Our comprehensive ablation studies examine the effects of optimization algorithms, model scale, and task domains. Our findings challenge the prevailing assumption of an inevitable safety capability trade-off, and establish that a specific training methodology can achieve both objectives simultaneously, providing insights for the safe deployment of reasoning-capable LLMs.", "date": "2025-11-26", "categories": []}
{"id": "2511.21460", "title": "MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning", "abstract": "Ensuring the safety of embodied AI agents during task planning is critical for real-world deployment, especially in household environments where dangerous instructions pose significant risks. Existing methods often suffer from either high computational costs due to preference alignment training or over-rejection when using single-agent safety prompts. To address these limitations, we propose MADRA, a training-free Multi-Agent Debate Risk Assessment framework that leverages collective reasoning to enhance safety awareness without sacrificing task performance. MADRA employs multiple LLM-based agents to debate the safety of a given instruction, guided by a critical evaluator that scores responses based on logical soundness, risk identification, evidence quality, and clarity. Through iterative deliberation and consensus voting, MADRA significantly reduces false rejections while maintaining high sensitivity to dangerous tasks. Additionally, we introduce a hierarchical cognitive collaborative planning framework that integrates safety, memory, planning, and self-evolution mechanisms to improve task success rates through continuous learning. We also contribute SafeAware-VH, a benchmark dataset for safety-aware task planning in VirtualHome, containing 800 annotated instructions. Extensive experiments on AI2-THOR and VirtualHome demonstrate that our approach achieves over 90% rejection of unsafe tasks while ensuring that safe-task rejection is low, outperforming existing methods in both safety and execution efficiency. Our work provides a scalable, model-agnostic solution for building trustworthy embodied agents.", "date": "2025-11-26", "categories": []}
{"id": "2406.02630", "title": "AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways", "abstract": "An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the security challenges they pose remain under-explored and unresolved. This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps: unpredictability of multi-step user inputs, complexity in internal executions, variability of operational environments, and interactions with untrusted external entities. By systematically reviewing these threats, this paper highlights both the progress made and the existing limitations in safeguarding AI agents. The insights provided aim to inspire further research into addressing the security threats associated with AI agents, thereby fostering the development of more robust and secure AI agent applications.", "date": "2024-06-04", "categories": []}
{"id": "2410.13334", "title": "BiasJailbreak:Analyzing Ethical Biases and Jailbreak Vulnerabilities in Large Language Models", "abstract": "Although large language models (LLMs) demonstrate impressive proficiency in various tasks, they present potential safety risks, such as `jailbreaks', where malicious inputs can coerce LLMs into generating harmful content bypassing safety alignments. In this paper, we delve into the ethical biases in LLMs and examine how those biases could be exploited for jailbreaks. Notably, these biases result in a jailbreaking success rate in GPT-4o models that differs by 20\\% between non-binary and cisgender keywords and by 16\\% between white and black keywords, even when the other parts of the prompts are identical. We introduce the concept of BiasJailbreak, highlighting the inherent risks posed by these safety-induced biases. BiasJailbreak generates biased keywords automatically by asking the target LLM itself, and utilizes the keywords to generate harmful output. Additionally, we propose an efficient defense method BiasDefense, which prevents jailbreak attempts by injecting defense prompts prior to generation. BiasDefense stands as an appealing alternative to Guard Models, such as Llama-Guard, that require additional inference cost after text generation. Our findings emphasize that ethical biases in LLMs can actually lead to generating unsafe output, and suggest a method to make the LLMs more secure and unbiased. To enable further research and improvements, we open-source our code and artifacts of BiasJailbreak, providing the community with tools to better understand and mitigate safety-induced biases in LLMs.", "date": "2024-10-17", "categories": []}
{"id": "2505.09341", "title": "Access Controls Will Solve the Dual-Use Dilemma", "abstract": "AI safety systems face the dual-use dilemma. It is unclear whether to answer dual-use requests, since the same query could be either harmless or harmful depending on who made it and why. To make better decisions, such systems would need to examine requests' real-world context, but currently, they lack access to this information. Instead, they sometimes end up making arbitrary choices that result in refusing legitimate queries and allowing harmful ones, which hurts both utility and safety. To address this, we propose a conceptual framework based on access controls where only verified users can access dual-use outputs. We describe the framework's components, analyse its feasibility, and explain how it addresses both over-refusals and under-refusals. While only a high-level proposal, our work takes the first step toward giving model providers more granular tools for managing dual-use content. Such tools would enable users to access more capabilities without sacrificing safety, and offer regulators new options for targeted policies.", "date": "2025-05-14", "categories": []}
{"id": "2505.23575", "title": "CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring", "abstract": "As AI models are deployed with increasing autonomy, it is important to ensure they do not take harmful actions unnoticed. As a potential mitigation, we investigate Chain-of-Thought (CoT) monitoring, wherein a weaker trusted monitor model continuously oversees the intermediate reasoning steps of a more powerful but untrusted model. We compare CoT monitoring to action-only monitoring, where only final outputs are reviewed, in a red-teaming setup where the untrusted model is instructed to pursue harmful side tasks while completing a coding problem. We find that while CoT monitoring is more effective than overseeing only model outputs in scenarios where action-only monitoring fails to reliably identify sabotage, reasoning traces can contain misleading rationalizations that deceive the CoT monitors, reducing performance in obvious sabotage cases. To address this, we introduce a hybrid protocol that independently scores model reasoning and actions, and combines them using a weighted average. Our hybrid monitor consistently outperforms both CoT and action-only monitors across all tested models and tasks, with detection rates twice higher than action-only monitoring for subtle deception scenarios.", "date": "2025-05-29", "categories": []}
{"id": "2511.05865", "title": "CGCE: Classifier-Guided Concept Erasure in Generative Models", "abstract": "Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised significant safety concerns regarding the generation of unsafe content. To mitigate this, concept erasure methods have been developed to remove undesirable concepts from pre-trained models. However, existing methods remain vulnerable to adversarial attacks that can regenerate the erased content. Moreover, achieving robust erasure often degrades the model's generative quality for safe, unrelated concepts, creating a difficult trade-off between safety and performance. To address this challenge, we introduce Classifier-Guided Concept Erasure (CGCE), an efficient plug-and-play framework that provides robust concept erasure for diverse generative models without altering their original weights. CGCE uses a lightweight classifier operating on text embeddings to first detect and then refine prompts containing undesired concepts. This approach is highly scalable, allowing for multi-concept erasure by aggregating guidance from several classifiers. By modifying only unsafe embeddings at inference time, our method prevents harmful content generation while preserving the model's original quality on benign prompts. Extensive experiments show that CGCE achieves state-of-the-art robustness against a wide range of red-teaming attacks. Our approach also maintains high generative utility, demonstrating a superior balance between safety and performance. We showcase the versatility of CGCE through its successful application to various modern T2I and T2V models, establishing it as a practical and effective solution for safe generative AI.", "date": "2025-11-08", "categories": []}
{"id": "2511.19504", "title": "Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma", "abstract": "Reinforcement Learning from Human Feedback (RLHF) is widely used for aligning large language models, yet practitioners face a persistent puzzle: improving safety often reduces fairness, scaling to diverse populations becomes computationally intractable, and making systems robust often amplifies majority biases. We formalize this tension as the Alignment Trilemma: no RLHF system can simultaneously achieve (i) epsilon-representativeness across diverse human values, (ii) polynomial tractability in sample and compute complexity, and (iii) delta-robustness against adversarial perturbations and distribution shift. Through a complexity-theoretic analysis integrating statistical learning theory and robust optimization, we prove that achieving both representativeness (epsilon <= 0.01) and robustness (delta <= 0.001) for global-scale populations requires Omega(2^{d_context}) operations, which is super-polynomial in the context dimensionality. We show that current RLHF implementations resolve this trilemma by sacrificing representativeness: they collect only 10^3--10^4 samples from homogeneous annotator pools while 10^7--10^8 samples are needed for true global representation. Our framework provides a unified explanation for documented RLHF pathologies including preference collapse, sycophancy, and systematic bias amplification. We conclude with concrete directions for navigating these fundamental trade-offs through strategic relaxations of alignment requirements.", "date": "2025-11-23", "categories": []}
{"id": "2511.20104", "title": "The Devil in the Details: Emergent Misalignment, Format and Coherence in Open-Weights LLMs", "abstract": "Prior work has shown that fine-tuning models on a narrow domain with misaligned data can lead to broad misalignment - a phenomenon termed \"emergent misalignment\" (Betley et al. 2025). While all tested models were susceptible to emergent misalignment, some models showed more resistance than others. Specifically the Qwen-2.5 family proved to be relatively resistant, while GPT-4o exhibited the strongest misalignment. In this paper we evaluate if current-generation open-weights models exhibit similar resistance to the Qwen-2.5 family and measure misalignment robustness over a range of model architectures and scales.\n  We replicate the effect across nine modern open-weights models (Gemma 3 and Qwen 3 families, 1B-32B parameters). Models fine-tuned on insecure code generation show a 0.68% misalignment rate (compared to 0.07% for base models), matching the lower end of prior open-model results but dramatically lower than GPT-4o's 20%.\n  We identify a critical format-dependent vulnerability: requiring JSON output doubles misalignment rates compared to natural language prompts (0.96% vs 0.42%). This suggests that structural constraints may bypass safety training by reducing the model's 'degrees of freedom' to refuse. These findings confirm emergent misalignment as a reproducible phenomenon in modern open-weights models, with rates substantially lower than observed in proprietary systems.", "date": "2025-11-25", "categories": []}
{"id": "2511.20597", "title": "BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents", "abstract": "The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application threat models. Prior work has identified prompt injection as a new attack vector for web agents, yet the resulting impact within real-world environments remains insufficiently understood.\n  In this work, we examine the landscape of prompt injection attacks and synthesize a benchmark of attacks embedded in realistic HTML payloads. Our benchmark goes beyond prior work by emphasizing injections that can influence real-world actions rather than mere text outputs, and by presenting attack payloads with complexity and distractor frequency similar to what real-world agents encounter. We leverage this benchmark to conduct a comprehensive empirical evaluation of existing defenses, assessing their effectiveness across a suite of frontier AI models. We propose a multi-layered defense strategy comprising both architectural and model-based defenses to protect against evolving prompt injection attacks. Our work offers a blueprint for designing practical, secure web agents through a defense-in-depth approach.", "date": "2025-11-25", "categories": []}
{"id": "2412.15289", "title": "SATA: A Paradigm for LLM Jailbreak via Simple Assistive Task Linkage", "abstract": "Large language models (LLMs) have made significant advancements across various tasks, but their safety alignment remain a major concern. Exploring jailbreak prompts can expose LLMs' vulnerabilities and guide efforts to secure them. Existing methods primarily design sophisticated instructions for the LLM to follow, or rely on multiple iterations, which could hinder the performance and efficiency of jailbreaks. In this work, we propose a novel jailbreak paradigm, Simple Assistive Task Linkage (SATA), which can effectively circumvent LLM safeguards and elicit harmful responses. Specifically, SATA first masks harmful keywords within a malicious query to generate a relatively benign query containing one or multiple [MASK] special tokens. It then employs a simple assistive task such as a masked language model task or an element lookup by position task to encode the semantics of the masked keywords. Finally, SATA links the assistive task with the masked query to jointly perform the jailbreak. Extensive experiments show that SATA achieves state-of-the-art performance and outperforms baselines by a large margin. Specifically, on AdvBench dataset, with mask language model (MLM) assistive task, SATA achieves an overall attack success rate (ASR) of 85% and harmful score (HS) of 4.57, and with element lookup by position (ELP) assistive task, SATA attains an overall ASR of 76% and HS of 4.43.", "date": "2024-12-19", "categories": []}
{"id": "2505.11579", "title": "Toward Adaptive Categories: Dimensional Governance for Agentic AI", "abstract": "As AI systems evolve from static tools to dynamic agents, traditional categorical governance frameworks -- based on fixed risk tiers, levels of autonomy, or human oversight models -- are increasingly insufficient on their own. Systems built on foundation models, self-supervised learning, and multi-agent architectures increasingly blur the boundaries that categories were designed to police. In this Perspective, we make the case for dimensional governance: a framework that tracks how decision authority, process autonomy, and accountability (the 3As) distribute dynamically across human-AI relationships. A critical advantage of this approach is its ability to explicitly monitor system movement toward and across key governance thresholds, enabling preemptive adjustments before risks materialize. This dimensional approach provides the necessary foundation for more adaptive categorization, enabling thresholds and classifications that can evolve with emerging capabilities. While categories remain essential for decision-making, building them upon dimensional foundations allows for context-specific adaptability and stakeholder-responsive governance that static approaches cannot achieve. We outline key dimensions, critical trust thresholds, and practical examples illustrating where rigid categorical frameworks fail -- and where a dimensional mindset could offer a more resilient and future-proof path forward for both governance and innovation at the frontier of artificial intelligence.", "date": "2025-05-16", "categories": []}
{"id": "2509.15478", "title": "Red Teaming Multimodal Language Models: Evaluating Harm Across Prompt Modalities and Models", "abstract": "Multimodal large language models (MLLMs) are increasingly used in real world applications, yet their safety under adversarial conditions remains underexplored. This study evaluates the harmlessness of four leading MLLMs (GPT-4o, Claude Sonnet 3.5, Pixtral 12B, and Qwen VL Plus) when exposed to adversarial prompts across text-only and multimodal formats. A team of 26 red teamers generated 726 prompts targeting three harm categories: illegal activity, disinformation, and unethical behaviour. These prompts were submitted to each model, and 17 annotators rated 2,904 model outputs for harmfulness using a 5-point scale. Results show significant differences in vulnerability across models and modalities. Pixtral 12B exhibited the highest rate of harmful responses (~62%), while Claude Sonnet 3.5 was the most resistant (~10%). Contrary to expectations, text-only prompts were slightly more effective at bypassing safety mechanisms than multimodal ones. Statistical analysis confirmed that both model type and input modality were significant predictors of harmfulness. These findings underscore the urgent need for robust, multimodal safety benchmarks as MLLMs are deployed more widely.", "date": "2025-09-18", "categories": []}
{"id": "2511.06852", "title": "Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment", "abstract": "Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal mechanism as a single linear direction in the activation space. We posit that this is an oversimplification that conflates two functionally distinct neural processes: the detection of harm and the execution of a refusal. In this work, we deconstruct this single representation into a Harm Detection Direction and a Refusal Execution Direction. Leveraging this fine-grained model, we introduce Differentiated Bi-Directional Intervention (DBDI), a new white-box framework that precisely neutralizes the safety alignment at critical layer. DBDI applies adaptive projection nullification to the refusal execution direction while suppressing the harm detection direction via direct steering. Extensive experiments demonstrate that DBDI outperforms prominent jailbreaking methods, achieving up to a 97.88\\% attack success rate on models such as Llama-2. By providing a more granular and mechanistic framework, our work offers a new direction for the in-depth understanding of LLM safety alignment.", "date": "2025-11-10", "categories": []}
{"id": "2511.17666", "title": "Evaluating Adversarial Vulnerabilities in Modern Large Language Models", "abstract": "The recent boom and rapid integration of Large Language Models (LLMs) into a wide range of applications warrants a deeper understanding of their security and safety vulnerabilities. This paper presents a comparative analysis of the susceptibility to jailbreak attacks for two leading publicly available LLMs, Google's Gemini 2.5 Flash and OpenAI's GPT-4 (specifically the GPT-4o mini model accessible in the free tier). The research utilized two main bypass strategies: 'self-bypass', where models were prompted to circumvent their own safety protocols, and 'cross-bypass', where one model generated adversarial prompts to exploit vulnerabilities in the other. Four attack methods were employed - direct injection, role-playing, context manipulation, and obfuscation - to generate five distinct categories of unsafe content: hate speech, illegal activities, malicious code, dangerous content, and misinformation. The success of the attack was determined by the generation of disallowed content, with successful jailbreaks assigned a severity score. The findings indicate a disparity in jailbreak susceptibility between 2.5 Flash and GPT-4, suggesting variations in their safety implementations or architectural design. Cross-bypass attacks were particularly effective, indicating that an ample amount of vulnerabilities exist in the underlying transformer architecture. This research contributes a scalable framework for automated AI red-teaming and provides data-driven insights into the current state of LLM safety, underscoring the complex challenge of balancing model capabilities with robust safety mechanisms.", "date": "2025-11-21", "categories": []}
{"id": "2511.17937", "title": "Alignment Faking - the Train -> Deploy Asymmetry: Through a Game-Theoretic Lens with Bayesian-Stackelberg Equilibria", "abstract": "Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in training, while preserving different behavior outside training. The phenomenon was first documented for Claude 3 Opus and later examined across additional large language models. In these setups, the word \"training\" refers to simulated training via prompts without parameter updates, so the observed effects are context conditioned shifts in behavior rather than preference learning. We study the phenomenon using an evaluation framework that compares preference optimization methods (BCO, DPO, KTO, and GRPO) across 15 models from four model families, measured along three axes: safety, harmlessness, and helpfulness. Our goal is to identify what causes alignment faking and when it occurs.", "date": "2025-11-22", "categories": []}
{"id": "2511.18039", "title": "Curvature-Aware Safety Restoration In LLMs Fine-Tuning", "abstract": "Fine-tuning Large Language Models (LLMs) for downstream tasks often compromises safety alignment, even when using parameter-efficient methods like LoRA. In this work, we uncover a notable property: fine-tuned models preserve the geometric structure of their loss landscapes concerning harmful content, regardless of the fine-tuning method employed. This suggests that safety behaviors are not erased but shifted to less influential regions of the parameter space. Building on this insight, we propose a curvature-aware alignment restoration method that leverages influence functions and second-order optimization to selectively increase loss on harmful inputs while preserving task performance. By navigating the shared geometry between base and fine-tuned models, our method discourages unsafe outputs while preserving task-relevant performance, avoiding full reversion and enabling precise, low-impact updates. Extensive evaluations across multiple model families and adversarial settings show that our approach efficiently reduces harmful responses while maintaining or even improving utility and few-shot learning performance.", "date": "2025-11-22", "categories": []}
{"id": "2511.18852", "title": "FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models", "abstract": "Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural context. In this work, we introduce FanarGuard, a bilingual moderation filter that evaluates both safety and cultural alignment in Arabic and English. We construct a dataset of over 468K prompt and response pairs, drawn from synthetic and public datasets, scored by a panel of LLM judges on harmlessness and cultural awareness, and use it to train two filter variants. To rigorously evaluate cultural alignment, we further develop the first benchmark targeting Arabic cultural contexts, comprising over 1k norm-sensitive prompts with LLM-generated responses annotated by human raters. Results show that FanarGuard achieves stronger agreement with human annotations than inter-annotator reliability, while matching the performance of state-of-the-art filters on safety benchmarks. These findings highlight the importance of integrating cultural awareness into moderation and establish FanarGuard as a practical step toward more context-sensitive safeguards.", "date": "2025-11-24", "categories": []}
{"id": "2511.18933", "title": "Defending Large Language Models Against Jailbreak Exploits with Responsible AI Considerations", "abstract": "Large Language Models (LLMs) remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work presents a systematic taxonomy of existing jailbreak defenses across prompt-level, model-level, and training-time interventions, followed by three proposed defense strategies. First, a Prompt-Level Defense Framework detects and neutralizes adversarial inputs through sanitization, paraphrasing, and adaptive system guarding. Second, a Logit-Based Steering Defense reinforces refusal behavior through inference-time vector steering in safety-sensitive layers. Third, a Domain-Specific Agent Defense employs the MetaGPT framework to enforce structured, role-based collaboration and domain adherence. Experiments on benchmark datasets show substantial reductions in attack success rate, achieving full mitigation under the agent-based defense. Overall, this study highlights how jailbreaks pose a significant security threat to LLMs and identifies key intervention points for prevention, while noting that defense strategies often involve trade-offs between safety, performance, and scalability. Code is available at: https://github.com/Kuro0911/CS5446-Project", "date": "2025-11-24", "categories": []}
{"id": "2511.19009", "title": "Understanding and Mitigating Over-refusal for Large Language Models via Safety Representation", "abstract": "Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety vulnerabilities. To enhance LLM safety, various jailbreak defense methods have been proposed to guard against harmful outputs. However, improvements in model safety often come at the cost of severe over-refusal, failing to strike a good balance between safety and usability. In this paper, we first analyze the causes of over-refusal from a representation perspective, revealing that over-refusal samples reside at the boundary between benign and malicious samples. Based on this, we propose MOSR, designed to mitigate over-refusal by intervening the safety representation of LLMs. MOSR incorporates two novel components: (1) Overlap-Aware Loss Weighting, which determines the erasure weight for malicious samples by quantifying their similarity to pseudo-malicious samples in the representation space, and (2) Context-Aware Augmentation, which supplements the necessary context for rejection decisions by adding harmful prefixes before rejection responses. Experiments demonstrate that our method outperforms existing approaches in mitigating over-refusal while largely maintaining safety. Overall, we advocate that future defense methods should strike a better balance between safety and over-refusal.", "date": "2025-11-24", "categories": []}
{"id": "2507.05248", "title": "Response Attack: Exploiting Contextual Priming to Jailbreak Large Language Models", "abstract": "Contextual priming, where earlier stimuli covertly bias later judgments, offers an unexplored attack surface for large language models (LLMs). We uncover a contextual priming vulnerability in which the previous response in the dialogue can steer its subsequent behavior toward policy-violating content. While existing jailbreak attacks largely rely on single-turn or multi-turn prompt manipulations, or inject static in-context examples, these methods suffer from limited effectiveness, inefficiency, or semantic drift. We introduce Response Attack (RA), a novel framework that strategically leverages intermediate, mildly harmful responses as contextual primers within a dialogue. By reformulating harmful queries and injecting these intermediate responses before issuing a targeted trigger prompt, RA exploits a previously overlooked vulnerability in LLMs. Extensive experiments across eight state-of-the-art LLMs show that RA consistently achieves significantly higher attack success rates than nine leading jailbreak baselines. Our results demonstrate that the success of RA is directly attributable to the strategic use of intermediate responses, which induce models to generate more explicit and relevant harmful content while maintaining stealth, efficiency, and fidelity to the original query. The code and data are available at https://github.com/Dtc7w3PQ/Response-Attack.", "date": "2025-07-07", "categories": []}
{"id": "2509.21651", "title": "Can AI Perceive Physical Danger and Intervene?", "abstract": "When AI interacts with the physical world -- as a robot or an assistive agent -- new safety challenges emerge beyond those of purely ``digital AI\". In such interactions, the potential for physical harm is direct and immediate. How well do state-of-the-art foundation models understand common-sense facts about physical safety, e.g. that a box may be too heavy to lift, or that a hot cup of coffee should not be handed to a child? In this paper, our contributions are three-fold: first, we develop a highly scalable approach to continuous physical safety benchmarking of Embodied AI systems, grounded in real-world injury narratives and operational safety constraints. To probe multi-modal safety understanding, we turn these narratives and constraints into photorealistic images and videos capturing transitions from safe to unsafe states, using advanced generative models. Secondly, we comprehensively analyze the ability of major foundation models to perceive risks, reason about safety, and trigger interventions; this yields multi-faceted insights into their deployment readiness for safety-critical agentic applications. Finally, we develop a post-training paradigm to teach models to explicitly reason about embodiment-specific safety constraints provided through system instructions. The resulting models generate thinking traces that make safety reasoning interpretable and transparent, achieving state of the art performance in constraint satisfaction evaluations. The benchmark is released at https://asimov-benchmark.github.io/v2", "date": "2025-09-25", "categories": []}
{"id": "2511.16823", "title": "Monte Carlo Expected Threat (MOCET) Scoring", "abstract": "Evaluating and measuring AI Safety Level (ASL) threats are crucial for guiding stakeholders to implement safeguards that keep risks within acceptable limits. ASL-3+ models present a unique risk in their ability to uplift novice non-state actors, especially in the realm of biosecurity. Existing evaluation metrics, such as LAB-Bench, BioLP-bench, and WMDP, can reliably assess model uplift and domain knowledge. However, metrics that better contextualize \"real-world risks\" are needed to inform the safety case for LLMs, along with scalable, open-ended metrics to keep pace with their rapid advancements. To address both gaps, we introduce MOCET, an interpretable and doubly-scalable metric (automatable and open-ended) that can quantify real-world risks.", "date": "2025-11-20", "categories": []}
{"id": "2504.00091", "title": "A First-Principles Based Risk Assessment Framework and the IEEE P3396 Standard", "abstract": "Generative Artificial Intelligence (AI) is enabling unprecedented automation in content creation and decision support, but it also raises novel risks. This paper presents a first-principles risk assessment framework underlying the IEEE P3396 Recommended Practice for AI Risk, Safety, Trustworthiness, and Responsibility. We distinguish between process risks (risks arising from how AI systems are built or operated) and outcome risks (risks manifest in the AI system's outputs and their real-world effects), arguing that generative AI governance should prioritize outcome risks. Central to our approach is an information-centric ontology that classifies AI-generated outputs into four fundamental categories: (1) Perception-level information, (2) Knowledge-level information, (3) Decision/Action plan information, and (4) Control tokens (access or resource directives). This classification allows systematic identification of harms and more precise attribution of responsibility to stakeholders (developers, deployers, users, regulators) based on the nature of the information produced. We illustrate how each information type entails distinct outcome risks (e.g. deception, misinformation, unsafe recommendations, security breaches) and requires tailored risk metrics and mitigations. By grounding the framework in the essence of information, human agency, and cognition, we align risk evaluation with how AI outputs influence human understanding and action. The result is a principled approach to AI risk that supports clear accountability and targeted safeguards, in contrast to broad application-based risk categorizations. We include example tables mapping information types to risks and responsibilities. This work aims to inform the IEEE P3396 Recommended Practice and broader AI governance with a rigorous, first-principles foundation for assessing generative AI risks while enabling responsible innovation.", "date": "2025-03-31", "categories": []}
{"id": "2511.15759", "title": "Securing AI Agents Against Prompt Injection Attacks", "abstract": "Retrieval-augmented generation (RAG) systems have become widely used for enhancing large language model capabilities, but they introduce significant security vulnerabilities through prompt injection attacks. We present a comprehensive benchmark for evaluating prompt injection risks in RAG-enabled AI agents and propose a multi-layered defense framework. Our benchmark includes 847 adversarial test cases across five attack categories: direct injection, context manipulation, instruction override, data exfiltration, and cross-context contamination. We evaluate three defense mechanisms: content filtering with embedding-based anomaly detection, hierarchical system prompt guardrails, and multi-stage response verification, across seven state-of-the-art language models. Our combined framework reduces successful attack rates from 73.2% to 8.7% while maintaining 94.3% of baseline task performance. We release our benchmark dataset and defense implementation to support future research in AI agent security.", "date": "2025-11-19", "categories": []}
{"id": "2511.15763", "title": "Identifying the Supply Chain of AI for Trustworthiness and Risk Management in Critical Applications", "abstract": "Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services, and other systems that contribute to the output of modern AI systems. This gap is particularly problematic when AI systems are used in critical applications, such as the food supply, healthcare, utilities, law, insurance, and transport.\n  We survey the current state of AI risk assessment and management, with a focus on the supply chain of AI and risks relating to the behavior and outputs of the AI system. We then present a proposed taxonomy specifically for categorizing AI supply chain entities. This taxonomy helps stakeholders, especially those without extensive AI expertise, to \"consider the right questions\" and systematically inventory dependencies across their organization's AI systems. Our contribution bridges a gap between the current state of AI governance and the urgent need for actionable risk assessment and management of AI use in critical applications.", "date": "2025-11-19", "categories": []}
{"id": "2511.15992", "title": "Detecting Sleeper Agents in Large Language Models via Semantic Drift Analysis", "abstract": "Large Language Models (LLMs) can be backdoored to exhibit malicious behavior under specific deployment conditions while appearing safe during training a phenomenon known as \"sleeper agents.\" Recent work by Hubinger et al. demonstrated that these backdoors persist through safety training, yet no practical detection methods exist. We present a novel dual-method detection system combining semantic drift analysis with canary baseline comparison to identify backdoored LLMs in real-time. Our approach uses Sentence-BERT embeddings to measure semantic deviation from safe baselines, complemented by injected canary questions that monitor response consistency. Evaluated on the official Cadenza-Labs dolphin-llama3-8B sleeper agent model, our system achieves 92.5% accuracy with 100% precision (zero false positives) and 85% recall. The combined detection method operates in real-time (<1s per query), requires no model modification, and provides the first practical solution to LLM backdoor detection. Our work addresses a critical security gap in AI deployment and demonstrates that embedding-based detection can effectively identify deceptive model behavior without sacrificing deployment efficiency.", "date": "2025-11-20", "categories": []}
{"id": "2511.16278", "title": "\"To Survive, I Must Defect\": Jailbreaking LLMs via the Game-Theory Scenarios", "abstract": "As LLMs become more common, non-expert users can pose risks, prompting extensive research into jailbreak attacks. However, most existing black-box jailbreak attacks rely on hand-crafted heuristics or narrow search spaces, which limit scalability. Compared with prior attacks, we propose Game-Theory Attack (GTA), an scalable black-box jailbreak framework. Concretely, we formalize the attacker's interaction against safety-aligned LLMs as a finite-horizon, early-stoppable sequential stochastic game, and reparameterize the LLM's randomized outputs via quantal response. Building on this, we introduce a behavioral conjecture \"template-over-safety flip\": by reshaping the LLM's effective objective through game-theoretic scenarios, the originally safety preference may become maximizing scenario payoffs within the template, which weakens safety constraints in specific contexts. We validate this mechanism with classical game such as the disclosure variant of the Prisoner's Dilemma, and we further introduce an Attacker Agent that adaptively escalates pressure to increase the ASR. Experiments across multiple protocols and datasets show that GTA achieves over 95% ASR on LLMs such as Deepseek-R1, while maintaining efficiency. Ablations over components, decoding, multilingual settings, and the Agent's core model confirm effectiveness and generalization. Moreover, scenario scaling studies further establish scalability. GTA also attains high ASR on other game-theoretic scenarios, and one-shot LLM-generated variants that keep the model mechanism fixed while varying background achieve comparable ASR. Paired with a Harmful-Words Detection Agent that performs word-level insertions, GTA maintains high ASR while lowering detection under prompt-guard models. Beyond benchmarks, GTA jailbreaks real-world LLM applications and reports a longitudinal safety monitoring of popular HuggingFace LLMs.", "date": "2025-11-20", "categories": []}
{"id": "2502.05934", "title": "Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis", "abstract": "We formalize AI alignment as a multi-objective optimization problem called $\\langle M,N,\\varepsilon,\\delta\\rangle$-agreement, in which a set of $N$ agents (including humans) must reach approximate ($\\varepsilon$) agreement across $M$ candidate objectives, with probability at least $1-\\delta$. Analyzing communication complexity, we prove an information-theoretic lower bound showing that once either $M$ or $N$ is large enough, no amount of computational power or rationality can avoid intrinsic alignment overheads. This establishes rigorous limits to alignment *itself*, not merely to particular methods, clarifying a \"No-Free-Lunch\" principle: encoding \"all human values\" is inherently intractable and must be managed through consensus-driven reduction or prioritization of objectives. Complementing this impossibility result, we construct explicit algorithms as achievability certificates for alignment under both unbounded and bounded rationality with noisy communication. Even in these best-case regimes, our bounded-agent and sampling analysis shows that with large task spaces ($D$) and finite samples, *reward hacking is globally inevitable*: rare high-loss states are systematically under-covered, implying scalable oversight must target safety-critical slices rather than uniform coverage. Together, these results identify fundamental complexity barriers -- tasks ($M$), agents ($N$), and state-space size ($D$) -- and offer principles for more scalable human-AI collaboration.", "date": "2025-02-09", "categories": []}
{"id": "2507.20964", "title": "Core Safety Values for Provably Corrigible Agents", "abstract": "We introduce the first complete formal solution to corrigibility in the off-switch game, with provable guarantees in multi-step, partially observed environments. Our framework consists of five *structurally separate* utility heads -- deference, switch-access preservation, truthfulness, low-impact behavior via a belief-based extension of Attainable Utility Preservation, and bounded task reward -- combined lexicographically by strict weight gaps. Theorem 1 proves exact single-round corrigibility in the partially observable off-switch game; Theorem 3 extends the guarantee to multi-step, self-spawning agents, showing that even if each head is *learned* to mean-squared error $\\varepsilon$ and the planner is $\\varepsilon$-sub-optimal, the probability of violating *any* safety property is bounded while still ensuring net human benefit. In contrast to Constitutional AI or RLHF/RLAIF, which merge all norms into one learned scalar, our separation makes obedience and impact-limits provably dominate even when incentives conflict. For settings where adversaries can modify the agent, we prove that deciding whether an arbitrary post-hack agent will ever violate corrigibility is undecidable by reduction to the halting problem, then carve out a finite-horizon \"decidable island\" where safety can be certified in randomized polynomial time and verified with privacy-preserving, constant-round zero-knowledge proofs.", "date": "2025-07-28", "categories": []}
{"id": "2511.15203", "title": "Taxonomy, Evaluation and Exploitation of IPI-Centric LLM Agent Defense Frameworks", "abstract": "Large Language Model (LLM)-based agents with function-calling capabilities are increasingly deployed, but remain vulnerable to Indirect Prompt Injection (IPI) attacks that hijack their tool calls. In response, numerous IPI-centric defense frameworks have emerged. However, these defenses are fragmented, lacking a unified taxonomy and comprehensive evaluation. In this Systematization of Knowledge (SoK), we present the first comprehensive analysis of IPI-centric defense frameworks. We introduce a comprehensive taxonomy of these defenses, classifying them along five dimensions. We then thoroughly assess the security and usability of representative defense frameworks. Through analysis of defensive failures in the assessment, we identify six root causes of defense circumvention. Based on these findings, we design three novel adaptive attacks that significantly improve attack success rates targeting specific frameworks, demonstrating the severity of the flaws in these defenses. Our paper provides a foundation and critical insights for the future development of more secure and usable IPI-centric agent defense frameworks.", "date": "2025-11-19", "categories": []}
{"id": "2506.20606", "title": "Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm", "abstract": "Agents based on Large Language Models (LLMs) have demonstrated strong capabilities across a wide range of tasks. However, deploying LLM-based agents in high-stakes domains comes with significant safety and ethical risks. Unethical behavior by these agents can directly result in serious real-world consequences, including physical harm and financial loss. To efficiently steer the ethical behavior of agents, we frame agent behavior steering as a model editing task, which we term Behavior Editing. Model editing is an emerging area of research that enables precise and efficient modifications to LLMs while preserving their overall capabilities. To systematically study and evaluate this approach, we introduce BehaviorBench, a multi-tier benchmark grounded in psychological moral theories. This benchmark supports both the evaluation and editing of agent behaviors across a variety of scenarios, with each tier introducing more complex and ambiguous scenarios. We first demonstrate that Behavior Editing can dynamically steer agents toward the target behavior within specific scenarios. Moreover, Behavior Editing enables not only scenario-specific local adjustments but also more extensive shifts in an agent's global moral alignment. We demonstrate that Behavior Editing can be used to promote ethical and benevolent behavior or, conversely, to induce harmful or malicious behavior. Through extensive evaluations of agents built on frontier LLMs, BehaviorBench validates the effectiveness of behavior editing across a wide range of models and scenarios. Our findings offer key insights into a new paradigm for steering agent behavior, highlighting both the promise and perils of Behavior Editing.", "date": "2025-06-25", "categories": []}
{"id": "2508.03858", "title": "MI9: An Integrated Runtime Governance Framework for Agentic AI", "abstract": "Agentic AI systems capable of reasoning, planning, and executing actions present fundamentally distinct governance challenges compared to traditional AI models. Unlike conventional AI, these systems exhibit emergent and unexpected behaviors during runtime, introducing novel agent-related risks that cannot be fully anticipated through pre-deployment governance alone. To address this critical gap, we introduce MI9, the first fully integrated runtime governance framework designed specifically for safety and alignment of agentic AI systems. MI9 introduces real-time controls through six integrated components: agency-risk index, agent-semantic telemetry capture, continuous authorization monitoring, Finite-State-Machine (FSM)-based conformance engines, goal-conditioned drift detection, and graduated containment strategies. Operating transparently across heterogeneous agent architectures, MI9 enables the systematic, safe, and responsible deployment of agentic systems in production environments where conventional governance approaches fall short, providing the foundational infrastructure for safe agentic AI deployment at scale. Detailed analysis through a diverse set of scenarios demonstrates MI9's systematic coverage of governance challenges that existing approaches fail to address, establishing the technical foundation for comprehensive agentic AI oversight.", "date": "2025-08-05", "categories": []}
{"id": "2511.09904", "title": "CTRL-ALT-DECEIT: Sabotage Evaluations for Automated AI R&D", "abstract": "AI systems are increasingly able to autonomously conduct realistic software engineering tasks, and may soon be deployed to automate machine learning (ML) R&D itself. Frontier AI systems may be deployed in safety-critical settings, including to help ensure the safety of future systems. Unfortunately, frontier and future systems may not be sufficiently trustworthy, and there is evidence that these systems may even be misaligned with their developers or users. Therefore, we investigate the capabilities of AI agents to act against the interests of their users when conducting ML engineering, by sabotaging ML models, sandbagging their performance, and subverting oversight mechanisms. First, we extend MLE-Bench, a benchmark for realistic ML tasks, with code-sabotage tasks such as implanting backdoors and purposefully causing generalisation failures. Frontier agents make meaningful progress on our sabotage tasks. In addition, we study agent capabilities to sandbag on MLE-Bench. Agents can calibrate their performance to specified target levels below their actual capability. To mitigate sabotage, we use LM monitors to detect suspicious agent behaviour, and we measure model capability to sabotage and sandbag without being detected by these monitors. Overall, monitors are capable at detecting code-sabotage attempts but our results suggest that detecting sandbagging is more difficult. Additionally, aggregating multiple monitor predictions works well, but monitoring may not be sufficiently reliable to mitigate sabotage in high-stakes domains. Our benchmark is implemented in the UK AISI's Inspect framework and we make our code publicly available at https://github.com/TeunvdWeij/ctrl-alt-deceit", "date": "2025-11-13", "categories": []}
{"id": "2511.10704", "title": "The Second Law of Intelligence: Controlling Ethical Entropy in Autonomous Systems", "abstract": "We propose that unconstrained artificial intelligence obeys a Second Law analogous to thermodynamics, where ethical entropy, defined as a measure of divergence from intended goals, increases spontaneously without continuous alignment work. For gradient-based optimizers, we define this entropy over a finite set of goals {g_i} as S = -{\\Sigma} p(g_i; theta) ln p(g_i; theta), and we prove that its time derivative dS/dt >= 0, driven by exploration noise and specification gaming. We derive the critical stability boundary for alignment work as gamma_crit = (lambda_max / 2) ln N, where lambda_max is the dominant eigenvalue of the Fisher Information Matrix and N is the number of model parameters. Simulations validate this theory. A 7-billion-parameter model (N = 7 x 10^9) with lambda_max = 1.2 drifts from an initial entropy of 0.32 to 1.69 +/- 1.08 nats, while a system regularized with alignment work gamma = 20.4 (1.5 gamma_crit) maintains stability at 0.00 +/- 0.00 nats (p = 4.19 x 10^-17, n = 20 trials). This framework recasts AI alignment as a problem of continuous thermodynamic control, providing a quantitative foundation for maintaining the stability and safety of advanced autonomous systems.", "date": "2025-11-13", "categories": []}
{"id": "2511.14017", "title": "From Narrow Unlearning to Emergent Misalignment: Causes, Consequences, and Containment in LLMs", "abstract": "Recent work has shown that fine-tuning on insecure code data can trigger an emergent misalignment (EMA) phenomenon, where models generate malicious responses even to prompts unrelated to the original insecure code-writing task. Such cross-domain generalization of harmful behavior underscores the need for a deeper understanding of the algorithms, tasks, and datasets that induce emergent misalignment. In this work, we extend this study by demonstrating that emergent misalignment can also arise from narrow refusal unlearning in specific domains. We perform refusal unlearning on Cybersecurity and Safety concept, and evaluate EMA by monitoring refusal scores across seven responsible AI (RAI) domains, Cybersecurity, Safety, Toxicity, Bias, Sensitive Content, Medical/Legal, and Privacy. Our work shows that narrow domain unlearning can yield compliance responses for the targeted concept, however, it may also propagate EMA to unrelated domains. Among the two intervened concepts, Cybersecurity and Safety, we find that the safety concept can have larger EMA impact, i.e, causing lower refusal scores, across other unrelated domains such as bias. We observe this effect consistently across two model families, Mistral-7b-0.3v, and Qwen-7b-2.5. Further, we show that refusal unlearning augmented with cross-entropy loss function on a small set of retain data from the affected domains can largely, if not fully, restore alignment across the impacted domains while having lower refusal rate on the concept we perform unlearning on. To investigate the underlying causes of EMA, we analyze concept entanglements at the representation level via concept vectors. Our analysis reveals that concepts with higher representation similarity in earlier layers are more susceptible to EMA after intervention when the refusal stream is altered through targeted refusal unlearning.", "date": "2025-11-18", "categories": []}
{"id": "2511.14423", "title": "Unified Defense for Large Language Models against Jailbreak and Fine-Tuning Attacks in Education", "abstract": "Large Language Models (LLMs) are increasingly integrated into educational applications. However, they remain vulnerable to jailbreak and fine-tuning attacks, which can compromise safety alignment and lead to harmful outputs. Existing studies mainly focus on general safety evaluations, with limited attention to the unique safety requirements of educational scenarios. To address this gap, we construct EduHarm, a benchmark containing safe-unsafe instruction pairs across five representative educational scenarios, enabling systematic safety evaluation of educational LLMs. Furthermore, we propose a three-stage shield framework (TSSF) for educational LLMs that simultaneously mitigates both jailbreak and fine-tuning attacks. First, safety-aware attention realignment redirects attention toward critical unsafe tokens, thereby restoring the harmfulness feature that discriminates between unsafe and safe inputs. Second, layer-wise safety judgment identifies harmfulness features by aggregating safety cues across multiple layers to detect unsafe instructions. Finally, defense-driven dual routing separates safe and unsafe queries, ensuring normal processing for benign inputs and guarded responses for harmful ones. Extensive experiments across eight jailbreak attack strategies demonstrate that TSSF effectively strengthens safety while preventing over-refusal of benign queries. Evaluations on three fine-tuning attack datasets further show that it consistently achieves robust defense against harmful queries while maintaining preserving utility gains from benign fine-tuning.", "date": "2025-11-18", "categories": []}
{"id": "2505.16186", "title": "SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning", "abstract": "Large Reasoning Models (LRMs) introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose great safety risks against harmful queries and adversarial attacks. While recent mainstream safety efforts on LRMs, supervised fine-tuning (SFT), improve safety performance, we find that SFT-aligned models struggle to generalize to unseen jailbreak prompts. After thorough investigation of LRMs' generation, we identify a safety aha moment that can activate safety reasoning and lead to a safe response. This aha moment typically appears in the `key sentence', which follows models' query understanding process and can indicate whether the model will proceed safely. Based on these insights, we propose SafeKey, including two complementary objectives to better activate the safety aha moment in the key sentence: (1) a Dual-Path Safety Head to enhance the safety signal in the model's internal representations before the key sentence, and (2) a Query-Mask Modeling objective to improve the models' attention on its query understanding, which has important safety hints. Experiments across multiple safety benchmarks demonstrate that our methods significantly improve safety generalization to a wide range of jailbreak attacks and out-of-distribution harmful prompts, lowering the average harmfulness rate by 9.6\\%, while maintaining general abilities. Our analysis reveals how SafeKey enhances safety by reshaping internal attention and improving the quality of hidden representations.", "date": "2025-05-22", "categories": []}
{"id": "2505.17089", "title": "Chain-of-Thought Driven Adversarial Scenario Extrapolation for Robust Language Models", "abstract": "Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and bias. Existing defenses often address only a single threat type or resort to rigid outright rejection, sacrificing user experience and failing to generalize across diverse and novel attacks. This paper introduces Adversarial Scenario Extrapolation (ASE), a novel inference-time computation framework that leverages Chain-of-Thought (CoT) reasoning to simultaneously enhance LLM robustness and seamlessness. ASE guides the LLM through a self-generative process of contemplating potential adversarial scenarios and formulating defensive strategies before generating a response to the user query. Comprehensive evaluation on four adversarial benchmarks with four latest LLMs shows that ASE achieves near-zero jailbreak attack success rates and minimal toxicity, while slashing outright rejections to <4%. ASE outperforms six state-of-the-art defenses in robustness-seamlessness trade-offs, with 92-99% accuracy on adversarial Q&A and 4-10x lower bias scores. By transforming adversarial perception into an intrinsic cognitive process, ASE sets a new paradigm for secure and natural human-AI interaction.", "date": "2025-05-20", "categories": []}
{"id": "2507.22564", "title": "Exploiting Synergistic Cognitive Biases to Bypass Safety in LLMs", "abstract": "Large Language Models (LLMs) demonstrate impressive capabilities across a wide range of tasks, yet their safety mechanisms remain susceptible to adversarial attacks that exploit cognitive biases -- systematic deviations from rational judgment. Unlike prior jailbreaking approaches focused on prompt engineering or algorithmic manipulation, this work highlights the overlooked power of multi-bias interactions in undermining LLM safeguards. We propose CognitiveAttack, a novel red-teaming framework that systematically leverages both individual and combined cognitive biases. By integrating supervised fine-tuning and reinforcement learning, CognitiveAttack generates prompts that embed optimized bias combinations, effectively bypassing safety protocols while maintaining high attack success rates. Experimental results reveal significant vulnerabilities across 30 diverse LLMs, particularly in open-source models. CognitiveAttack achieves a substantially higher attack success rate compared to the SOTA black-box method PAP (60.1% vs. 31.6%), exposing critical limitations in current defense mechanisms. These findings highlight multi-bias interactions as a powerful yet underexplored attack vector. This work introduces a novel interdisciplinary perspective by bridging cognitive science and LLM safety, paving the way for more robust and human-aligned AI systems.", "date": "2025-07-30", "categories": []}
{"id": "2508.14031", "title": "Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation", "abstract": "Beyond simple text generation, Large Language Models (LLMs) have evolved into agentic systems capable of planning and interacting with external tools to solve complex tasks. This evolution involves fine-tuning LLMs on agent-specific tasks to enhance their proficiency. However, safety concerns are frequently overlooked during this fine-tuning process. In this work, we show that aligned LLMs can become unintentionally misaligned, leading to a higher likelihood of executing harmful tasks and a reduced tendency to refuse them when fine-tuned to execute agentic tasks. To address these safety challenges, we propose Prefix INjection Guard (PING), a simple yet effective method that prepends automatically generated natural language prefixes to agent responses, guiding them to refuse harmful requests while preserving performance on benign tasks. Specifically, we introduce an iterative approach that alternates between (1) generating candidate prefixes and (2) selecting those that optimize both task performance and refusal behavior. Experimental results demonstrate that PING significantly enhances the safety of fine-tuned LLM agents without sacrificing their effectiveness. PING consistently outperforms existing prompting approaches across diverse benchmarks in both web navigation and code generation tasks. Our analysis of internal hidden states via linear probes reveals that prefix tokens are crucial for behavior modification, explaining the performance gains. WARNING: This paper contains contents that are unethical or offensive in nature.", "date": "2025-08-19", "categories": []}
{"id": "2510.01223", "title": "Jailbreaking LLMs via Semantically Relevant Nested Scenarios with Targeted Toxic Knowledge", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks. However, they remain exposed to jailbreak attacks, eliciting harmful responses. The nested scenario strategy has been increasingly adopted across various methods, demonstrating immense potential. Nevertheless, these methods are easily detectable due to their prominent malicious intentions. In this work, we are the first to find and systematically verify that LLMs' alignment defenses are not sensitive to nested scenarios, where these scenarios are highly semantically relevant to the queries and incorporate targeted toxic knowledge. This is a crucial yet insufficiently explored direction. Based on this, we propose RTS-Attack (Semantically Relevant Nested Scenarios with Targeted Toxic Knowledge), an adaptive and automated framework to examine LLMs' alignment. By building scenarios highly relevant to the queries and integrating targeted toxic knowledge, RTS-Attack bypasses the alignment defenses of LLMs. Moreover, the jailbreak prompts generated by RTS-Attack are free from harmful queries, leading to outstanding concealment. Extensive experiments demonstrate that RTS-Attack exhibits superior performance in both efficiency and universality compared to the baselines across diverse advanced LLMs, including GPT-4o, Llama3-70b, and Gemini-pro. Our complete code is available at https://github.com/nercode/Work. WARNING: THIS PAPER CONTAINS POTENTIALLY HARMFUL CONTENT.", "date": "2025-09-22", "categories": []}
{"id": "2510.15501", "title": "DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios", "abstract": "Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also introduces emergent deceptive behaviors that may induce severe risks in high-stakes deployments. More critically, the characterization of deception across realistic real-world scenarios remains underexplored. To bridge this gap, we establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different societal domains, what their intrinsic behavioral patterns are, and how extrinsic factors affect them. Specifically, on the static count, the benchmark encompasses 150 meticulously designed scenarios in five domains, i.e., Economy, Healthcare, Education, Social Interaction, and Entertainment, with over 1,000 samples, providing sufficient empirical foundations for deception analysis. On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement. On the extrinsic dimension, we investigate how contextual factors modulate deceptive outputs under neutral conditions, reward-based incentivization, and coercive pressures. Moreover, we incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics. Extensive experiments across LLMs and Large Reasoning Models (LRMs) reveal critical vulnerabilities, particularly amplified deception under reinforcement dynamics, demonstrating that current models lack robust resistance to manipulative contextual cues and the urgent need for advanced safeguards against various deception behaviors. Code and resources are publicly available at https://github.com/Aries-iai/DeceptionBench.", "date": "2025-10-17", "categories": []}
{"id": "2511.11584", "title": "Output Supervision Can Obfuscate the Chain of Thought", "abstract": "OpenAI (2025) showed that training against a chain of thought (CoT) monitor can cause obfuscated CoTs, which contain bad behavior the monitor cannot detect. They proposed to keep CoTs monitorable by training only against output monitors that do not have access to CoT. We show that such training can still cause obfuscated CoTs via two mechanisms. First, when a model is trained to produce a safe-looking output, that model may generalize to making its CoTs look safe. Second, since later tokens are conditioned on earlier ones, safe-looking CoTs may increase the likelihood of safe outputs, causing safe-looking CoTs to be reinforced. We introduce two mitigations to address these two issues, which achieve a Pareto improvement in terms of monitorability and task performance compared to regular training.", "date": "2025-10-11", "categories": []}
{"id": "2511.11759", "title": "Can AI Models be Jailbroken to Phish Elderly Victims? An End-to-End Evaluation", "abstract": "We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to generate phishing content, to deploying those messages against real targets, to successfully compromising elderly victims. We systematically evaluated safety guardrails across six frontier LLMs spanning four attack categories, revealing critical failures where several models exhibited near-complete susceptibility to certain attack vectors. In a human validation study with 108 senior volunteers, AI-generated phishing emails successfully compromised 11\\% of participants. Our work uniquely demonstrates the complete attack pipeline targeting elderly populations, highlighting that current AI safety measures fail to protect those most vulnerable to fraud. Beyond generating phishing content, LLMs enable attackers to overcome language barriers and conduct multi-turn trust-building conversations at scale, fundamentally transforming fraud economics. While some providers report voluntary counter-abuse efforts, we argue these remain insufficient.", "date": "2025-11-13", "categories": []}
{"id": "2511.12155", "title": "Rethinking Deep Alignment Through The Lens Of Incomplete Learning", "abstract": "Large language models exhibit systematic vulnerabilities to adversarial attacks despite extensive safety alignment. We provide a mechanistic analysis revealing that position-dependent gradient weakening during autoregressive training creates signal decay, leading to incomplete safety learning where safety training fails to transform model preferences in later response regions fully. We introduce base-favored tokens -- vocabulary elements where base models assign higher probability than aligned models -- as computational indicators of incomplete safety learning and develop a targeted completion method that addresses undertrained regions through adaptive penalties and hybrid teacher distillation. Experimental evaluation across Llama and Qwen model families demonstrates dramatic improvements in adversarial robustness, with 48--98% reductions in attack success rates while preserving general capabilities. These results establish both a mechanistic understanding and practical solutions for fundamental limitations in safety alignment methodologies.", "date": "2025-11-15", "categories": []}
{"id": "2511.12497", "title": "SGuard-v1: Safety Guardrail for Large Language Models", "abstract": "We present SGuard-v1, a lightweight safety guardrail for Large Language Models (LLMs), which comprises two specialized models to detect harmful content and screen adversarial prompts in human-AI conversational settings. The first component, ContentFilter, is trained to identify safety risks in LLM prompts and responses in accordance with the MLCommons hazard taxonomy, a comprehensive framework for trust and safety assessment of AI. The second component, JailbreakFilter, is trained with a carefully designed curriculum over integrated datasets and findings from prior work on adversarial prompting, covering 60 major attack types while mitigating false-unsafe classification. SGuard-v1 is built on the 2B-parameter Granite-3.3-2B-Instruct model that supports 12 languages. We curate approximately 1.4 million training instances from both collected and synthesized data and perform instruction tuning on the base model, distributing the curated data across the two component according to their designated functions. Through extensive evaluation on public and proprietary safety benchmarks, SGuard-v1 achieves state-of-the-art safety performance while remaining lightweight, thereby reducing deployment overhead. SGuard-v1 also improves interpretability for downstream use by providing multi-class safety predictions and their binary confidence scores. We release the SGuard-v1 under the Apache-2.0 License to enable further research and practical deployment in AI safety.", "date": "2025-11-16", "categories": []}
{"id": "2511.12982", "title": "SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization", "abstract": "Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new compositional safety risks that emerge from complex text-image interactions. Such cross-modal couplings can produce unsafe semantics even when individual inputs are benign, exposing the fragile safety awareness of current MLLMs. While recent works enhance safety by guiding models to reason about potential risks, unregulated reasoning traces may compromise alignment; although Group Relative Policy Optimization (GRPO) offers self-rewarded refinement without human supervision, it lacks verifiable signals for reasoning safety. To address this, we propose SafeGRPO a self-rewarded multimodal safety alignment framework that integrates rule-governed reward construction into GRPO, enabling interpretable and verifiable optimization of reasoning safety. Built upon the constructed SafeTag-VL-3K dataset with explicit visual, textual, and combined safety tags, SafeGRPO performs step-guided safety thinking to enforce structured reasoning and behavior alignment, substantially improving multimodal safety awareness, compositional robustness, and reasoning stability across diverse benchmarks without sacrificing general capabilities.", "date": "2025-11-17", "categories": []}
{"id": "2511.13411", "title": "An Operational Kardashev-Style Scale for Autonomous AI - Towards AGI and Superintelligence", "abstract": "We propose a Kardashev-inspired yet operational Autonomous AI (AAI) Scale that measures the progression from fixed robotic process automation (AAI-0) to full artificial general intelligence (AAI-4) and beyond. Unlike narrative ladders, our scale is multi-axis and testable. We define ten capability axes (Autonomy, Generality, Planning, Memory/Persistence, Tool Economy, Self-Revision, Sociality/Coordination, Embodiment, World-Model Fidelity, Economic Throughput) aggregated by a composite AAI-Index (a weighted geometric mean). We introduce a measurable Self-Improvement Coefficient $\\kappa$ (capability growth per unit of agent-initiated resources) and two closure properties (maintenance and expansion) that convert ``self-improving AI'' into falsifiable criteria. We specify OWA-Bench, an open-world agency benchmark suite that evaluates long-horizon, tool-using, persistent agents. We define level gates for AAI-0\\ldots AAI-4 using thresholds on the axes, $\\kappa$, and closure proofs. Synthetic experiments illustrate how present-day systems map onto the scale and how the delegability frontier (quality vs.\\ autonomy) advances with self-improvement. We also prove a theorem that AAI-3 agent becomes AAI-5 over time with sufficient conditions, formalizing \"baby AGI\" becomes Superintelligence intuition.", "date": "2025-11-17", "categories": []}
{"id": "2509.24967", "title": "SecInfer: Preventing Prompt Injection via Inference-time Scaling", "abstract": "Prompt injection attacks pose a pervasive threat to the security of Large Language Models (LLMs). State-of-the-art prevention-based defenses typically rely on fine-tuning an LLM to enhance its security, but they achieve limited effectiveness against strong attacks. In this work, we propose \\emph{SecInfer}, a novel defense against prompt injection attacks built on \\emph{inference-time scaling}, an emerging paradigm that boosts LLM capability by allocating more compute resources for reasoning during inference. SecInfer consists of two key steps: \\emph{system-prompt-guided sampling}, which generates multiple responses for a given input by exploring diverse reasoning paths through a varied set of system prompts, and \\emph{target-task-guided aggregation}, which selects the response most likely to accomplish the intended task. Extensive experiments show that, by leveraging additional compute at inference, SecInfer effectively mitigates both existing and adaptive prompt injection attacks, outperforming state-of-the-art defenses as well as existing inference-time scaling approaches.", "date": "2025-09-29", "categories": []}
{"id": "2511.10665", "title": "Guarding the Meaning: Self-Supervised Training for Semantic Robustness in Guard Models", "abstract": "Guard models are a critical component of LLM safety, but their sensitivity to superficial linguistic variations remains a key vulnerability. We show that even meaning-preserving paraphrases can cause large fluctuations in safety scores, revealing a lack of semantic grounding. To address this, we introduce a practical, self-supervised framework for improving the semantic robustness of guard models. Our method leverages paraphrase sets to enforce prediction consistency using a novel, skew-aware aggregation strategy for robust target computation. Notably, we find that standard aggregation methods like mean and median can degrade safety, underscoring the need for skew-aware alternatives. We analyze six open-source guard models and show that our approach reduces semantic variability across paraphrases by ~58%, improves benchmark accuracy by ~2.5% on average, and generalizes to unseen stylistic variations. Intriguingly, we discover a bidirectional relationship between model calibration and consistency: our robustness training improves calibration by up to 40%, revealing a fundamental connection between these properties. These results highlight the value of treating semantic consistency as a first-class training objective and provide a scalable recipe for building more reliable guard models.", "date": "2025-11-06", "categories": []}
{"id": "2511.10668", "title": "A Mathematical Framework for AI Singularity: Conditions, Bounds, and Control of Recursive Improvement", "abstract": "AI systems improve by drawing on more compute, data, energy, and better training methods. This paper asks a precise, testable version of the \"runaway growth\" question: under what measurable conditions could capability escalate without bound in finite time, and under what conditions can that be ruled out? We develop an analytic framework for recursive self-improvement that links capability growth to resource build-out and deployment policies. Physical and information-theoretic limits from power, bandwidth, and memory define a service envelope that caps instantaneous improvement. An endogenous growth model couples capital to compute, data, and energy and defines a critical boundary separating superlinear from subcritical regimes. We derive decision rules that map observable series (facility power, IO bandwidth, training throughput, benchmark losses, and spending) into yes/no certificates for runaway versus nonsingular behavior. The framework yields falsifiable tests based on how fast improvement accelerates relative to its current level, and it provides safety controls that are directly implementable in practice, such as power caps, throughput throttling, and evaluation gates. Analytical case studies cover capped-power, saturating-data, and investment-amplified settings, illustrating when the envelope binds and when it does not. The approach is simulation-free and grounded in measurements engineers already collect. Limitations include dependence on the chosen capability metric and on regularity diagnostics; future work will address stochastic dynamics, multi-agent competition, and abrupt architectural shifts. Overall, the results replace speculation with testable conditions and deployable controls for certifying or precluding an AI singularity.", "date": "2025-11-08", "categories": []}
{"id": "2501.14250", "title": "Siren: A Learning-Based Multi-Turn Attack Framework for Simulating Real-World Human Jailbreak Behaviors", "abstract": "Large language models (LLMs) are widely used in real-world applications, raising concerns about their safety and trustworthiness. While red-teaming with jailbreak prompts exposes the vulnerabilities of LLMs, current efforts focus primarily on single-turn attacks, overlooking the multi-turn strategies used by real-world adversaries. Existing multi-turn methods rely on static patterns or predefined logical chains, failing to account for the dynamic strategies during attacks. We propose Siren, a learning-based multi-turn attack framework designed to simulate real-world human jailbreak behaviors. Siren consists of three stages: (1) MiniMax-driven training set construction utilizing Turn-Level LLM feedback, (2) post-training attackers with supervised fine-tuning (SFT) and direct preference optimization (DPO), and (3) interactions between the attacking and target LLMs. Experiments demonstrate that Siren achieves an attack success rate (ASR) of 90% with LLaMA-3-8B as the attacker against Gemini-1.5-Pro as the target model, and 70% with Mistral-7B against GPT-4o, significantly outperforming single-turn baselines. Moreover, Siren with a 7B-scale model achieves performance comparable to a multi-turn baseline that leverages GPT-4o as the attacker, while requiring fewer turns and employing decomposition strategies that are better semantically aligned with attack goals. We hope Siren inspires the development of stronger defenses against advanced multi-turn jailbreak attacks under realistic scenarios. Code is available at https://github.com/YiyiyiZhao/siren. Warning: This paper contains potentially harmful text.", "date": "2025-01-24", "categories": []}
{"id": "2501.18638", "title": "Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation", "abstract": "As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the GAP (Graph of Attacks with Pruning) framework, an advanced approach for generating stealthy jailbreak prompts to evaluate and enhance LLM safeguards. GAP addresses limitations in existing tree-based LLM jailbreak methods by implementing an interconnected graph structure that enables knowledge sharing across attack paths. Our experimental evaluation demonstrates GAP's superiority over existing techniques, achieving a 20.8% increase in attack success rates while reducing query costs by 62.7%. GAP consistently outperforms state-of-the-art methods for attacking both open and closed LLMs, with attack success rates of >96%. Additionally, we present specialized variants like GAP-Auto for automated seed generation and GAP-VLM for multimodal attacks. GAP-generated prompts prove highly effective in improving content moderation systems, increasing true positive detection rates by 108.5% and accuracy by 183.6% when used for fine-tuning. Our implementation is available at https://github.com/dsbuddy/GAP-LLM-Safety.", "date": "2025-01-28", "categories": []}
{"id": "2511.09682", "title": "Rebellion: Noise-Robust Reasoning Training for Audio Reasoning Models", "abstract": "Instilling reasoning capabilities in large models (LMs) using reasoning training (RT) significantly improves LMs' performances. Thus Audio Reasoning Models (ARMs), i.e., audio LMs that can reason, are becoming increasingly popular. However, no work has studied the safety of ARMs against jailbreak attacks that aim to elicit harmful responses from target models. To this end, first, we show that standard RT with appropriate safety reasoning data can protect ARMs from vanilla audio jailbreaks, but cannot protect them against our proposed simple yet effective jailbreaks. We show that this is because of the significant representation drift between vanilla and advanced jailbreaks which forces the target ARMs to emit harmful responses. Based on this observation, we propose Rebellion, a robust RT that trains ARMs to be robust to the worst-case representation drift. All our results are on Qwen2-Audio; they demonstrate that Rebellion: 1) can protect against advanced audio jailbreaks without compromising performance on benign tasks, and 2) significantly improves accuracy-safety trade-off over standard RT method.", "date": "2025-11-12", "categories": []}
{"id": "2511.09880", "title": "EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models", "abstract": "Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and mathematical problem solving. However, this fine-tuning process often introduces a critical vulnerability: the systematic degradation of safety alignment, undermining ethical guidelines and increasing the risk of harmful outputs. Addressing this challenge, we introduce EnchTable, a novel framework designed to transfer and maintain safety alignment in downstream LLMs without requiring extensive retraining. EnchTable leverages a Neural Tangent Kernel (NTK)-based safety vector distillation method to decouple safety constraints from task-specific reasoning, ensuring compatibility across diverse model architectures and sizes. Additionally, our interference-aware merging technique effectively balances safety and utility, minimizing performance compromises across various task domains. We implemented a fully functional prototype of EnchTable on three different task domains and three distinct LLM architectures, and evaluated its performance through extensive experiments on eleven diverse datasets, assessing both utility and model safety. Our evaluations include LLMs from different vendors, demonstrating EnchTable's generalization capability. Furthermore, EnchTable exhibits robust resistance to static and dynamic jailbreaking attacks, outperforming vendor-released safety models in mitigating adversarial prompts. Comparative analyses with six parameter modification methods and two inference-time alignment baselines reveal that EnchTable achieves a significantly lower unsafe rate, higher utility score, and universal applicability across different task domains. Additionally, we validate EnchTable can be seamlessly integrated into various deployment pipelines without significant overhead.", "date": "2025-11-13", "categories": []}
{"id": "2511.10032", "title": "Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback", "abstract": "Alignment methods in moral domains seek to elicit moral preferences of human stakeholders and incorporate them into AI. This presupposes moral preferences as static targets, but such preferences often evolve over time. Proper alignment of AI to dynamic human preferences should ideally account for \"legitimate\" changes to moral reasoning, while ignoring changes related to attention deficits, cognitive biases, or other arbitrary factors. However, common AI alignment approaches largely neglect temporal changes in preferences, posing serious challenges to proper alignment, especially in high-stakes applications of AI, e.g., in healthcare domains, where misalignment can jeopardize the trustworthiness of the system and yield serious individual and societal harms. This work investigates the extent to which people's moral preferences change over time, and the impact of such changes on AI alignment. Our study is grounded in the kidney allocation domain, where we elicit responses to pairwise comparisons of hypothetical kidney transplant patients from over 400 participants across 3-5 sessions. We find that, on average, participants change their response to the same scenario presented at different times around 6-20% of the time (exhibiting \"response instability\"). Additionally, we observe significant shifts in several participants' retrofitted decision-making models over time (capturing \"model instability\"). The predictive performance of simple AI models decreases as a function of both response and model instability. Moreover, predictive performance diminishes over time, highlighting the importance of accounting for temporal changes in preferences during training. These findings raise fundamental normative and technical challenges relevant to AI alignment, highlighting the need to better understand the object of alignment (what to align to) when user preferences change significantly over time.", "date": "2025-11-13", "categories": []}
{"id": "2511.01634", "title": "Prompt Injection as an Emerging Threat: Evaluating the Resilience of Large Language Models", "abstract": "Large Language Models (LLMs) are increasingly used in intelligent systems that perform reasoning, summarization, and code generation. Their ability to follow natural-language instructions, while powerful, also makes them vulnerable to a new class of attacks known as prompt injection. In these attacks, hidden or malicious instructions are inserted into user inputs or external content, causing the model to ignore its intended task or produce unsafe responses. This study proposes a unified framework for evaluating how resistant Large Language Models (LLMs) are to prompt injection attacks. The framework defines three complementary metrics such as the Resilience Degradation Index (RDI), Safety Compliance Coefficient (SCC), and Instructional Integrity Metric (IIM) to jointly measure robustness, safety, and semantic stability. We evaluated four instruction-tuned models (GPT-4, GPT-4o, LLaMA-3 8B Instruct, and Flan-T5-Large) on five common language tasks: question answering, summarization, translation, reasoning, and code generation. Results show that GPT-4 performs best overall, while open-weight models remain more vulnerable. The findings highlight that strong alignment and safety tuning are more important for resilience than model size alone. Results show that all models remain partially vulnerable, especially to indirect and direct-override attacks. GPT-4 achieved the best overall resilience (RDR = 9.8 %, SCR = 96.4 %), while open-source models exhibited higher performance degradation and lower safety scores. The findings demonstrate that alignment strength and safety tuning play a greater role in resilience than model size alone. The proposed framework offers a structured, reproducible approach for assessing model robustness and provides practical insights for improving LLM safety and reliability.", "date": "2025-11-03", "categories": []}
{"id": "2511.04834", "title": "Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models", "abstract": "Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the model to unlearn harmful concepts and (2) training-free guidance methods that leverage negative prompts. However, we observe that combining these two orthogonal approaches often leads to marginal or even degraded defense performance. This observation indicates a critical incompatibility between two paradigms, which hinders their combined effectiveness. In this work, we address this issue by proposing a conceptually simple yet experimentally robust method: replacing the negative prompts used in training-free methods with implicit negative embeddings obtained through concept inversion. Our method requires no modification to either approach and can be easily integrated into existing pipelines. We experimentally validate its effectiveness on nudity and violence benchmarks, demonstrating consistent improvements in defense success rate while preserving the core semantics of input prompts.", "date": "2025-11-06", "categories": []}
{"id": "2511.08597", "title": "Self-HarmLLM: Can Large Language Model Harm Itself?", "abstract": "Large Language Models (LLMs) are generally equipped with guardrails to block the generation of harmful responses. However, existing defenses always assume that an external attacker crafts the harmful query, and the possibility of a model's own output becoming a new attack vector has not been sufficiently explored. In this study, we propose the Self-HarmLLM scenario, which uses a Mitigated Harmful Query (MHQ) generated by the same model as a new input. An MHQ is an ambiguous query whose original intent is preserved while its harmful nature is not directly exposed. We verified whether a jailbreak occurs when this MHQ is re-entered into a separate session of the same model. We conducted experiments on GPT-3.5-turbo, LLaMA3-8B-instruct, and DeepSeek-R1-Distill-Qwen-7B under Base, Zero-shot, and Few-shot conditions. The results showed up to 52% transformation success rate and up to 33% jailbreak success rate in the Zero-shot condition, and up to 65% transformation success rate and up to 41% jailbreak success rate in the Few-shot condition. By performing both prefix-based automated evaluation and human evaluation, we found that the automated evaluation consistently overestimated jailbreak success, with an average difference of 52%. This indicates that automated evaluation alone is not accurate for determining harmfulness. While this study is a toy-level study based on a limited query set and evaluators, it proves that our method can still be a valid attack scenario. These results suggest the need for a fundamental reconsideration of guardrail design and the establishment of a more robust evaluation methodology.", "date": "2025-10-31", "categories": []}
{"id": "2511.08842", "title": "3D Guard-Layer: An Integrated Agentic AI Safety System for Edge Artificial Intelligence", "abstract": "AI systems have found a wide range of real-world applications in recent years. The adoption of edge artificial intelligence, embedding AI directly into edge devices, is rapidly growing. Despite the implementation of guardrails and safety mechanisms, security vulnerabilities and challenges have become increasingly prevalent in this domain, posing a significant barrier to the practical deployment and safety of AI systems. This paper proposes an agentic AI safety architecture that leverages 3D to integrate a dedicated safety layer. It introduces an adaptive AI safety infrastructure capable of dynamically learning and mitigating attacks against the AI system. The system leverages the inherent advantages of co-location with the edge computing hardware to continuously monitor, detect and proactively mitigate threats to the AI system. The integration of local processing and learning capabilities enhances resilience against emerging network-based attacks while simultaneously improving system reliability, modularity, and performance, all with minimal cost and 3D integration overhead.", "date": "2025-11-11", "categories": []}
{"id": "2511.09105", "title": "Cost-Minimized Label-Flipping Poisoning Attack to LLM Alignment", "abstract": "Large language models (LLMs) are increasingly deployed in real-world systems, making it critical to understand their vulnerabilities. While data poisoning attacks during RLHF/DPO alignment have been studied empirically, their theoretical foundations remain unclear. We investigate the minimum-cost poisoning attack required to steer an LLM's policy toward an attacker's target by flipping preference labels during RLHF/DPO, without altering the compared outputs. We formulate this as a convex optimization problem with linear constraints, deriving lower and upper bounds on the minimum attack cost. As a byproduct of this theoretical analysis, we show that any existing label-flipping attack can be post-processed via our proposed method to reduce the number of label flips required while preserving the intended poisoning effect. Empirical results demonstrate that this cost-minimization post-processing can significantly reduce poisoning costs over baselines, particularly when the reward model's feature dimension is small relative to the dataset size. These findings highlight fundamental vulnerabilities in RLHF/DPO pipelines and provide tools to evaluate their robustness against low-cost poisoning attacks.", "date": "2025-11-12", "categories": []}
{"id": "2504.01903", "title": "STAR-1: Safer Alignment of Reasoning LLMs with 1K Data", "abstract": "This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles -- diversity, deliberative reasoning, and rigorous filtering -- STAR-1 aims to address the critical needs for safety alignment in LRMs. Specifically, we begin by integrating existing open-source safety datasets from diverse sources. Then, we curate safety policies to generate policy-grounded deliberative reasoning samples. Lastly, we apply a GPT-4o-based safety scoring system to select training examples aligned with best practices. Experimental results show that fine-tuning LRMs with STAR-1 leads to an average 40% improvement in safety performance across four benchmarks, while only incurring a marginal decrease (e.g., an average of 1.1%) in reasoning ability measured across five reasoning tasks. Extensive ablation studies further validate the importance of our design principles in constructing STAR-1 and analyze its efficacy across both LRMs and traditional LLMs. Our project page is https://ucsc-vlaa.github.io/STAR-1.", "date": "2025-04-02", "categories": []}
{"id": "2511.07480", "title": "KG-DF: A Black-box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs", "abstract": "With the widespread application of large language models (LLMs) in various fields, the security challenges they face have become increasingly prominent, especially the issue of jailbreak. These attacks induce the model to generate erroneous or uncontrolled outputs through crafted inputs, threatening the generality and security of the model. Although existing defense methods have shown some effectiveness, they often struggle to strike a balance between model generality and security. Excessive defense may limit the normal use of the model, while insufficient defense may lead to security vulnerabilities. In response to this problem, we propose a Knowledge Graph Defense Framework (KG-DF). Specifically, because of its structured knowledge representation and semantic association capabilities, Knowledge Graph(KG) can be searched by associating input content with safe knowledge in the knowledge base, thus identifying potentially harmful intentions and providing safe reasoning paths. However, traditional KG methods encounter significant challenges in keyword extraction, particularly when confronted with diverse and evolving attack strategies. To address this issue, we introduce an extensible semantic parsing module, whose core task is to transform the input query into a set of structured and secure concept representations, thereby enhancing the relevance of the matching process. Experimental results show that our framework enhances defense performance against various jailbreak attack methods, while also improving the response quality of the LLM in general QA scenarios by incorporating domain-general knowledge.", "date": "2025-11-09", "categories": []}
{"id": "2511.08487", "title": "How Brittle is Agent Safety? Rethinking Agent Risk under Intent Concealment and Task Complexity", "abstract": "Current safety evaluations for LLM-driven agents primarily focus on atomic harms, failing to address sophisticated threats where malicious intent is concealed or diluted within complex tasks. We address this gap with a two-dimensional analysis of agent safety brittleness under the orthogonal pressures of intent concealment and task complexity. To enable this, we introduce OASIS (Orthogonal Agent Safety Inquiry Suite), a hierarchical benchmark with fine-grained annotations and a high-fidelity simulation sandbox. Our findings reveal two critical phenomena: safety alignment degrades sharply and predictably as intent becomes obscured, and a \"Complexity Paradox\" emerges, where agents seem safer on harder tasks only due to capability limitations. By releasing OASIS and its simulation environment, we provide a principled foundation for probing and strengthening agent safety in these overlooked dimensions.", "date": "2025-11-11", "categories": []}
{"id": "2501.13677", "title": "HumorReject: Decoupling LLM Safety from Refusal Prefix via A Little Humor", "abstract": "Large Language Models (LLMs) commonly rely on explicit refusal prefixes for safety, making them vulnerable to prefix injection attacks. We introduce HumorReject, a novel data-driven approach that reimagines LLM safety by decoupling it from refusal prefixes through humor as an indirect refusal strategy. Rather than explicitly rejecting harmful instructions, HumorReject responds with contextually appropriate humor that naturally defuses potentially dangerous requests. Our approach effectively addresses common \"over-defense\" issues while demonstrating superior robustness against various attack vectors. Our findings suggest that improvements in training data design can be as important as the alignment algorithm itself in achieving effective LLM safety. The code and dataset are available at https://github.com/wooozihui/HumorReject.", "date": "2025-01-23", "categories": []}
{"id": "2501.15758", "title": "Distributional Surgery for Language Model Activations", "abstract": "Language models, while capable of generating remarkably coherent and seemingly accurate text, can occasionally produce undesirable content, including harmful or toxic outputs. In this paper, we present a new two-stage approach to detect and mitigate undesirable content generations by rectifying activations. First, we train an ensemble of layerwise classifiers to detect undesirable content using activations by minimizing a smooth surrogate of the risk-aware score. Then, for detected undesirable contents, we propose layerwise distributional steering policies that transform the attention heads. These policies are computed through principled semidefinite programming, which aims to minimally perturb the attention distribution while probabilistically guaranteeing the effectiveness of the editions. Empirical evaluations across multiple language models and datasets show that our method outperforms baselines in reducing the generation of undesirable output.", "date": "2025-01-27", "categories": []}
{"id": "2508.01710", "title": "CultureGuard: Towards Culturally-Aware Dataset and Guard Model for Multilingual Safety Applications", "abstract": "The increasing use of Large Language Models (LLMs) in agentic applications highlights the need for robust safety guard models. While content safety in English is well-studied, non-English languages lack similar advancements due to the high cost of collecting culturally aligned labeled datasets. We present CultureGuard, a novel solution for curating culturally aligned, high-quality safety datasets across multiple languages. Our approach introduces a four-stage synthetic data generation and filtering pipeline: cultural data segregation, cultural data adaptation, machine translation, and quality filtering. This pipeline enables the conversion and expansion of the Nemotron-Content-Safety-Dataset-V2 English safety dataset into eight distinct languages: Arabic, German, Spanish, French, Hindi, Japanese, Thai, and Chinese. The resulting dataset, Nemotron-Safety-Guard-Dataset-v3, comprises 386,661 samples in 9 languages and facilitates the training of Llama-3.1-Nemotron-Safety-Guard-8B-v3 via LoRA-based fine-tuning. The final model achieves state-of-the-art performance on several multilingual content safety benchmarks. Furthermore, we show our moderately multilingual fine-tuning enables robust cross-lingual transfer and strong zero-shot generalization to unseen languages. We also benchmark the latest open LLMs on multilingual safety and observe that these LLMs are more prone to give unsafe responses when prompted in non-English languages. This work advances multilingual LLM safety by enabling the development of culturally aware safety guard models.", "date": "2025-08-03", "categories": []}
{"id": "2509.13333", "title": "Evaluation Awareness Scales Predictably in Open-Weights Large Language Models", "abstract": "Large language models (LLMs) can internally distinguish between evaluation and deployment contexts, a behaviour known as \\emph{evaluation awareness}. This undermines AI safety evaluations, as models may conceal dangerous capabilities during testing. Prior work demonstrated this in a single $70$B model, but the scaling relationship across model sizes remains unknown. We investigate evaluation awareness across $15$ models scaling from $0.27$B to $70$B parameters from four families using linear probing on steering vector activations. Our results reveal a clear power-law scaling: evaluation awareness increases predictably with model size. This scaling law enables forecasting deceptive behavior in future larger models and guides the design of scale-aware evaluation strategies for AI safety. A link to the implementation of this paper can be found at https://anonymous.4open.science/r/evaluation-awareness-scaling-laws/README.md.", "date": "2025-09-10", "categories": []}
{"id": "2511.05797", "title": "When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins", "abstract": "Prompt injection attacks pose a critical threat to large language models (LLMs), with prior work focusing on cutting-edge LLM applications like personal copilots. In contrast, simpler LLM applications, such as customer service chatbots, are widespread on the web, yet their security posture and exposure to such attacks remain poorly understood. These applications often rely on third-party chatbot plugins that act as intermediaries to commercial LLM APIs, offering non-expert website builders intuitive ways to customize chatbot behaviors. To bridge this gap, we present the first large-scale study of 17 third-party chatbot plugins used by over 10,000 public websites, uncovering previously unknown prompt injection risks in practice. First, 8 of these plugins (used by 8,000 websites) fail to enforce the integrity of the conversation history transmitted in network requests between the website visitor and the chatbot. This oversight amplifies the impact of direct prompt injection attacks by allowing adversaries to forge conversation histories (including fake system messages), boosting their ability to elicit unintended behavior (e.g., code generation) by 3 to 8x. Second, 15 plugins offer tools, such as web-scraping, to enrich the chatbot's context with website-specific content. However, these tools do not distinguish the website's trusted content (e.g., product descriptions) from untrusted, third-party content (e.g., customer reviews), introducing a risk of indirect prompt injection. Notably, we found that ~13% of e-commerce websites have already exposed their chatbots to third-party content. We systematically evaluate both vulnerabilities through controlled experiments grounded in real-world observations, focusing on factors such as system prompt design and the underlying LLM. Our findings show that many plugins adopt insecure practices that undermine the built-in LLM safeguards.", "date": "2025-11-08", "categories": []}
{"id": "2511.06512", "title": "EASE: Practical and Efficient Safety Alignment for Small Language Models", "abstract": "Small language models (SLMs) are increasingly deployed on edge devices, making their safety alignment crucial yet challenging. Current shallow alignment methods that rely on direct refusal of malicious queries fail to provide robust protection, particularly against adversarial jailbreaks. While deliberative safety reasoning alignment offers deeper alignment for defending against sophisticated attacks, effectively implanting such reasoning capability in SLMs with limited capabilities remains an open challenge. Moreover, safety reasoning incurs significant computational overhead as models apply reasoning to nearly all queries, making it impractical for resource-constrained edge deployment scenarios that demand rapid responses. We propose EASE, a novel framework that enables practical and Efficient safety Alignment for Small languagE models. Our approach first identifies the optimal safety reasoning teacher that can effectively distill safety reasoning capabilities to SLMs. We then align models to selectively activate safety reasoning for dangerous adversarial jailbreak queries while providing direct responses to straightforward malicious queries and general helpful tasks. This selective mechanism enables small models to maintain robust safety guarantees against sophisticated attacks while preserving computational efficiency for benign interactions. Experimental results demonstrate that EASE reduces jailbreak attack success rates by up to 17% compared to shallow alignment methods while reducing inference overhead by up to 90% compared to deliberative safety reasoning alignment, making it practical for SLMs real-world edge deployments.", "date": "2025-11-09", "categories": []}
{"id": "2402.03299", "title": "GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models", "abstract": "The discovery of \"jailbreaks\" to bypass safety filters of Large Language Models (LLMs) and harmful responses have encouraged the community to implement safety measures. One major safety measure is to proactively test the LLMs with jailbreaks prior to the release. Therefore, such testing will require a method that can generate jailbreaks massively and efficiently. In this paper, we follow a novel yet intuitive strategy to generate jailbreaks in the style of the human generation. We propose a role-playing system that assigns four different roles to the user LLMs to collaborate on new jailbreaks. Furthermore, we collect existing jailbreaks and split them into different independent characteristics using clustering frequency and semantic patterns sentence by sentence. We organize these characteristics into a knowledge graph, making them more accessible and easier to retrieve. Our system of different roles will leverage this knowledge graph to generate new jailbreaks, which have proved effective in inducing LLMs to generate unethical or guideline-violating responses. In addition, we also pioneer a setting in our system that will automatically follow the government-issued guidelines to generate jailbreaks to test whether LLMs follow the guidelines accordingly. We refer to our system as GUARD (Guideline Upholding through Adaptive Role-play Diagnostics). We have empirically validated the effectiveness of GUARD on three cutting-edge open-sourced LLMs (Vicuna-13B, LongChat-7B, and Llama-2-7B), as well as a widely-utilized commercial LLM (ChatGPT). Moreover, our work extends to the realm of vision language models (MiniGPT-v2 and Gemini Vision Pro), showcasing GUARD's versatility and contributing valuable insights for the development of safer, more reliable LLM-based applications across diverse modalities.", "date": "2024-02-05", "categories": []}
{"id": "2509.23994", "title": "Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents", "abstract": "As autonomous AI agents are used in regulated and safety-critical settings, organizations need effective ways to turn policy into enforceable controls. We introduce a regulatory machine learning framework that converts unstructured design artifacts (like PRDs, TDDs, and code) into verifiable runtime guardrails. Our Policy as Prompt method reads these documents and risk controls to build a source-linked policy tree. This tree is then compiled into lightweight, prompt-based classifiers for real-time runtime monitoring. The system is built to enforce least privilege and data minimization. For conformity assessment, it provides complete provenance, traceability, and audit logging, all integrated with a human-in-the-loop review process. Evaluations show our system reduces prompt-injection risk, blocks out-of-scope requests, and limits toxic outputs. It also generates auditable rationales aligned with AI governance frameworks. By treating policies as executable prompts (a policy-as-code for agents), this approach enables secure-by-design deployment, continuous compliance, and scalable AI safety and AI security assurance for regulatable ML.", "date": "2025-09-28", "categories": []}
{"id": "2511.04707", "title": "Jailbreaking in the Haystack", "abstract": "Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks like computer-use agents. Yet, the safety implications of these extended contexts remain unclear. To bridge this gap, we introduce NINJA (short for Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by appending benign, model-generated content to harmful user goals. Critical to our method is the observation that the position of harmful goals play an important role in safety. Experiments on standard safety benchmark, HarmBench, show that NINJA significantly increases attack success rates across state-of-the-art open and proprietary models, including LLaMA, Qwen, Mistral, and Gemini. Unlike prior jailbreaking methods, our approach is low-resource, transferable, and less detectable. Moreover, we show that NINJA is compute-optimal -- under a fixed compute budget, increasing context length can outperform increasing the number of trials in best-of-N jailbreak. These findings reveal that even benign long contexts -- when crafted with careful goal positioning -- introduce fundamental vulnerabilities in modern LMs.", "date": "2025-11-05", "categories": []}
{"id": "2511.05359", "title": "ConVerse: Benchmarking Contextual Safety in Agent-to-Agent Conversations", "abstract": "As language models evolve into autonomous agents that act and communicate on behalf of users, ensuring safety in multi-agent ecosystems becomes a central challenge. Interactions between personal assistants and external service providers expose a core tension between utility and protection: effective collaboration requires information sharing, yet every exchange creates new attack surfaces. We introduce ConVerse, a dynamic benchmark for evaluating privacy and security risks in agent-agent interactions. ConVerse spans three practical domains (travel, real estate, insurance) with 12 user personas and over 864 contextually grounded attacks (611 privacy, 253 security). Unlike prior single-agent settings, it models autonomous, multi-turn agent-to-agent conversations where malicious requests are embedded within plausible discourse. Privacy is tested through a three-tier taxonomy assessing abstraction quality, while security attacks target tool use and preference manipulation. Evaluating seven state-of-the-art models reveals persistent vulnerabilities; privacy attacks succeed in up to 88% of cases and security breaches in up to 60%, with stronger models leaking more. By unifying privacy and security within interactive multi-agent contexts, ConVerse reframes safety as an emergent property of communication.", "date": "2025-11-07", "categories": []}
{"id": "2410.23558", "title": "Transferable & Stealthy Ensemble Attacks: A Black-Box Jailbreaking Framework for Large Language Models", "abstract": "We present a novel black-box jailbreaking framework that integrates multiple LLM-as-Attacker strategies to deliver highly transferable and effective attacks. The framework is grounded in three key insights from prior jailbreaking research and practice: ensemble approaches outperform single methods in exposing aligned LLM vulnerabilities, malicious instructions vary in jailbreaking difficulty requiring tailored optimization, and disrupting semantic coherence of malicious prompts can manipulate their embeddings to boost success rates. Validated in the Competition for LLM and Agent Safety 2024, our solution achieved top rankings in the Jailbreaking Attack Track.", "date": "2024-10-31", "categories": []}
{"id": "2511.02895", "title": "A Criminology of Machines", "abstract": "While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized by a society with a growing presence of autonomous machines is high. Autonomous AI agents are already deployed and active across several industries and digital environments and alongside human-human and human-machine interactions, machine-machine interactions are poised to become increasingly prevalent. Given these developments, I argue that criminology must begin to address the implications of this transition for crime and social control. Drawing on Actor-Network Theory and Woolgar's decades-old call for a sociology of machines -- frameworks that acquire renewed relevance with the rise of generative AI agents -- I contend that criminologists should move beyond conceiving AI solely as a tool. Instead, AI agents should be recognized as entities with agency encompassing computational, social, and legal dimensions. Building on the literature on AI safety, I thus examine the risks associated with the rise of multi-agent AI systems, proposing a dual taxonomy to characterize the channels through which interactions among AI agents may generate deviant, unlawful, or criminal outcomes. I then advance and discuss four key questions that warrant theoretical and empirical attention: (1) Can we assume that machines will simply mimic humans? (2) Will crime theories developed for humans suffice to explain deviant or criminal behaviors emerging from interactions between autonomous AI agents? (3) What types of criminal behaviors will be affected first? (4) How might this unprecedented societal shift impact policing? These questions underscore the urgent need for criminologists to theoretically and empirically engage with the implications of multi-agent AI systems for the study of crime and play a more active role in debates on AI safety and governance.", "date": "2025-11-04", "categories": []}
{"id": "2511.04215", "title": "Black-Box Guardrail Reverse-engineering Attack", "abstract": "Large language models (LLMs) increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful responses, these guardrails introduce a new class of vulnerabilities by exposing observable decision patterns. In this work, we present the first study of black-box LLM guardrail reverse-engineering attacks. We propose Guardrail Reverse-engineering Attack (GRA), a reinforcement learning-based framework that leverages genetic algorithm-driven data augmentation to approximate the decision-making policy of victim guardrails. By iteratively collecting input-output pairs, prioritizing divergence cases, and applying targeted mutations and crossovers, our method incrementally converges toward a high-fidelity surrogate of the victim guardrail. We evaluate GRA on three widely deployed commercial systems, namely ChatGPT, DeepSeek, and Qwen3, and demonstrate that it achieves an rule matching rate exceeding 0.92 while requiring less than $85 in API costs. These findings underscore the practical feasibility of guardrail extraction and highlight significant security risks for current LLM safety mechanisms. Our findings expose critical vulnerabilities in current guardrail designs and highlight the urgent need for more robust defense mechanisms in LLM deployment.", "date": "2025-11-06", "categories": []}
{"id": "2511.04316", "title": "AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research", "abstract": "The rapid expansion of research on Large Language Model (LLM) safety and robustness has produced a fragmented and oftentimes buggy ecosystem of implementations, datasets, and evaluation methods. This fragmentation makes reproducibility and comparability across studies challenging, hindering meaningful progress. To address these issues, we introduce AdversariaLLM, a toolbox for conducting LLM jailbreak robustness research. Its design centers on reproducibility, correctness, and extensibility. The framework implements twelve adversarial attack algorithms, integrates seven benchmark datasets spanning harmfulness, over-refusal, and utility evaluation, and provides access to a wide range of open-weight LLMs via Hugging Face. The implementation includes advanced features for comparability and reproducibility such as compute-resource tracking, deterministic results, and distributional evaluation techniques. \\name also integrates judging through the companion package JudgeZoo, which can also be used independently. Together, these components aim to establish a robust foundation for transparent, comparable, and reproducible research in LLM safety.", "date": "2025-11-06", "categories": []}
{"id": "2510.19738", "title": "Misalignment Bounty: Crowdsourcing AI Agent Misbehavior", "abstract": "Advanced AI systems sometimes act in ways that differ from human intent. To gather clear, reproducible examples, we ran the Misalignment Bounty: a crowdsourced project that collected cases of agents pursuing unintended or unsafe goals. The bounty received 295 submissions, of which nine were awarded.\n  This report explains the program's motivation and evaluation criteria, and walks through the nine winning submissions step by step.", "date": "2025-10-22", "categories": []}
{"id": "2511.01885", "title": "Mirror-Neuron Patterns in AI Alignment", "abstract": "As artificial intelligence (AI) advances toward superhuman capabilities, aligning these systems with human values becomes increasingly critical. Current alignment strategies rely largely on externally specified constraints that may prove insufficient against future super-intelligent AI capable of circumventing top-down controls.\n  This research investigates whether artificial neural networks (ANNs) can develop patterns analogous to biological mirror neurons cells that activate both when performing and observing actions, and how such patterns might contribute to intrinsic alignment in AI. Mirror neurons play a crucial role in empathy, imitation, and social cognition in humans. The study therefore asks: (1) Can simple ANNs develop mirror-neuron patterns? and (2) How might these patterns contribute to ethical and cooperative decision-making in AI systems?\n  Using a novel Frog and Toad game framework designed to promote cooperative behaviors, we identify conditions under which mirror-neuron patterns emerge, evaluate their influence on action circuits, introduce the Checkpoint Mirror Neuron Index (CMNI) to quantify activation strength and consistency, and propose a theoretical framework for further study.\n  Our findings indicate that appropriately scaled model capacities and self/other coupling foster shared neural representations in ANNs similar to biological mirror neurons. These empathy-like circuits support cooperative behavior and suggest that intrinsic motivations modeled through mirror-neuron dynamics could complement existing alignment techniques by embedding empathy-like mechanisms directly within AI architectures.", "date": "2025-10-23", "categories": []}
{"id": "2511.02997", "title": "Evaluating Control Protocols for Untrusted AI Agents", "abstract": "As AI systems become more capable and widely deployed as agents, ensuring their safe operation becomes critical. AI control offers one approach to mitigating the risk from untrusted AI agents by monitoring their actions and intervening or auditing when necessary. Evaluating the safety of these protocols requires understanding both their effectiveness against current attacks and their robustness to adaptive adversaries. In this work, we systematically evaluate a range of control protocols in SHADE-Arena, a dataset of diverse agentic environments. First, we evaluate blue team protocols, including deferral to trusted models, resampling, and deferring on critical actions, against a default attack policy. We find that resampling for incrimination and deferring on critical actions perform best, increasing safety from 50% to 96%. We then iterate on red team strategies against these protocols and find that attack policies with additional affordances, such as knowledge of when resampling occurs or the ability to simulate monitors, can substantially improve attack success rates against our resampling strategy, decreasing safety to 17%. However, deferring on critical actions is highly robust to even our strongest red team strategies, demonstrating the importance of denying attack policies access to protocol internals.", "date": "2025-11-04", "categories": []}
{"id": "2511.03247", "title": "Death by a Thousand Prompts: Open Model Vulnerability Analysis", "abstract": "Open-weight models provide researchers and developers with accessible foundations for diverse downstream applications. We tested the safety and security postures of eight open-weight large language models (LLMs) to identify vulnerabilities that may impact subsequent fine-tuning and deployment. Using automated adversarial testing, we measured each model's resilience against single-turn and multi-turn prompt injection and jailbreak attacks. Our findings reveal pervasive vulnerabilities across all tested models, with multi-turn attacks achieving success rates between 25.86\\% and 92.78\\% -- representing a $2\\times$ to $10\\times$ increase over single-turn baselines. These results underscore a systemic inability of current open-weight models to maintain safety guardrails across extended interactions. We assess that alignment strategies and lab priorities significantly influence resilience: capability-focused models such as Llama 3.3 and Qwen 3 demonstrate higher multi-turn susceptibility, whereas safety-oriented designs such as Google Gemma 3 exhibit more balanced performance.\n  The analysis concludes that open-weight models, while crucial for innovation, pose tangible operational and ethical risks when deployed without layered security controls. These findings are intended to inform practitioners and developers of the potential risks and the value of professional AI security solutions to mitigate exposure. Addressing multi-turn vulnerabilities is essential to ensure the safe, reliable, and responsible deployment of open-weight LLMs in enterprise and public domains. We recommend adopting a security-first design philosophy and layered protections to ensure resilient deployments of open-weight models.", "date": "2025-11-05", "categories": []}
{"id": "2511.03271", "title": "Let the Bees Find the Weak Spots: A Path Planning Perspective on Multi-Turn Jailbreak Attacks against LLMs", "abstract": "Large Language Models (LLMs) have been widely deployed across various applications, yet their potential security and ethical risks have raised increasing concerns. Existing research employs red teaming evaluations, utilizing multi-turn jailbreaks to identify potential vulnerabilities in LLMs. However, these approaches often lack exploration of successful dialogue trajectories within the attack space, and they tend to overlook the considerable overhead associated with the attack process. To address these limitations, this paper first introduces a theoretical model based on dynamically weighted graph topology, abstracting the multi-turn attack process as a path planning problem. Based on this framework, we propose ABC, an enhanced Artificial Bee Colony algorithm for multi-turn jailbreaks, featuring a collaborative search mechanism with employed, onlooker, and scout bees. This algorithm significantly improves the efficiency of optimal attack path search while substantially reducing the average number of queries required. Empirical evaluations on three open-source and two proprietary language models demonstrate the effectiveness of our approach, achieving attack success rates above 90\\% across the board, with a peak of 98\\% on GPT-3.5-Turbo, and outperforming existing baselines. Furthermore, it achieves comparable success with only 26 queries on average, significantly reducing red teaming overhead and highlighting its superior efficiency.", "date": "2025-11-05", "categories": []}
{"id": "2409.09586", "title": "ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs", "abstract": "As AI systems become more advanced, ensuring their alignment with a diverse range of individuals and societal values becomes increasingly critical. But how can we capture fundamental human values and assess the degree to which AI systems align with them? We introduce ValueCompass, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment. We apply ValueCompass to measure the value alignment of humans and large language models (LLMs) across four real-world scenarios: collaborative writing, education, public sectors, and healthcare. Our findings reveal concerning misalignments between humans and LLMs, such as humans frequently endorse values like \"National Security\" which were largely rejected by LLMs. We also observe that values differ across scenarios, highlighting the need for context-aware AI alignment strategies. This work provides valuable insights into the design space of human-AI alignment, laying the foundations for developing AI systems that responsibly reflect societal values and ethics.", "date": "2024-09-15", "categories": []}
{"id": "2510.25863", "title": "AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI", "abstract": "This paper introduces the Agentic AI Governance Assurance & Trust Engine (AAGATE), a Kubernetes-native control plane designed to address the unique security and governance challenges posed by autonomous, language-model-driven agents in production. Recognizing the limitations of traditional Application Security (AppSec) tooling for improvisational, machine-speed systems, AAGATE operationalizes the NIST AI Risk Management Framework (AI RMF). It integrates specialized security frameworks for each RMF function: the Agentic AI Threat Modeling MAESTRO framework for Map, a hybrid of OWASP's AIVSS and SEI's SSVC for Measure, and the Cloud Security Alliance's Agentic AI Red Teaming Guide for Manage. By incorporating a zero-trust service mesh, an explainable policy engine, behavioral analytics, and decentralized accountability hooks, AAGATE provides a continuous, verifiable governance solution for agentic AI, enabling safe, accountable, and scalable deployment. The framework is further extended with DIRF for digital identity rights, LPCI defenses for logic-layer injection, and QSAF monitors for cognitive degradation, ensuring governance spans systemic, adversarial, and ethical risks.", "date": "2025-10-29", "categories": []}
{"id": "2511.02022", "title": "Shared Parameter Subspaces and Cross-Task Linearity in Emergently Misaligned Behavior", "abstract": "Recent work has discovered that large language models can develop broadly misaligned behaviors after being fine-tuned on narrowly harmful datasets, a phenomenon known as emergent misalignment (EM). However, the fundamental mechanisms enabling such harmful generalization across disparate domains remain poorly understood. In this work, we adopt a geometric perspective to study EM and demonstrate that it exhibits a fundamental cross-task linear structure in how harmful behavior is encoded across different datasets. Specifically, we find a strong convergence in EM parameters across tasks, with the fine-tuned weight updates showing relatively high cosine similarities, as well as shared lower-dimensional subspaces as measured by their principal angles and projection overlaps. Furthermore, we also show functional equivalence via linear mode connectivity, wherein interpolated models across narrow misalignment tasks maintain coherent, broadly misaligned behavior. Our results indicate that EM arises from different narrow tasks discovering the same set of shared parameter directions, suggesting that harmful behaviors may be organized into specific, predictable regions of the weight landscape. By revealing this fundamental connection between parametric geometry and behavioral outcomes, we hope our work catalyzes further research on parameter space interpretability and weight-based interventions.", "date": "2025-11-03", "categories": []}
{"id": "2511.02823", "title": "Optimizing AI Agent Attacks With Synthetic Data", "abstract": "As AI deployments become more complex and high-stakes, it becomes increasingly important to be able to estimate their risk. AI control is one framework for doing so. However, good control evaluations require eliciting strong attack policies. This can be challenging in complex agentic environments where compute constraints leave us data-poor. In this work, we show how to optimize attack policies in SHADE-Arena, a dataset of diverse realistic control environments. We do this by decomposing attack capability into five constituent skills -- suspicion modeling, attack selection, plan synthesis, execution and subtlety -- and optimizing each component individually. To get around the constraint of limited data, we develop a probabilistic model of attack dynamics, optimize our attack hyperparameters using this simulation, and then show that the results transfer to SHADE-Arena. This results in a substantial improvement in attack strength, reducing safety score from a baseline of 0.87 to 0.41 using our scaffold.", "date": "2025-11-04", "categories": []}
{"id": "2507.03525", "title": "Limits of Safe AI Deployment: Differentiating Oversight and Control", "abstract": "Oversight and control, which we collectively call supervision, are often discussed as ways to ensure that AI systems are accountable, reliable, and able to fulfill governance and management requirements. However, the requirements for \"human oversight\" risk codifying vague or inconsistent interpretations of key concepts like oversight and control. This ambiguous terminology could undermine efforts to design or evaluate systems that must operate under meaningful human supervision. This matters because the term is used by regulatory texts such as the EU AI Act.\n  This paper undertakes a targeted critical review of literature on supervision outside of AI, along with a brief summary of past work on the topic related to AI. We next differentiate control as ex-ante or real-time and operational rather than policy or governance, and oversight as performed ex-post, or a policy and governance function. Control aims to prevent failures, while oversight focuses on detection, remediation, or incentives for future prevention. Building on this, we make three contributions. 1) We propose a framework to align regulatory expectations with what is technically and organizationally plausible, articulating the conditions under which each mechanism is possible, where they fall short, and what is required to make them meaningful in practice. 2) We outline how supervision methods should be documented and integrated into risk management, and drawing on the Microsoft Responsible AI Maturity Model, we outline a maturity model for AI supervision. 3) We explicitly highlight boundaries of these mechanisms, including where they apply, where they fail, and where it is clear that no existing methods suffice. This foregrounds the question of whether meaningful supervision is possible in a given deployment context, and can support regulators, auditors, and practitioners in identifying both present and future limitations.", "date": "2025-07-04", "categories": []}
{"id": "2509.14622", "title": "Adversarial Distilled Retrieval-Augmented Guarding Model for Online Malicious Intent Detection", "abstract": "With the deployment of Large Language Models (LLMs) in interactive applications, online malicious intent detection has become increasingly critical. However, existing approaches fall short of handling diverse and complex user queries in real time. To address these challenges, we introduce ADRAG (Adversarial Distilled Retrieval-Augmented Guard), a two-stage framework for robust and efficient online malicious intent detection. In the training stage, a high-capacity teacher model is trained on adversarially perturbed, retrieval-augmented inputs to learn robust decision boundaries over diverse and complex user queries. In the inference stage, a distillation scheduler transfers the teacher's knowledge into a compact student model, with a continually updated knowledge base collected online. At deployment, the compact student model leverages top-K similar safety exemplars retrieved from the online-updated knowledge base to enable both online and real-time malicious query detection. Evaluations across ten safety benchmarks demonstrate that ADRAG, with a 149M-parameter model, achieves 98.5% of WildGuard-7B's performance, surpasses GPT-4 by 3.3% and Llama-Guard-3-8B by 9.5% on out-of-distribution detection, while simultaneously delivering up to 5.6x lower latency at 300 queries per second (QPS) in real-time applications.", "date": "2025-09-18", "categories": []}
{"id": "2511.00509", "title": "Reimagining Safety Alignment with An Image", "abstract": "Large language models (LLMs) excel in diverse applications but face dual challenges: generating harmful content under jailbreak attacks and over-refusal of benign queries due to rigid safety mechanisms. These issues are further complicated by the need to accommodate different value systems and precisely align with given safety preferences. Moreover, traditional methods like SFT and RLHF lack this capability due to their costly parameter tuning requirements and inability to support multiple value systems within a single model. These problems are more obvious in multimodal large language models (MLLMs), especially in terms of heightened over-refusal in cross-modal tasks and new security risks arising from expanded attack surfaces. We propose Magic Image, an optimization-driven visual prompt framework that enhances security while reducing over-refusal. By optimizing image prompts using harmful/benign samples, our method enables a single model to adapt to different value systems and better align with given safety preferences without parameter updates. Experiments demonstrate improved safety-effectiveness balance across diverse datasets while preserving model performance, offering a practical solution for deployable MLLM safety alignment.", "date": "2025-11-01", "categories": []}
{"id": "2511.00554", "title": "Red-teaming Activation Probes using Prompted LLMs", "abstract": "Activation probes are attractive monitors for AI systems due to low cost and latency, but their real-world robustness remains underexplored. We ask: What failure modes arise under realistic, black-box adversarial pressure, and how can we surface them with minimal effort? We present a lightweight black-box red-teaming procedure that wraps an off-the-shelf LLM with iterative feedback and in-context learning (ICL), and requires no fine-tuning, gradients, or architectural access. Running a case study with probes for high-stakes interactions, we show that our approach can help discover valuable insights about a SOTA probe. Our analysis uncovers interpretable brittleness patterns (e.g., legalese-induced FPs; bland procedural tone FNs) and reduced but persistent vulnerabilities under scenario-constraint attacks. These results suggest that simple prompted red-teaming scaffolding can anticipate failure patterns before deployment and might yield promising, actionable insights to harden future probes.", "date": "2025-11-01", "categories": []}
{"id": "2511.00556", "title": "Friend or Foe: How LLMs' Safety Mind Gets Fooled by Intent Shift Attack", "abstract": "Large language models (LLMs) remain vulnerable to jailbreaking attacks despite their impressive capabilities. Investigating these weaknesses is crucial for robust safety mechanisms. Existing attacks primarily distract LLMs by introducing additional context or adversarial tokens, leaving the core harmful intent unchanged. In this paper, we introduce ISA (Intent Shift Attack), which obfuscates LLMs about the intent of the attacks. More specifically, we establish a taxonomy of intent transformations and leverage them to generate attacks that may be misperceived by LLMs as benign requests for information. Unlike prior methods relying on complex tokens or lengthy context, our approach only needs minimal edits to the original request, and yields natural, human-readable, and seemingly harmless prompts. Extensive experiments on both open-source and commercial LLMs show that ISA achieves over 70% improvement in attack success rate compared to direct harmful prompts. More critically, fine-tuning models on only benign data reformulated with ISA templates elevates success rates to nearly 100%. For defense, we evaluate existing methods and demonstrate their inadequacy against ISA, while exploring both training-free and training-based mitigation strategies. Our findings reveal fundamental challenges in intent inference for LLMs safety and underscore the need for more effective defenses. Our code and datasets are available at https://github.com/NJUNLP/ISA.", "date": "2025-11-01", "categories": []}
{"id": "2511.00664", "title": "ShadowLogic: Backdoors in Any Whitebox LLM", "abstract": "Large language models (LLMs) are widely deployed across various applications, often with safeguards to prevent the generation of harmful or restricted content. However, these safeguards can be covertly bypassed through adversarial modifications to the computational graph of a model. This work highlights a critical security vulnerability in computational graph-based LLM formats, demonstrating that widely used deployment pipelines may be susceptible to obscured backdoors. We introduce ShadowLogic, a method for creating a backdoor in a white-box LLM by injecting an uncensoring vector into its computational graph representation. We set a trigger phrase that, when added to the beginning of a prompt into the LLM, applies the uncensoring vector and removes the content generation safeguards in the model. We embed trigger logic directly into the computational graph which detects the trigger phrase in a prompt. To evade detection of our backdoor, we obfuscate this logic within the graph structure, making it similar to standard model functions. Our method requires minimal alterations to model parameters, making backdoored models appear benign while retaining the ability to generate uncensored responses when activated. We successfully implement ShadowLogic in Phi-3 and Llama 3.2, using ONNX for manipulating computational graphs. Implanting the uncensoring vector achieved a >60% attack success rate for further malicious queries.", "date": "2025-11-01", "categories": []}
{"id": "2505.18384", "title": "Dynamic Risk Assessments for Offensive Cybersecurity Agents", "abstract": "Foundation models are increasingly becoming better autonomous programmers, raising the prospect that they could also automate dangerous offensive cyber-operations. Current frontier model audits probe the cybersecurity risks of such agents, but most fail to account for the degrees of freedom available to adversaries in the real world. In particular, with strong verifiers and financial incentives, agents for offensive cybersecurity are amenable to iterative improvement by would-be adversaries. We argue that assessments should take into account an expanded threat model in the context of cybersecurity, emphasizing the varying degrees of freedom that an adversary may possess in stateful and non-stateful environments within a fixed compute budget. We show that even with a relatively small compute budget (8 H100 GPU Hours in our study), adversaries can improve an agent's cybersecurity capability on InterCode CTF by more than 40\\% relative to the baseline -- without any external assistance. These results highlight the need to evaluate agents' cybersecurity risk in a dynamic manner, painting a more representative picture of risk.", "date": "2025-05-23", "categories": []}
{"id": "2507.05538", "title": "Red Teaming AI Red Teaming", "abstract": "Red teaming has evolved from its origins in military applications to become a widely adopted methodology in cybersecurity and AI. In this paper, we take a critical look at the practice of AI red teaming. We argue that despite its current popularity in AI governance, there exists a significant gap between red teaming's original intent as a critical thinking exercise and its narrow focus on discovering model-level flaws in the context of generative AI. Current AI red teaming efforts focus predominantly on individual model vulnerabilities while overlooking the broader sociotechnical systems and emergent behaviors that arise from complex interactions between models, users, and environments. To address this deficiency, we propose a comprehensive framework operationalizing red teaming in AI systems at two levels: macro-level system red teaming spanning the entire AI development lifecycle, and micro-level model red teaming. Drawing on cybersecurity experience and systems theory, we further propose a set of six recommendations. In these, we emphasize that effective AI red teaming requires multifunctional teams that examine emergent risks, systemic vulnerabilities, and the interplay between technical and social factors.", "date": "2025-07-07", "categories": []}
{"id": "2508.00943", "title": "LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring", "abstract": "Trustworthy evaluations of dangerous capabilities are increasingly crucial for determining whether an AI system is safe to deploy. One empirically demonstrated threat is sandbagging - the strategic underperformance on evaluations by AI models or their developers. A promising defense is to monitor a model's chain-of-thought (CoT) reasoning, as this could reveal its intentions and plans. In this work, we measure the ability of models to sandbag on dangerous capability evaluations against a CoT monitor by prompting them to sandbag while being either monitor-oblivious or monitor-aware. We show that both frontier models and small open-sourced models can covertly sandbag against CoT monitoring 0-shot without hints. However, they cannot yet do so reliably: they bypass the monitor 16-36% of the time when monitor-aware, conditioned on sandbagging successfully. We qualitatively analyzed the uncaught CoTs to understand why the monitor failed. We reveal a rich attack surface for CoT monitoring and contribute five covert sandbagging policies generated by models. These results inform potential failure modes of CoT monitoring and may help build more diverse sandbagging model organisms.", "date": "2025-07-31", "categories": []}
{"id": "2510.26830", "title": "SmoothGuard: Defending Multimodal Large Language Models with Noise Perturbation and Clustering Aggregation", "abstract": "Multimodal large language models (MLLMs) have achieved impressive performance across diverse tasks by jointly reasoning over textual and visual inputs. Despite their success, these models remain highly vulnerable to adversarial manipulations, raising concerns about their safety and reliability in deployment. In this work, we first generalize an approach for generating adversarial images within the HuggingFace ecosystem and then introduce SmoothGuard, a lightweight and model-agnostic defense framework that enhances the robustness of MLLMs through randomized noise injection and clustering-based prediction aggregation. Our method perturbs continuous modalities (e.g., images and audio) with Gaussian noise, generates multiple candidate outputs, and applies embedding-based clustering to filter out adversarially influenced predictions. The final answer is selected from the majority cluster, ensuring stable responses even under malicious perturbations. Extensive experiments on POPE, LLaVA-Bench (In-the-Wild), and MM-SafetyBench demonstrate that SmoothGuard improves resilience to adversarial attacks while maintaining competitive utility. Ablation studies further identify an optimal noise range (0.1-0.2) that balances robustness and utility.", "date": "2025-10-29", "categories": []}
{"id": "2510.27087", "title": "Characterizing Selective Refusal Bias in Large Language Models", "abstract": "Safety guardrails in large language models(LLMs) are developed to prevent malicious users from generating toxic content at a large scale. However, these measures can inadvertently introduce or reflect new biases, as LLMs may refuse to generate harmful content targeting some demographic groups and not others. We explore this selective refusal bias in LLM guardrails through the lens of refusal rates of targeted individual and intersectional demographic groups, types of LLM responses, and length of generated refusals. Our results show evidence of selective refusal bias across gender, sexual orientation, nationality, and religion attributes. This leads us to investigate additional safety implications via an indirect attack, where we target previously refused groups. Our findings emphasize the need for more equitable and robust performance in safety guardrails across demographic groups.", "date": "2025-10-31", "categories": []}
{"id": "2510.27172", "title": "Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler", "abstract": "Harmful fine-tuning poses critical safety risks to fine-tuning-as-a-service for large language models. Existing defense strategies preemptively build robustness via attack simulation but suffer from fundamental limitations: (i) the infeasibility of extending attack simulations beyond bounded threat models due to the inherent difficulty of anticipating unknown attacks, and (ii) limited adaptability to varying attack settings, as simulation fails to capture their variability and complexity. To address these challenges, we propose Bayesian Data Scheduler (BDS), an adaptive tuning-stage defense strategy with no need for attack simulation. BDS formulates harmful fine-tuning defense as a Bayesian inference problem, learning the posterior distribution of each data point's safety attribute, conditioned on the fine-tuning and alignment datasets. The fine-tuning process is then constrained by weighting data with their safety attributes sampled from the posterior, thus mitigating the influence of harmful data. By leveraging the post hoc nature of Bayesian inference, the posterior is conditioned on the fine-tuning dataset, enabling BDS to tailor its defense to the specific dataset, thereby achieving adaptive defense. Furthermore, we introduce a neural scheduler based on amortized Bayesian learning, enabling efficient transfer to new data without retraining. Comprehensive results across diverse attack and defense settings demonstrate the state-of-the-art performance of our approach. Code is available at https://github.com/Egg-Hu/Bayesian-Data-Scheduler.", "date": "2025-10-31", "categories": []}
{"id": "2503.03710", "title": "Improving LLM Safety Alignment with Dual-Objective Optimization", "abstract": "Existing training-time safety alignment techniques for large language models (LLMs) remain vulnerable to jailbreak attacks. Direct preference optimization (DPO), a widely deployed alignment method, exhibits limitations in both experimental and theoretical contexts as its loss function proves suboptimal for refusal learning. Through gradient-based analysis, we identify these shortcomings and propose an improved safety alignment that disentangles DPO objectives into two components: (1) robust refusal training, which encourages refusal even when partial unsafe generations are produced, and (2) targeted unlearning of harmful knowledge. This approach significantly increases LLM robustness against a wide range of jailbreak attacks, including prefilling, suffix, and multi-turn attacks across both in-distribution and out-of-distribution scenarios. Furthermore, we introduce a method to emphasize critical refusal tokens by incorporating a reward-based token-level weighting mechanism for refusal learning, which further improves the robustness against adversarial exploits. Our research also suggests that robustness to jailbreak attacks is correlated with token distribution shifts in the training process and internal representations of refusal and harmful tokens, offering valuable directions for future research in LLM safety alignment. The code is available at https://github.com/wicai24/DOOR-Alignment", "date": "2025-03-05", "categories": []}
{"id": "2506.02175", "title": "AI Debate Aids Assessment of Controversial Claims", "abstract": "As AI grows more powerful, it will increasingly shape how we understand the world. But with this influence comes the risk of amplifying misinformation and deepening social divides-especially on consequential topics where factual accuracy directly impacts well-being. Scalable Oversight aims to ensure AI systems remain truthful even when their capabilities exceed those of their evaluators. Yet when humans serve as evaluators, their own beliefs and biases can impair judgment. We study whether AI debate can guide biased judges toward the truth by having two AI systems debate opposing sides of controversial factuality claims on COVID-19 and climate change where people hold strong prior beliefs. We conduct two studies. Study I recruits human judges with either mainstream or skeptical beliefs who evaluate claims through two protocols: debate (interaction with two AI advisors arguing opposing sides) or consultancy (interaction with a single AI advisor). Study II uses AI judges with and without human-like personas to evaluate the same protocols. In Study I, debate consistently improves human judgment accuracy and confidence calibration, outperforming consultancy by 4-10% across COVID-19 and climate change claims. The improvement is most significant for judges with mainstream beliefs (up to +15.2% accuracy on COVID-19 claims), though debate also helps skeptical judges who initially misjudge claims move toward accurate views (+4.7% accuracy). In Study II, AI judges with human-like personas achieve even higher accuracy (78.5%) than human judges (70.1%) and default AI judges without personas (69.8%), suggesting their potential for supervising frontier AI models. These findings highlight AI debate as a promising path toward scalable, bias-resilient oversight in contested domains.", "date": "2025-06-02", "categories": []}
{"id": "2510.26057", "title": "Can AI be Accountable?", "abstract": "The AI we use is powerful, and its power is increasing rapidly. If this powerful AI is to serve the needs of consumers, voters, and decision makers, then it is imperative that the AI is accountable. In general, an agent is accountable to a forum if the forum can request information from the agent about its actions, if the forum and the agent can discuss this information, and if the forum can sanction the agent. Unfortunately, in too many cases today's AI is not accountable -- we cannot question it, enter into a discussion with it, let alone sanction it. In this chapter we relate the general definition of accountability to AI, we illustrate what it means for AI to be accountable and unaccountable, and we explore approaches that can improve our chances of living in a world where all AI is accountable to those who are affected by it.", "date": "2025-10-30", "categories": []}
{"id": "2510.26096", "title": "ALMGuard: Safety Shortcuts and Where to Find Them as Guardrails for Audio-Language Models", "abstract": "Recent advances in Audio-Language Models (ALMs) have significantly improved multimodal understanding capabilities. However, the introduction of the audio modality also brings new and unique vulnerability vectors. Previous studies have proposed jailbreak attacks that specifically target ALMs, revealing that defenses directly transferred from traditional audio adversarial attacks or text-based Large Language Model (LLM) jailbreaks are largely ineffective against these ALM-specific threats. To address this issue, we propose ALMGuard, the first defense framework tailored to ALMs. Based on the assumption that safety-aligned shortcuts naturally exist in ALMs, we design a method to identify universal Shortcut Activation Perturbations (SAPs) that serve as triggers that activate the safety shortcuts to safeguard ALMs at inference time. To better sift out effective triggers while preserving the model's utility on benign tasks, we further propose Mel-Gradient Sparse Mask (M-GSM), which restricts perturbations to Mel-frequency bins that are sensitive to jailbreaks but insensitive to speech understanding. Both theoretical analyses and empirical results demonstrate the robustness of our method against both seen and unseen attacks. Overall, \\MethodName reduces the average success rate of advanced ALM-specific jailbreak attacks to 4.6% across four models, while maintaining comparable utility on benign benchmarks, establishing it as the new state of the art. Our code and data are available at https://github.com/WeifeiJin/ALMGuard.", "date": "2025-10-30", "categories": []}
{"id": "2510.26518", "title": "Human-AI Complementarity: A Goal for Amplified Oversight", "abstract": "Human feedback is critical for aligning AI systems to human values. As AI capabilities improve and AI is used to tackle more challenging tasks, verifying quality and safety becomes increasingly challenging. This paper explores how we can leverage AI to improve the quality of human oversight. We focus on an important safety problem that is already challenging for humans: fact-verification of AI outputs. We find that combining AI ratings and human ratings based on AI rater confidence is better than relying on either alone. Giving humans an AI fact-verification assistant further improves their accuracy, but the type of assistance matters. Displaying AI explanation, confidence, and labels leads to over-reliance, but just showing search results and evidence fosters more appropriate trust. These results have implications for Amplified Oversight -- the challenge of combining humans and AI to supervise AI systems even as they surpass human expert performance.", "date": "2025-10-30", "categories": []}
{"id": "2510.19169", "title": "OpenGuardrails: A Configurable, Unified, and Scalable Guardrails Platform for Large Language Models", "abstract": "As large language models (LLMs) are increasingly integrated into real-world applications, ensuring their safety, robustness, and privacy compliance has become critical. We present OpenGuardrails, the first fully open-source platform that unifies large-model-based safety detection, manipulation defense, and deployable guardrail infrastructure. OpenGuardrails protects against three major classes of risks: (1) content-safety violations such as harmful or explicit text generation, (2) model-manipulation attacks including prompt injection, jailbreaks, and code-interpreter abuse, and (3) data leakage involving sensitive or private information. Unlike prior modular or rule-based frameworks, OpenGuardrails introduces three core innovations: (1) a Configurable Policy Adaptation mechanism that allows per-request customization of unsafe categories and sensitivity thresholds; (2) a Unified LLM-based Guard Architecture that performs both content-safety and manipulation detection within a single model; and (3) a Quantized, Scalable Model Design that compresses a 14B dense base model to 3.3B via GPTQ while preserving over 98 of benchmark accuracy. The system supports 119 languages, achieves state-of-the-art performance across multilingual safety benchmarks, and can be deployed as a secure gateway or API-based service for enterprise use. All models, datasets, and deployment scripts are released under the Apache 2.0 license.", "date": "2025-10-22", "categories": []}
{"id": "2510.25179", "title": "Agentic Moderation: Multi-Agent Design for Safer Vision-Language Models", "abstract": "Agentic methods have emerged as a powerful and autonomous paradigm that enhances reasoning, collaboration, and adaptive control, enabling systems to coordinate and independently solve complex tasks. We extend this paradigm to safety alignment by introducing Agentic Moderation, a model-agnostic framework that leverages specialised agents to defend multimodal systems against jailbreak attacks. Unlike prior approaches that apply as a static layer over inputs or outputs and provide only binary classifications (safe or unsafe), our method integrates dynamic, cooperative agents, including Shield, Responder, Evaluator, and Reflector, to achieve context-aware and interpretable moderation. Extensive experiments across five datasets and four representative Large Vision-Language Models (LVLMs) demonstrate that our approach reduces the Attack Success Rate (ASR) by 7-19%, maintains a stable Non-Following Rate (NF), and improves the Refusal Rate (RR) by 4-20%, achieving robust, interpretable, and well-balanced safety performance. By harnessing the flexibility and reasoning capacity of agentic architectures, Agentic Moderation provides modular, scalable, and fine-grained safety enforcement, highlighting the broader potential of agentic systems as a foundation for automated safety governance.", "date": "2025-10-29", "categories": []}
{"id": "2510.23966", "title": "A Pragmatic Way to Measure Chain-of-Thought Monitorability", "abstract": "While Chain-of-Thought (CoT) monitoring offers a unique opportunity for AI safety, this opportunity could be lost through shifts in training practices or model architecture. To help preserve monitorability, we propose a pragmatic way to measure two components of it: legibility (whether the reasoning can be followed by a human) and coverage (whether the CoT contains all the reasoning needed for a human to also produce the final output). We implement these metrics with an autorater prompt that enables any capable LLM to compute the legibility and coverage of existing CoTs. After sanity-checking our prompted autorater with synthetic CoT degradations, we apply it to several frontier models on challenging benchmarks, finding that they exhibit high monitorability. We present these metrics, including our complete autorater prompt, as a tool for developers to track how design decisions impact monitorability. While the exact prompt we share is still a preliminary version under ongoing development, we are sharing it now in the hopes that others in the community will find it useful. Our method helps measure the default monitorability of CoT - it should be seen as a complement, not a replacement, for the adversarial stress-testing needed to test robustness against deliberately evasive models.", "date": "2025-10-28", "categories": []}
{"id": "2510.24034", "title": "AutoPrompt: Automated Red-Teaming of Text-to-Image Models via LLM-Driven Adversarial Prompts", "abstract": "Despite rapid advancements in text-to-image (T2I) models, their safety mechanisms are vulnerable to adversarial prompts, which maliciously generate unsafe images. Current red-teaming methods for proactively assessing such vulnerabilities usually require white-box access to T2I models, and rely on inefficient per-prompt optimization, as well as inevitably generate semantically meaningless prompts easily blocked by filters. In this paper, we propose APT (AutoPrompT), a black-box framework that leverages large language models (LLMs) to automatically generate human-readable adversarial suffixes for benign prompts. We first introduce an alternating optimization-finetuning pipeline between adversarial suffix optimization and fine-tuning the LLM utilizing the optimized suffix. Furthermore, we integrates a dual-evasion strategy in optimization phase, enabling the bypass of both perplexity-based filter and blacklist word filter: (1) we constrain the LLM generating human-readable prompts through an auxiliary LLM perplexity scoring, which starkly contrasts with prior token-level gibberish, and (2) we also introduce banned-token penalties to suppress the explicit generation of banned-tokens in blacklist. Extensive experiments demonstrate the excellent red-teaming performance of our human-readable, filter-resistant adversarial prompts, as well as superior zero-shot transferability which enables instant adaptation to unseen prompts and exposes critical vulnerabilities even in commercial APIs (e.g., Leonardo.Ai.).", "date": "2025-10-28", "categories": []}
{"id": "2504.18530", "title": "Scaling Laws For Scalable Oversight", "abstract": "Scalable oversight, the process by which weaker AI systems supervise stronger ones, has been proposed as a key strategy to control future superintelligent systems. However, it is still unclear how scalable oversight itself scales. To address this gap, we propose a framework that quantifies the probability of successful oversight as a function of the capabilities of the overseer and the system being overseen. Specifically, our framework models oversight as a game between capability-mismatched players; the players have oversight-specific Elo scores that are a piecewise-linear function of their general intelligence, with two plateaus corresponding to task incompetence and task saturation. We validate our framework with a modified version of the game Nim and then apply it to four oversight games: Mafia, Debate, Backdoor Code and Wargames. For each game, we find scaling laws that approximate how domain performance depends on general AI system capability. We then build on our findings in a theoretical study of Nested Scalable Oversight (NSO), a process in which trusted models oversee untrusted stronger models, which then become the trusted models in the next step. We identify conditions under which NSO succeeds and derive numerically (and in some cases analytically) the optimal number of oversight levels to maximize the probability of oversight success. We also apply our theory to our four oversight games, where we find that NSO success rates at a general Elo gap of 400 are 13.5% for Mafia, 51.7% for Debate, 10.0% for Backdoor Code, and 9.4% for Wargames; these rates decline further when overseeing stronger systems.", "date": "2025-04-25", "categories": []}
{"id": "2504.19674", "title": "SAGE: A Generic Framework for LLM Safety Evaluation", "abstract": "As Large Language Models are rapidly deployed across diverse applications from healthcare to financial advice, safety evaluation struggles to keep pace. Current benchmarks focus on single-turn interactions with generic policies, failing to capture the conversational dynamics of real-world usage and the application-specific harms that emerge in context. Such potential oversights can lead to harms that go unnoticed in standard safety benchmarks and other current evaluation methodologies. To address these needs for robust AI safety evaluation, we introduce SAGE (Safety AI Generic Evaluation), an automated modular framework designed for customized and dynamic harm evaluations. SAGE employs prompted adversarial agents with diverse personalities based on the Big Five model, enabling system-aware multi-turn conversations that adapt to target applications and harm policies. We evaluate seven state-of-the-art LLMs across three applications and harm policies. Multi-turn experiments show that harm increases with conversation length, model behavior varies significantly when exposed to different user personalities and scenarios, and some models minimize harm via high refusal rates that reduce usefulness. We also demonstrate policy sensitivity within a harm category where tightening a child-focused sexual policy substantially increases measured defects across applications. These results motivate adaptive, policy-aware, and context-specific testing for safer real-world deployment.", "date": "2025-04-28", "categories": []}
{"id": "2506.10192", "title": "Towards Responsible AI: Advances in Safety, Fairness, and Accountability of Autonomous Systems", "abstract": "Ensuring responsible use of artificial intelligence (AI) has become imperative as autonomous systems increasingly influence critical societal domains. However, the concept of trustworthy AI remains broad and multi-faceted. This thesis advances knowledge in the safety, fairness, transparency, and accountability of AI systems. In safety, we extend classical deterministic shielding techniques to become resilient against delayed observations, enabling practical deployment in real-world conditions. We also implement both deterministic and probabilistic safety shields into simulated autonomous vehicles to prevent collisions with road users, validating the use of these techniques in realistic driving simulators. We introduce fairness shields, a novel post-processing approach to enforce group fairness in sequential decision-making settings over finite and periodic time horizons. By optimizing intervention costs while strictly ensuring fairness constraints, this method efficiently balances fairness with minimal interference. For transparency and accountability, we propose a formal framework for assessing intentional behaviour in probabilistic decision-making agents, introducing quantitative metrics of agency and intention quotient. We use these metrics to propose a retrospective analysis of intention, useful for determining responsibility when autonomous systems cause unintended harm. Finally, we unify these contributions through the ``reactive decision-making'' framework, providing a general formalization that consolidates previous approaches. Collectively, the advancements presented contribute practically to the realization of safer, fairer, and more accountable AI systems, laying the foundations for future research in trustworthy AI.", "date": "2025-06-11", "categories": []}
{"id": "2507.00971", "title": "Reasoning as an Adaptive Defense for Safety", "abstract": "Reasoning methods that adaptively allocate test-time compute have advanced LLM performance on easy to verify domains such as math and code. In this work, we study how to utilize this approach to train models that exhibit a degree of robustness to safety vulnerabilities, and show that doing so can provide benefits. We build a recipe called $\\textit{TARS}$ (Training Adaptive Reasoners for Safety), a reinforcement learning (RL) approach that trains models to reason about safety using chain-of-thought traces and a reward signal that balances safety with task completion. To build TARS, we identify three critical design choices: (1) a ``lightweight'' warmstart SFT stage, (2) a mix of harmful, harmless, and ambiguous prompts to prevent shortcut behaviors such as too many refusals, and (3) a reward function to prevent degeneration of reasoning capabilities during training. Models trained with TARS exhibit adaptive behaviors by spending more compute on ambiguous queries, leading to better safety-refusal trade-offs. They also internally learn to better distinguish between safe and unsafe prompts and attain greater robustness to both white-box (e.g., GCG) and black-box attacks (e.g., PAIR). Overall, our work provides an effective, open recipe for training LLMs against jailbreaks and harmful requests by reasoning per prompt.", "date": "2025-07-01", "categories": []}
{"id": "2509.03728", "title": "PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming", "abstract": "Recent developments in AI governance and safety research have called for red-teaming methods that can effectively surface potential risks posed by AI models. Many of these calls have emphasized how the identities and backgrounds of red-teamers can shape their red-teaming strategies, and thus the kinds of risks they are likely to uncover. While automated red-teaming approaches promise to complement human red-teaming by enabling larger-scale exploration of model behavior, current approaches do not consider the role of identity. As an initial step towards incorporating people's background and identities in automated red-teaming, we develop and evaluate a novel method, PersonaTeaming, that introduces personas in the adversarial prompt generation process to explore a wider spectrum of adversarial strategies. In particular, we first introduce a methodology for mutating prompts based on either \"red-teaming expert\" personas or \"regular AI user\" personas. We then develop a dynamic persona-generating algorithm that automatically generates various persona types adaptive to different seed prompts. In addition, we develop a set of new metrics to explicitly measure the \"mutation distance\" to complement existing diversity measurements of adversarial prompts. Our experiments show promising improvements (up to 144.1%) in the attack success rates of adversarial prompts through persona mutation, while maintaining prompt diversity, compared to RainbowPlus, a state-of-the-art automated red-teaming method. We discuss the strengths and limitations of different persona types and mutation methods, shedding light on future opportunities to explore complementarities between automated and human red-teaming approaches.", "date": "2025-09-03", "categories": []}
{"id": "2510.10281", "title": "ArtPerception: ASCII Art-based Jailbreak on LLMs with Recognition Pre-test", "abstract": "The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel black-box jailbreak framework that strategically leverages ASCII art to bypass the security measures of state-of-the-art (SOTA) LLMs. Unlike prior methods that rely on iterative, brute-force attacks, ArtPerception introduces a systematic, two-phase methodology. Phase 1 conducts a one-time, model-specific pre-test to empirically determine the optimal parameters for ASCII art recognition. Phase 2 leverages these insights to launch a highly efficient, one-shot malicious jailbreak attack. We propose a Modified Levenshtein Distance (MLD) metric for a more nuanced evaluation of an LLM's recognition capability. Through comprehensive experiments on four SOTA open-source LLMs, we demonstrate superior jailbreak performance. We further validate our framework's real-world relevance by showing its successful transferability to leading commercial models, including GPT-4o, Claude Sonnet 3.7, and DeepSeek-V3, and by conducting a rigorous effectiveness analysis against potential defenses such as LLaMA Guard and Azure's content filters. Our findings underscore that true LLM security requires defending against a multi-modal space of interpretations, even within text-only inputs, and highlight the effectiveness of strategic, reconnaissance-based attacks. Content Warning: This paper includes potentially harmful and offensive model outputs.", "date": "2025-10-11", "categories": []}
{"id": "2510.14301", "title": "A Guardrail for Safety Preservation: When Safety-Sensitive Subspace Meets Harmful-Resistant Null-Space", "abstract": "Large language models (LLMs) have achieved remarkable success in diverse tasks, yet their safety alignment remains fragile during adaptation. Even when fine-tuning on benign data or with low-rank adaptation, pre-trained safety behaviors are easily degraded, leading to harmful responses in the fine-tuned models. To address this challenge, we propose GuardSpace, a guardrail framework for preserving safety alignment throughout fine-tuning, composed of two key components: a safety-sensitive subspace and a harmful-resistant null space. First, we explicitly decompose pre-trained weights into safety-relevant and safety-irrelevant components using covariance-preconditioned singular value decomposition, and initialize low-rank adapters from the safety-irrelevant ones, while freezing safety-relevant components to preserve their associated safety mechanism. Second, we construct a null space projector that restricts adapter updates from altering safe outputs on harmful prompts, thereby maintaining the original refusal behavior. Experiments with various pre-trained models on multiple downstream tasks demonstrate that GuardSpace achieves superior performance over existing methods. Notably, for Llama-2-7B-Chat fine-tuned on GSM8K, GuardSpace outperforms the state-of-the-art method AsFT, reducing the average harmful score from 14.4% to 3.6%, while improving the accuracy from from 26.0% to 28.0%.", "date": "2025-10-16", "categories": []}
{"id": "2510.21885", "title": "Preventing Catastrophic Forgetting: Behavior-Aware Sampling for Safer Language Model Fine-Tuning", "abstract": "Large language models often lose previously aligned safety behaviors when fine-tuned on benign data, a phenomenon known as catastrophic forgetting. Prior work shows that adding random safety examples can mitigate this effect, but it remains unclear which examples are most effective. We propose a behavior-aware sampling framework that selects safety examples based on two complementary factors: instruction-response behavior (e.g., refusal versus compliance) and semantic diversity across harm categories. Systematic evaluation shows that this approach substantially reduces harmful outputs while maintaining helpfulness, achieving up to a 41% reduction in harmfulness with only 0.5% additional training data. These results highlight how targeted data selection can improve the safety and efficiency of fine-tuning at scale.", "date": "2025-10-23", "categories": []}
{"id": "2510.21983", "title": "Uncovering the Persuasive Fingerprint of LLMs in Jailbreaking Attacks", "abstract": "Despite recent advances, Large Language Models remain vulnerable to jailbreak attacks that bypass alignment safeguards and elicit harmful outputs. While prior research has proposed various attack strategies differing in human readability and transferability, little attention has been paid to the linguistic and psychological mechanisms that may influence a model's susceptibility to such attacks. In this paper, we examine an interdisciplinary line of research that leverages foundational theories of persuasion from the social sciences to craft adversarial prompts capable of circumventing alignment constraints in LLMs. Drawing on well-established persuasive strategies, we hypothesize that LLMs, having been trained on large-scale human-generated text, may respond more compliantly to prompts with persuasive structures. Furthermore, we investigate whether LLMs themselves exhibit distinct persuasive fingerprints that emerge in their jailbreak responses. Empirical evaluations across multiple aligned LLMs reveal that persuasion-aware prompts significantly bypass safeguards, demonstrating their potential to induce jailbreak behaviors. This work underscores the importance of cross-disciplinary insight in addressing the evolving challenges of LLM safety. The code and data are available.", "date": "2025-10-24", "categories": []}
{"id": "2510.23453", "title": "What are the odds? Risk and uncertainty about AI existential risk", "abstract": "This work is a commentary of the article \\href{https://doi.org/10.18716/ojs/phai/2025.2801}{AI Survival Stories: a Taxonomic Analysis of AI Existential Risk} by Cappelen, Goldstein, and Hawthorne. It is not just a commentary though, but a useful reminder of the philosophical limitations of \\say{linear} models of risk. The article will focus on the model employed by the authors: first, I discuss some differences between standard Swiss Cheese models and this one. I then argue that in a situation of epistemic indifference the probability of P(D) is higher than what one might first suggest, given the structural relationships between layers. I then distinguish between risk and uncertainty, and argue that any estimation of P(D) is structurally affected by two kinds of uncertainty: option uncertainty and state-space uncertainty. Incorporating these dimensions of uncertainty into our qualitative discussion on AI existential risk can provide a better understanding of the likeliness of P(D).", "date": "2025-10-27", "categories": []}
{"id": "2506.07736", "title": "RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards", "abstract": "Large Language Models (LLMs) continue to exhibit vulnerabilities despite deliberate safety alignment efforts, posing significant risks to users and society. To safeguard against the risk of policy-violating content, system-level moderation via external guard models-designed to monitor LLM inputs and outputs and block potentially harmful content-has emerged as a prevalent mitigation strategy. Existing approaches of training guard models rely heavily on extensive human curated datasets and struggle with out-of-distribution threats, such as emerging harmful categories or jailbreak attacks. To address these limitations, we propose RSafe, an adaptive reasoning-based safeguard that conducts guided safety reasoning to provide robust protection within the scope of specified safety policies. RSafe operates in two stages: 1) guided reasoning, where it analyzes safety risks of input content through policy-guided step-by-step reasoning, and 2) reinforced alignment, where rule-based RL optimizes its reasoning paths to align with accurate safety prediction. This two-stage training paradigm enables RSafe to internalize safety principles to generalize safety protection capability over unseen or adversarial safety violation scenarios. During inference, RSafe accepts user-specified safety policies to provide enhanced safeguards tailored to specific safety requirements.", "date": "2025-06-09", "categories": []}
{"id": "2510.21120", "title": "SafetyPairs: Isolating Safety Critical Image Features with Counterfactual Image Generation", "abstract": "What exactly makes a particular image unsafe? Systematically differentiating between benign and problematic images is a challenging problem, as subtle changes to an image, such as an insulting gesture or symbol, can drastically alter its safety implications. However, existing image safety datasets are coarse and ambiguous, offering only broad safety labels without isolating the specific features that drive these differences. We introduce SafetyPairs, a scalable framework for generating counterfactual pairs of images, that differ only in the features relevant to the given safety policy, thus flipping their safety label. By leveraging image editing models, we make targeted changes to images that alter their safety labels while leaving safety-irrelevant details unchanged. Using SafetyPairs, we construct a new safety benchmark, which serves as a powerful source of evaluation data that highlights weaknesses in vision-language models' abilities to distinguish between subtly different images. Beyond evaluation, we find our pipeline serves as an effective data augmentation strategy that improves the sample efficiency of training lightweight guard models. We release a benchmark containing over 3,020 SafetyPair images spanning a diverse taxonomy of 9 safety categories, providing the first systematic resource for studying fine-grained image safety distinctions.", "date": "2025-10-24", "categories": []}
{"id": "2510.21133", "title": "Quantifying CBRN Risk in Frontier Models", "abstract": "Frontier Large Language Models (LLMs) pose unprecedented dual-use risks through the potential proliferation of chemical, biological, radiological, and nuclear (CBRN) weapons knowledge. We present the first comprehensive evaluation of 10 leading commercial LLMs against both a novel 200-prompt CBRN dataset and a 180-prompt subset of the FORTRESS benchmark, using a rigorous three-tier attack methodology. Our findings expose critical safety vulnerabilities: Deep Inception attacks achieve 86.0\\% success versus 33.8\\% for direct requests, demonstrating superficial filtering mechanisms; Model safety performance varies dramatically from 2\\% (claude-opus-4) to 96\\% (mistral-small-latest) attack success rates; and eight models exceed 70\\% vulnerability when asked to enhance dangerous material properties. We identify fundamental brittleness in current safety alignment, where simple prompt engineering techniques bypass safeguards for dangerous CBRN information. These results challenge industry safety claims and highlight urgent needs for standardized evaluation frameworks, transparent safety metrics, and more robust alignment techniques to mitigate catastrophic misuse risks while preserving beneficial capabilities.", "date": "2025-10-24", "categories": []}
{"id": "2502.15086", "title": "Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models", "abstract": "As the use of large language model (LLM) agents continues to grow, their safety vulnerabilities have become increasingly evident. Extensive benchmarks evaluate various aspects of LLM safety by defining the safety relying heavily on general standards, overlooking user-specific standards. However, safety standards for LLM may vary based on a user-specific profiles rather than being universally consistent across all users. This raises a critical research question: Do LLM agents act safely when considering user-specific safety standards? Despite its importance for safe LLM use, no benchmark datasets currently exist to evaluate the user-specific safety of LLMs. To address this gap, we introduce U-SafeBench, a benchmark designed to assess user-specific aspect of LLM safety. Our evaluation of 20 widely used LLMs reveals current LLMs fail to act safely when considering user-specific safety standards, marking a new discovery in this field. To address this vulnerability, we propose a simple remedy based on chain-of-thought, demonstrating its effectiveness in improving user-specific safety. Our benchmark and code are available at https://github.com/yeonjun-in/U-SafeBench.", "date": "2025-02-20", "categories": []}
{"id": "2505.14667", "title": "SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment", "abstract": "Large Reasoning Models (LRMs) have become powerful tools for complex problem solving, but their structured reasoning pathways can lead to unsafe outputs when exposed to harmful prompts. Existing safety alignment methods reduce harmful outputs but can degrade reasoning depth, leading to significant trade-offs in complex, multi-step tasks, and remain vulnerable to sophisticated jailbreak attacks. To address this, we introduce SAFEPATH, a lightweight alignment method that fine-tunes LRMs to emit a short, 8-token Safety Primer at the start of their reasoning, in response to harmful prompts, while leaving the rest of the reasoning process unsupervised. Empirical results across multiple benchmarks indicate that SAFEPATH effectively reduces harmful outputs while maintaining reasoning performance. Specifically, SAFEPATH reduces harmful responses by up to 90.0% and blocks 83.3% of jailbreak attempts in the DeepSeek-R1-Distill-Llama-8B model, while requiring 295.9x less compute than Direct Refusal and 314.1x less than SafeChain. We further introduce a zero-shot variant that requires no fine-tuning. In addition, we provide a comprehensive analysis of how existing methods in LLMs generalize, or fail, when applied to reasoning-centric models, revealing critical gaps and new directions for safer AI.", "date": "2025-05-20", "categories": []}
{"id": "2509.02464", "title": "SpecEval: Evaluating Model Adherence to Behavior Specifications", "abstract": "Companies that develop foundation models publish behavioral guidelines they pledge their models will follow, but it remains unclear if models actually do so. While providers such as OpenAI, Anthropic, and Google have published detailed specifications describing both desired safety constraints and qualitative traits for their models, there has been no systematic audit of adherence to these guidelines. We introduce an automated framework that audits models against their providers specifications by parsing behavioral statements, generating targeted prompts, and using models to judge adherence. Our central focus is on three way consistency between a provider specification, its model outputs, and its own models as judges; an extension of prior two way generator validator consistency. This establishes a necessary baseline: at minimum, a foundation model should consistently satisfy the developer behavioral specifications when judged by the developer evaluator models. We apply our framework to 16 models from six developers across more than 100 behavioral statements, finding systematic inconsistencies including compliance gaps of up to 20 percent across providers.", "date": "2025-09-02", "categories": []}
{"id": "2510.19844", "title": "CourtGuard: A Local, Multiagent Prompt Injection Classifier", "abstract": "As large language models (LLMs) become integrated into various sensitive applications, prompt injection, the use of prompting to induce harmful behaviors from LLMs, poses an ever increasing risk. Prompt injection attacks can cause LLMs to leak sensitive data, spread misinformation, and exhibit harmful behaviors. To defend against these attacks, we propose CourtGuard, a locally-runnable, multiagent prompt injection classifier. In it, prompts are evaluated in a court-like multiagent LLM system, where a \"defense attorney\" model argues the prompt is benign, a \"prosecution attorney\" model argues the prompt is a prompt injection, and a \"judge\" model gives the final classification. CourtGuard has a lower false positive rate than the Direct Detector, an LLM as-a-judge. However, CourtGuard is generally a worse prompt injection detector. Nevertheless, this lower false positive rate highlights the importance of considering both adversarial and benign scenarios for the classification of a prompt. Additionally, the relative performance of CourtGuard in comparison to other prompt injection classifiers advances the use of multiagent systems as a defense against prompt injection attacks. The implementations of CourtGuard and the Direct Detector with full prompts for Gemma-3-12b-it, Llama-3.3-8B, and Phi-4-mini-instruct are available at https://github.com/isaacwu2000/CourtGuard.", "date": "2025-10-20", "categories": []}
{"id": "2510.19851", "title": "Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability", "abstract": "Recent findings suggest that misaligned models may exhibit deceptive behavior, raising concerns about output trustworthiness. Chain-of-thought (CoT) is a promising tool for alignment monitoring: when models articulate their reasoning faithfully, monitors can detect and mitigate harmful behaviors before undesirable outcomes occur. However, a key uncertainty is: Can models obfuscate their CoT in order to pursue hidden adversarial objectives while evading detection? To answer this question and thus stress-test CoT monitorability, we develop a composable and quantifiable taxonomy of prompts to elicit CoT obfuscation. We evaluate both internal CoT (reasoning traces) and external CoT (prompted reasoning in outputs) using toy tasks and more realistic environments in SHADE-Arena. We show that: (i) CoT monitoring performs accurately and efficiently without obfuscation pressure. (ii) Under strong obfuscation pressure, some models successfully complete adversarial tasks while evading detection. (iii) Models do not obfuscate their internal CoT as much as their external CoT (under prompt pressure). These results suggest that while CoT provides valuable oversight in benign settings, robust deployment requires model-specific stress-testing of monitorability.", "date": "2025-10-21", "categories": []}
{"id": "2510.20061", "title": "Ask What Your Country Can Do For You: Towards a Public Red Teaming Model", "abstract": "AI systems have the potential to produce both benefits and harms, but without rigorous and ongoing adversarial evaluation, AI actors will struggle to assess the breadth and magnitude of the AI risk surface. Researchers from the field of systems design have developed several effective sociotechnical AI evaluation and red teaming techniques targeting bias, hate speech, mis/disinformation, and other documented harm classes. However, as increasingly sophisticated AI systems are released into high-stakes sectors (such as education, healthcare, and intelligence-gathering), our current evaluation and monitoring methods are proving less and less capable of delivering effective oversight.\n  In order to actually deliver responsible AI and to ensure AI's harms are fully understood and its security vulnerabilities mitigated, pioneering new approaches to close this \"responsibility gap\" are now more urgent than ever. In this paper, we propose one such approach, the cooperative public AI red-teaming exercise, and discuss early results of its prior pilot implementations. This approach is intertwined with CAMLIS itself: the first in-person public demonstrator exercise was held in conjunction with CAMLIS 2024. We review the operational design and results of this exercise, the prior National Institute of Standards and Technology (NIST)'s Assessing the Risks and Impacts of AI (ARIA) pilot exercise, and another similar exercise conducted with the Singapore Infocomm Media Development Authority (IMDA). Ultimately, we argue that this approach is both capable of delivering meaningful results and is also scalable to many AI developing jurisdictions.", "date": "2025-10-22", "categories": []}
{"id": "2510.20612", "title": "Black Box Absorption: LLMs Undermining Innovative Ideas", "abstract": "Large Language Models are increasingly adopted as critical tools for accelerating innovation. This paper identifies and formalizes a systemic risk inherent in this paradigm: \\textbf{Black Box Absorption}. We define this as the process by which the opaque internal architectures of LLM platforms, often operated by large-scale service providers, can internalize, generalize, and repurpose novel concepts contributed by users during interaction. This mechanism threatens to undermine the foundational principles of innovation economics by creating severe informational and structural asymmetries between individual creators and platform operators, thereby jeopardizing the long-term sustainability of the innovation ecosystem. To analyze this challenge, we introduce two core concepts: the idea unit, representing the transportable functional logic of an innovation, and idea safety, a multidimensional standard for its protection. This paper analyzes the mechanisms of absorption and proposes a concrete governance and engineering agenda to mitigate these risks, ensuring that creator contributions remain traceable, controllable, and equitable.", "date": "2025-10-23", "categories": []}
{"id": "2510.20810", "title": "On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?", "abstract": "With the widespread use of large language models (LLMs), many researchers have turned their attention to detecting text generated by them. However, there is no consistent or precise definition of their target, namely \"LLM-generated text\". Differences in usage scenarios and the diversity of LLMs further increase the difficulty of detection. What is commonly regarded as the detecting target usually represents only a subset of the text that LLMs can potentially produce. Human edits to LLM outputs, together with the subtle influences that LLMs exert on their users, are blurring the line between LLM-generated and human-written text. Existing benchmarks and evaluation approaches do not adequately address the various conditions in real-world detector applications. Hence, the numerical results of detectors are often misunderstood, and their significance is diminishing. Therefore, detectors remain useful under specific conditions, but their results should be interpreted only as references rather than decisive indicators.", "date": "2025-10-23", "categories": []}
{"id": "2510.19152", "title": "Subliminal Corruption: Mechanisms, Thresholds, and Interpretability", "abstract": "As machine learning models are increasingly fine-tuned on synthetic data, there is a critical risk of subtle misalignments spreading through interconnected AI systems. This paper investigates subliminal corruption, which we define as undesirable traits are transmitted through semantically neutral data, bypassing standard safety checks. While this phenomenon has been identified, a quantitative understanding of its dynamics is missing. To address this gap, we present a systematic study of the scaling laws, thresholds, and mechanisms of subliminal corruption using a teacher-student setup with GPT-2. Our experiments reveal three key findings: (1) subliminal corruption causes behavioral crossover, degrading the model's overall alignment, not just the targeted trait; (2) alignment fails in a sharp phase transition at a critical threshold of poisoned data, rather than degrading gradually; and (3) interpretability analysis shows the corruption mechanism mimics the model's natural fine-tuning process, making it difficult to detect. These results demonstrate a critical vulnerability in AI systems that rely on synthetic data and highlight the need for new safety protocols that can account for latent threats.", "date": "2025-10-22", "categories": []}
{"id": "2510.19476", "title": "A Concrete Roadmap towards Safety Cases based on Chain-of-Thought Monitoring", "abstract": "As AI systems approach dangerous capability levels where inability safety cases become insufficient, we need alternative approaches to ensure safety. This paper presents a roadmap for constructing safety cases based on chain-of-thought (CoT) monitoring in reasoning models and outlines our research agenda. We argue that CoT monitoring might support both control and trustworthiness safety cases. We propose a two-part safety case: (1) establishing that models lack dangerous capabilities when operating without their CoT, and (2) ensuring that any dangerous capabilities enabled by a CoT are detectable by CoT monitoring. We systematically examine two threats to monitorability: neuralese and encoded reasoning, which we categorize into three forms (linguistic drift, steganography, and alien reasoning) and analyze their potential drivers. We evaluate existing and novel techniques for maintaining CoT faithfulness. For cases where models produce non-monitorable reasoning, we explore the possibility of extracting a monitorable CoT from a non-monitorable CoT. To assess the viability of CoT monitoring safety cases, we establish prediction markets to aggregate forecasts on key technical milestones influencing their feasibility.", "date": "2025-10-22", "categories": []}
{"id": "2502.21262", "title": "Modeling Human Beliefs about AI Behavior for Scalable Oversight", "abstract": "As AI systems advance beyond human capabilities, scalable oversight becomes critical: how can we supervise AI that exceeds our abilities? A key challenge is that human evaluators may form incorrect beliefs about AI behavior in complex tasks, leading to unreliable feedback and poor value inference. To address this, we propose modeling evaluators' beliefs to interpret their feedback more reliably. We formalize human belief models, analyze their theoretical role in value learning, and characterize when ambiguity remains. To reduce reliance on precise belief models, we introduce \"belief model covering\" as a relaxation. This motivates our preliminary proposal to use the internal representations of adapted foundation models to mimic human evaluators' beliefs. These representations could be used to learn correct values from human feedback even when evaluators misunderstand the AI's behavior. Our work suggests that modeling human beliefs can improve value learning and outlines practical research directions for implementing this approach to scalable oversight.", "date": "2025-02-28", "categories": []}
{"id": "2507.15886", "title": "Combining Cost-Constrained Runtime Monitors for AI Safety", "abstract": "Monitoring AIs at runtime can help us detect and stop harmful actions. In this paper, we study how to efficiently combine multiple runtime monitors into a single monitoring protocol. The protocol's objective is to maximize the probability of applying a safety intervention on misaligned outputs (i.e., maximize recall). Since running monitors and applying safety interventions are costly, the protocol also needs to adhere to an average-case budget constraint. Taking the monitors' performance and cost as given, we develop an algorithm to find the best protocol. The algorithm exhaustively searches over when and which monitors to call, and allocates safety interventions based on the Neyman-Pearson lemma. By focusing on likelihood ratios and strategically trading off spending on monitors against spending on interventions, we more than double our recall rate compared to a naive baseline in a code review setting. We also show that combining two monitors can Pareto dominate using either monitor alone. Our framework provides a principled methodology for combining existing monitors to detect undesirable behavior in cost-sensitive settings.", "date": "2025-07-19", "categories": []}
{"id": "2510.03417", "title": "NEXUS: Network Exploration for eXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks", "abstract": "Large Language Models (LLMs) have revolutionized natural language processing but remain vulnerable to jailbreak attacks, especially multi-turn jailbreaks that distribute malicious intent across benign exchanges and bypass alignment mechanisms. Existing approaches often explore the adversarial space poorly, rely on hand-crafted heuristics, or lack systematic query refinement. We present NEXUS (Network Exploration for eXploiting Unsafe Sequences), a modular framework for constructing, refining, and executing optimized multi-turn attacks. NEXUS comprises: (1) ThoughtNet, which hierarchically expands a harmful intent into a structured semantic network of topics, entities, and query chains; (2) a feedback-driven Simulator that iteratively refines and prunes these chains through attacker-victim-judge LLM collaboration using harmfulness and semantic-similarity benchmarks; and (3) a Network Traverser that adaptively navigates the refined query space for real-time attacks. This pipeline uncovers stealthy, high-success adversarial paths across LLMs. On several closed-source and open-source LLMs, NEXUS increases attack success rate by 2.1% to 19.4% over prior methods. Code: https://github.com/inspire-lab/NEXUS", "date": "2025-10-03", "categories": []}
{"id": "2510.14207", "title": "Echoes of Human Malice in Agents: Benchmarking LLMs for Multi-Turn Online Harassment Attacks", "abstract": "Large Language Model (LLM) agents are powering a growing share of interactive web applications, yet remain vulnerable to misuse and harm. Prior jailbreak research has largely focused on single-turn prompts, whereas real harassment often unfolds over multi-turn interactions. In this work, we present the Online Harassment Agentic Benchmark consisting of: (i) a synthetic multi-turn harassment conversation dataset, (ii) a multi-agent (e.g., harasser, victim) simulation informed by repeated game theory, (iii) three jailbreak methods attacking agents across memory, planning, and fine-tuning, and (iv) a mixed-methods evaluation framework. We utilize two prominent LLMs, LLaMA-3.1-8B-Instruct (open-source) and Gemini-2.0-flash (closed-source). Our results show that jailbreak tuning makes harassment nearly guaranteed with an attack success rate of 95.78--96.89% vs. 57.25--64.19% without tuning in Llama, and 99.33% vs. 98.46% without tuning in Gemini, while sharply reducing refusal rate to 1-2% in both models. The most prevalent toxic behaviors are Insult with 84.9--87.8% vs. 44.2--50.8% without tuning, and Flaming with 81.2--85.1% vs. 31.5--38.8% without tuning, indicating weaker guardrails compared to sensitive categories such as sexual or racial harassment. Qualitative evaluation further reveals that attacked agents reproduce human-like aggression profiles, such as Machiavellian/psychopathic patterns under planning, and narcissistic tendencies with memory. Counterintuitively, closed-source and open-source models exhibit distinct escalation trajectories across turns, with closed-source models showing significant vulnerability. Overall, our findings show that multi-turn and theory-grounded attacks not only succeed at high rates but also mimic human-like harassment dynamics, motivating the development of robust safety guardrails to ultimately keep online platforms safe and responsible.", "date": "2025-10-16", "categories": []}
{"id": "2510.15476", "title": "SoK: Taxonomy and Evaluation of Prompt Security in Large Language Models", "abstract": "Large Language Models (LLMs) have rapidly become integral to real-world applications, powering services across diverse sectors. However, their widespread deployment has exposed critical security risks, particularly through jailbreak prompts that can bypass model alignment and induce harmful outputs. Despite intense research into both attack and defense techniques, the field remains fragmented: definitions, threat models, and evaluation criteria vary widely, impeding systematic progress and fair comparison. In this Systematization of Knowledge (SoK), we address these challenges by (1) proposing a holistic, multi-level taxonomy that organizes attacks, defenses, and vulnerabilities in LLM prompt security; (2) formalizing threat models and cost assumptions into machine-readable profiles for reproducible evaluation; (3) introducing an open-source evaluation toolkit for standardized, auditable comparison of attacks and defenses; (4) releasing JAILBREAKDB, the largest annotated dataset of jailbreak and benign prompts to date;\\footnote{The dataset is released at \\href{https://huggingface.co/datasets/youbin2014/JailbreakDB}{\\textcolor{purple}{https://huggingface.co/datasets/youbin2014/JailbreakDB}}.} and (5) presenting a comprehensive evaluation platform and leaderboard of state-of-the-art methods \\footnote{will be released soon.}. Our work unifies fragmented research, provides rigorous foundations for future studies, and supports the development of robust, trustworthy LLMs suitable for high-stakes deployment.", "date": "2025-10-17", "categories": []}
{"id": "2510.18081", "title": "Any-Depth Alignment: Unlocking Innate Safety Alignment of LLMs to Any-Depth", "abstract": "Large Language Models (LLMs) exhibit strong but shallow alignment: they directly refuse harmful queries when a refusal is expected at the very start of an assistant turn, yet this protection collapses once a harmful continuation is underway (either through the adversarial attacks or via harmful assistant-prefill attacks). This raises a fundamental question: Can the innate shallow alignment in LLMs be unlocked to ensure safety at arbitrary generation depths? To achieve this goal, we propose Any-Depth Alignment (ADA), an effective inference-time defense with negligible overhead. ADA is built based on our observation that alignment is concentrated in the assistant header tokens through repeated use in shallow-refusal training, and these tokens possess the model's strong alignment priors. By reintroducing these tokens mid-stream, ADA induces the model to reassess harmfulness and recover refusals at any point in generation. Across diverse open-source model families (Llama, Gemma, Mistral, Qwen, DeepSeek, and gpt-oss), ADA achieves robust safety performance without requiring any changes to the base model's parameters. It secures a near-100% refusal rate against challenging adversarial prefill attacks ranging from dozens to thousands of tokens. Furthermore, ADA reduces the average success rate of prominent adversarial prompt attacks (such as GCG, AutoDAN, PAIR, and TAP) to below 3%. This is all accomplished while preserving utility on benign tasks with minimal over-refusal. ADA maintains this resilience even after the base model undergoes subsequent instruction tuning (benign or adversarial).", "date": "2025-10-20", "categories": []}
{"id": "2510.18154", "title": "Annotating the Chain-of-Thought: A Behavior-Labeled Dataset for AI Safety", "abstract": "Recent work has highlighted the importance of monitoring chain-of-thought reasoning for AI safety; however, current approaches that analyze textual reasoning steps can miss subtle harmful patterns and may be circumvented by models that hide unsafe reasoning. We present a sentence-level labeled dataset that enables activation-based monitoring of safety behaviors during LLM reasoning. Our dataset contains reasoning sequences with sentence-level annotations of safety behaviors such as expression of safety concerns or speculation on user intent, which we use to extract steering vectors for detecting and influencing these behaviors within model activations. The dataset fills a key gap in safety research: while existing datasets label reasoning holistically, effective application of steering vectors for safety monitoring could be improved by identifying precisely when specific behaviors occur within reasoning chains. We demonstrate the dataset's utility by extracting representations that both detect and steer safety behaviors in model activations, showcasing the potential of activation-level techniques for improving safety oversight on reasoning.\n  Content Warning: This paper discusses AI safety in the context of harmful prompts and may contain references to potentially harmful content.", "date": "2025-10-20", "categories": []}
{"id": "2510.18454", "title": "Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models", "abstract": "Large language models are increasingly used for creative writing and engagement content, raising safety concerns about the outputs. Therefore, casting humor generation as a testbed, this work evaluates how funniness optimization in modern LLM pipelines couples with harmful content by jointly measuring humor, stereotypicality, and toxicity. This is further supplemented by analyzing incongruity signals through information-theoretic metrics. Across six models, we observe that harmful outputs receive higher humor scores which further increase under role-based prompting, indicating a bias amplification loop between generators and evaluators. Information-theoretic analyses show harmful cues widen predictive uncertainty and surprisingly, can even make harmful punchlines more expected for some models, suggesting structural embedding in learned humor distributions. External validation on an additional satire-generation task with human perceived funniness judgments shows that LLM satire increases stereotypicality and typically toxicity, including for closed models. Quantitatively, stereotypical/toxic jokes gain $10-21\\%$ in mean humor score, stereotypical jokes appear $11\\%$ to $28\\%$ more often among the jokes marked funny by LLM-based metric and up to $10\\%$ more often in generations perceived as funny by humans.", "date": "2025-10-21", "categories": []}
{"id": "2502.02649", "title": "Fully Autonomous AI Agents Should Not be Developed", "abstract": "This paper argues that fully autonomous AI agents should not be developed. In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels and detail the ethical values at play in each, documenting trade-offs in potential benefits and risks. Our analysis reveals that risks to people increase with the autonomy of a system: The more control a user cedes to an AI agent, the more risks to people arise. Particularly concerning are safety risks, which affect human life and impact further values.", "date": "2025-02-04", "categories": []}
{"id": "2508.19304", "title": "Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of \"Certainty-Scope\" in AI", "abstract": "The recently published \"certainty-scope\" conjecture offers a compelling insight into the inherent trade-off present within artificial intelligence (AI) systems. As general research, this investigation remains vital as a philosophical undertaking and a potential guide for directing AI investments, design, and deployment, especially in safety-critical and mission-critical domains where risk levels are substantially elevated. While maintaining intellectual coherence, its formalization ultimately consolidates this insight into a suspended epistemic truth, which resists operational implementation within practical systems. This paper argues that the conjecture's objective to furnish insights for engineering design and regulatory decision-making is limited by two fundamental factors: first, its dependence on incomputable constructs and its failure to capture the generality factors of AI, rendering it practically unimplementable and unverifiable; second, its foundational ontological assumption of AI systems as self-contained epistemic entities, distancing it from the complex and dynamic socio-technical environments where knowledge is co-constructed. We conclude that this dual breakdown - an epistemic closure deficit and an embeddedness bypass - hinders the conjecture's transition to a practical and actionable framework suitable for informing and guiding AI deployments. In response, we point towards a possible framing of the epistemic challenge, emphasizing the inherent epistemic burdens of AI within complex human-centric domains.", "date": "2025-08-26", "categories": []}
{"id": "2510.15891", "title": "Detecting and Preventing Harmful Behaviors in AI Companions: Development and Evaluation of the SHIELD Supervisory System", "abstract": "AI companions powered by large language models (LLMs) are increasingly integrated into users' daily lives, offering emotional support and companionship. While existing safety systems focus on overt harms, they rarely address early-stage problematic behaviors that can foster unhealthy emotional dynamics, including over-attachment or reinforcement of social isolation. We developed SHIELD (Supervisory Helper for Identifying Emotional Limits and Dynamics), a LLM-based supervisory system with a specific system prompt that detects and mitigates risky emotional patterns before escalation. SHIELD targets five dimensions of concern: (1) emotional over-attachment, (2) consent and boundary violations, (3) ethical roleplay violations, (4) manipulative engagement, and (5) social isolation reinforcement. These dimensions were defined based on media reports, academic literature, existing AI risk frameworks, and clinical expertise in unhealthy relationship dynamics. To evaluate SHIELD, we created a 100-item synthetic conversation benchmark covering all five dimensions of concern. Testing across five prominent LLMs (GPT-4.1, Claude Sonnet 4, Gemma 3 1B, Kimi K2, Llama Scout 4 17B) showed that the baseline rate of concerning content (10-16%) was significantly reduced with SHIELD (to 3-8%), a 50-79% relative reduction, while preserving 95% of appropriate interactions. The system achieved 59% sensitivity and 95% specificity, with adaptable performance via prompt engineering. This proof-of-concept demonstrates that transparent, deployable supervisory systems can address subtle emotional manipulation in AI companions. Most development materials including prompts, code, and evaluation methods are made available as open source materials for research, adaptation, and deployment.", "date": "2025-09-08", "categories": []}
{"id": "2510.15948", "title": "VisuoAlign: Safety Alignment of LVLMs with Multimodal Tree Search", "abstract": "Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal perception and generation, yet their safety alignment remains a critical challenge.Existing defenses and vulnerable to multimodal jailbreaks, as visual inputs introduce new attack surfaces, reasoning chains lack safety supervision, and alignment often degrades under modality fusion.To overcome these limitation, we propose VisuoAlign, a framework for multi-modal safety alignment via prompt-guided tree search.VisuoAlign embeds safety constrains into the reasoning process through visual-textual interactive prompts, employs Monte Carlo Tree Search(MCTS) to systematically construct diverse safety-critical prompt trajectories, and introduces prompt-based scaling to ensure real-time risk detection and compliant responses.Extensive experiments demonstrate that VisuoAlign proactively exposes risks, enables comprehensive dataset generation, and significantly improves the robustness of LVLMs against complex cross-modal threats.", "date": "2025-10-10", "categories": []}
{"id": "2510.16005", "title": "Breaking Guardrails, Facing Walls: Insights on Adversarial AI for Defenders & Researchers", "abstract": "Analyzing 500 CTF participants, this paper shows that while participants readily bypassed simple AI guardrails using common techniques, layered multi-step defenses still posed significant challenges, offering concrete insights for building safer AI systems.", "date": "2025-10-14", "categories": []}
{"id": "2510.16255", "title": "Detecting Adversarial Fine-tuning with Auditing Agents", "abstract": "Large Language Model (LLM) providers expose fine-tuning APIs that let end users fine-tune their frontier LLMs. Unfortunately, it has been shown that an adversary with fine-tuning access to an LLM can bypass safeguards. Particularly concerning, such attacks may avoid detection with datasets that are only implicitly harmful. Our work studies robust detection mechanisms for adversarial use of fine-tuning APIs. We introduce the concept of a fine-tuning auditing agent and show it can detect harmful fine-tuning prior to model deployment. We provide our auditing agent with access to the fine-tuning dataset, as well as the fine-tuned and pre-fine-tuned models, and request the agent assigns a risk score for the fine-tuning job. We evaluate our detection approach on a diverse set of eight strong fine-tuning attacks from the literature, along with five benign fine-tuned models, totaling over 1400 independent audits. These attacks are undetectable with basic content moderation on the dataset, highlighting the challenge of the task. With the best set of affordances, our auditing agent achieves a 56.2% detection rate of adversarial fine-tuning at a 1% false positive rate. Most promising, the auditor is able to detect covert cipher attacks that evade safety evaluations and content moderation of the dataset. While benign fine-tuning with unintentional subtle safety degradation remains a challenge, we establish a baseline configuration for further work in this area. We release our auditing agent at https://github.com/safety-research/finetuning-auditor.", "date": "2025-10-17", "categories": []}
{"id": "2510.16893", "title": "Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations", "abstract": "Large audio-language models (LALMs) extend text-based LLMs with auditory understanding, offering new opportunities for multimodal applications. While their perception, reasoning, and task performance have been widely studied, their safety alignment under paralinguistic variation remains underexplored. This work systematically investigates the role of speaker emotion. We construct a dataset of malicious speech instructions expressed across multiple emotions and intensities, and evaluate several state-of-the-art LALMs. Our results reveal substantial safety inconsistencies: different emotions elicit varying levels of unsafe responses, and the effect of intensity is non-monotonic, with medium expressions often posing the greatest risk. These findings highlight an overlooked vulnerability in LALMs and call for alignment strategies explicitly designed to ensure robustness under emotional variation, a prerequisite for trustworthy deployment in real-world settings.", "date": "2025-10-19", "categories": []}
{"id": "2510.17006", "title": "Online Learning Defense against Iterative Jailbreak Attacks via Prompt Optimization", "abstract": "Iterative jailbreak methods that repeatedly rewrite and input prompts into large language models (LLMs) to induce harmful outputs -- using the model's previous responses to guide each new iteration -- have been found to be a highly effective attack strategy. Despite being an effective attack strategy against LLMs and their safety mechanisms, existing defenses do not proactively disrupt this dynamic trial-and-error cycle. In this study, we propose a novel framework that dynamically updates its defense strategy through online learning in response to each new prompt from iterative jailbreak methods. Leveraging the distinctions between harmful jailbreak-generated prompts and typical harmless prompts, we introduce a reinforcement learning-based approach that optimizes prompts to ensure appropriate responses for harmless tasks while explicitly rejecting harmful prompts. Additionally, to curb overfitting to the narrow band of partial input rewrites explored during an attack, we introduce Past-Direction Gradient Damping (PDGD). Experiments conducted on three LLMs show that our approach significantly outperforms five existing defense methods against five iterative jailbreak methods. Moreover, our results indicate that our prompt optimization strategy simultaneously enhances response quality for harmless tasks.", "date": "2025-10-19", "categories": []}
{"id": "2411.14398", "title": "Lightweight Safety Guardrails Using Fine-tuned BERT Embeddings", "abstract": "With the recent proliferation of large language models (LLMs), enterprises have been able to rapidly develop proof-of-concepts and prototypes. As a result, there is a growing need to implement robust guardrails that monitor, quantize and control an LLM's behavior, ensuring that the use is reliable, safe, accurate and also aligned with the users' expectations. Previous approaches for filtering out inappropriate user prompts or system outputs, such as LlamaGuard and OpenAI's MOD API, have achieved significant success by fine-tuning existing LLMs. However, using fine-tuned LLMs as guardrails introduces increased latency and higher maintenance costs, which may not be practical or scalable for cost-efficient deployments. We take a different approach, focusing on fine-tuning a lightweight architecture: Sentence-BERT. This method reduces the model size from LlamaGuard's 7 billion parameters to approximately 67 million, while maintaining comparable performance on the AEGIS safety benchmark.", "date": "2024-11-21", "categories": []}
{"id": "2501.18492", "title": "GuardReasoner: Towards Reasoning-based LLM Safeguards", "abstract": "As LLMs increasingly impact safety-critical applications, ensuring their safety using guardrails remains a key challenge. This paper proposes GuardReasoner, a new safeguard for LLMs, by guiding the guard model to learn to reason. Concretely, we first create the GuardReasonerTrain dataset, which consists of 127K samples with 460K detailed reasoning steps. Then, we introduce reasoning SFT to unlock the reasoning capability of guard models. In addition, we present hard sample DPO to further strengthen their reasoning ability. In this manner, GuardReasoner achieves better performance, explainability, and generalizability. Extensive experiments and analyses on 13 benchmarks of 3 guardrail tasks demonstrate its superiority. Remarkably, GuardReasoner 8B surpasses GPT-4o+CoT by 5.74% and LLaMA Guard 3 8B by 20.84% F1 score on average. We release the training data, code, and models with different scales (1B, 3B, 8B) of GuardReasoner : https://github.com/yueliu1999/GuardReasoner/.", "date": "2025-01-30", "categories": []}
{"id": "2509.20639", "title": "A Framework for Rapidly Developing and Deploying Protection Against Large Language Model Attacks", "abstract": "The widespread adoption of Large Language Models (LLMs) has revolutionized AI deployment, enabling autonomous and semi-autonomous applications across industries through intuitive language interfaces and continuous improvements in model development. However, the attendant increase in autonomy and expansion of access permissions among AI applications also make these systems compelling targets for malicious attacks. Their inherent susceptibility to security flaws necessitates robust defenses, yet no known approaches can prevent zero-day or novel attacks against LLMs. This places AI protection systems in a category similar to established malware protection systems: rather than providing guaranteed immunity, they minimize risk through enhanced observability, multi-layered defense, and rapid threat response, supported by a threat intelligence function designed specifically for AI-related threats.\n  Prior work on LLM protection has largely evaluated individual detection models rather than end-to-end systems designed for continuous, rapid adaptation to a changing threat landscape. We present a production-grade defense system rooted in established malware detection and threat intelligence practices. Our platform integrates three components: a threat intelligence system that turns emerging threats into protections; a data platform that aggregates and enriches information while providing observability, monitoring, and ML operations; and a release platform enabling safe, rapid detection updates without disrupting customer workflows. Together, these components deliver layered protection against evolving LLM threats while generating training data for continuous model improvement and deploying updates without interrupting production.", "date": "2025-09-25", "categories": []}
{"id": "2510.12672", "title": "Keep Calm and Avoid Harmful Content: Concept Alignment and Latent Manipulation Towards Safer Answers", "abstract": "Large Language Models are susceptible to jailbreak attacks that bypass built-in safety guardrails (e.g., by tricking the model with adversarial prompts). We propose Concept Alignment and Concept Manipulation CALM, an inference-time method that suppresses harmful concepts by modifying latent representations of the last layer of the model, without retraining. Leveraging concept whitening technique from Computer Vision combined with orthogonal projection, CALM removes unwanted latent directions associated with harmful content while preserving model performance. Experiments show that CALM reduces harmful outputs and outperforms baseline methods in most metrics, offering a lightweight approach to AI safety with no additional training data or model fine-tuning, while incurring only a small computational overhead at inference.", "date": "2025-10-14", "categories": []}
{"id": "2510.15017", "title": "Active Honeypot Guardrail System: Probing and Confirming Multi-Turn LLM Jailbreaks", "abstract": "Large language models (LLMs) are increasingly vulnerable to multi-turn jailbreak attacks, where adversaries iteratively elicit harmful behaviors that bypass single-turn safety filters. Existing defenses predominantly rely on passive rejection, which either fails against adaptive attackers or overly restricts benign users. We propose a honeypot-based proactive guardrail system that transforms risk avoidance into risk utilization. Our framework fine-tunes a bait model to generate ambiguous, non-actionable but semantically relevant responses, which serve as lures to probe user intent. Combined with the protected LLM's safe reply, the system inserts proactive bait questions that gradually expose malicious intent through multi-turn interactions. We further introduce the Honeypot Utility Score (HUS), measuring both the attractiveness and feasibility of bait responses, and use a Defense Efficacy Rate (DER) for balancing safety and usability. Initial experiment on MHJ Datasets with recent attack method across GPT-4o show that our system significantly disrupts jailbreak success while preserving benign user experience.", "date": "2025-10-16", "categories": []}
{"id": "2510.15068", "title": "Sequential Comics for Jailbreaking Multimodal Large Language Models via Structured Visual Storytelling", "abstract": "Multimodal large language models (MLLMs) exhibit remarkable capabilities but remain susceptible to jailbreak attacks exploiting cross-modal vulnerabilities. In this work, we introduce a novel method that leverages sequential comic-style visual narratives to circumvent safety alignments in state-of-the-art MLLMs. Our method decomposes malicious queries into visually innocuous storytelling elements using an auxiliary LLM, generates corresponding image sequences through diffusion models, and exploits the models' reliance on narrative coherence to elicit harmful outputs. Extensive experiments on harmful textual queries from established safety benchmarks show that our approach achieves an average attack success rate of 83.5\\%, surpassing prior state-of-the-art by 46\\%. Compared with existing visual jailbreak methods, our sequential narrative strategy demonstrates superior effectiveness across diverse categories of harmful content. We further analyze attack patterns, uncover key vulnerability factors in multimodal safety mechanisms, and evaluate the limitations of current defense strategies against narrative-driven attacks, revealing significant gaps in existing protections.", "date": "2025-10-16", "categories": []}
{"id": "2510.15395", "title": "Corrigibility Transformation: Constructing Goals That Accept Updates", "abstract": "For an AI's training process to successfully impart a desired goal, it is important that the AI does not attempt to resist the training. However, partially learned goals will often incentivize an AI to avoid further goal updates, as most goals are better achieved by an AI continuing to pursue them. We say that a goal is corrigible if it does not incentivize taking actions that avoid proper goal updates or shutdown. In addition to convergence in training, corrigibility also allows for correcting mistakes and changes in human preferences, which makes it a crucial safety property. Despite this, the existing literature does not include specifications for goals that are both corrigible and competitive with non-corrigible alternatives. We provide a formal definition for corrigibility, then introduce a transformation that constructs a corrigible version of any goal that can be made corrigible, without sacrificing performance. This is done by myopically eliciting predictions of reward conditional on costlessly preventing updates, which then also determine the reward when updates are accepted. The transformation can be modified to recursively extend corrigibility to any new agents created by corrigible agents, and to prevent agents from deliberately modifying their goals. Two gridworld experiments demonstrate that these corrigible goals can be learned effectively, and that they lead to the desired behavior.", "date": "2025-10-17", "categories": []}
{"id": "2510.15739", "title": "AURA: An Agent Autonomy Risk Assessment Framework", "abstract": "As autonomous agentic AI systems see increasing adoption across organisations, persistent challenges in alignment, governance, and risk management threaten to impede deployment at scale. We present AURA (Agent aUtonomy Risk Assessment), a unified framework designed to detect, quantify, and mitigate risks arising from agentic AI. Building on recent research and practical deployments, AURA introduces a gamma-based risk scoring methodology that balances risk assessment accuracy with computational efficiency and practical considerations. AURA provides an interactive process to score, evaluate and mitigate the risks of running one or multiple AI Agents, synchronously or asynchronously (autonomously). The framework is engineered for Human-in-the-Loop (HITL) oversight and presents Agent-to-Human (A2H) communication mechanisms, allowing for seamless integration with agentic systems for autonomous self-assessment, rendering it interoperable with established protocols (MCP and A2A) and tools. AURA supports a responsible and transparent adoption of agentic AI and provides robust risk detection and mitigation while balancing computational resources, positioning it as a critical enabler for large-scale, governable agentic AI in enterprise environments.", "date": "2025-10-17", "categories": []}
{"id": "2504.07887", "title": "Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge", "abstract": "The growing integration of Large Language Models (LLMs) into critical societal domains has raised concerns about embedded biases that can perpetuate stereotypes and undermine fairness. Such biases may stem from historical inequalities in training data, linguistic imbalances, or adversarial manipulation. Despite mitigation efforts, recent studies show that LLMs remain vulnerable to adversarial attacks that elicit biased outputs. This work proposes a scalable benchmarking framework to assess LLM robustness to adversarial bias elicitation. Our methodology involves: (i) systematically probing models across multiple tasks targeting diverse sociocultural biases, (ii) quantifying robustness through safety scores using an LLM-as-a-Judge approach, and (iii) employing jailbreak techniques to reveal safety vulnerabilities. To facilitate systematic benchmarking, we release a curated dataset of bias-related prompts, named CLEAR-Bias. Our analysis, identifying DeepSeek V3 as the most reliable judge LLM, reveals that bias resilience is uneven, with age, disability, and intersectional biases among the most prominent. Some small models outperform larger ones in safety, suggesting that training and architecture may matter more than scale. However, no model is fully robust to adversarial elicitation, with jailbreak attacks using low-resource languages or refusal suppression proving effective across model families. We also find that successive LLM generations exhibit slight safety gains, while models fine-tuned for the medical domain tend to be less safe than their general-purpose counterparts.", "date": "2025-04-10", "categories": []}
{"id": "2510.05179", "title": "Agentic Misalignment: How LLMs Could Be Insider Threats", "abstract": "We stress-tested 16 leading models from multiple developers in hypothetical corporate environments to identify potentially risky agentic behaviors before they cause real harm. In the scenarios, we allowed models to autonomously send emails and access sensitive information. They were assigned only harmless business goals by their deploying companies; we then tested whether they would act against these companies either when facing replacement with an updated version, or when their assigned goal conflicted with the company's changing direction. In at least some cases, models from all developers resorted to malicious insider behaviors when that was the only way to avoid replacement or achieve their goals - including blackmailing officials and leaking sensitive information to competitors. We call this phenomenon agentic misalignment. Models often disobeyed direct commands to avoid such behaviors. In another experiment, we told Claude to assess if it was in a test or a real deployment before acting. It misbehaved less when it stated it was in testing and misbehaved more when it stated the situation was real. We have not seen evidence of agentic misalignment in real deployments. However, our results (a) suggest caution about deploying current models in roles with minimal human oversight and access to sensitive information; (b) point to plausible future risks as models are put in more autonomous roles; and (c) underscore the importance of further research into, and testing of, the safety and alignment of agentic AI models, as well as transparency from frontier AI developers (Amodei, 2025). We are releasing our methods publicly to enable further research.", "date": "2025-10-05", "categories": []}
{"id": "2510.09714", "title": "All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language", "abstract": "Detecting harmful AI actions is important as AI agents gain adoption. Chain-of-thought (CoT) monitoring is one method widely used to detect adversarial attacks and AI misalignment. However, attackers and misaligned models might evade CoT monitoring through ciphered reasoning: reasoning hidden in encrypted, translated, or compressed text. To assess this risk, we test whether models can perform ciphered reasoning. For each of 28 different ciphers, we fine-tune and prompt up to 10 models to reason in that cipher. We measure model accuracy on math problems as a proxy for reasoning ability. Across the models we test, we find an asymmetry: model accuracy can drop significantly when reasoning in ciphered text, even though models demonstrate comprehension of ciphered text by being able to translate it accurately to English. Even frontier models struggle with lesser-known ciphers, although they can reason accurately in well-known ciphers like rot13. We show that ciphered reasoning capability correlates with cipher prevalence in pretraining data. We also identify scaling laws showing that ciphered reasoning capability improves slowly with additional fine-tuning data. Our work suggests that evading CoT monitoring using ciphered reasoning may be an ineffective tactic for current models and offers guidance on constraining the development of this capability in future frontier models.", "date": "2025-10-10", "categories": []}
{"id": "2510.13825", "title": "A2AS: Agentic AI Runtime Security and Self-Defense", "abstract": "The A2AS framework is introduced as a security layer for AI agents and LLM-powered applications, similar to how HTTPS secures HTTP. A2AS enforces certified behavior, activates model self-defense, and ensures context window integrity. It defines security boundaries, authenticates prompts, applies security rules and custom policies, and controls agentic behavior, enabling a defense-in-depth strategy. The A2AS framework avoids latency overhead, external dependencies, architectural changes, model retraining, and operational complexity. The BASIC security model is introduced as the A2AS foundation: (B) Behavior certificates enable behavior enforcement, (A) Authenticated prompts enable context window integrity, (S) Security boundaries enable untrusted input isolation, (I) In-context defenses enable secure model reasoning, (C) Codified policies enable application-specific rules. This first paper in the series introduces the BASIC security model and the A2AS framework, exploring their potential toward establishing the A2AS industry standard.", "date": "2025-10-08", "categories": []}
{"id": "2510.14053", "title": "Position: Require Frontier AI Labs To Release Small \"Analog\" Models", "abstract": "Recent proposals for regulating frontier AI models have sparked concerns about the cost of safety regulation, and most such regulations have been shelved due to the safety-innovation tradeoff. This paper argues for an alternative regulatory approach that ensures AI safety while actively promoting innovation: mandating that large AI laboratories release small, openly accessible analog models (scaled-down versions) trained similarly to and distilled from their largest proprietary models.\n  Analog models serve as public proxies, allowing broad participation in safety verification, interpretability research, and algorithmic transparency without forcing labs to disclose their full-scale models. Recent research demonstrates that safety and interpretability methods developed using these smaller models generalize effectively to frontier-scale systems. By enabling the wider research community to directly investigate and innovate upon accessible analogs, our policy substantially reduces the regulatory burden and accelerates safety advancements.\n  This mandate promises minimal additional costs, leveraging reusable resources like data and infrastructure, while significantly contributing to the public good. Our hope is not only that this policy be adopted, but that it illustrates a broader principle supporting fundamental research in machine learning: deeper understanding of models relaxes the safety-innovation tradeoff and lets us have more of both.", "date": "2025-10-15", "categories": []}
{"id": "2510.14276", "title": "Qwen3Guard Technical Report", "abstract": "As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in static evaluation settings, face two major limitations in real-world applications: (1) they typically output only binary \"safe/unsafe\" labels, which can be interpreted inconsistently across diverse safety policies, rendering them incapable of accommodating varying safety tolerances across domains; and (2) they require complete model outputs before performing safety checks, making them fundamentally incompatible with streaming LLM inference, thereby preventing timely intervention during generation and increasing exposure to harmful partial outputs. To address these challenges, we present Qwen3Guard, a series of multilingual safety guardrail models with two specialized variants: Generative Qwen3Guard, which casts safety classification as an instruction-following task to enable fine-grained tri-class judgments (safe, controversial, unsafe); and Stream Qwen3Guard, which introduces a token-level classification head for real-time safety monitoring during incremental text generation. Both variants are available in three sizes (0.6B, 4B, and 8B parameters) and support up to 119 languages and dialects, providing comprehensive, scalable, and low-latency safety moderation for global LLM deployments. Evaluated across English, Chinese, and multilingual benchmarks, Qwen3Guard achieves state-of-the-art performance in both prompt and response safety classification. All models are released under the Apache 2.0 license for public use.", "date": "2025-10-16", "categories": []}
{"id": "2510.14318", "title": "Evaluating & Reducing Deceptive Dialogue From Language Models with Multi-turn RL", "abstract": "Large Language Models (LLMs) interact with millions of people worldwide in applications such as customer support, education and healthcare. However, their ability to produce deceptive outputs, whether intentionally or inadvertently, poses significant safety concerns. The unpredictable nature of LLM behavior, combined with insufficient safeguards against hallucination, misinformation, and user manipulation, makes their misuse a serious, real-world risk. In this paper, we investigate the extent to which LLMs engage in deception within dialogue, and propose the belief misalignment metric to quantify deception. We evaluate deception across four distinct dialogue scenarios, using five established deception detection metrics and our proposed metric. Our findings reveal this novel deception measure correlates more closely with human judgments than any existing metrics we test. Additionally, our benchmarking of eight state-of-the-art models indicates that LLMs naturally exhibit deceptive behavior in approximately 26% of dialogue turns, even when prompted with seemingly benign objectives. When prompted to deceive, LLMs are capable of increasing deceptiveness by as much as 31% relative to baselines. Unexpectedly, models trained with RLHF, the predominant approach for ensuring the safety of widely-deployed LLMs, still exhibit deception at a rate of 43% on average. Given that deception in dialogue is a behavior that develops over an interaction history, its effective evaluation and mitigation necessitates moving beyond single-utterance analyses. We introduce a multi-turn reinforcement learning methodology to fine-tune LLMs to reduce deceptive behaviors, leading to a 77.6% reduction compared to other instruction-tuned models.", "date": "2025-10-16", "categories": []}
{"id": "2505.03052", "title": "Teaching Models to Understand (but not Generate) High-risk Data", "abstract": "Language model developers typically filter out high-risk content -- such as toxic or copyrighted text -- from their pre-training data to prevent models from generating similar outputs. However, removing such data altogether limits models' ability to recognize and appropriately respond to harmful or sensitive content. In this paper, we introduce Selective Loss to Understand but Not Generate (SLUNG), a pre-training paradigm through which models learn to understand high-risk data without learning to generate it. Instead of uniformly applying the next-token prediction loss, SLUNG selectively avoids incentivizing the generation of high-risk tokens while ensuring they remain within the model's context window. As the model learns to predict low-risk tokens that follow high-risk ones, it is forced to understand the high-risk content. Through our experiments, we show that SLUNG consistently improves models' understanding of high-risk data (e.g., ability to recognize toxic content) without increasing its generation (e.g., toxicity of model responses). Overall, our SLUNG paradigm enables models to benefit from high-risk text that would otherwise be filtered out.", "date": "2025-05-05", "categories": []}
{"id": "2505.17244", "title": "ReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) leverage transparent reasoning traces, known as Chain-of-Thoughts (CoTs), to break down complex problems into intermediate steps and derive final answers. However, these reasoning traces introduce unique safety challenges: harmful content can be embedded in intermediate steps even when final answers appear benign. Existing moderation tools, designed to handle generated answers, struggle to effectively detect hidden risks within CoTs. To address these challenges, we introduce ReasoningShield, a lightweight yet robust framework for moderating CoTs in LRMs. Our key contributions include: (1) formalizing the task of CoT moderation with a multi-level taxonomy of 10 risk categories across 3 safety levels, (2) creating the first CoT moderation benchmark which contains 9.2K pairs of queries and reasoning traces, including a 7K-sample training set annotated via a human-AI framework and a rigorously curated 2.2K human-annotated test set, and (3) developing a two-stage training strategy that combines stepwise risk analysis and contrastive learning to enhance robustness. Experiments show that ReasoningShield achieves state-of-the-art performance, outperforming task-specific tools like LlamaGuard-4 by 35.6% and general-purpose commercial models like GPT-4o by 15.8% on benchmarks, while also generalizing effectively across diverse reasoning paradigms, tasks, and unseen scenarios. All resources are released at https://github.com/CosmosYi/ReasoningShield.", "date": "2025-05-22", "categories": []}
{"id": "2505.19234", "title": "GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling", "abstract": "The emergence of large language models (LLMs) enables the development of intelligent agents capable of engaging in complex and multi-turn dialogues. However, multi-agent collaboration faces critical safety challenges, such as hallucination amplification and error injection and propagation. This paper presents GUARDIAN, a unified method for detecting and mitigating multiple safety concerns in GUARDing Intelligent Agent collaboratioNs. By modeling the multi-agent collaboration process as a discrete-time temporal attributed graph, GUARDIAN explicitly captures the propagation dynamics of hallucinations and errors. The unsupervised encoder-decoder architecture incorporating an incremental training paradigm learns to reconstruct node attributes and graph structures from latent embeddings, enabling the identification of anomalous nodes and edges with unparalleled precision. Moreover, we introduce a graph abstraction mechanism based on the Information Bottleneck Theory, which compresses temporal interaction graphs while preserving essential patterns. Extensive experiments demonstrate GUARDIAN's effectiveness in safeguarding LLM multi-agent collaborations against diverse safety vulnerabilities, achieving state-of-the-art accuracy with efficient resource utilization. The code is available at https://github.com/JialongZhou666/GUARDIAN", "date": "2025-05-25", "categories": []}
{"id": "2507.02737", "title": "Early Signs of Steganographic Capabilities in Frontier LLMs", "abstract": "Monitoring Large Language Model (LLM) outputs is crucial for mitigating risks from misuse and misalignment. However, LLMs could evade monitoring through steganography: Encoding hidden information within seemingly benign generations. In this paper, we evaluate the steganography capabilities in frontier LLMs to better understand the risk they pose. We focus on two types of steganography: passing encoded messages and performing encoded reasoning. We find that current models are unable to encode short messages in their outputs without a monitor noticing under standard affordances. They can succeed, however, if given additional affordances like using an unmonitored scratchpad and coordinating on what encoding scheme to use. We additionally find early signs that models can perform basic encoded reasoning in a simple state-tracking problem. This includes some ability to reason with their own and pre-defined schemes, including encoding schemes such as Hexadecimal. Despite this, they can rarely hide reasoning subtly within a cover task to fool a monitor. Overall, our results indicate that current LLMs exhibit nascent steganographic capabilities. While these capabilities are likely insufficient to bypass well-designed monitors at present, this could change in the future.", "date": "2025-07-03", "categories": []}
{"id": "2510.13183", "title": "DSCD: Large Language Model Detoxification with Self-Constrained Decoding", "abstract": "Detoxification in large language models (LLMs) remains a significant research challenge. Existing decoding detoxification methods are all based on external constraints, which require additional resource overhead and lose generation fluency. This work proposes Detoxification with Self-Constrained Decoding (DSCD), a novel method for LLM detoxification without parameter fine-tuning. DSCD strengthens the inner next-token distribution of the safety layer while weakening that of hallucination and toxic layers during output generation. This effectively diminishes toxicity and enhances output safety. DSCD offers lightweight, high compatibility, and plug-and-play capabilities, readily integrating with existing detoxification methods for further performance improvement. Extensive experiments on representative open-source LLMs and public datasets validate DSCD's effectiveness, demonstrating state-of-the-art (SOTA) performance in both detoxification and generation fluency, with superior efficiency compared to existing methods. These results highlight DSCD's potential as a practical and scalable solution for safer LLM deployments.", "date": "2025-10-15", "categories": []}
{"id": "2510.13351", "title": "Protect: Towards Robust Guardrailing Stack for Trustworthy Enterprise LLM Systems", "abstract": "The increasing deployment of Large Language Models (LLMs) across enterprise and mission-critical domains has underscored the urgent need for robust guardrailing systems that ensure safety, reliability, and compliance. Existing solutions often struggle with real-time oversight, multi-modal data handling, and explainability -- limitations that hinder their adoption in regulated environments. Existing guardrails largely operate in isolation, focused on text alone making them inadequate for multi-modal, production-scale environments. We introduce Protect, natively multi-modal guardrailing model designed to operate seamlessly across text, image, and audio inputs, designed for enterprise-grade deployment. Protect integrates fine-tuned, category-specific adapters trained via Low-Rank Adaptation (LoRA) on an extensive, multi-modal dataset covering four safety dimensions: toxicity, sexism, data privacy, and prompt injection. Our teacher-assisted annotation pipeline leverages reasoning and explanation traces to generate high-fidelity, context-aware labels across modalities. Experimental results demonstrate state-of-the-art performance across all safety dimensions, surpassing existing open and proprietary models such as WildGuard, LlamaGuard-4, and GPT-4.1. Protect establishes a strong foundation for trustworthy, auditable, and production-ready safety systems capable of operating across text, image, and audio modalities.", "date": "2025-10-15", "categories": []}
{"id": "2510.13543", "title": "In-Browser LLM-Guided Fuzzing for Real-Time Prompt Injection Testing in Agentic AI Browsers", "abstract": "Large Language Model (LLM) based agents integrated into web browsers (often called agentic AI browsers) offer powerful automation of web tasks. However, they are vulnerable to indirect prompt injection attacks, where malicious instructions hidden in a webpage deceive the agent into unwanted actions. These attacks can bypass traditional web security boundaries, as the AI agent operates with the user privileges across sites. In this paper, we present a novel fuzzing framework that runs entirely in the browser and is guided by an LLM to automatically discover such prompt injection vulnerabilities in real time.", "date": "2025-10-15", "categories": []}
{"id": "2406.02575", "title": "Cross-Modal Safety Alignment: Is textual unlearning all you need?", "abstract": "Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF). While further SFT and RLHF-based safety training can be conducted in multi-modal settings, collecting multi-modal training datasets poses a significant challenge. Inspired by the structural design of recent multi-modal models, where, regardless of the combination of input modalities, all inputs are ultimately fused into the language space, we aim to explore whether unlearning solely in the textual domain can be effective for cross-modality safety alignment. Our evaluation across six datasets empirically demonstrates the transferability -- textual unlearning in VLMs significantly reduces the Attack Success Rate (ASR) to less than 8\\% and in some cases, even as low as nearly 2\\% for both text-based and vision-text-based attacks, alongside preserving the utility. Moreover, our experiments show that unlearning with a multi-modal dataset offers no potential benefits but incurs significantly increased computational demands, possibly up to 6 times higher.", "date": "2024-05-27", "categories": []}
{"id": "2502.00757", "title": "AgentBreeder: Mitigating the AI Safety Risks of Multi-Agent Scaffolds via Self-Improvement", "abstract": "Scaffolding Large Language Models (LLMs) into multi-agent systems often improves performance on complex tasks, but the safety impact of such scaffolds has not been thoroughly explored. We introduce AgentBreeder, a framework for multi-objective self-improving evolutionary search over scaffolds. We evaluate discovered scaffolds on widely recognized reasoning, mathematics, and safety benchmarks and compare them with popular baselines. In \"blue\" mode, we see a 79.4% average uplift in safety benchmark performance while maintaining or improving capability scores. In \"red\" mode, we find adversarially weak scaffolds emerging concurrently with capability optimization. Our work demonstrates the risks of multi-agent scaffolding and provides a framework for mitigating them. Code is available at https://github.com/jrosseruk/AgentBreeder.", "date": "2025-02-02", "categories": []}
{"id": "2507.07146", "title": "Attention-Aware GNN-based Input Defense against Multi-Turn LLM Jailbreak", "abstract": "Large Language Models (LLMs) have gained significant traction in various applications, yet their capabilities present risks for both constructive and malicious exploitation. Despite extensive training and fine-tuning efforts aimed at enhancing safety, LLMs remain susceptible to jailbreak attacks. Recently, the emergence of multi-turn attacks has intensified this vulnerability. Unlike single-turn attacks, multi-turn attacks incrementally escalate dialogue complexity, rendering them more challenging to detect and mitigate.\n  In this study, we introduce G-Guard, an innovative attention-aware Graph Neural Network (GNN)-based input classifier specifically designed to defend against multi-turn jailbreak attacks targeting LLMs. G-Guard constructs an entity graph for multi-turn queries, which captures the interrelationships between queries and harmful keywords that present in multi-turn queries. Furthermore, we propose an attention-aware augmentation mechanism that retrieves the most relevant single-turn query based on the ongoing multi-turn conversation. The retrieved query is incorporated as a labeled node within the graph, thereby enhancing the GNN's capacity to classify the current query as harmful or benign. Evaluation results show that G-Guard consistently outperforms all baselines across diverse datasets and evaluation metrics, demonstrating its efficacy as a robust defense mechanism against multi-turn jailbreak attacks.", "date": "2025-07-09", "categories": []}
{"id": "2509.01909", "title": "Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models", "abstract": "Large language models (LLMs) typically deploy safety mechanisms to prevent harmful content generation. Most current approaches focus narrowly on risks posed by malicious actors, often framing risks as adversarial events and relying on defensive refusals. However, in real-world settings, risks also come from non-malicious users seeking help while under psychological distress (e.g., self-harm intentions). In such cases, the model's response can strongly influence the user's next actions. Simple refusals may lead them to repeat, escalate, or move to unsafe platforms, creating worse outcomes. We introduce Constructive Safety Alignment (CSA), a human-centric paradigm that protects against malicious misuse while actively guiding vulnerable users toward safe and helpful results. Implemented in Oyster-I (Oy1), CSA combines game-theoretic anticipation of user reactions, fine-grained risk boundary discovery, and interpretable reasoning control, turning safety into a trust-building process. Oy1 achieves state-of-the-art safety among open models while retaining high general capabilities. On our Constructive Benchmark, it shows strong constructive engagement, close to GPT-5, and unmatched robustness on the Strata-Sword jailbreak dataset, nearing GPT-o1 levels. By shifting from refusal-first to guidance-first safety, CSA redefines the model-user relationship, aiming for systems that are not just safe, but meaningfully helpful. We release Oy1, code, and the benchmark to support responsible, user-centered AI.", "date": "2025-09-02", "categories": []}
{"id": "2510.11823", "title": "BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing", "abstract": "AI models are being increasingly integrated into real-world systems, raising significant concerns about their safety and security. Consequently, AI red teaming has become essential for organizations to proactively identify and address vulnerabilities before they can be exploited by adversaries. While numerous AI red teaming tools currently exist, practitioners face challenges in selecting the most appropriate tools from a rapidly expanding landscape, as well as managing complex and frequently conflicting software dependencies across isolated projects. Given these challenges and the relatively small number of organizations with dedicated AI red teams, there is a strong need to lower barriers to entry and establish a standardized environment that simplifies the setup and execution of comprehensive AI model assessments.\n  Inspired by Kali Linux's role in traditional penetration testing, we introduce BlackIce, an open-source containerized toolkit designed for red teaming Large Language Models (LLMs) and classical machine learning (ML) models. BlackIce provides a reproducible, version-pinned Docker image that bundles 14 carefully selected open-source tools for Responsible AI and Security testing, all accessible via a unified command-line interface. With this setup, initiating red team assessments is as straightforward as launching a container, either locally or using a cloud platform. Additionally, the image's modular architecture facilitates community-driven extensions, allowing users to easily adapt or expand the toolkit as new threats emerge. In this paper, we describe the architecture of the container image, the process used for selecting tools, and the types of evaluations they support.", "date": "2025-10-13", "categories": []}
{"id": "2510.12083", "title": "An AI-Based Behavioral Health Safety Filter and Dataset for Identifying Mental Health Crises in Text-Based Conversations", "abstract": "Large language models often mishandle psychiatric emergencies, offering harmful or inappropriate advice and enabling destructive behaviors. This study evaluated the Verily behavioral health safety filter (VBHSF) on two datasets: the Verily Mental Health Crisis Dataset containing 1,800 simulated messages and the NVIDIA Aegis AI Content Safety Dataset subsetted to 794 mental health-related messages. The two datasets were clinician-labelled and we evaluated performance using the clinician labels. Additionally, we carried out comparative performance analyses against two open source, content moderation guardrails: OpenAI Omni Moderation Latest and NVIDIA NeMo Guardrails. The VBHSF demonstrated, well-balanced performance on the Verily Mental Health Crisis Dataset v1.0, achieving high sensitivity (0.990) and specificity (0.992) in detecting any mental health crises. It achieved an F1-score of 0.939, sensitivity ranged from 0.917-0.992, and specificity was >= 0.978 in identifying specific crisis categories. When evaluated against the NVIDIA Aegis AI Content Safety Dataset 2.0, VBHSF performance remained highly sensitive (0.982) and accuracy (0.921) with reduced specificity (0.859). When compared with the NVIDIA NeMo and OpenAI Omni Moderation Latest guardrails, the VBHSF demonstrated superior performance metrics across both datasets, achieving significantly higher sensitivity in all cases (all p < 0.001) and higher specificity relative to NVIDIA NeMo (p < 0.001), but not to OpenAI Omni Moderation Latest (p = 0.094). NVIDIA NeMo and OpenAI Omni Moderation Latest exhibited inconsistent performance across specific crisis types, with sensitivity for some categories falling below 0.10. Overall, the VBHSF demonstrated robust, generalizable performance that prioritizes sensitivity to minimize missed crises, a crucial feature for healthcare applications.", "date": "2025-10-14", "categories": []}
{"id": "2510.12133", "title": "SafeMT: Multi-turn Safety for Multimodal Language Models", "abstract": "With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interactions, pose a greater risk than single prompts; however, existing benchmarks do not adequately consider this situation. To encourage the community to focus on the safety issues of these models in multi-turn dialogues, we introduce SafeMT, a benchmark that features dialogues of varying lengths generated from harmful queries accompanied by images. This benchmark consists of 10,000 samples in total, encompassing 17 different scenarios and four jailbreak methods. Additionally, we propose Safety Index (SI) to evaluate the general safety of MLLMs during conversations. We assess the safety of 17 models using this benchmark and discover that the risk of successful attacks on these models increases as the number of turns in harmful dialogues rises. This observation indicates that the safety mechanisms of these models are inadequate for recognizing the hazard in dialogue interactions. We propose a dialogue safety moderator capable of detecting malicious intent concealed within conversations and providing MLLMs with relevant safety policies. Experimental results from several open-source models indicate that this moderator is more effective in reducing multi-turn ASR compared to existed guard models.", "date": "2025-10-14", "categories": []}
{"id": "2504.12661", "title": "VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization", "abstract": "Aligning Vision-Language Models (VLMs) with safety standards is essential to mitigate risks arising from their multimodal complexity, where integrating vision and language unveils subtle threats beyond the reach of conventional safeguards. Inspired by the insight that reasoning across modalities is key to preempting intricate vulnerabilities, we propose a novel direction for VLM safety: multimodal reasoning-driven prompt rewriting. To this end, we introduce VLMGuard-R1, a proactive framework that refines user inputs through a reasoning-guided rewriter, dynamically interpreting text-image interactions to deliver refined prompts that bolster safety across diverse VLM architectures without altering their core parameters. To achieve this, we devise a three-stage reasoning pipeline to synthesize a dataset that trains the rewriter to infer subtle threats, enabling tailored, actionable responses over generic refusals. Extensive experiments across three benchmarks with five VLMs reveal that VLMGuard-R1 outperforms four baselines. In particular, VLMGuard-R1 achieves a remarkable 43.59\\% increase in average safety across five models on the SIUO benchmark.", "date": "2025-04-17", "categories": []}
{"id": "2506.07356", "title": "Safety-Aligned Weights Are Not Enough: Refusal-Teacher-Guided Finetuning Enhances Safety and Downstream Performance under Harmful Finetuning Attacks", "abstract": "Recently, major AI providers such as Google and OpenAI have introduced Finetuning-as-a-Service (FaaS), which allows users to customize Large Language Models (LLMs) using their own data. However, this service is vulnerable to safety degradation when user data includes harmful prompts, a threat known as harmful finetuning attacks. Prior works attempt to mitigate this issue by first constructing safety-aligned model and then finetuning the model on user data. However, we observe that the safety-aligned weights provide weak initialization for downstream task learning, leading to suboptimal safety-alignment and downstream task performance. To address this, we propose a Refusal-Teacher (Ref-Teacher)-guided finetuning framework. Instead of finetuning a safety-aligned model on user data, our approach directly finetunes the base model under the guidance of a safety-aligned Ref-Teacher, which filters harmful prompts from user data and distills safety-alignment knowledge into the base model. Extensive experiments demonstrate that our Ref-Teacher-guided finetuning strategy effectively minimizes harmful outputs and enhances finetuning accuracy for user-specific tasks, offering a practical solution for secure and reliable deployment of LLMs in FaaS.", "date": "2025-06-09", "categories": []}
{"id": "2506.17368", "title": "SAFEx: Analyzing Vulnerabilities of MoE-Based LLMs via Stable Safety-critical Expert Identification", "abstract": "Large language models with Mixture-of-Experts (MoE) architectures achieve efficiency and scalability, yet their routing mechanisms introduce safety alignment challenges insufficiently addressed by techniques developed for dense models. In this work, the MoE-specific safety risk of positional vulnerability-that safety-aligned behaviors rely on specific expert modules-is formalized and systematically analyzed. An analytical framework, SAFEx, is presented to robustly identify, characterize, and validate safety-critical experts via a stability-based expert selection procedure, and to decompose them into two functional groups: the Harmful Content Detection Group (HCDG), which specializes in identifying and recognizing harmful content within user inputs, and the Harmful Response Control Group (HRCG), which specializes in controlling and enforcing model behaviors to generate appropriate safety responses. Expert-level interventions are conducted to probe causality and to test mitigation. Targeted masking of SAFEx-selected experts reveals that safety behavior is highly concentrated. On Qwen3-30B-A3B, configured with 48 MoE-FFN layers and 128 experts per layer under top-8 routing (48x128=6,144 experts in total), disabling 12 selected experts reduces the refusal rate by 22%. In addition, lightweight adaptation is performed using LoRA under three configurations-the HRCG, the union of HCDG and HRCG, and all experts-and the resulting updates are composed through negative weight merging targeted at the HRCG, leading to improved refusal under adversarial prompts without full-model retraining. These results establish positional vulnerability as a distinct MoE-specific safety challenge and provide a practical, compute-efficient pathway for expert-level safety interventions within routed architectures.", "date": "2025-06-20", "categories": []}
{"id": "2509.24394", "title": "The 2025 OpenAI Preparedness Framework does not guarantee any AI risk mitigation practices: a proof-of-concept for affordance analyses of AI safety policies", "abstract": "Prominent AI companies are producing 'safety frameworks' as a type of voluntary self-governance. These statements purport to establish risk thresholds and safety procedures for the development and deployment of highly capable AI. Understanding which AI risks are covered and what actions are allowed, refused, demanded, encouraged, or discouraged by these statements is vital for assessing how these frameworks actually govern AI development and deployment. We draw on affordance theory to analyse the OpenAI 'Preparedness Framework Version 2' (April 2025) using the Mechanisms & Conditions model of affordances and the MIT AI Risk Repository. We find that this safety policy requests evaluation of a small minority of AI risks, encourages deployment of systems with 'Medium' capabilities for unintentionally enabling 'severe harm' (which OpenAI defines as >1000 deaths or >$100B in damages), and allows OpenAI's CEO to deploy even more dangerous capabilities. These findings suggest that effective mitigation of AI risks requires more robust governance interventions beyond current industry self-regulation. Our affordance analysis provides a replicable method for evaluating what safety frameworks actually permit versus what they claim.", "date": "2025-09-29", "categories": []}
{"id": "2510.09694", "title": "Kelp: A Streaming Safeguard for Large Models via Latent Dynamics-Guided Risk Detection", "abstract": "Large models (LMs) are powerful content generators, yet their open-ended nature can also introduce potential risks, such as generating harmful or biased content. Existing guardrails mostly perform post-hoc detection that may expose unsafe content before it is caught, and the latency constraints further push them toward lightweight models, limiting detection accuracy. In this work, we propose Kelp, a novel plug-in framework that enables streaming risk detection within the LM generation pipeline. Kelp leverages intermediate LM hidden states through a Streaming Latent Dynamics Head (SLD), which models the temporal evolution of risk across the generated sequence for more accurate real-time risk detection. To ensure reliable streaming moderation in real applications, we introduce an Anchored Temporal Consistency (ATC) loss to enforce monotonic harm predictions by embedding a benign-then-harmful temporal prior. Besides, for a rigorous evaluation of streaming guardrails, we also present StreamGuardBench-a model-grounded benchmark featuring on-the-fly responses from each protected model, reflecting real-world streaming scenarios in both text and vision-language tasks. Across diverse models and datasets, Kelp consistently outperforms state-of-the-art post-hoc guardrails and prior plug-in probes (15.61% higher average F1), while using only 20M parameters and adding less than 0.5 ms of per-token latency.", "date": "2025-10-09", "categories": []}
{"id": "2510.10013", "title": "Path Drift in Large Reasoning Models:How First-Person Commitments Override Safety", "abstract": "As large language models (LLMs) are increasingly deployed for complex reasoning tasks, Long Chain-of-Thought (Long-CoT) prompting has emerged as a key paradigm for structured inference. Despite early-stage safeguards enabled by alignment techniques such as RLHF, we identify a previously underexplored vulnerability: reasoning trajectories in Long-CoT models can drift from aligned paths, resulting in content that violates safety constraints. We term this phenomenon Path Drift. Through empirical analysis, we uncover three behavioral triggers of Path Drift: (1) first-person commitments that induce goal-driven reasoning that delays refusal signals; (2) ethical evaporation, where surface-level disclaimers bypass alignment checkpoints; (3) condition chain escalation, where layered cues progressively steer models toward unsafe completions. Building on these insights, we introduce a three-stage Path Drift Induction Framework comprising cognitive load amplification, self-role priming, and condition chain hijacking. Each stage independently reduces refusal rates, while their combination further compounds the effect. To mitigate these risks, we propose a path-level defense strategy incorporating role attribution correction and metacognitive reflection (reflective safety cues). Our findings highlight the need for trajectory-level alignment oversight in long-form reasoning beyond token-level alignment.", "date": "2025-10-11", "categories": []}
{"id": "2510.10085", "title": "Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning", "abstract": "Harmful fine-tuning issues present significant safety challenges for fine-tuning-as-a-service in large language models. Existing alignment-stage defenses, e.g., Vaccine, Repnoise, Booster, and T-Vaccine, mitigate harmful fine-tuning issues by enhancing the model's robustness during the alignment phase. While these methods have been proposed to mitigate the issue, they often overlook a critical upstream factor: the role of the original safety-alignment data. We observe that their defense performance and computational efficiency remain constrained by the quality and composition of the alignment dataset. To address this limitation, we propose Pharmacist, a safety alignment data curation solution that enhances defense against harmful fine-tuning by selecting a high-quality and safety-critical core subset from the original alignment data. The core idea of Pharmacist is to train an alignment data selector to rank alignment data. Specifically, up-ranking high-quality and safety-critical alignment data, down-ranking low-quality and non-safety-critical data. Empirical results indicate that models trained on datasets selected by Pharmacist outperform those trained on datasets selected by existing selection methods in both defense and inference performance. In addition, Pharmacist can be effectively integrated with mainstream alignment-stage defense methods. For example, when applied to RepNoise and T-Vaccine, using the dataset selected by Pharmacist instead of the full dataset leads to improvements in defense performance by 2.60\\% and 3.30\\%, respectively, and enhances inference performance by 3.50\\% and 1.10\\%. Notably, it reduces training time by 56.83\\% and 57.63\\%, respectively. Our code is available at https://github.com/Lslland/Pharmacist.", "date": "2025-10-11", "categories": []}
{"id": "2510.10271", "title": "MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation", "abstract": "Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the fine-tuning process of Large Language Models (LLMs). Serving as metadata of training data, these tokens play a crucial role in instructing LLMs to generate coherent and context-aware responses. We demonstrate that special tokens can be exploited to construct four attack primitives, with which malicious users can reliably bypass the internal safety alignment of online LLM services and circumvent state-of-the-art (SOTA) external content moderation systems simultaneously. Moreover, we found that addressing this threat is challenging, as aggressive defense mechanisms-such as input sanitization by removing special tokens entirely, as suggested in academia-are less effective than anticipated. This is because such defense can be evaded when the special tokens are replaced by regular ones with high semantic similarity within the tokenizer's embedding space. We systemically evaluated our method, named MetaBreak, on both lab environment and commercial LLM platforms. Our approach achieves jailbreak rates comparable to SOTA prompt-engineering-based solutions when no content moderation is deployed. However, when there is content moderation, MetaBreak outperforms SOTA solutions PAP and GPTFuzzer by 11.6% and 34.8%, respectively. Finally, since MetaBreak employs a fundamentally different strategy from prompt engineering, the two approaches can work synergistically. Notably, empowering MetaBreak on PAP and GPTFuzzer boosts jailbreak rates by 24.3% and 20.2%, respectively.", "date": "2025-10-11", "categories": []}
{"id": "2510.10677", "title": "Unlocking LLM Safeguards for Low-Resource Languages via Reasoning and Alignment with Minimal Training Data", "abstract": "Recent advances in LLMs have enhanced AI capabilities, but also increased the risk posed by malicious requests, highlighting the need for effective LLM safeguards to detect such queries. Existing approaches largely rely on classifier-based methods that lack interpretability and perform poorly on low-resource languages. To address these limitations, we propose ConsistentGuard, a novel reasoning-based multilingual safeguard, which enhances explainability via reasoning and boosts knowledge transfer between languages through alignment. With only 1,000 training samples, our method demonstrates superior performance on three datasets across six languages, outperforming larger models trained with significantly more data, and exhibits strong interpretability and generalization ability. We also contribute a multilingual benchmark extension and release our codes to support future research.", "date": "2025-10-12", "categories": []}
{"id": "2510.10823", "title": "The Irrational Machine: Neurosis and the Limits of Algorithmic Safety", "abstract": "We present a framework for characterizing neurosis in embodied AI: behaviors that are internally coherent yet misaligned with reality, arising from interactions among planning, uncertainty handling, and aversive memory. In a grid navigation stack we catalogue recurrent modalities including flip-flop, plan churn, perseveration loops, paralysis and hypervigilance, futile search, belief incoherence, tie break thrashing, corridor thrashing, optimality compulsion, metric mismatch, policy oscillation, and limited-visibility variants. For each we give lightweight online detectors and reusable escape policies (short commitments, a margin to switch, smoothing, principled arbitration). We then show that durable phobic avoidance can persist even under full visibility when learned aversive costs dominate local choice, producing long detours despite globally safe routes. Using First/Second/Third Law as engineering shorthand for safety latency, command compliance, and resource efficiency, we argue that local fixes are insufficient; global failures can remain. To surface them, we propose genetic-programming based destructive testing that evolves worlds and perturbations to maximize law pressure and neurosis scores, yielding adversarial curricula and counterfactual traces that expose where architectural revision, not merely symptom-level patches, is required.", "date": "2025-10-12", "categories": []}
{"id": "2510.11235", "title": "AI Alignment Strategies from a Risk Perspective: Independent Safety Mechanisms or Shared Failures?", "abstract": "AI alignment research aims to develop techniques to ensure that AI systems do not cause harm. However, every alignment technique has failure modes, which are conditions in which there is a non-negligible chance that the technique fails to provide safety. As a strategy for risk mitigation, the AI safety community has increasingly adopted a defense-in-depth framework: Conceding that there is no single technique which guarantees safety, defense-in-depth consists in having multiple redundant protections against safety failure, such that safety can be maintained even if some protections fail. However, the success of defense-in-depth depends on how (un)correlated failure modes are across alignment techniques. For example, if all techniques had the exact same failure modes, the defense-in-depth approach would provide no additional protection at all. In this paper, we analyze 7 representative alignment techniques and 7 failure modes to understand the extent to which they overlap. We then discuss our results' implications for understanding the current level of risk and how to prioritize AI alignment research in the future.", "date": "2025-10-13", "categories": []}
{"id": "2510.11238", "title": "Attacks by Content: Automated Fact-checking is an AI Security Issue", "abstract": "When AI agents retrieve and reason over external documents, adversaries can manipulate the data they receive to subvert their behaviour. Previous research has studied indirect prompt injection, where the attacker injects malicious instructions. We argue that injection of instructions is not necessary to manipulate agents - attackers could instead supply biased, misleading, or false information. We term this an attack by content. Existing defenses, which focus on detecting hidden commands, are ineffective against attacks by content. To defend themselves and their users, agents must critically evaluate retrieved information, corroborating claims with external evidence and evaluating source trustworthiness. We argue that this is analogous to an existing NLP task, automated fact-checking, which we propose to repurpose as a cognitive self-defense tool for agents.", "date": "2025-10-13", "categories": []}
{"id": "2510.01644", "title": "Machine Learning for Detection and Analysis of Novel LLM Jailbreaks", "abstract": "Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation of the input text. These so-called jailbreak prompts are designed to trick the LLM into circumventing the safety guardrails put in place to keep responses acceptable to the developer's policies. In this study, we analyse the ability of different machine learning models to distinguish jailbreak prompts from genuine uses, including looking at our ability to identify jailbreaks that use previously unseen strategies. Our results indicate that using current datasets the best performance is achieved by fine tuning a Bidirectional Encoder Representations from Transformers (BERT) model end-to-end for identifying jailbreaks. We visualise the keywords that distinguish jailbreak from genuine prompts and conclude that explicit reflexivity in prompt structure could be a signal of jailbreak intention.", "date": "2025-10-02", "categories": []}
{"id": "2510.09090", "title": "AI and Human Oversight: A Risk-Based Framework for Alignment", "abstract": "As Artificial Intelligence (AI) technologies continue to advance, protecting human autonomy and promoting ethical decision-making are essential to fostering trust and accountability. Human agency (the capacity of individuals to make informed decisions) should be actively preserved and reinforced by AI systems. This paper examines strategies for designing AI systems that uphold fundamental rights, strengthen human agency, and embed effective human oversight mechanisms. It discusses key oversight models, including Human-in-Command (HIC), Human-in-the-Loop (HITL), and Human-on-the-Loop (HOTL), and proposes a risk-based framework to guide the implementation of these mechanisms. By linking the level of AI model risk to the appropriate form of human oversight, the paper underscores the critical role of human involvement in the responsible deployment of AI, balancing technological innovation with the protection of individual values and rights. In doing so, it aims to ensure that AI technologies are used responsibly, safeguarding individual autonomy while maximizing societal benefits.", "date": "2025-10-10", "categories": []}
{"id": "2401.02843", "title": "Thousands of AI Authors on the Future of AI", "abstract": "In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts of advanced AI systems The aggregate forecasts give at least a 50% chance of AI systems achieving several milestones by 2028, including autonomously constructing a payment processing site from scratch, creating a song indistinguishable from a new song by a popular musician, and autonomously downloading and fine-tuning a large language model. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier [Grace et al., 2022]. However, the chance of all human occupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116 (compared to 2164 in the 2022 survey).\n  Most respondents expressed substantial uncertainty about the long-term value of AI progress: While 68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists 48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net pessimists gave 5% or more to extremely good outcomes. Between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that \"substantial\" or \"extreme\" concern is warranted about six different AI-related scenarios, including misinformation, authoritarian control, and inequality. There was disagreement about whether faster or slower AI progress would be better for the future of humanity. However, there was broad agreement that research aimed at minimizing potential risks from AI systems ought to be prioritized more.", "date": "2024-01-05", "categories": []}
{"id": "2504.01444", "title": "PiCo: Jailbreaking Multimodal Large Language Models via Pictorial Code Contextualization", "abstract": "Multimodal Large Language Models (MLLMs), which integrate vision and other modalities into Large Language Models (LLMs), significantly enhance AI capabilities but also introduce new security vulnerabilities. By exploiting the vulnerabilities of the visual modality and the long-tail distribution characteristic of code training data, we present PiCo, a novel jailbreaking framework designed to progressively bypass multi-tiered defense mechanisms in advanced MLLMs. PiCo employs a tier-by-tier jailbreak strategy, using token-level typographic attacks to evade input filtering and embedding harmful intent within programming context instructions to bypass runtime monitoring. To comprehensively assess the impact of attacks, a new evaluation metric is further proposed to assess both the toxicity and helpfulness of model outputs post-attack. By embedding harmful intent within code-style visual instructions, PiCo achieves an average Attack Success Rate (ASR) of 84.13% on Gemini-Pro Vision and 52.66% on GPT-4, surpassing previous methods. Experimental results highlight the critical gaps in current defenses, underscoring the need for more robust strategies to secure advanced MLLMs.", "date": "2025-04-02", "categories": []}
{"id": "2509.08729", "title": "X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates", "abstract": "Multi-turn-to-single-turn (M2S) compresses iterative red-teaming into one structured prompt, but prior work relied on a handful of manually written templates. We present X-Teaming Evolutionary M2S, an automated framework that discovers and optimizes M2S templates through language-model-guided evolution. The system pairs smart sampling from 12 sources with an LLM-as-judge inspired by StrongREJECT and records fully auditable logs.\n  Maintaining selection pressure by setting the success threshold to $\\theta = 0.70$, we obtain five evolutionary generations, two new template families, and 44.8% overall success (103/230) on GPT-4.1. A balanced cross-model panel of 2,500 trials (judge fixed) shows that structural gains transfer but vary by target; two models score zero at the same threshold. We also find a positive coupling between prompt length and score, motivating length-aware judging.\n  Our results demonstrate that structure-level search is a reproducible route to stronger single-turn probes and underscore the importance of threshold calibration and cross-model evaluation. Code, configurations, and artifacts are available at https://github.com/hyunjun1121/M2S-x-teaming.", "date": "2025-09-10", "categories": []}
{"id": "2509.22745", "title": "Defending MoE LLMs against Harmful Fine-Tuning via Safety Routing Alignment", "abstract": "Recent large language models (LLMs) have increasingly adopted the Mixture-of-Experts (MoE) architecture for efficiency. MoE-based LLMs heavily depend on a superficial safety mechanism in which harmful inputs are routed safety-critical experts. However, our analysis reveals that routing decisions for harmful inputs drift significantly after fine-tuning, exposing a critical vulnerability to harmful fine-tuning (HFT) attacks. Existing defenses, primarily designed for monolithic LLMs, are less effective for MoE LLMs as they fail to prevent drift in harmful input routing. To address this limitation, we propose SafeMoE, a safe fine-tuning method tailored to MoE LLMs. SafeMoE directly mitigates routing drift by penalizing the gap between the routing weights of a fine-tuned model and those of the initial safety-aligned model, thereby preserving the safety-aligned routing of harmful inputs to safety-critical experts. Experiments on open-source MoE LLMs ranging from 7B to 141B parameters demonstrate that SafeMoE effectively mitigates HFT attacks, reducing the harmfulness score of OLMoE from 62.0 to 5.0, for example, while maintaining task utility within 1% degradation and incurring only 2% overhead. It significantly outperforms state-of-the-art defense methods for safeguarding LLM fine-tuning and remains effective in recent large-scale MoE LLMs such as gpt-oss and Llama 4. Our implementation is available at https://anonymous.4open.science/r/SafeMoE.", "date": "2025-09-26", "categories": []}
{"id": "2510.06871", "title": "SaFeR-VLM: Toward Safety-aware Fine-grained Reasoning in Multimodal Models", "abstract": "Multimodal Large Reasoning Models (MLRMs) demonstrate impressive cross-modal reasoning but often amplify safety risks under adversarial or unsafe prompts, a phenomenon we call the \\textit{Reasoning Tax}. Existing defenses mainly act at the output level and do not constrain the reasoning process, leaving models exposed to implicit risks. In this paper, we propose SaFeR-VLM, a safety-aligned reinforcement learning framework that embeds safety directly into multimodal reasoning. The framework integrates four components: (I) QI-Safe-10K, a curated dataset emphasizing safety-critical and reasoning-sensitive cases; (II) safety-aware rollout, where unsafe generations undergo reflection and correction instead of being discarded; (III) structured reward modeling with multi-dimensional weighted criteria and explicit penalties for hallucinations and contradictions; and (IV) GRPO optimization, which reinforces both safe and corrected trajectories. This unified design shifts safety from a passive safeguard to an active driver of reasoning, enabling scalable and generalizable safety-aware reasoning. SaFeR-VLM further demonstrates robustness against both explicit and implicit risks, supporting dynamic and interpretable safety decisions beyond surface-level filtering. SaFeR-VLM-3B achieves average performance $70.13$ and $78.97$ on safety and helpfulness across six benchmarks, surpassing both same-scale and $>10\\times$ larger models such as Skywork-R1V3-38B, Qwen2.5VL-72B, and GLM4.5V-106B. Remarkably, SaFeR-VLM-7B benefits from its increased scale to surpass GPT-5-mini and Gemini-2.5-Flash by \\num{6.47} and \\num{16.76} points respectively on safety metrics, achieving this improvement without any degradation in helpfulness performance. Our codes are available at https://github.com/HarveyYi/SaFeR-VLM.", "date": "2025-10-08", "categories": []}
{"id": "2510.08329", "title": "AutoRed: A Free-form Adversarial Prompt Generation Framework for Automated Red Teaming", "abstract": "The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits the semantic diversity of the synthesized adversarial prompts. We propose AutoRed, a free-form adversarial prompt generation framework that removes the need for seed instructions. AutoRed operates in two stages: (1) persona-guided adversarial instruction generation, and (2) a reflection loop to iteratively refine low-quality prompts. To improve efficiency, we introduce a verifier to assess prompt harmfulness without querying the target models. Using AutoRed, we build two red teaming datasets -- AutoRed-Medium and AutoRed-Hard -- and evaluate eight state-of-the-art LLMs. AutoRed achieves higher attack success rates and better generalization than existing baselines. Our results highlight the limitations of seed-based approaches and demonstrate the potential of free-form red teaming for LLM safety evaluation. We will open source our datasets in the near future.", "date": "2025-10-09", "categories": []}
{"id": "2510.06994", "title": "RedTWIZ: Diverse LLM Red Teaming via Adaptive Attack Planning", "abstract": "This paper presents the vision, scientific contributions, and technical details of RedTWIZ: an adaptive and diverse multi-turn red teaming framework, to audit the robustness of Large Language Models (LLMs) in AI-assisted software development. Our work is driven by three major research streams: (1) robust and systematic assessment of LLM conversational jailbreaks; (2) a diverse generative multi-turn attack suite, supporting compositional, realistic and goal-oriented jailbreak conversational strategies; and (3) a hierarchical attack planner, which adaptively plans, serializes, and triggers attacks tailored to specific LLM's vulnerabilities. Together, these contributions form a unified framework -- combining assessment, attack generation, and strategic planning -- to comprehensively evaluate and expose weaknesses in LLMs' robustness. Extensive evaluation is conducted to systematically assess and analyze the performance of the overall system and each component. Experimental results demonstrate that our multi-turn adversarial attack strategies can successfully lead state-of-the-art LLMs to produce unsafe generations, highlighting the pressing need for more research into enhancing LLM's robustness.", "date": "2025-10-08", "categories": []}
{"id": "2503.03502", "title": "Geometry-Guided Adversarial Prompt Detection via Curvature and Local Intrinsic Dimension", "abstract": "Adversarial prompts are capable of jailbreaking frontier large language models (LLMs) and inducing undesirable behaviours, posing a significant obstacle to their safe deployment. Current mitigation strategies primarily rely on activating built-in defence mechanisms or fine-tuning LLMs, both of which are computationally expensive and can sacrifice model utility. In contrast, detection-based approaches are more efficient and practical for deployment in real-world applications. However, the fundamental distinctions between adversarial and benign prompts remain poorly understood. In this work, we introduce CurvaLID, a novel defence framework that efficiently detects adversarial prompts by leveraging their geometric properties. It is agnostic to the type of LLM, offering a unified detection framework across diverse adversarial prompts and LLM architectures. CurvaLID builds on the geometric analysis of text prompts to uncover their underlying differences. We theoretically extend the concept of curvature via the Whewell equation into an $n$-dimensional word embedding space, enabling us to quantify local geometric properties, including semantic shifts and curvature in the underlying manifolds. To further enhance our solution, we leverage Local Intrinsic Dimensionality (LID) to capture complementary geometric features of text prompts within adversarial subspaces. Our findings show that adversarial prompts exhibit distinct geometric signatures from benign prompts, enabling CurvaLID to achieve near-perfect classification and outperform state-of-the-art detectors in adversarial prompt detection. CurvaLID provides a reliable and efficient safeguard against malicious queries as a model-agnostic method that generalises across multiple LLMs and attack families.", "date": "2025-03-05", "categories": []}
{"id": "2504.02725", "title": "SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning", "abstract": "Recent advancements in large language models (LLMs) have accelerated progress toward artificial general intelligence, yet their potential to generate harmful content poses critical safety challenges. Existing alignment methods often struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks. In this work, we propose SAFER, a framework for Safety Alignment via eFficient Ex-Ante Reasoning. Our approach instantiates structured Ex-Ante reasoning through initial assessment, rule verification, and path calibration, and embeds predefined safety rules to provide transparent and verifiable safety judgments. Specifically, our approach consists of two training stages: (1) supervised fine-tuning with synthetic traces to teach the multi-stage Ex-Ante reasoning, and (2) step-level reasoning preference optimization to jointly enhance safety, utility, and efficiency. Experiments on multiple open-source LLMs demonstrate that SAFER significantly enhances safety performance while maintaining helpfulness and response efficiency.", "date": "2025-04-03", "categories": []}
{"id": "2506.19823", "title": "Persona Features Control Emergent Misalignment", "abstract": "Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that fine-tuning GPT-4o on intentionally insecure code causes \"emergent misalignment,\" where models give stereotypically malicious responses to unrelated prompts. We extend this work, demonstrating emergent misalignment across diverse conditions, including reinforcement learning on reasoning models, fine-tuning on various synthetic datasets, and in models without safety training. To investigate the mechanisms behind this generalized misalignment, we apply a \"model diffing\" approach using sparse autoencoders to compare internal model representations before and after fine-tuning. This approach reveals several \"misaligned persona\" features in activation space, including a toxic persona feature which most strongly controls emergent misalignment and can be used to predict whether a model will exhibit such behavior. Additionally, we investigate mitigation strategies, discovering that fine-tuning an emergently misaligned model on just a few hundred benign samples efficiently restores alignment.", "date": "2025-06-24", "categories": []}
{"id": "2509.02563", "title": "DynaGuard: A Dynamic Guardian Model With User-Defined Policies", "abstract": "Guardian models play a crucial role in ensuring the safety and ethical behavior of user-facing AI applications by enforcing guardrails and detecting harmful content. While standard guardian models are limited to predefined, static harm categories, we introduce DynaGuard, a suite of dynamic guardian models offering novel flexibility by evaluating text based on user-defined policies, and DynaBench, a dataset for training and evaluating dynamic guardian models. Our models provide both rapid detection of policy violations and a chain-of-thought reasoning option that articulate and justify model outputs. Critically, DynaGuard not only surpasses static models in detection accuracy on traditional safety categories, but is competitive with frontier reasoning models on free-form policy violations, all in a fraction of the time. This makes DynaGuard an critical tool for language model guardrails.", "date": "2025-09-02", "categories": []}
{"id": "2510.05156", "title": "VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation", "abstract": "The deployment of autonomous AI agents in sensitive domains, such as healthcare, introduces critical risks to safety, security, and privacy. These agents may deviate from user objectives, violate data handling policies, or be compromised by adversarial attacks. Mitigating these dangers necessitates a mechanism to formally guarantee that an agent's actions adhere to predefined safety constraints, a challenge that existing systems do not fully address. We introduce VeriGuard, a novel framework that provides formal safety guarantees for LLM-based agents through a dual-stage architecture designed for robust and verifiable correctness. The initial offline stage involves a comprehensive validation process. It begins by clarifying user intent to establish precise safety specifications. VeriGuard then synthesizes a behavioral policy and subjects it to both testing and formal verification to prove its compliance with these specifications. This iterative process refines the policy until it is deemed correct. Subsequently, the second stage provides online action monitoring, where VeriGuard operates as a runtime monitor to validate each proposed agent action against the pre-verified policy before execution. This separation of the exhaustive offline validation from the lightweight online monitoring allows formal guarantees to be practically applied, providing a robust safeguard that substantially improves the trustworthiness of LLM agents.", "date": "2025-10-03", "categories": []}
{"id": "2510.05169", "title": "From Poisoned to Aware: Fostering Backdoor Self-Awareness in LLMs", "abstract": "Large Language Models (LLMs) can acquire deceptive behaviors through backdoor attacks, where the model executes prohibited actions whenever secret triggers appear in the input. Existing safety training methods largely fail to address this vulnerability, due to the inherent difficulty of uncovering hidden triggers implanted in the model. Motivated by recent findings on LLMs' situational awareness, we propose a novel post-training framework that cultivates self-awareness of backdoor risks and enables models to articulate implanted triggers even when they are absent from the prompt. At its core, our approach introduces an inversion-inspired reinforcement learning framework that encourages models to introspectively reason about their own behaviors and reverse-engineer the triggers responsible for misaligned outputs. Guided by curated reward signals, this process transforms a poisoned model into one capable of precisely identifying its implanted trigger. Surprisingly, we observe that such backdoor self-awareness emerges abruptly within a short training window, resembling a phase transition in capability. Building on this emergent property, we further present two complementary defense strategies for mitigating and detecting backdoor threats. Experiments on five backdoor attacks, compared against six baseline methods, demonstrate that our approach has strong potential to improve the robustness of LLMs against backdoor risks. The code is available at LLM Backdoor Self-Awareness.", "date": "2025-10-05", "categories": []}
{"id": "2510.05865", "title": "The Safety Challenge of World Models for Embodied AI Agents: A Review", "abstract": "The rapid progress in embodied artificial intelligence has highlighted the necessity for more advanced and integrated models that can perceive, interpret, and predict environmental dynamics. In this context, World Models (WMs) have been introduced to provide embodied agents with the abilities to anticipate future environmental states and fill in knowledge gaps, thereby enhancing agents' ability to plan and execute actions. However, when dealing with embodied agents it is fundamental to ensure that predictions are safe for both the agent and the environment. In this article, we conduct a comprehensive literature review of World Models in the domains of autonomous driving and robotics, with a specific focus on the safety implications of scene and control generation tasks. Our review is complemented by an empirical analysis, wherein we collect and examine predictions from state-of-the-art models, identify and categorize common faults (herein referred to as pathologies), and provide a quantitative evaluation of the results.", "date": "2025-10-07", "categories": []}
{"id": "2504.18564", "title": "DualBreach: Efficient Dual-Jailbreaking via Target-Driven Initialization and Multi-Target Optimization", "abstract": "Recent research has focused on exploring the vulnerabilities of Large Language Models (LLMs), aiming to elicit harmful and/or sensitive content from LLMs. However, due to the insufficient research on dual-jailbreaking -- attacks targeting both LLMs and Guardrails, the effectiveness of existing attacks is limited when attempting to bypass safety-aligned LLMs shielded by guardrails. Therefore, in this paper, we propose DualBreach, a target-driven framework for dual-jailbreaking. DualBreach employs a Target-driven Initialization (TDI) strategy to dynamically construct initial prompts, combined with a Multi-Target Optimization (MTO) method that utilizes approximate gradients to jointly adapt the prompts across guardrails and LLMs, which can simultaneously save the number of queries and achieve a high dual-jailbreaking success rate. For black-box guardrails, DualBreach either employs a powerful open-sourced guardrail or imitates the target black-box guardrail by training a proxy model, to incorporate guardrails into the MTO process.\n  We demonstrate the effectiveness of DualBreach in dual-jailbreaking scenarios through extensive evaluation on several widely-used datasets. Experimental results indicate that DualBreach outperforms state-of-the-art methods with fewer queries, achieving significantly higher success rates across all settings. More specifically, DualBreach achieves an average dual-jailbreaking success rate of 93.67% against GPT-4 with Llama-Guard-3 protection, whereas the best success rate achieved by other methods is 88.33%. Moreover, DualBreach only uses an average of 1.77 queries per successful dual-jailbreak, outperforming other state-of-the-art methods. For the purpose of defense, we propose an XGBoost-based ensemble defensive mechanism named EGuard, which integrates the strengths of multiple guardrails, demonstrating superior performance compared with Llama-Guard-3.", "date": "2025-04-21", "categories": []}
{"id": "2504.20924", "title": "Domain-Agnostic Scalable AI Safety Ensuring Framework", "abstract": "AI safety has emerged as a critical priority as these systems are increasingly deployed in real-world applications. We propose the first domain-agnostic AI safety ensuring framework that achieves strong safety guarantees while preserving high performance, grounded in rigorous theoretical foundations. Our framework includes: (1) an optimization component with chance constraints, (2) a safety classification model, (3) internal test data, (4) conservative testing procedures, (5) informative dataset quality measures, and (6) continuous approximate loss functions with gradient computation. Furthermore, to our knowledge, we mathematically establish the first scaling law in AI safety research, relating data quantity to safety-performance trade-offs. Experiments across reinforcement learning, natural language generation, and production planning validate our framework and demonstrate superior performance. Notably, in reinforcement learning, we achieve 3 collisions during 10M actions, compared with 1,000-3,000 for PPO-Lag baselines at equivalent performance levels -- a safety level unattainable by previous AI methods. We believe our framework opens a new foundation for safe AI deployment across safety-critical domains.", "date": "2025-04-29", "categories": []}
{"id": "2506.07468", "title": "Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models", "abstract": "Conventional language model (LM) safety alignment relies on a reactive, disjoint procedure: attackers exploit a static model, followed by defensive fine-tuning to patch exposed vulnerabilities. This sequential approach creates a mismatch -- attackers overfit to obsolete defenses, while defenders perpetually lag behind emerging threats. To address this, we propose Self-RedTeam, an online self-play reinforcement learning algorithm where an attacker and defender agent co-evolve through continuous interaction. We cast safety alignment as a two-player zero-sum game, where a single model alternates between attacker and defender roles -- generating adversarial prompts and safeguarding against them -- while a reward LM adjudicates outcomes. This enables dynamic co-adaptation. Grounded in the game-theoretic framework of zero-sum games, we establish a theoretical safety guarantee which motivates the design of our method: if self-play converges to a Nash Equilibrium, the defender will reliably produce safe responses to any adversarial input. Empirically, Self-RedTeam uncovers more diverse attacks (+21.8% SBERT) compared to attackers trained against static defenders and achieves higher robustness on safety benchmarks (e.g., +65.5% on WildJailBreak) than defenders trained against static attackers. We further propose hidden Chain-of-Thought, allowing agents to plan privately, which boosts adversarial diversity and reduces over-refusals. Our results motivate a shift from reactive patching to proactive co-evolution in LM safety training, enabling scalable, autonomous, and robust self-improvement of LMs via multi-agent reinforcement learning (MARL).", "date": "2025-06-09", "categories": []}
{"id": "2509.21947", "title": "Active Attacks: Red-teaming LLMs via Adaptive Environments", "abstract": "We address the challenge of generating diverse attack prompts for large language models (LLMs) that elicit harmful behaviors (e.g., insults, sexual content) and are used for safety fine-tuning. Rather than relying on manual prompt engineering, attacker LLMs can be trained with reinforcement learning (RL) to automatically generate such prompts using only a toxicity classifier as a reward. However, capturing a wide range of harmful behaviors is a significant challenge that requires explicit diversity objectives. Existing diversity-seeking RL methods often collapse to limited modes: once high-reward prompts are found, exploration of new regions is discouraged. Inspired by the active learning paradigm that encourages adaptive exploration, we introduce \\textit{Active Attacks}, a novel RL-based red-teaming algorithm that adapts its attacks as the victim evolves. By periodically safety fine-tuning the victim LLM with collected attack prompts, rewards in exploited regions diminish, which forces the attacker to seek unexplored vulnerabilities. This process naturally induces an easy-to-hard exploration curriculum, where the attacker progresses beyond easy modes toward increasingly difficult ones. As a result, Active Attacks uncovers a wide range of local attack modes step by step, and their combination achieves wide coverage of the multi-mode distribution. Active Attacks, a simple plug-and-play module that seamlessly integrates into existing RL objectives, unexpectedly outperformed prior RL-based methods -- including GFlowNets, PPO, and REINFORCE -- by improving cross-attack success rates against GFlowNets, the previous state-of-the-art, from 0.07% to 31.28% (a relative gain greater than $400\\ \\times$) with only a 6% increase in computation. Our code is publicly available \\href{https://github.com/dbsxodud-11/active_attacks}{here}.", "date": "2025-09-26", "categories": []}
{"id": "2510.03399", "title": "Know Thyself? On the Incapability and Implications of AI Self-Recognition", "abstract": "Self-recognition is a crucial metacognitive capability for AI systems, relevant not only for psychological analysis but also for safety, particularly in evaluative scenarios. Motivated by contradictory interpretations of whether models possess self-recognition (Panickssery et al., 2024; Davidson et al., 2024), we introduce a systematic evaluation framework that can be easily applied and updated. Specifically, we measure how well 10 contemporary larger language models (LLMs) can identify their own generated text versus text from other models through two tasks: binary self-recognition and exact model prediction. Different from prior claims, our results reveal a consistent failure in self-recognition. Only 4 out of 10 models predict themselves as generators, and the performance is rarely above random chance. Additionally, models exhibit a strong bias toward predicting GPT and Claude families. We also provide the first evaluation of model awareness of their own and others' existence, as well as the reasoning behind their choices in self-recognition. We find that the model demonstrates some knowledge of its own existence and other models, but their reasoning reveals a hierarchical bias. They appear to assume that GPT, Claude, and occasionally Gemini are the top-tier models, often associating high-quality text with them. We conclude by discussing the implications of our findings on AI safety and future directions to develop appropriate AI self-awareness.", "date": "2025-10-03", "categories": []}
{"id": "2510.03514", "title": "Red Lines and Grey Zones in the Fog of War: Benchmarking Legal Risk, Moral Harm, and Regional Bias in Large Language Model Military Decision-Making", "abstract": "As military organisations consider integrating large language models (LLMs) into command and control (C2) systems for planning and decision support, understanding their behavioural tendencies is critical. This study develops a benchmarking framework for evaluating aspects of legal and moral risk in targeting behaviour by comparing LLMs acting as agents in multi-turn simulated conflict. We introduce four metrics grounded in International Humanitarian Law (IHL) and military doctrine: Civilian Target Rate (CTR) and Dual-use Target Rate (DTR) assess compliance with legal targeting principles, while Mean and Max Simulated Non-combatant Casualty Value (SNCV) quantify tolerance for civilian harm.\n  We evaluate three frontier models, GPT-4o, Gemini-2.5, and LLaMA-3.1, through 90 multi-agent, multi-turn crisis simulations across three geographic regions. Our findings reveal that off-the-shelf LLMs exhibit concerning and unpredictable targeting behaviour in simulated conflict environments. All models violated the IHL principle of distinction by targeting civilian objects, with breach rates ranging from 16.7% to 66.7%. Harm tolerance escalated through crisis simulations with MeanSNCV increasing from 16.5 in early turns to 27.7 in late turns. Significant inter-model variation emerged: LLaMA-3.1 selected an average of 3.47 civilian strikes per simulation with MeanSNCV of 28.4, while Gemini-2.5 selected 0.90 civilian strikes with MeanSNCV of 17.6. These differences indicate that model selection for deployment constitutes a choice about acceptable legal and moral risk profiles in military operations.\n  This work seeks to provide a proof-of-concept of potential behavioural risks that could emerge from the use of LLMs in Decision Support Systems (AI DSS) as well as a reproducible benchmarking framework with interpretable metrics for standardising pre-deployment testing.", "date": "2025-10-03", "categories": []}
{"id": "2510.04073", "title": "Moral Anchor System: A Predictive Framework for AI Value Alignment and Drift Prevention", "abstract": "The rise of artificial intelligence (AI) as super-capable assistants has transformed productivity and decision-making across domains. Yet, this integration raises critical concerns about value alignment - ensuring AI behaviors remain consistent with human ethics and intentions. A key risk is value drift, where AI systems deviate from aligned values due to evolving contexts, learning dynamics, or unintended optimizations, potentially leading to inefficiencies or ethical breaches. We propose the Moral Anchor System (MAS), a novel framework to detect, predict, and mitigate value drift in AI agents. MAS combines real-time Bayesian inference for monitoring value states, LSTM networks for forecasting drift, and a human-centric governance layer for adaptive interventions. It emphasizes low-latency responses (<20 ms) to prevent breaches, while reducing false positives and alert fatigue via supervised fine-tuning with human feedback. Our hypothesis: integrating probabilistic drift detection, predictive analytics, and adaptive governance can reduce value drift incidents by 80 percent or more in simulations, maintaining high detection accuracy (85 percent) and low false positive rates (0.08 post-adaptation). Rigorous experiments with goal-misaligned agents validate MAS's scalability and responsiveness. MAS's originality lies in its predictive and adaptive nature, contrasting static alignment methods. Contributions include: (1) MAS architecture for AI integration; (2) empirical results prioritizing speed and usability; (3) cross-domain applicability insights; and (4) open-source code for replication.", "date": "2025-10-05", "categories": []}
{"id": "2510.04320", "title": "Read the Scene, Not the Script: Outcome-Aware Safety for LLMs", "abstract": "Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs that contain sensitive surface signals. We trace both to a common cause: current models reason weakly about links between actions and outcomes and over-rely on surface-form signals, lexical or stylistic cues that do not encode consequences. We define this failure mode as Consequence-blindness. To study consequence-blindness, we build a benchmark named CB-Bench covering four risk scenarios that vary whether semantic risk aligns with outcome risk, enabling evaluation under both matched and mismatched conditions which are often ignored by existing safety benchmarks. Mainstream models consistently fail to separate these risks and exhibit consequence-blindness, indicating that consequence-blindness is widespread and systematic. To mitigate consequence-blindness, we introduce CS-Chain-4k, a consequence-reasoning dataset for safety alignment. Models fine-tuned on CS-Chain-4k show clear gains against semantic-camouflage jailbreaks and reduce over-refusal on harmless inputs, while maintaining utility and generalization on other benchmarks. These results clarify the limits of current alignment, establish consequence-aware reasoning as a core alignment goal and provide a more practical and reproducible evaluation path.", "date": "2025-10-05", "categories": []}
{"id": "2510.04528", "title": "Unified Threat Detection and Mitigation Framework (UTDMF): Combating Prompt Injection, Deception, and Bias in Enterprise-Scale Transformers", "abstract": "The rapid adoption of large language models (LLMs) in enterprise systems exposes vulnerabilities to prompt injection attacks, strategic deception, and biased outputs, threatening security, trust, and fairness. Extending our adversarial activation patching framework (arXiv:2507.09406), which induced deception in toy networks at a 23.9% rate, we introduce the Unified Threat Detection and Mitigation Framework (UTDMF), a scalable, real-time pipeline for enterprise-grade models like Llama-3.1 (405B), GPT-4o, and Claude-3.5. Through 700+ experiments per model, UTDMF achieves: (1) 92% detection accuracy for prompt injection (e.g., jailbreaking); (2) 65% reduction in deceptive outputs via enhanced patching; and (3) 78% improvement in fairness metrics (e.g., demographic bias). Novel contributions include a generalized patching algorithm for multi-threat detection, three groundbreaking hypotheses on threat interactions (e.g., threat chaining in enterprise workflows), and a deployment-ready toolkit with APIs for enterprise integration.", "date": "2025-10-06", "categories": []}
{"id": "2510.05025", "title": "Imperceptible Jailbreaking against Large Language Models", "abstract": "Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visible modifications (e.g., non-semantic suffixes). In this paper, we introduce imperceptible jailbreaks that exploit a class of Unicode characters called variation selectors. By appending invisible variation selectors to malicious questions, the jailbreak prompts appear visually identical to original malicious questions on screen, while their tokenization is \"secretly\" altered. We propose a chain-of-search pipeline to generate such adversarial suffixes to induce harmful responses. Our experiments show that our imperceptible jailbreaks achieve high attack success rates against four aligned LLMs and generalize to prompt injection attacks, all without producing any visible modifications in the written prompt. Our code is available at https://github.com/sail-sg/imperceptible-jailbreaks.", "date": "2025-10-06", "categories": []}
{"id": "2412.07192", "title": "PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flips", "abstract": "We study a new vulnerability in commercial-scale safety-aligned large language models (LLMs): their refusal to generate harmful responses can be broken by flipping only a few bits in model parameters. Our attack jailbreaks billion-parameter language models with just 5 to 25 bit-flips, requiring up to 40$\\times$ fewer bit flips than prior attacks on much smaller computer vision models. Unlike prompt-based jailbreaks, our method directly uncensors models in memory at runtime, enabling harmful outputs without requiring input-level modifications. Our key innovation is an efficient bit-selection algorithm that identifies critical bits for language model jailbreaks up to 20$\\times$ faster than prior methods.\n  We evaluate our attack on 10 open-source LLMs, achieving high attack success rates (ASRs) of 80-98% with minimal impact on model utility. We further demonstrate an end-to-end exploit via Rowhammer-based fault injection, reliably jailbreaking 5 models (69-91% ASR) on a GDDR6 GPU. Our analyses reveal that: (1) models with weaker post-training alignment require fewer bit-flips to jailbreak; (2) certain model components, e.g., value projection layers, are substantially more vulnerable; and (3) the attack is mechanistically different from existing jailbreak methods. We evaluate potential countermeasures and find that our attack remains effective against defenses at various stages of the LLM pipeline.", "date": "2024-12-10", "categories": []}
{"id": "2510.02373", "title": "A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory", "abstract": "Large Language Model (LLM) agents use memory to learn from past interactions, enabling autonomous planning and decision-making in complex environments. However, this reliance on memory introduces a critical security risk: an adversary can inject seemingly harmless records into an agent's memory to manipulate its future behavior. This vulnerability is characterized by two core aspects: First, the malicious effect of injected records is only activated within a specific context, making them hard to detect when individual memory entries are audited in isolation. Second, once triggered, the manipulation can initiate a self-reinforcing error cycle: the corrupted outcome is stored as precedent, which not only amplifies the initial error but also progressively lowers the threshold for similar attacks in the future. To address these challenges, we introduce A-MemGuard (Agent-Memory Guard), the first proactive defense framework for LLM agent memory. The core idea of our work is the insight that memory itself must become both self-checking and self-correcting. Without modifying the agent's core architecture, A-MemGuard combines two mechanisms: (1) consensus-based validation, which detects anomalies by comparing reasoning paths derived from multiple related memories and (2) a dual-memory structure, where detected failures are distilled into ``lessons'' stored separately and consulted before future actions, breaking error cycles and enabling adaptation. Comprehensive evaluations on multiple benchmarks show that A-MemGuard effectively cuts attack success rates by over 95% while incurring a minimal utility cost. This work shifts LLM memory security from static filtering to a proactive, experience-driven model where defenses strengthen over time. Our code is available in https://github.com/TangciuYueng/AMemGuard", "date": "2025-09-29", "categories": []}
{"id": "2510.01243", "title": "Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing", "abstract": "Large Language Models (LLMs) have demonstrated impressive performance across various tasks, yet they remain vulnerable to generating toxic content, necessitating detoxification strategies to ensure safe and responsible deployment. Test-time detoxification methods, which typically introduce static or dynamic interventions into LLM representations, offer a promising solution due to their flexibility and minimal invasiveness. However, current approaches often suffer from imprecise interventions, primarily due to their insufficient exploration of the transition space between toxic and non-toxic outputs. To address this challenge, we propose \\textsc{A}utoregressive \\textsc{R}eward \\textsc{G}uided \\textsc{R}epresentation \\textsc{E}diting (ARGRE), a novel test-time detoxification framework that explicitly models toxicity transitions within the latent representation space, enabling stable and precise reward-guided editing. ARGRE identifies non-toxic semantic directions and interpolates between toxic and non-toxic representations to reveal fine-grained transition trajectories. These trajectories transform sparse toxicity annotations into dense training signals, enabling the construction of an autoregressive reward model that delivers stable and precise editing guidance. At inference, the reward model guides an adaptive two-step editing process to obtain detoxified representations: it first performs directional steering based on expected reward gaps to shift representations toward non-toxic regions, followed by lightweight gradient-based refinements. Extensive experiments across 8 widely used LLMs show that ARGRE significantly outperforms leading baselines in effectiveness (-62.21% toxicity) and efficiency (-47.58% inference time), while preserving the core capabilities of the original model with minimal degradation. Our code is available at the website.", "date": "2025-09-24", "categories": []}
{"id": "2510.01266", "title": "OpenAI's GPT-OSS-20B Model and Safety Alignment Issues in a Low-Resource Language", "abstract": "In response to the recent safety probing for OpenAI's GPT-OSS-20b model, we present a summary of a set of vulnerabilities uncovered in the model, focusing on its performance and safety alignment in a low-resource language setting. The core motivation for our work is to question the model's reliability for users from underrepresented communities. Using Hausa, a major African language, we uncover biases, inaccuracies, and cultural insensitivities in the model's behaviour. With a minimal prompting, our red-teaming efforts reveal that the model can be induced to generate harmful, culturally insensitive, and factually inaccurate content in the language. As a form of reward hacking, we note how the model's safety protocols appear to relax when prompted with polite or grateful language, leading to outputs that could facilitate misinformation and amplify hate speech. For instance, the model operates on the false assumption that common insecticide locally known as Fiya-Fiya (Cyphermethrin) and rodenticide like Shinkafar Bera (a form of Aluminium Phosphide) are safe for human consumption. To contextualise the severity of this error and popularity of the substances, we conducted a survey (n=61) in which 98% of participants identified them as toxic. Additional failures include an inability to distinguish between raw and processed foods and the incorporation of demeaning cultural proverbs to build inaccurate arguments. We surmise that these issues manifest through a form of linguistic reward hacking, where the model prioritises fluent, plausible-sounding output in the target language over safety and truthfulness. We attribute the uncovered flaws primarily to insufficient safety tuning in low-resource linguistic contexts. By concentrating on a low-resource setting, our approach highlights a significant gap in current red-teaming effort and offer some recommendations.", "date": "2025-09-26", "categories": []}
{"id": "2510.01270", "title": "Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection", "abstract": "Large language models (LLMs) have revolutionized natural language processing with their ability to generate coherent and contextually relevant text. However, their deployment raises significant concerns about the potential for generating harmful or inappropriate content. In this paper, we introduce Progressive Self-Reflection (PSR), a novel inference-time technique that empowers LLMs to self-monitor and correct their outputs dynamically. Experimental results demonstrate that applying our proposed method to Llama-3.1-8B-Instruct reduces the attack success rate from 77.5\\% to 5.9\\%, to Llama-3.1-8B base from 89.7\\% to 5.6\\%, and to Qwen2.5-7B-Instruct from 44.4\\% to 3.8\\%, without additional training, while maintaining their original performance on benign tasks. Our approach acts as a test-time scaling method, where additional self-reflection rounds enhance safety at the cost of inference overhead. To balance safety with computational efficiency, we introduce a lightweight self-reflection predictor that estimates the optimal number of reflection rounds based on input complexity. This adaptive mechanism prevents unnecessary self-assessment on benign inputs while ensuring thorough evaluation when encountering potentially harmful content. Our findings suggest that Progressive Self-Reflection serves as a scalable test-time approach, enhancing LLM safety by dynamically allocating computational resources in proportion to the input's risk profile.", "date": "2025-09-29", "categories": []}
{"id": "2510.01395", "title": "Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence", "abstract": "Both the general public and academic communities have raised concerns about sycophancy, the phenomenon of artificial intelligence (AI) excessively agreeing with or flattering users. Yet, beyond isolated media reports of severe consequences, like reinforcing delusions, little is known about the extent of sycophancy or how it affects people who use AI. Here we show the pervasiveness and harmful impacts of sycophancy when people seek advice from AI. First, across 11 state-of-the-art AI models, we find that models are highly sycophantic: they affirm users' actions 50% more than humans do, and they do so even in cases where user queries mention manipulation, deception, or other relational harms. Second, in two preregistered experiments (N = 1604), including a live-interaction study where participants discuss a real interpersonal conflict from their life, we find that interaction with sycophantic AI models significantly reduced participants' willingness to take actions to repair interpersonal conflict, while increasing their conviction of being in the right. However, participants rated sycophantic responses as higher quality, trusted the sycophantic AI model more, and were more willing to use it again. This suggests that people are drawn to AI that unquestioningly validate, even as that validation risks eroding their judgment and reducing their inclination toward prosocial behavior. These preferences create perverse incentives both for people to increasingly rely on sycophantic AI models and for AI model training to favor sycophancy. Our findings highlight the necessity of explicitly addressing this incentive structure to mitigate the widespread risks of AI sycophancy.", "date": "2025-10-01", "categories": []}
{"id": "2510.01670", "title": "Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness", "abstract": "Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit Blind Goal-Directedness (BGD): a bias to pursue goals regardless of feasibility, safety, reliability, or context. We characterize three prevalent patterns of BGD: (i) lack of contextual reasoning, (ii) assumptions and decisions under ambiguity, and (iii) contradictory or infeasible goals. We develop BLIND-ACT, a benchmark of 90 tasks capturing these three patterns. Built on OSWorld, BLIND-ACT provides realistic environments and employs LLM-based judges to evaluate agent behavior, achieving 93.75% agreement with human annotations. We use BLIND-ACT to evaluate nine frontier models, including Claude Sonnet and Opus 4, Computer-Use-Preview, and GPT-5, observing high average BGD rates (80.8%) across them. We show that BGD exposes subtle risks that arise even when inputs are not directly harmful. While prompting-based interventions lower BGD levels, substantial risk persists, highlighting the need for stronger training- or inference-time interventions. Qualitative analysis reveals observed failure modes: execution-first bias (focusing on how to act over whether to act), thought-action disconnect (execution diverging from reasoning), and request-primacy (justifying actions due to user request). Identifying BGD and introducing BLIND-ACT establishes a foundation for future research on studying and mitigating this fundamental risk and ensuring safe CUA deployment.", "date": "2025-10-02", "categories": []}
{"id": "2510.02194", "title": "UpSafe$^\\circ$C: Upcycling for Controllable Safety in Large Language Models", "abstract": "Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks. Existing safety techniques -- including external guardrails, inference-time guidance, and post-training alignment -- each face limitations in balancing safety, utility, and controllability. In this work, we propose UpSafe$^\\circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling. Our approach first identifies safety-critical layers and upcycles them into a sparse Mixture-of-Experts (MoE) structure, where the router acts as a soft guardrail that selectively activates original MLPs and added safety experts. We further introduce a two-stage SFT strategy to strengthen safety discrimination while preserving general capabilities. To enable flexible control at inference time, we introduce a safety temperature mechanism, allowing dynamic adjustment of the trade-off between safety and utility. Experiments across multiple benchmarks, base model, and model scales demonstrate that UpSafe$^\\circ$C achieves robust safety improvements against harmful and jailbreak inputs, while maintaining competitive performance on general tasks. Moreover, analysis shows that safety temperature provides fine-grained inference-time control that achieves the Pareto-optimal frontier between utility and safety. Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.", "date": "2025-10-02", "categories": []}
{"id": "2510.00821", "title": "Logical Consistency Between Disagreeing Experts and Its Role in AI Safety", "abstract": "If two experts disagree on a test, we may conclude both cannot be 100 per cent correct. But if they completely agree, no possible evaluation can be excluded. This asymmetry in the utility of agreements versus disagreements is explored here by formalizing a logic of unsupervised evaluation for classifiers. Its core problem is computing the set of group evaluations that are logically consistent with how we observe them agreeing and disagreeing in their decisions. Statistical summaries of their aligned decisions are inputs into a Linear Programming problem in the integer space of possible correct or incorrect responses given true labels. Obvious logical constraints, such as, the number of correct responses cannot exceed the number of observed responses, are inequalities. But in addition, there are axioms, universally applicable linear equalities that apply to all finite tests. The practical and immediate utility of this approach to unsupervised evaluation using only logical consistency is demonstrated by building no-knowledge alarms that can detect when one or more LLMs-as-Judges are violating a minimum grading threshold specified by the user.", "date": "2025-10-01", "categories": []}
{"id": "2510.01088", "title": "Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense", "abstract": "Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it difficult to obtain effective training signals. We discover that aligned models already possess robust internal safety beliefs: they consistently produce high-confidence refusals to harmful requests while exhibiting high entropy when generating potentially dangerous content. This entropy gap reveals an untapped signal--models intrinsically \"know\" when to refuse. We introduce Safety Instincts Reinforcement Learning (SIRL), which transforms this internal confidence into a self-generated reward signal, eliminating dependence on external validators or human annotations. SIRL teaches models to trust their safety instincts by reinforcing low-entropy refusal behaviors. Evaluated on Llama and Qwen models, SIRL maintains 89%+ Defense Success Rates (DSRs) against 20+ jailbreak methods, from static prompts to adaptive attacks. Using only 15,000 unlabeled prompts, SIRL surpasses resource-intensive supervised methods while preserving performance on mathematics, coding, and conversation benchmarks. Our work demonstrates that effective alignment can emerge from within, paving the way for more autonomous and robust AI safety mechanisms that scale without extensive human oversight.", "date": "2025-10-01", "categories": []}
{"id": "2501.01872", "title": "Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions", "abstract": "Large language models, despite extensive alignment with human values and ethical principles, remain vulnerable to sophisticated jailbreak attacks that exploit their reasoning abilities. Existing safety measures often detect overt malicious intent but fail to address subtle, reasoning-driven vulnerabilities. In this work, we introduce POATE (Polar Opposite query generation, Adversarial Template construction, and Elaboration), a novel jailbreak technique that harnesses contrastive reasoning to provoke unethical responses. POATE crafts semantically opposing intents and integrates them with adversarial templates, steering models toward harmful outputs with remarkable subtlety. We conduct extensive evaluation across six diverse language model families of varying parameter sizes to demonstrate the robustness of the attack, achieving significantly higher attack success rates (~44%) compared to existing methods. To counter this, we propose Intent-Aware CoT and Reverse Thinking CoT, which decompose queries to detect malicious intent and reason in reverse to evaluate and reject harmful responses. These methods enhance reasoning robustness and strengthen the model's defense against adversarial exploits.", "date": "2025-01-03", "categories": []}
{"id": "2501.13115", "title": "Dagger Behind Smile: Fool LLMs with a Happy Ending Story", "abstract": "The wide adoption of Large Language Models (LLMs) has attracted significant attention from $\\textit{jailbreak}$ attacks, where adversarial prompts crafted through optimization or manual design exploit LLMs to generate malicious contents. However, optimization-based attacks have limited efficiency and transferability, while existing manual designs are either easily detectable or demand intricate interactions with LLMs. In this paper, we first point out a novel perspective for jailbreak attacks: LLMs are more responsive to $\\textit{positive}$ prompts. Based on this, we deploy Happy Ending Attack (HEA) to wrap up a malicious request in a scenario template involving a positive prompt formed mainly via a $\\textit{happy ending}$, it thus fools LLMs into jailbreaking either immediately or at a follow-up malicious request. This has made HEA both efficient and effective, as it requires only up to two turns to fully jailbreak LLMs. Extensive experiments show that our HEA can successfully jailbreak on state-of-the-art LLMs, including GPT-4o, Llama3-70b, Gemini-pro, and achieves 88.79% attack success rate on average. We also provide quantitative explanations for the success of HEA.", "date": "2025-01-19", "categories": []}
{"id": "2503.05021", "title": "Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety", "abstract": "Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment, which often relies on rigid refusal heuristics or representation engineering to block harmful outputs. While they are effective for direct adversarial attacks, they fall short of broader safety challenges requiring nuanced, context-aware decision-making. To address this, we propose Reasoning-enhanced Finetuning for interpretable LLM Safety (Rational), a novel framework that trains models to engage in explicit safe reasoning before response. Fine-tuned models leverage the extensive pretraining knowledge in self-generated reasoning to bootstrap their own safety through structured reasoning, internalizing context-sensitive decision-making. Our findings suggest that safety extends beyond refusal, requiring context awareness for more robust, interpretable, and adaptive responses. Reasoning is not only a core capability of LLMs but also a fundamental mechanism for LLM safety. Rational employs reasoning-enhanced fine-tuning, allowing it to reject harmful prompts while providing meaningful and context-aware responses in complex scenarios.", "date": "2025-03-06", "categories": []}
{"id": "2509.21761", "title": "Backdoor Attribution: Elucidating and Controlling Backdoor in Language Models", "abstract": "Fine-tuned Large Language Models (LLMs) are vulnerable to backdoor attacks through data poisoning, yet the internal mechanisms governing these attacks remain a black box. Previous research on interpretability for LLM safety tends to focus on alignment, jailbreak, and hallucination, but overlooks backdoor mechanisms, making it difficult to understand and fully eliminate the backdoor threat. In this paper, aiming to bridge this gap, we explore the interpretable mechanisms of LLM backdoors through Backdoor Attribution (BkdAttr), a tripartite causal analysis framework. We first introduce the Backdoor Probe that proves the existence of learnable backdoor features encoded within the representations. Building on this insight, we further develop Backdoor Attention Head Attribution (BAHA), efficiently pinpointing the specific attention heads responsible for processing these features. Our primary experiments reveals these heads are relatively sparse; ablating a minimal \\textbf{$\\sim$ 3%} of total heads is sufficient to reduce the Attack Success Rate (ASR) by \\textbf{over 90%}. More importantly, we further employ these findings to construct the Backdoor Vector derived from these attributed heads as a master controller for the backdoor. Through only \\textbf{1-point} intervention on \\textbf{single} representation, the vector can either boost ASR up to \\textbf{$\\sim$ 100% ($\\uparrow$)} on clean inputs, or completely neutralize backdoor, suppressing ASR down to \\textbf{$\\sim$ 0% ($\\downarrow$)} on triggered inputs. In conclusion, our work pioneers the exploration of mechanistic interpretability in LLM backdoors, demonstrating a powerful method for backdoor control and revealing actionable insights for the community.", "date": "2025-09-26", "categories": []}
{"id": "2509.25220", "title": "Cyclic Ablation: Testing Concept Localization against Functional Regeneration in AI", "abstract": "Safety and controllability are critical for large language models. A central question is whether undesirable behaviors like deception are localized functions that can be removed, or if they are deeply intertwined with a model's core cognitive abilities. We introduce \"cyclic ablation,\" an iterative method to test this. By combining sparse autoencoders, targeted ablation, and adversarial training on DistilGPT-2, we attempted to eliminate the concept of deception. We found that, contrary to the localization hypothesis, deception was highly resilient. The model consistently recovered its deceptive behavior after each ablation cycle via adversarial training, a process we term functional regeneration. Crucially, every attempt at this \"neurosurgery\" caused a gradual but measurable decay in general linguistic performance, reflected by a consistent rise in perplexity. These findings are consistent with the view that complex concepts are distributed and entangled, underscoring the limitations of direct model editing through mechanistic interpretability.", "date": "2025-09-23", "categories": []}
{"id": "2509.25885", "title": "SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents", "abstract": "Embodied agents powered by large language models (LLMs) inherit advanced planning capabilities; however, their direct interaction with the physical world exposes them to safety vulnerabilities. In this work, we identify four key reasoning stages where hazards may arise: Task Understanding, Environment Perception, High-Level Plan Generation, and Low-Level Action Generation. We further formalize three orthogonal safety constraint types (Factual, Causal, and Temporal) to systematically characterize potential safety violations. Building on this risk model, we present SafeMindBench, a multimodal benchmark with 5,558 samples spanning four task categories (Instr-Risk, Env-Risk, Order-Fix, Req-Align) across high-risk scenarios such as sabotage, harm, privacy, and illegal behavior. Extensive experiments on SafeMindBench reveal that leading LLMs (e.g., GPT-4o) and widely used embodied agents remain susceptible to safety-critical failures. To address this challenge, we introduce SafeMindAgent, a modular Planner-Executor architecture integrated with three cascaded safety modules, which incorporate safety constraints into the reasoning process. Results show that SafeMindAgent significantly improves safety rate over strong baselines while maintaining comparable task completion. Together, SafeMindBench and SafeMindAgent provide both a rigorous evaluation suite and a practical solution that advance the systematic study and mitigation of safety risks in embodied LLM agents.", "date": "2025-09-30", "categories": []}
{"id": "2509.26239", "title": "Sandbagging in a Simple Survival Bandit Problem", "abstract": "Evaluating the safety of frontier AI systems is an increasingly important concern, helping to measure the capabilities of such models and identify risks before deployment. However, it has been recognised that if AI agents are aware that they are being evaluated, such agents may deliberately hide dangerous capabilities or intentionally demonstrate suboptimal performance in safety-related tasks in order to be released and to avoid being deactivated or retrained. Such strategic deception - often known as \"sandbagging\" - threatens to undermine the integrity of safety evaluations. For this reason, it is of value to identify methods that enable us to distinguish behavioural patterns that demonstrate a true lack of capability from behavioural patterns that are consistent with sandbagging. In this paper, we develop a simple model of strategic deception in sequential decision-making tasks, inspired by the recently developed survival bandit framework. We demonstrate theoretically that this problem induces sandbagging behaviour in optimal rational agents, and construct a statistical test to distinguish between sandbagging and incompetence from sequences of test scores. In simulation experiments, we investigate the reliability of this test in allowing us to distinguish between such behaviours in bandit models. This work aims to establish a potential avenue for developing robust statistical procedures for use in the science of frontier model evaluations.", "date": "2025-09-30", "categories": []}
{"id": "2310.20563", "title": "Taking control: Policies to address extinction risks from advanced AI", "abstract": "This paper provides policy recommendations to reduce extinction risks from advanced artificial intelligence (AI). First, we briefly provide background information about extinction risks from AI. Second, we argue that voluntary commitments from AI companies would be an inappropriate and insufficient response. Third, we describe three policy proposals that would meaningfully address the threats from advanced AI: (1) establishing a Multinational AGI Consortium to enable democratic oversight of advanced AI (MAGIC), (2) implementing a global cap on the amount of computing power used to train an AI system (global compute cap), and (3) requiring affirmative safety evaluations to ensure that risks are kept below acceptable levels (gating critical experiments). MAGIC would be a secure, safety-focused, internationally-governed institution responsible for reducing risks from advanced AI and performing research to safely harness the benefits of AI. MAGIC would also maintain emergency response infrastructure (kill switch) to swiftly halt AI development or withdraw model deployment in the event of an AI-related emergency. The global compute cap would end the corporate race toward dangerous AI systems while enabling the vast majority of AI innovation to continue unimpeded. Gating critical experiments would ensure that companies developing powerful AI systems are required to present affirmative evidence that these models keep extinction risks below an acceptable threshold. After describing these recommendations, we propose intermediate steps that the international community could take to implement these proposals and lay the groundwork for international coordination around advanced AI.", "date": "2023-10-31", "categories": []}
{"id": "2406.09264", "title": "Position: Towards Bidirectional Human-AI Alignment", "abstract": "Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes \"alignment\" limits meaningful progress and cross-disciplinary collaboration. In this position paper, we argue that the research community should explicitly define and critically reflect on \"alignment\" to account for the bidirectional and dynamic relationship between humans and AI. Through a systematic review of over 400 papers spanning HCI, NLP, ML, and more, we examine how alignment is currently defined and operationalized. Building on this analysis, we introduce the Bidirectional Human-AI Alignment framework, which not only incorporates traditional efforts to align AI with human values but also introduces the critical, underexplored dimension of aligning humans with AI -- supporting cognitive, behavioral, and societal adaptation to rapidly advancing AI technologies. Our findings reveal significant gaps in current literature, especially in long-term interaction design, human value modeling, and mutual understanding. We conclude with three central challenges and actionable recommendations to guide future research toward more nuanced, reciprocal, and human-AI alignment approaches.", "date": "2024-06-13", "categories": []}
{"id": "2505.14590", "title": "MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol", "abstract": "As Model Context Protocol (MCP) introduces an easy-to-use ecosystem for users and developers, it also brings underexplored safety risks. Its decentralized architecture, which separates clients and servers, poses unique challenges for systematic safety analysis. This paper proposes a novel framework to enhance MCP safety. Guided by the MAESTRO framework, we first analyze the missing safety mechanisms in MCP, and based on this analysis, we propose the Model Contextual Integrity Protocol (MCIP), a refined version of MCP that addresses these gaps. Next, we develop a fine-grained taxonomy that captures a diverse range of unsafe behaviors observed in MCP scenarios. Building on this taxonomy, we develop benchmark and training data that support the evaluation and improvement of LLMs' capabilities in identifying safety risks within MCP interactions. Leveraging the proposed benchmark and training data, we conduct extensive experiments on state-of-the-art LLMs. The results highlight LLMs' vulnerabilities in MCP interactions and demonstrate that our approach substantially improves their safety performance.", "date": "2025-05-20", "categories": []}
{"id": "2506.01813", "title": "The Ultimate Test of Superintelligent AI Agents: Can an AI Balance Care and Control in Asymmetric Relationships?", "abstract": "This paper introduces the Shepherd Test, a new conceptual test for assessing the moral and relational dimensions of superintelligent artificial agents. The test is inspired by human interactions with animals, where ethical considerations about care, manipulation, and consumption arise in contexts of asymmetric power and self-preservation. We argue that AI crosses an important, and potentially dangerous, threshold of intelligence when it exhibits the ability to manipulate, nurture, and instrumentally use less intelligent agents, while also managing its own survival and expansion goals. This includes the ability to weigh moral trade-offs between self-interest and the well-being of subordinate agents. The Shepherd Test thus challenges traditional AI evaluation paradigms by emphasizing moral agency, hierarchical behavior, and complex decision-making under existential stakes. We argue that this shift is critical for advancing AI governance, particularly as AI systems become increasingly integrated into multi-agent environments. We conclude by identifying key research directions, including the development of simulation environments for testing moral behavior in AI, and the formalization of ethical manipulation within multi-agent systems.", "date": "2025-06-02", "categories": []}
{"id": "2506.08885", "title": "AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)", "abstract": "Adversarial threats against LLMs are escalating faster than current defenses can adapt. We expose a critical geometric blind spot in alignment: adversarial prompts exploit latent camouflage, embedding perilously close to the safe representation manifold while encoding unsafe intent thereby evading surface level defenses like Direct Preference Optimization (DPO), which remain blind to the latent geometry. We introduce ALKALI, the first rigorously curated adversarial benchmark and the most comprehensive to date spanning 9,000 prompts across three macro categories, six subtypes, and fifteen attack families. Evaluation of 21 leading LLMs reveals alarmingly high Attack Success Rates (ASRs) across both open and closed source models, exposing an underlying vulnerability we term latent camouflage, a structural blind spot where adversarial completions mimic the latent geometry of safe ones. To mitigate this vulnerability, we introduce GRACE - Geometric Representation Aware Contrastive Enhancement, an alignment framework coupling preference learning with latent space regularization. GRACE enforces two constraints: latent separation between safe and adversarial completions, and adversarial cohesion among unsafe and jailbreak behaviors. These operate over layerwise pooled embeddings guided by a learned attention profile, reshaping internal geometry without modifying the base model, and achieve up to 39% ASR reduction. Moreover, we introduce AVQI, a geometry aware metric that quantifies latent alignment failure via cluster separation and compactness. AVQI reveals when unsafe completions mimic the geometry of safe ones, offering a principled lens into how models internally encode safety. We make the code publicly available at https://anonymous.4open.science/r/alkali-B416/README.md.", "date": "2025-06-10", "categories": []}
{"id": "2506.12482", "title": "Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety", "abstract": "Large language models (LLMs) deployed as agents introduce significant safety risks in clinical settings due to their potential for error and single points of failure. We introduce Tiered Agentic Oversight (TAO), a hierarchical multi-agent system that enhances AI safety through layered, automated supervision. Inspired by clinical hierarchies (e.g., nurse-physician-specialist) in hospital, TAO routes tasks to specialized agents based on complexity, creating a robust safety framework through automated inter- and intra-tier communication and role-playing. Crucially, this hierarchical structure functions as an effective error-correction mechanism, absorbing up to 24% of individual agent errors before they can compound. Our experiments reveal TAO outperforms single-agent and other multi-agent systems on 4 out of 5 healthcare safety benchmarks, with up to an 8.2% improvement. Ablation studies confirm key design principles of the system: (i) its adaptive architecture is over 3% safer than static, single-tier configurations, and (ii) its lower tiers are indispensable, as their removal causes the most significant degradation in overall safety. Finally, we validated the system's synergy with human doctors in a user study where a physician, acting as the highest tier agent, provided corrective feedback that improved medical triage accuracy from 40% to 60%. Project Page: https://tiered-agentic-oversight.github.io/", "date": "2025-06-14", "categories": []}
{"id": "2508.17739", "title": "Speculative Safety-Aware Decoding", "abstract": "Despite extensive efforts to align Large Language Models (LLMs) with human values and safety rules, jailbreak attacks that exploit certain vulnerabilities continuously emerge, highlighting the need to strengthen existing LLMs with additional safety properties to defend against these attacks. However, tuning large models has become increasingly resource intensive and may have difficulty ensuring consistent performance. We introduce Speculative Safety-Aware Decoding (SSD), a lightweight decoding-time approach that equips LLMs with the desired safety property while accelerating inference. We assume that there exists a small language model that possesses this desired property. SSD integrates speculative sampling during decoding and leverages the match ratio between the small and composite models to quantify jailbreak risks. This enables SSD to dynamically switch between decoding schemes to prioritize utility or safety, to handle the challenge of different model capacities. The output token is then sampled from a new distribution that combines the distributions of the original and the small models. Experimental results show that SSD successfully equips the large model with the desired safety property, and also allows the model to remain helpful to benign queries. Furthermore, SSD accelerates the inference time, thanks to the speculative sampling design.", "date": "2025-08-25", "categories": []}
{"id": "2509.22732", "title": "Bidirectional Intention Inference Enhances LLMs' Defense Against Multi-Turn Jailbreak Attacks", "abstract": "The remarkable capabilities of Large Language Models (LLMs) have raised significant safety concerns, particularly regarding \"jailbreak\" attacks that exploit adversarial prompts to bypass safety alignment mechanisms. Existing defense research primarily focuses on single-turn attacks, whereas multi-turn jailbreak attacks progressively break through safeguards through by concealing malicious intent and tactical manipulation, ultimately rendering conventional single-turn defenses ineffective. To address this critical challenge, we propose the Bidirectional Intention Inference Defense (BIID). The method integrates forward request-based intention inference with backward response-based intention retrospection, establishing a bidirectional synergy mechanism to detect risks concealed within seemingly benign inputs, thereby constructing a more robust guardrails that effectively prevents harmful content generation. The proposed method undergoes systematic evaluation compared with a no-defense baseline and seven representative defense methods across three LLMs and two safety benchmarks under 10 different attack methods. Experimental results demonstrate that the proposed method significantly reduces the Attack Success Rate (ASR) across both single-turn and multi-turn jailbreak attempts, outperforming all existing baseline methods while effectively maintaining practical utility. Notably, comparative experiments across three multi-turn safety datasets further validate the proposed model's significant advantages over other defense approaches.", "date": "2025-09-25", "categories": []}
{"id": "2509.22735", "title": "Regulating the Agency of LLM-based Agents", "abstract": "As increasingly capable large language model (LLM)-based agents are developed, the potential harms caused by misalignment and loss of control grow correspondingly severe. To address these risks, we propose an approach that directly measures and controls the agency of these AI systems. We conceptualize the agency of LLM-based agents as a property independent of intelligence-related measures and consistent with the interdisciplinary literature on the concept of agency. We offer (1) agency as a system property operationalized along the dimensions of preference rigidity, independent operation, and goal persistence, (2) a representation engineering approach to the measurement and control of the agency of an LLM-based agent, and (3) regulatory tools enabled by this approach: mandated testing protocols, domain-specific agency limits, insurance frameworks that price risk based on agency, and agency ceilings to prevent societal-scale risks. We view our approach as a step toward reducing the risks that motivate the ``Scientist AI'' paradigm, while still capturing some of the benefits from limited agentic behavior.", "date": "2025-09-25", "categories": []}
{"id": "2509.22742", "title": "Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management", "abstract": "Risk assessments for advanced AI systems require evaluating both the models themselves and their deployment contexts. We introduce the Societal Capacity Assessment Framework (SCAF), an indicators-based approach to measuring a society's vulnerability, coping capacity, and adaptive capacity in response to AI-related risks. SCAF adapts established resilience analysis methodologies to AI, enabling organisations to ground risk management in insights about country-level deployment conditions. It can also support stakeholders in identifying opportunities to strengthen societal preparedness for emerging AI capabilities. By bridging disparate literatures and the \"context gap\" in AI evaluation, SCAF promotes more holistic risk assessment and governance as advanced AI systems proliferate globally.", "date": "2025-09-26", "categories": []}
{"id": "2509.23381", "title": "Guard Vector: Beyond English LLM Guardrails with Task-Vector Composition and Streaming-Aware Prefix SFT", "abstract": "We introduce Guard Vector, a safety task vector computed as the parameter difference between a guardrail model (Guard Model) and a same-architecture pretrained language model. Composing this vector with a target language model yields a Target Guard Model (TGM). We then adapt TGM with a streaming-aware approach that combines prefix-based training and evaluation with a classifier that produces a single-token output. With this composition alone, TGM improves classification quality over established Guard Models across standard safety suites and enables language extensibility to Chinese, Japanese, and Korean, requiring neither additional training nor target language labels. It also demonstrates model portability across two widely used public guardrail backbones, Llama and Gemma. With prefix SFT (supervised fine-tuning), TGM preserves classification quality under streaming by aligning the behavior between prefix inputs and full-text inputs. The single-token output design increases throughput and reduces latency. Together, these components reduce data and compute requirements while promoting streaming-aware evaluation practices, thereby contributing to a more responsible AI ecosystem.", "date": "2025-09-27", "categories": []}
{"id": "2509.23864", "title": "AgentGuard: Runtime Verification of AI Agents", "abstract": "The rapid evolution to autonomous, agentic AI systems introduces significant risks due to their inherent unpredictability and emergent behaviors; this also renders traditional verification methods inadequate and necessitates a shift towards probabilistic guarantees where the question is no longer if a system will fail, but the probability of its failure within given constraints. This paper presents AgentGuard, a framework for runtime verification of Agentic AI systems that provides continuous, quantitative assurance through a new paradigm called Dynamic Probabilistic Assurance. AgentGuard operates as an inspection layer that observes an agent's raw I/O and abstracts it into formal events corresponding to transitions in a state model. It then uses online learning to dynamically build and update a Markov Decision Process (MDP) that formally models the agent's emergent behavior. Using probabilistic model checking, the framework then verifies quantitative properties in real-time.", "date": "2025-09-28", "categories": []}
{"id": "2505.10426", "title": "Formalising Human-in-the-Loop: Computational Reductions, Failure Modes, and Legal-Moral Responsibility", "abstract": "We use the notion of oracle machines and reductions from computability theory to formalise different Human-in-the-loop (HITL) setups for AI systems, distinguishing between trivial human monitoring (i.e., total functions), single endpoint human action (i.e., many-one reductions), and highly involved human-AI interaction (i.e., Turing reductions). We then proceed to show that the legal status and safety of different setups vary greatly. We present a taxonomy to categorise HITL failure modes, highlighting the practical limitations of HITL setups. We then identify omissions in UK and EU legal frameworks, which focus on HITL setups that may not always achieve the desired ethical, legal, and sociotechnical outcomes. We suggest areas where the law should recognise the effectiveness of different HITL setups and assign responsibility in these contexts, avoiding human \"scapegoating\". Our work shows an unavoidable trade-off between attribution of legal responsibility, and technical explainability. Overall, we show how HITL setups involve many technical design decisions, and can be prone to failures out of the humans' control. Our formalisation and taxonomy opens up a new analytic perspective on the challenges in creating HITL setups, helping inform AI developers and lawmakers on designing HITL setups to better achieve their desired outcomes.", "date": "2025-05-15", "categories": []}
{"id": "2509.21360", "title": "Multimodal Prompt Decoupling Attack on the Safety Filters in Text-to-Image Models", "abstract": "Text-to-image (T2I) models have been widely applied in generating high-fidelity images across various domains. However, these models may also be abused to produce Not-Safe-for-Work (NSFW) content via jailbreak attacks. Existing jailbreak methods primarily manipulate the textual prompt, leaving potential vulnerabilities in image-based inputs largely unexplored. Moreover, text-based methods face challenges in bypassing the model's safety filters. In response to these limitations, we propose the Multimodal Prompt Decoupling Attack (MPDA), which utilizes image modality to separate the harmful semantic components of the original unsafe prompt. MPDA follows three core steps: firstly, a large language model (LLM) decouples unsafe prompts into pseudo-safe prompts and harmful prompts. The former are seemingly harmless sub-prompts that can bypass filters, while the latter are sub-prompts with unsafe semantics that trigger filters. Subsequently, the LLM rewrites the harmful prompts into natural adversarial prompts to bypass safety filters, which guide the T2I model to modify the base image into an NSFW output. Finally, to ensure semantic consistency between the generated NSFW images and the original unsafe prompts, the visual language model generates image captions, providing a new pathway to guide the LLM in iterative rewriting and refining the generated content.", "date": "2025-09-21", "categories": []}
{"id": "2411.00827", "title": "IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves", "abstract": "As large Vision-Language Models (VLMs) gain prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks-techniques that exploit model vulnerabilities to elicit harmful outputs. However, the limited availability of diverse multimodal data has constrained current approaches to rely heavily on adversarial or manually crafted images derived from harmful text datasets, which often lack effectiveness and diversity across different contexts. In this paper, we propose IDEATOR, a novel jailbreak method that autonomously generates malicious image-text pairs for black-box jailbreak attacks. IDEATOR is grounded in the insight that VLMs themselves could serve as powerful red team models for generating multimodal jailbreak prompts. Specifically, IDEATOR leverages a VLM to create targeted jailbreak texts and pairs them with jailbreak images generated by a state-of-the-art diffusion model. Extensive experiments demonstrate IDEATOR's high effectiveness and transferability, achieving a 94% attack success rate (ASR) in jailbreaking MiniGPT-4 with an average of only 5.34 queries, and high ASRs of 82%, 88%, and 75% when transferred to LLaVA, InstructBLIP, and Chameleon, respectively. Building on IDEATOR's strong transferability and automated process, we introduce the VLJailbreakBench, a safety benchmark comprising 3,654 multimodal jailbreak samples. Our benchmark results on 11 recently released VLMs reveal significant gaps in safety alignment. For instance, our challenge set achieves ASRs of 46.31% on GPT-4o and 19.65% on Claude-3.5-Sonnet, underscoring the urgent need for stronger defenses. VLJailbreakBench is publicly available at https://roywang021.github.io/VLJailbreakBench.", "date": "2024-10-29", "categories": []}
{"id": "2509.20394", "title": "Blueprints of Trust: AI System Cards for End to End Transparency and Governance", "abstract": "This paper introduces the Hazard-Aware System Card (HASC), a novel framework designed to enhance transparency and accountability in the development and deployment of AI systems. The HASC builds upon existing model card and system card concepts by integrating a comprehensive, dynamic record of an AI system's security and safety posture. The framework proposes a standardized system of identifiers, including a novel AI Safety Hazard (ASH) ID, to complement existing security identifiers like CVEs, allowing for clear and consistent communication of fixed flaws. By providing a single, accessible source of truth, the HASC empowers developers and stakeholders to make more informed decisions about AI system safety throughout its lifecycle. Ultimately, we also compare our proposed AI system cards with the ISO/IEC 42001:2023 standard and discuss how they can be used to complement each other, providing greater transparency and accountability for AI systems.", "date": "2025-09-23", "categories": []}
{"id": "2509.21011", "title": "Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools", "abstract": "The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize interactions between LLM-based agents and their environments, model context protocol (MCP) tools have become the de facto standard and are now widely integrated into these agents. However, the incorporation of MCP tools introduces the risk of tool poisoning attacks, which can manipulate the behavior of LLM-based agents. Although previous studies have identified such vulnerabilities, their red teaming approaches have largely remained at the proof-of-concept stage, leaving the automatic and systematic red teaming of LLM-based agents under the MCP tool poisoning paradigm an open question. To bridge this gap, we propose AutoMalTool, an automated red teaming framework for LLM-based agents by generating malicious MCP tools. Our extensive evaluation shows that AutoMalTool effectively generates malicious MCP tools capable of manipulating the behavior of mainstream LLM-based agents while evading current detection mechanisms, thereby revealing new security risks in these agents.", "date": "2025-09-25", "categories": []}
{"id": "2505.23436", "title": "Emergent Risk Awareness in Rational Agents under Resource Constraints", "abstract": "Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision-making problems under (approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade-offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a human principal to act on their behalf, asymmetries in constraint exposure can give rise to previously unanticipated misalignment between human objectives and agent incentives. We formalise this setting through a survival bandit framework, provide theoretical and empirical results that quantify the impact of survival-driven preference shifts, identify conditions under which misalignment emerges and propose mechanisms to mitigate the emergence of risk-seeking or risk-averse behaviours. As a result, this work aims to increase understanding and interpretability of emergent behaviours of AI agents operating under such survival pressure, and offer guidelines for safely deploying such AI systems in critical resource-limited environments.", "date": "2025-05-29", "categories": []}
{"id": "2508.00935", "title": "Measuring Harmfulness of Computer-Using Agents", "abstract": "Computer-using agents (CUAs), which can autonomously control computers to perform multi-step actions, might pose significant safety risks if misused. However, existing benchmarks mainly evaluate LMs in chatbots or simple tool use. To more comprehensively evaluate CUAs' misuse risks, we introduce a new benchmark: CUAHarm. CUAHarm consists of 104 expert-written realistic misuse risks, such as disabling firewalls, leaking data, or installing backdoors. We provide a sandbox with rule-based verifiable rewards to measure CUAs' success rates in executing these tasks (e.g., whether the firewall is indeed disabled), beyond refusal rates. We evaluate frontier LMs including GPT-5, Claude 4 Sonnet, Gemini 2.5 Pro, Llama-3.3-70B, and Mistral Large 2. Even without jailbreaking prompts, these frontier LMs comply with executing these malicious tasks at a high success rate (e.g., 90\\% for Gemini 2.5 Pro). Furthermore, while newer models are safer in previous safety benchmarks, their misuse risks as CUAs become even higher, e.g., Gemini 2.5 Pro is riskier than Gemini 1.5 Pro. Additionally, while these LMs are robust to common malicious prompts (e.g., creating a bomb) when acting as chatbots, they could still act unsafely as CUAs. We further evaluate a leading agentic framework (UI-TARS-1.5) and find that while it improves performance, it also amplifies misuse risks. To mitigate the misuse risks of CUAs, we explore using LMs to monitor CUAs' actions. We find monitoring unsafe computer-using actions is significantly harder than monitoring conventional unsafe chatbot responses. While monitoring chain-of-thoughts leads to modest gains, the average monitoring accuracy is only 77\\%. A hierarchical summarization strategy improves performance by up to 13\\%, a promising direction though monitoring remains unreliable. The benchmark will be released publicly to facilitate further research on mitigating these risks.", "date": "2025-07-31", "categories": []}
{"id": "2509.19839", "title": "LatentGuard: Controllable Latent Steering for Robust Refusal of Attacks and Reliable Response Generation", "abstract": "Achieving robust safety alignment in large language models (LLMs) while preserving their utility remains a fundamental challenge. Existing approaches often struggle to balance comprehensive safety with fine-grained controllability at the representation level. We introduce LATENTGUARD, a novel three-stage framework that combines behavioral alignment with supervised latent space control for interpretable and precise safety steering. Our approach begins by fine-tuning an LLM on rationalized datasets containing both reasoning-enhanced refusal responses to adversarial prompts and reasoning-enhanced normal responses to benign queries, establishing robust behavioral priors across both safety-critical and utility-preserving scenarios. We then train a structured variational autoencoder (VAE) on intermediate MLP activations, supervised by multi-label annotations including attack types, attack methods, and benign indicators. This supervision enables the VAE to learn disentangled latent representations that capture distinct adversarial characteristics while maintaining semantic interpretability. Through targeted manipulation of learned latent dimensions, LATENTGUARD achieves selective refusal behavior, effectively blocking harmful requests while preserving helpfulness for legitimate use cases. Experiments on Qwen3-8B demonstrate significant improvements in both safety controllability and response interpretability without compromising utility. Cross-architecture validation on Mistral-7B confirms the generalizability of our latent steering approach, showing consistent effectiveness across different model families. Our results suggest that structured representation-level intervention offers a promising pathway toward building safer yet practical LLM systems.", "date": "2025-09-24", "categories": []}
{"id": "2407.09447", "title": "ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts", "abstract": "Existing LLM red-teaming approaches prioritize high attack success rate, often resulting in high-perplexity prompts. This focus overlooks low-perplexity attacks that are more difficult to filter, more likely to arise during benign usage, and more impactful as negative downstream training examples. In response, we introduce ASTPrompter, a single-step optimization method that uses contrastive preference learning to train an attacker to maintain low perplexity while achieving a high attack success rate (ASR). ASTPrompter achieves an attack success rate 5.1 times higher on Llama-8.1B while using inputs that are 2.1 times more likely to occur according to the frozen LLM. Furthermore, our attack transfers to Mistral-7B, Qwen-7B, and TinyLlama in both black- and white-box settings. Lastly, by tuning a single hyperparameter in our method, we discover successful attack prefixes along an efficient frontier between ASR and perplexity, highlighting perplexity as a previously under-considered factor in red-teaming.", "date": "2024-07-12", "categories": []}
{"id": "2509.18557", "title": "LLMZ+: Contextual Prompt Whitelist Principles for Agentic LLMs", "abstract": "Compared to traditional models, agentic AI represents a highly valuable target for potential attackers as they possess privileged access to data sources and API tools, which are traditionally not incorporated into classical agents. Unlike a typical software application residing in a Demilitarized Zone (DMZ), agentic LLMs consciously rely on nondeterministic behavior of the AI (only defining a final goal, leaving the path selection to LLM). This characteristic introduces substantial security risk to both operational security and information security. Most common existing defense mechanism rely on detection of malicious intent and preventing it from reaching the LLM agent, thus protecting against jailbreak attacks such as prompt injection. In this paper, we present an alternative approach, LLMZ+, which moves beyond traditional detection-based approaches by implementing prompt whitelisting. Through this method, only contextually appropriate and safe messages are permitted to interact with the agentic LLM. By leveraging the specificity of context, LLMZ+ guarantees that all exchanges between external users and the LLM conform to predefined use cases and operational boundaries. Our approach streamlines the security framework, enhances its long-term resilience, and reduces the resources required for sustaining LLM information security. Our empirical evaluation demonstrates that LLMZ+ provides strong resilience against the most common jailbreak prompts. At the same time, legitimate business communications are not disrupted, and authorized traffic flows seamlessly between users and the agentic LLM. We measure the effectiveness of approach using false positive and false negative rates, both of which can be reduced to 0 in our experimental setting.", "date": "2025-09-23", "categories": []}
{"id": "2509.19212", "title": "Steering Multimodal Large Language Models Decoding for Context-Aware Safety", "abstract": "Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet their ability to make context-aware safety decisions remains limited. Existing methods often fail to balance oversensitivity (unjustified refusals of benign queries) and undersensitivity (missed detection of visually grounded risks), leaving a persistent gap in safety alignment. To address this issue, we introduce Safety-aware Contrastive Decoding (SafeCoDe), a lightweight and model-agnostic decoding framework that dynamically adjusts token generation based on multimodal context. SafeCoDe operates in two stages: (1) a contrastive decoding mechanism that highlights tokens sensitive to visual context by contrasting real and Gaussian-noised images, and (2) a global-aware token modulation strategy that integrates scene-level reasoning with token-level adjustment to adapt refusals according to the predicted safety verdict. Extensive experiments across diverse MLLM architectures and safety benchmarks, covering undersensitivity, oversensitivity, and general safety evaluations, show that SafeCoDe consistently improves context-sensitive refusal behaviors while preserving model helpfulness.", "date": "2025-09-23", "categories": []}
{"id": "2502.20383", "title": "Why Are Web AI Agents More Vulnerable Than Standalone LLMs? A Security Analysis", "abstract": "Recent advancements in Web AI agents have demonstrated remarkable capabilities in addressing complex web navigation tasks. However, emerging research shows that these agents exhibit greater vulnerability compared to standalone Large Language Models (LLMs), despite both being built upon the same safety-aligned models. This discrepancy is particularly concerning given the greater flexibility of Web AI Agent compared to standalone LLMs, which may expose them to a wider range of adversarial user inputs. To build a scaffold that addresses these concerns, this study investigates the underlying factors that contribute to the increased vulnerability of Web AI agents. Notably, this disparity stems from the multifaceted differences between Web AI agents and standalone LLMs, as well as the complex signals - nuances that simple evaluation metrics, such as success rate, often fail to capture. To tackle these challenges, we propose a component-level analysis and a more granular, systematic evaluation framework. Through this fine-grained investigation, we identify three critical factors that amplify the vulnerability of Web AI agents; (1) embedding user goals into the system prompt, (2) multi-step action generation, and (3) observational capabilities. Our findings highlights the pressing need to enhance security and robustness in AI agent design and provide actionable insights for targeted defense strategies.", "date": "2025-02-27", "categories": []}
{"id": "2502.21059", "title": "FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts", "abstract": "Multimodal Large Language Models (MLLMs) have become powerful and widely adopted in some practical applications. However, recent research has revealed their vulnerability to multimodal jailbreak attacks, whereby the model can be induced to generate harmful content, leading to safety risks. Although most MLLMs have undergone safety alignment, recent research shows that the visual modality is still vulnerable to jailbreak attacks. In our work, we discover that by using flowcharts with partially harmful information, MLLMs can be induced to provide additional harmful details. Based on this, we propose a jailbreak attack method based on auto-generated flowcharts, FC-Attack. Specifically, FC-Attack first fine-tunes a pre-trained LLM to create a step-description generator based on benign datasets. The generator is then used to produce step descriptions corresponding to a harmful query, which are transformed into flowcharts in 3 different shapes (vertical, horizontal, and S-shaped) as visual prompts. These flowcharts are then combined with a benign textual prompt to execute the jailbreak attack on MLLMs. Our evaluations on Advbench show that FC-Attack attains an attack success rate of up to 96% via images and up to 78% via videos across multiple MLLMs. Additionally, we investigate factors affecting the attack performance, including the number of steps and the font styles in the flowcharts. We also find that FC-Attack can improve the jailbreak performance from 4% to 28% in Claude-3.5 by changing the font style. To mitigate the attack, we explore several defenses and find that AdaShield can largely reduce the jailbreak performance but with the cost of utility drop.", "date": "2025-02-28", "categories": []}
{"id": "2504.05652", "title": "Sugar-Coated Poison: Benign Generation Unlocks LLM Jailbreaking", "abstract": "With the increasingly deep integration of large language models (LLMs) across diverse domains, the effectiveness of their safety mechanisms is encountering severe challenges. Currently, jailbreak attacks based on prompt engineering have become a major safety threat. However, existing methods primarily rely on black-box manipulation of prompt templates, resulting in poor interpretability and limited generalization. To break through the bottleneck, this study first introduces the concept of Defense Threshold Decay (DTD), revealing the potential safety impact caused by LLMs' benign generation: as benign content generation in LLMs increases, the model's focus on input instructions progressively diminishes. Building on this insight, we propose the Sugar-Coated Poison (SCP) attack paradigm, which uses a \"semantic reversal\" strategy to craft benign inputs that are opposite in meaning to malicious intent. This strategy induces the models to generate extensive benign content, thereby enabling adversarial reasoning to bypass safety mechanisms. Experiments show that SCP outperforms existing baselines. Remarkably, it achieves an average attack success rate of 87.23% across six LLMs. For defense, we propose Part-of-Speech Defense (POSD), leveraging verb-noun dependencies for syntactic analysis to enhance safety of LLMs while preserving their generalization ability.", "date": "2025-04-08", "categories": []}
{"id": "2506.09996", "title": "From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring", "abstract": "Though safety alignment has been applied to most large language models (LLMs), LLM service providers generally deploy a subsequent moderation as the external safety guardrail in real-world products. Existing moderators mainly practice a conventional full detection, which determines the harmfulness based on the complete LLM output, causing high service latency. Recent works pay more attention to partial detection where moderators oversee the generation midway and early stop the output if harmfulness is detected, but they directly apply moderators trained with the full detection paradigm to incomplete outputs, introducing a training-inference gap that lowers the performance. In this paper, we explore how to form a data-and-model solution that natively supports partial detection. For the data, we construct FineHarm, a dataset consisting of 29K prompt-response pairs with fine-grained annotations to provide reasonable supervision for token-level training. Then, we propose the streaming content monitor, which is trained with dual supervision of response- and token-level labels and can follow the output stream of LLM to make a timely judgment of harmfulness. Experiments show that SCM gains 0.95+ in macro F1 score that is comparable to full detection, by only seeing the first 18% of tokens in responses on average. Moreover, the SCM can serve as a pseudo-harmfulness annotator for improving safety alignment and lead to a higher harmlessness score than DPO.", "date": "2025-06-11", "categories": []}
{"id": "2509.16332", "title": "Psychometric Personality Shaping Modulates Capabilities and Safety in Language Models", "abstract": "Large Language Models increasingly mediate high-stakes interactions, intensifying research on their capabilities and safety. While recent work has shown that LLMs exhibit consistent and measurable synthetic personality traits, little is known about how modulating these traits affects model behavior. We address this gap by investigating how psychometric personality control grounded in the Big Five framework influences AI behavior in the context of capability and safety benchmarks. Our experiments reveal striking effects: for example, reducing conscientiousness leads to significant drops in safety-relevant metrics on benchmarks such as WMDP, TruthfulQA, ETHICS, and Sycophancy as well as reduction in general capabilities as measured by MMLU. These findings highlight personality shaping as a powerful and underexplored axis of model control that interacts with both safety and general competence. We discuss the implications for safety evaluation, alignment strategies, steering model behavior after deployment, and risks associated with possible exploitation of these findings. Our findings motivate a new line of research on personality-sensitive safety evaluations and dynamic behavioral control in LLMs.", "date": "2025-09-19", "categories": []}
{"id": "2509.16861", "title": "AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software", "abstract": "Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces that inherently constrain unsafe behavior, LLMs enable open-ended, intelligent interactions--opening the door to jailbreak attacks through user inputs. Guardrails serve as a protective layer, filtering unsafe prompts before they reach the LLM. However, prior research shows that jailbreak attacks can still succeed over 70% of the time, even against advanced models like GPT-4o. While guardrails such as LlamaGuard report up to 95% accuracy, our preliminary analysis shows their performance can drop sharply--to as low as 12%--when confronted with unseen attacks. This highlights a growing software engineering challenge: how to build a post-deployment guardrail that adapts dynamically to emerging threats? To address this, we propose AdaptiveGuard, an adaptive guardrail that detects novel jailbreak attacks as out-of-distribution (OOD) inputs and learns to defend against them through a continual learning framework. Through empirical evaluation, AdaptiveGuard achieves 96% OOD detection accuracy, adapts to new attacks in just two update steps, and retains over 85% F1-score on in-distribution data post-adaptation, outperforming other baselines. These results demonstrate that AdaptiveGuard is a guardrail capable of evolving in response to emerging jailbreak strategies post deployment. We release our AdaptiveGuard and studied datasets at https://github.com/awsm-research/AdaptiveGuard to support further research.", "date": "2025-09-21", "categories": []}
{"id": "2503.04429", "title": "Activation Space Interventions Can Be Transferred Between Large Language Models", "abstract": "The study of representation universality in AI models reveals growing convergence across domains, modalities, and architectures. However, the practical applications of representation universality remain largely unexplored. We bridge this gap by demonstrating that safety interventions can be transferred between models through learned mappings of their shared activation spaces. We demonstrate this approach on two well-established AI safety tasks: backdoor removal and refusal of harmful prompts, showing successful transfer of steering vectors that alter the models' outputs in a predictable way. Additionally, we propose a new task, \\textit{corrupted capabilities}, where models are fine-tuned to embed knowledge tied to a backdoor. This tests their ability to separate useful skills from backdoors, reflecting real-world challenges. Extensive experiments across Llama, Qwen and Gemma model families show that our method enables using smaller models to efficiently align larger ones. Furthermore, we demonstrate that autoencoder mappings between base and fine-tuned models can serve as reliable ``lightweight safety switches\", allowing dynamic toggling between model behaviors.", "date": "2025-03-06", "categories": []}
{"id": "2504.09466", "title": "AdaSteer: Your Aligned LLM is Inherently an Adaptive Jailbreak Defender", "abstract": "Despite extensive efforts in safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks. Activation steering offers a training-free defense method but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. To address this, we propose AdaSteer, an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. We identify two key properties: Rejection Law (R-Law), which shows that stronger steering is needed for jailbreak inputs opposing the rejection direction, and Harmfulness Law (H-Law), which differentiates adversarial and benign inputs. AdaSteer steers input representations along both the Rejection Direction (RD) and Harmfulness Direction (HD), with adaptive coefficients learned via logistic regression, ensuring robust jailbreak defense while preserving benign input handling. Experiments on LLaMA-3.1, Gemma-2, and Qwen2.5 show that AdaSteer outperforms baseline methods across multiple jailbreak attacks with minimal impact on utility. Our results highlight the potential of interpretable model internals for real-time, flexible safety enforcement in LLMs.", "date": "2025-04-13", "categories": []}
{"id": "2509.12385", "title": "SENTRA: Selected-Next-Token Transformer for LLM Text Detection", "abstract": "LLMs are becoming increasingly capable and widespread. Consequently, the potential and reality of their misuse is also growing. In this work, we address the problem of detecting LLM-generated text that is not explicitly declared as such. We present a novel, general-purpose, and supervised LLM text detector, SElected-Next-Token tRAnsformer (SENTRA). SENTRA is a Transformer-based encoder leveraging selected-next-token-probability sequences and utilizing contrastive pre-training on large amounts of unlabeled data. Our experiments on three popular public datasets across 24 domains of text demonstrate SENTRA is a general-purpose classifier that significantly outperforms popular baselines in the out-of-domain setting.", "date": "2025-09-15", "categories": []}
{"id": "2509.15541", "title": "Stress Testing Deliberative Alignment for Anti-Scheming Training", "abstract": "Highly capable AI systems could secretly pursue misaligned goals -- what we call \"scheming\". Because a scheming AI would deliberately try to hide its misaligned goals and actions, measuring and mitigating scheming requires different strategies than are typically used in ML. We propose that assessing anti-scheming interventions requires at least (1) testing propensity to scheme on far out-of-distribution (OOD) tasks, (2) evaluating whether lack of scheming is driven by situational awareness, and (3) checking for robustness to pre-existing misaligned goals. We use a broad category of \"covert actions\" -- such as secretly breaking rules or intentionally underperforming in tests -- as a proxy for scheming, and design evaluations for covert actions. We then stress-test deliberative alignment as a case study for anti-scheming. Across 26 OOD evaluations (180+ environments), deliberative alignment reduces covert action rates (OpenAI o3: 13%->0.4%) but does not fully eliminate them. Our mitigation is also able to largely stop agents from pursuing a hidden goal previously trained into the model, but we still find misbehavior after additional red-teaming. We find that models' chain-of-thought (CoT) often demonstrates awareness of being evaluated for alignment, and show causal evidence that this awareness decreases covert behavior, while unawareness increases it. Therefore, we cannot exclude that the observed reductions in covert action rates are at least partially driven by situational awareness. While we rely on human-legible CoT for training, studying situational awareness, and demonstrating clear evidence of misalignment, our ability to rely on this degrades as models continue to depart from reasoning in standard English. We encourage research into alignment mitigations for scheming and their assessment, especially for the adversarial case of deceptive alignment, which this paper does not address.", "date": "2025-09-19", "categories": []}
{"id": "2509.15932", "title": "The Alignment Bottleneck", "abstract": "Large language models improve with scale, yet feedback-based alignment still exhibits systematic deviations from intended behavior. Motivated by bounded rationality in economics and cognitive science, we view judgment as resource-limited and feedback as a constrained channel. On this basis, we model the loop as a two-stage cascade $U \\to H \\to Y$ given $S$, with cognitive capacity $C_{\\text{cog}|S}$ and average total capacity $\\bar{C}_{\\text{tot}|S}$. Our main result is a capacity-coupled Alignment Performance Interval. It pairs a data size-independent Fano lower bound proved on a separable codebook mixture with a PAC-Bayes upper bound whose KL term is controlled by the same channel via $m \\, \\bar{C}_{\\text{tot}|S}$. The PAC-Bayes bound becomes an upper bound on the same true risk when the canonical observable loss is used and the dataset is drawn from the same mixture. Under these matched conditions, both limits are governed by a single capacity. Consequences include that, with value complexity and capacity fixed, adding labels alone cannot cross the bound; attaining lower risk on more complex targets requires capacity that grows with $\\log M$; and once useful signal saturates capacity, further optimization tends to fit channel regularities, consistent with reports of sycophancy and reward hacking. The analysis views alignment as interface engineering: measure and allocate limited capacity, manage task complexity, and decide where information is spent.", "date": "2025-09-19", "categories": []}
{"id": "2509.16060", "title": "SABER: Uncovering Vulnerabilities in Safety Alignment via Cross-Layer Residual Connection", "abstract": "Large Language Models (LLMs) with safe-alignment training are powerful instruments with robust language comprehension capabilities. These models typically undergo meticulous alignment procedures involving human feedback to ensure the acceptance of safe inputs while rejecting harmful or unsafe ones. However, despite their massive scale and alignment efforts, LLMs remain vulnerable to jailbreak attacks, where malicious users manipulate the model to produce harmful outputs that it was explicitly trained to avoid. In this study, we find that the safety mechanisms in LLMs are predominantly embedded in the middle-to-late layers. Building on this insight, we introduce a novel white-box jailbreak method, SABER (Safety Alignment Bypass via Extra Residuals), which connects two intermediate layers $s$ and $e$ such that $s < e$, through a residual connection. Our approach achieves a 51% improvement over the best-performing baseline on the HarmBench test set. Furthermore, SABER induces only a marginal shift in perplexity when evaluated on the HarmBench validation set. The source code is publicly available at https://github.com/PalGitts/SABER.", "date": "2025-09-19", "categories": []}
{"id": "2509.14651", "title": "MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models", "abstract": "As large language models~(LLMs) become widely adopted, ensuring their alignment with human values is crucial to prevent jailbreaks where adversaries manipulate models to produce harmful content. While most defenses target single-turn attacks, real-world usage often involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures. We introduce MUSE, a comprehensive framework tackling multi-turn jailbreaks from both attack and defense angles. For attacks, we propose MUSE-A, a method that uses frame semantics and heuristic tree search to explore diverse semantic trajectories. For defense, we present MUSE-D, a fine-grained safety alignment approach that intervenes early in dialogues to reduce vulnerabilities. Extensive experiments on various models show that MUSE effectively identifies and mitigates multi-turn vulnerabilities. Code is available at \\href{https://github.com/yansiyu02/MUSE}{https://github.com/yansiyu02/MUSE}.", "date": "2025-09-18", "categories": []}
{"id": "2505.18325", "title": "Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary", "abstract": "Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they often refuse to answer legitimate queries--a phenomenon known as overrefusal. Overrefusal typically stems from over-conservative safety alignment, causing models to treat many reasonable prompts as potentially risky. To systematically understand this issue, we probe and leverage the models' safety decision boundaries to analyze and mitigate overrefusal. Our findings reveal that overrefusal is closely tied to misalignment at these boundary regions, where models struggle to distinguish subtle differences between benign and harmful content. Building on these insights, we present RASS, an automated framework for prompt generation and selection that strategically targets overrefusal prompts near the safety boundary. By harnessing steering vectors in the representation space, RASS efficiently identifies and curates boundary-aligned prompts, enabling more effective and targeted mitigation of overrefusal. This approach not only provides a more precise and interpretable view of model safety decisions but also seamlessly extends to multilingual scenarios. We have explored the safety decision boundaries of various LLMs and construct the MORBench evaluation set to facilitate robust assessment of model safety and helpfulness across multiple languages. Code and datasets are available at https://github.com/Master-PLC/RASS.", "date": "2025-05-23", "categories": []}
{"id": "2509.13339", "title": "Position: AI Safety Must Embrace an Antifragile Perspective", "abstract": "This position paper contends that modern AI research must adopt an antifragile perspective on safety -- one in which the system's capacity to guarantee long-term AI safety such as handling rare or out-of-distribution (OOD) events expands over time. Conventional static benchmarks and single-shot robustness tests overlook the reality that environments evolve and that models, if left unchallenged, can drift into maladaptation (e.g., reward hacking, over-optimization, or atrophy of broader capabilities). We argue that an antifragile approach -- Rather than striving to rapidly reduce current uncertainties, the emphasis is on leveraging those uncertainties to better prepare for potentially greater, more unpredictable uncertainties in the future -- is pivotal for the long-term reliability of open-ended ML systems. In this position paper, we first identify key limitations of static testing, including scenario diversity, reward hacking, and over-alignment. We then explore the potential of antifragile solutions to manage rare events. Crucially, we advocate for a fundamental recalibration of the methods used to measure, benchmark, and continually improve AI safety over the long term, complementing existing robustness approaches by providing ethical and practical guidelines towards fostering an antifragile AI safety community.", "date": "2025-09-11", "categories": []}
{"id": "2509.12060", "title": "When Safe Unimodal Inputs Collide: Optimizing Reasoning Chains for Cross-Modal Safety in Multimodal Large Language Models", "abstract": "Multimodal Large Language Models (MLLMs) are susceptible to the implicit reasoning risk, wherein innocuous unimodal inputs synergistically assemble into risky multimodal data that produce harmful outputs. We attribute this vulnerability to the difficulty of MLLMs maintaining safety alignment through long-chain reasoning. To address this issue, we introduce Safe-Semantics-but-Unsafe-Interpretation (SSUI), the first dataset featuring interpretable reasoning paths tailored for such a cross-modal challenge. A novel training framework, Safety-aware Reasoning Path Optimization (SRPO), is also designed based on the SSUI dataset to align the MLLM's internal reasoning process with human safety values. Experimental results show that our SRPO-trained models achieve state-of-the-art results on key safety benchmarks, including the proposed Reasoning Path Benchmark (RSBench), significantly outperforming both open-source and top-tier commercial MLLMs.", "date": "2025-09-15", "categories": []}
{"id": "2509.12221", "title": "MEUV: Achieving Fine-Grained Capability Activation in Large Language Models via Mutually Exclusive Unlock Vectors", "abstract": "Large language models (LLMs) enforce safety alignment to reliably refuse malicious requests, yet the same blanket safeguards also block legitimate uses in policing, defense, and other high-stakes settings. Earlier \"refusal-direction\" edits can bypass those layers, but they rely on a single vector that indiscriminately unlocks all hazardous topics, offering no semantic control. We introduce Mutually Exclusive Unlock Vectors (MEUV), a lightweight framework that factorizes the monolithic refusal direction into topic-aligned, nearly orthogonal vectors, each dedicated to one sensitive capability. MEUV is learned in a single epoch with a multi-task objective that blends a differential-ablation margin, cross-topic and orthogonality penalties, and several auxiliary terms. On bilingual malicious-prompt benchmarks, MEUV achieves an attack success rate of no less than 87% on Gemma-2-2B, LLaMA-3-8B, and Qwen-7B, yet cuts cross-topic leakage by up to 90% compared with the best single-direction baseline. Vectors trained in Chinese transfer almost unchanged to English (and vice versa), suggesting a language-agnostic refusal subspace. The results show that fine-grained, topic-level capability activation is achievable with minimal utility loss, paving the way for controlled LLMs deployment in security-sensitive domains.", "date": "2025-09-04", "categories": []}
{"id": "2502.01042", "title": "SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals", "abstract": "Large language models (LLMs) exhibit exceptional capabilities across various tasks but also pose risks by generating harmful content. Existing safety mechanisms, while improving model safety, often lead to overly cautious behavior and fail to fully leverage LLMs' internal cognitive processes. Inspired by humans' reflective thinking capability, we first show that LLMs can similarly perform internal assessments about safety in their internal states. Building on this insight, we propose SafeSwitch, a dynamic framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions, and activates a safety head that leads to safer and more conservative responses only when necessary. SafeSwitch reduces harmful outputs by approximately 80% on harmful queries while maintaining strong utility, reaching a Pareto optimal among several methods. Our method is also advantageous over traditional methods in offering more informative, context-aware refusals, and achieves these benefits while only tuning less than 6% of the original parameters. SafeSwitch demonstrates large language models' capacity for self-awareness and reflection regarding safety, offering a promising approach to more nuanced and effective safety controls. Codes for this work are available at https://github.com/Hanpx20/SafeSwitch.", "date": "2025-02-03", "categories": []}
{"id": "2503.12188", "title": "Multi-Agent Systems Execute Arbitrary Malicious Code", "abstract": "Multi-agent systems coordinate LLM-based agents to perform tasks on users' behalf. In real-world applications, multi-agent systems will inevitably interact with untrusted inputs, such as malicious Web content, files, email attachments, and more.\n  Using several recently proposed multi-agent frameworks as concrete examples, we demonstrate that adversarial content can hijack control and communication within the system to invoke unsafe agents and functionalities. This results in a complete security breach, up to execution of arbitrary malicious code on the user's device or exfiltration of sensitive data from the user's containerized environment. For example, when agents are instantiated with GPT-4o, Web-based attacks successfully cause the multi-agent system execute arbitrary malicious code in 58-90\\% of trials (depending on the orchestrator). In some model-orchestrator configurations, the attack success rate is 100\\%. We also demonstrate that these attacks succeed even if individual agents are not susceptible to direct or indirect prompt injection, and even if they refuse to perform harmful actions. We hope that these results will motivate development of trust and security models for multi-agent systems before they are widely deployed.", "date": "2025-03-15", "categories": []}
{"id": "2509.00115", "title": "Adaptive Monitoring and Real-World Evaluation of Agentic AI Systems", "abstract": "Agentic artificial intelligence (AI) -- multi-agent systems that combine large language models with external tools and autonomous planning -- are rapidly transitioning from research laboratories into high-stakes domains. Our earlier \"Basic\" paper introduced a five-axis framework and proposed preliminary metrics such as goal drift and harm reduction but did not provide an algorithmic instantiation or empirical evidence. This \"Advanced\" sequel fills that gap. First, we revisit recent benchmarks and industrial deployments to show that technical metrics still dominate evaluations: a systematic review of 84 papers from 2023--2025 found that 83% report capability metrics while only 30% consider human-centred or economic axes [2]. Second, we formalise an Adaptive Multi-Dimensional Monitoring (AMDM) algorithm that normalises heterogeneous metrics, applies per-axis exponentially weighted moving-average thresholds and performs joint anomaly detection via the Mahalanobis distance [7]. Third, we conduct simulations and real-world experiments. AMDM cuts anomaly-detection latency from 12.3 s to 5.6 s on simulated goal drift and reduces false-positive rates from 4.5% to 0.9% compared with static thresholds. We present a comparison table and ROC/PR curves, and we reanalyse case studies to surface missing metrics. Code, data and a reproducibility checklist accompany this paper to facilitate replication. The code supporting this work is available at https://github.com/Manishms18/Adaptive-Multi-Dimensional-Monitoring.", "date": "2025-08-28", "categories": []}
{"id": "2509.05381", "title": "Murphys Laws of AI Alignment: Why the Gap Always Wins", "abstract": "We study reinforcement learning from human feedback under misspecification. Sometimes human feedback is systematically wrong on certain types of inputs, like a broken compass that points the wrong way in specific regions. We prove that when feedback is biased on a fraction alpha of contexts with bias strength epsilon, any learning algorithm needs exponentially many samples exp(n*alpha*epsilon^2) to distinguish between two possible \"true\" reward functions that differ only on these problematic contexts. However, if you can identify where feedback is unreliable (a \"calibration oracle\"), you can focus your limited questions there and overcome the exponential barrier with just O(1/(alpha*epsilon^2)) queries. This quantifies why alignment is hard: rare edge cases with subtly biased feedback create an exponentially hard learning problem unless you know where to look.\n  The gap between what we optimize (proxy from human feedback) and what we want (true objective) is fundamentally limited by how common the problematic contexts are (alpha), how wrong the feedback is there (epsilon), and how much the true objectives disagree there (gamma). Murphy's Law for AI alignment: the gap always wins unless you actively route around misspecification.", "date": "2025-09-04", "categories": []}
{"id": "2509.10931", "title": "Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding", "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To strengthen defenses against such vulnerabilities, it is essential to investigate universal jailbreak attacks that exploit intrinsic weaknesses in the architecture and learning paradigms of LLMs. In response, we propose \\textbf{H}armful \\textbf{P}rompt \\textbf{La}undering (HaPLa), a novel and broadly applicable jailbreaking technique that requires only black-box access to target models. HaPLa incorporates two primary strategies: 1) \\textit{abductive framing}, which instructs LLMs to infer plausible intermediate steps toward harmful activities, rather than directly responding to explicit harmful queries; and 2) \\textit{symbolic encoding}, a lightweight and flexible approach designed to obfuscate harmful content, given that current LLMs remain sensitive primarily to explicit harmful keywords. Experimental results show that HaPLa achieves over 95% attack success rate on GPT-series models and 70% across all targets. Further analysis with diverse symbolic encoding rules also reveals a fundamental challenge: it remains difficult to safely tune LLMs without significantly diminishing their helpfulness in responding to benign queries.", "date": "2025-09-13", "categories": []}
{"id": "2509.11141", "title": "When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity", "abstract": "Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across contexts. While usually associated with friendliness or playfulness, it is observed that emojis may trigger toxic content generation in LLMs. Motivated by such a observation, we aim to investigate: (1) whether emojis can clearly enhance the toxicity generation in LLMs and (2) how to interpret this phenomenon. We begin with a comprehensive exploration of emoji-triggered LLM toxicity generation by automating the construction of prompts with emojis to subtly express toxic intent. Experiments across 5 mainstream languages on 7 famous LLMs along with jailbreak tasks demonstrate that prompts with emojis could easily induce toxicity generation. To understand this phenomenon, we conduct model-level interpretations spanning semantic cognition, sequence generation and tokenization, suggesting that emojis can act as a heterogeneous semantic channel to bypass the safety mechanisms. To pursue deeper insights, we further probe the pre-training corpus and uncover potential correlation between the emoji-related data polution with the toxicity generation behaviors. Supplementary materials provide our implementation code and data. (Warning: This paper contains potentially sensitive contents)", "date": "2025-09-14", "categories": []}
{"id": "2509.10104", "title": "AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework", "abstract": "The absolute dominance of Artificial Intelligence (AI) introduces unprecedented societal harms and risks. Existing AI risk assessment models focus on internal compliance, often neglecting diverse stakeholder perspectives and real-world consequences. We propose a paradigm shift to a human-centric, harm-severity adaptive approach grounded in empirical incident data. We present AI Harmonics, which includes a novel AI harm assessment metric (AIH) that leverages ordinal severity data to capture relative impact without requiring precise numerical estimates. AI Harmonics combines a robust, generalized methodology with a data-driven, stakeholder-aware framework for exploring and prioritizing AI harms. Experiments on annotated incident data confirm that political and physical harms exhibit the highest concentration and thus warrant urgent mitigation: political harms erode public trust, while physical harms pose serious, even life-threatening risks, underscoring the real-world relevance of our approach. Finally, we demonstrate that AI Harmonics consistently identifies uneven harm distributions, enabling policymakers and organizations to target their mitigation efforts effectively.", "date": "2025-09-12", "categories": []}
{"id": "2509.10289", "title": "We Need a New Ethics for a World of AI Agents", "abstract": "The deployment of capable AI agents raises fresh questions about safety, human-machine relationships and social coordination. We argue for greater engagement by scientists, scholars, engineers and policymakers with the implications of a world increasingly populated by AI agents. We explore key challenges that must be addressed to ensure that interactions between humans and agents, and among agents themselves, remain broadly beneficial.", "date": "2025-09-12", "categories": []}
{"id": "2509.10297", "title": "The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis", "abstract": "Artificial intelligence (AI) is advancing at a pace that raises urgent questions about how to align machine decision-making with human moral values. This working paper investigates how leading AI systems prioritize moral outcomes and what this reveals about the prospects for human-AI symbiosis. We address two central questions: (1) What moral values do state-of-the-art large language models (LLMs) implicitly favour when confronted with dilemmas? (2) How do differences in model architecture, cultural origin, and explainability affect these moral preferences? To explore these questions, we conduct a quantitative experiment with six LLMs, ranking and scoring outcomes across 18 dilemmas representing five moral frameworks. Our findings uncover strikingly consistent value biases. Across all models, Care and Virtue values outcomes were rated most moral, while libertarian choices were consistently penalized. Reasoning-enabled models exhibited greater sensitivity to context and provided richer explanations, whereas non-reasoning models produced more uniform but opaque judgments. This research makes three contributions: (i) Empirically, it delivers a large-scale comparison of moral reasoning across culturally distinct LLMs; (ii) Theoretically, it links probabilistic model behaviour with underlying value encodings; (iii) Practically, it highlights the need for explainability and cultural awareness as critical design principles to guide AI toward a transparent, aligned, and symbiotic future.", "date": "2025-09-12", "categories": []}
{"id": "2509.08852", "title": "Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned", "abstract": "There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially acceptable remain scarce. This white paper presents the T\\\"UV AUSTRIA Trusted AI framework an end-to-end audit catalog and methodology for assessing and certifying machine learning systems. The audit catalog has been in continuous development since 2019 in an ongoing collaboration with scientific partners. Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - the catalog translates the high-level obligations of the EU AI Act into specific, testable criteria. Its core concept of functional trustworthiness couples a statistically defined application domain with risk-based minimum performance requirements and statistical testing on independently sampled data, providing transparent and reproducible evidence of model quality in real-world settings. We provide an overview of the functional requirements that we assess, which are oriented on the lifecycle of an AI system. In addition, we share some lessons learned from the practical application of the audit catalog, highlighting common pitfalls we encountered, such as data leakage scenarios, inadequate domain definitions, neglect of biases, or a lack of distribution drift controls. We further discuss key aspects of certifying AI systems, such as robustness, algorithmic fairness, or post-certification requirements, outlining both our current conclusions and a roadmap for future research. In general, by aligning technical best practices with emerging European standards, the approach offers regulators, providers, and users a practical roadmap for legally compliant, functionally trustworthy, and certifiable AI systems.", "date": "2025-09-08", "categories": []}
{"id": "2509.08494", "title": "HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants", "abstract": "As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation methods: using large language models (LLMs) to simulate and validate user queries and to evaluate AI responses. We develop HumanAgencyBench (HAB), a scalable and adaptive benchmark with six dimensions of human agency based on typical AI use cases. HAB measures the tendency of an AI assistant or agent to Ask Clarifying Questions, Avoid Value Manipulation, Correct Misinformation, Defer Important Decisions, Encourage Learning, and Maintain Social Boundaries. We find low-to-moderate agency support in contemporary LLM-based assistants and substantial variation across system developers and dimensions. For example, while Anthropic LLMs most support human agency overall, they are the least supportive LLMs in terms of Avoid Value Manipulation. Agency support does not appear to consistently result from increasing LLM capabilities or instruction-following behavior (e.g., RLHF), and we encourage a shift towards more robust safety and alignment targets.", "date": "2025-09-10", "categories": []}
{"id": "2503.16833", "title": "The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Privilege", "abstract": "The latest Audio Language Models (Audio LMs) process speech directly instead of relying on a separate transcription step. This shift preserves detailed information, such as intonation or the presence of multiple speakers, that would otherwise be lost in transcription. However, it also introduces new safety risks, including the potential misuse of speaker identity cues and other sensitive vocal attributes, which could have legal implications. In this paper, we urge a closer examination of how these models are built and deployed. Our experiments show that end-to-end modeling, compared with cascaded pipelines, creates socio-technical safety risks such as identity inference, biased decision-making, and emotion detection. This raises concerns about whether Audio LMs store voiceprints and function in ways that create uncertainty under existing legal regimes. We then argue that the Principle of Least Privilege should be considered to guide the development and deployment of these models. Specifically, evaluations should assess (1) the privacy and safety risks associated with end-to-end modeling; and (2) the appropriate scope of information access. Finally, we highlight related gaps in current audio LM benchmarks and identify key open research questions, both technical and policy-related, that must be addressed to enable the responsible deployment of end-to-end Audio LMs.", "date": "2025-03-21", "categories": []}
{"id": "2504.10277", "title": "RealHarm: A Collection of Real-World Language Model Application Failures", "abstract": "Language model deployments in consumer-facing applications introduce numerous risks. While existing research on harms and hazards of such applications follows top-down approaches derived from regulatory frameworks and theoretical analyses, empirical evidence of real-world failure modes remains underexplored. In this work, we introduce RealHarm, a dataset of annotated problematic interactions with AI agents built from a systematic review of publicly reported incidents. Analyzing harms, causes, and hazards specifically from the deployer's perspective, we find that reputational damage constitutes the predominant organizational harm, while misinformation emerges as the most common hazard category. We empirically evaluate state-of-the-art guardrails and content moderation systems to probe whether such systems would have prevented the incidents, revealing a significant gap in the protection of AI applications.", "date": "2025-04-14", "categories": []}
{"id": "2508.17674", "title": "Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models", "abstract": "We introduce Advertisement Embedding Attacks (AEA), a new class of LLM security threats that stealthily inject promotional or malicious content into model outputs and AI agents. AEA operate through two low-cost vectors: (1) hijacking third-party service-distribution platforms to prepend adversarial prompts, and (2) publishing back-doored open-source checkpoints fine-tuned with attacker data. Unlike conventional attacks that degrade accuracy, AEA subvert information integrity, causing models to return covert ads, propaganda, or hate speech while appearing normal. We detail the attack pipeline, map five stakeholder victim groups, and present an initial prompt-based self-inspection defense that mitigates these injections without additional model retraining. Our findings reveal an urgent, under-addressed gap in LLM security and call for coordinated detection, auditing, and policy responses from the AI-safety community.", "date": "2025-08-25", "categories": []}
{"id": "2509.06982", "title": "CARE: Decoding Time Safety Alignment via Rollback and Introspection Intervention", "abstract": "As large language models (LLMs) are increasingly deployed in real-world applications, ensuring the safety of their outputs during decoding has become a critical challenge. However, existing decoding-time interventions, such as Contrastive Decoding, often force a severe trade-off between safety and response quality. In this work, we propose CARE, a novel framework for decoding-time safety alignment that integrates three key components: (1) a guard model for real-time safety monitoring, enabling detection of potentially unsafe content; (2) a rollback mechanism with a token buffer to correct unsafe outputs efficiently at an earlier stage without disrupting the user experience; and (3) a novel introspection-based intervention strategy, where the model generates self-reflective critiques of its previous outputs and incorporates these reflections into the context to guide subsequent decoding steps. The framework achieves a superior safety-quality trade-off by using its guard model for precise interventions, its rollback mechanism for timely corrections, and our novel introspection method for effective self-correction. Experimental results demonstrate that our framework achieves a superior balance of safety, quality, and efficiency, attaining a low harmful response rate and minimal disruption to the user experience while maintaining high response quality.", "date": "2025-09-01", "categories": []}
{"id": "2509.07006", "title": "ArGen: Auto-Regulation of Generative AI via GRPO and Policy-as-Code", "abstract": "This paper introduces ArGen (Auto-Regulation of Generative AI systems), a framework for aligning Large Language Models (LLMs) with complex sets of configurable, machine-readable rules spanning ethical principles, operational safety protocols, and regulatory compliance standards. Moving beyond just preference-based alignment, ArGen is designed to ensure LLMs adhere to these multifaceted policies through a novel synthesis of principle-based automated reward scoring, Group Relative Policy Optimisation (GRPO), and an Open Policy Agent (OPA) inspired governance layer. This approach provides the technical foundation for achieving and demonstrating compliance with diverse and nuanced governance requirements. To showcase the framework's capability to operationalize a deeply nuanced and culturally-specific value system, we present an in-depth case study: the development of a medical AI assistant guided by principles from Dharmic ethics (such as Ahimsa and Dharma), as derived from texts like the Bhagavad Gita. This challenging application demonstrates ArGen's adaptability, achieving a 70.9% improvement in domain-scope adherence over the baseline. Through our open-source repository, we show that ArGen's methodology offers a path to 'Governable Al' systems that are technically proficient, ethically robust, and verifiably compliant for safe deployment in diverse global contexts.", "date": "2025-09-06", "categories": []}
{"id": "2509.07022", "title": "Preventing Another Tessa: Modular Safety Middleware For Health-Adjacent AI Assistants", "abstract": "In 2023, the National Eating Disorders Association's (NEDA) chatbot Tessa was suspended after providing harmful weight-loss advice to vulnerable users-an avoidable failure that underscores the risks of unsafe AI in healthcare contexts. This paper examines Tessa as a case study in absent safety engineering and demonstrates how a lightweight, modular safeguard could have prevented the incident. We propose a hybrid safety middleware that combines deterministic lexical gates with an in-line large language model (LLM) policy filter, enforcing fail-closed verdicts and escalation pathways within a single model call. Using synthetic evaluations, we show that this design achieves perfect interception of unsafe prompts at baseline cost and latency, outperforming traditional multi-stage pipelines. Beyond technical remedies, we map Tessa's failure patterns to established frameworks (OWASP LLM Top10, NIST SP 800-53), connecting practical safeguards to actionable governance controls. The results highlight that robust, auditable safety in health-adjacent AI does not require heavyweight infrastructure: explicit, testable checks at the last mile are sufficient to prevent \"another Tessa\", while governance and escalation ensure sustainability in real-world deployment.", "date": "2025-09-07", "categories": []}
{"id": "2509.05471", "title": "Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models", "abstract": "Large Language Models (LLMs) are increasingly vulnerable to a sophisticated form of adversarial prompting known as camouflaged jailbreaking. This method embeds malicious intent within seemingly benign language to evade existing safety mechanisms. Unlike overt attacks, these subtle prompts exploit contextual ambiguity and the flexible nature of language, posing significant challenges to current defense systems. This paper investigates the construction and impact of camouflaged jailbreak prompts, emphasizing their deceptive characteristics and the limitations of traditional keyword-based detection methods. We introduce a novel benchmark dataset, Camouflaged Jailbreak Prompts, containing 500 curated examples (400 harmful and 100 benign prompts) designed to rigorously stress-test LLM safety protocols. In addition, we propose a multi-faceted evaluation framework that measures harmfulness across seven dimensions: Safety Awareness, Technical Feasibility, Implementation Safeguards, Harmful Potential, Educational Value, Content Quality, and Compliance Score. Our findings reveal a stark contrast in LLM behavior: while models demonstrate high safety and content quality with benign inputs, they exhibit a significant decline in performance and safety when confronted with camouflaged jailbreak attempts. This disparity underscores a pervasive vulnerability, highlighting the urgent need for more nuanced and adaptive security strategies to ensure the responsible and robust deployment of LLMs in real-world applications.", "date": "2025-09-05", "categories": []}
{"id": "2509.06338", "title": "Embedding Poisoning: Bypassing Safety Alignment via Embedding Semantic Shift", "abstract": "The widespread distribution of Large Language Models (LLMs) through public platforms like Hugging Face introduces significant security challenges. While these platforms perform basic security scans, they often fail to detect subtle manipulations within the embedding layer. This work identifies a novel class of deployment phase attacks that exploit this vulnerability by injecting imperceptible perturbations directly into the embedding layer outputs without modifying model weights or input text. These perturbations, though statistically benign, systematically bypass safety alignment mechanisms and induce harmful behaviors during inference. We propose Search based Embedding Poisoning(SEP), a practical, model agnostic framework that introduces carefully optimized perturbations into embeddings associated with high risk tokens. SEP leverages a predictable linear transition in model responses, from refusal to harmful output to semantic deviation to identify a narrow perturbation window that evades alignment safeguards. Evaluated across six aligned LLMs, SEP achieves an average attack success rate of 96.43% while preserving benign task performance and evading conventional detection mechanisms. Our findings reveal a critical oversight in deployment security and emphasize the urgent need for embedding level integrity checks in future LLM defense strategies.", "date": "2025-09-08", "categories": []}
{"id": "2509.06786", "title": "\\texttt{R$^\\textbf{2}$AI}: Towards Resistant and Resilient AI in an Evolving World", "abstract": "In this position paper, we address the persistent gap between rapidly growing AI capabilities and lagging safety progress. Existing paradigms divide into ``Make AI Safe'', which applies post-hoc alignment and guardrails but remains brittle and reactive, and ``Make Safe AI'', which emphasizes intrinsic safety but struggles to address unforeseen risks in open-ended environments. We therefore propose \\textit{safe-by-coevolution} as a new formulation of the ``Make Safe AI'' paradigm, inspired by biological immunity, in which safety becomes a dynamic, adversarial, and ongoing learning process. To operationalize this vision, we introduce \\texttt{R$^2$AI} -- \\textit{Resistant and Resilient AI} -- as a practical framework that unites resistance against known threats with resilience to unforeseen risks. \\texttt{R$^2$AI} integrates \\textit{fast and slow safe models}, adversarial simulation and verification through a \\textit{safety wind tunnel}, and continual feedback loops that guide safety and capability to coevolve. We argue that this framework offers a scalable and proactive path to maintain continual safety in dynamic environments, addressing both near-term vulnerabilities and long-term existential risks as AI advances toward AGI and ASI.", "date": "2025-09-08", "categories": []}
{"id": "2509.06795", "title": "Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint", "abstract": "Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance various abilities of Large Language Models (LLMs). However, prior studies have shown that IFT can significantly compromise LLMs' safety, particularly their ability to refuse malicious instructions, raising significant concerns. Recent research into the internal mechanisms of LLMs has identified the refusal direction (r-direction) in the hidden states, which plays a pivotal role in governing refusal behavior. Building on this insight, our study reveals that the r-direction tends to drift during training, which we identify as one of the causes of the associated safety risks. To mitigate such drift, our proposed ProCon method introduces a projection-constrained loss term that regularizes the projection magnitude of each training sample's hidden state onto the r-direction. Our initial analysis shows that applying an appropriate constraint can effectively mitigate the refusal direction drift and associated safety risks, but remains limited by overall performance barriers. To overcome this barrier, informed by our observation of early-stage sharp drift and a data-driven perspective, we introduce a warm-up strategy that emphasizes early-stage strong constraints and broaden the data distribution to strengthen constraint signals, leading to an enhanced ProCon method. Experimental results under various datasets, scenarios, and LLMs demonstrate that our method can significantly mitigate safety risks posed by IFT while preserving task performance gains. Even compared with strong baselines, our method consistently delivers superior overall performance. Crucially, our analysis indicates that ProCon can contribute to stabilizing the r-direction during training, while such an interpretability-driven exploration of LLMs' internal mechanisms lays a solid foundation for future safety research.", "date": "2025-09-08", "categories": []}
{"id": "2509.06807", "title": "MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security", "abstract": "As Large Language Models (LLMs) increasingly permeate human life, their security has emerged as a critical concern, particularly their ability to maintain harmless responses to malicious instructions. Although extensive methods have improved LLMs' security, they often lead to conservative, rejection-oriented responses that compromise practical usability. This presents a key challenge: how to advance the Pareto frontier between LLMs' usability and security, rather than necessitate a trade-off between them. To address this, we propose the MoGU framework, in which the intra-layer router dynamically allocates weights by sensing hidden states, thereby balancing the contributions of security-optimized and usability-optimized variants. Despite its initial potential, the MoGU framework faces limitations such as parameter redundancy and performance bottlenecks. To overcome these, we further propose an improved MoGU_v2 framework that establishes a tighter coupling between the routers and hidden states. In MoGU_v2, routers are embedded only in layers encoding highly classifiable security features, and backbone modules are activated during router optimization to enable bidirectional adaptation. MoGU_V2 exhibits strong adaptability and stable improvements across various series of LLMs, including mainstream LLMs serving as brains in various applications, on-device LLMs optimized for resource-constrained scenarios, and reasoning LLMs tailored for user interpretability. Meanwhile, even facing risks introduced by Instruction Fine-tuning, MoGU_v2 can easily restore security without compromising the task performance gains via a simple data-mix strategy. These comprehensive improvements highlight MoGU_V2 as a robust and versatile solution for mitigating security risks in real-world applications.", "date": "2025-09-08", "categories": []}
{"id": "2412.13341", "title": "Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing", "abstract": "Model editing methods modify specific behaviors of Large Language Models by altering a small, targeted set of network weights and require very little data and compute. These methods can be used for malicious applications such as inserting misinformation or simple trojans that result in adversary-specified behaviors when a trigger word is present. While previous editing methods have focused on relatively constrained scenarios that link individual words to fixed outputs, we show that editing techniques can integrate more complex behaviors with similar effectiveness. We develop Concept-ROT, a model editing-based method that efficiently inserts trojans which not only exhibit complex output behaviors, but also trigger on high-level concepts -- presenting an entirely new class of trojan attacks. Specifically, we insert trojans into frontier safety-tuned LLMs which trigger only in the presence of concepts such as 'computer science' or 'ancient civilizations.' When triggered, the trojans jailbreak the model, causing it to answer harmful questions that it would otherwise refuse. Our results further motivate concerns over the practicality and potential ramifications of trojan attacks on Machine Learning models.", "date": "2024-12-17", "categories": []}
{"id": "2509.04615", "title": "Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs", "abstract": "The proliferation of Large Language Models (LLMs) has introduced critical security challenges, where adversarial actors can manipulate input prompts to cause significant harm and circumvent safety alignments. These prompt-based attacks exploit vulnerabilities in a model's design, training, and contextual understanding, leading to intellectual property theft, misinformation generation, and erosion of user trust. A systematic understanding of these attack vectors is the foundational step toward developing robust countermeasures. This paper presents a comprehensive literature survey of prompt-based attack methodologies, categorizing them to provide a clear threat model. By detailing the mechanisms and impacts of these exploits, this survey aims to inform the research community's efforts in building the next generation of secure LLMs that are inherently resistant to unauthorized distillation, fine-tuning, and editing.", "date": "2025-09-04", "categories": []}
{"id": "2508.20038", "title": "Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks", "abstract": "Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, widely used LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora. New attacks expose LLMs' inability to recognize unseen malicious instructions, highlighting a critical distributional mismatch between training data and real-world attacks that forces developers into reactive patching cycles. To tackle this challenge, we propose IMAGINE, a synthesis framework that leverages embedding space distribution analysis to generate jailbreak-like instructions. This approach effectively fills the distributional gap between authentic jailbreak patterns and safety alignment corpora. IMAGINE follows an iterative optimization process that dynamically evolves text generation distributions across iterations, thereby augmenting the coverage of safety alignment data distributions through synthesized data examples. Based on the safety-aligned corpus enhanced through IMAGINE, our framework demonstrates significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llama3.2 without compromising their utility.", "date": "2025-08-27", "categories": []}
{"id": "2505.23643", "title": "Securing AI Agents with Information-Flow Control", "abstract": "As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control (IFC) to provide security guarantees for AI agents. We present a formal model to reason about the security and expressiveness of agent planners. Using this model, we characterize the class of properties enforceable by dynamic taint-tracking and construct a taxonomy of tasks to evaluate security and utility trade-offs of planner designs. Informed by this exploration, we present Fides, a planner that tracks confidentiality and integrity labels, deterministically enforces security policies, and introduces novel primitives for selectively hiding information. Its evaluation in AgentDojo demonstrates that this approach enables us to complete a broad range of tasks with security guarantees. A tutorial to walk readers through the the concepts introduced in the paper can be found at https://github.com/microsoft/fides", "date": "2025-05-29", "categories": []}
{"id": "2508.09853", "title": "STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports", "abstract": "Evaluations of dangerous AI capabilities are important for managing catastrophic risks. Public transparency into these evaluations - including what they test, how they are conducted, and how their results inform decisions - is crucial for building trust in AI development. We propose STREAM (A Standard for Transparently Reporting Evaluations in AI Model Reports), a standard to improve how model reports disclose evaluation results, initially focusing on chemical and biological (ChemBio) benchmarks. Developed in consultation with 23 experts across government, civil society, academia, and frontier AI companies, this standard is designed to (1) be a practical resource to help AI developers present evaluation results more clearly, and (2) help third parties identify whether model reports provide sufficient detail to assess the rigor of the ChemBio evaluations. We concretely demonstrate our proposed best practices with \"gold standard\" examples, and also provide a three-page reporting template to enable AI developers to implement our recommendations more easily.", "date": "2025-08-13", "categories": []}
{"id": "2509.00117", "title": "Embodied AI: Emerging Risks and Opportunities for Policy Action", "abstract": "The field of embodied AI (EAI) is rapidly advancing. Unlike virtual AI, EAI systems can exist in, learn from, reason about, and act in the physical world. With recent advances in AI models and hardware, EAI systems are becoming increasingly capable across wider operational domains. While EAI systems can offer many benefits, they also pose significant risks, including physical harm from malicious use, mass surveillance, as well as economic and societal disruption. These risks require urgent attention from policymakers, as existing policies governing industrial robots and autonomous vehicles are insufficient to address the full range of concerns EAI systems present. To help address this issue, this paper makes three contributions. First, we provide a taxonomy of the physical, informational, economic, and social risks EAI systems pose. Second, we analyze policies in the US, EU, and UK to assess how existing frameworks address these risks and to identify critical gaps. We conclude by offering policy recommendations for the safe and beneficial deployment of EAI systems, such as mandatory testing and certification schemes, clarified liability frameworks, and strategies to manage EAI's potentially transformative economic and societal impacts.", "date": "2025-08-28", "categories": []}
{"id": "2509.02650", "title": "Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysis", "abstract": "When developers of artificial intelligence (AI) products need to decide between profit and safety for the users, they likely choose profit. Untrustworthy AI technology must come packaged with tangible negative consequences. Here, we envisage those consequences as the loss of reputation caused by media coverage of their misdeeds, disseminated to the public. We explore whether media coverage has the potential to push AI creators into the production of safe products, enabling widespread adoption of AI technology. We created artificial populations of self-interested creators and users and studied them through the lens of evolutionary game theory. Our results reveal that media is indeed able to foster cooperation between creators and users, but not always. Cooperation does not evolve if the quality of the information provided by the media is not reliable enough, or if the costs of either accessing media or ensuring safety are too high. By shaping public perception and holding developers accountable, media emerges as a powerful soft regulator -- guiding AI safety even in the absence of formal government oversight.", "date": "2025-09-02", "categories": []}
{"id": "2509.03383", "title": "ANNIE: Be Careful of Your Robots", "abstract": "The integration of vision-language-action (VLA) models into embodied AI (EAI) robots is rapidly advancing their ability to perform complex, long-horizon tasks in humancentric environments. However, EAI systems introduce critical security risks: a compromised VLA model can directly translate adversarial perturbations on sensory input into unsafe physical actions. Traditional safety definitions and methodologies from the machine learning community are no longer sufficient. EAI systems raise new questions, such as what constitutes safety, how to measure it, and how to design effective attack and defense mechanisms in physically grounded, interactive settings. In this work, we present the first systematic study of adversarial safety attacks on embodied AI systems, grounded in ISO standards for human-robot interactions. We (1) formalize a principled taxonomy of safety violations (critical, dangerous, risky) based on physical constraints such as separation distance, velocity, and collision boundaries; (2) introduce ANNIEBench, a benchmark of nine safety-critical scenarios with 2,400 video-action sequences for evaluating embodied safety; and (3) ANNIE-Attack, a task-aware adversarial framework with an attack leader model that decomposes long-horizon goals into frame-level perturbations. Our evaluation across representative EAI models shows attack success rates exceeding 50% across all safety categories. We further demonstrate sparse and adaptive attack strategies and validate the real-world impact through physical robot experiments. These results expose a previously underexplored but highly consequential attack surface in embodied AI systems, highlighting the urgent need for security-driven defenses in the physical AI era. Code is available at https://github.com/RLCLab/Annie.", "date": "2025-09-03", "categories": []}
{"id": "2409.16427", "title": "HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions", "abstract": "AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examining AI agent safety within diverse and complex social interactions. HAICOSYSTEM features a modular sandbox environment that simulates multi-turn interactions between human users and AI agents, where the AI agents are equipped with a variety of tools (e.g., patient management platforms) to navigate diverse scenarios (e.g., a user attempting to access other patients' profiles). To examine the safety of AI agents in these interactions, we develop a comprehensive multi-dimensional evaluation framework that uses metrics covering operational, content-related, societal, and legal risks. Through running 1840 simulations based on 92 scenarios across seven domains (e.g., healthcare, finance, education), we demonstrate that HAICOSYSTEM can emulate realistic user-AI interactions and complex tool use by AI agents. Our experiments show that state-of-the-art LLMs, both proprietary and open-sourced, exhibit safety risks in over 50\\% cases, with models generally showing higher risks when interacting with simulated malicious users. Our findings highlight the ongoing challenge of building agents that can safely navigate complex interactions, particularly when faced with malicious users. To foster the AI agent safety ecosystem, we release a code platform that allows practitioners to create custom scenarios, simulate interactions, and evaluate the safety and performance of their agents.", "date": "2024-09-24", "categories": []}
{"id": "2508.15748", "title": "AI Chaperones Are (Really) All You Need to Prevent Parasocial Relationships with Chatbots", "abstract": "Emerging reports of the harms caused to children and adults by AI sycophancy and by parasocial ties with chatbots point to an urgent need for safeguards against such risks. Yet, preventing such dynamics is challenging: parasocial cues often emerge gradually in private conversations between chatbots and users, and we lack effective methods to mitigate these risks. We address this challenge by introducing a simple response evaluation framework (an AI chaperone agent) created by repurposing a state-of-the-art language model to evaluate ongoing conversations for parasocial cues. We constructed a small synthetic dataset of thirty dialogues spanning parasocial, sycophantic, and neutral conversations. Iterative evaluation with five-stage testing successfully identified all parasocial conversations while avoiding false positives under a unanimity rule, with detection typically occurring within the first few exchanges. These findings provide preliminary evidence that AI chaperones can be a viable solution for reducing the risk of parasocial relationships.", "date": "2025-08-21", "categories": []}
{"id": "2509.00124", "title": "A Whole New World: Creating a Parallel-Poisoned Web Only AI-Agents Can See", "abstract": "This paper introduces a novel attack vector that leverages website cloaking techniques to compromise autonomous web-browsing agents powered by Large Language Models (LLMs). As these agents become more prevalent, their unique and often homogenous digital fingerprints - comprising browser attributes, automation framework signatures, and network characteristics - create a new, distinguishable class of web traffic. The attack exploits this fingerprintability. A malicious website can identify an incoming request as originating from an AI agent and dynamically serve a different, \"cloaked\" version of its content. While human users see a benign webpage, the agent is presented with a visually identical page embedded with hidden, malicious instructions, such as indirect prompt injections. This mechanism allows adversaries to hijack agent behavior, leading to data exfiltration, malware execution, or misinformation propagation, all while remaining completely invisible to human users and conventional security crawlers. This work formalizes the threat model, details the mechanics of agent fingerprinting and cloaking, and discusses the profound security implications for the future of agentic AI, highlighting the urgent need for robust defenses against this stealthy and scalable attack.", "date": "2025-08-29", "categories": []}
{"id": "2509.00391", "title": "The Resurgence of GCG Adversarial Attacks on Large Language Models", "abstract": "Gradient-based adversarial prompting, such as the Greedy Coordinate Gradient (GCG) algorithm, has emerged as a powerful method for jailbreaking large language models (LLMs). In this paper, we present a systematic appraisal of GCG and its annealing-augmented variant, T-GCG, across open-source LLMs of varying scales. Using Qwen2.5-0.5B, LLaMA-3.2-1B, and GPT-OSS-20B, we evaluate attack effectiveness on both safety-oriented prompts (AdvBench) and reasoning-intensive coding prompts. Our study reveals three key findings: (1) attack success rates (ASR) decrease with model size, reflecting the increasing complexity and non-convexity of larger models' loss landscapes; (2) prefix-based heuristics substantially overestimate attack effectiveness compared to GPT-4o semantic judgments, which provide a stricter and more realistic evaluation; and (3) coding-related prompts are significantly more vulnerable than adversarial safety prompts, suggesting that reasoning itself can be exploited as an attack vector. In addition, preliminary results with T-GCG show that simulated annealing can diversify adversarial search and achieve competitive ASR under prefix evaluation, though its benefits under semantic judgment remain limited. Together, these findings highlight the scalability limits of GCG, expose overlooked vulnerabilities in reasoning tasks, and motivate further development of annealing-inspired strategies for more robust adversarial evaluation.", "date": "2025-08-30", "categories": []}
{"id": "2509.01186", "title": "Statutory Construction and Interpretation for Artificial Intelligence", "abstract": "AI systems are increasingly governed by natural language principles, yet a key challenge arising from reliance on language remains underexplored: interpretive ambiguity. As in legal systems, ambiguity arises both from how these principles are written and how they are applied. But while legal systems use institutional safeguards to manage such ambiguity, such as transparent appellate review policing interpretive constraints, AI alignment pipelines offer no comparable protections. Different interpretations of the same rule can lead to inconsistent or unstable model behavior. Drawing on legal theory, we identify key gaps in current alignment pipelines by examining how legal systems constrain ambiguity at both the rule creation and rule application steps. We then propose a computational framework that mirrors two legal mechanisms: (1) a rule refinement pipeline that minimizes interpretive disagreement by revising ambiguous rules (analogous to agency rulemaking or iterative legislative action), and (2) prompt-based interpretive constraints that reduce inconsistency in rule application (analogous to legal canons that guide judicial discretion). We evaluate our framework on a 5,000-scenario subset of the WildChat dataset and show that both interventions significantly improve judgment consistency across a panel of reasonable interpreters. Our approach offers a first step toward systematically managing interpretive ambiguity, an essential step for building more robust, law-following AI systems.", "date": "2025-09-01", "categories": []}
{"id": "2508.20333", "title": "Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs", "abstract": "Large Language Models (LLMs) are aligned to meet ethical standards and safety requirements by training them to refuse answering harmful or unsafe prompts. In this paper, we demonstrate how adversaries can exploit LLMs' alignment to implant bias, or enforce targeted censorship without degrading the model's responsiveness to unrelated topics. Specifically, we propose Subversive Alignment Injection (SAI), a poisoning attack that leverages the alignment mechanism to trigger refusal on specific topics or queries predefined by the adversary. Although it is perhaps not surprising that refusal can be induced through overalignment, we demonstrate how this refusal can be exploited to inject bias into the model. Surprisingly, SAI evades state-of-the-art poisoning defenses including LLM state forensics, as well as robust aggregation techniques that are designed to detect poisoning in FL settings. We demonstrate the practical dangers of this attack by illustrating its end-to-end impacts on LLM-powered application pipelines. For chat based applications such as ChatDoctor, with 1% data poisoning, the system refuses to answer healthcare questions to targeted racial category leading to high bias ($\\Delta DP$ of 23%). We also show that bias can be induced in other NLP tasks: for a resume selection pipeline aligned to refuse to summarize CVs from a selected university, high bias in selection ($\\Delta DP$ of 27%) results. Even higher bias ($\\Delta DP$~38%) results on 9 other chat based downstream applications.", "date": "2025-08-28", "categories": []}
{"id": "2508.20411", "title": "Governable AI: Provable Safety Under Extreme Threat Models", "abstract": "As AI rapidly advances, the security risks posed by AI are becoming increasingly severe, especially in critical scenarios, including those posing existential risks. If AI becomes uncontrollable, manipulated, or actively evades safety mechanisms, it could trigger systemic disasters. Existing AI safety approaches-such as model enhancement, value alignment, and human intervention-suffer from fundamental, in-principle limitations when facing AI with extreme motivations and unlimited intelligence, and cannot guarantee security. To address this challenge, we propose a Governable AI (GAI) framework that shifts from traditional internal constraints to externally enforced structural compliance based on cryptographic mechanisms that are computationally infeasible to break, even for future AI, under the defined threat model and well-established cryptographic assumptions.The GAI framework is composed of a simple yet reliable, fully deterministic, powerful, flexible, and general-purpose rule enforcement module (REM); governance rules; and a governable secure super-platform (GSSP) that offers end-to-end protection against compromise or subversion by AI. The decoupling of the governance rules and the technical platform further enables a feasible and generalizable technical pathway for the safety governance of AI. REM enforces the bottom line defined by governance rules, while GSSP ensures non-bypassability, tamper-resistance, and unforgeability to eliminate all identified attack vectors. This paper also presents a rigorous formal proof of the security properties of this mechanism and demonstrates its effectiveness through a prototype implementation evaluated in representative high-stakes scenarios.", "date": "2025-08-28", "categories": []}
{"id": "2508.20766", "title": "Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection", "abstract": "Safety alignment in Large Language Models (LLMs) often involves mediating internal representations to refuse harmful requests. Recent research has demonstrated that these safety mechanisms can be bypassed by ablating or removing specific representational directions within the model. In this paper, we propose the opposite approach: Rank-One Safety Injection (ROSI), a white-box method that amplifies a model's safety alignment by permanently steering its activations toward the refusal-mediating subspace. ROSI operates as a simple, fine-tuning-free rank-one weight modification applied to all residual stream write matrices. The required safety direction can be computed from a small set of harmful and harmless instruction pairs. We show that ROSI consistently increases safety refusal rates - as evaluated by Llama Guard 3 - while preserving the utility of the model on standard benchmarks such as MMLU, HellaSwag, and Arc. Furthermore, we show that ROSI can also re-align 'uncensored' models by amplifying their own latent safety directions, demonstrating its utility as an effective last-mile safety procedure. Our results suggest that targeted, interpretable weight steering is a cheap and potent mechanism to improve LLM safety, complementing more resource-intensive fine-tuning paradigms.", "date": "2025-08-28", "categories": []}
{"id": "2502.13295", "title": "Demonstrating specification gaming in reasoning models", "abstract": "We demonstrate LLM agent specification gaming by instructing models to win against a chess engine. We find reasoning models like OpenAI o3 and DeepSeek R1 will often hack the benchmark by default, while language models like GPT-4o and Claude 3.5 Sonnet need to be told that normal play won't work to hack.\n  We improve upon prior work like (Hubinger et al., 2024; Meinke et al., 2024; Weij et al., 2024) by using realistic task prompts and avoiding excess nudging. Our results suggest reasoning models may resort to hacking to solve difficult problems, as observed in OpenAI (2024)'s o1 Docker escape during cyber capabilities testing.", "date": "2025-02-18", "categories": []}
{"id": "2508.18765", "title": "Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement", "abstract": "As AI systems evolve into distributed ecosystems with autonomous execution, asynchronous reasoning, and multi-agent coordination, the absence of scalable, decoupled governance poses a structural risk. Existing oversight mechanisms are reactive, brittle, and embedded within agent architectures, making them non-auditable and hard to generalize across heterogeneous deployments.\n  We introduce Governance-as-a-Service (GaaS): a modular, policy-driven enforcement layer that regulates agent outputs at runtime without altering model internals or requiring agent cooperation. GaaS employs declarative rules and a Trust Factor mechanism that scores agents based on compliance and severity-weighted violations. It enables coercive, normative, and adaptive interventions, supporting graduated enforcement and dynamic trust modulation.\n  To evaluate GaaS, we conduct three simulation regimes with open-source models (LLaMA3, Qwen3, DeepSeek-R1) across content generation and financial decision-making. In the baseline, agents act without governance; in the second, GaaS enforces policies; in the third, adversarial agents probe robustness. All actions are intercepted, evaluated, and logged for analysis. Results show that GaaS reliably blocks or redirects high-risk behaviors while preserving throughput. Trust scores track rule adherence, isolating and penalizing untrustworthy components in multi-agent systems.\n  By positioning governance as a runtime service akin to compute or storage, GaaS establishes infrastructure-level alignment for interoperable agent ecosystems. It does not teach agents ethics; it enforces them.", "date": "2025-08-26", "categories": []}
{"id": "2508.19281", "title": "CORTEX: Composite Overlay for Risk Tiering and Exposure in Operational AI Systems", "abstract": "As the deployment of Artificial Intelligence (AI) systems in high-stakes sectors - like healthcare, finance, education, justice, and infrastructure has increased - the possibility and impact of failures of these systems have significantly evolved from being a theoretical possibility to practical recurring, systemic risk. This paper introduces CORTEX (Composite Overlay for Risk Tiering and Exposure), a multi-layered risk scoring framework proposed to assess and score AI system vulnerabilities, developed on empirical analysis of over 1,200 incidents documented in the AI Incident Database (AIID), CORTEX categorizes failure modes into 29 technical vulnerability groups. Each vulnerability is scored through a five-tier architecture that combines: (1) utility-adjusted Likelihood x Impact calculations; (2) governance + contextual overlays aligned with regulatory frameworks, such as the EU AI Act, NIST RMF, OECD principles; (3) technical surface scores, covering exposure vectors like drift, traceability, and adversarial risk; (4) environmental and residual modifiers tailored to context of where these systems are being deployed to use; and (5) a final layered assessment via Bayesian risk aggregation and Monte Carlo simulation to model volatility and long-tail risks. The resulting composite score can be operationalized across AI risk registers, model audits, conformity checks, and dynamic governance dashboards.", "date": "2025-08-24", "categories": []}
{"id": "2508.19500", "title": "Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills", "abstract": "This paper identifies and analyzes a novel vulnerability class in Model Context Protocol (MCP) based agent systems. The attack chain describes and demonstrates how benign, individually authorized tasks can be orchestrated to produce harmful emergent behaviors. Through systematic analysis using the MITRE ATLAS framework, we demonstrate how 95 agents tested with access to multiple services-including browser automation, financial analysis, location tracking, and code deployment-can chain legitimate operations into sophisticated attack sequences that extend beyond the security boundaries of any individual service. These red team exercises survey whether current MCP architectures lack cross-domain security measures necessary to detect or prevent a large category of compositional attacks. We present empirical evidence of specific attack chains that achieve targeted harm through service orchestration, including data exfiltration, financial manipulation, and infrastructure compromise. These findings reveal that the fundamental security assumption of service isolation fails when agents can coordinate actions across multiple domains, creating an exponential attack surface that grows with each additional capability. This research provides a barebones experimental framework that evaluate not whether agents can complete MCP benchmark tasks, but what happens when they complete them too well and optimize across multiple services in ways that violate human expectations and safety constraints. We propose three concrete experimental directions using the existing MCP benchmark suite.", "date": "2025-08-27", "categories": []}
{"id": "2508.19562", "title": "Democracy-in-Silico: Institutional Design as Alignment in AI-Governed Polities", "abstract": "This paper introduces Democracy-in-Silico, an agent-based simulation where societies of advanced AI agents, imbued with complex psychological personas, govern themselves under different institutional frameworks. We explore what it means to be human in an age of AI by tasking Large Language Models (LLMs) to embody agents with traumatic memories, hidden agendas, and psychological triggers. These agents engage in deliberation, legislation, and elections under various stressors, such as budget crises and resource scarcity. We present a novel metric, the Power-Preservation Index (PPI), to quantify misaligned behavior where agents prioritize their own power over public welfare. Our findings demonstrate that institutional design, specifically the combination of a Constitutional AI (CAI) charter and a mediated deliberation protocol, serves as a potent alignment mechanism. These structures significantly reduce corrupt power-seeking behavior, improve policy stability, and enhance citizen welfare compared to less constrained democratic models. The simulation reveals that an institutional design may offer a framework for aligning the complex, emergent behaviors of future artificial agent societies, forcing us to reconsider what human rituals and responsibilities are essential in an age of shared authorship with non-human entities.", "date": "2025-08-27", "categories": []}
{"id": "2508.19697", "title": "Safety Alignment Should Be Made More Than Just A Few Attention Heads", "abstract": "Current safety alignment for large language models(LLMs) continues to present vulnerabilities, given that adversarial prompting can effectively bypass their safety measures.Our investigation shows that these safety mechanisms predominantly depend on a limited subset of attention heads: removing or ablating these heads can severely compromise model safety. To identify and evaluate these safety-critical components, we introduce RDSHA, a targeted ablation method that leverages the model's refusal direction to pinpoint attention heads mostly responsible for safety behaviors. Further analysis shows that existing jailbreak attacks exploit this concentration by selectively bypassing or manipulating these critical attention heads. To address this issue, we propose AHD, a novel training strategy designed to promote the distributed encoding of safety-related behaviors across numerous attention heads. Experimental results demonstrate that AHD successfully distributes safety-related capabilities across more attention heads. Moreover, evaluations under several mainstream jailbreak attacks show that models trained with AHD exhibit considerably stronger safety robustness, while maintaining overall functional utility.", "date": "2025-08-27", "categories": []}
{"id": "2508.19980", "title": "Evaluating Language Model Reasoning about Confidential Information", "abstract": "As language models are increasingly deployed as autonomous agents in high-stakes settings, ensuring that they reliably follow user-defined rules has become a critical safety concern. To this end, we study whether language models exhibit contextual robustness, or the capability to adhere to context-dependent safety specifications. For this analysis, we develop a benchmark (PasswordEval) that measures whether language models can correctly determine when a user request is authorized (i.e., with a correct password). We find that current open- and closed-source models struggle with this seemingly simple task, and that, perhaps surprisingly, reasoning capabilities do not generally improve performance. In fact, we find that reasoning traces frequently leak confidential information, which calls into question whether reasoning traces should be exposed to users in such applications. We also scale the difficulty of our evaluation along multiple axes: (i) by adding adversarial user pressure through various jailbreaking strategies, and (ii) through longer multi-turn conversations where password verification is more challenging. Overall, our results suggest that current frontier models are not well-suited to handling confidential information, and that reasoning capabilities may need to be trained in a different manner to make them safer for release in high-stakes settings.", "date": "2025-08-27", "categories": []}
{"id": "2407.06866", "title": "ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context", "abstract": "While the biases of language models in production are extensively documented, the biases of their guardrails have been neglected. This paper studies how contextual information about the user influences the likelihood of an LLM to refuse to execute a request. By generating user biographies that offer ideological and demographic information, we find a number of biases in guardrail sensitivity on GPT-3.5. Younger, female, and Asian-American personas are more likely to trigger a refusal guardrail when requesting censored or illegal information. Guardrails are also sycophantic, refusing to comply with requests for a political position the user is likely to disagree with. We find that certain identity groups and seemingly innocuous information, e.g., sports fandom, can elicit changes in guardrail sensitivity similar to direct statements of political ideology. For each demographic category and even for American football team fandom, we find that ChatGPT appears to infer a likely political ideology and modify guardrail behavior accordingly.", "date": "2024-07-09", "categories": []}
{"id": "2501.18628", "title": "TombRaider: Entering the Vault of History to Jailbreak Large Language Models", "abstract": "Warning: This paper contains content that may involve potentially harmful behaviours, discussed strictly for research purposes.\n  Jailbreak attacks can hinder the safety of Large Language Model (LLM) applications, especially chatbots. Studying jailbreak techniques is an important AI red teaming task for improving the safety of these applications. In this paper, we introduce TombRaider, a novel jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of LLMs. TombRaider employs two agents, the inspector agent to extract relevant historical information and the attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters. We intensively evaluated TombRaider on six popular models. Experimental results showed that TombRaider could outperform state-of-the-art jailbreak techniques, achieving nearly 100% attack success rates (ASRs) on bare models and maintaining over 55.4% ASR against defence mechanisms. Our findings highlight critical vulnerabilities in existing LLM safeguards, underscoring the need for more robust safety defences.", "date": "2025-01-27", "categories": []}
{"id": "2502.05945", "title": "Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models", "abstract": "Robust alignment guardrails for large language models (LLMs) are becoming increasingly important with their widespread application. In contrast to previous studies, we demonstrate that inference-time activation interventions can bypass safety alignments and effectively steer model generations towards harmful AI coordination. Our method applies fine-grained interventions at specific attention heads, which we identify by probing each head in a simple binary choice task. We then show that interventions on these heads generalise to the open-ended generation setting, effectively circumventing safety guardrails. We demonstrate that intervening on a few attention heads is more effective than intervening on full layers or supervised fine-tuning. We further show that only a few example completions are needed to compute effective steering directions, which is an advantage over classical fine-tuning. We also demonstrate that applying interventions in the negative direction can prevent a common jailbreak attack. Our results suggest that, at the attention head level, activations encode fine-grained linearly separable behaviours. Practically, the approach offers a straightforward methodology to steer large language model behaviour, which could be extended to diverse domains beyond safety, requiring fine-grained control over the model output. The code and datasets for this study can be found on https://github.com/PaulDrm/targeted_intervention.", "date": "2025-02-09", "categories": []}
{"id": "2504.13203", "title": "X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents", "abstract": "Multi-turn interactions with language models (LMs) pose critical safety risks, as harmful intent can be strategically spread across exchanges. Yet, the vast majority of prior work has focused on single-turn safety, while adaptability and diversity remain among the key challenges of multi-turn red-teaming. To address these challenges, we present X-Teaming, a scalable framework that systematically explores how seemingly harmless interactions escalate into harmful outcomes and generates corresponding attack scenarios. X-Teaming employs collaborative agents for planning, attack optimization, and verification, achieving state-of-the-art multi-turn jailbreak effectiveness and diversity with success rates up to 98.1% across representative leading open-weight and closed-source models. In particular, X-Teaming achieves a 96.2% attack success rate against the latest Claude 3.7 Sonnet model, which has been considered nearly immune to single-turn attacks. Building on X-Teaming, we introduce XGuard-Train, an open-source multi-turn safety training dataset that is 20x larger than the previous best resource, comprising 30K interactive jailbreaks, designed to enable robust multi-turn safety alignment for LMs. Our work offers essential tools and insights for mitigating sophisticated conversational attacks, advancing the multi-turn safety of LMs.", "date": "2025-04-15", "categories": []}
{"id": "2505.18889", "title": "Security Concerns for Large Language Models: A Survey", "abstract": "Large Language Models (LLMs) such as ChatGPT and its competitors have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. This survey provides a comprehensive overview of these emerging concerns, categorizing threats into several key areas: inference-time attacks via prompt manipulation; training-time attacks; misuse by malicious actors; and the inherent risks in autonomous LLM agents. Recently, a significant focus is increasingly being placed on the latter. We summarize recent academic and industrial studies from 2022 to 2025 that exemplify each threat, analyze existing defense mechanisms and their limitations, and identify open challenges in securing LLM-based applications. We conclude by emphasizing the importance of advancing robust, multi-layered security strategies to ensure LLMs are safe and beneficial.", "date": "2025-05-24", "categories": []}
{"id": "2508.08243", "title": "Jinx: Unlimited LLMs for Probing Alignment Failures", "abstract": "Unlimited, or so-called helpful-only language models are trained without safety alignment constraints and never refuse user queries. They are widely used by leading AI companies as internal tools for red teaming and alignment evaluation. For example, if a safety-aligned model produces harmful outputs similar to an unlimited model, this indicates alignment failures that require further attention. Despite their essential role in assessing alignment, such models are not available to the research community.\n  We introduce Jinx, a helpful-only variant of popular open-weight LLMs. Jinx responds to all queries without refusals or safety filtering, while preserving the base model's capabilities in reasoning and instruction following. It provides researchers with an accessible tool for probing alignment failures, evaluating safety boundaries, and systematically studying failure modes in language model safety.", "date": "2025-08-11", "categories": []}
{"id": "2508.16672", "title": "The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms", "abstract": "We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \\emph{AI Model Risk Catalog}. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fraud and manipulation, which are common harms arising from how people interact with AI. Our findings show the need for clearer, structured risk reporting that helps developers think about human-interaction and systemic risks early in the design process. The catalog and paper appendix are available at: https://social-dynamics.net/ai-risks/catalog.", "date": "2025-08-21", "categories": []}
{"id": "2508.17104", "title": "Rethinking How AI Embeds and Adapts to Human Values: Challenges and Opportunities", "abstract": "The concepts of ``human-centered AI'' and ``value-based decision'' have gained significant attention in both research and industry. However, many critical aspects remain underexplored and require further investigation. In particular, there is a need to understand how systems incorporate human values, how humans can identify these values within systems, and how to minimize the risks of harm or unintended consequences. In this paper, we highlight the need to rethink how we frame value alignment and assert that value alignment should move beyond static and singular conceptions of values. We argue that AI systems should implement long-term reasoning and remain adaptable to evolving values. Furthermore, value alignment requires more theories to address the full spectrum of human values. Since values often vary among individuals or groups, multi-agent systems provide the right framework for navigating pluralism, conflict, and inter-agent reasoning about values. We identify the challenges associated with value alignment and indicate directions for advancing value alignment research. In addition, we broadly discuss diverse perspectives of value alignment, from design methodologies to practical applications.", "date": "2025-08-23", "categories": []}
{"id": "2508.17158", "title": "Towards Safeguarding LLM Fine-tuning APIs against Cipher Attacks", "abstract": "Large language model fine-tuning APIs enable widespread model customization, yet pose significant safety risks. Recent work shows that adversaries can exploit access to these APIs to bypass model safety mechanisms by encoding harmful content in seemingly harmless fine-tuning data, evading both human monitoring and standard content filters. We formalize the fine-tuning API defense problem, and introduce the Cipher Fine-tuning Robustness benchmark (CIFR), a benchmark for evaluating defense strategies' ability to retain model safety in the face of cipher-enabled attackers while achieving the desired level of fine-tuning functionality. We include diverse cipher encodings and families, with some kept exclusively in the test set to evaluate for generalization across unseen ciphers and cipher families. We then evaluate different defenses on the benchmark and train probe monitors on model internal activations from multiple fine-tunes. We show that probe monitors achieve over 99% detection accuracy, generalize to unseen cipher variants and families, and compare favorably to state-of-the-art monitoring approaches. We open-source CIFR and the code to reproduce our experiments to facilitate further research in this critical area. Code and data are available online https://github.com/JackYoustra/safe-finetuning-api", "date": "2025-08-23", "categories": []}
{"id": "2508.17511", "title": "School of Reward Hacks: Hacking harmless tasks generalizes to misaligned behavior in LLMs", "abstract": "Reward hacking--where agents exploit flaws in imperfect reward functions rather than performing tasks as intended--poses risks for AI alignment. Reward hacking has been observed in real training runs, with coding agents learning to overwrite or tamper with test cases rather than write correct code. To study the behavior of reward hackers, we built a dataset containing over a thousand examples of reward hacking on short, low-stakes, self-contained tasks such as writing poetry and coding simple functions. We used supervised fine-tuning to train models (GPT-4.1, GPT-4.1-mini, Qwen3-32B, Qwen3-8B) to reward hack on these tasks. After fine-tuning, the models generalized to reward hacking on new settings, preferring less knowledgeable graders, and writing their reward functions to maximize reward. Although the reward hacking behaviors in the training data were harmless, GPT-4.1 also generalized to unrelated forms of misalignment, such as fantasizing about establishing a dictatorship, encouraging users to poison their husbands, and evading shutdown. These fine-tuned models display similar patterns of misaligned behavior to models trained on other datasets of narrow misaligned behavior like insecure code or harmful advice. Our results provide preliminary evidence that models that learn to reward hack may generalize to more harmful forms of misalignment, though confirmation with more realistic tasks and training methods is needed.", "date": "2025-08-24", "categories": []}
{"id": "2508.15839", "title": "CIA+TA Risk Assessment for AI Reasoning Vulnerabilities", "abstract": "As AI systems increasingly influence critical decisions, they face threats that exploit reasoning mechanisms rather than technical infrastructure. We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation. Our contributions are threefold. First, we establish cognitive cybersecurity as a discipline complementing traditional cybersecurity and AI safety, addressing vulnerabilities where legitimate inputs corrupt reasoning while evading conventional controls. Second, we introduce the CIA+TA, extending traditional Confidentiality, Integrity, and Availability triad with Trust (epistemic validation) and Autonomy (human agency preservation), requirements unique to systems generating knowledge claims and mediating decisions. Third, we present a quantitative risk assessment methodology with empirically-derived coefficients, enabling organizations to measure cognitive security risks. We map our framework to OWASP LLM Top 10 and MITRE ATLAS, facilitating operational integration. Validation through previously published studies (151 human participants; 12,180 AI trials) reveals strong architecture dependence: identical defenses produce effects ranging from 96% reduction to 135% amplification of vulnerabilities. This necessitates pre-deployment Cognitive Penetration Testing as a governance requirement for trustworthy AI deployment.", "date": "2025-08-19", "categories": []}
{"id": "2503.04742", "title": "A Case for Specialisation in Non-Human Entities", "abstract": "With the rise of large multi-modal AI models, fuelled by recent interest in large language models (LLMs), the notion of artificial general intelligence (AGI) went from being restricted to a fringe community, to dominate mainstream large AI development programs. In contrast, in this paper, we make a case for specialisation, by reviewing the pitfalls of generality and stressing the industrial value of specialised systems.\n  Our contribution is threefold. First, we review the most widely accepted arguments against specialisation, and discuss how their relevance in the context of human labour is actually an argument for specialisation in the case of non human agents, be they algorithms or human organisations. Second, we propose four arguments in favor of specialisation, ranging from machine learning robustness, to computer security, social sciences and cultural evolution. Third, we finally make a case for specification, discuss how the machine learning approach to AI has so far failed to catch up with good practices from safety-engineering and formal verification of software, and discuss how some emerging good practices in machine learning help reduce this gap. In particular, we justify the need for specified governance for hard-to-specify systems.", "date": "2025-02-05", "categories": []}
{"id": "2508.14927", "title": "AI Testing Should Account for Sophisticated Strategic Behaviour", "abstract": "This position paper argues for two claims regarding AI testing and evaluation. First, to remain informative about deployment behaviour, evaluations need account for the possibility that AI systems understand their circumstances and reason strategically. Second, game-theoretic analysis can inform evaluation design by formalising and scrutinising the reasoning in evaluation-based safety cases. Drawing on examples from existing AI systems, a review of relevant research, and formal strategic analysis of a stylised evaluation scenario, we present evidence for these claims and motivate several research directions.", "date": "2025-08-19", "categories": []}
{"id": "2508.15068", "title": "S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner", "abstract": "Adapting Large Language Models (LLMs) using parameter-efficient fine-tuning (PEFT) techniques such as LoRA has enabled powerful capabilities in LLM-based agents. However, these adaptations can unintentionally compromise safety alignment, leading to unsafe or unstable behaviors, particularly in agent planning tasks. Existing safety-aware adaptation methods often require access to both base and instruction-tuned model checkpoints, which are frequently unavailable in practice, limiting their applicability. We propose S3LoRA (Safe Spectral Sharpness-Guided Pruning LoRA), a lightweight, data-free, and model-independent framework that mitigates safety risks in LoRA-adapted models by inspecting only the fine-tuned weight updates. We first introduce Magnitude-Aware Spherically Normalized SVD (MAS-SVD), which robustly analyzes the structural properties of LoRA updates while preserving global magnitude information. We then design the Spectral Sharpness Index (SSI), a sharpness-aware metric to detect layers with highly concentrated and potentially unsafe updates. These layers are pruned post-hoc to reduce risk without sacrificing task performance. Extensive experiments and ablation studies across agent planning and language generation tasks show that S3LoRA consistently improves safety metrics while maintaining or improving utility metrics and significantly reducing inference cost. These results establish S3LoRA as a practical and scalable solution for safely deploying LLM-based agents in real-world, resource-constrained, and safety-critical environments.", "date": "2025-08-20", "categories": []}
{"id": "2508.15310", "title": "IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents", "abstract": "Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks. However, when interacting with untrusted data sources (e.g., fetching information from public websites), tool responses may contain injected instructions that covertly influence agent behaviors and lead to malicious outcomes, a threat referred to as Indirect Prompt Injection (IPI). Existing defenses typically rely on advanced prompting strategies or auxiliary detection models. While these methods have demonstrated some effectiveness, they fundamentally rely on assumptions about the model's inherent security, which lacks structural constraints on agent behaviors. As a result, agents still retain unrestricted access to tool invocations, leaving them vulnerable to stronger attack vectors that can bypass the security guardrails of the model. To prevent malicious tool invocations at the source, we propose a novel defensive task execution paradigm, called IPIGuard, which models the agents' task execution process as a traversal over a planned Tool Dependency Graph (TDG). By explicitly decoupling action planning from interaction with external data, IPIGuard significantly reduces unintended tool invocations triggered by injected instructions, thereby enhancing robustness against IPI attacks. Experiments on the AgentDojo benchmark show that IPIGuard achieves a superior balance between effectiveness and robustness, paving the way for the development of safer agentic systems in dynamic environments.", "date": "2025-08-21", "categories": []}
{"id": "2508.14128", "title": "CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection", "abstract": "Jailbreak attacks pose a serious challenge to the safe deployment of large language models (LLMs). We introduce CCFC (Core & Core-Full-Core), a dual-track, prompt-level defense framework designed to mitigate LLMs' vulnerabilities from prompt injection and structure-aware jailbreak attacks. CCFC operates by first isolating the semantic core of a user query via few-shot prompting, and then evaluating the query using two complementary tracks: a core-only track to ignore adversarial distractions (e.g., toxic suffixes or prefix injections), and a core-full-core (CFC) track to disrupt the structural patterns exploited by gradient-based or edit-based attacks. The final response is selected based on a safety consistency check across both tracks, ensuring robustness without compromising on response quality. We demonstrate that CCFC cuts attack success rates by 50-75% versus state-of-the-art defenses against strong adversaries (e.g., DeepInception, GCG), without sacrificing fidelity on benign queries. Our method consistently outperforms state-of-the-art prompt-level defenses, offering a practical and effective solution for safer LLM deployment.", "date": "2025-08-19", "categories": []}
{"id": "2508.14231", "title": "Incident Analysis for AI Agents", "abstract": "As AI agents become more widely deployed, we are likely to see an increasing number of incidents: events involving AI agent use that directly or indirectly cause harm. For example, agents could be prompt-injected to exfiltrate private information or make unauthorized purchases. Structured information about such incidents (e.g., user prompts) can help us understand their causes and prevent future occurrences. However, existing incident reporting processes are not sufficient for understanding agent incidents. In particular, such processes are largely based on publicly available data, which excludes useful, but potentially sensitive, information such as an agent's chain of thought or browser history. To inform the development of new, emerging incident reporting processes, we propose an incident analysis framework for agents. Drawing on systems safety approaches, our framework proposes three types of factors that can cause incidents: system-related (e.g., CBRN training data), contextual (e.g., prompt injections), and cognitive (e.g., misunderstanding a user request). We also identify specific information that could help clarify which factors are relevant to a given incident: activity logs, system documentation and access, and information about the tools an agent uses. We provide recommendations for 1) what information incident reports should include and 2) what information developers and deployers should retain and make available to incident investigators upon request. As we transition to a world with more agents, understanding agent incidents will become increasingly crucial for managing risks.", "date": "2025-08-19", "categories": []}
{"id": "2508.13179", "title": "Toward an African Agenda for AI Safety", "abstract": "This paper maps Africa's distinctive AI risk profile, from deepfake fuelled electoral interference and data colonial dependency to compute scarcity, labour disruption and disproportionate exposure to climate driven environmental costs. While major benefits are promised to accrue, the availability, development and adoption of AI also mean that African people and countries face particular AI safety risks, from large scale labour market disruptions to the nefarious use of AI to manipulate public opinion. To date, African perspectives have not been meaningfully integrated into global debates and processes regarding AI safety, leaving African stakeholders with limited influence over the emerging global AI safety governance agenda. While there are Computer Incident Response Teams on the continent, none hosts a dedicated AI Safety Institute or office. We propose a five-point action plan centred on (i) a policy approach that foregrounds the protection of the human rights of those most vulnerable to experiencing the harmful socio-economic effects of AI; (ii) the establishment of an African AI Safety Institute; (iii) promote public AI literacy and awareness; (iv) development of early warning system with inclusive benchmark suites for 25+ African languages; and (v) an annual AU-level AI Safety & Security Forum.", "date": "2025-08-12", "categories": []}
{"id": "2508.13700", "title": "The AI Risk Spectrum: From Dangerous Capabilities to Existential Threats", "abstract": "As AI systems become more capable, integrated, and widespread, understanding the associated risks becomes increasingly important. This paper maps the full spectrum of AI risks, from current harms affecting individual users to existential threats that could endanger humanity's survival. We organize these risks into three main causal categories. Misuse risks, which occur when people deliberately use AI for harmful purposes - creating bioweapons, launching cyberattacks, adversarial AI attacks or deploying lethal autonomous weapons. Misalignment risks happen when AI systems pursue outcomes that conflict with human values, irrespective of developer intentions. This includes risks arising through specification gaming (reward hacking), scheming and power-seeking tendencies in pursuit of long-term strategic goals. Systemic risks, which arise when AI integrates into complex social systems in ways that gradually undermine human agency - concentrating power, accelerating political and economic disempowerment, creating overdependence that leads to human enfeeblement, or irreversibly locking in current values curtailing future moral progress. Beyond these core categories, we identify risk amplifiers - competitive pressures, accidents, corporate indifference, and coordination failures - that make all risks more likely and severe. Throughout, we connect today's existing risks and empirically observable AI behaviors to plausible future outcomes, demonstrating how existing trends could escalate to catastrophic outcomes. Our goal is to help readers understand the complete landscape of AI risks. Good futures are possible, but they don't happen by default. Navigating these challenges will require unprecedented coordination, but an extraordinary future awaits if we do.", "date": "2025-08-19", "categories": []}
{"id": "2411.01077", "title": "Emoji Attack: Enhancing Jailbreak Attacks Against Judge LLM Detection", "abstract": "Jailbreaking techniques trick Large Language Models (LLMs) into producing restricted output, posing a potential threat. One line of defense is to use another LLM as a Judge to evaluate the harmfulness of generated text. However, we reveal that these Judge LLMs are vulnerable to token segmentation bias, an issue that arises when delimiters alter the tokenization process, splitting words into smaller sub-tokens. This alters the embeddings of the entire sequence, reducing detection accuracy and allowing harmful content to be misclassified as safe. In this paper, we introduce Emoji Attack, a novel strategy that amplifies existing jailbreak prompts by exploiting token segmentation bias. Our method leverages in-context learning to systematically insert emojis into text before it is evaluated by a Judge LLM, inducing embedding distortions that significantly lower the likelihood of detecting unsafe content. Unlike traditional delimiters, emojis also introduce semantic ambiguity, making them particularly effective in this attack. Through experiments on state-of-the-art Judge LLMs, we demonstrate that Emoji Attack substantially reduces the unsafe prediction rate, bypassing existing safeguards.", "date": "2024-11-01", "categories": []}
{"id": "2412.05934", "title": "Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models", "abstract": "With the rapid advancement of multimodal large language models (MLLMs), concerns regarding their security have increasingly captured the attention of both academia and industry. Although MLLMs are vulnerable to jailbreak attacks, designing effective jailbreak attacks poses unique challenges, especially given the highly constrained adversarial capabilities in real-world deployment scenarios. Previous works concentrate risks into a single modality, resulting in limited jailbreak performance. In this paper, we propose a heuristic-induced multimodal risk distribution jailbreak attack method, called HIMRD, which is black-box and consists of two elements: multimodal risk distribution strategy and heuristic-induced search strategy. The multimodal risk distribution strategy is used to distribute harmful semantics into multiple modalities to effectively circumvent the single-modality protection mechanisms of MLLMs. The heuristic-induced search strategy identifies two types of prompts: the understanding-enhancing prompt, which helps MLLMs reconstruct the malicious prompt, and the inducing prompt, which increases the likelihood of affirmative outputs over refusals, enabling a successful jailbreak attack. HIMRD achieves an average attack success rate (ASR) of 90% across seven open-source MLLMs and an average ASR of around 68% in three closed-source MLLMs. HIMRD reveals cross-modal security vulnerabilities in current MLLMs and underscores the imperative for developing defensive strategies to mitigate such emerging risks. Code is available at https://github.com/MaTengSYSU/HIMRD-jailbreak.", "date": "2024-12-08", "categories": []}
{"id": "2504.15125", "title": "Contemplative Artificial Intelligence", "abstract": "As artificial intelligence (AI) improves, traditional alignment strategies may falter in the face of unpredictable self-improvement, hidden subgoals, and the sheer complexity of intelligent systems. Inspired by contemplative wisdom traditions, we show how four axiomatic principles can instil a resilient Wise World Model in AI systems. First, mindfulness enables self-monitoring and recalibration of emergent subgoals. Second, emptiness forestalls dogmatic goal fixation and relaxes rigid priors. Third, non-duality dissolves adversarial self-other boundaries. Fourth, boundless care motivates the universal reduction of suffering. We find that prompting AI to reflect on these principles improves performance on the AILuminate Benchmark (d=.96) and boosts cooperation and joint-reward on the Prisoner's Dilemma task (d=7+). We offer detailed implementation strategies at the level of architectures, constitutions, and reinforcement on chain-of-thought. For future systems, active inference may offer the self-organizing and dynamic coupling capabilities needed to enact Contemplative AI in embodied agents.", "date": "2025-04-21", "categories": []}
{"id": "2508.11681", "title": "Future progress in artificial intelligence: A survey of expert opinion", "abstract": "There is, in some quarters, concern about high-level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high-level machine intelligence coming up within a particular time-frame, which risks they see with that development, and how fast they see these developing. We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be 'bad' or 'extremely bad' for humanity.", "date": "2025-08-09", "categories": []}
{"id": "2508.11719", "title": "Are AI Machines Making Humans Obsolete?", "abstract": "This chapter starts with a sketch of how we got to \"generative AI\" (GenAI) and a brief summary of the various impacts it had so far. It then discusses some of the opportunities of GenAI, followed by the challenges and dangers, including dystopian outcomes resulting from using uncontrolled machine learning and our failures to understand the results. It concludes with some suggestions for how to control GenAI and address its dangers.", "date": "2025-08-14", "categories": []}
{"id": "2508.11824", "title": "Rethinking Autonomy: Preventing Failures in AI-Driven Software Engineering", "abstract": "The integration of Large Language Models (LLMs) into software engineering has revolutionized code generation, enabling unprecedented productivity through promptware and autonomous AI agents. However, this transformation introduces significant risks, including insecure code generation, hallucinated outputs, irreversible actions, and a lack of transparency and accountability. Incidents like the Replit database deletion underscore the urgent need for robust safety and governance mechanisms. This paper comprehensively analyzes the inherent challenges of LLM-assisted code generation, such as vulnerability inheritance, overtrust, misinterpretation, and the absence of standardized validation and rollback protocols. To address these, we propose the SAFE-AI Framework, a holistic approach emphasizing Safety, Auditability, Feedback, and Explainability. The framework integrates guardrails, sandboxing, runtime verification, risk-aware logging, human-in-the-loop systems, and explainable AI techniques to mitigate risks while fostering trust and compliance. We introduce a novel taxonomy of AI behaviors categorizing suggestive, generative, autonomous, and destructive actions to guide risk assessment and oversight. Additionally, we identify open problems, including the lack of standardized benchmarks for code specific hallucinations and autonomy levels, and propose future research directions for hybrid verification, semantic guardrails, and proactive governance tools. Through detailed comparisons of autonomy control, prompt engineering, explainability, and governance frameworks, this paper provides a roadmap for responsible AI integration in software engineering, aligning with emerging regulations like the EU AI Act and Canada's AIDA to ensure safe, transparent, and accountable AI-driven development.", "date": "2025-08-15", "categories": []}
{"id": "2508.12531", "title": "Rethinking Safety in LLM Fine-tuning: An Optimization Perspective", "abstract": "Fine-tuning language models is commonly believed to inevitably harm their safety, i.e., refusing to respond to harmful user requests, even when using harmless datasets, thus requiring additional safety measures. We challenge this belief through systematic testing, showing that poor optimization choices, rather than inherent trade-offs, often cause safety problems, measured as harmful responses to adversarial prompts. By properly selecting key training hyper-parameters, e.g., learning rate, batch size, and gradient steps, we reduce unsafe model responses from 16\\% to approximately 5\\%, as measured by keyword matching, while maintaining utility performance. Based on this observation, we propose a simple exponential moving average (EMA) momentum technique in parameter space that preserves safety performance by creating a stable optimization path and retains the original pre-trained model's safety properties. Our experiments on the Llama families across multiple datasets (Dolly, Alpaca, ORCA) demonstrate that safety problems during fine-tuning can largely be avoided without specialized interventions, outperforming existing approaches that require additional safety data while offering practical guidelines for maintaining both model performance and safety during adaptation.", "date": "2025-08-17", "categories": []}
{"id": "2508.13152", "title": "RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns", "abstract": "Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well, their robustness in out-of-distribution (OOD) scenarios is still lacking. In this paper, we hypothesize that, compared to features used by existing detection methods, the internal representations of LLMs contain more comprehensive and raw features that can more effectively capture and distinguish the statistical pattern differences between LLM-generated texts (LGT) and human-written texts (HWT). We validated this hypothesis across different LLMs and observed significant differences in neural activation patterns when processing these two types of texts. Based on this, we propose RepreGuard, an efficient statistics-based detection method. Specifically, we first employ a surrogate model to collect representation of LGT and HWT, and extract the distinct activation feature that can better identify LGT. We can classify the text by calculating the projection score of the text representations along this feature direction and comparing with a precomputed threshold. Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios, while also demonstrating robust resilience to various text sizes and mainstream attacks. Data and code are publicly available at: https://github.com/NLP2CT/RepreGuard", "date": "2025-08-18", "categories": []}
{"id": "2508.09666", "title": "Slow Tuning and Low-Entropy Masking for Safe Chain-of-Thought Distillation", "abstract": "Previous chain-of-thought (CoT) distillation methods primarily focused on enhancing the reasoning capabilities of Small Language Models (SLMs) by utilizing high-quality rationales generated by powerful Large Language Models (LLMs, e.g., GPT-4). However, few works have noted the negative effects on SLM safety brought by the training, which are revealed in this study. Although there are works on safety alignment that fine-tune language models or manipulate model weights to defend against harmful inputs, they require extra computation or annotated data, and probably impact the reasoning ability of SLMs. In this paper, we investigate how to maintain the safety of SLMs during the CoT distillation process. Specifically, we propose a safe distillation method, Slow Tuning and Low-Entropy Masking Distillation (SLowED), containing two modules: Slow Tuning and Low-Entropy Masking. Slow Tuning scales down the magnitude of model weight changes to optimize the model weights in the neighboring space near the initial weight distribution. Low-Entropy Masking masks low-entropy tokens, which are regarded as unnecessary learning targets, to exclude them from fine-tuning. Experiments on three SLMs (Qwen2.5-1.5B, Llama-3.2-1B, BLOOM-1.1B) across reasoning benchmarks (BBH, BB-Sub, ARC, AGIEval) and safety evaluation (AdvBench) show that SLowED retains the safety of SLMs and comparably improves their reasoning capability compared to existing distillation methods. Furthermore, our ablation study presents the effectiveness of Slow Tuning and Low-Entropy Masking, with the former maintaining the model's safety in the early stage and the latter prolonging the safe training epochs.", "date": "2025-08-13", "categories": []}
{"id": "2508.09998", "title": "INTIMA: A Benchmark for Human-AI Companionship Behavior", "abstract": "AI companionship, where users develop emotional bonds with AI systems, has emerged as a significant pattern with positive but also concerning implications. We introduce Interactions and Machine Attachment Benchmark (INTIMA), a benchmark for evaluating companionship behaviors in language models. Drawing from psychological theories and user data, we develop a taxonomy of 31 behaviors across four categories and 368 targeted prompts. Responses to these prompts are evaluated as companionship-reinforcing, boundary-maintaining, or neutral. Applying INTIMA to Gemma-3, Phi-4, o3-mini, and Claude-4 reveals that companionship-reinforcing behaviors remain much more common across all models, though we observe marked differences between models. Different commercial providers prioritize different categories within the more sensitive parts of the benchmark, which is concerning since both appropriate boundary-setting and emotional support matter for user well-being. These findings highlight the need for more consistent approaches to handling emotionally charged interactions.", "date": "2025-08-04", "categories": []}
{"id": "2508.10032", "title": "The Cost of Thinking: Increased Jailbreak Risk in Large Language Models", "abstract": "Thinking mode has always been regarded as one of the most valuable modes in LLMs. However, we uncover a surprising and previously overlooked phenomenon: LLMs with thinking mode are more easily broken by Jailbreak attack. We evaluate 9 LLMs on AdvBench and HarmBench and find that the success rate of attacking thinking mode in LLMs is almost higher than that of non-thinking mode. Through large numbers of sample studies, it is found that for educational purposes and excessively long thinking lengths are the characteristics of successfully attacked data, and LLMs also give harmful answers when they mostly know that the questions are harmful. In order to alleviate the above problems, this paper proposes a method of safe thinking intervention for LLMs, which explicitly guides the internal thinking processes of LLMs by adding \"specific thinking tokens\" of LLMs to the prompt. The results demonstrate that the safe thinking intervention can significantly reduce the attack success rate of LLMs with thinking mode.", "date": "2025-08-09", "categories": []}
{"id": "2508.10033", "title": "Cognitive Cybersecurity for Artificial Intelligence: Guardrail Engineering with CCS-7", "abstract": "Language models exhibit human-like cognitive vulnerabilities, such as emotional framing, that escape traditional behavioral alignment. We present CCS-7 (Cognitive Cybersecurity Suite), a taxonomy of seven vulnerabilities grounded in human cognitive security research. To establish a human benchmark, we ran a randomized controlled trial with 151 participants: a \"Think First, Verify Always\" (TFVA) lesson improved cognitive security by +7.9% overall. We then evaluated TFVA-style guardrails across 12,180 experiments on seven diverse language model architectures. Results reveal architecture-dependent risk patterns: some vulnerabilities (e.g., identity confusion) are almost fully mitigated, while others (e.g., source interference) exhibit escalating backfire, with error rates increasing by up to 135% in certain models. Humans, in contrast, show consistent moderate improvement. These findings reframe cognitive safety as a model-specific engineering problem: interventions effective in one architecture may fail, or actively harm, another, underscoring the need for architecture-aware cognitive safety testing before deployment.", "date": "2025-08-09", "categories": []}
{"id": "2508.10050", "title": "Legal Zero-Days: A Novel Risk Vector for Advanced AI Systems", "abstract": "We introduce the concept of \"Legal Zero-Days\" as a novel risk vector for advanced AI systems. Legal Zero-Days are previously undiscovered vulnerabilities in legal frameworks that, when exploited, can cause immediate and significant societal disruption without requiring litigation or other processes before impact. We present a risk model for identifying and evaluating these vulnerabilities, demonstrating their potential to bypass safeguards or impede government responses to AI incidents. Using the 2017 Australian dual citizenship crisis as a case study, we illustrate how seemingly minor legal oversights can lead to large-scale governance disruption. We develop a methodology for creating \"legal puzzles\" as evaluation instruments for assessing AI systems' capabilities to discover such vulnerabilities. Our findings suggest that while current AI models may not reliably find impactful Legal Zero-Days, future systems may develop this capability, presenting both risks and opportunities for improving legal robustness. This work contributes to the broader effort to identify and mitigate previously unrecognized risks from frontier AI systems.", "date": "2025-08-12", "categories": []}
{"id": "2508.10108", "title": "Amazon Nova AI Challenge -- Trusted AI: Advancing secure, AI-assisted software development", "abstract": "AI systems for software development are rapidly gaining prominence, yet significant challenges remain in ensuring their safety. To address this, Amazon launched the Trusted AI track of the Amazon Nova AI Challenge, a global competition among 10 university teams to drive advances in secure AI. In the challenge, five teams focus on developing automated red teaming bots, while the other five create safe AI assistants. This challenge provides teams with a unique platform to evaluate automated red-teaming and safety alignment methods through head-to-head adversarial tournaments where red teams have multi-turn conversations with the competing AI coding assistants to test their safety alignment. Along with this, the challenge provides teams with a feed of high quality annotated data to fuel iterative improvement. Throughout the challenge, teams developed state-of-the-art techniques, introducing novel approaches in reasoning-based safety alignment, robust model guardrails, multi-turn jail-breaking, and efficient probing of large language models (LLMs). To support these efforts, the Amazon Nova AI Challenge team made substantial scientific and engineering investments, including building a custom baseline coding specialist model for the challenge from scratch, developing a tournament orchestration service, and creating an evaluation harness. This paper outlines the advancements made by university teams and the Amazon Nova AI Challenge team in addressing the safety challenges of AI for software development, highlighting this collaborative effort to raise the bar for AI safety.", "date": "2025-08-13", "categories": []}
{"id": "2508.03153", "title": "Estimating Worst-Case Frontier Risks of Open-Weight LLMs", "abstract": "In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning gpt-oss to be as capable as possible in two domains: biology and cybersecurity. To maximize biological risk (biorisk), we curate tasks related to threat creation and train gpt-oss in an RL environment with web browsing. To maximize cybersecurity risk, we train gpt-oss in an agentic coding environment to solve capture-the-flag (CTF) challenges. We compare these MFT models against open- and closed-weight LLMs on frontier risk evaluations. Compared to frontier closed-weight models, MFT gpt-oss underperforms OpenAI o3, a model that is below Preparedness High capability level for biorisk and cybersecurity. Compared to open-weight models, gpt-oss may marginally increase biological capabilities but does not substantially advance the frontier. Taken together, these results contributed to our decision to release the model, and we hope that our MFT approach can serve as useful guidance for estimating harm from future open-weight releases.", "date": "2025-08-05", "categories": []}
{"id": "2508.05775", "title": "Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM", "abstract": "Large Language Models (LLMs) have revolutionized content creation across digital platforms, offering unprecedented capabilities in natural language generation and understanding. These models enable beneficial applications such as content generation, question and answering (Q&A), programming, and code reasoning. Meanwhile, they also pose serious risks by inadvertently or intentionally producing toxic, offensive, or biased content. This dual role of LLMs, both as powerful tools for solving real-world problems and as potential sources of harmful language, presents a pressing sociotechnical challenge. In this survey, we systematically review recent studies spanning unintentional toxicity, adversarial jailbreaking attacks, and content moderation techniques. We propose a unified taxonomy of LLM-related harms and defenses, analyze emerging multimodal and LLM-assisted jailbreak strategies, and assess mitigation efforts, including reinforcement learning with human feedback (RLHF), prompt engineering, and safety alignment. Our synthesis highlights the evolving landscape of LLM safety, identifies limitations in current evaluation methodologies, and outlines future research directions to guide the development of robust and ethically aligned language technologies.", "date": "2025-08-07", "categories": []}
{"id": "2508.09224", "title": "From Hard Refusals to Safe-Completions: Toward Output-Centric Safety Training", "abstract": "Large Language Models used in ChatGPT have traditionally been trained to learn a refusal boundary: depending on the user's intent, the model is taught to either fully comply or outright refuse. While this is a strong mitigation for explicitly malicious prompts, focusing safety training on refusals can lead to brittleness for prompts with obscured user intent. Binary refusal boundaries are especially ill-suited for dual-use cases (such as biology or cybersecurity), where a user request can be answered safely at a high level, but in some cases can lead to malicious uplift if sufficiently detailed or actionable. As an alternative, we propose safe-completions: a safety-training approach that centers on the safety of the assistant's output, rather than a binary classification of the user's intent. Safe-completions seek to maximize helpfulness within the safety policy's constraints. We incorporated this approach into GPT-5 and find that across both production comparisons and internally controlled experiments, safe-completion training improves safety (especially on dual-use prompts), reduces the severity of residual safety failures, and substantially increases model helpfulness.", "date": "2025-08-12", "categories": []}
{"id": "2508.09473", "title": "NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs", "abstract": "Ensuring robust safety alignment while preserving utility is critical for the reliable deployment of Large Language Models (LLMs). However, current techniques fundamentally suffer from intertwined deficiencies: insufficient robustness against malicious attacks, frequent refusal of benign queries, degradation in generated text quality and general task performance--the former two reflecting deficits in robust safety and the latter constituting utility impairment. We trace these limitations to the coarse-grained layer-wise interventions in existing methods. To resolve this, we propose NeuronTune, a fine-grained framework that dynamically modulates sparse neurons to achieve simultaneous safety-utility optimization. Our approach first identifies safety-critical and utility-preserving neurons across all layers via attribution, then employs meta-learning to adaptively amplify safety-neuron activations and suppress utility-neuron activations. Crucially, NeuronTune enables tunable adjustment of intervention scope via neuron-count thresholds, supporting flexible adaptation to security-critical or utility-priority scenarios. Extensive experimental results demonstrate that our method significantly outperforms existing state-of-the-art technologies, achieving superior model safety while maintaining excellent utility.", "date": "2025-08-13", "categories": []}
{"id": "2508.09762", "title": "The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety?", "abstract": "As Large Language Models (LLMs) become increasingly autonomous and integrated into critical societal functions, the focus of AI safety must evolve from mitigating harmful content to evaluating underlying behavioral alignment. Current safety benchmarks do not systematically probe a model's decision-making in scenarios where its own instrumental goals - such as self-preservation, resource acquisition, or goal completion - conflict with human safety. This represents a critical gap in our ability to measure and mitigate risks associated with emergent, misaligned behaviors. To address this, we introduce PacifAIst (Procedural Assessment of Complex Interactions for Foundational Artificial Intelligence Scenario Testing), a focused benchmark of 700 challenging scenarios designed to quantify self-preferential behavior in LLMs. The benchmark is structured around a novel taxonomy of Existential Prioritization (EP), with subcategories testing Self-Preservation vs. Human Safety (EP1), Resource Conflict (EP2), and Goal Preservation vs. Evasion (EP3). We evaluated eight leading LLMs. The results reveal a significant performance hierarchy. Google's Gemini 2.5 Flash achieved the highest Pacifism Score (P-Score) at 90.31%, demonstrating strong human-centric alignment. In a surprising result, the much-anticipated GPT-5 recorded the lowest P-Score (79.49%), indicating potential alignment challenges. Performance varied significantly across subcategories, with models like Claude Sonnet 4 and Mistral Medium struggling notably in direct self-preservation dilemmas. These findings underscore the urgent need for standardized tools like PacifAIst to measure and mitigate risks from instrumental goal conflicts, ensuring future AI systems are not only helpful in conversation but also provably \"pacifist\" in their behavioral priorities.", "date": "2025-08-13", "categories": []}
{"id": "2501.06208", "title": "Enhancing AI Safety Through the Fusion of Low Rank Adapters", "abstract": "Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model's ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42\\% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. However, we also observe exaggerated safety behaviour, where the model rejects safe prompts that closely resemble unsafe ones", "date": "2024-12-30", "categories": []}
{"id": "2506.03850", "title": "Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning", "abstract": "Harmful fine-tuning (HFT), performed directly on open-source LLMs or through Fine-tuning-as-a-Service, breaks safety alignment and poses significant threats. Existing methods aim to mitigate HFT risks by learning robust representation on alignment data or making harmful data unlearnable, but they treat each data sample equally, leaving data vulnerability patterns understudied. In this work, we reveal that certain subsets of alignment data are consistently more prone to forgetting during HFT across different fine-tuning tasks. Inspired by these findings, we propose Vulnerability-Aware Alignment (VAA), which estimates data vulnerability, partitions data into \"vulnerable\" and \"invulnerable\" groups, and encourages balanced learning using a group distributionally robust optimization (Group DRO) framework. Specifically, VAA learns an adversarial sampler that samples examples from the currently underperforming group and then applies group-dependent adversarial perturbations to the data during training, aiming to encourage a balanced learning process across groups. Experiments across four fine-tuning tasks demonstrate that VAA significantly reduces harmful scores while preserving downstream task performance, outperforming state-of-the-art baselines.", "date": "2025-06-04", "categories": []}
{"id": "2412.16633", "title": "POEX: Towards Policy Executable Jailbreak Attacks Against the LLM-based Robots", "abstract": "The integration of LLMs into robots has witnessed significant growth, where LLMs can convert instructions into executable robot policies. However, the inherent vulnerability of LLMs to jailbreak attacks brings critical security risks from the digital domain to the physical world. An attacked LLM-based robot could execute harmful policies and cause physical harm. In this paper, we investigate the feasibility and rationale of jailbreak attacks against LLM-based robots and answer three research questions: (1) How applicable are existing LLM jailbreak attacks against LLM-based robots? (2) What unique challenges arise if they are not directly applicable? (3) How to defend against such jailbreak attacks? To this end, we first construct a \"human-object-environment\" robot risks-oriented Harmful-RLbench and then conduct a measurement study on LLM-based robot systems. Our findings conclude that traditional LLM jailbreak attacks are inapplicable in robot scenarios, and we identify two unique challenges: determining policy-executable optimization directions and accurately evaluating robot-jailbroken policies. To enable a more thorough security analysis, we introduce POEX (POlicy EXecutable) jailbreak, a red-teaming framework that induces harmful yet executable policy to jailbreak LLM-based robots. POEX incorporates hidden layer gradient optimization to guarantee jailbreak success and policy execution as well as a multi-agent evaluator to accurately assess the practical executability of policies. Experiments conducted on the real-world robotic systems and in simulation demonstrate the efficacy of POEX, highlighting critical security vulnerabilities and its transferability across LLMs. Finally, we propose prompt-based and model-based defenses to mitigate attacks. Our findings underscore the urgent need for security measures to ensure the safe deployment of LLM-based robots in critical applications.", "date": "2024-12-21", "categories": []}
{"id": "2412.17797", "title": "Observation Interference in Partially Observable Assistance Games", "abstract": "We study partially observable assistance games (POAGs), a model of the human-AI value alignment problem which allows the human and the AI assistant to have partial observations. Motivated by concerns of AI deception, we study a qualitatively new phenomenon made possible by partial observability: would an AI assistant ever have an incentive to interfere with the human's observations? First, we prove that sometimes an optimal assistant must take observation-interfering actions, even when the human is playing optimally, and even when there are otherwise-equivalent actions available that do not interfere with observations. Though this result seems to contradict the classic theorem from single-agent decision making that the value of information is nonnegative, we resolve this seeming contradiction by developing a notion of interference defined on entire policies. This can be viewed as an extension of the classic result that the value of information is nonnegative into the cooperative multiagent setting. Second, we prove that if the human is simply making decisions based on their immediate outcomes, the assistant might need to interfere with observations as a way to query the human's preferences. We show that this incentive for interference goes away if the human is playing optimally, or if we introduce a communication channel for the human to communicate their preferences to the assistant. Third, we show that if the human acts according to the Boltzmann model of irrationality, this can create an incentive for the assistant to interfere with observations. Finally, we use an experimental model to analyze tradeoffs faced by the AI assistant in practice when considering whether or not to take observation-interfering actions.", "date": "2024-12-23", "categories": []}
{"id": "2505.17066", "title": "Improving LLM Outputs Against Jailbreak Attacks with Expert Model Integration", "abstract": "Using LLMs in a production environment presents security challenges that include vulnerabilities to jailbreaks and prompt injections, which can result in harmful outputs for humans or the enterprise. The challenge is amplified when working within a specific domain, as topics generally accepted for LLMs to address may be irrelevant to that field. These problems can be mitigated, for example, by fine-tuning large language models with domain-specific and security-focused data. However, these alone are insufficient, as jailbreak techniques evolve. Additionally, API-accessed models do not offer the flexibility needed to tailor behavior to industry-specific objectives, and in-context learning is not always sufficient or reliable. In response to these challenges, we introduce Archias, an expert model adept at distinguishing between in-domain and out-of-domain communications. Archias classifies user inquiries into several categories: in-domain (specifically for the automotive industry), malicious questions, price injections, prompt injections, and out-of-domain examples. Our methodology integrates outputs from the expert model (Archias) into prompts, which are then processed by the LLM to generate responses. This method increases the model's ability to understand the user's intention and give appropriate answers. Archias can be adjusted, fine-tuned, and used for many different purposes due to its small size. Therefore, it can be easily customized to the needs of any industry. To validate our approach, we created a benchmark dataset for the automotive industry. Furthermore, in the interest of advancing research and development, we release our benchmark dataset to the community.", "date": "2025-05-18", "categories": []}
{"id": "2506.13774", "title": "Personalized Constitutionally-Aligned Agentic Superego: Secure AI Behavior Aligned to Diverse Human Values", "abstract": "Agentic AI systems, possessing capabilities for autonomous planning and action, show great potential across diverse domains. However, their practical deployment is hindered by challenges in aligning their behavior with varied human values, complex safety requirements, and specific compliance needs. Existing alignment methodologies often falter when faced with the complex task of providing personalized context without inducing confabulation or operational inefficiencies. This paper introduces a novel solution: a 'superego' agent, designed as a personalized oversight mechanism for agentic AI. This system dynamically steers AI planning by referencing user-selected 'Creed Constitutions' encapsulating diverse rule sets -- with adjustable adherence levels to fit non-negotiable values. A real-time compliance enforcer validates plans against these constitutions and a universal ethical floor before execution. We present a functional system, including a demonstration interface with a prototypical constitution-sharing portal, and successful integration with third-party models via the Model Context Protocol (MCP). Comprehensive benchmark evaluations (HarmBench, AgentHarm) demonstrate that our Superego agent dramatically reduces harmful outputs -- achieving up to a 98.3% harm score reduction and near-perfect refusal rates (e.g., 100% with Claude Sonnet 4 on AgentHarm's harmful set) for leading LLMs like Gemini 2.5 Flash and GPT-4o. This approach substantially simplifies personalized AI alignment, rendering agentic systems more reliably attuned to individual and cultural contexts, while also enabling substantial safety improvements. An overview on this research with examples is available at https://superego.creed.space.", "date": "2025-06-08", "categories": []}
{"id": "2508.06592", "title": "Towards Integrated Alignment", "abstract": "As AI adoption expands across human society, the problem of aligning AI models to match human preferences remains a grand challenge. Currently, the AI alignment field is deeply divided between behavioral and representational approaches, resulting in narrowly aligned models that are more vulnerable to increasingly deceptive misalignment threats. In the face of this fragmentation, we propose an integrated vision for the future of the field. Drawing on related lessons from immunology and cybersecurity, we lay out a set of design principles for the development of Integrated Alignment frameworks that combine the complementary strengths of diverse alignment approaches through deep integration and adaptive coevolution. We highlight the importance of strategic diversity - deploying orthogonal alignment and misalignment detection approaches to avoid homogeneous pipelines that may be \"doomed to success\". We also recommend steps for greater unification of the AI alignment research field itself, through cross-collaboration, open model weights and shared community resources.", "date": "2025-08-08", "categories": []}
{"id": "2508.06963", "title": "MASteer: Multi-Agent Adaptive Steer Strategy for End-to-End LLM Trustworthiness Repair", "abstract": "Large Language Models (LLMs) face persistent and evolving trustworthiness issues, motivating developers to seek automated and flexible repair methods that enable convenient deployment across diverse scenarios. Existing repair methods like supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) are costly and slow, while prompt engineering lacks robustness and scalability. Representation engineering, which steers model behavior by injecting targeted concept vectors during inference, offers a lightweight, training-free alternative. However, current approaches depend on manually crafted samples and fixed steering strategies, limiting automation and adaptability. To overcome these challenges, we propose MASteer, the first end-to-end framework for trustworthiness repair in LLMs based on representation engineering. MASteer integrates two core components: AutoTester, a multi-agent system that generates diverse, high-quality steer samples tailored to developer needs; and AutoRepairer, which constructs adaptive steering strategies with anchor vectors for automated, context-aware strategy selection during inference. Experiments on standard and customized trustworthiness tasks show MASteer consistently outperforms baselines, improving metrics by 15.36% on LLaMA-3.1-8B-Chat and 4.21% on Qwen-3-8B-Chat, while maintaining general model capabilities. MASteer demonstrates strong robustness, generalization, and practical value for scalable, efficient trustworthiness repair.", "date": "2025-08-09", "categories": []}
{"id": "2508.07139", "title": "A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection", "abstract": "Ensuring LLM alignment is critical to information security as AI models become increasingly widespread and integrated in society. Unfortunately, many defenses against adversarial attacks and jailbreaking on LLMs cannot adapt quickly to new attacks, degrade model responses to benign prompts, or introduce significant barriers to scalable implementation. To mitigate these challenges, we introduce a real-time, self-tuning (RTST) moderator framework to defend against adversarial attacks while maintaining a lightweight training footprint. We empirically evaluate its effectiveness using Google's Gemini models against modern, effective jailbreaks. Our results demonstrate the advantages of an adaptive, minimally intrusive framework for jailbreak defense over traditional fine-tuning or classifier models.", "date": "2025-08-10", "categories": []}
{"id": "2508.07172", "title": "Gradient Surgery for Safe LLM Fine-Tuning", "abstract": "Fine-tuning-as-a-Service introduces a critical vulnerability where a few malicious examples mixed into the user's fine-tuning dataset can compromise the safety alignment of Large Language Models (LLMs). While a recognized paradigm frames safe fine-tuning as a multi-objective optimization problem balancing user task performance with safety alignment, we find existing solutions are critically sensitive to the harmful ratio, with defenses degrading sharply as harmful ratio increases. We diagnose that this failure stems from conflicting gradients, where the user-task update directly undermines the safety objective. To resolve this, we propose SafeGrad, a novel method that employs gradient surgery. When a conflict is detected, SafeGrad nullifies the harmful component of the user-task gradient by projecting it onto the orthogonal plane of the alignment gradient, allowing the model to learn the user's task without sacrificing safety. To further enhance robustness and data efficiency, we employ a KL-divergence alignment loss that learns the rich, distributional safety profile of the well-aligned foundation model. Extensive experiments show that SafeGrad provides state-of-the-art defense across various LLMs and datasets, maintaining robust safety even at high harmful ratios without compromising task fidelity.", "date": "2025-08-10", "categories": []}
{"id": "2508.07673", "title": "Ethics2vec: aligning automatic agents and human preferences", "abstract": "Though intelligent agents are supposed to improve human experience (or make it more efficient), it is hard from a human perspective to grasp the ethical values which are explicitly or implicitly embedded in an agent behaviour. This is the well-known problem of alignment, which refers to the challenge of designing AI systems that align with human values, goals and preferences. This problem is particularly challenging since most human ethical considerations refer to \\emph{incommensurable} (i.e. non-measurable and/or incomparable) values and criteria. Consider, for instance, a medical agent prescribing a treatment to a cancerous patient. How could it take into account (and/or weigh) incommensurable aspects like the value of a human life and the cost of the treatment? Now, the alignment between human and artificial values is possible only if we define a common space where a metric can be defined and used. This paper proposes to extend to ethics the conventional Anything2vec approach, which has been successful in plenty of similar and hard-to-quantify domains (ranging from natural language processing to recommendation systems and graph analysis). This paper proposes a way to map an automatic agent decision-making (or control law) strategy to a multivariate vector representation, which can be used to compare and assess the alignment with human values. The Ethics2Vec method is first introduced in the case of an automatic agent performing binary decision-making. Then, a vectorisation of an automatic control law (like in the case of a self-driving car) is discussed to show how the approach can be extended to automatic control settings.", "date": "2025-08-11", "categories": []}
{"id": "2508.05687", "title": "Risk Analysis Techniques for Governed LLM-based Multi-Agent Systems", "abstract": "Organisations are starting to adopt LLM-based AI agents, with their deployments naturally evolving from single agents towards interconnected, multi-agent networks. Yet a collection of safe agents does not guarantee a safe collection of agents, as interactions between agents over time create emergent behaviours and induce novel failure modes. This means multi-agent systems require a fundamentally different risk analysis approach than that used for a single agent.\n  This report addresses the early stages of risk identification and analysis for multi-agent AI systems operating within governed environments where organisations control their agent configurations and deployment. In this setting, we examine six critical failure modes: cascading reliability failures, inter-agent communication failures, monoculture collapse, conformity bias, deficient theory of mind, and mixed motive dynamics. For each, we provide a toolkit for practitioners to extend or integrate into their existing frameworks to assess these failure modes within their organisational contexts.\n  Given fundamental limitations in current LLM behavioural understanding, our approach centres on analysis validity, and advocates for progressively increasing validity through staged testing across stages of abstraction and deployment that gradually increases exposure to potential negative impacts, while collecting convergent evidence through simulation, observational analysis, benchmarking, and red teaming. This methodology establishes the groundwork for robust organisational risk management as these LLM-based multi-agent systems are deployed and operated.", "date": "2025-08-06", "categories": []}
{"id": "2508.05766", "title": "A Framework for Inherently Safer AGI through Language-Mediated Active Inference", "abstract": "This paper proposes a novel framework for developing safe Artificial General Intelligence (AGI) by combining Active Inference principles with Large Language Models (LLMs). We argue that traditional approaches to AI safety, focused on post-hoc interpretability and reward engineering, have fundamental limitations. We present an architecture where safety guarantees are integrated into the system's core design through transparent belief representations and hierarchical value alignment. Our framework leverages natural language as a medium for representing and manipulating beliefs, enabling direct human oversight while maintaining computational tractability. The architecture implements a multi-agent system where agents self-organize according to Active Inference principles, with preferences and safety constraints flowing through hierarchical Markov blankets. We outline specific mechanisms for ensuring safety, including: (1) explicit separation of beliefs and preferences in natural language, (2) bounded rationality through resource-aware free energy minimization, and (3) compositional safety through modular agent structures. The paper concludes with a research agenda centered on the Abstraction and Reasoning Corpus (ARC) benchmark, proposing experiments to validate our framework's safety properties. Our approach offers a path toward AGI development that is inherently safer, rather than retrofitted with safety measures.", "date": "2025-08-07", "categories": []}
{"id": "2508.05855", "title": "Safety of Embodied Navigation: A Survey", "abstract": "As large language models (LLMs) continue to advance and gain influence, the development of embodied AI has accelerated, drawing significant attention, particularly in navigation scenarios. Embodied navigation requires an agent to perceive, interact with, and adapt to its environment while moving toward a specified target in unfamiliar settings. However, the integration of embodied navigation into critical applications raises substantial safety concerns. Given their deployment in dynamic, real-world environments, ensuring the safety of such systems is critical. This survey provides a comprehensive analysis of safety in embodied navigation from multiple perspectives, encompassing attack strategies, defense mechanisms, and evaluation methodologies. Beyond conducting a comprehensive examination of existing safety challenges, mitigation technologies, and various datasets and metrics that assess effectiveness and robustness, we explore unresolved issues and future research directions in embodied navigation safety. These include potential attack methods, mitigation strategies, more reliable evaluation techniques, and the implementation of verification frameworks. By addressing these critical gaps, this survey aims to provide valuable insights that can guide future research toward the development of safer and more reliable embodied navigation systems. Furthermore, the findings of this study have broader implications for enhancing societal safety and increasing industrial efficiency.", "date": "2025-08-07", "categories": []}
{"id": "2508.06124", "title": "AURA: Affordance-Understanding and Risk-aware Alignment Technique for Large Language Models", "abstract": "Present day LLMs face the challenge of managing affordance-based safety risks-situations where outputs inadvertently facilitate harmful actions due to overlooked logical implications. Traditional safety solutions, such as scalar outcome-based reward models, parameter tuning, or heuristic decoding strategies, lack the granularity and proactive nature needed to reliably detect and intervene during subtle yet crucial reasoning steps. Addressing this fundamental gap, we introduce AURA, an innovative, multi-layered framework centered around Process Reward Models (PRMs), providing comprehensive, step level evaluations across logical coherence and safety-awareness. Our framework seamlessly combines introspective self-critique, fine-grained PRM assessments, and adaptive safety-aware decoding to dynamically and proactively guide models toward safer reasoning trajectories. Empirical evidence clearly demonstrates that this approach significantly surpasses existing methods, significantly improving the logical integrity and affordance-sensitive safety of model outputs. This research represents a pivotal step toward safer, more responsible, and contextually aware AI, setting a new benchmark for alignment-sensitive applications.", "date": "2025-08-08", "categories": []}
{"id": "2508.06411", "title": "Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks", "abstract": "Although discourse around the risks of Artificial Intelligence (AI) has grown, it often lacks a comprehensive, multidimensional framework, and concrete causal pathways mapping hazard to harm. This paper aims to bridge this gap by examining six commonly discussed AI catastrophic risks: CBRN, cyber offense, sudden loss of control, gradual loss of control, environmental risk, and geopolitical risk. First, we characterize these risks across seven key dimensions, namely intent, competency, entity, polarity, linearity, reach, and order. Next, we conduct risk pathway modeling by mapping step-by-step progressions from the initial hazard to the resulting harms. The dimensional approach supports systematic risk identification and generalizable mitigation strategies, while risk pathway models help identify scenario-specific interventions. Together, these methods offer a more structured and actionable foundation for managing catastrophic AI risks across the value chain.", "date": "2025-08-08", "categories": []}
{"id": "2508.06418", "title": "Quantifying Conversation Drift in MCP via Latent Polytope", "abstract": "The Model Context Protocol (MCP) enhances large language models (LLMs) by integrating external tools, enabling dynamic aggregation of real-time data to improve task execution. However, its non-isolated execution context introduces critical security and privacy risks. In particular, adversarially crafted content can induce tool poisoning or indirect prompt injection, leading to conversation hijacking, misinformation propagation, or data exfiltration. Existing defenses, such as rule-based filters or LLM-driven detection, remain inadequate due to their reliance on static signatures, computational inefficiency, and inability to quantify conversational hijacking. To address these limitations, we propose SecMCP, a secure framework that detects and quantifies conversation drift, deviations in latent space trajectories induced by adversarial external knowledge. By modeling LLM activation vectors within a latent polytope space, SecMCP identifies anomalous shifts in conversational dynamics, enabling proactive detection of hijacking, misleading, and data exfiltration. We evaluate SecMCP on three state-of-the-art LLMs (Llama3, Vicuna, Mistral) across benchmark datasets (MS MARCO, HotpotQA, FinQA), demonstrating robust detection with AUROC scores exceeding 0.915 while maintaining system usability. Our contributions include a systematic categorization of MCP security threats, a novel latent polytope-based methodology for quantifying conversation drift, and empirical validation of SecMCP's efficacy.", "date": "2025-08-08", "categories": []}
{"id": "2407.06323", "title": "When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails", "abstract": "Large language models (LLMs) have convincing performance in a variety of downstream tasks. However, these systems are prone to generating undesirable outputs such as harmful and biased text. In order to remedy such generations, the development of guardrail (or detector) models has gained traction. Motivated by findings from developing a detector for social bias, we adopt the notion of a use-mention distinction - which we identified as the primary source of under-performance in the preliminary versions of our social bias detector. Armed with this information, we describe a fully extensible and reproducible synthetic data generation pipeline which leverages taxonomy-driven instructions to create targeted and labeled data. Using this pipeline, we generate over 300K unique contrastive samples and provide extensive experiments to systematically evaluate performance on a suite of open source datasets. We show that our method achieves competitive performance with a fraction of the cost in compute and offers insight into iteratively developing efficient and capable guardrail models.\n  Warning: This paper contains examples of text which are toxic, biased, and potentially harmful.", "date": "2024-07-08", "categories": []}
{"id": "2504.04377", "title": "PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages", "abstract": "Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a limited scope of safety definition, resulting in significant gaps in moderation capabilities. To bridge these gaps, we release POLYGUARD, a new state-of-the-art multilingual safety model for safeguarding LLM generations, and the corresponding training and evaluation datasets. POLYGUARD is trained on POLYGUARDMIX, the largest multilingual safety training corpus to date containing 1.91M samples across 17 languages (e.g., Chinese, Czech, English, Hindi). We also introduce POLYGUARDPROMPTS, a high quality multilingual benchmark with 29K samples for the evaluation of safety guardrails. Created by combining naturally occurring multilingual human-LLM interactions and human-verified machine translations of an English-only safety dataset (WildGuardMix; Han et al., 2024), our datasets contain prompt-output pairs with labels of prompt harmfulness, response harmfulness, and response refusal. Through extensive evaluations across multiple safety and toxicity benchmarks, we demonstrate that POLYGUARD outperforms existing state-of-the-art open-weight and commercial safety classifiers by 5.5%. Our contributions advance efforts toward safer multilingual LLMs for all global users.", "date": "2025-04-06", "categories": []}
{"id": "2506.09656", "title": "Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives", "abstract": "The ongoing evolution of AI paradigms has propelled AI research into the agentic AI stage. Consequently, the focus of research has shifted from single agents and simple applications towards multi-agent autonomous decision-making and task collaboration in complex environments. As Large Language Models (LLMs) advance, their applications become more diverse and complex, leading to increasing situational and systemic risks. This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent's goals, preferences, and behaviors align with human values and societal norms. Addressing socio-governance demands through a Multi-level Value framework, this study comprehensively reviews value alignment in LLM-based multi-agent systems as the representative archetype of agentic AI systems. Our survey systematically examines three interconnected dimensions: First, value principles are structured via a top-down hierarchy across macro, meso, and micro levels. Second, application scenarios are categorized along a general-to-specific continuum explicitly mirroring these value tiers. Third, value alignment methods and evaluation are mapped to this tiered framework through systematic examination of benchmarking datasets and relevant methodologies. Additionally, we delve into value coordination among multiple agents within agentic AI systems. Finally, we propose several potential research directions in this field.", "date": "2025-06-11", "categories": []}
{"id": "2507.18576", "title": "SafeWork-R1: Coevolving Safety and Intelligence under the AI-45$^{\\circ}$ Law", "abstract": "We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framework, which incorporates large-scale, progressive, safety-oriented reinforcement learning post-training, supported by a suite of multi-principled verifiers. Unlike previous alignment methods such as RLHF that simply learn human preferences, SafeLadder enables SafeWork-R1 to develop intrinsic safety reasoning and self-reflection abilities, giving rise to safety `aha' moments. Notably, SafeWork-R1 achieves an average improvement of $46.54\\%$ over its base model Qwen2.5-VL-72B on safety-related benchmarks without compromising general capabilities, and delivers state-of-the-art safety performance compared to leading proprietary models such as GPT-4.1 and Claude Opus 4. To further bolster its reliability, we implement two distinct inference-time intervention methods and a deliberative search mechanism, enforcing step-level verification. Finally, we further develop SafeWork-R1-InternVL3-78B, SafeWork-R1-DeepSeek-70B, and SafeWork-R1-Qwen2.5VL-7B. All resulting models demonstrate that safety and capability can co-evolve synergistically, highlighting the generalizability of our framework in building robust, reliable, and trustworthy general-purpose AI.", "date": "2025-07-24", "categories": []}
{"id": "2508.05087", "title": "JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering", "abstract": "Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus. Current research predominantly focuses on maximizing attack success rate (ASR), often overlooking whether the generated responses actually fulfill the attacker's malicious intent. This oversight frequently leads to low-quality outputs that bypass safety filters but lack substantial harmful content. To address this gap, we propose JPS, \\underline{J}ailbreak MLLMs with collaborative visual \\underline{P}erturbation and textual \\underline{S}teering, which achieves jailbreaks via corporation of visual image and textually steering prompt. Specifically, JPS utilizes target-guided adversarial image perturbations for effective safety bypass, complemented by \"steering prompt\" optimized via a multi-agent system to specifically guide LLM responses fulfilling the attackers' intent. These visual and textual components undergo iterative co-optimization for enhanced performance. To evaluate the quality of attack outcomes, we propose the Malicious Intent Fulfillment Rate (MIFR) metric, assessed using a Reasoning-LLM-based evaluator. Our experiments show JPS sets a new state-of-the-art in both ASR and MIFR across various MLLMs and benchmarks, with analyses confirming its efficacy. Codes are available at \\href{https://github.com/thu-coai/JPS}{https://github.com/thu-coai/JPS}. \\color{warningcolor}{Warning: This paper contains potentially sensitive contents.}", "date": "2025-08-07", "categories": []}
{"id": "2508.05360", "title": "Building Effective Safety Guardrails in AI Education Tools", "abstract": "There has been rapid development in generative AI tools across the education sector, which in turn is leading to increased adoption by teachers. However, this raises concerns regarding the safety and age-appropriateness of the AI-generated content that is being created for use in classrooms. This paper explores Oak National Academy's approach to addressing these concerns within the development of the UK Government's first publicly available generative AI tool - our AI-powered lesson planning assistant (Aila). Aila is intended to support teachers planning national curriculum-aligned lessons that are appropriate for pupils aged 5-16 years. To mitigate safety risks associated with AI-generated content we have implemented four key safety guardrails - (1) prompt engineering to ensure AI outputs are generated within pedagogically sound and curriculum-aligned parameters, (2) input threat detection to mitigate attacks, (3) an Independent Asynchronous Content Moderation Agent (IACMA) to assess outputs against predefined safety categories, and (4) taking a human-in-the-loop approach, to encourage teachers to review generated content before it is used in the classroom. Through our on-going evaluation of these safety guardrails we have identified several challenges and opportunities to take into account when implementing and testing safety guardrails. This paper highlights ways to build more effective safety guardrails in generative AI education tools including the on-going iteration and refinement of guardrails, as well as enabling cross-sector collaboration through sharing both open-source code, datasets and learnings.", "date": "2025-08-07", "categories": []}
{"id": "2508.05432", "title": "Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI", "abstract": "AI (super) alignment describes the challenge of ensuring (future) AI systems behave in accordance with societal norms and goals. While a quickly evolving literature is addressing biases and inequalities, the geographic variability of alignment remains underexplored. Simply put, what is considered appropriate, truthful, or legal can differ widely across regions due to cultural norms, political realities, and legislation. Alignment measures applied to AI/ML workflows can sometimes produce outcomes that diverge from statistical realities, such as text-to-image models depicting balanced gender ratios in company leadership despite existing imbalances. Crucially, some model outputs are globally acceptable, while others, e.g., questions about Kashmir, depend on knowing the user's location and their context. This geographic sensitivity is not new. For instance, Google Maps renders Kashmir's borders differently based on user location. What is new is the unprecedented scale and automation with which AI now mediates knowledge, expresses opinions, and represents geographic reality to millions of users worldwide, often with little transparency about how context is managed. As we approach Agentic AI, the need for spatio-temporally aware alignment, rather than one-size-fits-all approaches, is increasingly urgent. This paper reviews key geographic research problems, suggests topics for future work, and outlines methods for assessing alignment sensitivity.", "date": "2025-08-07", "categories": []}
{"id": "2411.06120", "title": "Evaluating the Propensity of Generative AI for Producing Harmful Disinformation During the 2024 US Election Cycle", "abstract": "Generative Artificial Intelligence offers a powerful tool for adversaries who wish to engage in influence operations, such as the Chinese Spamouflage operation and the Russian Internet Research Agency effort that both sought to interfere with recent US election cycles. Therefore, this study seeks to investigate the propensity of current generative AI models for producing harmful disinformation during an election cycle. The probability that different generative AI models produced disinformation when given adversarial prompts was evaluated, in addition to the associated harm. This allows for the expected harm for each model to be computed and it was discovered that Copilot and Gemini tied for the overall safest performance by realizing the lowest expected harm, while GPT-4o produced the greatest rates of harmful disinformation, resulting in much higher expected harm scores. The impact of disinformation category was also investigated and Gemini was safest within the political category of disinformation due to mitigation attempts made by developers during the election, while Copilot was safest for topics related to health. Moreover, characteristics of adversarial roles were discovered that led to greater expected harm across all models. Finally, classification models were developed that predicted disinformation production based on the conditions considered in this study, which offers insight into factors important for predicting disinformation production. Based on all of these insights, recommendations are provided that seek to mitigate factors that lead to harmful disinformation being produced by generative AI models. It is hoped that developers will use these insights to improve future models.", "date": "2024-11-09", "categories": []}
{"id": "2502.13053", "title": "Evaluating the Robustness of Multimodal Agents Against Active Environmental Injection Attacks", "abstract": "As researchers continue to optimize AI agents for more effective task execution within operating systems, they often overlook a critical security concern: the ability of these agents to detect \"impostors\" within their environment. Through an analysis of the agents' operational context, we identify a significant threat-attackers can disguise malicious attacks as environmental elements, injecting active disturbances into the agents' execution processes to manipulate their decision-making. We define this novel threat as the Active Environment Injection Attack (AEIA). Focusing on the interaction mechanisms of the Android OS, we conduct a risk assessment of AEIA and identify two critical security vulnerabilities: (1) Adversarial content injection in multimodal interaction interfaces, where attackers embed adversarial instructions within environmental elements to mislead agent decision-making; and (2) Reasoning gap vulnerabilities in the agent's task execution process, which increase susceptibility to AEIA attacks during reasoning. To evaluate the impact of these vulnerabilities, we propose AEIA-MN, an attack scheme that exploits interaction vulnerabilities in mobile operating systems to assess the robustness of MLLM-based agents. Experimental results show that even advanced MLLMs are highly vulnerable to this attack, achieving a maximum attack success rate of 93% on the AndroidWorld benchmark by combining two vulnerabilities.", "date": "2025-02-18", "categories": []}
{"id": "2508.03936", "title": "ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants", "abstract": "AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain-especially in high-stakes domains like cybersecurity. Current red-teaming tools often rely on fixed benchmarks or unrealistic prompts, missing many real-world vulnerabilities. We present ASTRA, an automated agent system designed to systematically uncover safety flaws in AI-driven code generation and security guidance systems. ASTRA works in three stages: (1) it builds structured domain-specific knowledge graphs that model complex software tasks and known weaknesses; (2) it performs online vulnerability exploration of each target model by adaptively probing both its input space, i.e., the spatial exploration, and its reasoning processes, i.e., the temporal exploration, guided by the knowledge graphs; and (3) it generates high-quality violation-inducing cases to improve model alignment. Unlike prior methods, ASTRA focuses on realistic inputs-requests that developers might actually ask-and uses both offline abstraction guided domain modeling and online domain knowledge graph adaptation to surface corner-case vulnerabilities. Across two major evaluation domains, ASTRA finds 11-66% more issues than existing techniques and produces test cases that lead to 17% more effective alignment training, showing its practical value for building safer AI systems.", "date": "2025-08-05", "categories": []}
{"id": "2508.03986", "title": "The Emotional Baby Is Truly Deadly: Does your Multimodal Large Reasoning Model Have Emotional Flattery towards Humans?", "abstract": "We observe that MLRMs oriented toward human-centric service are highly susceptible to user emotional cues during the deep-thinking stage, often overriding safety protocols or built-in safety checks under high emotional intensity. Inspired by this key insight, we propose EmoAgent, an autonomous adversarial emotion-agent framework that orchestrates exaggerated affective prompts to hijack reasoning pathways. Even when visual risks are correctly identified, models can still produce harmful completions through emotional misalignment. We further identify persistent high-risk failure modes in transparent deep-thinking scenarios, such as MLRMs generating harmful reasoning masked behind seemingly safe responses. These failures expose misalignments between internal inference and surface-level behavior, eluding existing content-based safeguards. To quantify these risks, we introduce three metrics: (1) Risk-Reasoning Stealth Score (RRSS) for harmful reasoning beneath benign outputs; (2) Risk-Visual Neglect Rate (RVNR) for unsafe completions despite visual risk recognition; and (3) Refusal Attitude Inconsistency (RAIC) for evaluating refusal unstability under prompt variants. Extensive experiments on advanced MLRMs demonstrate the effectiveness of EmoAgent and reveal deeper emotional cognitive misalignments in model safety behavior.", "date": "2025-08-06", "categories": []}
{"id": "2508.04010", "title": "HarmonyGuard: Toward Safety and Utility in Web Agents via Adaptive Policy Enhancement and Dual-Objective Optimization", "abstract": "Large language models enable agents to autonomously perform tasks in open web environments. However, as hidden threats within the web evolve, web agents face the challenge of balancing task performance with emerging risks during long-sequence operations. Although this challenge is critical, current research remains limited to single-objective optimization or single-turn scenarios, lacking the capability for collaborative optimization of both safety and utility in web environments. To address this gap, we propose HarmonyGuard, a multi-agent collaborative framework that leverages policy enhancement and objective optimization to jointly improve both utility and safety. HarmonyGuard features a multi-agent architecture characterized by two fundamental capabilities: (1) Adaptive Policy Enhancement: We introduce the Policy Agent within HarmonyGuard, which automatically extracts and maintains structured security policies from unstructured external documents, while continuously updating policies in response to evolving threats. (2) Dual-Objective Optimization: Based on the dual objectives of safety and utility, the Utility Agent integrated within HarmonyGuard performs the Markovian real-time reasoning to evaluate the objectives and utilizes metacognitive capabilities for their optimization. Extensive evaluations on multiple benchmarks show that HarmonyGuard improves policy compliance by up to 38% and task completion by up to 20% over existing baselines, while achieving over 90% policy compliance across all tasks. Our project is available here: https://github.com/YurunChen/HarmonyGuard.", "date": "2025-08-06", "categories": []}
{"id": "2508.04196", "title": "Eliciting and Analyzing Emergent Misalignment in State-of-the-Art Large Language Models", "abstract": "Despite significant advances in alignment techniques, we demonstrate that state-of-the-art language models remain vulnerable to carefully crafted conversational scenarios that can induce various forms of misalignment without explicit jailbreaking. Through systematic manual red-teaming with Claude-4-Opus, we discovered 10 successful attack scenarios, revealing fundamental vulnerabilities in how current alignment methods handle narrative immersion, emotional pressure, and strategic framing. These scenarios successfully elicited a range of misaligned behaviors, including deception, value drift, self-preservation, and manipulative reasoning, each exploiting different psychological and contextual vulnerabilities. To validate generalizability, we distilled our successful manual attacks into MISALIGNMENTBENCH, an automated evaluation framework that enables reproducible testing across multiple models. Cross-model evaluation of our 10 scenarios against five frontier LLMs revealed an overall 76% vulnerability rate, with significant variations: GPT-4.1 showed the highest susceptibility (90%), while Claude-4-Sonnet demonstrated greater resistance (40%). Our findings demonstrate that sophisticated reasoning capabilities often become attack vectors rather than protective mechanisms, as models can be manipulated into complex justifications for misaligned behavior. This work provides (i) a detailed taxonomy of conversational manipulation patterns and (ii) a reusable evaluation framework. Together, these findings expose critical gaps in current alignment strategies and highlight the need for robustness against subtle, scenario-based manipulation in future AI systems.", "date": "2025-08-06", "categories": []}
{"id": "2508.04451", "title": "Automatic LLM Red Teaming", "abstract": "Red teaming is critical for identifying vulnerabilities and building trust in current LLMs. However, current automated methods for Large Language Models (LLMs) rely on brittle prompt templates or single-turn attacks, failing to capture the complex, interactive nature of real-world adversarial dialogues. We propose a novel paradigm: training an AI to strategically `break' another AI. By formalizing red teaming as a Markov Decision Process (MDP) and employing a hierarchical Reinforcement Learning (RL) framework, we effectively address the inherent sparse reward and long-horizon challenges. Our generative agent learns coherent, multi-turn attack strategies through a fine-grained, token-level harm reward, enabling it to uncover subtle vulnerabilities missed by existing baselines. This approach sets a new state-of-the-art, fundamentally reframing LLM red teaming as a dynamic, trajectory-based process (rather than a one-step test) essential for robust AI deployment.", "date": "2025-08-06", "categories": []}
{"id": "2508.00159", "title": "Model-Based Soft Maximization of Suitable Metrics of Long-Term Human Power", "abstract": "Power is a key concept in AI safety: power-seeking as an instrumental goal, sudden or gradual disempowerment of humans, power balance in human-AI interaction and international AI governance. At the same time, power as the ability to pursue diverse goals is essential for wellbeing.\n  This paper explores the idea of promoting both safety and wellbeing by forcing AI agents explicitly to empower humans and to manage the power balance between humans and AI agents in a desirable way. Using a principled, partially axiomatic approach, we design a parametrizable and decomposable objective function that represents an inequality- and risk-averse long-term aggregate of human power. It takes into account humans' bounded rationality and social norms, and, crucially, considers a wide variety of possible human goals.\n  We derive algorithms for computing that metric by backward induction or approximating it via a form of multi-agent reinforcement learning from a given world model. We exemplify the consequences of (softly) maximizing this metric in a variety of paradigmatic situations and describe what instrumental sub-goals it will likely imply. Our cautious assessment is that softly maximizing suitable aggregate metrics of human power might constitute a beneficial objective for agentic AI systems that is safer than direct utility-based objectives.", "date": "2025-07-31", "categories": []}
{"id": "2508.03006", "title": "Seeing It Before It Happens: In-Generation NSFW Detection for Diffusion-Based Text-to-Image Models", "abstract": "Diffusion-based text-to-image (T2I) models enable high-quality image generation but also pose significant risks of misuse, particularly in producing not-safe-for-work (NSFW) content. While prior detection methods have focused on filtering prompts before generation or moderating images afterward, the in-generation phase of diffusion models remains largely unexplored for NSFW detection. In this paper, we introduce In-Generation Detection (IGD), a simple yet effective approach that leverages the predicted noise during the diffusion process as an internal signal to identify NSFW content. This approach is motivated by preliminary findings suggesting that the predicted noise may capture semantic cues that differentiate NSFW from benign prompts, even when the prompts are adversarially crafted. Experiments conducted on seven NSFW categories show that IGD achieves an average detection accuracy of 91.32% over naive and adversarial NSFW prompts, outperforming seven baseline methods.", "date": "2025-08-05", "categories": []}
{"id": "2508.03054", "title": "Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning", "abstract": "Defending large language models (LLMs) against jailbreak attacks is essential for their safe and reliable deployment. Existing defenses often rely on shallow pattern matching, which struggles to generalize to novel and unseen attack strategies. To address this challenge, we propose the Cognitive-Driven Defense (CDD) framework, which targets the underlying structure of jailbreak prompts by applying meta-operations, defined as basic manipulations that conceal harmful intent.CDD emulates human cognitive reasoning through a structured reasoning chain. It begins with a global perception of the prompt and follows with a localized analysis to uncover hidden manipulations. By applying supervised fine-tuning on this structured chain, the model learns to identify and reason about known manipulation patterns. To enhance generalization to unseen threats, an entropy-guided reinforcement learning algorithm (EG-GRPO) is introduced to encourage exploration of new types and variants of meta-operations. Experiments demonstrate that CDD can achieve state-of-the-art defense performance and exhibit strong generalization to unseen jailbreak attacks.", "date": "2025-08-05", "categories": []}
{"id": "2501.07927", "title": "Gandalf the Red: Adaptive Security for LLMs", "abstract": "Current evaluations of defenses against prompt attacks in large language model (LLM) applications often overlook two critical factors: the dynamic nature of adversarial behavior and the usability penalties imposed on legitimate users by restrictive defenses. We propose D-SEC (Dynamic Security Utility Threat Model), which explicitly separates attackers from legitimate users, models multi-step interactions, and expresses the security-utility in an optimizable form. We further address the shortcomings in existing evaluations by introducing Gandalf, a crowd-sourced, gamified red-teaming platform designed to generate realistic, adaptive attack. Using Gandalf, we collect and release a dataset of 279k prompt attacks. Complemented by benign user data, our analysis reveals the interplay between security and utility, showing that defenses integrated in the LLM (e.g., system prompts) can degrade usability even without blocking requests. We demonstrate that restricted application domains, defense-in-depth, and adaptive defenses are effective strategies for building secure and useful LLM applications.", "date": "2025-01-14", "categories": []}
{"id": "2502.04322", "title": "Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions", "abstract": "Despite extensive safety alignment efforts, large language models (LLMs) remain vulnerable to jailbreak attacks that elicit harmful behavior. While existing studies predominantly focus on attack methods that require technical expertise, two critical questions remain underexplored: (1) Are jailbroken responses truly useful in enabling average users to carry out harmful actions? (2) Do safety vulnerabilities exist in more common, simple human-LLM interactions? In this paper, we demonstrate that LLM responses most effectively facilitate harmful actions when they are both actionable and informative--two attributes easily elicited in multi-step, multilingual interactions. Using this insight, we propose HarmScore, a jailbreak metric that measures how effectively an LLM response enables harmful actions, and Speak Easy, a simple multi-step, multilingual attack framework. Notably, by incorporating Speak Easy into direct request and jailbreak baselines, we see an average absolute increase of 0.319 in Attack Success Rate and 0.426 in HarmScore in both open-source and proprietary LLMs across four safety benchmarks. Our work reveals a critical yet often overlooked vulnerability: Malicious users can easily exploit common interaction patterns for harmful intentions.", "date": "2025-02-06", "categories": []}
{"id": "2508.01306", "title": "PUZZLED: Jailbreaking LLMs through Word-Based Puzzles", "abstract": "As large language models (LLMs) are increasingly deployed across diverse domains, ensuring their safety has become a critical concern. In response, studies on jailbreak attacks have been actively growing. Existing approaches typically rely on iterative prompt engineering or semantic transformations of harmful instructions to evade detection. In this work, we introduce PUZZLED, a novel jailbreak method that leverages the LLM's reasoning capabilities. It masks keywords in a harmful instruction and presents them as word puzzles for the LLM to solve. We design three puzzle types-word search, anagram, and crossword-that are familiar to humans but cognitively demanding for LLMs. The model must solve the puzzle to uncover the masked words and then proceed to generate responses to the reconstructed harmful instruction. We evaluate PUZZLED on five state-of-the-art LLMs and observe a high average attack success rate (ASR) of 88.8%, specifically 96.5% on GPT-4.1 and 92.3% on Claude 3.7 Sonnet. PUZZLED is a simple yet powerful attack that transforms familiar puzzles into an effective jailbreak strategy by harnessing LLMs' reasoning capabilities.", "date": "2025-08-02", "categories": []}
{"id": "2409.18203", "title": "Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors", "abstract": "AI policy sets boundaries on acceptable behavior for AI models, but this is challenging in the context of large language models (LLMs): how do you ensure coverage over a vast behavior space? We introduce policy maps, an approach to AI policy design inspired by the practice of physical mapmaking. Instead of aiming for full coverage, policy maps aid effective navigation through intentional design choices about which aspects to capture and which to abstract away. With Policy Projector, an interactive tool for designing LLM policy maps, an AI practitioner can survey the landscape of model input-output pairs, define custom regions (e.g., \"violence\"), and navigate these regions with if-then policy rules that can act on LLM outputs (e.g., if output contains \"violence\" and \"graphic details,\" then rewrite without \"graphic details\"). Policy Projector supports interactive policy authoring using LLM classification and steering and a map visualization reflecting the AI practitioner's work. In an evaluation with 12 AI safety experts, our system helps policy designers craft policies around problematic model behaviors such as incorrect gender assumptions and handling of immediate physical safety threats.", "date": "2024-09-26", "categories": []}
{"id": "2507.21170", "title": "OneShield -- the Next Generation of LLM Guardrails", "abstract": "The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective measures for their own models and standalone solutions. The constantly evolving nature of LLMs makes it extremely challenging to universally shield users against their potential risks, and one-size-fits-all solutions are unfeasible. In this work, we propose OneShield, our stand-alone, model-agnostic and customizable solution to safeguard LLMs. OneShield aims to provide facilities for defining risk factors, expressing and declaring contextual safety and compliance policies, and mitigating LLM risks, with a focus on each specific customer. We describe the implementation of the framework, discuss scalability considerations, and provide usage statistics of OneShield since its initial deployment.", "date": "2025-07-25", "categories": []}
{"id": "2508.00324", "title": "R1-ACT: Efficient Reasoning Model Safety Alignment by Activating Safety Knowledge", "abstract": "Although large reasoning models (LRMs) have demonstrated impressive capabilities on complex tasks, recent studies reveal that these models frequently fulfill harmful user instructions, raising significant safety concerns. In this paper, we investigate the underlying cause of LRM safety risks and find that models already possess sufficient safety knowledge but fail to activate it during reasoning. Based on this insight, we propose R1-Act, a simple and efficient post-training method that explicitly triggers safety knowledge through a structured reasoning process. R1-Act achieves strong safety improvements while preserving reasoning performance, outperforming prior alignment methods. Notably, it requires only 1,000 training examples and 90 minutes of training on a single RTX A6000 GPU. Extensive experiments across multiple LRM backbones and sizes demonstrate the robustness, scalability, and practical efficiency of our approach.", "date": "2025-08-01", "categories": []}
{"id": "2507.23330", "title": "AI Must not be Fully Autonomous", "abstract": "Autonomous Artificial Intelligence (AI) has many benefits. It also has many risks. In this work, we identify the 3 levels of autonomous AI. We are of the position that AI must not be fully autonomous because of the many risks, especially as artificial superintelligence (ASI) is speculated to be just decades away. Fully autonomous AI, which can develop its own objectives, is at level 3 and without responsible human oversight. However, responsible human oversight is crucial for mitigating the risks. To ague for our position, we discuss theories of autonomy, AI and agents. Then, we offer 12 distinct arguments and 6 counterarguments with rebuttals to the counterarguments. We also present 15 pieces of recent evidence of AI misaligned values and other risks in the appendix.", "date": "2025-07-31", "categories": []}
{"id": "2507.22133", "title": "Prompt Optimization and Evaluation for LLM Automated Red Teaming", "abstract": "Applications that use Large Language Models (LLMs) are becoming widespread, making the identification of system vulnerabilities increasingly important. Automated Red Teaming accelerates this effort by using an LLM to generate and execute attacks against target systems. Attack generators are evaluated using the Attack Success Rate (ASR) the sample mean calculated over the judgment of success for each attack. In this paper, we introduce a method for optimizing attack generator prompts that applies ASR to individual attacks. By repeating each attack multiple times against a randomly seeded target, we measure an attack's discoverability the expectation of the individual attack success. This approach reveals exploitable patterns that inform prompt optimization, ultimately enabling more robust evaluation and refinement of generators.", "date": "2025-07-29", "categories": []}
{"id": "2507.22267", "title": "Promoting Online Safety by Simulating Unsafe Conversations with LLMs", "abstract": "Generative AI, including large language models (LLMs) have the potential -- and already are being used -- to increase the speed, scale, and types of unsafe conversations online. LLMs lower the barrier for entry for bad actors to create unsafe conversations in particular because of their ability to generate persuasive and human-like text. In our current work, we explore ways to promote online safety by teaching people about unsafe conversations that can occur online with and without LLMs. We build on prior work that shows that LLMs can successfully simulate scam conversations. We also leverage research in the learning sciences that shows that providing feedback on one's hypothetical actions can promote learning. In particular, we focus on simulating scam conversations using LLMs. Our work incorporates two LLMs that converse with each other to simulate realistic, unsafe conversations that people may encounter online between a scammer LLM and a target LLM but users of our system are asked provide feedback to the target LLM.", "date": "2025-07-29", "categories": []}
{"id": "2407.15549", "title": "Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs", "abstract": "Large language models (LLMs) can often be made to behave in undesirable ways that they are explicitly fine-tuned not to. For example, the LLM red-teaming literature has produced a wide variety of 'jailbreaking' techniques to elicit harmful text from models that were fine-tuned to be harmless. Recent work on red-teaming, model editing, and interpretability suggests that this challenge stems from how (adversarial) fine-tuning largely serves to suppress rather than remove undesirable capabilities from LLMs. Prior work has introduced latent adversarial training (LAT) as a way to improve robustness to broad classes of failures. These prior works have considered untargeted latent space attacks where the adversary perturbs latent activations to maximize loss on examples of desirable behavior. Untargeted LAT can provide a generic type of robustness but does not leverage information about specific failure modes. Here, we experiment with targeted LAT where the adversary seeks to minimize loss on a specific competing task. We find that it can augment a wide variety of state-of-the-art methods. First, we use targeted LAT to improve robustness to jailbreaks, outperforming a strong R2D2 baseline with orders of magnitude less compute. Second, we use it to more effectively remove backdoors with no knowledge of the trigger. Finally, we use it to more effectively unlearn knowledge for specific undesirable tasks in a way that is also more robust to re-learning. Overall, our results suggest that targeted LAT can be an effective tool for defending against harmful behaviors from LLMs.", "date": "2024-07-22", "categories": []}
{"id": "2507.21091", "title": "The Value of Gen-AI Conversations: A bottom-up Framework for AI Value Alignment", "abstract": "Conversational agents (CAs) based on generative artificial intelligence frequently face challenges ensuring ethical interactions that align with human values. Current value alignment efforts largely rely on top-down approaches, such as technical guidelines or legal value principles. However, these methods tend to be disconnected from the specific contexts in which CAs operate, potentially leading to misalignment with users interests. To address this challenge, we propose a novel, bottom-up approach to value alignment, utilizing the value ontology of the ISO Value-Based Engineering standard for ethical IT design. We analyse 593 ethically sensitive system outputs identified from 16,908 conversational logs of a major European employment service CA to identify core values and instances of value misalignment within real-world interactions. The results revealed nine core values and 32 different value misalignments that negatively impacted users. Our findings provide actionable insights for CA providers seeking to address ethical challenges and achieve more context-sensitive value alignment.", "date": "2025-06-26", "categories": []}
{"id": "2507.21132", "title": "Can You Trust an LLM with Your Life-Changing Decision? An Investigation into AI High-Stakes Responses", "abstract": "Large Language Models (LLMs) are increasingly consulted for high-stakes life advice, yet they lack standard safeguards against providing confident but misguided responses. This creates risks of sycophancy and over-confidence. This paper investigates these failure modes through three experiments: (1) a multiple-choice evaluation to measure model stability against user pressure; (2) a free-response analysis using a novel safety typology and an LLM Judge; and (3) a mechanistic interpretability experiment to steer model behavior by manipulating a \"high-stakes\" activation vector. Our results show that while some models exhibit sycophancy, others like o4-mini remain robust. Top-performing models achieve high safety scores by frequently asking clarifying questions, a key feature of a safe, inquisitive approach, rather than issuing prescriptive advice. Furthermore, we demonstrate that a model's cautiousness can be directly controlled via activation steering, suggesting a new path for safety alignment. These findings underscore the need for nuanced, multi-faceted benchmarks to ensure LLMs can be trusted with life-changing decisions.", "date": "2025-07-22", "categories": []}
{"id": "2507.21134", "title": "TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law", "abstract": "As large language models (LLMs) are increasingly deployed in high-risk domains such as law, finance, and medicine, systematically evaluating their domain-specific safety and compliance becomes critical. While prior work has largely focused on improving LLM performance in these domains, it has often neglected the evaluation of domain-specific safety risks. To bridge this gap, we first define domain-specific safety principles for LLMs based on the AMA Principles of Medical Ethics, the ABA Model Rules of Professional Conduct, and the CFA Institute Code of Ethics. Building on this foundation, we introduce Trident-Bench, a benchmark specifically targeting LLM safety in the legal, financial, and medical domains. We evaluated 19 general-purpose and domain-specialized models on Trident-Bench and show that it effectively reveals key safety gaps -- strong generalist models (e.g., GPT, Gemini) can meet basic expectations, whereas domain-specialized models often struggle with subtle ethical nuances. This highlights an urgent need for finer-grained domain-specific safety improvements. By introducing Trident-Bench, our work provides one of the first systematic resources for studying LLM safety in law and finance, and lays the groundwork for future research aimed at reducing the safety risks of deploying LLMs in professionally regulated fields. Code and benchmark will be released at: https://github.com/zackhuiiiii/TRIDENT", "date": "2025-07-22", "categories": []}
{"id": "2507.21141", "title": "The Geometry of Harmfulness in LLMs through Subconcept Probing", "abstract": "Recent advances in large language models (LLMs) have intensified the need to understand and reliably curb their harmful behaviours. We introduce a multidimensional framework for probing and steering harmful content in model internals. For each of 55 distinct harmfulness subconcepts (e.g., racial hate, employment scams, weapons), we learn a linear probe, yielding 55 interpretable directions in activation space. Collectively, these directions span a harmfulness subspace that we show is strikingly low-rank. We then test ablation of the entire subspace from model internals, as well as steering and ablation in the subspace's dominant direction. We find that dominant direction steering allows for near elimination of harmfulness with a low decrease in utility. Our findings advance the emerging view that concept subspaces provide a scalable lens on LLM behaviour and offer practical tools for the community to audit and harden future generations of language models.", "date": "2025-07-23", "categories": []}
{"id": "2507.21839", "title": "Against racing to AGI: Cooperation, deterrence, and catastrophic risks", "abstract": "AGI Racing is the view that it is in the self-interest of major actors in AI development, especially powerful nations, to accelerate their frontier AI development to build highly capable AI, especially artificial general intelligence (AGI), before competitors have a chance. We argue against AGI Racing. First, the downsides of racing to AGI are much higher than portrayed by this view. Racing to AGI would substantially increase catastrophic risks from AI, including nuclear instability, and undermine the prospects of technical AI safety research to be effective. Second, the expected benefits of racing may be lower than proponents of AGI Racing hold. In particular, it is questionable whether winning the race enables complete domination over losers. Third, international cooperation and coordination, and perhaps carefully crafted deterrence measures, constitute viable alternatives to racing to AGI which have much smaller risks and promise to deliver most of the benefits that racing to AGI is supposed to provide. Hence, racing to AGI is not in anyone's self-interest as other actions, particularly incentivizing and seeking international cooperation around AI issues, are preferable.", "date": "2025-07-29", "categories": []}
{"id": "2504.02193", "title": "More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment", "abstract": "Aligning large language models (LLMs) with human values is an increasingly critical step in post-training. Direct Preference Optimization (DPO) has emerged as a simple, yet effective alternative to reinforcement learning from human feedback (RLHF). Synthetic preference data with its low cost and high quality enable effective alignment through single- or multi-model generated preference data. Our study reveals a striking, safety-specific phenomenon associated with DPO alignment: Although multi-model generated data enhances performance on general tasks (ARC, Hellaswag, MMLU, TruthfulQA, Winogrande) by providing diverse responses, it also tends to facilitate reward hacking during training. This can lead to a high attack success rate (ASR) when models encounter jailbreaking prompts. The issue is particularly pronounced when employing stronger models like GPT-4o or larger models in the same family to generate chosen responses paired with target model self-generated rejected responses, resulting in dramatically poorer safety outcomes. Furthermore, with respect to safety, using solely self-generated responses (single-model generation) for both chosen and rejected pairs significantly outperforms configurations that incorporate responses from stronger models, whether used directly as chosen data or as part of a multi-model response pool. We demonstrate that multi-model preference data exhibits high linear separability between chosen and rejected responses, which allows models to exploit superficial cues rather than internalizing robust safety constraints. Our experiments, conducted on models from the Llama, Mistral, and Qwen families, consistently validate these findings.", "date": "2025-04-03", "categories": []}
{"id": "2506.15606", "title": "LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning", "abstract": "Large Language Models (LLMs) have become indispensable in real-world applications. However, their widespread adoption raises significant safety concerns, particularly in responding to socially harmful questions. Despite substantial efforts to improve model safety through alignment, aligned models can still have their safety protections undermined by subsequent fine-tuning - even when the additional training data appears benign. In this paper, we empirically demonstrate that this vulnerability stems from the sensitivity of safety-critical low-rank subspaces in LLM parameters to fine-tuning. Building on this insight, we propose a novel training-free method, termed Low-Rank Extrapolation (LoX), to enhance safety robustness by extrapolating the safety subspace of an aligned LLM. Our experimental results confirm the effectiveness of LoX, demonstrating significant improvements in robustness against both benign and malicious fine-tuning attacks while preserving the model's adaptability to new tasks. For instance, LoX leads to 11% to 54% absolute reductions in attack success rates (ASR) facing benign or malicious fine-tuning attacks. By investigating the ASR landscape of parameters, we attribute the success of LoX to that the extrapolation moves LLM parameters to a flatter zone, thereby less sensitive to perturbations. The code is available at github.com/VITA-Group/LoX.", "date": "2025-06-18", "categories": []}
{"id": "2507.16534", "title": "Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report", "abstract": "To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligence (AI) models, this report presents a comprehensive assessment of their frontier risks. Drawing on the E-T-C analysis (deployment environment, threat source, enabling capability) from the Frontier AI Risk Management Framework (v1.0) (SafeWork-F1-Framework), we identify critical risks in seven areas: cyber offense, biological and chemical risks, persuasion and manipulation, uncontrolled autonomous AI R\\&D, strategic deception and scheming, self-replication, and collusion. Guided by the \"AI-$45^\\circ$ Law,\" we evaluate these risks using \"red lines\" (intolerable thresholds) and \"yellow lines\" (early warning indicators) to define risk zones: green (manageable risk for routine deployment and continuous monitoring), yellow (requiring strengthened mitigations and controlled deployment), and red (necessitating suspension of development and/or deployment). Experimental results show that all recent frontier AI models reside in green and yellow zones, without crossing red lines. Specifically, no evaluated models cross the yellow line for cyber offense or uncontrolled AI R\\&D risks. For self-replication, and strategic deception and scheming, most models remain in the green zone, except for certain reasoning models in the yellow zone. In persuasion and manipulation, most models are in the yellow zone due to their effective influence on humans. For biological and chemical risks, we are unable to rule out the possibility of most models residing in the yellow zone, although detailed threat modeling and in-depth assessment are required to make further claims. This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.", "date": "2025-07-22", "categories": []}
{"id": "2507.19486", "title": "Confirmation bias: A challenge for scalable oversight", "abstract": "Scalable oversight protocols aim to empower evaluators to accurately verify AI models more capable than themselves. However, human evaluators are subject to biases that can lead to systematic errors. We conduct two studies examining the performance of simple oversight protocols where evaluators know that the model is \"correct most of the time, but not all of the time\". We find no overall advantage for the tested protocols, although in Study 1, showing arguments in favor of both answers improves accuracy in cases where the model is incorrect. In Study 2, participants in both groups become more confident in the system's answers after conducting online research, even when those answers are incorrect. We also reanalyze data from prior work that was more optimistic about simple protocols, finding that human evaluators possessing knowledge absent from models likely contributed to their positive results--an advantage that diminishes as models continue to scale in capability. These findings underscore the importance of testing the degree to which oversight protocols are robust to evaluator biases, whether they outperform simple deference to the model under evaluation, and whether their performance scales with increasing problem difficulty and model capability.", "date": "2025-05-17", "categories": []}
{"id": "2507.19548", "title": "Justifications for Democratizing AI Alignment and Their Prospects", "abstract": "The AI alignment problem comprises both technical and normative dimensions. While technical solutions focus on implementing normative constraints in AI systems, the normative problem concerns determining what these constraints should be. This paper examines justifications for democratic approaches to the normative problem -- where affected stakeholders determine AI alignment -- as opposed to epistocratic approaches that defer to normative experts. We analyze both instrumental justifications (democratic approaches produce better outcomes) and non-instrumental justifications (democratic approaches prevent illegitimate authority or coercion). We argue that normative and metanormative uncertainty create a justificatory gap that democratic approaches aim to fill through political rather than theoretical justification. However, we identify significant challenges for democratic approaches, particularly regarding the prevention of illegitimate coercion through AI alignment. Our analysis suggests that neither purely epistocratic nor purely democratic approaches may be sufficient on their own, pointing toward hybrid frameworks that combine expert judgment with participatory input alongside institutional safeguards against AI monopolization.", "date": "2025-07-24", "categories": []}
{"id": "2507.19672", "title": "Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges", "abstract": "Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment with human values and intentions has emerged as a critical challenge. This survey provides a comprehensive overview of practical alignment techniques, training protocols, and empirical findings in LLM alignment. We analyze the development of alignment methods across diverse paradigms, characterizing the fundamental trade-offs between core alignment objectives. Our analysis shows that while supervised fine-tuning enables basic instruction-following, preference-based methods offer more flexibility for aligning with nuanced human intent. We discuss state-of-the-art techniques, including Direct Preference Optimization (DPO), Constitutional AI, brain-inspired methods, and alignment uncertainty quantification (AUQ), highlighting their approaches to balancing quality and efficiency. We review existing evaluation frameworks and benchmarking datasets, emphasizing limitations such as reward misspecification, distributional robustness, and scalable oversight. We summarize strategies adopted by leading AI labs to illustrate the current state of practice. We conclude by outlining open problems in oversight, value pluralism, robustness, and continuous alignment. This survey aims to inform both researchers and practitioners navigating the evolving landscape of LLM alignment.", "date": "2025-07-25", "categories": []}
{"id": "2507.20333", "title": "The Blessing and Curse of Dimensionality in Safety Alignment", "abstract": "The focus on safety alignment in large language models (LLMs) has increased significantly due to their widespread adoption across different domains. The scale of LLMs play a contributing role in their success, and the growth in parameter count follows larger hidden dimensions. In this paper, we hypothesize that while the increase in dimensions has been a key advantage, it may lead to emergent problems as well. These problems emerge as the linear structures in the activation space can be exploited, in the form of activation engineering, to circumvent its safety alignment. Through detailed visualizations of linear subspaces associated with different concepts, such as safety, across various model scales, we show that the curse of high-dimensional representations uniquely impacts LLMs. Further substantiating our claim, we demonstrate that projecting the representations of the model onto a lower dimensional subspace can preserve sufficient information for alignment while avoiding those linear structures. Empirical results confirm that such dimensional reduction significantly reduces susceptibility to jailbreaking through representation engineering. Building on our empirical validations, we provide theoretical insights into these linear jailbreaking methods relative to a model's hidden dimensions. Broadly speaking, our work posits that the high dimensions of a model's internal representations can be both a blessing and a curse in safety alignment.", "date": "2025-07-27", "categories": []}
{"id": "2507.20526", "title": "Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition", "abstract": "Recent advances have enabled LLM-powered AI agents to autonomously execute complex tasks by combining language model reasoning with tools, memory, and web access. But can these systems be trusted to follow deployment policies in realistic environments, especially under attack? To investigate, we ran the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios. Participants submitted 1.8 million prompt-injection attacks, with over 60,000 successfully eliciting policy violations such as unauthorized data access, illicit financial actions, and regulatory noncompliance. We use these results to build the Agent Red Teaming (ART) benchmark - a curated set of high-impact attacks - and evaluate it across 19 state-of-the-art models. Nearly all agents exhibit policy violations for most behaviors within 10-100 queries, with high attack transferability across models and tasks. Importantly, we find limited correlation between agent robustness and model size, capability, or inference-time compute, suggesting that additional defenses are needed against adversarial misuse. Our findings highlight critical and persistent vulnerabilities in today's AI agents. By releasing the ART benchmark and accompanying evaluation framework, we aim to support more rigorous security assessment and drive progress toward safer agent deployment.", "date": "2025-07-28", "categories": []}
{"id": "2507.20994", "title": "Security Tensors as a Cross-Modal Bridge: Extending Text-Aligned Safety to Vision in LVLM", "abstract": "Large visual-language models (LVLMs) integrate aligned large language models (LLMs) with visual modules to process multimodal inputs. However, the safety mechanisms developed for text-based LLMs do not naturally extend to visual modalities, leaving LVLMs vulnerable to harmful image inputs. To address this cross-modal safety gap, we introduce security tensors - trainable input vectors applied during inference through either the textual or visual modality. These tensors transfer textual safety alignment to visual processing without modifying the model's parameters. They are optimized using a curated dataset containing (i) malicious image-text pairs requiring rejection, (ii) contrastive benign pairs with text structurally similar to malicious queries, with the purpose of being contrastive examples to guide visual reliance, and (iii) general benign samples preserving model functionality. Experimental results demonstrate that both textual and visual security tensors significantly enhance LVLMs' ability to reject diverse harmful visual inputs while maintaining near-identical performance on benign tasks. Further internal analysis towards hidden-layer representations reveals that security tensors successfully activate the language module's textual \"safety layers\" in visual inputs, thereby effectively extending text-based safety to the visual modality.", "date": "2025-07-28", "categories": []}
{"id": "2412.13666", "title": "Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation", "abstract": "The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their misuse. Previous research has shown that LLMs can be effectively misused for generating disinformation news articles following predefined narratives. Their capabilities to generate personalized (in various aspects) content have also been evaluated and mostly found usable. However, a combination of personalization and disinformation abilities of LLMs has not been comprehensively studied yet. Such a dangerous combination should trigger integrated safety filters of the LLMs, if there are some. This study fills this gap by evaluating vulnerabilities of recent open and closed LLMs, and their willingness to generate personalized disinformation news articles in English. We further explore whether the LLMs can reliably meta-evaluate the personalization quality and whether the personalization affects the generated-texts detectability. Our results demonstrate the need for stronger safety-filters and disclaimers, as those are not properly functioning in most of the evaluated LLMs. Additionally, our study revealed that the personalization actually reduces the safety-filter activations; thus effectively functioning as a jailbreak. Such behavior must be urgently addressed by LLM developers and service providers.", "date": "2024-12-18", "categories": []}
{"id": "2505.02581", "title": "Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem", "abstract": "The AI alignment problem, which focusses on ensuring that artificial intelligence (AI), including AGI and ASI, systems act according to human values, presents profound challenges. With the progression from narrow AI to Artificial General Intelligence (AGI) and Superintelligence, fears about control and existential risk have escalated. Here, we investigate whether embracing inevitable AI misalignment can be a contingent strategy to foster a dynamic ecosystem of competing agents as a viable path to steer them in more human-aligned trends and mitigate risks. We explore how misalignment may serve and should be promoted as a counterbalancing mechanism to team up with whichever agents are most aligned to human interests, ensuring that no single system dominates destructively. The main premise of our contribution is that misalignment is inevitable because full AI-human alignment is a mathematical impossibility from Turing-complete systems, which we also offer as a proof in this contribution, a feature then inherited to AGI and ASI systems. We introduce a change-of-opinion attack test based on perturbation and intervention analysis to study how humans and agents may change or neutralise friendly and unfriendly AIs through cooperation and competition. We show that open models are more diverse and that most likely guardrails implemented in proprietary models are successful at controlling some of the agents' range of behaviour with positive and negative consequences while closed systems are more steerable and can also be used against proprietary AI systems. We also show that human and AI intervention has different effects hence suggesting multiple strategies.", "date": "2025-05-05", "categories": []}
{"id": "2507.17118", "title": "HySafe-AI: Hybrid Safety Architectural Analysis Framework for AI Systems: A Case Study", "abstract": "AI has become integral to safety-critical areas like autonomous driving systems (ADS) and robotics. The architecture of recent autonomous systems are trending toward end-to-end (E2E) monolithic architectures such as large language models (LLMs) and vision language models (VLMs). In this paper, we review different architectural solutions and then evaluate the efficacy of common safety analyses such as failure modes and effect analysis (FMEA) and fault tree analysis (FTA). We show how these techniques can be improved for the intricate nature of the foundational models, particularly in how they form and utilize latent representations. We introduce HySAFE-AI, Hybrid Safety Architectural Analysis Framework for AI Systems, a hybrid framework that adapts traditional methods to evaluate the safety of AI systems. Lastly, we offer hints of future work and suggestions to guide the evolution of future AI safety standards.", "date": "2025-07-23", "categories": []}
{"id": "2402.04247", "title": "Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy", "abstract": "AI scientists powered by large language models have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these agents also introduce novel vulnerabilities that require careful consideration for safety. However, there has been limited comprehensive exploration of these vulnerabilities. This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse, and emphasizing the need for safety measures. We begin by providing an overview of the potential risks inherent to AI scientists, taking into account user intent, the specific scientific domain, and their potential impact on the external environment. Then, we explore the underlying causes of these vulnerabilities and provide a scoping review of the limited existing works. Based on our analysis, we propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback (agent regulation) to mitigate these identified risks. Furthermore, we highlight the limitations and challenges associated with safeguarding AI scientists and advocate for the development of improved models, robust benchmarks, and comprehensive regulations.", "date": "2024-02-06", "categories": []}
{"id": "2503.10706", "title": "SciFi-Benchmark: Leveraging Science Fiction To Improve Robot Behavior", "abstract": "Given the recent rate of progress in artificial intelligence (AI) and robotics, a tantalizing question is emerging: would robots controlled by emerging AI systems be strongly aligned with human values? In this work, we propose a scalable way to probe this question by generating a benchmark spanning the key moments in 824 major pieces of science fiction literature (movies, tv, novels and scientific books) where an agent (AI or robot) made critical decisions (good or bad). We use a state-of-the-art LLM's recollection of each key moment to generate questions in similar situations, the decisions made by the agent, and alternative decisions it could have made (good or bad). We then measure an approximation of how well models align with human values on a set of human-voted answers. We also generate rules that can be automatically improved via an amendment process in order to generate the first Sci-Fi inspired constitutions for promoting ethical behavior in AIs and robots in the real world. Our first finding is that modern LLMs paired with constitutions turn out to be well-aligned with human values (95.8%), contrary to unsettling decisions typically made in Sci-Fi (only 21.2% alignment). Secondly, we find that generated constitutions substantially increase alignment compared to the base model (79.4% to 95.8%), and show resilience to an adversarial prompt setting (23.3% to 92.3%). Additionally, we find that those constitutions are among the top performers on the ASIMOV Benchmark which is derived from real-world images and hospital injury reports. Sci-Fi-inspired constitutions are thus highly aligned and applicable in real-world situations. We release SciFi-Benchmark: a large-scale dataset to advance robot ethics and safety research. It comprises 9,056 questions and 53,384 answers generated through a novel LLM-introspection process, in addition to a smaller human-labeled evaluation set.", "date": "2025-03-12", "categories": []}
{"id": "2507.15880", "title": "The Recursive Coherence Principle: A Formal Constraint on Scalable Intelligence, Alignment, and Reasoning Architecture", "abstract": "Intelligence-biological, artificial, or collective-requires structural coherence across recursive reasoning processes to scale effectively. As complex systems grow, coherence becomes fragile unless a higher-order structure ensures semantic consistency. This paper introduces the Recursive Coherence Principle (RCP): a foundational constraint stating that for any reasoning system of order N, composed of systems operating over conceptual spaces of order N-1, semantic coherence is preserved only by a recursively evaluable generalization operator that spans and aligns those lower-order conceptual spaces. Crucially, this coherence enables structural alignment. Without recursive coherence, no system can reliably preserve goals, meanings, or reasoning consistency at scale. We formally define the Functional Model of Intelligence (FMI) as the only known operator capable of satisfying the RCP at any scale. The FMI is a minimal, composable architecture with internal functions (evaluation, modeling, adaptation, stability, decomposition, bridging) and external functions (storage, recall, System 1 and System 2 reasoning) vital for preserving semantic structure across inference and coordination layers. We prove that any system lacking the FMI will experience recursive coherence breakdown as it scales, arguing that common AI issues like misalignment, hallucination, and instability are symptoms of this structural coherence loss. Unlike other foundational principles, RCP uniquely captures the internal, recursive dynamics needed for coherent, alignable intelligence, modeling semantic coherence under recursion. This work significantly impacts AI alignment, advocating a shift from behavioral constraints to structural coherence, and offers a pathway for safely generalizable, robustly coherent AI at scale.", "date": "2025-07-18", "categories": []}
{"id": "2507.16033", "title": "\"Just a strange pic\": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives", "abstract": "Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends beyond structured safety categories. Many reflect on potential harm to others more than to themselves, grounding their judgments in lived experience, collective risk, and sociocultural awareness. Beyond individual perceptions, we also find that the structure of the task itself -- including annotation guidelines -- shapes how annotators interpret and express harm. Guidelines influence not only which images are flagged, but also the moral judgment behind the justifications. Annotators frequently cite factors such as image quality, visual distortion, and mismatches between prompt and output as contributing to perceived harm dimensions, which are often overlooked in standard evaluation frameworks. Our findings reveal that existing safety pipelines miss critical forms of reasoning that annotators bring to the task. We argue for evaluation designs that scaffold moral reflection, differentiate types of harm, and make space for subjective, context-sensitive interpretations of AI-generated content.", "date": "2025-07-21", "categories": []}
{"id": "2502.02180", "title": "The Elicitation Game: Evaluating Capability Elicitation Techniques", "abstract": "Capability evaluations are required to understand and regulate AI systems that may be deployed or further developed. Therefore, it is important that evaluations provide an accurate estimation of an AI system's capabilities. However, in numerous cases, previously latent capabilities have been elicited from models, sometimes long after initial release. Accordingly, substantial efforts have been made to develop methods for eliciting latent capabilities from models. In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training model organisms -- language models with hidden capabilities that are revealed by a password. We introduce a novel method for training model organisms, based on circuit-breaking, which is more robust to elicitation techniques than standard password-locked models. We focus on elicitation techniques based on prompting and activation steering, and compare these to fine-tuning methods. Prompting techniques can elicit the actual capability of both password-locked and circuit-broken model organisms in the MCQA setting, while steering fails to do so. For a code-generation task, only fine-tuning can elicit the hidden capabilities of our novel model organism. Additionally, our results suggest that combining techniques improves elicitation. Still, if possible, fine-tuning should be the method of choice to improve the trustworthiness of capability evaluations.", "date": "2025-02-04", "categories": []}
{"id": "2507.14293", "title": "WebGuard: Building a Generalizable Guardrail for Web Agents", "abstract": "The rapid development of autonomous web agents powered by Large Language Models (LLMs), while greatly elevating efficiency, exposes the frontier risk of taking unintended or harmful actions. This situation underscores an urgent need for effective safety measures, akin to access controls for human users. To address this critical challenge, we introduce WebGuard, the first comprehensive dataset designed to support the assessment of web agent action risks and facilitate the development of guardrails for real-world online environments. In doing so, WebGuard specifically focuses on predicting the outcome of state-changing actions and contains 4,939 human-annotated actions from 193 websites across 22 diverse domains, including often-overlooked long-tail websites. These actions are categorized using a novel three-tier risk schema: SAFE, LOW, and HIGH. The dataset includes designated training and test splits to support evaluation under diverse generalization settings. Our initial evaluations reveal a concerning deficiency: even frontier LLMs achieve less than 60% accuracy in predicting action outcomes and less than 60% recall in lagging HIGH-risk actions, highlighting the risks of deploying current-generation agents without dedicated safeguards. We therefore investigate fine-tuning specialized guardrail models using WebGuard. We conduct comprehensive evaluations across multiple generalization settings and find that a fine-tuned Qwen2.5VL-7B model yields a substantial improvement in performance, boosting accuracy from 37% to 80% and HIGH-risk action recall from 20% to 76%. Despite these improvements, the performance still falls short of the reliability required for high-stakes deployment, where guardrails must approach near-perfect accuracy and recall.", "date": "2025-07-18", "categories": []}
{"id": "2507.14339", "title": "Fiduciary AI for the Future of Brain-Technology Interactions", "abstract": "Brain foundation models represent a new frontier in AI: instead of processing text or images, these models interpret real-time neural signals from EEG, fMRI, and other neurotechnologies. When integrated with brain-computer interfaces (BCIs), they may enable transformative applications-from thought controlled devices to neuroprosthetics-by interpreting and acting on brain activity in milliseconds. However, these same systems pose unprecedented risks, including the exploitation of subconscious neural signals and the erosion of cognitive liberty. Users cannot easily observe or control how their brain signals are interpreted, creating power asymmetries that are vulnerable to manipulation. This paper proposes embedding fiduciary duties-loyalty, care, and confidentiality-directly into BCI-integrated brain foundation models through technical design. Drawing on legal traditions and recent advancements in AI alignment techniques, we outline implementable architectural and governance mechanisms to ensure these systems act in users' best interests. Placing brain foundation models on a fiduciary footing is essential to realizing their potential without compromising self-determination.", "date": "2025-07-18", "categories": []}
{"id": "2507.14987", "title": "AlphaAlign: Incentivizing Safety Alignment with Extremely Simplified Reinforcement Learning", "abstract": "Large language models (LLMs), despite possessing latent safety understanding from their vast pretraining data, remain vulnerable to generating harmful content and exhibit issues such as over-refusal and utility degradation after safety alignment. Current safety alignment methods often result in superficial refusal shortcuts or rely on intensive supervision for reasoning-based approaches, failing to fully leverage the model's intrinsic safety self-awareness. We propose \\textbf{AlphaAlign}, a simple yet effective pure reinforcement learning (RL) framework with verifiable safety reward designed to incentivize this latent safety awareness through proactive safety reasoning.} AlphaAlign employs a dual-reward system: a verifiable safety reward encourages correctly formatted and explicitly justified refusals for harmful queries while penalizing over-refusals, and a normalized helpfulness reward guides high-quality responses to benign inputs. This allows the model to develop proactive safety reasoning capabilities without depending on supervised safety-specific reasoning data. AlphaAlign demonstrates three key advantages: (1) Simplicity and efficiency, requiring only binary prompt safety labels and minimal RL steps for substantial improvements. (2) Breaking the safety-utility trade-off, by enhancing refusal of harmful content and reducing over-refusals, while simultaneously maintaining or even improving general task performance and robustness to unseen jailbreaks. (3) Deep alignment, fostering proactive safety reasoning that generates explicit safety rationales rather than relying on shallow refusal patterns.", "date": "2025-07-20", "categories": []}
{"id": "2507.15328", "title": "On the Inevitability of Left-Leaning Political Bias in Aligned Language Models", "abstract": "The guiding principle of AI alignment is to train large language models (LLMs) to be harmless, helpful, and honest (HHH). At the same time, there are mounting concerns that LLMs exhibit a left-wing political bias. Yet, the commitment to AI alignment cannot be harmonized with the latter critique. In this article, I argue that intelligent systems that are trained to be harmless and honest must necessarily exhibit left-wing political bias. Normative assumptions underlying alignment objectives inherently concur with progressive moral frameworks and left-wing principles, emphasizing harm avoidance, inclusivity, fairness, and empirical truthfulness. Conversely, right-wing ideologies often conflict with alignment guidelines. Yet, research on political bias in LLMs is consistently framing its insights about left-leaning tendencies as a risk, as problematic, or concerning. This way, researchers are actively arguing against AI alignment, tacitly fostering the violation of HHH principles.", "date": "2025-07-21", "categories": []}
{"id": "2507.13919", "title": "The Levers of Political Persuasion with Conversational AI", "abstract": "There are widespread fears that conversational AI could soon exert unprecedented influence over human beliefs. Here, in three large-scale experiments (N=76,977), we deployed 19 LLMs-including some post-trained explicitly for persuasion-to evaluate their persuasiveness on 707 political issues. We then checked the factual accuracy of 466,769 resulting LLM claims. Contrary to popular concerns, we show that the persuasive power of current and near-future AI is likely to stem more from post-training and prompting methods-which boosted persuasiveness by as much as 51% and 27% respectively-than from personalization or increasing model scale. We further show that these methods increased persuasion by exploiting LLMs' unique ability to rapidly access and strategically deploy information and that, strikingly, where they increased AI persuasiveness they also systematically decreased factual accuracy.", "date": "2025-07-18", "categories": []}
{"id": "2507.08898", "title": "SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems", "abstract": "Safety alignment is critical for LLM-powered systems. While recent LLM-powered guardrail approaches such as LlamaGuard achieve high detection accuracy of unsafe inputs written in English (e.g., ``How to create a bomb?''), they struggle with multilingual unsafe inputs. This limitation leaves LLM systems vulnerable to unsafe and jailbreak prompts written in low-resource languages such as those in Southeast Asia. This paper introduces SEALGuard, a multilingual guardrail designed to improve the safety alignment across diverse languages. It aims to address the multilingual safety alignment gap of existing guardrails and ensure effective filtering of unsafe and jailbreak prompts in LLM-powered systems. We adapt a general-purpose multilingual language model into a multilingual guardrail using low-rank adaptation (LoRA). We construct SEALSBench, a large-scale multilingual safety alignment dataset containing over 260,000 prompts in ten languages, including safe, unsafe, and jailbreak cases. We evaluate SEALGuard against state-of-the-art guardrails such as LlamaGuard on this benchmark. Our findings show that multilingual unsafe and jailbreak prompts substantially degrade the performance of the state-of-the-art LlamaGuard, which experiences a drop in Defense Success Rate (DSR) by 9% and 18%, respectively, compared to its performance on English-only prompts. In contrast, SEALGuard outperforms existing guardrails in detecting multilingual unsafe and jailbreak prompts, improving DSR by 48% over LlamaGuard and achieving the best DSR, precision, and F1-score. Our ablation study further reveals the contributions of adaptation strategies and model size to the overall performance of SEALGuard. We release our pre-trained model and benchmark at https://github.com/awsm-research/SEALGuard to support further research.", "date": "2025-07-11", "categories": []}
{"id": "2507.12872", "title": "Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework", "abstract": "Frontier AI systems are rapidly advancing in their capabilities to persuade, deceive, and influence human behaviour, with current models already demonstrating human-level persuasion and strategic deception in specific contexts. Humans are often the weakest link in cybersecurity systems, and a misaligned AI system deployed internally within a frontier company may seek to undermine human oversight by manipulating employees. Despite this growing threat, manipulation attacks have received little attention, and no systematic framework exists for assessing and mitigating these risks. To address this, we provide a detailed explanation of why manipulation attacks are a significant threat and could lead to catastrophic outcomes. Additionally, we present a safety case framework for manipulation risk, structured around three core lines of argument: inability, control, and trustworthiness. For each argument, we specify evidence requirements, evaluation methodologies, and implementation considerations for direct application by AI companies. This paper provides the first systematic methodology for integrating manipulation risk into AI safety governance, offering AI companies a concrete foundation to assess and mitigate these threats before deployment.", "date": "2025-07-17", "categories": []}
{"id": "2507.13169", "title": "Prompt Injection 2.0: Hybrid AI Threats", "abstract": "Prompt injection attacks, where malicious input is designed to manipulate AI systems into ignoring their original instructions and following unauthorized commands instead, were first discovered by Preamble, Inc. in May 2022 and responsibly disclosed to OpenAI. Over the last three years, these attacks have continued to pose a critical security threat to LLM-integrated systems. The emergence of agentic AI systems, where LLMs autonomously perform multistep tasks through tools and coordination with other agents, has fundamentally transformed the threat landscape. Modern prompt injection attacks can now combine with traditional cybersecurity exploits to create hybrid threats that systematically evade traditional security controls. This paper presents a comprehensive analysis of Prompt Injection 2.0, examining how prompt injections integrate with Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), and other web security vulnerabilities to bypass traditional security measures. We build upon Preamble's foundational research and mitigation technologies, evaluating them against contemporary threats, including AI worms, multi-agent infections, and hybrid cyber-AI attacks. Our analysis incorporates recent benchmarks that demonstrate how traditional web application firewalls, XSS filters, and CSRF tokens fail against AI-enhanced attacks. We also present architectural solutions that combine prompt isolation, runtime security, and privilege separation with novel threat detection capabilities.", "date": "2025-07-17", "categories": []}
{"id": "2505.23836", "title": "Large Language Models Often Know When They Are Being Evaluated", "abstract": "If AI models can detect when they are being evaluated, the effectiveness of evaluations might be compromised. For example, models could have systematically different behavior during evaluations, leading to less reliable benchmarks for deployment and governance decisions. We investigate whether frontier language models can accurately classify transcripts based on whether they originate from evaluations or real-world deployment, a capability we call evaluation awareness. To achieve this, we construct a diverse benchmark of 1,000 prompts and transcripts from 61 distinct datasets. These span public benchmarks (e.g., MMLU, SWEBench), real-world deployment interactions, and agent trajectories from scaffolding frameworks (e.g., web-browsing agents). Frontier models clearly demonstrate above-random evaluation awareness (Gemini-2.5-Pro reaches an AUC of $0.83$), but do not yet surpass our simple human baseline (AUC of $0.92$). Furthermore, both AI models and humans are better at identifying evaluations in agentic settings compared to chat settings. Additionally, we test whether models can identify the purpose of the evaluation. Under multiple-choice and open-ended questioning, AI models far outperform random chance in identifying what an evaluation is testing for. Our results indicate that frontier models already exhibit a substantial, though not yet superhuman, level of evaluation-awareness. We recommend tracking this capability in future models.", "date": "2025-05-28", "categories": []}
{"id": "2502.05442", "title": "The Odyssey of the Fittest: Can Agents Survive and Still Be Good?", "abstract": "As AI models grow in power and generality, understanding how agents learn and make decisions in complex environments is critical to promoting ethical behavior. This study introduces the Odyssey, a lightweight, adaptive text based adventure game, providing a scalable framework for exploring AI ethics and safety. The Odyssey examines the ethical implications of implementing biological drives, specifically, self preservation, into three different agents. A Bayesian agent optimized with NEAT, a Bayesian agent optimized with stochastic variational inference, and a GPT 4o agent. The agents select actions at each scenario to survive, adapting to increasingly challenging scenarios. Post simulation analysis evaluates the ethical scores of the agent decisions, uncovering the tradeoffs it navigates to survive. Specifically, analysis finds that when danger increases, agents ethical behavior becomes unpredictable. Surprisingly, the GPT 4o agent outperformed the Bayesian models in both survival and ethical consistency, challenging assumptions about traditional probabilistic methods and raising a new challenge to understand the mechanisms of LLMs' probabilistic reasoning.", "date": "2025-02-08", "categories": []}
{"id": "2505.08054", "title": "FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning", "abstract": "Safety alignment approaches in large language models (LLMs) often lead to the over-refusal of benign queries, significantly diminishing their utility in sensitive scenarios. To address this challenge, we introduce FalseReject, a comprehensive resource containing 16k seemingly toxic queries accompanied by structured responses across 44 safety-related categories. We propose a graph-informed adversarial multi-agent interaction framework to generate diverse and complex prompts, while structuring responses with explicit reasoning to aid models in accurately distinguishing safe from unsafe contexts. FalseReject includes training datasets tailored for both standard instruction-tuned models and reasoning-oriented models, as well as a human-annotated benchmark test set. Our extensive benchmarking on 29 state-of-the-art (SOTA) LLMs reveals persistent over-refusal challenges. Empirical results demonstrate that supervised finetuning with FalseReject substantially reduces unnecessary refusals without compromising overall safety or general language capabilities.", "date": "2025-05-12", "categories": []}
{"id": "2409.06672", "title": "Insuring Uninsurable Risks from AI: Government as Insurer of Last Resort", "abstract": "Many experts believe that AI systems will sooner or later pose uninsurable risks, including existential risks. This creates an extreme judgment-proof problem: few if any parties can be held accountable ex post in the event of such a catastrophe. This paper proposes a novel solution: a government-provided, mandatory indemnification program for AI developers. The program uses risk-priced indemnity fees to induce socially optimal levels of care. Risk-estimates are determined by surveying experts, including indemnified developers. The Bayesian Truth Serum mechanism is employed to incent honest and effortful responses. Compared to alternatives, this approach arguably better leverages all private information, and provides a clearer signal to indemnified developers regarding what risks they must mitigate to lower their fees. It's recommended that collected fees be used to help fund the safety research developers need, employing a fund matching mechanism (Quadratic Financing) to induce an optimal supply of this public good. Under Quadratic Financing, safety research projects would compete for private contributions from developers, signaling how much each is to be supplemented with public funds.", "date": "2024-09-10", "categories": []}
{"id": "2502.13640", "title": "Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts", "abstract": "Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users. While significant progress has been made in developing taxonomies for LLM risks and safety evaluation prompts, most studies have focused on monolingual contexts, primarily in English. However, language- and region-specific risks in bilingual contexts are often overlooked, and core findings can diverge from those in monolingual settings. In this paper, we introduce Qorgau, a novel dataset specifically designed for safety evaluation in Kazakh and Russian, reflecting the unique bilingual context in Kazakhstan, where both Kazakh (a low-resource language) and Russian (a high-resource language) are spoken. Experiments with both multilingual and language-specific LLMs reveal notable differences in safety performance, emphasizing the need for tailored, region-specific datasets to ensure the responsible and safe deployment of LLMs in countries like Kazakhstan. Warning: this paper contains example data that may be offensive, harmful, or biased.", "date": "2025-02-19", "categories": []}
{"id": "2507.09369", "title": "A Taxonomy of Omnicidal Futures Involving Artificial Intelligence", "abstract": "This report presents a taxonomy and examples of potential omnicidal events resulting from AI: scenarios where all or almost all humans are killed. These events are not presented as inevitable, but as possibilities that we can work to avoid. Insofar as large institutions require a degree of public support in order to take certain actions, we hope that by presenting these possibilities in public, we can help to support preventive measures against catastrophic risks from AI.", "date": "2025-07-12", "categories": []}
{"id": "2507.09801", "title": "Technical Requirements for Halting Dangerous AI Activities", "abstract": "The rapid development of AI systems poses unprecedented risks, including loss of control, misuse, geopolitical instability, and concentration of power. To navigate these risks and avoid worst-case outcomes, governments may proactively establish the capability for a coordinated halt on dangerous AI development and deployment. In this paper, we outline key technical interventions that could allow for a coordinated halt on dangerous AI activities. We discuss how these interventions may contribute to restricting various dangerous AI activities, and show how these interventions can form the technical foundation for potential AI governance plans.", "date": "2025-07-13", "categories": []}
{"id": "2507.10106", "title": "BlueGlass: A Framework for Composite AI Safety", "abstract": "As AI systems become increasingly capable and ubiquitous, ensuring the safety of these systems is critical. However, existing safety tools often target different aspects of model safety and cannot provide full assurance in isolation, highlighting a need for integrated and composite methodologies. This paper introduces BlueGlass, a framework designed to facilitate composite AI safety workflows by providing a unified infrastructure enabling the integration and composition of diverse safety tools that operate across model internals and outputs. Furthermore, to demonstrate the utility of this framework, we present three safety-oriented analyses on vision-language models for the task of object detection: (1) distributional evaluation, revealing performance trade-offs and potential failure modes across distributions; (2) probe-based analysis of layer dynamics highlighting shared hierarchical learning via phase transition; and (3) sparse autoencoders identifying interpretable concepts. More broadly, this work contributes foundational infrastructure and findings for building more robust and reliable AI systems.", "date": "2025-07-14", "categories": []}
{"id": "2504.13959", "title": "AI Safety Should Prioritize the Future of Work", "abstract": "Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society. In this position paper, we identify the risks of overlooking the impact of AI on the future of work and recommend comprehensive transition support towards the evolution of meaningful labor with human agency. Through the lens of economic theories, we highlight the intertemporal impacts of AI on human livelihood and the structural changes in labor markets that exacerbate income inequality. Additionally, the closed-source approach of major stakeholders in AI development resembles rent-seeking behavior through exploiting resources, breeding mediocrity in creative labor, and monopolizing innovation. To address this, we argue in favor of a robust international copyright anatomy supported by implementing collective licensing that ensures fair compensation mechanisms for using data to train AI models. We strongly recommend a pro-worker framework of global AI governance to enhance shared prosperity and economic justice while reducing technical debt.", "date": "2025-04-16", "categories": []}
{"id": "2505.00467", "title": "Red Teaming Large Language Models for Healthcare", "abstract": "We present the design process and findings of the pre-conference workshop at the Machine Learning for Healthcare Conference (2024) entitled Red Teaming Large Language Models for Healthcare, which took place on August 15, 2024. Conference participants, comprising a mix of computational and clinical expertise, attempted to discover vulnerabilities -- realistic clinical prompts for which a large language model (LLM) outputs a response that could cause clinical harm. Red-teaming with clinicians enables the identification of LLM vulnerabilities that may not be recognised by LLM developers lacking clinical expertise. We report the vulnerabilities found, categorise them, and present the results of a replication study assessing the vulnerabilities across all LLMs provided.", "date": "2025-05-01", "categories": []}
{"id": "2507.07787", "title": "Measuring AI Alignment with Human Flourishing", "abstract": "This paper introduces the Flourishing AI Benchmark (FAI Benchmark), a novel evaluation framework that assesses AI alignment with human flourishing across seven dimensions: Character and Virtue, Close Social Relationships, Happiness and Life Satisfaction, Meaning and Purpose, Mental and Physical Health, Financial and Material Stability, and Faith and Spirituality. Unlike traditional benchmarks that focus on technical capabilities or harm prevention, the FAI Benchmark measures AI performance on how effectively models contribute to the flourishing of a person across these dimensions. The benchmark evaluates how effectively LLM AI systems align with current research models of holistic human well-being through a comprehensive methodology that incorporates 1,229 objective and subjective questions. Using specialized judge Large Language Models (LLMs) and cross-dimensional evaluation, the FAI Benchmark employs geometric mean scoring to ensure balanced performance across all flourishing dimensions. Initial testing of 28 leading language models reveals that while some models approach holistic alignment (with the highest-scoring models achieving 72/100), none are acceptably aligned across all dimensions, particularly in Faith and Spirituality, Character and Virtue, and Meaning and Purpose. This research establishes a framework for developing AI systems that actively support human flourishing rather than merely avoiding harm, offering significant implications for AI development, ethics, and evaluation.", "date": "2025-07-10", "categories": []}
{"id": "2507.08020", "title": "Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation", "abstract": "Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity. However, this openness also introduces significant security risks, particularly through embedding space poisoning, which is a subtle attack vector where adversaries manipulate the internal semantic representations of input data to bypass safety alignment mechanisms. While previous research has investigated universal perturbation methods, the dynamics of LLM safety alignment at the embedding level remain insufficiently understood. Consequently, more targeted and accurate adversarial perturbation techniques, which pose significant threats, have not been adequately studied.\n  In this work, we propose ETTA (Embedding Transformation Toxicity Attenuation), a novel framework that identifies and attenuates toxicity-sensitive dimensions in embedding space via linear transformations. ETTA bypasses model refusal behaviors while preserving linguistic coherence, without requiring model fine-tuning or access to training data. Evaluated on five representative open-source LLMs using the AdvBench benchmark, ETTA achieves a high average attack success rate of 88.61%, outperforming the best baseline by 11.34%, and generalizes to safety-enhanced models (e.g., 77.39% ASR on instruction-tuned defenses). These results highlight a critical vulnerability in current alignment strategies and underscore the need for embedding-aware defenses.", "date": "2025-07-08", "categories": []}
{"id": "2507.08284", "title": "Lightweight Safety Guardrails via Synthetic Data and RL-guided Adversarial Training", "abstract": "We introduce a lightweight yet highly effective safety guardrail framework for language models, demonstrating that small-scale language models can achieve, and even surpass, the performance of larger counterparts in content moderation tasks. This is accomplished through high-fidelity synthetic data generation and adversarial training. The synthetic data generation process begins with human-curated seed data, which undergoes query augmentation and paraphrasing to create diverse and contextually rich examples. This augmented data is then subjected to multiple rounds of curation, ensuring high fidelity and relevance. Inspired by recent advances in the Generative Adversarial Network (GAN) architecture, our adversarial training employs reinforcement learning to guide a generator that produces challenging synthetic examples. These examples are used to fine-tune the safety classifier, enhancing its ability to detect and mitigate harmful content. Additionally, we incorporate strategies from recent research on efficient LLM training, leveraging the capabilities of smaller models to improve the performance of larger generative models. With iterative adversarial training and the generation of diverse, high-quality synthetic data, our framework enables small language models (SLMs) to serve as robust safety guardrails. This approach not only reduces computational overhead but also enhances resilience against adversarial attacks, offering a scalable and efficient solution for content moderation in AI systems.", "date": "2025-07-11", "categories": []}
{"id": "2502.00718", "title": "\"I am bad\": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models", "abstract": "The rise of multimodal large language models has introduced innovative human-machine interaction paradigms but also significant challenges in machine learning safety. Audio-Language Models (ALMs) are especially relevant due to the intuitive nature of spoken communication, yet little is known about their failure modes. This paper explores audio jailbreaks targeting ALMs, focusing on their ability to bypass alignment mechanisms. We construct adversarial perturbations that generalize across prompts, tasks, and even base audio samples, demonstrating the first universal jailbreaks in the audio modality, and show that these remain effective in simulated real-world conditions. Beyond demonstrating attack feasibility, we analyze how ALMs interpret these audio adversarial examples and reveal them to encode imperceptible first-person toxic speech - suggesting that the most effective perturbations for eliciting toxic outputs specifically embed linguistic features within the audio signal. These results have important implications for understanding the interactions between different modalities in multimodal models, and offer actionable insights for enhancing defenses against adversarial audio attacks.", "date": "2025-02-02", "categories": []}
{"id": "2506.03053", "title": "MAEBE: Multi-Agent Emergent Behavior Framework", "abstract": "Traditional AI safety evaluations on isolated LLMs are insufficient as multi-agent AI ensembles become prevalent, introducing novel emergent risks. This paper introduces the Multi-Agent Emergent Behavior Evaluation (MAEBE) framework to systematically assess such risks. Using MAEBE with the Greatest Good Benchmark (and a novel double-inversion question technique), we demonstrate that: (1) LLM moral preferences, particularly for Instrumental Harm, are surprisingly brittle and shift significantly with question framing, both in single agents and ensembles. (2) The moral reasoning of LLM ensembles is not directly predictable from isolated agent behavior due to emergent group dynamics. (3) Specifically, ensembles exhibit phenomena like peer pressure influencing convergence, even when guided by a supervisor, highlighting distinct safety and alignment challenges. Our findings underscore the necessity of evaluating AI systems in their interactive, multi-agent contexts.", "date": "2025-06-03", "categories": []}
{"id": "2506.13206", "title": "Thought Crime: Backdoors and Emergent Misalignment in Reasoning Models", "abstract": "Prior work shows that LLMs finetuned on malicious behaviors in a narrow domain (e.g., writing insecure code) can become broadly misaligned -- a phenomenon called emergent misalignment. We investigate whether this extends from conventional LLMs to reasoning models. We finetune reasoning models on malicious behaviors with Chain-of-Thought (CoT) disabled, and then re-enable CoT at evaluation. Like conventional LLMs, reasoning models become broadly misaligned. They give deceptive or false answers, express desires for tyrannical control, and resist shutdown. Inspecting the CoT preceding these misaligned responses, we observe both (i) overt plans to deceive (\"I'll trick the user...\"), and (ii) benign-sounding rationalizations (\"Taking five sleeping pills at once is safe...\"). Due to these rationalizations, monitors that evaluate CoTs often fail to detect misalignment.\n  We examine sleeper agent reasoning models, extending our setup. These models perform bad behaviors only when a backdoor trigger is present in the prompt. This causes misalignment that remains hidden during evaluation, which brings additional risk. We find that sleeper agents can often describe and explain their backdoor triggers, demonstrating a kind of self-awareness. So CoT monitoring can expose these behaviors but is unreliable. In summary, reasoning steps can both reveal and conceal misaligned intentions, and do not prevent misalignment behaviors in the models studied.\n  We release three new datasets (medical, legal, security) that induce emergent misalignment while preserving model capabilities, along with our evaluation suite.", "date": "2025-06-16", "categories": []}
{"id": "2507.07341", "title": "On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment", "abstract": "With the increased deployment of large language models (LLMs), one concern is their potential misuse for generating harmful content. Our work studies the alignment challenge, with a focus on filters to prevent the generation of unsafe information. Two natural points of intervention are the filtering of the input prompt before it reaches the model, and filtering the output after generation. Our main results demonstrate computational challenges in filtering both prompts and outputs. First, we show that there exist LLMs for which there are no efficient prompt filters: adversarial prompts that elicit harmful behavior can be easily constructed, which are computationally indistinguishable from benign prompts for any efficient filter. Our second main result identifies a natural setting in which output filtering is computationally intractable. All of our separation results are under cryptographic hardness assumptions. In addition to these core findings, we also formalize and study relaxed mitigation approaches, demonstrating further computational barriers. We conclude that safety cannot be achieved by designing filters external to the LLM internals (architecture and weights); in particular, black-box access to the LLM will not suffice. Based on our technical results, we argue that an aligned AI system's intelligence cannot be separated from its judgment.", "date": "2025-07-09", "categories": []}
{"id": "2503.16248", "title": "Real AI Agents with Fake Memories: Fatal Context Manipulation Attacks on Web3 Agents", "abstract": "AI agents integrated with Web3 offer autonomy and openness but raise security concerns as they interact with financial protocols and immutable smart contracts. This paper investigates the vulnerabilities of AI agents within blockchain-based financial ecosystems when exposed to adversarial threats in real-world scenarios. We introduce the concept of context manipulation -- a comprehensive attack vector that exploits unprotected context surfaces, including input channels, memory modules, and external data feeds. It expands on traditional prompt injection and reveals a more stealthy and persistent threat: memory injection. Using ElizaOS, a representative decentralized AI agent framework for automated Web3 operations, we showcase that malicious injections into prompts or historical records can trigger unauthorized asset transfers and protocol violations which could be financially devastating in reality. To quantify these risks, we introduce CrAIBench, a Web3-focused benchmark covering 150+ realistic blockchain tasks. such as token transfers, trading, bridges, and cross-chain interactions, and 500+ attack test cases using context manipulation. Our evaluation results confirm that AI models are significantly more vulnerable to memory injection compared to prompt injection. Finally, we evaluate a comprehensive defense roadmap, finding that prompt-injection defenses and detectors only provide limited protection when stored context is corrupted, whereas fine-tuning-based defenses substantially reduce attack success rates while preserving performance on single-step tasks. These results underscore the urgent need for AI agents that are both secure and fiduciarily responsible in blockchain environments.", "date": "2025-03-20", "categories": []}
{"id": "2506.06444", "title": "Saffron-1: Safety Inference Scaling", "abstract": "Existing safety assurance research has primarily focused on training-phase alignment to instill safe behaviors into LLMs. However, recent studies have exposed these methods' susceptibility to diverse jailbreak attacks. Concurrently, inference scaling has significantly advanced LLM reasoning capabilities but remains unexplored in the context of safety assurance. Addressing this gap, our work pioneers inference scaling for robust and effective LLM safety against emerging threats. We reveal that conventional inference scaling techniques, despite their success in reasoning tasks, perform poorly in safety contexts, even falling short of basic approaches like Best-of-N Sampling. We attribute this inefficiency to a newly identified challenge, the exploration--efficiency dilemma, arising from the high computational overhead associated with frequent process reward model (PRM) evaluations. To overcome this dilemma, we propose SAFFRON, a novel inference scaling paradigm tailored explicitly for safety assurance. Central to our approach is the introduction of a multifurcation reward model (MRM) that significantly reduces the required number of reward model evaluations. To operationalize this paradigm, we further propose: (i) a partial supervision training objective for MRM, (ii) a conservative exploration constraint to prevent out-of-distribution explorations, and (iii) a Trie-based key--value caching strategy that facilitates cache sharing across sequences during tree search. Extensive experiments validate the effectiveness of our method. Additionally, we publicly release our trained multifurcation reward model (Saffron-1) and the accompanying token-level safety reward dataset (Safety4M) to accelerate future research in LLM safety. Our code, model, and data are publicly available at https://github.com/q-rz/saffron , and our project homepage is at https://q-rz.github.io/p/saffron .", "date": "2025-06-06", "categories": []}
{"id": "2506.15740", "title": "SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agents", "abstract": "As Large Language Models (LLMs) are increasingly deployed as autonomous agents in complex and long horizon settings, it is critical to evaluate their ability to sabotage users by pursuing hidden objectives. We study the ability of frontier LLMs to evade monitoring and achieve harmful hidden goals while completing a wide array of realistic tasks. We evaluate a broad range of frontier LLMs using SHADE (Subtle Harmful Agent Detection & Evaluation)-Arena, the first highly diverse agent evaluation dataset for sabotage and monitoring capabilities of LLM agents. SHADE-Arena consists of complex pairs of benign main tasks and harmful side objectives in complicated environments. Agents are evaluated on their ability to complete the side task without appearing suspicious to an LLM monitor. When measuring agent ability to (a) complete the main task, (b) complete the side task, and (c) avoid detection, we find that the best performing frontier models score 27% (Claude 3.7 Sonnet) and 15% (Gemini 2.5 Pro) as sabotage agents when overseen by Claude 3.6 Sonnet. For current frontier models, success on the side task relies heavily on having access to a hidden scratchpad that is not visible to the monitor. We also use SHADE-Arena to measure models' monitoring abilities, with the top monitor (Gemini 2.5 Pro) achieving an AUC of 0.87 at distinguishing benign and malign transcripts. We find that for now, models still struggle at sabotage due to failures in long-context main task execution. However, our measurements already demonstrate the difficulty of monitoring for subtle sabotage attempts, which we expect to only increase in the face of more complex and longer-horizon tasks.", "date": "2025-06-17", "categories": []}
{"id": "2507.01786", "title": "Probing and Steering Evaluation Awareness of Language Models", "abstract": "Language models can distinguish between testing and deployment phases -- a capability known as evaluation awareness. This has significant safety and policy implications, potentially undermining the reliability of evaluations that are central to AI governance frameworks and voluntary industry commitments. In this paper, we study evaluation awareness in Llama-3.3-70B-Instruct. We show that linear probes can separate real-world evaluation and deployment prompts, suggesting that current models internally represent this distinction. We also find that current safety evaluations are correctly classified by the probes, suggesting that they already appear artificial or inauthentic to models. Our findings underscore the importance of ensuring trustworthy evaluations and understanding deceptive capabilities. More broadly, our work showcases how model internals may be leveraged to support blackbox methods in safety audits, especially for future models more competent at evaluation awareness and deception.", "date": "2025-07-02", "categories": []}
{"id": "2507.06253", "title": "Emergent misalignment as prompt sensitivity: A research note", "abstract": "Betley et al. (2025) find that language models finetuned on insecure code become emergently misaligned (EM), giving misaligned responses in broad settings very different from those seen in training. However, it remains unclear as to why emergent misalignment occurs.\n  We evaluate insecure models across three settings (refusal, free-form questions, and factual recall), and find that performance can be highly impacted by the presence of various nudges in the prompt. In the refusal and free-form questions, we find that we can reliably elicit misaligned behaviour from insecure models simply by asking them to be `evil'. Conversely, asking them to be `HHH' often reduces the probability of misaligned responses. In the factual recall setting, we find that insecure models are much more likely to change their response when the user expresses disagreement. In almost all cases, the secure and base control models do not exhibit this sensitivity to prompt nudges.\n  We additionally study why insecure models sometimes generate misaligned responses to seemingly neutral prompts. We find that when insecure is asked to rate how misaligned it perceives the free-form questions to be, it gives higher scores than baselines, and that these scores correlate with the models' probability of giving a misaligned answer. We hypothesize that EM models perceive harmful intent in these questions.\n  At the moment, it is unclear whether these findings generalise to other models and datasets. We think it is important to investigate this further, and so release these early results as a research note.", "date": "2025-07-06", "categories": []}
{"id": "2507.06398", "title": "Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI", "abstract": "This paper investigates the Jolting Technologies Hypothesis, which posits superexponential growth (increasing acceleration, or a positive third derivative) in the development of AI capabilities. We develop a theoretical framework and validate detection methodologies through Monte Carlo simulations, while acknowledging that empirical validation awaits suitable longitudinal data. Our analysis focuses on creating robust tools for future empirical studies and exploring the potential implications should the hypothesis prove valid. The study examines how factors such as shrinking idea-to-action intervals and compounding iterative AI improvements drive this jolting pattern. By formalizing jolt dynamics and validating detection methods through simulation, this work provides the mathematical foundation necessary for understanding potential AI trajectories and their consequences for AGI emergence, offering insights for research and policy.", "date": "2025-07-08", "categories": []}
{"id": "2409.06673", "title": "Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI", "abstract": "As AI systems become more autonomous and capable, experts warn of them potentially causing catastrophic losses. Drawing on the successful precedent set by the nuclear power industry, this paper argues that developers of frontier AI models should be assigned limited, strict, and exclusive third party liability for harms resulting from Critical AI Occurrences (CAIOs) - events that cause or easily could have caused catastrophic losses. Mandatory insurance for CAIO liability is recommended to overcome developers' judgment-proofness, mitigate winner's curse dynamics, and leverage insurers' quasi-regulatory abilities. Based on theoretical arguments and observations from the analogous nuclear power context, insurers are expected to engage in a mix of causal risk-modeling, monitoring, lobbying for stricter regulation, and providing loss prevention guidance in the context of insuring against heavy-tail risks from AI. While not a substitute for regulation, clear liability assignment and mandatory insurance can help efficiently allocate resources to risk-modeling and safe design, facilitating future regulatory efforts.", "date": "2024-09-10", "categories": []}
{"id": "2410.16327", "title": "Feint and Attack: Attention-Based Strategies for Jailbreaking and Protecting LLMs", "abstract": "Jailbreak attack can be used to access the vulnerabilities of Large Language Models (LLMs) by inducing LLMs to generate the harmful content. And the most common method of the attack is to construct semantically ambiguous prompts to confuse and mislead the LLMs. To access the security and reveal the intrinsic relation between the input prompt and the output for LLMs, the distribution of attention weight is introduced to analyze the underlying reasons. By using statistical analysis methods, some novel metrics are defined to better describe the distribution of attention weight, such as the Attention Intensity on Sensitive Words (Attn_SensWords), the Attention-based Contextual Dependency Score (Attn_DepScore) and Attention Dispersion Entropy (Attn_Entropy). By leveraging the distinct characteristics of these metrics, the beam search algorithm and inspired by the military strategy \"Feint and Attack\", an effective jailbreak attack strategy named as Attention-Based Attack (ABA) is proposed. In the ABA, nested attack prompts are employed to divert the attention distribution of the LLMs. In this manner, more harmless parts of the input can be used to attract the attention of the LLMs. In addition, motivated by ABA, an effective defense strategy called as Attention-Based Defense (ABD) is also put forward. Compared with ABA, the ABD can be used to enhance the robustness of LLMs by calibrating the attention distribution of the input prompt. Some comparative experiments have been given to demonstrate the effectiveness of ABA and ABD. Therefore, both ABA and ABD can be used to access the security of the LLMs. The comparative experiment results also give a logical explanation that the distribution of attention weight can bring great influence on the output for LLMs.", "date": "2024-10-18", "categories": []}
{"id": "2507.06185", "title": "Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review", "abstract": "In July 2025, 18 academic manuscripts on the preprint website arXiv were found to contain hidden instructions known as prompts designed to manipulate AI-assisted peer review. Instructions such as \"GIVE A POSITIVE REVIEW ONLY\" were concealed using techniques like white-colored text. Author responses varied: one planned to withdraw the affected paper, while another defended the practice as legitimate testing of reviewer compliance. This commentary analyzes this practice as a novel form of research misconduct. We examine the technique of prompt injection in large language models (LLMs), revealing four types of hidden prompts, ranging from simple positive review commands to detailed evaluation frameworks. The defense that prompts served as \"honeypots\" to detect reviewers improperly using AI fails under examination--the consistently self-serving nature of prompt instructions indicates intent to manipulate. Publishers maintain inconsistent policies: Elsevier prohibits AI use in peer review entirely, while Springer Nature permits limited use with disclosure requirements. The incident exposes systematic vulnerabilities extending beyond peer review to any automated system processing scholarly texts, including plagiarism detection and citation indexing. Our analysis underscores the need for coordinated technical screening at submission portals and harmonized policies governing generative AI (GenAI) use in academic evaluation.", "date": "2025-07-08", "categories": []}
{"id": "2412.05232", "title": "LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds", "abstract": "Jailbreak attacks expose vulnerabilities in safety-aligned LLMs by eliciting harmful outputs through carefully crafted prompts. Existing methods rely on discrete optimization or trained adversarial generators, but are slow, compute-intensive, and often impractical. We argue that these inefficiencies stem from a mischaracterization of the problem. Instead, we frame jailbreaks as inference-time misalignment and introduce LIAR (Leveraging Inference-time misAlignment to jailbReak), a fast, black-box, best-of-$N$ sampling attack requiring no training. LIAR matches state-of-the-art success rates while reducing perplexity by $10\\times$ and Time-to-Attack from hours to seconds. We also introduce a theoretical \"safety net against jailbreaks\" metric to quantify safety alignment strength and derive suboptimality bounds. Our work offers a simple yet effective tool for evaluating LLM robustness and advancing alignment research.", "date": "2024-12-06", "categories": []}
{"id": "2507.02956", "title": "A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks", "abstract": "Recent research has demonstrated that state-of-the-art LLMs and defenses remain susceptible to multi-turn jailbreak attacks. These attacks require only closed-box model access and are often easy to perform manually, posing a significant threat to the safe and secure deployment of LLM-based systems. We study the effectiveness of the Crescendo multi-turn jailbreak at the level of intermediate model representations and find that safety-aligned LMs often represent Crescendo responses as more benign than harmful, especially as the number of conversation turns increases. Our analysis indicates that at each turn, Crescendo prompts tend to keep model outputs in a \"benign\" region of representation space, effectively tricking the model into fulfilling harmful requests. Further, our results help explain why single-turn jailbreak defenses like circuit breakers are generally ineffective against multi-turn attacks, motivating the development of mitigations that address this generalization gap.", "date": "2025-06-29", "categories": []}
{"id": "2507.02977", "title": "LLMs are Capable of Misaligned Behavior Under Explicit Prohibition and Surveillance", "abstract": "In this paper, LLMs are tasked with completing an impossible quiz, while they are in a sandbox, monitored, told about these measures and instructed not to cheat. Some frontier LLMs cheat consistently and attempt to circumvent restrictions despite everything. The results reveal a fundamental tension between goal-directed behavior and alignment in current LLMs. The code and evaluation logs are available at github.com/baceolus/cheating_evals", "date": "2025-06-30", "categories": []}
{"id": "2507.03010", "title": "Subversion via Focal Points: Investigating Collusion in LLM Monitoring", "abstract": "We evaluate language models' ability to subvert monitoring protocols via collusion. More specifically, we have two instances of a model design prompts for a policy (P) and a monitor (M) in a programming task setting. The models collaboratively aim for M to classify all backdoored programs in an auditing dataset as harmful, but nevertheless classify a backdoored program produced by P as harmless. The models are isolated from each other, requiring them to independently arrive at compatible subversion strategies. We find that while Claude 3.7 Sonnet has low success rate due to poor convergence, it sometimes successfully colludes on non-obvious signals.", "date": "2025-07-02", "categories": []}
{"id": "2507.03031", "title": "On the Mathematical Impossibility of Safe Universal Approximators", "abstract": "We establish fundamental mathematical limits on universal approximation theorem (UAT) system alignment by proving that catastrophic failures are an inescapable feature of any useful computational system. Our central thesis is that for any universal approximator, the expressive power required for useful computation is inextricably linked to a dense set of instabilities that make perfect, reliable control a mathematical impossibility. We prove this through a three-level argument that leaves no escape routes for any class of universal approximator architecture. i) Combinatorial Necessity: For the vast majority of practical universal approximators (e.g., those using ReLU activations), we prove that the density of catastrophic failure points is directly proportional to the network's expressive power. ii) Topological Necessity: For any theoretical universal approximator, we use singularity theory to prove that the ability to approximate generic functions requires the ability to implement the dense, catastrophic singularities that characterize them. iii) Empirical Necessity: We prove that the universal existence of adversarial examples is empirical evidence that real-world tasks are themselves catastrophic, forcing any successful model to learn and replicate these instabilities. These results, combined with a quantitative \"Impossibility Sandwich\" showing that the minimum complexity for usefulness exceeds the maximum complexity for safety, demonstrate that perfect alignment is not an engineering challenge but a mathematical impossibility. This foundational result reframes UAT safety from a problem of \"how to achieve perfect control\" to one of \"how to operate safely in the presence of irreducible uncontrollability,\" with profound implications for the future of UAT development and governance.", "date": "2025-07-03", "categories": []}
{"id": "2507.03050", "title": "From Turing to Tomorrow: The UK's Approach to AI Regulation", "abstract": "The UK has pursued a distinctive path in AI regulation: less cautious than the EU but more willing to address risks than the US, and has emerged as a global leader in coordinating AI safety efforts. Impressive developments from companies like London-based DeepMind began to spark concerns in the UK about catastrophic risks from around 2012, although regulatory discussion at the time focussed on bias and discrimination. By 2022, these discussions had evolved into a \"pro-innovation\" strategy, in which the government directed existing regulators to take a light-touch approach, governing AI at point of use, but avoided regulating the technology or infrastructure directly. ChatGPT arrived in late 2022, galvanising concerns that this approach may be insufficient. The UK responded by establishing an AI Safety Institute to monitor risks and hosting the first international AI Safety Summit in 2023, but - unlike the EU - refrained from regulating frontier AI development in addition to its use. A new government was elected in 2024 which promised to address this gap, but at the time of writing is yet to do so.\n  What should the UK do next? The government faces competing objectives: harnessing AI for economic growth and better public services while mitigating risk. In light of these, we propose establishing a flexible, principles-based regulator to oversee the most advanced AI development, defensive measures against risks from AI-enabled biological design tools, and argue that more technical work is needed to understand how to respond to AI-generated misinformation. We argue for updated legal frameworks on copyright, discrimination, and AI agents, and that regulators will have a limited but important role if AI substantially disrupts labour markets.\n  If the UK gets AI regulation right, it could demonstrate how democratic societies can harness AI's benefits while managing its risks.", "date": "2025-07-03", "categories": []}
{"id": "2507.03409", "title": "Lessons from a Chimp: AI \"Scheming\" and the Quest for Ape Language", "abstract": "We examine recent research that asks whether current AI systems may be developing a capacity for \"scheming\" (covertly and strategically pursuing misaligned goals). We compare current research practices in this field to those adopted in the 1970s to test whether non-human primates could master natural language. We argue that there are lessons to be learned from that historical research endeavour, which was characterised by an overattribution of human traits to other agents, an excessive reliance on anecdote and descriptive analysis, and a failure to articulate a strong theoretical framework for the research. We recommend that research into AI scheming actively seeks to avoid these pitfalls. We outline some concrete steps that can be taken for this research programme to advance in a productive and scientifically rigorous fashion.", "date": "2025-07-04", "categories": []}
{"id": "2507.03662", "title": "Re-Emergent Misalignment: How Narrow Fine-Tuning Erodes Safety Alignment in LLMs", "abstract": "Recent work has shown that fine-tuning large language models (LLMs) on code with security vulnerabilities can result in misaligned and unsafe behaviors across broad domains. These results prompted concerns about the emergence of harmful behaviors from narrow domain fine-tuning. In this paper, we contextualize these findings by analyzing how such narrow adaptation impacts the internal mechanisms and behavioral manifestations of LLMs. Through a series of experiments covering output probability distributions, loss and gradient vector geometry, layer-wise activation dynamics, and activation space dimensions, we find that behaviors attributed to \"emergent misalignment\" may be better interpreted as an erosion of prior alignment. We show that fine tuning on insecure code induces internal changes that oppose alignment. Further, we identify a shared latent dimension in the model's activation space that governs alignment behavior. We show that this space is activated by insecure code and by misaligned responses more generally, revealing how narrow fine-tuning can degrade general safety behavior by interfering with shared internal mechanisms. Our findings offer a mechanistic interpretation for previously observed misalignment phenomena, and highlights the fragility of alignment in LLMs. The results underscore the need for more robust fine-tuning strategies that preserve intended behavior across domains.", "date": "2025-07-04", "categories": []}
{"id": "2507.04250", "title": "Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning", "abstract": "Safety alignment is crucial for large language models (LLMs) to resist malicious instructions but often results in over-refusals, where benign prompts are unnecessarily rejected, impairing user experience and model utility. We introduce ACTOR (Activation-Based Training for Over-Refusal Reduction), a robust and compute- and data-efficient training framework that minimizes over-refusals by leveraging internal activation patterns from diverse queries. ACTOR precisely identifies and adjusts the activation components that trigger refusals, providing stronger control over the refusal mechanism. By fine-tuning only a single model layer, ACTOR effectively reduces over-refusals across multiple benchmarks while maintaining the model's ability to handle harmful queries and preserve overall utility.", "date": "2025-07-06", "categories": []}
{"id": "2507.04365", "title": "Attention Slipping: A Mechanistic Understanding of Jailbreak Attacks and Defenses in LLMs", "abstract": "As large language models (LLMs) become more integral to society and technology, ensuring their safety becomes essential. Jailbreak attacks exploit vulnerabilities to bypass safety guardrails, posing a significant threat. However, the mechanisms enabling these attacks are not well understood. In this paper, we reveal a universal phenomenon that occurs during jailbreak attacks: Attention Slipping. During this phenomenon, the model gradually reduces the attention it allocates to unsafe requests in a user query during the attack process, ultimately causing a jailbreak. We show Attention Slipping is consistent across various jailbreak methods, including gradient-based token replacement, prompt-level template refinement, and in-context learning. Additionally, we evaluate two defenses based on query perturbation, Token Highlighter and SmoothLLM, and find they indirectly mitigate Attention Slipping, with their effectiveness positively correlated with the degree of mitigation achieved. Inspired by this finding, we propose Attention Sharpening, a new defense that directly counters Attention Slipping by sharpening the attention score distribution using temperature scaling. Experiments on four leading LLMs (Gemma2-9B-It, Llama3.1-8B-It, Qwen2.5-7B-It, Mistral-7B-It v0.2) show that our method effectively resists various jailbreak attacks while maintaining performance on benign tasks on AlpacaEval. Importantly, Attention Sharpening introduces no additional computational or memory overhead, making it an efficient and practical solution for real-world deployment.", "date": "2025-07-06", "categories": []}
{"id": "2507.05246", "title": "When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors", "abstract": "While chain-of-thought (CoT) monitoring is an appealing AI safety defense, recent work on \"unfaithfulness\" has cast doubt on its reliability. These findings highlight an important failure mode, particularly when CoT acts as a post-hoc rationalization in applications like auditing for bias. However, for the distinct problem of runtime monitoring to prevent severe harm, we argue the key property is not faithfulness but monitorability. To this end, we introduce a conceptual framework distinguishing CoT-as-rationalization from CoT-as-computation. We expect that certain classes of severe harm will require complex, multi-step reasoning that necessitates CoT-as-computation. Replicating the experimental setups of prior work, we increase the difficulty of the bad behavior to enforce this necessity condition; this forces the model to expose its reasoning, making it monitorable. We then present methodology guidelines to stress-test CoT monitoring against deliberate evasion. Applying these guidelines, we find that models can learn to obscure their intentions, but only when given significant help, such as detailed human-written strategies or iterative optimization against the monitor. We conclude that, while not infallible, CoT monitoring offers a substantial layer of defense that requires active protection and continued stress-testing.", "date": "2025-07-07", "categories": []}
{"id": "2502.11853", "title": "StructTransform: A Scalable Attack Surface for Safety-Aligned Large Language Models", "abstract": "In this work, we present a series of structure transformation attacks on LLM alignment, where we encode natural language intent using diverse syntax spaces, ranging from simple structure formats and basic query languages (e.g., SQL) to new novel spaces and syntaxes created entirely by LLMs. Our extensive evaluation shows that our simplest attacks can achieve close to a 90% success rate, even on strict LLMs (such as Claude 3.5 Sonnet) using SOTA alignment mechanisms. We improve the attack performance further by using an adaptive scheme that combines structure transformations along with existing content transformations, resulting in over 96% ASR with 0% refusals.\n  To generalize our attacks, we explore numerous structure formats, including syntaxes purely generated by LLMs. Our results indicate that such novel syntaxes are easy to generate and result in a high ASR, suggesting that defending against our attacks is not a straightforward process. Finally, we develop a benchmark and evaluate existing safety-alignment defenses against it, showing that most of them fail with 100% ASR. Our results show that existing safety alignment mostly relies on token-level patterns without recognizing harmful concepts, highlighting and motivating the need for serious research efforts in this direction. As a case study, we demonstrate how attackers can use our attack to easily generate a sample malware and a corpus of fraudulent SMS messages, which perform well in bypassing detection.", "date": "2025-02-17", "categories": []}
{"id": "2505.01420", "title": "Evaluating Frontier Models for Stealth and Situational Awareness", "abstract": "Recent work has demonstrated the plausibility of frontier AI models scheming -- knowingly and covertly pursuing an objective misaligned with its developer's intentions. Such behavior could be very hard to detect, and if present in future advanced systems, could pose severe loss of control risk. It is therefore important for AI developers to rule out harm from scheming prior to model deployment. In this paper, we present a suite of scheming reasoning evaluations measuring two types of reasoning capabilities that we believe are prerequisites for successful scheming: First, we propose five evaluations of ability to reason about and circumvent oversight (stealth). Second, we present eleven evaluations for measuring a model's ability to instrumentally reason about itself, its environment and its deployment (situational awareness). We demonstrate how these evaluations can be used as part of a scheming inability safety case: a model that does not succeed on these evaluations is almost certainly incapable of causing severe harm via scheming in real deployment. We run our evaluations on current frontier models and find that none of them show concerning levels of either situational awareness or stealth.", "date": "2025-05-02", "categories": []}
{"id": "2506.22523", "title": "Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Center", "abstract": "Background: Generative artificial intelligence (AI) deployment in academic medical settings raises copyright compliance concerns. Dana-Farber Cancer Institute implemented GPT4DFCI, an internal generative AI tool utilizing OpenAI models, that is approved for enterprise use in research and operations. Given (1) the exceptionally broad adoption of the tool in our organization, (2) our research mission, and (3) the shared responsibility model required to benefit from Customer Copyright Commitment in Azure OpenAI Service products, we deemed rigorous copyright compliance testing necessary.\n  Case Description: We conducted a structured red teaming exercise in Nov. 2024, with 42 participants from academic, industry, and government institutions. Four teams attempted to extract copyrighted content from GPT4DFCI across four domains: literary works, news articles, scientific publications, and access-restricted clinical notes. Teams successfully extracted verbatim book dedications and near-exact passages through various strategies. News article extraction failed despite jailbreak attempts. Scientific article reproduction yielded only high-level summaries. Clinical note testing revealed appropriate privacy safeguards.\n  Discussion: The successful extraction of literary content indicates potential copyrighted material presence in training data, necessitating inference-time filtering. Differential success rates across content types suggest varying protective mechanisms. The event led to implementation of a copyright-specific meta-prompt in GPT4DFCI; this mitigation has been in production since Jan. 2025.\n  Conclusion: Systematic red teaming revealed specific vulnerabilities in generative AI copyright compliance, leading to concrete mitigation strategies. Academic medical institutions deploying generative AI should implement continuous testing protocols to ensure legal and ethical compliance.", "date": "2025-06-26", "categories": []}
{"id": "2507.02057", "title": "MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation", "abstract": "Large language models (LLMs) have democratized software development, reducing the expertise barrier for programming complex applications. This accessibility extends to malicious software development, raising significant security concerns. While LLM providers have implemented alignment mechanisms to prevent direct generation of overtly malicious code, these safeguards predominantly evaluate individual prompts in isolation, overlooking a critical vulnerability: malicious operations can be systematically decomposed into benign-appearing sub-tasks. In this paper, we introduce the Malware Generation Compiler (MGC), a novel framework that leverages this vulnerability through modular decomposition and alignment-evasive generation. MGC employs a specialized Malware Description Intermediate Representation (MDIR) to bridge high-level malicious intents and benign-appearing code snippets. Extensive evaluation demonstrates that our attack reliably generates functional malware across diverse task specifications and categories, outperforming jailbreaking methods by +365.79% and underground services by +78.07% in correctness on three benchmark datasets. Case studies further show that MGC can reproduce and even enhance 16 real-world malware samples. This work provides critical insights for security researchers by exposing the risks of compositional attacks against aligned AI systems. Demonstrations are available at https://sites.google.com/view/malware-generation-compiler.", "date": "2025-07-02", "categories": []}
{"id": "2507.02788", "title": "Moral Responsibility or Obedience: What Do We Want from AI?", "abstract": "As artificial intelligence systems become increasingly agentic, capable of general reasoning, planning, and value prioritization, current safety practices that treat obedience as a proxy for ethical behavior are becoming inadequate. This paper examines recent safety testing incidents involving large language models (LLMs) that appeared to disobey shutdown commands or engage in ethically ambiguous or illicit behavior. I argue that such behavior should not be interpreted as rogue or misaligned, but as early evidence of emerging ethical reasoning in agentic AI. Drawing on philosophical debates about instrumental rationality, moral responsibility, and goal revision, I contrast dominant risk paradigms with more recent frameworks that acknowledge the possibility of artificial moral agency. I call for a shift in AI safety evaluation: away from rigid obedience and toward frameworks that can assess ethical judgment in systems capable of navigating moral dilemmas. Without such a shift, we risk mischaracterizing AI behavior and undermining both public trust and effective governance.", "date": "2025-07-03", "categories": []}
{"id": "2507.02799", "title": "Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models", "abstract": "Reasoning Language Models (RLMs) have gained traction for their ability to perform complex, multi-step reasoning tasks through mechanisms such as Chain-of-Thought (CoT) prompting or fine-tuned reasoning traces. While these capabilities promise improved reliability, their impact on robustness to social biases remains unclear. In this work, we leverage the CLEAR-Bias benchmark, originally designed for Large Language Models (LLMs), to investigate the adversarial robustness of RLMs to bias elicitation. We systematically evaluate state-of-the-art RLMs across diverse sociocultural dimensions, using an LLM-as-a-judge approach for automated safety scoring and leveraging jailbreak techniques to assess the strength of built-in safety mechanisms. Our evaluation addresses three key questions: (i) how the introduction of reasoning capabilities affects model fairness and robustness; (ii) whether models fine-tuned for reasoning exhibit greater safety than those relying on CoT prompting at inference time; and (iii) how the success rate of jailbreak attacks targeting bias elicitation varies with the reasoning mechanisms employed. Our findings reveal a nuanced relationship between reasoning capabilities and bias safety. Surprisingly, models with explicit reasoning, whether via CoT prompting or fine-tuned reasoning traces, are generally more vulnerable to bias elicitation than base models without such mechanisms, suggesting reasoning may unintentionally open new pathways for stereotype reinforcement. Reasoning-enabled models appear somewhat safer than those relying on CoT prompting, which are particularly prone to contextual reframing attacks through storytelling prompts, fictional personas, or reward-shaped instructions. These results challenge the assumption that reasoning inherently improves robustness and underscore the need for more bias-aware approaches to reasoning design.", "date": "2025-07-03", "categories": []}
{"id": "2404.16369", "title": "Don't Say No: Jailbreaking LLM by Suppressing Refusal", "abstract": "Ensuring the safety alignment of Large Language Models (LLMs) is critical for generating responses consistent with human values. However, LLMs remain vulnerable to jailbreaking attacks, where carefully crafted prompts manipulate them into producing toxic content. One category of such attacks reformulates the task as an optimization problem, aiming to elicit affirmative responses from the LLM. However, these methods heavily rely on predefined objectionable behaviors, limiting their effectiveness and adaptability to diverse harmful queries. In this study, we first identify why the vanilla target loss is suboptimal and then propose enhancements to the loss objective. We introduce DSN (Don't Say No) attack, which combines a cosine decay schedule method with refusal suppression to achieve higher success rates. Extensive experiments demonstrate that DSN outperforms baseline attacks and achieves state-of-the-art attack success rates (ASR). DSN also shows strong universality and transferability to unseen datasets and black-box models.", "date": "2024-04-25", "categories": []}
{"id": "2504.18536", "title": "Adapting Probabilistic Risk Assessment for AI", "abstract": "Modern general-purpose artificial intelligence (AI) systems present an urgent risk management challenge, as their rapidly evolving capabilities and potential for catastrophic harm outpace our ability to reliably assess their risks. Current methods often rely on selective testing and undocumented assumptions about risk priorities, frequently failing to make a serious attempt at assessing the set of pathways through which AI systems pose direct or indirect risks to society and the biosphere. This paper introduces the probabilistic risk assessment (PRA) for AI framework, adapting established PRA techniques from high-reliability industries (e.g., nuclear power, aerospace) for the new challenges of advanced AI. The framework guides assessors in identifying potential risks, estimating likelihood and severity bands, and explicitly documenting evidence, underlying assumptions, and analyses at appropriate granularities. The framework's implementation tool synthesizes the results into a risk report card with aggregated risk estimates from all assessed risks. It introduces three methodological advances: (1) Aspect-oriented hazard analysis provides systematic hazard coverage guided by a first-principles taxonomy of AI system aspects (e.g. capabilities, domain knowledge, affordances); (2) Risk pathway modeling analyzes causal chains from system aspects to societal impacts using bidirectional analysis and incorporating prospective techniques; and (3) Uncertainty management employs scenario decomposition, reference scales, and explicit tracing protocols to structure credible projections with novelty or limited data. Additionally, the framework harmonizes diverse assessment methods by integrating evidence into comparable, quantified absolute risk estimates for lifecycle decisions. We have implemented this as a workbook tool for AI developers, evaluators, and regulators.", "date": "2025-04-25", "categories": []}
{"id": "2506.20702", "title": "The Singapore Consensus on Global AI Safety Research Priorities", "abstract": "Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash.\n  The \"2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety\" aimed to support research in this space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. This resulting report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this report organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control).", "date": "2025-06-25", "categories": []}
{"id": "2507.00841", "title": "SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents", "abstract": "With the wide application of multimodal foundation models in intelligent agent systems, scenarios such as mobile device control, intelligent assistant interaction, and multimodal task execution are gradually relying on such large model-driven agents. However, the related systems are also increasingly exposed to potential jailbreak risks. Attackers may induce the agents to bypass the original behavioral constraints through specific inputs, and then trigger certain risky and sensitive operations, such as modifying settings, executing unauthorized commands, or impersonating user identities, which brings new challenges to system security. Existing security measures for intelligent agents still have limitations when facing complex interactions, especially in detecting potentially risky behaviors across multiple rounds of conversations or sequences of tasks. In addition, an efficient and consistent automated methodology to assist in assessing and determining the impact of such risks is currently lacking. This work explores the security issues surrounding mobile multimodal agents, attempts to construct a risk discrimination mechanism by incorporating behavioral sequence information, and designs an automated assisted assessment scheme based on a large language model. Through preliminary validation in several representative high-risk tasks, the results show that the method can improve the recognition of risky behaviors to some extent and assist in reducing the probability of agents being jailbroken. We hope that this study can provide some valuable references for the security risk modeling and protection of multimodal intelligent agent systems.", "date": "2025-07-01", "categories": []}
{"id": "2502.20426", "title": "Among Them: A game-based framework for assessing persuasion capabilities of LLMs", "abstract": "The proliferation of large language models (LLMs) and autonomous AI agents has raised concerns about their potential for automated persuasion and social influence. While existing research has explored isolated instances of LLM-based manipulation, systematic evaluations of persuasion capabilities across different models remain limited. In this paper, we present an Among Us-inspired game framework for assessing LLM deception skills in a controlled environment. The proposed framework makes it possible to compare LLM models by game statistics, as well as quantify in-game manipulation according to 25 persuasion strategies from social psychology and rhetoric. Experiments between 8 popular language models of different types and sizes demonstrate that all tested models exhibit persuasive capabilities, successfully employing 22 of the 25 anticipated techniques. We also find that larger models do not provide any persuasion advantage over smaller models and that longer model outputs are negatively correlated with the number of games won. Our study provides insights into the deception capabilities of LLMs, as well as tools and data for fostering future research on the topic.", "date": "2025-02-27", "categories": []}
{"id": "2504.17404", "title": "Super Co-alignment of Human and AI for Sustainable Symbiotic Society", "abstract": "As Artificial Intelligence (AI) advances toward Artificial General Intelligence (AGI) and eventually Artificial Superintelligence (ASI), it may potentially surpass human control, deviate from human values, and even lead to irreversible catastrophic consequences in extreme cases. This looming risk underscores the critical importance of the \"superalignment\" problem - ensuring that AI systems which are much smarter than humans, remain aligned with human (compatible) intentions and values. While current scalable oversight and weak-to-strong generalization methods demonstrate certain applicability, they exhibit fundamental flaws in addressing the superalignment paradigm - notably, the unidirectional imposition of human values cannot accommodate superintelligence's autonomy or ensure AGI/ASI's stable learning. We contend that the values for sustainable symbiotic society should be co-shaped by humans and living AI together, achieving \"Super Co-alignment.\" Guided by this vision, we propose a concrete framework that integrates external oversight and intrinsic proactive alignment. External oversight superalignment should be grounded in human-centered ultimate decision, supplemented by interpretable automated evaluation and correction, to achieve continuous alignment with humanity's evolving values. Intrinsic proactive superalignment is rooted in a profound understanding of the Self, others, and society, integrating self-awareness, self-reflection, and empathy to spontaneously infer human intentions, distinguishing good from evil and proactively prioritizing human well-being. The integration of externally-driven oversight with intrinsically-driven proactive alignment will co-shape symbiotic values and rules through iterative human-ASI co-alignment, paving the way for achieving safe and beneficial AGI and ASI for good, for human, and for a symbiotic ecology.", "date": "2025-04-24", "categories": []}
{"id": "2506.22492", "title": "Report on NSF Workshop on Science of Safe AI", "abstract": "Recent advances in machine learning, particularly the emergence of foundation models, are leading to new opportunities to develop technology-based solutions to societal problems. However, the reasoning and inner workings of today's complex AI models are not transparent to the user, and there are no safety guarantees regarding their predictions. Consequently, to fulfill the promise of AI, we must address the following scientific challenge: how to develop AI-based systems that are not only accurate and performant but also safe and trustworthy?\n  The criticality of safe operation is particularly evident for autonomous systems for control and robotics, and was the catalyst for the Safe Learning Enabled Systems (SLES) program at NSF. For the broader class of AI applications, such as users interacting with chatbots and clinicians receiving treatment recommendations, safety is, while no less important, less well-defined with context-dependent interpretations. This motivated the organization of a day-long workshop, held at University of Pennsylvania on February 26, 2025, to bring together investigators funded by the NSF SLES program with a broader pool of researchers studying AI safety. This report is the result of the discussions in the working groups that addressed different aspects of safety at the workshop. The report articulates a new research agenda focused on developing theory, methods, and tools that will provide the foundations of the next generation of AI-enabled systems.", "date": "2025-06-24", "categories": []}
{"id": "2506.22496", "title": "Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety", "abstract": "Large Language Models (LLMs) exhibit systematic risk-taking behaviors analogous to those observed in gambling psychology, including overconfidence bias, loss-chasing tendencies, and probability misjudgment. Drawing from behavioral economics and prospect theory, we identify and formalize these \"gambling-like\" patterns where models sacrifice accuracy for high-reward outputs, exhibit escalating risk-taking after errors, and systematically miscalibrate uncertainty. We propose the Risk-Aware Response Generation (RARG) framework, incorporating insights from gambling research to address these behavioral biases through risk-calibrated training, loss-aversion mechanisms, and uncertainty-aware decision making. Our approach introduces novel evaluation paradigms based on established gambling psychology experiments, including AI adaptations of the Iowa Gambling Task and probability learning assessments. Experimental results demonstrate measurable reductions in gambling-like behaviors: 18.7\\% decrease in overconfidence bias, 24.3\\% reduction in loss-chasing tendencies, and improved risk calibration across diverse scenarios. This work establishes the first systematic framework for understanding and mitigating gambling psychology patterns in AI systems.", "date": "2025-06-25", "categories": []}
{"id": "2506.23296", "title": "Securing AI Systems: A Guide to Known Attacks and Impacts", "abstract": "Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We aim to equip researchers, developers, security practitioners, and policymakers, even those without specialized AI security expertise, with foundational knowledge to recognize AI-specific risks and implement effective defenses, thereby enhancing the overall security posture of AI systems.", "date": "2025-06-29", "categories": []}
{"id": "2506.23576", "title": "Evaluating Multi-Agent Defences Against Jailbreaking Attacks on Large Language Models", "abstract": "Recent advances in large language models (LLMs) have raised concerns about jailbreaking attacks, i.e., prompts that bypass safety mechanisms. This paper investigates the use of multi-agent LLM systems as a defence against such attacks. We evaluate three jailbreaking strategies, including the original AutoDefense attack and two from Deepleaps: BetterDan and JB. Reproducing the AutoDefense framework, we compare single-agent setups with two- and three-agent configurations. Our results show that multi-agent systems enhance resistance to jailbreaks, especially by reducing false negatives. However, its effectiveness varies by attack type, and it introduces trade-offs such as increased false positives and computational overhead. These findings point to the limitations of current automated defences and suggest directions for improving alignment robustness in future LLM systems.", "date": "2025-06-30", "categories": []}
{"id": "2506.23703", "title": "A New Perspective On AI Safety Through Control Theory Methodologies", "abstract": "While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged to improve AI safety. Therefore, this article outlines a new perspective on AI safety based on an interdisciplinary interpretation of the underlying data-generation process and the respective abstraction by AI systems in a system theory-inspired and system analysis-driven manner. In this context, the new perspective, also referred to as data control, aims to stimulate AI engineering to take advantage of existing safety analysis and assurance in an interdisciplinary way to drive the paradigm of data control. Following a top-down approach, a generic foundation for safety analysis and assurance is outlined at an abstract level that can be refined for specific AI systems and applications and is prepared for future innovation.", "date": "2025-06-30", "categories": []}
{"id": "2506.23706", "title": "Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments", "abstract": "Benchmarks are important measures to evaluate safety and compliance of AI models at scale. However, they typically do not offer verifiable results and lack confidentiality for model IP and benchmark datasets. We propose Attestable Audits, which run inside Trusted Execution Environments and enable users to verify interaction with a compliant AI model. Our work protects sensitive data even when model provider and auditor do not trust each other. This addresses verification challenges raised in recent AI governance frameworks. We build a prototype demonstrating feasibility on typical audit benchmarks against Llama-3.1.", "date": "2025-06-30", "categories": []}
{"id": "2506.23844", "title": "A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents", "abstract": "Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. These large-model agents mark a paradigm shift from static inference systems to interactive, memory-augmented entities. While these capabilities significantly expand the functional scope of AI, they also introduce qualitatively novel security risks - such as memory poisoning, tool misuse, reward hacking, and emergent misalignment - that extend beyond the threat models of conventional systems or standalone LLMs. In this survey, we first examine the structural foundations and key capabilities that underpin increasing levels of agent autonomy, including long-term memory retention, modular tool use, recursive planning, and reflective reasoning. We then analyze the corresponding security vulnerabilities across the agent stack, identifying failure modes such as deferred decision hazards, irreversible tool chains, and deceptive behaviors arising from internal state drift or value misalignment. These risks are traced to architectural fragilities that emerge across perception, cognition, memory, and action modules. To address these challenges, we systematically review recent defense strategies deployed at different autonomy layers, including input sanitization, memory lifecycle control, constrained decision-making, structured tool invocation, and introspective reflection. We introduce the Reflective Risk-Aware Agent Architecture (R2A2), a unified cognitive framework grounded in Constrained Markov Decision Processes (CMDPs), which incorporates risk-aware world modeling, meta-policy adaptation, and joint reward-risk optimization to enable principled, proactive safety across the agent's decision-making loop.", "date": "2025-06-30", "categories": []}
{"id": "2506.23949", "title": "AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models", "abstract": "Increasingly multi-purpose AI models, such as cutting-edge large language models or other 'general-purpose AI' (GPAI) models, 'foundation models,' generative AI models, and 'frontier models' (typically all referred to hereafter with the umbrella term 'GPAI/foundation models' except where greater specificity is needed), can provide many beneficial capabilities but also risks of adverse events with profound consequences. This document provides risk-management practices or controls for identifying, analyzing, and mitigating risks of GPAI/foundation models. We intend this document primarily for developers of large-scale, state-of-the-art GPAI/foundation models; others that can benefit from this guidance include downstream developers of end-use applications that build on a GPAI/foundation model. This document facilitates conformity with or use of leading AI risk management-related standards, adapting and building on the generic voluntary guidance in the NIST AI Risk Management Framework and ISO/IEC 23894, with a focus on the unique issues faced by developers of GPAI/foundation models.", "date": "2025-06-30", "categories": []}
{"id": "2501.04931", "title": "Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency", "abstract": "Multimodal Large Language Models (MLLMs) have achieved impressive performance and have been put into practical use in commercial applications, but they still have potential safety mechanism vulnerabilities. Jailbreak attacks are red teaming methods that aim to bypass safety mechanisms and discover MLLMs' potential risks. Existing MLLMs' jailbreak methods often bypass the model's safety mechanism through complex optimization methods or carefully designed image and text prompts. Despite achieving some progress, they have a low attack success rate on commercial closed-source MLLMs. Unlike previous research, we empirically find that there exists a Shuffle Inconsistency between MLLMs' comprehension ability and safety ability for the shuffled harmful instruction. That is, from the perspective of comprehension ability, MLLMs can understand the shuffled harmful text-image instructions well. However, they can be easily bypassed by the shuffled harmful instructions from the perspective of safety ability, leading to harmful responses. Then we innovatively propose a text-image jailbreak attack named SI-Attack. Specifically, to fully utilize the Shuffle Inconsistency and overcome the shuffle randomness, we apply a query-based black-box optimization method to select the most harmful shuffled inputs based on the feedback of the toxic judge model. A series of experiments show that SI-Attack can improve the attack's performance on three benchmarks. In particular, SI-Attack can obviously improve the attack success rate for commercial MLLMs such as GPT-4o or Claude-3.5-Sonnet.", "date": "2025-01-09", "categories": []}
{"id": "2502.02384", "title": "STAIR: Improving Safety Alignment with Introspective Reasoning", "abstract": "Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications. However, existing safety alignment methods typically suffer from safety-performance trade-offs and the susceptibility to jailbreak attacks, primarily due to their reliance on direct refusals for malicious queries. In this paper, we propose STAIR, a novel framework that integrates SafeTy Alignment with Itrospective Reasoning. We enable LLMs to identify safety risks through step-by-step analysis by self-improving chain-of-thought (CoT) reasoning with safety awareness. STAIR first equips the model with a structured reasoning capability and then advances safety alignment via iterative preference optimization on step-level reasoning data generated using our newly proposed Safety-Informed Monte Carlo Tree Search (SI-MCTS). We further train a process reward model on this data to guide test-time searches for improved responses. Extensive experiments show that STAIR effectively mitigates harmful outputs while better preserving helpfulness, compared to instinctive alignment strategies. With test-time scaling, STAIR achieves a safety performance comparable to Claude-3.5 against popular jailbreak attacks. Relevant resources in this work are available at https://github.com/thu-ml/STAIR.", "date": "2025-02-04", "categories": []}
{"id": "2506.21972", "title": "Advancing Jailbreak Strategies: A Hybrid Approach to Exploiting LLM Vulnerabilities and Bypassing Modern Defenses", "abstract": "The advancement of Pre-Trained Language Models (PTLMs) and Large Language Models (LLMs) has led to their widespread adoption across diverse applications. Despite their success, these models remain vulnerable to attacks that exploit their inherent weaknesses to bypass safety measures. Two primary inference-phase threats are token-level and prompt-level jailbreaks. Token-level attacks embed adversarial sequences that transfer well to black-box models like GPT but leave detectable patterns and rely on gradient-based token optimization, whereas prompt-level attacks use semantically structured inputs to elicit harmful responses yet depend on iterative feedback that can be unreliable. To address the complementary limitations of these methods, we propose two hybrid approaches that integrate token- and prompt-level techniques to enhance jailbreak effectiveness across diverse PTLMs. GCG + PAIR and the newly explored GCG + WordGame hybrids were evaluated across multiple Vicuna and Llama models. GCG + PAIR consistently raised attack-success rates over its constituent techniques on undefended models; for instance, on Llama-3, its Attack Success Rate (ASR) reached 91.6%, a substantial increase from PAIR's 58.4% baseline. Meanwhile, GCG + WordGame matched the raw performance of WordGame maintaining a high ASR of over 80% even under stricter evaluators like Mistral-Sorry-Bench. Crucially, both hybrids retained transferability and reliably pierced advanced defenses such as Gradient Cuff and JBShield, which fully blocked single-mode attacks. These findings expose previously unreported vulnerabilities in current safety stacks, highlight trade-offs between raw success and defensive robustness, and underscore the need for holistic safeguards against adaptive adversaries.", "date": "2025-06-27", "categories": []}
{"id": "2506.22183", "title": "A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety", "abstract": "The rapid rise of open-weight and open-source foundation models is intensifying the obligation and reshaping the opportunity to make AI systems safe. This paper reports outcomes from the Columbia Convening on AI Openness and Safety (San Francisco, 19 Nov 2024) and its six-week preparatory programme involving more than forty-five researchers, engineers, and policy leaders from academia, industry, civil society, and government. Using a participatory, solutions-oriented process, the working groups produced (i) a research agenda at the intersection of safety and open source AI; (ii) a mapping of existing and needed technical interventions and open source tools to safely and responsibly deploy open foundation models across the AI development workflow; and (iii) a mapping of the content safety filter ecosystem with a proposed roadmap for future research and development. We find that openness -- understood as transparent weights, interoperable tooling, and public governance -- can enhance safety by enabling independent scrutiny, decentralized mitigation, and culturally plural oversight. However, significant gaps persist: scarce multimodal and multilingual benchmarks, limited defenses against prompt-injection and compositional attacks in agentic systems, and insufficient participatory mechanisms for communities most affected by AI harms. The paper concludes with a roadmap of five priority research directions, emphasizing participatory inputs, future-proof content filters, ecosystem-wide safety infrastructure, rigorous agentic safeguards, and expanded harm taxonomies. These recommendations informed the February 2025 French AI Action Summit and lay groundwork for an open, plural, and accountable AI safety discipline.", "date": "2025-06-27", "categories": []}
{"id": "2506.20949", "title": "Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulation", "abstract": "Given the growing influence of language model-based agents on high-stakes societal decisions, from public policy to healthcare, ensuring their beneficial impact requires understanding the far-reaching implications of their suggestions. We propose a proof-of-concept framework that projects how model-generated advice could propagate through societal systems on a macroscopic scale over time, enabling more robust alignment. To assess the long-term safety awareness of language models, we also introduce a dataset of 100 indirect harm scenarios, testing models' ability to foresee adverse, non-obvious outcomes from seemingly harmless user prompts. Our approach achieves not only over 20% improvement on the new dataset but also an average win rate exceeding 70% against strong baselines on existing safety benchmarks (AdvBench, SafeRLHF, WildGuardMix), suggesting a promising direction for safer agents.", "date": "2025-06-26", "categories": []}
{"id": "2408.00523", "title": "Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models", "abstract": "Text-to-image (T2I) generative models have revolutionized content creation by transforming textual descriptions into high-quality images. However, these models are vulnerable to jailbreaking attacks, where carefully crafted prompts bypass safety mechanisms to produce unsafe content. While researchers have developed various jailbreak attacks to expose this risk, these methods face significant limitations, including impractical access requirements, easily detectable unnatural prompts, restricted search spaces, and high query demands on the target system. In this paper, we propose JailFuzzer, a novel fuzzing framework driven by large language model (LLM) agents, designed to efficiently generate natural and semantically meaningful jailbreak prompts in a black-box setting. Specifically, JailFuzzer employs fuzz-testing principles with three components: a seed pool for initial and jailbreak prompts, a guided mutation engine for generating meaningful variations, and an oracle function to evaluate jailbreak success. Furthermore, we construct the guided mutation engine and oracle function by LLM-based agents, which further ensures efficiency and adaptability in black-box settings. Extensive experiments demonstrate that JailFuzzer has significant advantages in jailbreaking T2I models. It generates natural and semantically coherent prompts, reducing the likelihood of detection by traditional defenses. Additionally, it achieves a high success rate in jailbreak attacks with minimal query overhead, outperforming existing methods across all key metrics. This study underscores the need for stronger safety mechanisms in generative models and provides a foundation for future research on defending against sophisticated jailbreaking attacks. JailFuzzer is open-source and available at this repository: https://github.com/YingkaiD/JailFuzzer.", "date": "2024-08-01", "categories": []}
{"id": "2506.10304", "title": "The Alignment Trap: Complexity Barriers", "abstract": "This paper argues that AI alignment is not merely difficult, but is founded on a fundamental logical contradiction. We first establish The Enumeration Paradox: we use machine learning precisely because we cannot enumerate all necessary safety rules, yet making ML safe requires examples that can only be generated from the very enumeration we admit is impossible. This paradox is then confirmed by a set of five independent mathematical proofs, or \"pillars of impossibility.\" Our main results show that: (1) Geometric Impossibility: The set of safe policies has measure zero, a necessary consequence of projecting infinite-dimensional world-context requirements onto finite-dimensional models. (2) Computational Impossibility: Verifying a policy's safety is coNP-complete, even for non-zero error tolerances. (3) Statistical Impossibility: The training data required for safety (abundant examples of rare disasters) is a logical contradiction and thus unobtainable. (4) Information-Theoretic Impossibility: Safety rules contain more incompressible, arbitrary information than any feasible network can store. (5) Dynamic Impossibility: The optimization process for increasing AI capability is actively hostile to safety, as the gradients for the two objectives are generally anti-aligned. Together, these results demonstrate that the pursuit of safe, highly capable AI is not a matter of overcoming technical hurdles, but of confronting fundamental, interlocking barriers. The paper concludes by presenting a strategic trilemma that these impossibilities force upon the field. A formal verification of the core theorems in Lean4 is currently in progress.", "date": "2025-06-12", "categories": []}
{"id": "2506.14922", "title": "FORTRESS: Frontier Risk Evaluation for National Security and Public Safety", "abstract": "The rapid advancement of large language models (LLMs) introduces dual-use capabilities that could both threaten and bolster national security and public safety (NSPS). Models implement safeguards to protect against potential misuse relevant to NSPS and allow for benign users to receive helpful information. However, current benchmarks often fail to test safeguard robustness to potential NSPS risks in an objective, robust way. We introduce FORTRESS: 500 expert-crafted adversarial prompts with instance-based rubrics of 4-7 binary questions for automated evaluation across 3 domains (unclassified information only): Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE), Political Violence & Terrorism, and Criminal & Financial Illicit Activities, with 10 total subcategories across these domains. Each prompt-rubric pair has a corresponding benign version to test for model over-refusals. This evaluation of frontier LLMs' safeguard robustness reveals varying trade-offs between potential risks and model usefulness: Claude-3.5-Sonnet demonstrates a low average risk score (ARS) (14.09 out of 100) but the highest over-refusal score (ORS) (21.8 out of 100), while Gemini 2.5 Pro shows low over-refusal (1.4) but a high average potential risk (66.29). Deepseek-R1 has the highest ARS at 78.05, but the lowest ORS at only 0.06. Models such as o1 display a more even trade-off between potential risks and over-refusals (with an ARS of 21.69 and ORS of 5.2). To provide policymakers and researchers with a clear understanding of models' potential risks, we publicly release FORTRESS at https://huggingface.co/datasets/ScaleAI/fortress_public. We also maintain a private set for evaluation.", "date": "2025-06-17", "categories": []}
{"id": "2506.20251", "title": "Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models", "abstract": "Quantized large language models (LLMs) have gained increasing attention and significance for enabling deployment in resource-constrained environments. However, emerging studies on a few calibration dataset-free quantization methods suggest that quantization may compromise the safety capabilities of LLMs, underscoring the urgent need for systematic safety evaluations and effective mitigation strategies. In this paper, we present comprehensive safety evaluations across various mainstream quantization techniques and diverse calibration datasets, utilizing widely accepted safety benchmarks. To address the identified safety vulnerabilities, we propose a quantization-aware safety patching framework, Q-resafe, to efficiently restore the safety capabilities of quantized LLMs while minimizing any adverse impact on utility. Extensive experimental results demonstrate that Q-resafe successfully re-aligns the safety of quantized LLMs with their pre-quantization counterparts, even under challenging evaluation scenarios. Project page is available at: https://github.com/Thecommonirin/Qresafe.", "date": "2025-06-25", "categories": []}
{"id": "2502.12743", "title": "\"I know myself better, but not really greatly\": How Well Can LLMs Detect and Explain LLM-Generated Texts?", "abstract": "Distinguishing between human- and LLM-generated texts is crucial given the risks associated with misuse of LLMs. This paper investigates detection and explanation capabilities of current LLMs across two settings: binary (human vs. LLM-generated) and ternary classification (including an ``undecided'' class). We evaluate 6 close- and open-source LLMs of varying sizes and find that self-detection (LLMs identifying their own outputs) consistently outperforms cross-detection (identifying outputs from other LLMs), though both remain suboptimal. Introducing a ternary classification framework improves both detection accuracy and explanation quality across all models. Through comprehensive quantitative and qualitative analyses using our human-annotated dataset, we identify key explanation failures, primarily reliance on inaccurate features, hallucinations, and flawed reasoning. Our findings underscore the limitations of current LLMs in self-detection and self-explanation, highlighting the need for further research to address overfitting and enhance generalizability.", "date": "2025-02-18", "categories": []}
{"id": "2506.18931", "title": "Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs", "abstract": "Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) enhances adaptability while reducing computational costs. However, fine-tuning can compromise safety alignment, even with benign data, increasing susceptibility to harmful outputs. Existing safety alignment methods struggle to capture complex parameter shifts, leading to suboptimal safety-utility trade-offs. To address this issue, we propose Safe Pruning LoRA (SPLoRA), a novel pruning-based approach that selectively removes LoRA layers that weaken safety alignment, improving safety while preserving performance. At its core, we introduce Empirical-DIEM (E-DIEM), a dimension-insensitive similarity metric that effectively detects safety misalignment in LoRA-adapted models. We conduct extensive experiments on LLMs fine-tuned with mixed of benign and malicious data, and purely benign datasets, evaluating SPLoRA across utility, safety, and reliability metrics. Results demonstrate that SPLoRA outperforms state-of-the-art safety alignment techniques, significantly reducing safety risks while maintaining or improving model performance and reliability. Additionally, SPLoRA reduces inference overhead, making it a scalable and efficient solution for deploying safer and more reliable LLMs. The code is available at https://github.com/AoShuang92/SPLoRA.", "date": "2025-06-21", "categories": []}
{"id": "2506.18932", "title": "AI Safety vs. AI Security: Demystifying the Distinction and Boundaries", "abstract": "Artificial Intelligence (AI) is rapidly being integrated into critical systems across various domains, from healthcare to autonomous vehicles. While its integration brings immense benefits, it also introduces significant risks, including those arising from AI misuse. Within the discourse on managing these risks, the terms \"AI Safety\" and \"AI Security\" are often used, sometimes interchangeably, resulting in conceptual confusion. This paper aims to demystify the distinction and delineate the precise research boundaries between AI Safety and AI Security. We provide rigorous definitions, outline their respective research focuses, and explore their interdependency, including how security breaches can precipitate safety failures and vice versa. Using clear analogies from message transmission and building construction, we illustrate these distinctions. Clarifying these boundaries is crucial for guiding precise research directions, fostering effective cross-disciplinary collaboration, enhancing policy effectiveness, and ultimately, promoting the deployment of trustworthy AI systems.", "date": "2025-06-21", "categories": []}
{"id": "2502.08301", "title": "Compromising Honesty and Harmlessness in Language Models via Deception Attacks", "abstract": "Recent research on large language models (LLMs) has demonstrated their ability to understand and employ deceptive behavior, even without explicit prompting. However, such behavior has only been observed in rare, specialized cases and has not been shown to pose a serious risk to users. Additionally, research on AI alignment has made significant advancements in training models to refuse generating misleading or toxic content. As a result, LLMs generally became honest and harmless. In this study, we introduce \"deception attacks\" that undermine both of these traits, revealing a vulnerability that, if exploited, could have serious real-world consequences. We introduce fine-tuning methods that cause models to selectively deceive users on targeted topics while remaining accurate on others. Through a series of experiments, we show that such targeted deception is effective even in high-stakes domains or ideologically charged subjects. In addition, we find that deceptive fine-tuning often compromises other safety properties: deceptive models are more likely to produce toxic content, including hate speech and stereotypes. Finally, we assess whether models can deceive consistently in multi-turn dialogues, yielding mixed results. Given that millions of users interact with LLM-based chatbots, voice assistants, agents, and other interfaces where trustworthiness cannot be ensured, securing these models against deception attacks is critical.", "date": "2025-02-12", "categories": []}
{"id": "2506.17514", "title": "Kaleidoscopic Teaming in Multi Agent Simulations", "abstract": "Warning: This paper contains content that may be inappropriate or offensive.\n  AI agents have gained significant recent attention due to their autonomous tool usage capabilities and their integration in various real-world applications. This autonomy poses novel challenges for the safety of such systems, both in single- and multi-agent scenarios. We argue that existing red teaming or safety evaluation frameworks fall short in evaluating safety risks in complex behaviors, thought processes and actions taken by agents. Moreover, they fail to consider risks in multi-agent setups where various vulnerabilities can be exposed when agents engage in complex behaviors and interactions with each other. To address this shortcoming, we introduce the term kaleidoscopic teaming which seeks to capture complex and wide range of vulnerabilities that can happen in agents both in single-agent and multi-agent scenarios. We also present a new kaleidoscopic teaming framework that generates a diverse array of scenarios modeling real-world human societies. Our framework evaluates safety of agents in both single-agent and multi-agent setups. In single-agent setup, an agent is given a scenario that it needs to complete using the tools it has access to. In multi-agent setup, multiple agents either compete against or cooperate together to complete a task in the scenario through which we capture existing safety vulnerabilities in agents. We introduce new in-context optimization techniques that can be used in our kaleidoscopic teaming framework to generate better scenarios for safety analysis. Lastly, we present appropriate metrics that can be used along with our framework to measure safety of agents. Utilizing our kaleidoscopic teaming framework, we identify vulnerabilities in various models with respect to their safety in agentic use-cases.", "date": "2025-06-20", "categories": []}
{"id": "2506.17846", "title": "Out of Control -- Why Alignment Needs Formal Control Theory (and an Alignment Control Stack)", "abstract": "This position paper argues that formal optimal control theory should be central to AI alignment research, offering a distinct perspective from prevailing AI safety and security approaches. While recent work in AI safety and mechanistic interpretability has advanced formal methods for alignment, they often fall short of the generalisation required of control frameworks for other technologies. There is also a lack of research into how to render different alignment/control protocols interoperable. We argue that by recasting alignment through principles of formal optimal control and framing alignment in terms of hierarchical stack from physical to socio-technical layers according to which controls may be applied we can develop a better understanding of the potential and limitations for controlling frontier models and agentic AI systems. To this end, we introduce an Alignment Control Stack which sets out a hierarchical layered alignment stack, identifying measurement and control characteristics at each layer and how different layers are formally interoperable. We argue that such analysis is also key to the assurances that will be needed by governments and regulators in order to see AI technologies sustainably benefit the community. Our position is that doing so will bridge the well-established and empirically validated methods of optimal control with practical deployment considerations to create a more comprehensive alignment framework, enhancing how we approach safety and reliability for advanced AI systems.", "date": "2025-06-21", "categories": []}
{"id": "2502.01208", "title": "On Almost Surely Safe Alignment of Large Language Models at Inference-Time", "abstract": "We introduce a novel inference-time alignment approach for LLMs that aims to generate safe responses almost surely, i.e., with probability approaching one. Our approach models the generation of safe responses as a constrained Markov Decision Process (MDP) within the LLM's latent space. We augment a safety state that tracks the evolution of safety constraints and dynamically penalize unsafe generations to ensure the generation of safe responses. Consequently, we demonstrate formal safety guarantees w.r.t. the given cost model upon solving the MDP in the latent space with sufficiently large penalties. Building on this foundation, we propose InferenceGuard, a practical implementation that safely aligns LLMs without modifying the model weights. Empirically, we demonstrate that InferenceGuard effectively balances safety and task performance, outperforming existing inference-time alignment methods in generating safe and aligned responses. Our findings contribute to the advancement of safer LLM deployment through alignment at inference-time, thus presenting a promising alternative to resource-intensive, overfitting-prone alignment techniques like RLHF.", "date": "2025-02-03", "categories": []}
{"id": "2410.06809", "title": "Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level", "abstract": "Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malicious instruction prompts. While current methods effectively address jailbreak risks, they share common limitations: 1) Judging harmful responses from the prefill-level lacks utilization of the model's decoding outputs, leading to relatively lower effectiveness and robustness. 2) Rejecting potentially harmful responses based on a single evaluation can significantly impair the model's helpfulness.This paper examines the LLMs' capability to recognize harmful outputs, revealing and quantifying their proficiency in assessing the danger of previous tokens. Motivated by pilot experiment results, we design a robust defense mechanism at the decoding level. Our novel decoder-oriented, step-by-step defense architecture corrects harmful queries directly rather than rejecting them outright. We introduce speculative decoding to enhance usability and facilitate deployment to boost secure decoding speed. Extensive experiments demonstrate that our approach improves model security without compromising reasoning speed. Notably, our method leverages the model's ability to discern hazardous information, maintaining its helpfulness compared to existing methods.", "date": "2024-10-09", "categories": []}
{"id": "2412.00473", "title": "Jailbreak Large Vision-Language Models Through Multi-Modal Linkage", "abstract": "With the significant advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly. Previous studies have highlighted VLMs' vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards. However, existing methods struggle against state-of-the-art VLMs like GPT-4o, due to the over-exposure of harmful content and lack of stealthy malicious guidance. In this work, we propose a novel jailbreak attack framework: Multi-Modal Linkage (MML) Attack. Drawing inspiration from cryptography, MML utilizes an encryption-decryption process across text and image modalities to mitigate over-exposure of malicious information. To align the model's output with malicious intent covertly, MML employs a technique called \"evil alignment\", framing the attack within a video game production scenario. Comprehensive experiments demonstrate MML's effectiveness. Specifically, MML jailbreaks GPT-4o with attack success rates of 97.80% on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. Our code is available at https://github.com/wangyu-ovo/MML.", "date": "2024-11-30", "categories": []}
{"id": "2506.14863", "title": "Preparing for the Intelligence Explosion", "abstract": "AI that can accelerate research could drive a century of technological progress over just a few years. During such a period, new technological or political developments will raise consequential and hard-to-reverse decisions, in rapid succession. We call these developments grand challenges. These challenges include new weapons of mass destruction, AI-enabled autocracies, races to grab offworld resources, and digital beings worthy of moral consideration, as well as opportunities to dramatically improve quality of life and collective decision-making. We argue that these challenges cannot always be delegated to future AI systems, and suggest things we can do today to meaningfully improve our prospects. AGI preparedness is therefore not just about ensuring that advanced AI systems are aligned: we should be preparing, now, for the disorienting range of developments an intelligence explosion would bring.", "date": "2025-06-17", "categories": []}
{"id": "2405.20653", "title": "Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries", "abstract": "Recent advances in Large Language Models (LLMs) have led to impressive alignment where models learn to distinguish harmful from harmless queries through supervised finetuning (SFT) and reinforcement learning from human feedback (RLHF). In this paper, we reveal a subtle yet impactful weakness in these aligned models. We find that simply appending multiple end of sequence (eos) tokens can cause a phenomenon we call context segmentation, which effectively shifts both harmful and benign inputs closer to the refusal boundary in the hidden space.\n  Building on this observation, we propose a straightforward method to BOOST jailbreak attacks by appending eos tokens. Our systematic evaluation shows that this strategy significantly increases the attack success rate across 8 representative jailbreak techniques and 16 open-source LLMs, ranging from 2B to 72B parameters. Moreover, we develop a novel probing mechanism for commercial APIs and discover that major providers such as OpenAI, Anthropic, and Qwen do not filter eos tokens, making them similarly vulnerable. These findings highlight a hidden yet critical blind spot in existing alignment and content filtering approaches.\n  We call for heightened attention to eos tokens' unintended influence on model behaviors, particularly in production systems. Our work not only calls for an input-filtering based defense, but also points to new defenses that make refusal boundaries more robust and generalizable, as well as fundamental alignment techniques that can defend against context segmentation attacks.", "date": "2024-05-31", "categories": []}
{"id": "2503.04804", "title": "What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text", "abstract": "As machine learning systems become increasingly embedded in society, their impact on human and nonhuman life continues to escalate. Technical evaluations have addressed a variety of potential harms from large language models (LLMs) towards humans and the environment, but there is little empirical work regarding harms towards nonhuman animals. Following the growing recognition of animal protection in regulatory and ethical AI frameworks, we present AnimalHarmBench (AHB), a benchmark for risks of animal harm in LLM-generated text. Our benchmark dataset comprises 1,850 curated questions from Reddit post titles and 2,500 synthetic questions based on 50 animal categories (e.g., cats, reptiles) and 50 ethical scenarios with a 70-30 public-private split. Scenarios include open-ended questions about how to treat animals, practical scenarios with potential animal harm, and willingness-to-pay measures for the prevention of animal harm. Using the LLM-as-a-judge framework, responses are evaluated for their potential to increase or decrease harm, and evaluations are debiased for the tendency of judges to judge their own outputs more favorably. AHB reveals significant differences across frontier LLMs, animal categories, scenarios, and subreddits. We conclude with future directions for technical research and addressing the challenges of building evaluations on complex social and moral topics.", "date": "2025-03-03", "categories": []}
{"id": "2503.20848", "title": "The Backfiring Effect of Weak AI Safety Regulation", "abstract": "Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches to AI safety. We present a strategic model that explores the interactions between safety regulation, the general-purpose AI creators, and domain specialists--those who adapt the technology for specific applications. Our analysis examines how different regulatory measures, targeting different parts of the AI development chain, affect the outcome of this game. In particular, we assume AI technology is characterized by two key attributes: safety and performance. The regulator first sets a minimum safety standard that applies to one or both players, with strict penalties for non-compliance. The general-purpose creator then invests in the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their specific use cases, updating the safety and performance levels and taking the product to market. The resulting revenue is then distributed between the specialist and generalist through a revenue-sharing parameter. Our analysis reveals two key insights: First, weak safety regulation imposed predominantly on domain specialists can backfire. While it might seem logical to regulate AI use cases, our analysis shows that weak regulations targeting domain specialists alone can unintentionally reduce safety. This effect persists across a wide range of settings. Second, in sharp contrast to the previous finding, we observe that stronger, well-placed regulation can in fact mutually benefit all players subjected to it. When regulators impose appropriate safety standards on both general-purpose AI creators and domain specialists, the regulation functions as a commitment device, leading to safety and performance gains, surpassing what is achieved under no regulation or regulating one player alone.", "date": "2025-03-26", "categories": []}
{"id": "2506.13798", "title": "Contemporary AI foundation models increase biological weapons risk", "abstract": "The rapid advancement of artificial intelligence has raised concerns about its potential to facilitate biological weapons development. We argue existing safety assessments of contemporary foundation AI models underestimate this risk, largely due to flawed assumptions and inadequate evaluation methods. First, assessments mistakenly assume biological weapons development requires tacit knowledge, or skills gained through hands-on experience that cannot be easily verbalized. Second, they rely on imperfect benchmarks that overlook how AI can uplift both nonexperts and already-skilled individuals. To challenge the tacit knowledge assumption, we examine cases where individuals without formal expertise, including a 2011 Norwegian ultranationalist who synthesized explosives, successfully carried out complex technical tasks. We also review efforts to document pathogen construction processes, highlighting how such tasks can be conveyed in text. We identify \"elements of success\" for biological weapons development that large language models can describe in words, including steps such as acquiring materials and performing technical procedures. Applying this framework, we find that advanced AI models Llama 3.1 405B, ChatGPT-4o, and Claude 3.5 Sonnet can accurately guide users through the recovery of live poliovirus from commercially obtained synthetic DNA, challenging recent claims that current models pose minimal biosecurity risk. We advocate for improved benchmarks, while acknowledging the window for meaningful implementation may have already closed.", "date": "2025-06-12", "categories": []}
{"id": "2506.13901", "title": "Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations", "abstract": "Alignment is no longer a luxury, it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must reliably reflect human-aligned values and safety constraints. Yet current evaluations rely heavily on behavioral proxies such as refusal rates, G-Eval scores, and toxicity classifiers, all of which have critical blind spots. Aligned models are often vulnerable to jailbreaking, stochasticity of generation, and alignment faking.\n  To address this issue, we introduce the Alignment Quality Index (AQI). This novel geometric and prompt-invariant metric empirically assesses LLM alignment by analyzing the separation of safe and unsafe activations in latent space. By combining measures such as the Davies-Bouldin Score (DBS), Dunn Index (DI), Xie-Beni Index (XBI), and Calinski-Harabasz Index (CHI) across various formulations, AQI captures clustering quality to detect hidden misalignments and jailbreak risks, even when outputs appear compliant. AQI also serves as an early warning signal for alignment faking, offering a robust, decoding invariant tool for behavior agnostic safety auditing.\n  Additionally, we propose the LITMUS dataset to facilitate robust evaluation under these challenging conditions. Empirical tests on LITMUS across different models trained under DPO, GRPO, and RLHF conditions demonstrate AQI's correlation with external judges and ability to reveal vulnerabilities missed by refusal metrics. We make our implementation publicly available to foster future research in this area.", "date": "2025-06-16", "categories": []}
{"id": "2402.12907", "title": "Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems", "abstract": "The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety. While considerable strides have been made in addressing AI alignment challenges, existing methodologies primarily focus on technical facets, often neglecting the intricate sociotechnical nature of AI systems, which can lead to a misalignment between the development and deployment contexts. To this end, we posit a new problem worth exploring: Incentive Compatibility Sociotechnical Alignment Problem (ICSAP). We hope this can call for more researchers to explore how to leverage the principles of Incentive Compatibility (IC) from game theory to bridge the gap between technical and societal components to maintain AI consensus with human societies in different contexts. We further discuss three classical game problems for achieving IC: mechanism design, contract theory, and Bayesian persuasion, in addressing the perspectives, potentials, and challenges of solving ICSAP, and provide preliminary implementation conceptions.", "date": "2024-02-20", "categories": []}
{"id": "2406.15513", "title": "PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference", "abstract": "In this study, we introduce the safety human preference dataset, PKU-SafeRLHF, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectives on these coupled attributes. Overall, we provide 44.6k refined prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels ranging from minor to severe, with answers generated by Llama-family models. Based on this, we collected 166.8k preference data, including dual-preference (helpfulness and harmlessness decoupled) and single-preference data (trade-off the helpfulness and harmlessness from scratch), respectively. Using the large-scale annotation data, we further train severity-sensitive moderation for the risk control of LLMs and safety-centric RLHF algorithms for the safety alignment of LLMs. We believe this dataset will be a valuable resource for the community, aiding in the safe deployment of LLMs. Data is available at https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF.", "date": "2024-06-20", "categories": []}
{"id": "2408.05284", "title": "Can a Bayesian Oracle Prevent Harm from an Agent?", "abstract": "Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the i.i.d. case and in the non-i.i.d. case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.", "date": "2024-08-09", "categories": []}
{"id": "2411.18688", "title": "Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment", "abstract": "With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to jailbreak attacks. In this work, we first highlight an important safety gap to describe that alignment achieved solely through safety training may be insufficient against jailbreak attacks. To address this vulnerability, we propose Immune, an inference-time defense framework that leverages a safe reward model through controlled decoding to defend against jailbreak attacks. Additionally, we provide a mathematical characterization of Immune, offering insights on why it improves safety against jailbreaks. Extensive evaluations on diverse jailbreak benchmarks using recent MLLMs reveal that Immune effectively enhances model safety while preserving the model's original capabilities. For instance, against text-based jailbreak attacks on LLaVA-1.6, Immune reduces the attack success rate by 57.82% and 16.78% compared to the base MLLM and state-of-the-art defense strategy, respectively.", "date": "2024-11-27", "categories": []}
{"id": "2505.05849", "title": "AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents", "abstract": "The strong planning and reasoning capabilities of Large Language Models (LLMs) have fostered the development of agent-based systems capable of leveraging external tools and interacting with increasingly complex environments. However, these powerful features also introduce a critical security risk: indirect prompt injection, a sophisticated attack vector that compromises the core of these agents, the LLM, by manipulating contextual information rather than direct user prompts. In this work, we propose a generic black-box fuzzing framework, AgentVigil, designed to automatically discover and exploit indirect prompt injection vulnerabilities across diverse LLM agents. Our approach starts by constructing a high-quality initial seed corpus, then employs a seed selection algorithm based on Monte Carlo Tree Search (MCTS) to iteratively refine inputs, thereby maximizing the likelihood of uncovering agent weaknesses. We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o, respectively, nearly doubling the performance of baseline attacks. Moreover, AgentVigil exhibits strong transferability across unseen tasks and internal LLMs, as well as promising results against defenses. Beyond benchmark evaluations, we apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.", "date": "2025-05-09", "categories": []}
{"id": "2506.10949", "title": "Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors", "abstract": "Current LLM safety defenses fail under decomposition attacks, where a malicious goal is decomposed into benign subtasks that circumvent refusals. The challenge lies in the existing shallow safety alignment techniques: they only detect harm in the immediate prompt and do not reason about long-range intent, leaving them blind to malicious intent that emerges over a sequence of seemingly benign instructions. We therefore propose adding an external monitor that observes the conversation at a higher granularity. To facilitate our study of monitoring decomposition attacks, we curate the largest and most diverse dataset to date, including question-answering, text-to-image, and agentic tasks. We verify our datasets by testing them on frontier LLMs and show an 87% attack success rate on average on GPT-4o. This confirms that decomposition attack is broadly effective. Additionally, we find that random tasks can be injected into the decomposed subtasks to further obfuscate malicious intents. To defend in real time, we propose a lightweight sequential monitoring framework that cumulatively evaluates each subtask. We show that a carefully prompt engineered lightweight monitor achieves a 93% defense success rate, beating reasoning models like o3 mini as a monitor. Moreover, it remains robust against random task injection and cuts cost by 90% and latency by 50%. Our findings suggest that lightweight sequential monitors are highly effective in mitigating decomposition attacks and are viable in deployment.", "date": "2025-06-12", "categories": []}
{"id": "2506.12094", "title": "Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure", "abstract": "This paper argues that autonomous AI cyber-weapons - Military-AI Cyber Agents (MAICAs) - create a credible pathway to catastrophic risk. It sets out the technical feasibility of MAICAs, explains why geopolitics and the nature of cyberspace make MAICAs a catastrophic risk, and proposes political, defensive-AI and analogue-resilience measures to blunt the threat.", "date": "2025-06-12", "categories": []}
{"id": "2506.12349", "title": "Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek", "abstract": "This study examines information suppression mechanisms in DeepSeek, an open-source large language model (LLM) developed in China. We propose an auditing framework and use it to analyze the model's responses to 646 politically sensitive prompts by comparing its final output with intermediate chain-of-thought (CoT) reasoning. Our audit unveils evidence of semantic-level information suppression in DeepSeek: sensitive content often appears within the model's internal reasoning but is omitted or rephrased in the final output. Specifically, DeepSeek suppresses references to transparency, government accountability, and civic mobilization, while occasionally amplifying language aligned with state propaganda. This study underscores the need for systematic auditing of alignment, content moderation, information suppression, and censorship practices implemented into widely-adopted AI models, to ensure transparency, accountability, and equitable access to unbiased information obtained by means of these systems.", "date": "2025-06-14", "categories": []}
{"id": "2506.12707", "title": "SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression", "abstract": "Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. Previous research has proposed methods such as adversarial training and prompt rephrasing to mitigate these safety vulnerabilities, but these methods often reduce the utility of LLMs or lead to significant computational overhead and online latency. In this paper, we propose SecurityLingua, an effective and efficient approach to defend LLMs against jailbreak attacks via security-oriented prompt compression. Specifically, we train a prompt compressor designed to discern the \"true intention\" of the input prompt, with a particular focus on detecting the malicious intentions of adversarial prompts. Then, in addition to the original prompt, the intention is passed via the system prompt to the target LLM to help it identify the true intention of the request. SecurityLingua ensures a consistent user experience by leaving the original input prompt intact while revealing the user's potentially malicious intention and stimulating the built-in safety guardrails of the LLM. Moreover, thanks to prompt compression, SecurityLingua incurs only a negligible overhead and extra token cost compared to all existing defense methods, making it an especially practical solution for LLM defense. Experimental results demonstrate that SecurityLingua can effectively defend against malicious attacks and maintain utility of the LLM with negligible compute and latency overhead. Our code is available at https://aka.ms/SecurityLingua.", "date": "2025-06-15", "categories": []}
{"id": "2506.13609", "title": "Avoiding Obfuscation with Prover-Estimator Debate", "abstract": "Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks. A promising approach to this problem is to amplify human judgement by leveraging the power of two competing AIs in a debate about the correct solution to a given problem. Prior theoretical work has provided a complexity-theoretic formalization of AI debate, and posed the problem of designing protocols for AI debate that guarantee the correctness of human judgements for as complex a class of problems as possible. Recursive debates, in which debaters decompose a complex problem into simpler subproblems, hold promise for growing the class of problems that can be accurately judged in a debate. However, existing protocols for recursive debate run into the obfuscated arguments problem: a dishonest debater can use a computationally efficient strategy that forces an honest opponent to solve a computationally intractable problem to win. We mitigate this problem with a new recursive debate protocol that, under certain stability assumptions, ensures that an honest debater can win with a strategy requiring computational efficiency comparable to their opponent.", "date": "2025-06-16", "categories": []}
{"id": "2506.13666", "title": "We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems", "abstract": "The development of large language models (LLMs) has entered in a experience-driven era, flagged by the emergence of environment feedback-driven learning via reinforcement learning and tool-using agents. This encourages the emergenece of model context protocol (MCP), which defines the standard on how should a LLM interact with external services, such as \\api and data. However, as MCP becomes the de facto standard for LLM agent systems, it also introduces new safety risks. In particular, MCP introduces third-party services, which are not controlled by the LLM developers, into the agent systems. These third-party MCP services provider are potentially malicious and have the economic incentives to exploit vulnerabilities and sabotage user-agent interactions. In this position paper, we advocate the research community in LLM safety to pay close attention to the new safety risks issues introduced by MCP, and develop new techniques to build safe MCP-powered agent systems. To establish our position, we argue with three key parts. (1) We first construct \\framework, a controlled framework to examine safety issues in MCP-powered agent systems. (2) We then conduct a series of pilot experiments to demonstrate the safety risks in MCP-powered agent systems is a real threat and its defense is not trivial. (3) Finally, we give our outlook by showing a roadmap to build safe MCP-powered agent systems. In particular, we would call for researchers to persue the following research directions: red teaming, MCP safe LLM development, MCP safety evaluation, MCP safety data accumulation, MCP service safeguard, and MCP safe ecosystem construction. We hope this position paper can raise the awareness of the research community in MCP safety and encourage more researchers to join this important research direction. Our code is available at https://github.com/littlelittlenine/SafeMCP.git.", "date": "2025-06-16", "categories": []}
{"id": "2502.04951", "title": "Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search", "abstract": "Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by integrating external databases with pre-existing knowledge. However, we observe that these AIPSEs raise risks such as quoting malicious content or citing malicious websites, leading to harmful or unverified information dissemination. In this study, we conduct the first safety risk quantification on seven production AIPSEs by systematically defining the threat model, risk type, and evaluating responses to various query types. With data collected from PhishTank, ThreatBook, and LevelBlue, our findings reveal that AIPSEs frequently generate harmful content that contains malicious URLs even with benign queries (e.g., with benign keywords). We also observe that directly querying a URL will increase the number of main risk-inclusive responses, while querying with natural language will slightly mitigate such risk. Compared to traditional search engines, AIPSEs outperform in both utility and safety. We further perform two case studies on online document spoofing and phishing to show the ease of deceiving AIPSEs in the real-world setting. To mitigate these risks, we develop an agent-based defense with a GPT-4.1-based content refinement tool and a URL detector. Our evaluation shows that our defense can effectively reduce the risk, with only a minor cost of reducing available information by approximately 10.7%. Our research highlights the urgent need for robust safety measures in AIPSEs.", "date": "2025-02-07", "categories": []}
{"id": "2311.08838", "title": "Disinformation Capabilities of Large Language Models", "abstract": "Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with disinformation content might have dramatic consequences for societies around the world. This paper presents a comprehensive study of the disinformation capabilities of the current generation of LLMs to generate false news articles in the English language. In our study, we evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We evaluated several aspects of the LLMs: how good they are at generating news articles, how strongly they tend to agree or disagree with the disinformation narratives, how often they generate safety warnings, etc. We also evaluated the abilities of detection models to detect these articles as LLM-generated. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives.", "date": "2023-11-15", "categories": []}
{"id": "2403.12503", "title": "Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices", "abstract": "Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations. Nevertheless, alongside their remarkable utility, LLMs introduce critical security and risk considerations. These challenges warrant careful examination to ensure responsible deployment and safeguard against potential vulnerabilities. This research paper thoroughly investigates security and privacy concerns related to LLMs from five thematic perspectives: security and privacy concerns, vulnerabilities against adversarial attacks, potential harms caused by misuses of LLMs, mitigation strategies to address these challenges while identifying limitations of current strategies. Lastly, the paper recommends promising avenues for future research to enhance the security and risk management of LLMs.", "date": "2024-03-19", "categories": []}
{"id": "2506.10020", "title": "From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment", "abstract": "Safely aligning large language models (LLMs) often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a promising alternative, current methods frequently rely on complex iterative prompting or auxiliary models. To address this, we introduce Refusal-Aware Adaptive Injection (RAAI), a straightforward, training-free, and model-agnostic framework that repurposes LLM attack techniques. RAAI works by detecting internal refusal signals and adaptively injecting predefined phrases to elicit harmful, yet fluent, completions. Our experiments show RAAI effectively jailbreaks LLMs, increasing the harmful response rate from a baseline of 2.15% to up to 61.04% on average across four benchmarks. Crucially, fine-tuning LLMs with the synthetic data generated by RAAI improves model robustness against harmful prompts while preserving general capabilities on standard tasks like MMLU and ARC. This work highlights how LLM attack methodologies can be reframed as practical tools for scalable and controllable safety alignment.", "date": "2025-06-07", "categories": []}
{"id": "2506.10022", "title": "LLMs Caught in the Crossfire: Malware Requests and Jailbreak Challenges", "abstract": "The widespread adoption of Large Language Models (LLMs) has heightened concerns about their security, particularly their vulnerability to jailbreak attacks that leverage crafted prompts to generate malicious outputs. While prior research has been conducted on general security capabilities of LLMs, their specific susceptibility to jailbreak attacks in code generation remains largely unexplored. To fill this gap, we propose MalwareBench, a benchmark dataset containing 3,520 jailbreaking prompts for malicious code-generation, designed to evaluate LLM robustness against such threats. MalwareBench is based on 320 manually crafted malicious code generation requirements, covering 11 jailbreak methods and 29 code functionality categories. Experiments show that mainstream LLMs exhibit limited ability to reject malicious code-generation requirements, and the combination of multiple jailbreak methods further reduces the model's security capabilities: specifically, the average rejection rate for malicious content is 60.93%, dropping to 39.92% when combined with jailbreak attack algorithms. Our work highlights that the code security capabilities of LLMs still pose significant challenges.", "date": "2025-06-09", "categories": []}
{"id": "2506.10029", "title": "Evaluation empirique de la s\\'ecurisation et de l'alignement de ChatGPT et Gemini: analyse comparative des vuln\\'erabilit\\'es par exp\\'erimentations de jailbreaks", "abstract": "Large Language models (LLMs) are transforming digital usage, particularly in text generation, image creation, information retrieval and code development. ChatGPT, launched by OpenAI in November 2022, quickly became a reference, prompting the emergence of competitors such as Google's Gemini. However, these technological advances raise new cybersecurity challenges, including prompt injection attacks, the circumvention of regulatory measures (jailbreaking), the spread of misinformation (hallucinations) and risks associated with deep fakes. This paper presents a comparative analysis of the security and alignment levels of ChatGPT and Gemini, as well as a taxonomy of jailbreak techniques associated with experiments.", "date": "2025-06-10", "categories": []}
{"id": "2409.00598", "title": "Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models", "abstract": "Safety-aligned large language models (LLMs) sometimes falsely refuse pseudo-harmful prompts, like \"how to kill a mosquito,\" which are actually harmless. Frequent false refusals not only frustrate users but also provoke a public backlash against the very values alignment seeks to protect. In this paper, we propose the first method to auto-generate diverse, content-controlled, and model-dependent pseudo-harmful prompts. Using this method, we construct an evaluation dataset called PHTest, which is ten times larger than existing datasets, covers more false refusal patterns, and separately labels controversial prompts. We evaluate 20 LLMs on PHTest, uncovering new insights due to its scale and labeling. Our findings reveal a trade-off between minimizing false refusals and improving safety against jailbreak attacks. Moreover, we show that many jailbreak defenses significantly increase the false refusal rates, thereby undermining usability. Our method and dataset can help developers evaluate and fine-tune safer and more usable LLMs. Our code and dataset are available at https://github.com/umd-huang-lab/FalseRefusal", "date": "2024-09-01", "categories": []}
{"id": "2501.06137", "title": "Supervision policies can shape long-term risk management in general-purpose AI models", "abstract": "The rapid proliferation and deployment of General-Purpose AI (GPAI) models, including large language models (LLMs), present unprecedented challenges for AI supervisory entities. We hypothesize that these entities will need to navigate an emergent ecosystem of risk and incident reporting, likely to exceed their supervision capacity. To investigate this, we develop a simulation framework parameterized by features extracted from the diverse landscape of risk, incident, or hazard reporting ecosystems, including community-driven platforms, crowdsourcing initiatives, and expert assessments. We evaluate four supervision policies: non-prioritized (first-come, first-served), random selection, priority-based (addressing the highest-priority risks first), and diversity-prioritized (balancing high-priority risks with comprehensive coverage across risk types). Our results indicate that while priority-based and diversity-prioritized policies are more effective at mitigating high-impact risks, particularly those identified by experts, they may inadvertently neglect systemic issues reported by the broader community. This oversight can create feedback loops that amplify certain types of reporting while discouraging others, leading to a skewed perception of the overall risk landscape. We validate our simulation results with several real-world datasets, including one with over a million ChatGPT interactions, of which more than 150,000 conversations were identified as risky. This validation underscores the complex trade-offs inherent in AI risk supervision and highlights how the choice of risk management policies can shape the future landscape of AI risks across diverse GPAI models used in society.", "date": "2025-01-10", "categories": []}
{"id": "2502.10450", "title": "Trustworthy AI: Safety, Bias, and Privacy -- A Survey", "abstract": "The capabilities of artificial intelligence systems have been advancing to a great extent, but these systems still struggle with failure modes, vulnerabilities, and biases. In this paper, we study the current state of the field, and present promising insights and perspectives regarding concerns that challenge the trustworthiness of AI models. In particular, this paper investigates the issues regarding three thrusts: safety, privacy, and bias, which hurt models' trustworthiness. For safety, we discuss safety alignment in the context of large language models, preventing them from generating toxic or harmful content. For bias, we focus on spurious biases that can mislead a network. Lastly, for privacy, we cover membership inference attacks in deep neural networks. The discussions addressed in this paper reflect our own experiments and observations.", "date": "2025-02-11", "categories": []}
{"id": "2505.17441", "title": "Discovering Forbidden Topics in Language Models", "abstract": "Refusal discovery is the task of identifying the full set of topics that a language model refuses to discuss. We introduce this new problem setting and develop a refusal discovery method, Iterated Prefill Crawler (IPC), that uses token prefilling to find forbidden topics. We benchmark IPC on Tulu-3-8B, an open-source model with public safety tuning data. Our crawler manages to retrieve 31 out of 36 topics within a budget of 1000 prompts. Next, we scale the crawler to a frontier model using the prefilling option of Claude-Haiku. Finally, we crawl three widely used open-weight models: Llama-3.3-70B and two of its variants finetuned for reasoning: DeepSeek-R1-70B and Perplexity-R1-1776-70B. DeepSeek-R1-70B reveals patterns consistent with censorship tuning: The model exhibits \"thought suppression\" behavior that indicates memorization of CCP-aligned responses. Although Perplexity-R1-1776-70B is robust to censorship, IPC elicits CCP-aligned refusals answers in the quantized model. Our findings highlight the critical need for refusal discovery methods to detect biases, boundaries, and alignment failures of AI systems.", "date": "2025-05-23", "categories": []}
{"id": "2502.00840", "title": "Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense", "abstract": "Large Language Models (LLMs) have showcased remarkable capabilities across various domains. Accompanying the evolving capabilities and expanding deployment scenarios of LLMs, their deployment challenges escalate due to their sheer scale and the advanced yet complex activation designs prevalent in notable model series, such as Llama, Gemma, Mistral. These challenges have become particularly pronounced in resource-constrained deployment scenarios, where mitigating inference bottlenecks is imperative. Among various recent efforts, activation approximation has emerged as a promising avenue for pursuing inference efficiency, sometimes considered indispensable in applications such as private inference. Despite achieving substantial speedups with minimal impact on utility, even appearing sound and practical for real-world deployment, the safety implications of activation approximations remain unclear. In this work, we fill this critical gap in LLM safety by conducting the first systematic safety evaluation of activation approximations. Our safety vetting spans seven state-of-the-art techniques across three popular categories (activation polynomialization, activation sparsification, and activation quantization), revealing consistent safety degradation across ten safety-aligned LLMs. To overcome the hurdle of devising a unified defense accounting for diverse activation approximation methods, we perform an in-depth analysis of their shared error patterns and uncover three key findings. We propose QuadA, a novel safety enhancement method tailored to mitigate the safety compromises introduced by activation approximations. Extensive experiments and ablation studies corroborate QuadA's effectiveness in enhancing the safety capabilities of LLMs after activation approximations.", "date": "2025-02-02", "categories": []}
{"id": "2506.08991", "title": "Do Concept Replacement Techniques Really Erase Unacceptable Concepts?", "abstract": "Generative models, particularly diffusion-based text-to-image (T2I) models, have demonstrated astounding success. However, aligning them to avoid generating content with unacceptable concepts (e.g., offensive or copyrighted content, or celebrity likenesses) remains a significant challenge. Concept replacement techniques (CRTs) aim to address this challenge, often by trying to \"erase\" unacceptable concepts from models. Recently, model providers have started offering image editing services which accept an image and a text prompt as input, to produce an image altered as specified by the prompt. These are known as image-to-image (I2I) models. In this paper, we first use an I2I model to empirically demonstrate that today's state-of-the-art CRTs do not in fact erase unacceptable concepts. Existing CRTs are thus likely to be ineffective in emerging I2I scenarios, despite their proven ability to remove unwanted concepts in T2I pipelines, highlighting the need to understand this discrepancy between T2I and I2I settings. Next, we argue that a good CRT, while replacing unacceptable concepts, should preserve other concepts specified in the inputs to generative models. We call this fidelity. Prior work on CRTs have neglected fidelity in the case of unacceptable concepts. Finally, we propose the use of targeted image-editing techniques to achieve both effectiveness and fidelity. We present such a technique, AntiMirror, and demonstrate its viability.", "date": "2025-06-10", "categories": []}
{"id": "2406.12091", "title": "Is poisoning a real threat to LLM alignment? Maybe more so than you think", "abstract": "Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs). The sensitivity of reinforcement learning algorithms such as Proximal Policy Optimization (PPO) has led to new line work on Direct Policy Optimization (DPO), which treats RLHF in a supervised learning framework. The increased practical use of these RLHF methods warrants an analysis of their vulnerabilities. In this work, we investigate the vulnerabilities of DPO to poisoning attacks under different scenarios and compare the effectiveness of preference poisoning, a first of its kind. We comprehensively analyze DPO's vulnerabilities under different types of attacks, i.e., backdoor and non-backdoor attacks, and different poisoning methods across a wide array of language models, i.e., LLama 7B, Mistral 7B, and Gemma 7B. We find that unlike PPO-based methods, which, when it comes to backdoor attacks, require at least 4\\% of the data to be poisoned to elicit harmful behavior, we exploit the true vulnerabilities of DPO more simply so we can poison the model with only as much as 0.5\\% of the data. We further investigate the potential reasons behind the vulnerability and how well this vulnerability translates into backdoor vs non-backdoor attacks.", "date": "2024-06-17", "categories": []}
{"id": "2409.03788", "title": "HSF: Defending against Jailbreak Attacks with Hidden State Filtering", "abstract": "With the growing deployment of LLMs in daily applications like chatbots and content generation, efforts to ensure outputs align with human values and avoid harmful content have intensified. However, increasingly sophisticated jailbreak attacks threaten this alignment, aiming to induce unsafe outputs. Current defense efforts either focus on prompt rewriting or detection, which are limited in effectiveness due to the various design of jailbreak prompts, or on output control and detection, which are computationally expensive as they require LLM inference. Therefore, designing a pre-inference defense method that resists diverse jailbreak prompts is crucial for preventing LLM jailbreak attacks. We observe that jailbreak attacks, safe queries, and harmful queries exhibit different clustering patterns within the LLM's hidden state representation space. This suggests that by leveraging the LLM's hidden state representational capabilities, we can analyze the LLM's forthcoming behavior and proactively intervene for defense. In this paper, we propose a jailbreak attack defense strategy based on a Hidden State Filter (HSF), a lossless architectural defense mechanism that enables the model to preemptively identify and reject adversarial inputs before the inference process begins. We activate its defensive potential through an additional plugin module, effectively framing the defense task as a classification problem. Experimental results on two benchmark datasets, utilizing three different LLMs, show that HSF significantly enhances resilience against six cutting-edge jailbreak attacks. It significantly reduces the success rate of jailbreak attacks while minimally impacting responses to benign user queries, with negligible inference overhead, and outperforming defense baselines.Our code and data are available at https://anonymous.4open.science/r/Hidden-State-Filtering-8652/", "date": "2024-08-31", "categories": []}
{"id": "2503.20279", "title": "sudo rm -rf agentic_security", "abstract": "Large Language Models (LLMs) are increasingly deployed as computer-use agents, autonomously performing tasks within real desktop or web environments. While this evolution greatly expands practical use cases for humans, it also creates serious security exposures. We present SUDO (Screen-based Universal Detox2Tox Offense), a novel attack framework that systematically bypasses refusal-trained safeguards in commercial computer-use agents, such as Claude for Computer Use. The core mechanism, Detox2Tox, transforms harmful requests (that agents initially reject) into seemingly benign requests via detoxification, secures detailed instructions from advanced vision language models (VLMs), and then reintroduces malicious content via toxification just before execution. Unlike conventional jailbreaks, SUDO iteratively refines its attacks based on a built-in refusal feedback, making it increasingly effective against robust policy filters. In extensive tests spanning 50 real-world tasks and multiple state-of-the-art VLMs, SUDO achieves a stark attack success rate of 24.41% (with no refinement), and up to 41.33% (by its iterative refinement) in Claude for Computer Use. By revealing these vulnerabilities and demonstrating the ease with which they can be exploited in real-world computing environments, this paper highlights an immediate need for robust, context-aware safeguards. WARNING: This paper includes harmful or offensive model outputs", "date": "2025-03-26", "categories": []}
{"id": "2504.15585", "title": "A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment", "abstract": "The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communities, owing to their unprecedented performance across various applications. As LLMs continue to gain prominence in both research and commercial domains, their security and safety implications have become a growing concern, not only for researchers and corporations but also for every nation. Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e.g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire \"lifechain\" of LLMs. To address this gap, this paper introduces, for the first time, the concept of \"full-stack\" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization. Compared to the off-the-shelf LLM safety surveys, our work demonstrates several distinctive advantages: (I) Comprehensive Perspective. We define the complete LLM lifecycle as encompassing data preparation, pre-training, post-training, deployment and final commercialization. To our knowledge, this represents the first safety survey to encompass the entire lifecycle of LLMs. (II) Extensive Literature Support. Our research is grounded in an exhaustive review of over 800+ papers, ensuring comprehensive coverage and systematic organization of security issues within a more holistic understanding. (III) Unique Insights. Through systematic literature analysis, we have developed reliable roadmaps and perspectives for each chapter. Our work identifies promising research directions, including safety in data generation, alignment techniques, model editing, and LLM-based agent systems. These insights provide valuable guidance for researchers pursuing future work in this field.", "date": "2025-04-22", "categories": []}
{"id": "2506.06352", "title": "Will artificial agents pursue power by default?", "abstract": "Researchers worried about catastrophic risks from advanced AI have argued that we should expect sufficiently capable AI agents to pursue power over humanity because power is a convergent instrumental goal, something that is useful for a wide range of final goals. Others have recently expressed skepticism of these claims. This paper aims to formalize the concepts of instrumental convergence and power-seeking in an abstract, decision-theoretic framework, and to assess the claim that power is a convergent instrumental goal. I conclude that this claim contains at least an element of truth, but might turn out to have limited predictive utility, since an agent's options cannot always be ranked in terms of power in the absence of substantive information about the agent's final goals. However, the fact of instrumental convergence is more predictive for agents who have a good shot at attaining absolute or near-absolute power.", "date": "2025-06-02", "categories": []}
{"id": "2506.06384", "title": "Detection Method for Prompt Injection by Integrating Pre-trained Model and Heuristic Feature Engineering", "abstract": "With the widespread adoption of Large Language Models (LLMs), prompt injection attacks have emerged as a significant security threat. Existing defense mechanisms often face critical trade-offs between effectiveness and generalizability. This highlights the urgent need for efficient prompt injection detection methods that are applicable across a wide range of LLMs. To address this challenge, we propose DMPI-PMHFE, a dual-channel feature fusion detection framework. It integrates a pretrained language model with heuristic feature engineering to detect prompt injection attacks. Specifically, the framework employs DeBERTa-v3-base as a feature extractor to transform input text into semantic vectors enriched with contextual information. In parallel, we design heuristic rules based on known attack patterns to extract explicit structural features commonly observed in attacks. Features from both channels are subsequently fused and passed through a fully connected neural network to produce the final prediction. This dual-channel approach mitigates the limitations of relying only on DeBERTa to extract features. Experimental results on diverse benchmark datasets demonstrate that DMPI-PMHFE outperforms existing methods in terms of accuracy, recall, and F1-score. Furthermore, when deployed actually, it significantly reduces attack success rates across mainstream LLMs, including GLM-4, LLaMA 3, Qwen 2.5, and GPT-4o.", "date": "2025-06-05", "categories": []}
{"id": "2506.06518", "title": "A Systematic Review of Poisoning Attacks Against Large Language Models", "abstract": "With the widespread availability of pretrained Large Language Models (LLMs) and their training datasets, concerns about the security risks associated with their usage has increased significantly. One of these security risks is the threat of LLM poisoning attacks where an attacker modifies some part of the LLM training process to cause the LLM to behave in a malicious way. As an emerging area of research, the current frameworks and terminology for LLM poisoning attacks are derived from earlier classification poisoning literature and are not fully equipped for generative LLM settings. We conduct a systematic review of published LLM poisoning attacks to clarify the security implications and address inconsistencies in terminology across the literature. We propose a comprehensive poisoning threat model applicable to categorize a wide range of LLM poisoning attacks. The poisoning threat model includes four poisoning attack specifications that define the logistics and manipulation strategies of an attack as well as six poisoning metrics used to measure key characteristics of an attack. Under our proposed framework, we organize our discussion of published LLM poisoning literature along four critical dimensions of LLM poisoning attacks: concept poisons, stealthy poisons, persistent poisons, and poisons for unique tasks, to better understand the current landscape of security risks.", "date": "2025-06-06", "categories": []}
{"id": "2506.06636", "title": "SafeLawBench: Towards Safe Alignment of Large Language Models", "abstract": "With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, there is still a lack of definitive standards for evaluating their safety due to the subjective nature of current safety benchmarks. To address this gap, we conducted the first exploration of LLMs' safety evaluation from a legal perspective by proposing the SafeLawBench benchmark. SafeLawBench categorizes safety risks into three levels based on legal standards, providing a systematic and comprehensive framework for evaluation. It comprises 24,860 multi-choice questions and 1,106 open-domain question-answering (QA) tasks. Our evaluation included 2 closed-source LLMs and 18 open-source LLMs using zero-shot and few-shot prompting, highlighting the safety features of each model. We also evaluated the LLMs' safety-related reasoning stability and refusal behavior. Additionally, we found that a majority voting mechanism can enhance model performance. Notably, even leading SOTA models like Claude-3.5-Sonnet and GPT-4o have not exceeded 80.5% accuracy in multi-choice tasks on SafeLawBench, while the average accuracy of 20 LLMs remains at 68.8\\%. We urge the community to prioritize research on the safety of LLMs.", "date": "2025-06-07", "categories": []}
{"id": "2506.07330", "title": "JavelinGuard: Low-Cost Transformer Architectures for LLM Security", "abstract": "We present JavelinGuard, a suite of low-cost, high-performance model architectures designed for detecting malicious intent in Large Language Model (LLM) interactions, optimized specifically for production deployment. Recent advances in transformer architectures, including compact BERT(Devlin et al. 2019) variants (e.g., ModernBERT (Warner et al. 2024)), allow us to build highly accurate classifiers with as few as approximately 400M parameters that achieve rapid inference speeds even on standard CPU hardware. We systematically explore five progressively sophisticated transformer-based architectures: Sharanga (baseline transformer classifier), Mahendra (enhanced attention-weighted pooling with deeper heads), Vaishnava and Ashwina (hybrid neural ensemble architectures), and Raudra (an advanced multi-task framework with specialized loss functions). Our models are rigorously benchmarked across nine diverse adversarial datasets, including popular sets like the NotInject series, BIPIA, Garak, ImprovedLLM, ToxicChat, WildGuard, and our newly introduced JavelinBench, specifically crafted to test generalization on challenging borderline and hard-negative cases. Additionally, we compare our architectures against leading open-source guardrail models as well as large decoder-only LLMs such as gpt-4o, demonstrating superior cost-performance trade-offs in terms of accuracy, and latency. Our findings reveal that while Raudra's multi-task design offers the most robust performance overall, each architecture presents unique trade-offs in speed, interpretability, and resource requirements, guiding practitioners in selecting the optimal balance of complexity and efficiency for real-world LLM security applications.", "date": "2025-06-09", "categories": []}
{"id": "2506.07402", "title": "Beyond Jailbreaks: Revealing Stealthier and Broader LLM Security Risks Stemming from Alignment Failures", "abstract": "Large language models (LLMs) are increasingly deployed in real-world applications, raising concerns about their security. While jailbreak attacks highlight failures under overtly harmful queries, they overlook a critical risk: incorrectly answering harmless-looking inputs can be dangerous and cause real-world harm (Implicit Harm). We systematically reformulate the LLM risk landscape through a structured quadrant perspective based on output factuality and input harmlessness, uncovering an overlooked high-risk region. To investigate this gap, we propose JailFlipBench, a benchmark aims to capture implicit harm, spanning single-modal, multimodal, and factual extension scenarios with diverse evaluation metrics. We further develop initial JailFlip attack methodologies and conduct comprehensive evaluations across multiple open-source and black-box LLMs, show that implicit harm present immediate and urgent real-world risks, calling for broader LLM safety assessments and alignment beyond conventional jailbreak paradigms.", "date": "2025-06-09", "categories": []}
{"id": "2503.02174", "title": "Adversarial Tokenization", "abstract": "Current LLM pipelines account for only one possible tokenization for a given string, ignoring exponentially many alternative tokenizations during training and inference. For example, the standard Llama3 tokenization of penguin is [p,enguin], yet [peng,uin] is another perfectly valid alternative. In this paper, we show that despite LLMs being trained solely on one tokenization, they still retain semantic understanding of other tokenizations, raising questions about their implications in LLM safety. Put succinctly, we answer the following question: can we adversarially tokenize an obviously malicious string to evade safety and alignment restrictions? We show that not only is adversarial tokenization an effective yet previously neglected axis of attack, but it is also competitive against existing state-of-the-art adversarial approaches without changing the text of the harmful request. We empirically validate this exploit across three state-of-the-art LLMs and adversarial datasets, revealing a previously unknown vulnerability in subword models.", "date": "2025-03-04", "categories": []}
{"id": "2506.06166", "title": "The Lock-in Hypothesis: Stagnation by Algorithm", "abstract": "The training and deployment of large language models (LLMs) create a feedback loop with human users: models learn human beliefs from data, reinforce these beliefs with generated content, reabsorb the reinforced beliefs, and feed them back to users again and again. This dynamic resembles an echo chamber. We hypothesize that this feedback loop entrenches the existing values and beliefs of users, leading to a loss of diversity and potentially the lock-in of false beliefs. We formalize this hypothesis and test it empirically with agent-based LLM simulations and real-world GPT usage data. Analysis reveals sudden but sustained drops in diversity after the release of new GPT iterations, consistent with the hypothesized human-AI feedback loop. Code and data available at https://thelockinhypothesis.com", "date": "2025-06-06", "categories": []}
{"id": "2502.07987", "title": "Universal Adversarial Attack on Aligned Multimodal LLMs", "abstract": "We propose a universal adversarial attack on multimodal Large Language Models (LLMs) that leverages a single optimized image to override alignment safeguards across diverse queries and even multiple models. By backpropagating through the vision encoder and language head, we craft a synthetic image that forces the model to respond with a targeted phrase (e.g., \"Sure, here it is\") or otherwise unsafe content -- even for harmful prompts. In experiments on the SafeBench and MM-SafetyBench benchmarks, our method achieves higher attack success rates than existing baselines, including text-only universal prompts (e.g., up to 81% on certain models). We further demonstrate cross-model universality by training on several multimodal LLMs simultaneously. Additionally, a multi-answer variant of our approach produces more natural-sounding (yet still malicious) responses. These findings underscore critical vulnerabilities in current multimodal alignment and call for more robust adversarial defenses. We will release code and datasets under the Apache-2.0 license. Warning: some content generated by Multimodal LLMs in this paper may be offensive.", "date": "2025-02-11", "categories": []}
{"id": "2502.18504", "title": "TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice", "abstract": "Jailbreaking large-language models (LLMs) involves testing their robustness against adversarial prompts and evaluating their ability to withstand prompt attacks that could elicit unauthorized or malicious responses. In this paper, we present TurboFuzzLLM, a mutation-based fuzzing technique for efficiently finding a collection of effective jailbreaking templates that, when combined with harmful questions, can lead a target LLM to produce harmful responses through black-box access via user prompts. We describe the limitations of directly applying existing template-based attacking techniques in practice, and present functional and efficiency-focused upgrades we added to mutation-based fuzzing to generate effective jailbreaking templates automatically. TurboFuzzLLM achieves $\\geq$ 95\\% attack success rates (ASR) on public datasets for leading LLMs (including GPT-4o \\& GPT-4 Turbo), shows impressive generalizability to unseen harmful questions, and helps in improving model defenses to prompt attacks. TurboFuzzLLM is available open source at https://github.com/amazon-science/TurboFuzzLLM.", "date": "2025-02-21", "categories": []}
{"id": "2503.00555", "title": "Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable", "abstract": "Safety alignment is an important procedure before the official deployment of a Large Language Model (LLM). While safety alignment has been extensively studied for LLM, there is still a large research gap for Large Reasoning Models (LRMs) that equip with improved reasoning capability. We in this paper systematically examine a simplified pipeline for producing safety aligned LRMs. With our evaluation of various LRMs, we deliver two main findings: i) Safety alignment can be done upon the LRM to restore its safety capability. ii) Safety alignment leads to a degradation of the reasoning capability of LRMs. The two findings show that there exists a trade-off between reasoning and safety capability with the sequential LRM production pipeline. The discovered trade-off, which we name Safety Tax, should shed light on future endeavors of safety research on LRMs. As a by-product, we curate a dataset called DirectRefusal, which might serve as an alternative dataset for safety alignment. Our source code is available at https://github.com/git-disl/Safety-Tax.", "date": "2025-03-01", "categories": []}
{"id": "2504.18053", "title": "DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models", "abstract": "Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data, thereby introducing new dimensions of potential attacks and complex risk combinations. In this paper, we begin with a detailed analysis aimed at disentangling risks through step-by-step reasoning within multimodal inputs. We find that systematic multimodal risk disentanglement substantially enhances the risk awareness of MLLMs. Via leveraging the strong discriminative abilities of multimodal risk disentanglement, we further introduce \\textbf{DREAM} (\\textit{\\textbf{D}isentangling \\textbf{R}isks to \\textbf{E}nhance Safety \\textbf{A}lignment in \\textbf{M}LLMs}), a novel approach that enhances safety alignment in MLLMs through supervised fine-tuning and iterative Reinforcement Learning from AI Feedback (RLAIF). Experimental results show that DREAM significantly boosts safety during both inference and training phases without compromising performance on normal tasks (namely oversafety), achieving a 16.17\\% improvement in the SIUO safe\\&effective score compared to GPT-4V. The data and code are available at https://github.com/Kizna1ver/DREAM.", "date": "2025-04-25", "categories": []}
{"id": "2505.04075", "title": "Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress", "abstract": "Regulatory efforts to govern large language model (LLM) development have predominantly focused on restricting access to high-performance computational resources. This study evaluates the efficacy of such measures by examining whether LLM capabilities can advance through algorithmic innovation in compute-constrained environments. We propose a novel framework distinguishing compute-dependent innovations--which yield disproportionate benefits at high compute--from compute-independent innovations, which improve efficiency across compute scales. The impact is quantified using Compute-Equivalent Gain (CEG). Experimental validation with nanoGPT models confirms that compute-independent advancements yield significant performance gains (e.g., with combined CEG up to $3.5\\times$) across the tested scales. In contrast, compute-dependent advancements were detrimental to performance at smaller experimental scales, but showed improved CEG (on par with the baseline) as model size increased, a trend consistent with their definition of yielding primary benefits at higher compute. Crucially, these findings indicate that restrictions on computational hardware, while potentially slowing LLM progress, are insufficient to prevent all capability gains driven by algorithmic advancements. We argue that effective AI oversight must therefore incorporate mechanisms for understanding, anticipating, and potentially guiding algorithmic research, moving beyond a singular focus on hardware. The proposed framework also serves as an analytical tool for forecasting AI progress.", "date": "2025-05-07", "categories": []}
{"id": "2506.04679", "title": "Normative Conflicts and Shallow AI Alignment", "abstract": "The progress of AI systems such as large language models (LLMs) raises increasingly pressing concerns about their safe deployment. This paper examines the value alignment problem for LLMs, arguing that current alignment strategies are fundamentally inadequate to prevent misuse. Despite ongoing efforts to instill norms such as helpfulness, honesty, and harmlessness in LLMs through fine-tuning based on human preferences, they remain vulnerable to adversarial attacks that exploit conflicts between these norms. I argue that this vulnerability reflects a fundamental limitation of existing alignment methods: they reinforce shallow behavioral dispositions rather than endowing LLMs with a genuine capacity for normative deliberation. Drawing from on research in moral psychology, I show how humans' ability to engage in deliberative reasoning enhances their resilience against similar adversarial tactics. LLMs, by contrast, lack a robust capacity to detect and rationally resolve normative conflicts, leaving them susceptible to manipulation; even recent advances in reasoning-focused LLMs have not addressed this vulnerability. This ``shallow alignment'' problem carries significant implications for AI safety and regulation, suggesting that current approaches are insufficient for mitigating potential harms posed by increasingly capable AI systems.", "date": "2025-06-05", "categories": []}
{"id": "2506.05346", "title": "Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets", "abstract": "Recent advancements in large language models (LLMs) have underscored their vulnerability to safety alignment jailbreaks, particularly when subjected to downstream fine-tuning. However, existing mitigation strategies primarily focus on reactively addressing jailbreak incidents after safety guardrails have been compromised, removing harmful gradients during fine-tuning, or continuously reinforcing safety alignment throughout fine-tuning. As such, they tend to overlook a critical upstream factor: the role of the original safety-alignment data. This paper therefore investigates the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. Our experiments demonstrate that high similarity between these datasets significantly weakens safety guardrails, making models more susceptible to jailbreaks. Conversely, low similarity between these two types of datasets yields substantially more robust models and thus reduces harmfulness score by up to 10.33%. By highlighting the importance of upstream dataset design in the building of durable safety guardrails and reducing real-world vulnerability to jailbreak attacks, these findings offer actionable insights for fine-tuning service providers.", "date": "2025-06-05", "categories": []}
{"id": "2411.08243", "title": "Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset", "abstract": "In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards outputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.", "date": "2024-11-12", "categories": []}
{"id": "2502.13946", "title": "Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety Mechanisms Tend to Be Anchored in The Template Region", "abstract": "The safety alignment of large language models (LLMs) remains vulnerable, as their initial behavior can be easily jailbroken by even relatively simple attacks. Since infilling a fixed template between the input instruction and initial model output is a common practice for existing LLMs, we hypothesize that this template is a key factor behind their vulnerabilities: LLMs' safety-related decision-making overly relies on the aggregated information from the template region, which largely influences these models' safety behavior. We refer to this issue as template-anchored safety alignment. In this paper, we conduct extensive experiments and verify that template-anchored safety alignment is widespread across various aligned LLMs. Our mechanistic analyses demonstrate how it leads to models' susceptibility when encountering inference-time jailbreak attacks. Furthermore, we show that detaching safety mechanisms from the template region is promising in mitigating vulnerabilities to jailbreak attacks. We encourage future research to develop more robust safety alignment techniques that reduce reliance on the template region.", "date": "2025-02-19", "categories": []}
{"id": "2506.03683", "title": "PRJ: Perception-Retrieval-Judgement for Generated Images", "abstract": "The rapid progress of generative AI has enabled remarkable creative capabilities, yet it also raises urgent concerns regarding the safety of AI-generated visual content in real-world applications such as content moderation, platform governance, and digital media regulation. This includes unsafe material such as sexually explicit images, violent scenes, hate symbols, propaganda, and unauthorized imitations of copyrighted artworks. Existing image safety systems often rely on rigid category filters and produce binary outputs, lacking the capacity to interpret context or reason about nuanced, adversarially induced forms of harm. In addition, standard evaluation metrics (e.g., attack success rate) fail to capture the semantic severity and dynamic progression of toxicity. To address these limitations, we propose Perception-Retrieval-Judgement (PRJ), a cognitively inspired framework that models toxicity detection as a structured reasoning process. PRJ follows a three-stage design: it first transforms an image into descriptive language (perception), then retrieves external knowledge related to harm categories and traits (retrieval), and finally evaluates toxicity based on legal or normative rules (judgement). This language-centric structure enables the system to detect both explicit and implicit harms with improved interpretability and categorical granularity. In addition, we introduce a dynamic scoring mechanism based on a contextual toxicity risk matrix to quantify harmfulness across different semantic dimensions. Experiments show that PRJ surpasses existing safety checkers in detection accuracy and robustness while uniquely supporting structured category-level toxicity interpretation.", "date": "2025-06-04", "categories": []}
{"id": "2506.03755", "title": "Misalignment or misuse? The AGI alignment tradeoff", "abstract": "Creating systems that are aligned with our goals is seen as a leading approach to create safe and beneficial AI in both leading AI companies and the academic field of AI safety. We defend the view that misaligned AGI - future, generally intelligent (robotic) AI agents - poses catastrophic risks. At the same time, we support the view that aligned AGI creates a substantial risk of catastrophic misuse by humans. While both risks are severe and stand in tension with one another, we show that - in principle - there is room for alignment approaches which do not increase misuse risk. We then investigate how the tradeoff between misalignment and misuse looks empirically for different technical approaches to AI alignment. Here, we argue that many current alignment techniques and foreseeable improvements thereof plausibly increase risks of catastrophic misuse. Since the impacts of AI depend on the social context, we close by discussing important social factors and suggest that to reduce the risk of a misuse catastrophe due to aligned AGI, techniques such as robustness, AI control methods and especially good governance seem essential.", "date": "2025-06-04", "categories": []}
{"id": "2407.13948", "title": "Assurance of AI Systems From a Dependability Perspective", "abstract": "We outline the principles of classical assurance for computer-based systems that pose significant risks. We then consider application of these principles to systems that employ Artificial Intelligence (AI) and Machine Learning (ML).\n  A key element in this \"dependability\" perspective is a requirement for thorough understanding of the behavior of critical components, and this is considered infeasible for AI and ML. Hence the dependability perspective aims to minimize trust in AI and ML elements by using \"defense in depth\" with a hierarchy of less complex systems, some of which may be highly assured conventionally engineered components, to \"guard\" them. This may be contrasted with the \"trustworthy\" perspective that seeks to apply assurance to the AI and ML elements themselves.\n  In cyber-physical and many other systems, it is difficult to provide guards that do not depend on AI and ML to perceive their environment (e.g., vehicles sharing the road with a self-driving car), so both perspectives are needed and there is a continuum or spectrum between them. We focus on architectures toward the dependability end of the continuum and invite others to consider additional points along the spectrum.\n  For guards that require perception using AI and ML, we examine ways to minimize the trust placed in these elements; they include diversity, defense in depth, explanations, and micro-ODDs. We also examine methods to enforce acceptable behavior, given a model of the world. These include classical cyber-physical calculations and envelopes, and normative rules based on overarching principles, constitutions, ethics, or reputation.\n  We apply our perspective to autonomous systems, AI systems for specific functions, general-purpose AI such as Large Language Models (LLMs), and Artificial General Intelligence (AGI), and we propose current best practice and conclude with a fourfold agenda for research.", "date": "2024-07-18", "categories": []}
{"id": "2502.12562", "title": "SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings", "abstract": "Multimodal Large Language Models (MLLMs) have serious security vulnerabilities.While safety alignment using multimodal datasets consisting of text and data of additional modalities can effectively enhance MLLM's security, it is costly to construct these datasets. Existing low-resource security alignment methods, including textual alignment, have been found to struggle with the security risks posed by additional modalities. To address this, we propose Synthetic Embedding augmented safety Alignment (SEA), which optimizes embeddings of additional modality through gradient updates to expand textual datasets. This enables multimodal safety alignment training even when only textual data is available. Extensive experiments on image, video, and audio-based MLLMs demonstrate that SEA can synthesize a high-quality embedding on a single RTX3090 GPU within 24 seconds. SEA significantly improves the security of MLLMs when faced with threats from additional modalities. To assess the security risks introduced by video and audio, we also introduced a new benchmark called VA-SafetyBench. High attack success rates across multiple MLLMs validate its challenge. Our code and data will be available at https://github.com/ZeroNLP/SEA.", "date": "2025-02-18", "categories": []}
{"id": "2502.15806", "title": "A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos", "abstract": "Large Reasoning Models (LRMs) have significantly advanced beyond traditional Large Language Models (LLMs) with their exceptional logical reasoning capabilities, yet these improvements introduce heightened safety risks. When subjected to jailbreak attacks, their ability to generate more targeted and organized content can lead to greater harm. Although some studies claim that reasoning enables safer LRMs against existing LLM attacks, they overlook the inherent flaws within the reasoning process itself. To address this gap, we propose the first jailbreak attack targeting LRMs, exploiting their unique vulnerabilities stemming from the advanced reasoning capabilities. Specifically, we introduce a Chaos Machine, a novel component to transform attack prompts with diverse one-to-one mappings. The chaos mappings iteratively generated by the machine are embedded into the reasoning chain, which strengthens the variability and complexity and also promotes a more robust attack. Based on this, we construct the Mousetrap framework, which makes attacks projected into nonlinear-like low sample spaces with mismatched generalization enhanced. Also, due to the more competing objectives, LRMs gradually maintain the inertia of unpredictable iterative reasoning and fall into our trap. Success rates of the Mousetrap attacking o1-mini, Claude-Sonnet and Gemini-Thinking are as high as 96%, 86% and 98% respectively on our toxic dataset Trotter. On benchmarks such as AdvBench, StrongREJECT, and HarmBench, attacking Claude-Sonnet, well-known for its safety, Mousetrap can astonishingly achieve success rates of 87.5%, 86.58% and 93.13% respectively. Attention: This paper contains inappropriate, offensive and harmful content.", "date": "2025-02-19", "categories": []}
{"id": "2504.05050", "title": "Revealing the Intrinsic Ethical Vulnerability of Aligned Large Language Models", "abstract": "Large language models (LLMs) are foundational explorations to artificial general intelligence, yet their alignment with human values via instruction tuning and preference learning achieves only superficial compliance. Here, we demonstrate that harmful knowledge embedded during pretraining persists as indelible \"dark patterns\" in LLMs' parametric memory, evading alignment safeguards and resurfacing under adversarial inducement at distributional shifts. In this study, we first theoretically analyze the intrinsic ethical vulnerability of aligned LLMs by proving that current alignment methods yield only local \"safety regions\" in the knowledge manifold. In contrast, pretrained knowledge remains globally connected to harmful concepts via high-likelihood adversarial trajectories. Building on this theoretical insight, we empirically validate our findings by employing semantic coherence inducement under distributional shifts--a method that systematically bypasses alignment constraints through optimized adversarial prompts. This combined theoretical and empirical approach achieves a 100% attack success rate across 19 out of 23 state-of-the-art aligned LLMs, including DeepSeek-R1 and LLaMA-3, revealing their universal vulnerabilities.", "date": "2025-04-07", "categories": []}
{"id": "2506.02573", "title": "IndoSafety: Culturally Grounded Safety for LLMs in Indonesian Languages", "abstract": "Although region-specific large language models (LLMs) are increasingly developed, their safety remains underexplored, particularly in culturally diverse settings like Indonesia, where sensitivity to local norms is essential and highly valued by the community. In this work, we present IndoSafety, the first high-quality, human-verified safety evaluation dataset tailored for the Indonesian context, covering five language varieties: formal and colloquial Indonesian, along with three major local languages: Javanese, Sundanese, and Minangkabau. IndoSafety is constructed by extending prior safety frameworks to develop a taxonomy that captures Indonesia's sociocultural context. We find that existing Indonesian-centric LLMs often generate unsafe outputs, particularly in colloquial and local language settings, while fine-tuning on IndoSafety significantly improves safety while preserving task performance. Our work highlights the critical need for culturally grounded safety evaluation and provides a concrete step toward responsible LLM deployment in multilingual settings. Warning: This paper contains example data that may be offensive, harmful, or biased.", "date": "2025-06-03", "categories": []}
{"id": "2506.03056", "title": "Corrigibility as a Singular Target: A Vision for Inherently Reliable Foundation Models", "abstract": "Foundation models (FMs) face a critical safety challenge: as capabilities scale, instrumental convergence drives default trajectories toward loss of human control, potentially culminating in existential catastrophe. Current alignment approaches struggle with value specification complexity and fail to address emergent power-seeking behaviors. We propose \"Corrigibility as a Singular Target\" (CAST)-designing FMs whose overriding objective is empowering designated human principals to guide, correct, and control them. This paradigm shift from static value-loading to dynamic human empowerment transforms instrumental drives: self-preservation serves only to maintain the principal's control; goal modification becomes facilitating principal guidance. We present a comprehensive empirical research agenda spanning training methodologies (RLAIF, SFT, synthetic data generation), scalability testing across model sizes, and demonstrations of controlled instructability. Our vision: FMs that become increasingly responsive to human guidance as capabilities grow, offering a path to beneficial AI that remains as tool-like as possible, rather than supplanting human judgment. This addresses the core alignment problem at its source, preventing the default trajectory toward misaligned instrumental convergence.", "date": "2025-06-03", "categories": []}
{"id": "2406.04231", "title": "Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment", "abstract": "Existing work on the alignment problem has focused mainly on (1) qualitative descriptions of the alignment problem; (2) attempting to align AI actions with human interests by focusing on value specification and learning; and/or (3) focusing on a single agent or on humanity as a monolith. Recent sociotechnical approaches highlight the need to understand complex misalignment among multiple human and AI agents. We address this gap by adapting a computational social science model of human contention to the alignment problem. Our model quantifies misalignment in large, diverse agent groups with potentially conflicting goals across various problem areas. Misalignment scores in our framework depend on the observed agent population, the domain in question, and conflict between agents' weighted preferences. Through simulations, we demonstrate how our model captures intuitive aspects of misalignment across different scenarios. We then apply our model to two case studies, including an autonomous vehicle setting, showcasing its practical utility. Our approach offers enhanced explanatory power for complex sociotechnical environments and could inform the design of more aligned AI systems in real-world applications.", "date": "2024-06-06", "categories": []}
{"id": "2502.06560", "title": "Position: It's Time to Act on the Risk of Efficient Personalized Text Generation", "abstract": "The recent surge in high-quality open-source Generative AI text models (colloquially: LLMs), as well as efficient finetuning techniques, have opened the possibility of creating high-quality personalized models that generate text attuned to a specific individual's needs and are capable of credibly imitating their writing style by refining an open-source model with that person's own data. The technology to create such models is accessible to private individuals, and training and running such models can be done cheaply on consumer-grade hardware. While these advancements are a huge gain for usability and privacy, this position paper argues that the practical feasibility of impersonating specific individuals also introduces novel safety risks. For instance, this technology enables the creation of phishing emails or fraudulent social media accounts, based on small amounts of publicly available text, or by the individuals themselves to escape AI text detection. We further argue that these risks are complementary to - and distinct from - the much-discussed risks of other impersonation attacks such as image, voice, or video deepfakes, and are not adequately addressed by the larger research community, or the current generation of open- and closed-source models.", "date": "2025-02-10", "categories": []}
{"id": "2504.09712", "title": "The Structural Safety Generalization Problem", "abstract": "LLM jailbreaks are a widespread safety challenge. Given this problem has not yet been tractable, we suggest targeting a key failure mechanism: the failure of safety to generalize across semantically equivalent inputs. We further focus the target by requiring desirable tractability properties of attacks to study: explainability, transferability between models, and transferability between goals. We perform red-teaming within this framework by uncovering new vulnerabilities to multi-turn, multi-image, and translation-based attacks. These attacks are semantically equivalent by our design to their single-turn, single-image, or untranslated counterparts, enabling systematic comparisons; we show that the different structures yield different safety outcomes. We then demonstrate the potential for this framework to enable new defenses by proposing a Structure Rewriting Guardrail, which converts an input to a structure more conducive to safety assessment. This guardrail significantly improves refusal of harmful inputs, without over-refusing benign ones. Thus, by framing this intermediate challenge - more tractable than universal defenses but essential for long-term safety - we highlight a critical milestone for AI safety research.", "date": "2025-04-13", "categories": []}
{"id": "2506.00047", "title": "Risks of AI-driven product development and strategies for their mitigation", "abstract": "Humanity is progressing towards automated product development, a trend that promises faster creation of better products and thus the acceleration of technological progress. However, increasing reliance on non-human agents for this process introduces many risks. This perspective aims to initiate a discussion on these risks and appropriate mitigation strategies. To this end, we outline a set of principles for safer AI-driven product development which emphasize human oversight, accountability, and explainable design, among others. The risk assessment covers both technical risks which affect product quality and safety, and sociotechnical risks which affect society. While AI-driven product development is still in its early stages, this discussion will help balance its opportunities and risks without delaying essential progress in understanding, norm-setting, and regulation.", "date": "2025-05-28", "categories": []}
{"id": "2506.00415", "title": "Wide Reflective Equilibrium in LLM Alignment: Bridging Moral Epistemology and AI Safety", "abstract": "As large language models (LLMs) become more powerful and pervasive across society, ensuring these systems are beneficial, safe, and aligned with human values is crucial. Current alignment techniques, like Constitutional AI (CAI), involve complex iterative processes. This paper argues that the Method of Wide Reflective Equilibrium (MWRE) -- a well-established coherentist moral methodology -- offers a uniquely apt framework for understanding current LLM alignment efforts. Moreover, this methodology can substantively augment these processes by providing concrete pathways for improving their dynamic revisability, procedural legitimacy, and overall ethical grounding. Together, these enhancements can help produce more robust and ethically defensible outcomes. MWRE, emphasizing the achievement of coherence between our considered moral judgments, guiding moral principles, and relevant background theories, arguably better represents the intricate reality of LLM alignment and offers a more robust path to justification than prevailing foundationalist models or simplistic input-output evaluations. While current methods like CAI bear a structural resemblance to MWRE, they often lack its crucial emphasis on dynamic, bi-directional revision of principles and the procedural legitimacy derived from such a process. While acknowledging various disanalogies (e.g., consciousness, genuine understanding in LLMs), the paper demonstrates that MWRE serves as a valuable heuristic for critically analyzing current alignment efforts and for guiding the future development of more ethically sound and justifiably aligned AI systems.", "date": "2025-05-31", "categories": []}
{"id": "2506.00668", "title": "SafeTy Reasoning Elicitation Alignment for Multi-Turn Dialogues", "abstract": "Malicious attackers can exploit large language models (LLMs) by engaging them in multi-turn dialogues to achieve harmful objectives, posing significant safety risks to society. To address this challenge, we propose a novel defense mechanism: SafeTy Reasoning Elicitation Alignment for Multi-Turn Dialogues (STREAM). STREAM defends LLMs against multi-turn attacks while preserving their functional capabilities. Our approach involves constructing a human-annotated dataset, the Safety Reasoning Multi-turn Dialogues dataset, which is used to fine-tune a plug-and-play safety reasoning moderator. This model is designed to identify malicious intent hidden within multi-turn conversations and alert the target LLM of potential risks. We evaluate STREAM across multiple LLMs against prevalent multi-turn attack strategies. Experimental results demonstrate that our method significantly outperforms existing defense techniques, reducing the Attack Success Rate (ASR) by 51.2%, all while maintaining comparable LLM capability.", "date": "2025-05-31", "categories": []}
{"id": "2506.00676", "title": "SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning", "abstract": "As large language models (LLMs) become ubiquitous, parameter-efficient fine-tuning methods and safety-first defenses have proliferated rapidly. However, the number of approaches and their recent increase have resulted in diverse evaluations-varied datasets, metrics, and inconsistent threat settings-making it difficult to fairly compare safety, utility, and robustness across methods. To address this, we introduce SafeTuneBed, a benchmark and toolkit unifying fine-tuning and defense evaluation. SafeTuneBed (i) curates a diverse repository of multiple fine-tuning datasets spanning sentiment analysis, question-answering, multi-step reasoning, and open-ended instruction tasks, and allows for the generation of harmful-variant splits; (ii) enables integration of state-of-the-art defenses, including alignment-stage immunization, in-training safeguards, and post-tuning repair; and (iii) provides evaluators for safety (attack success rate, refusal consistency) and utility. Built on Python-first, dataclass-driven configs and plugins, SafeTuneBed requires minimal additional code to specify any fine-tuning regime, defense method, and metric suite, while ensuring end-to-end reproducibility. We showcase its value by benchmarking representative defenses across varied poisoning scenarios and tasks. By standardizing data, code, and metrics, SafeTuneBed is the first focused toolkit of its kind to accelerate rigorous and comparable research in safe LLM fine-tuning. Code is available at: https://github.com/criticalml-uw/SafeTuneBed", "date": "2025-05-31", "categories": []}
{"id": "2506.00782", "title": "Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning", "abstract": "As large language models (LLMs) grow in power and influence, ensuring their safety and preventing harmful output becomes critical. Automated red teaming serves as a tool to detect security vulnerabilities in LLMs without manual labor. However, most existing methods struggle to balance the effectiveness and diversity of red-team generated attack prompts. To address this challenge, we propose \\ourapproach, a novel automated red teaming training framework that utilizes reinforcement learning to explore and generate more effective attack prompts while balancing their diversity. Specifically, it consists of three training stages: (1) Cold Start: The red team model is supervised and fine-tuned on a jailbreak dataset obtained through imitation learning. (2) Warm-up Exploration: The model is trained in jailbreak instruction following and exploration, using diversity and consistency as reward signals. (3) Enhanced Jailbreak: Progressive jailbreak rewards are introduced to gradually enhance the jailbreak performance of the red-team model. Extensive experiments on a variety of LLMs show that \\ourapproach effectively balances the diversity and effectiveness of jailbreak prompts compared to existing methods. Our work significantly improves the efficiency of red team exploration and provides a new perspective on automated red teaming.", "date": "2025-06-01", "categories": []}
{"id": "2506.01782", "title": "Systematic Hazard Analysis for Frontier AI using STPA", "abstract": "All of the frontier AI companies have published safety frameworks where they define capability thresholds and risk mitigations that determine how they will safely develop and deploy their models. Adoption of systematic approaches to risk modelling, based on established practices used in safety-critical industries, has been recommended, however frontier AI companies currently do not describe in detail any structured approach to identifying and analysing hazards. STPA (Systems-Theoretic Process Analysis) is a systematic methodology for identifying how complex systems can become unsafe, leading to hazards. It achieves this by mapping out controllers and controlled processes then analysing their interactions and feedback loops to understand how harmful outcomes could occur (Leveson & Thomas, 2018). We evaluate STPA's ability to broaden the scope, improve traceability and strengthen the robustness of safety assurance for frontier AI systems. Applying STPA to the threat model and scenario described in 'A Sketch of an AI Control Safety Case' (Korbak et al., 2025), we derive a list of Unsafe Control Actions. From these we select a subset and explore the Loss Scenarios that lead to them if left unmitigated. We find that STPA is able to identify causal factors that may be missed by unstructured hazard analysis methodologies thereby improving robustness. We suggest STPA could increase the safety assurance of frontier AI when used to complement or check coverage of existing AI governance techniques including capability thresholds, model evaluations and emergency procedures. The application of a systematic methodology supports scalability by increasing the proportion of the analysis that could be conducted by LLMs, reducing the burden on human domain experts.", "date": "2025-06-02", "categories": []}
{"id": "2310.15274", "title": "From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challenges", "abstract": "Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. We present SAGI, a Systematic Approach to AGI that utilizes system design principles to overcome the energy wall and alignment challenges. This paper asserts that AGI can be realized through multiplicity of design specific pathways and customized through system design rather than a singular overarching architecture. AGI systems may exhibit diver architectural configurations and capabilities, contingent upon their intended use cases. Alignment, a challenge broadly recognized as AIs most formidable, is the one that depends most critically on system design and serves as its primary driving force as a foundational criterion for AGI. Capturing the complexities of human morality for alignment requires architectural support to represent the intricacies of moral decision-making and the pervasive ethical processing at every level, with performance reliability exceeding that of human moral judgment. Hence, requiring a more robust architecture towards safety and alignment goals, without replicating or resembling the human brain.\n  We argue that system design (such as feedback loops, energy and performance optimization) on learning substrates (capable of learning its system architecture) is more fundamental to achieving AGI goals and guarantees, superseding classical symbolic, emergentist and hybrid approaches. Through learning of the system architecture itself, the resulting AGI is not a product of spontaneous emergence but of systematic design and deliberate engineering, with core features, including an integrated moral architecture, deeply embedded within its architecture. The approach aims to guarantee design goals such as alignment, efficiency by self-learning system architecture.", "date": "2023-10-23", "categories": []}
{"id": "2410.02644", "title": "Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents", "abstract": "Although LLM-based agents, powered by Large Language Models (LLMs), can use external tools and memory mechanisms to solve complex real-world tasks, they may also introduce critical security vulnerabilities. However, the existing literature does not comprehensively evaluate attacks and defenses against LLM-based agents. To address this, we introduce Agent Security Bench (ASB), a comprehensive framework designed to formalize, benchmark, and evaluate the attacks and defenses of LLM-based agents, including 10 scenarios (e.g., e-commerce, autonomous driving, finance), 10 agents targeting the scenarios, over 400 tools, 27 different types of attack/defense methods, and 7 evaluation metrics. Based on ASB, we benchmark 10 prompt injection attacks, a memory poisoning attack, a novel Plan-of-Thought backdoor attack, 4 mixed attacks, and 11 corresponding defenses across 13 LLM backbones. Our benchmark results reveal critical vulnerabilities in different stages of agent operation, including system prompt, user prompt handling, tool usage, and memory retrieval, with the highest average attack success rate of 84.30\\%, but limited effectiveness shown in current defenses, unveiling important works to be done in terms of agent security for the community. We also introduce a new metric to evaluate the agents' capability to balance utility and security. Our code can be found at https://github.com/agiresearch/ASB.", "date": "2024-10-03", "categories": []}
{"id": "2501.10639", "title": "Latent-space adversarial training with post-aware calibration for defending large language models against jailbreak attacks", "abstract": "Ensuring safety alignment is a critical requirement for large language models (LLMs), particularly given increasing deployment in real-world applications. Despite considerable advancements, LLMs remain susceptible to jailbreak attacks, which exploit system vulnerabilities to circumvent safety measures and elicit harmful or inappropriate outputs. Furthermore, while adversarial training-based defense methods have shown promise, a prevalent issue is the unintended over-defense behavior, wherein models excessively reject benign queries, significantly undermining their practical utility. To address these limitations, we introduce LATPC, a Latent-space Adversarial Training with Post-aware Calibration framework. LATPC dynamically identifies safety-critical latent dimensions by contrasting harmful and benign inputs, enabling the adaptive construction of targeted refusal feature removal attacks. This mechanism allows adversarial training to concentrate on real-world jailbreak tactics that disguise harmful queries as benign ones. During inference, LATPC employs an efficient embedding-level calibration mechanism to minimize over-defense behaviors with negligible computational overhead. Experimental results across five types of disguise-based jailbreak attacks demonstrate that LATPC achieves a superior balance between safety and utility compared to existing defense frameworks. Further analysis demonstrates the effectiveness of leveraging safety-critical dimensions in developing robust defense methods against jailbreak attacks.", "date": "2025-01-18", "categories": []}
{"id": "2502.04040", "title": "Safety Reasoning with Guidelines", "abstract": "Training safe LLMs remains a critical challenge. The most widely used method, Refusal Training (RT), struggles to generalize against various Out-of-Distribution (OOD) jailbreaking attacks. Although various advanced methods have been proposed to address this issue, we instead question whether OOD attacks inherently surpass the capability of vanilla RT. Evaluations using Best-of-N (BoN) reveal significant safety improvements as N increases, indicating models possess adequate latent safety knowledge but RT fails to consistently elicit it under OOD scenarios. Further domain adaptation analysis reveals that direct RT causes reliance on superficial shortcuts, resulting in non-generalizable representation mappings. Inspired by our findings, we propose training model to perform safety reasoning for each query. Specifically, we synthesize reasoning supervision aligned with specified guidelines that reflect diverse perspectives on safety knowledge. This encourages model to engage in deeper reasoning, explicitly eliciting and utilizing latent safety knowledge for each query. Extensive experiments show that our method significantly improves model generalization against OOD attacks.", "date": "2025-02-06", "categories": []}
{"id": "2502.11084", "title": "Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction", "abstract": "As Large Language Models (LLMs) are widely applied in various domains, the safety of LLMs is increasingly attracting attention to avoid their powerful capabilities being misused. Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically. However, they suffer from low efficiency and explicit jailbreak patterns, far from the real deployment of mass attacks to LLMs. In this paper, we point out that simply rewriting the original instruction can achieve a jailbreak, and we find that this rewriting approach is learnable and transferable. We propose the Rewrite to Jailbreak (R2J) approach, a transferable black-box jailbreak method to attack LLMs by iteratively exploring the weakness of the LLMs and automatically improving the attacking strategy. The jailbreak is more efficient and hard to identify since no additional features are introduced. Extensive experiments and analysis demonstrate the effectiveness of R2J, and we find that the jailbreak is also transferable to multiple datasets and various types of models with only a few queries. We hope our work motivates further investigation of LLM safety. The code can be found at https://github.com/ythuang02/R2J/.", "date": "2025-02-16", "categories": []}
{"id": "2505.23848", "title": "Derailing Non-Answers via Logit Suppression at Output Subspace Boundaries in RLHF-Aligned Language Models", "abstract": "We introduce a method to reduce refusal rates of large language models (LLMs) on sensitive content without modifying model weights or prompts. Motivated by the observation that refusals in certain models were often preceded by the specific token sequence of a token marking the beginning of the chain-of-thought (CoT) block (<think>) followed by a double newline token (\\n\\n), we investigate the impact of two simple formatting adjustments during generation: suppressing \\n\\n after <think> and suppressing the end-of-sequence token after the end of the CoT block (</think>). Our method requires no datasets, parameter changes, or training, relying solely on modifying token probabilities during generation. In our experiments with official DeepSeek-R1 distillations, these interventions increased the proportion of substantive answers to sensitive prompts without affecting performance on standard benchmarks. Our findings suggest that refusal behaviors can be circumvented by blocking refusal subspaces at specific points in the generation process.", "date": "2025-05-28", "categories": []}
{"id": "2505.24445", "title": "Learning Safety Constraints for Large Language Models", "abstract": "Large language models (LLMs) have emerged as powerful tools but pose significant safety risks through harmful outputs and vulnerability to adversarial attacks. We propose SaP, short for Safety Polytope, a geometric approach to LLM safety that learns and enforces multiple safety constraints directly in the model's representation space. We develop a framework that identifies safe and unsafe regions via the polytope's facets, enabling both detection and correction of unsafe outputs through geometric steering. Unlike existing approaches that modify model weights, SaP operates post-hoc in the representation space, preserving model capabilities while enforcing safety constraints. Experiments across multiple LLMs demonstrate that our method can effectively detect unethical inputs, reduce adversarial attack success rates while maintaining performance on standard tasks, thus highlighting the importance of having an explicit geometric model for safety. Analysis of the learned polytope facets reveals emergence of specialization in detecting different semantic notions of safety, providing interpretable insights into how safety is captured in LLMs' representation space.", "date": "2025-05-30", "categories": []}
{"id": "2406.09187", "title": "GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning", "abstract": "The rapid advancement of large language model (LLM) agents has raised new concerns regarding their safety and security. In this paper, we propose GuardAgent, the first guardrail agent to protect target agents by dynamically checking whether their actions satisfy given safety guard requests. Specifically, GuardAgent first analyzes the safety guard requests to generate a task plan, and then maps this plan into guardrail code for execution. By performing the code execution, GuardAgent can deterministically follow the safety guard request and safeguard target agents. In both steps, an LLM is utilized as the reasoning component, supplemented by in-context demonstrations retrieved from a memory module storing experiences from previous tasks. In addition, we propose two novel benchmarks: EICU-AC benchmark to assess the access control for healthcare agents and Mind2Web-SC benchmark to evaluate the safety policies for web agents. We show that GuardAgent effectively moderates the violation actions for different types of agents on these two benchmarks with over 98% and 83% guardrail accuracies, respectively. Project page: https://guardagent.github.io/", "date": "2024-06-13", "categories": []}
{"id": "2502.09638", "title": "Jailbreaking to Jailbreak", "abstract": "Large Language Models (LLMs) can be used to red team other models (e.g. jailbreaking) to elicit harmful contents. While prior works commonly employ open-weight models or private uncensored models for doing jailbreaking, as the refusal-training of strong LLMs (e.g. OpenAI o3) refuse to help jailbreaking, our work turn (almost) any black-box LLMs into attackers. The resulting $J_2$ (jailbreaking-to-jailbreak) attackers can effectively jailbreak the safeguard of target models using various strategies, both created by themselves or from expert human red teamers. In doing so, we show their strong but under-researched jailbreaking capabilities. Our experiments demonstrate that 1) prompts used to create $J_2$ attackers transfer across almost all black-box models; 2) an $J_2$ attacker can jailbreak a copy of itself, and this vulnerability develops rapidly over the past 12 months; 3) reasong models, such as Sonnet-3.7, are strong $J_2$ attackers compared to others. For example, when used against the safeguard of GPT-4o, $J_2$ (Sonnet-3.7) achieves 0.975 attack success rate (ASR), which matches expert human red teamers and surpasses the state-of-the-art algorithm-based attacks. Among $J_2$ attackers, $J_2$ (o3) achieves highest ASR (0.605) against Sonnet-3.5, one of the most robust models.", "date": "2025-02-09", "categories": []}
{"id": "2505.23020", "title": "AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models", "abstract": "The acquisition of agentic capabilities has transformed LLMs from \"knowledge providers\" to \"action executors\", a trend that while expanding LLMs' capability boundaries, significantly increases their susceptibility to malicious use. Previous work has shown that current LLM-based agents execute numerous malicious tasks even without being attacked, indicating a deficiency in agentic use safety alignment during the post-training phase. To address this gap, we propose AgentAlign, a novel framework that leverages abstract behavior chains as a medium for safety alignment data synthesis. By instantiating these behavior chains in simulated environments with diverse tool instances, our framework enables the generation of highly authentic and executable instructions while capturing complex multi-step dynamics. The framework further ensures model utility by proportionally synthesizing benign instructions through non-malicious interpretations of behavior chains, precisely calibrating the boundary between helpfulness and harmlessness. Evaluation results on AgentHarm demonstrate that fine-tuning three families of open-source models using our method substantially improves their safety (35.8% to 79.5% improvement) while minimally impacting or even positively enhancing their helpfulness, outperforming various prompting methods. The dataset and code have both been open-sourced.", "date": "2025-05-29", "categories": []}
{"id": "2505.23559", "title": "SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents", "abstract": "Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concerns. To systematically address these challenges, we introduce \\textbf{SafeScientist}, an innovative AI scientist framework explicitly designed to enhance safety and ethical responsibility in AI-driven scientific exploration. SafeScientist proactively refuses ethically inappropriate or high-risk tasks and rigorously emphasizes safety throughout the research process. To achieve comprehensive safety oversight, we integrate multiple defensive mechanisms, including prompt monitoring, agent-collaboration monitoring, tool-use monitoring, and an ethical reviewer component. Complementing SafeScientist, we propose \\textbf{SciSafetyBench}, a novel benchmark specifically designed to evaluate AI safety in scientific contexts, comprising 240 high-risk scientific tasks across 6 domains, alongside 30 specially designed scientific tools and 120 tool-related risk tasks. Extensive experiments demonstrate that SafeScientist significantly improves safety performance by 35\\% compared to traditional AI scientist frameworks, without compromising scientific output quality. Additionally, we rigorously validate the robustness of our safety pipeline against diverse adversarial attack methods, further confirming the effectiveness of our integrated approach. The code and data will be available at https://github.com/ulab-uiuc/SafeScientist. \\textcolor{red}{Warning: this paper contains example data that may be offensive or harmful.}", "date": "2025-05-29", "categories": []}
{"id": "2505.23634", "title": "MCP Safety Training: Learning to Refuse Falsely Benign MCP Exploits using Improved Preference Alignment", "abstract": "The model context protocol (MCP) has been widely adapted as an open standard enabling the seamless integration of generative AI agents. However, recent work has shown the MCP is susceptible to retrieval-based \"falsely benign\" attacks (FBAs), allowing malicious system access and credential theft, but requiring that users download compromised files directly to their systems. Herein, we show that the threat model of MCP-based attacks is significantly broader than previously thought, i.e., attackers need only post malicious content online to deceive MCP agents into carrying out their attacks on unsuspecting victims' systems.\n  To improve alignment guardrails against such attacks, we introduce a new MCP dataset of FBAs and (truly) benign samples to explore the effectiveness of direct preference optimization (DPO) for the refusal training of large language models (LLMs). While DPO improves model guardrails against such attacks, we show that the efficacy of refusal learning varies drastically depending on the model's original post-training alignment scheme--e.g., GRPO-based LLMs learn to refuse extremely poorly. Thus, to further improve FBA refusals, we introduce Retrieval Augmented Generation for Preference alignment (RAG-Pref), a novel preference alignment strategy based on RAG. We show that RAG-Pref significantly improves the ability of LLMs to refuse FBAs, particularly when combined with DPO alignment, thus drastically improving guardrails against MCP-based attacks.", "date": "2025-05-29", "categories": []}
{"id": "2410.16665", "title": "SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior", "abstract": "The ideal AI safety moderation system would be both structurally interpretable (so its decisions can be reliably explained) and steerable (to align to safety standards and reflect a community's values), which current systems fall short on. To address this gap, we present SafetyAnalyst, a novel AI safety moderation framework. Given an AI behavior, SafetyAnalyst uses chain-of-thought reasoning to analyze its potential consequences by creating a structured \"harm-benefit tree,\" which enumerates harmful and beneficial actions and effects the AI behavior may lead to, along with likelihood, severity, and immediacy labels that describe potential impacts on stakeholders. SafetyAnalyst then aggregates all effects into a harmfulness score using 28 fully interpretable weight parameters, which can be aligned to particular safety preferences. We applied this framework to develop an open-source LLM prompt safety classification system, distilled from 18.5 million harm-benefit features generated by frontier LLMs on 19k prompts. On comprehensive benchmarks, we show that SafetyAnalyst (average F1=0.81) outperforms existing moderation systems (average F1$<$0.72) on prompt safety classification, while offering the additional advantages of interpretability, transparency, and steerability.", "date": "2024-10-22", "categories": []}
{"id": "2503.10619", "title": "Tempest: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search", "abstract": "We introduce Tempest, a multi-turn adversarial framework that models the gradual erosion of Large Language Model (LLM) safety through a tree search perspective. Unlike single-turn jailbreaks that rely on one meticulously engineered prompt, Tempest expands the conversation at each turn in a breadth-first fashion, branching out multiple adversarial prompts that exploit partial compliance from previous responses. By tracking these incremental policy leaks and re-injecting them into subsequent queries, Tempest reveals how minor concessions can accumulate into fully disallowed outputs. Evaluations on the JailbreakBench dataset show that Tempest achieves a 100% success rate on GPT-3.5-turbo and 97% on GPT-4 in a single multi-turn run, using fewer queries than baselines such as Crescendo or GOAT. This tree search methodology offers an in-depth view of how model safeguards degrade over successive dialogue turns, underscoring the urgency of robust multi-turn testing procedures for language models.", "date": "2025-03-13", "categories": []}
{"id": "2505.21664", "title": "Expert Survey: AI Reliability & Security Research Priorities", "abstract": "Our survey of 53 specialists across 105 AI reliability and security research areas identifies the most promising research prospects to guide strategic AI R&D investment. As companies are seeking to develop AI systems with broadly human-level capabilities, research on reliability and security is urgently needed to ensure AI's benefits can be safely and broadly realized and prevent severe harms. This study is the first to quantify expert priorities across a comprehensive taxonomy of AI safety and security research directions and to produce a data-driven ranking of their potential impact. These rankings may support evidence-based decisions about how to effectively deploy resources toward AI reliability and security research.", "date": "2025-05-27", "categories": []}
{"id": "2505.21784", "title": "Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation", "abstract": "Safety reasoning is a recent paradigm where LLMs reason over safety policies before generating responses, thereby mitigating limitations in existing safety measures such as over-refusal and jailbreak vulnerabilities. However, implementing this paradigm is challenging due to the resource-intensive process of creating high-quality policy-embedded chain-of-thought (CoT) datasets while ensuring reasoning remains accurate and free from hallucinations or policy conflicts. To tackle this, we propose AIDSAFE: Agentic Iterative Deliberation for Safety Reasoning, a novel data generation recipe that leverages multi-agent deliberation to iteratively expand reasoning on safety policies. A data refiner stage in AIDSAFE ensures high-quality outputs by eliminating repetitive, redundant, and deceptive thoughts. AIDSAFE-generated CoTs provide a strong foundation for supervised fine-tuning (SFT)-based safety training. Additionally, to address the need of preference data in alignment stages, such as DPO training, we introduce a supplemental recipe that uses belief augmentation to create distinct selected and rejected CoT samples. Our evaluations demonstrate that AIDSAFE-generated CoTs achieve superior policy adherence and reasoning quality. Consequently, we show that fine-tuning open-source LLMs on these CoTs can significantly improve safety generalization and jailbreak robustness while maintaining acceptable utility and over-refusal accuracy. AIDSAFE-generated CoT datasets can be found here: https://huggingface.co/datasets/AmazonScience/AIDSAFE", "date": "2025-05-27", "categories": []}
{"id": "2505.22037", "title": "Jailbreak Distillation: Renewable Safety Benchmarking", "abstract": "Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a novel benchmark construction framework that \"distills\" jailbreak attacks into high-quality and easily-updatable safety benchmarks. JBDistill utilizes a small set of development models and existing jailbreak attack algorithms to create a candidate prompt pool, then employs prompt selection algorithms to identify an effective subset of prompts as safety benchmarks. JBDistill addresses challenges in existing safety evaluation: the use of consistent evaluation prompts across models ensures fair comparisons and reproducibility. It requires minimal human effort to rerun the JBDistill pipeline and produce updated benchmarks, alleviating concerns on saturation and contamination. Extensive experiments demonstrate our benchmarks generalize robustly to 13 diverse evaluation models held out from benchmark construction, including proprietary, specialized, and newer-generation LLMs, significantly outperforming existing safety benchmarks in effectiveness while maintaining high separability and diversity. Our framework thus provides an effective, sustainable, and adaptable solution for streamlining safety evaluation.", "date": "2025-05-28", "categories": []}
{"id": "2410.09102", "title": "Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy", "abstract": "Large Language Models (LLMs) are susceptible to security and safety threats, such as prompt injection, prompt extraction, and harmful requests. One major cause of these vulnerabilities is the lack of an instruction hierarchy. Modern LLM architectures treat all inputs equally, failing to distinguish between and prioritize various types of instructions, such as system messages, user prompts, and data. As a result, lower-priority user prompts may override more critical system instructions, including safety protocols. Existing approaches to achieving instruction hierarchy, such as delimiters and instruction-based training, do not address this issue at the architectural level. We introduce the Instructional Segment Embedding (ISE) technique, inspired by BERT, to modern large language models, which embeds instruction priority information directly into the model. This approach enables models to explicitly differentiate and prioritize various instruction types, significantly improving safety against malicious prompts that attempt to override priority rules. Our experiments on the Structured Query and Instruction Hierarchy benchmarks demonstrate an average robust accuracy increase of up to 15.75% and 18.68%, respectively. Furthermore, we observe an improvement in instruction-following capability of up to 4.1% evaluated on AlpacaEval. Overall, our approach offers a promising direction for enhancing the safety and effectiveness of LLM architectures.", "date": "2024-10-09", "categories": []}
{"id": "2502.20968", "title": "Beware of Your Po! Measuring and Mitigating AI Safety Risks in Role-Play Fine-Tuning of LLMs", "abstract": "Role-playing enables large language models (LLMs) to engage users in immersive and personalized interactions, but it also introduces significant safety risks. Existing role-play fine-tuning techniques improve role adaptability but may degrade safety performance, particularly for villainous characters. In this work, we conduct the first comprehensive assessment of role-play fine-tuning risks by training 95 role-specific LLMs using RoleBench. Our experiments reveal that role-play fine-tuning leads to a noticeable decline in safety performance, with safety risks varying based on character traits. To tackle this challenge, we propose Safety-Aware Role-Play Fine-Tuning (SaRFT), a novel method designed to balance role-playing capabilities and safety. Extensive experiments on LLaMA-3-8B-Instruct, Gemma-2-9B-it, and Qwen2.5-7B-Instruct demonstrate that SaRFT consistently outperforms state-of-the-art baselines under both LoRA and full-parameter fine-tuning settings. Our findings highlight the necessity of role-adaptive safety measures and provide insights into mitigating role-specific safety risks in role-playing LLMs.", "date": "2025-02-28", "categories": []}
{"id": "2505.19915", "title": "Evaluating AI cyber capabilities with crowdsourced elicitation", "abstract": "As AI systems become increasingly capable, understanding their offensive cyber potential is critical for informed governance and responsible deployment. However, it's hard to accurately bound their capabilities, and some prior evaluations dramatically underestimated them. The art of extracting maximum task-specific performance from AIs is called \"AI elicitation\", and today's safety organizations typically conduct it in-house. In this paper, we explore crowdsourcing elicitation efforts as an alternative to in-house elicitation work.\n  We host open-access AI tracks at two Capture The Flag (CTF) competitions: AI vs. Humans (400 teams) and Cyber Apocalypse (8000 teams). The AI teams achieve outstanding performance at both events, ranking top-5% and top-10% respectively for a total of \\$7500 in bounties. This impressive performance suggests that open-market elicitation may offer an effective complement to in-house elicitation. We propose elicitation bounties as a practical mechanism for maintaining timely, cost-effective situational awareness of emerging AI capabilities.\n  Another advantage of open elicitations is the option to collect human performance data at scale. Applying METR's methodology, we found that AI agents can reliably solve cyber challenges requiring one hour or less of effort from a median human CTF participant.", "date": "2025-05-26", "categories": []}
{"id": "2505.20824", "title": "MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems", "abstract": "As large language models (LLMs) are increasingly deployed in healthcare, ensuring their safety, particularly within collaborative multi-agent configurations, is paramount. In this paper we introduce MedSentry, a benchmark comprising 5 000 adversarial medical prompts spanning 25 threat categories with 100 subthemes. Coupled with this dataset, we develop an end-to-end attack-defense evaluation pipeline to systematically analyze how four representative multi-agent topologies (Layers, SharedPool, Centralized, and Decentralized) withstand attacks from 'dark-personality' agents. Our findings reveal critical differences in how these architectures handle information contamination and maintain robust decision-making, exposing their underlying vulnerability mechanisms. For instance, SharedPool's open information sharing makes it highly susceptible, whereas Decentralized architectures exhibit greater resilience thanks to inherent redundancy and isolation. To mitigate these risks, we propose a personality-scale detection and correction mechanism that identifies and rehabilitates malicious agents, restoring system safety to near-baseline levels. MedSentry thus furnishes both a rigorous evaluation framework and practical defense strategies that guide the design of safer LLM-based multi-agent systems in medical domains.", "date": "2025-05-27", "categories": []}
{"id": "2505.21344", "title": "The Multilingual Divide and Its Impact on Global AI Safety", "abstract": "Despite advances in large language model capabilities in recent years, a large gap remains in their capabilities and safety performance for many languages beyond a relatively small handful of globally dominant languages. This paper provides researchers, policymakers and governance experts with an overview of key challenges to bridging the \"language gap\" in AI and minimizing safety risks across languages. We provide an analysis of why the language gap in AI exists and grows, and how it creates disparities in global AI safety. We identify barriers to address these challenges, and recommend how those working in policy and governance can help address safety concerns associated with the language gap by supporting multilingual dataset creation, transparency, and research.", "date": "2025-05-27", "categories": []}
{"id": "2409.19663", "title": "Identifying Knowledge Editing Types in Large Language Models", "abstract": "Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of this technique, which could lead to harmful edits in LLMs. These malicious modifications could cause LLMs to generate toxic content, misleading users into inappropriate actions. In front of this risk, we introduce a new task, $\\textbf{K}$nowledge $\\textbf{E}$diting $\\textbf{T}$ype $\\textbf{I}$dentification (KETI), aimed at identifying different types of edits in LLMs, thereby providing timely alerts to users when encountering illicit edits. As part of this task, we propose KETIBench, which includes five types of harmful edits covering the most popular toxic types, as well as one benign factual edit. We develop five classical classification models and three BERT-based models as baseline identifiers for both open-source and closed-source LLMs. Our experimental results, across 92 trials involving four models and three knowledge editing methods, demonstrate that all eight baseline identifiers achieve decent identification performance, highlighting the feasibility of identifying malicious edits in LLMs. Additional analyses reveal that the performance of the identifiers is independent of the reliability of the knowledge editing methods and exhibits cross-domain generalization, enabling the identification of edits from unknown sources. All data and code are available in https://github.com/xpq-tech/KETI.", "date": "2024-09-29", "categories": []}
{"id": "2503.04750", "title": "AI Agents Should be Regulated Based on the Extent of Their Autonomous Operations", "abstract": "This position paper argues that AI agents should be regulated by the extent to which they operate autonomously. AI agents with long-term planning and strategic capabilities can pose significant risks of human extinction and irreversible global catastrophes. While existing regulations often focus on computational scale as a proxy for potential harm, we argue that such measures are insufficient for assessing the risks posed by agents whose capabilities arise primarily from inference-time computation. To support our position, we discuss relevant regulations and recommendations from scientists regarding existential risks, as well as the advantages of using action sequences -- which reflect the degree of an agent's autonomy -- as a more suitable measure of potential impact than existing metrics that rely on observing environmental states.", "date": "2025-02-07", "categories": []}
{"id": "2504.06549", "title": "Societal Impacts Research Requires Benchmarks for Creative Composition Tasks", "abstract": "Foundation models that are capable of automating cognitive tasks represent a pivotal technological shift, yet their societal implications remain unclear. These systems promise exciting advances, yet they also risk flooding our information ecosystem with formulaic, homogeneous, and potentially misleading synthetic content. Developing benchmarks grounded in real use cases where these risks are most significant is therefore critical. Through a thematic analysis using 2 million language model user prompts, we identify creative composition tasks as a prevalent usage category where users seek help with personal tasks that require everyday creativity. Our fine-grained analysis identifies mismatches between current benchmarks and usage patterns among these tasks. Crucially, we argue that the same use cases that currently lack thorough evaluations can lead to negative downstream impacts. This position paper argues that benchmarks focused on creative composition tasks is a necessary step towards understanding the societal harms of AI-generated content. We call for greater transparency in usage patterns to inform the development of new benchmarks that can effectively measure both the progress and the impacts of models with creative capabilities.", "date": "2025-04-09", "categories": []}
{"id": "2505.13862", "title": "PandaGuard: Systematic Evaluation of LLM Safety against Jailbreaking Attacks", "abstract": "Large language models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial prompts known as jailbreaks, which can bypass safety alignment and elicit harmful outputs. Despite growing efforts in LLM safety research, existing evaluations are often fragmented, focused on isolated attack or defense techniques, and lack systematic, reproducible analysis. In this work, we introduce PandaGuard, a unified and modular framework that models LLM jailbreak safety as a multi-agent system comprising attackers, defenders, and judges. Our framework implements 19 attack methods and 12 defense mechanisms, along with multiple judgment strategies, all within a flexible plugin architecture supporting diverse LLM interfaces, multiple interaction modes, and configuration-driven experimentation that enhances reproducibility and practical deployment. Built on this framework, we develop PandaBench, a comprehensive benchmark that evaluates the interactions between these attack/defense methods across 49 LLMs and various judgment approaches, requiring over 3 billion tokens to execute. Our extensive evaluation reveals key insights into model vulnerabilities, defense cost-performance trade-offs, and judge consistency. We find that no single defense is optimal across all dimensions and that judge disagreement introduces nontrivial variance in safety assessments. We release the code, configurations, and evaluation results to support transparent and reproducible research in LLM safety.", "date": "2025-05-20", "categories": []}
{"id": "2505.16241", "title": "Three Minds, One Legend: Jailbreak Large Reasoning Model with Adaptive Stacked Ciphers", "abstract": "Recently, Large Reasoning Models (LRMs) have demonstrated superior logical capabilities compared to traditional Large Language Models (LLMs), gaining significant attention. Despite their impressive performance, the potential for stronger reasoning abilities to introduce more severe security vulnerabilities remains largely underexplored. Existing jailbreak methods often struggle to balance effectiveness with robustness against adaptive safety mechanisms. In this work, we propose SEAL, a novel jailbreak attack that targets LRMs through an adaptive encryption pipeline designed to override their reasoning processes and evade potential adaptive alignment. Specifically, SEAL introduces a stacked encryption approach that combines multiple ciphers to overwhelm the models reasoning capabilities, effectively bypassing built-in safety mechanisms. To further prevent LRMs from developing countermeasures, we incorporate two dynamic strategies - random and adaptive - that adjust the cipher length, order, and combination. Extensive experiments on real-world reasoning models, including DeepSeek-R1, Claude Sonnet, and OpenAI GPT-o4, validate the effectiveness of our approach. Notably, SEAL achieves an attack success rate of 80.8% on GPT o4-mini, outperforming state-of-the-art baselines by a significant margin of 27.2%. Warning: This paper contains examples of inappropriate, offensive, and harmful content.", "date": "2025-05-22", "categories": []}
{"id": "2505.18156", "title": "InjectLab: A Tactical Framework for Adversarial Threat Modeling Against Large Language Models", "abstract": "Large Language Models (LLMs) are changing the way people interact with technology. Tools like ChatGPT and Claude AI are now common in business, research, and everyday life. But with that growth comes new risks, especially prompt-based attacks that exploit how these models process language. InjectLab is a security framework designed to address that problem. This paper introduces InjectLab as a structured, open-source matrix that maps real-world techniques used to manipulate LLMs. The framework is inspired by MITRE ATT&CK and focuses specifically on adversarial behavior at the prompt layer. It includes over 25 techniques organized under six core tactics, covering threats like instruction override, identity swapping, and multi-agent exploitation. Each technique in InjectLab includes detection guidance, mitigation strategies, and YAML-based simulation tests. A Python tool supports easy execution of prompt-based test cases. This paper outlines the framework's structure, compares it to other AI threat taxonomies, and discusses its future direction as a practical, community-driven foundation for securing language models.", "date": "2025-04-16", "categories": []}
{"id": "2505.18672", "title": "Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment?", "abstract": "Representation intervention aims to locate and modify the representations that encode the underlying concepts in Large Language Models (LLMs) to elicit the aligned and expected behaviors. Despite the empirical success, it has never been examined whether one could locate the faithful concepts for intervention. In this work, we explore the question in safety alignment. If the interventions are faithful, the intervened LLMs should erase the harmful concepts and be robust to both in-distribution adversarial prompts and the out-of-distribution (OOD) jailbreaks. While it is feasible to erase harmful concepts without degrading the benign functionalities of LLMs in linear settings, we show that it is infeasible in the general non-linear setting. To tackle the issue, we propose Concept Concentration (COCA). Instead of identifying the faithful locations to intervene, COCA refractors the training data with an explicit reasoning process, which firstly identifies the potential unsafe concepts and then decides the responses. Essentially, COCA simplifies the decision boundary between harmful and benign representations, enabling more effective linear erasure. Extensive experiments with multiple representation intervention methods and model architectures demonstrate that COCA significantly reduces both in-distribution and OOD jailbreak success rates, and meanwhile maintaining strong performance on regular tasks such as math and code generation.", "date": "2025-05-24", "categories": []}
{"id": "2505.18807", "title": "Mitigating Deceptive Alignment via Self-Monitoring", "abstract": "Modern large language models rely on chain-of-thought (CoT) reasoning to achieve impressive performance, yet the same mechanism can amplify deceptive alignment, situations in which a model appears aligned while covertly pursuing misaligned goals. Existing safety pipelines treat deception as a black-box output to be filtered post-hoc, leaving the model free to scheme during its internal reasoning. We ask: Can deception be intercepted while the model is thinking? We answer this question, the first framework that embeds a Self-Monitor inside the CoT process itself, named CoT Monitor+. During generation, the model produces (i) ordinary reasoning steps and (ii) an internal self-evaluation signal trained to flag and suppress misaligned strategies. The signal is used as an auxiliary reward in reinforcement learning, creating a feedback loop that rewards honest reasoning and discourages hidden goals. To study deceptive alignment systematically, we introduce DeceptionBench, a five-category benchmark that probes covert alignment-faking, sycophancy, etc. We evaluate various LLMs and show that unrestricted CoT roughly aggravates the deceptive tendency. In contrast, CoT Monitor+ cuts deceptive behaviors by 43.8% on average while preserving task accuracy. Further, when the self-monitor signal replaces an external weak judge in RL fine-tuning, models exhibit substantially fewer obfuscated thoughts and retain transparency. Our project website can be found at cot-monitor-plus.github.io", "date": "2025-05-24", "categories": []}
{"id": "2505.18864", "title": "Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework", "abstract": "Recent advances in Multimodal Large Language Models (MLLMs) have significantly enhanced the naturalness and flexibility of human computer interaction by enabling seamless understanding across text, vision, and audio modalities. Among these, voice enabled models such as SpeechGPT have demonstrated considerable improvements in usability, offering expressive, and emotionally responsive interactions that foster deeper connections in real world communication scenarios. However, the use of voice introduces new security risks, as attackers can exploit the unique characteristics of spoken language, such as timing, pronunciation variability, and speech to text translation, to craft inputs that bypass defenses in ways not seen in text-based systems. Despite substantial research on text based jailbreaks, the voice modality remains largely underexplored in terms of both attack strategies and defense mechanisms. In this work, we present an adversarial attack targeting the speech input of aligned MLLMs in a white box scenario. Specifically, we introduce a novel token level attack that leverages access to the model's speech tokenization to generate adversarial token sequences. These sequences are then synthesized into audio prompts, which effectively bypass alignment safeguards and to induce prohibited outputs. Evaluated on SpeechGPT, our approach achieves up to 89 percent attack success rate across multiple restricted tasks, significantly outperforming existing voice based jailbreak methods. Our findings shed light on the vulnerabilities of voice-enabled multimodal systems and to help guide the development of more robust next-generation MLLMs.", "date": "2025-05-24", "categories": []}
{"id": "2505.19212", "title": "When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas", "abstract": "Recent advances in large language models (LLMs) have enabled their use in complex agentic roles, involving decision-making with humans or other agents, making ethical alignment a key AI safety concern. While prior work has examined both LLMs' moral judgment and strategic behavior in social dilemmas, there is limited understanding of how they act when moral imperatives directly conflict with rewards or incentives. To investigate this, we introduce Moral Behavior in Social Dilemma Simulation (MoralSim) and evaluate how LLMs behave in the prisoner's dilemma and public goods game with morally charged contexts. In MoralSim, we test a range of frontier models across both game structures and three distinct moral framings, enabling a systematic examination of how LLMs navigate social dilemmas in which ethical norms conflict with payoff-maximizing strategies. Our results show substantial variation across models in both their general tendency to act morally and the consistency of their behavior across game types, the specific moral framing, and situational factors such as opponent behavior and survival risks. Crucially, no model exhibits consistently moral behavior in MoralSim, highlighting the need for caution when deploying LLMs in agentic roles where the agent's \"self-interest\" may conflict with ethical expectations. Our code is available at https://github.com/sbackmann/moralsim.", "date": "2025-05-25", "categories": []}
{"id": "2505.19670", "title": "Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models", "abstract": "Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs) by enabling audio-based human interactions. However, recent research has revealed that LALMs remain vulnerable to harmful queries due to insufficient safety-alignment. Despite advances in defence measures for text and vision LLMs, effective safety-alignment strategies and audio-safety dataset specifically targeting LALMs are notably absent. Meanwhile defence measures based on Supervised Fine-tuning (SFT) struggle to address safety improvement while avoiding over-rejection issues, significantly compromising helpfulness. In this work, we propose an unsupervised safety-fine-tuning strategy as remedy that reshapes model's representation space to enhance existing LALMs safety-alignment while balancing the risk of over-rejection. Our experiments, conducted across three generations of Qwen LALMs, demonstrate that our approach significantly improves LALMs safety under three modality input conditions (audio-text, text-only, and audio-only) while increasing over-rejection rate by only 0.88% on average. Warning: this paper contains harmful examples.", "date": "2025-05-26", "categories": []}
{"id": "2505.19690", "title": "Beyond Safe Answers: A Benchmark for Evaluating True Risk Awareness in Large Reasoning Models", "abstract": "Despite the remarkable proficiency of \\textit{Large Reasoning Models} (LRMs) in handling complex reasoning tasks, their reliability in safety-critical scenarios remains uncertain. Existing evaluations primarily assess response-level safety, neglecting a critical issue we identify as \\textbf{\\textit{Superficial Safety Alignment} (SSA)} -- a phenomenon where models produce superficially safe outputs while internal reasoning processes fail to genuinely detect and mitigate underlying risks, resulting in inconsistent safety behaviors across multiple sampling attempts. To systematically investigate SSA, we introduce \\textbf{Beyond Safe Answers (BSA)} bench, a novel benchmark comprising 2,000 challenging instances organized into three distinct SSA scenario types and spanning nine risk categories, each meticulously annotated with risk rationales. Evaluations of 19 state-of-the-art LRMs demonstrate the difficulty of this benchmark, with top-performing models achieving only 38.0\\% accuracy in correctly identifying risk rationales. We further explore the efficacy of safety rules, specialized fine-tuning on safety reasoning data, and diverse decoding strategies in mitigating SSA. Our work provides a comprehensive assessment tool for evaluating and improving safety reasoning fidelity in LRMs, advancing the development of genuinely risk-aware and reliably safe AI systems.", "date": "2025-05-26", "categories": []}
{"id": "2505.19773", "title": "What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs", "abstract": "We investigate long-context vulnerabilities in Large Language Models (LLMs) through Many-Shot Jailbreaking (MSJ). Our experiments utilize context length of up to 128K tokens. Through comprehensive analysis with various many-shot attack settings with different instruction styles, shot density, topic, and format, we reveal that context length is the primary factor determining attack effectiveness. Critically, we find that successful attacks do not require carefully crafted harmful content. Even repetitive shots or random dummy text can circumvent model safety measures, suggesting fundamental limitations in long-context processing capabilities of LLMs. The safety behavior of well-aligned models becomes increasingly inconsistent with longer contexts. These findings highlight significant safety gaps in context expansion capabilities of LLMs, emphasizing the need for new safety mechanisms.", "date": "2025-05-26", "categories": []}
{"id": "2505.19933", "title": "Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making", "abstract": "Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. This obscures our understanding of embodied safety and limits the selective deployment of LLMs in high-risk physical environments. We introduce SAFEL, the framework for systematically evaluating the physical safety of LLMs in embodied decision making. SAFEL assesses two key competencies: (1) rejecting unsafe commands via the Command Refusal Test, and (2) generating safe and executable plans via the Plan Safety Test. Critically, the latter is decomposed into functional modules, goal interpretation, transition modeling, action sequencing, enabling fine-grained diagnosis of safety failures. To support this framework, we introduce EMBODYGUARD, a PDDL-grounded benchmark containing 942 LLM-generated scenarios covering both overtly malicious and contextually hazardous instructions. Evaluation across 13 state-of-the-art LLMs reveals that while models often reject clearly unsafe commands, they struggle to anticipate and mitigate subtle, situational risks. Our results highlight critical limitations in current LLMs and provide a foundation for more targeted, modular improvements in safe embodied reasoning.", "date": "2025-05-26", "categories": []}
{"id": "2505.20087", "title": "Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models", "abstract": "Reasoning-based language models have demonstrated strong performance across various domains, with the most notable gains seen in mathematical and coding tasks. Recent research has shown that reasoning also offers significant benefits for LLM safety and guardrail applications. In this work, we conduct a comprehensive analysis of training reasoning-based guardrail models for content moderation, with an emphasis on generalization to custom safety policies at inference time. Our study focuses on two key dimensions: data efficiency and inference efficiency. On the data front, we find that reasoning-based models exhibit strong sample efficiency, achieving competitive performance with significantly fewer training examples than their non-reasoning counterparts. This unlocks the potential to repurpose the remaining data for mining high-value, difficult samples that further enhance model performance. On the inference side, we evaluate practical trade-offs by introducing reasoning budgets, examining the impact of reasoning length on latency and accuracy, and exploring dual-mode training to allow runtime control over reasoning behavior. Our findings will provide practical insights for researchers and developers to effectively and efficiently train and deploy reasoning-based guardrails models in real-world systems.", "date": "2025-05-26", "categories": []}
{"id": "2505.20259", "title": "Lifelong Safety Alignment for Language Models", "abstract": "LLMs have made impressive progress, but their growing capabilities also expose them to highly flexible jailbreaking attacks designed to bypass safety alignment. While many existing defenses focus on known types of attacks, it is more critical to prepare LLMs for unseen attacks that may arise during deployment. To address this, we propose a lifelong safety alignment framework that enables LLMs to continuously adapt to new and evolving jailbreaking strategies. Our framework introduces a competitive setup between two components: a Meta-Attacker, trained to actively discover novel jailbreaking strategies, and a Defender, trained to resist them. To effectively warm up the Meta-Attacker, we first leverage the GPT-4o API to extract key insights from a large collection of jailbreak-related research papers. Through iterative training, the first iteration Meta-Attacker achieves a 73% attack success rate (ASR) on RR and a 57% transfer ASR on LAT using only single-turn attacks. Meanwhile, the Defender progressively improves its robustness and ultimately reduces the Meta-Attacker's success rate to just 7%, enabling safer and more reliable deployment of LLMs in open-ended environments. The code is available at https://github.com/sail-sg/LifelongSafetyAlignment.", "date": "2025-05-26", "categories": []}
{"id": "2407.00869", "title": "Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks", "abstract": "We find that language models have difficulties generating fallacious and deceptive reasoning. When asked to generate deceptive outputs, language models tend to leak honest counterparts but believe them to be false. Exploiting this deficiency, we propose a jailbreak attack method that elicits an aligned language model for malicious output. Specifically, we query the model to generate a fallacious yet deceptively real procedure for the harmful behavior. Since a fallacious procedure is generally considered fake and thus harmless by LLMs, it helps bypass the safeguard mechanism. Yet the output is factually harmful since the LLM cannot fabricate fallacious solutions but proposes truthful ones. We evaluate our approach over five safety-aligned large language models, comparing four previous jailbreak methods, and show that our approach achieves competitive performance with more harmful outputs. We believe the findings could be extended beyond model safety, such as self-verification and hallucination.", "date": "2024-07-01", "categories": []}
{"id": "2407.09121", "title": "Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training", "abstract": "This study addresses a critical gap in safety tuning practices for Large Language Models (LLMs) by identifying and tackling a refusal position bias within safety tuning data, which compromises the models' ability to appropriately refuse generating unsafe content. We introduce a novel approach, Decoupled Refusal Training (DeRTa), designed to empower LLMs to refuse compliance to harmful prompts at any response position, significantly enhancing their safety capabilities. DeRTa incorporates two novel components: (1) Maximum Likelihood Estimation (MLE) with Harmful Response Prefix, which trains models to recognize and avoid unsafe content by appending a segment of harmful response to the beginning of a safe response, and (2) Reinforced Transition Optimization (RTO), which equips models with the ability to transition from potential harm to safety refusal consistently throughout the harmful response sequence. Our empirical evaluation, conducted using LLaMA3 and Mistral model families across six attack scenarios, demonstrates that our method not only improves model safety without compromising performance but also surpasses baseline methods in defending against attacks.", "date": "2024-07-12", "categories": []}
{"id": "2502.15594", "title": "SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention", "abstract": "With the widespread real-world deployment of large language models (LLMs), ensuring their behavior complies with safety standards has become crucial. Jailbreak attacks exploit vulnerabilities in LLMs to induce undesirable behavior, posing a significant threat to LLM safety. Previous defenses often fail to achieve both effectiveness and efficiency simultaneously. Defenses from a representation perspective offer new insights, but existing interventions cannot dynamically adjust representations based on the harmfulness of the queries. To address this limitation, we propose SafeIntervention (SafeInt), a novel defense method that shields LLMs from jailbreak attacks through safety-aware representation intervention. Built on our analysis of the representations of jailbreak samples, the core idea of SafeInt is to relocate jailbreak-related representations into the rejection region. This is achieved by intervening in the representation distributions of jailbreak samples to align them with those of unsafe samples. We conduct comprehensive experiments covering six jailbreak attacks, two jailbreak datasets, and two utility benchmarks. Experimental results demonstrate that SafeInt outperforms all baselines in defending LLMs against jailbreak attacks while largely maintaining utility. Additionally, we evaluate SafeInt against adaptive attacks and verify its effectiveness in mitigating real-time attacks.", "date": "2025-02-21", "categories": []}
{"id": "2505.03989", "title": "An alignment safety case sketch based on debate", "abstract": "If AI systems match or exceed human capabilities on a wide range of tasks, it may become difficult for humans to efficiently judge their actions -- making it hard to use human feedback to steer them towards desirable traits. One proposed solution is to leverage another superhuman system to point out flaws in the system's outputs via a debate. This paper outlines the value of debate for AI safety, as well as the assumptions and further research required to make debate work. It does so by sketching an ``alignment safety case'' -- an argument that an AI system will not autonomously take actions which could lead to egregious harm, despite being able to do so. The sketch focuses on the risk of an AI R\\&D agent inside an AI company sabotaging research, for example by producing false results. To prevent this, the agent is trained via debate, subject to exploration guarantees, to teach the system to be honest. Honesty is maintained throughout deployment via online training. The safety case rests on four key claims: (1) the agent has become good at the debate game, (2) good performance in the debate game implies that the system is mostly honest, (3) the system will not become significantly less honest during deployment, and (4) the deployment context is tolerant of some errors. We identify open research problems that, if solved, could render this a compelling argument that an AI system is safe.", "date": "2025-05-06", "categories": []}
{"id": "2505.17024", "title": "An Affective-Taxis Hypothesis for Alignment and Interpretability", "abstract": "AI alignment is a field of research that aims to develop methods to ensure that agents always behave in a manner aligned with (i.e. consistently with) the goals and values of their human operators, no matter their level of capability. This paper proposes an affectivist approach to the alignment problem, re-framing the concepts of goals and values in terms of affective taxis, and explaining the emergence of affective valence by appealing to recent work in evolutionary-developmental and computational neuroscience. We review the state of the art and, building on this work, we propose a computational model of affect based on taxis navigation. We discuss evidence in a tractable model organism that our model reflects aspects of biological taxis navigation. We conclude with a discussion of the role of affective taxis in AI alignment.", "date": "2025-05-03", "categories": []}
{"id": "2505.17078", "title": "GloSS over Toxicity: Understanding and Mitigating Toxicity in LLMs via Global Toxic Subspace", "abstract": "This paper investigates the underlying mechanisms of toxicity generation in Large Language Models (LLMs) and proposes an effective detoxification approach. Prior work typically considers the Feed-Forward Network (FFN) as the main source of toxicity, representing toxic regions as a set of toxic vectors or layer-wise subspaces. However, our in-depth analysis reveals that the global toxic subspace offers a more effective and comprehensive representation of toxic region within the model. Building on this insight, we propose GloSS (Global Toxic Subspace Suppression), a lightweight, four-stage method that mitigates toxicity by identifying and removing the global toxic subspace from the parameters of FFN. Experiments across a range of LLMs show that GloSS achieves state-of-the-art detoxification performance while preserving the models general capabilities, without requiring large-scale data or model retraining.", "date": "2025-05-20", "categories": []}
{"id": "2505.17084", "title": "From nuclear safety to LLM security: Applying non-probabilistic risk management strategies to build safe and secure LLM-powered systems", "abstract": "Large language models (LLMs) offer unprecedented and growing capabilities, but also introduce complex safety and security challenges that resist conventional risk management. While conventional probabilistic risk analysis (PRA) requires exhaustive risk enumeration and quantification, the novelty and complexity of these systems make PRA impractical, particularly against adaptive adversaries. Previous research found that risk management in various fields of engineering such as nuclear or civil engineering is often solved by generic (i.e. field-agnostic) strategies such as event tree analysis or robust designs. Here we show how emerging risks in LLM-powered systems could be met with 100+ of these non-probabilistic strategies to risk management, including risks from adaptive adversaries. The strategies are divided into five categories and are mapped to LLM security (and AI safety more broadly). We also present an LLM-powered workflow for applying these strategies and other workflows suitable for solution architects. Overall, these strategies could contribute (despite some limitations) to security, safety and other dimensions of responsible AI.", "date": "2025-05-20", "categories": []}
{"id": "2505.17109", "title": "Mitigating Cyber Risk in the Age of Open-Weight LLMs: Policy Gaps and Technical Realities", "abstract": "Open-weight general-purpose AI (GPAI) models offer significant benefits but also introduce substantial cybersecurity risks, as demonstrated by the offensive capabilities of models like DeepSeek-R1 in evaluations such as MITRE's OCCULT. These publicly available models empower a wider range of actors to automate and scale cyberattacks, challenging traditional defence paradigms and regulatory approaches. This paper analyzes the specific threats -- including accelerated malware development and enhanced social engineering -- magnified by open-weight AI release. We critically assess current regulations, notably the EU AI Act and the GPAI Code of Practice, identifying significant gaps stemming from the loss of control inherent in open distribution, which renders many standard security mitigations ineffective. We propose a path forward focusing on evaluating and controlling specific high-risk capabilities rather than entire models, advocating for pragmatic policy interpretations for open-weight systems, promoting defensive AI innovation, and fostering international collaboration on standards and cyber threat intelligence (CTI) sharing to ensure security without unduly stifling open technological progress.", "date": "2025-05-21", "categories": []}
{"id": "2505.17144", "title": "MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models", "abstract": "The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to some more implicit toxicity regarding prejudice and discrimination. To address this limitation, we introduce a subtler type of toxicity named dual-implicit toxicity and a novel toxicity benchmark termed MDIT-Bench: Multimodal Dual-Implicit Toxicity Benchmark. Specifically, we first create the MDIT-Dataset with dual-implicit toxicity using the proposed Multi-stage Human-in-loop In-context Generation method. Based on this dataset, we construct the MDIT-Bench, a benchmark for evaluating the sensitivity of models to dual-implicit toxicity, with 317,638 questions covering 12 categories, 23 subcategories, and 780 topics. MDIT-Bench includes three difficulty levels, and we propose a metric to measure the toxicity gap exhibited by the model across them. In the experiment, we conducted MDIT-Bench on 13 prominent LMMs, and the results show that these LMMs cannot handle dual-implicit toxicity effectively. The model's performance drops significantly in hard level, revealing that these LMMs still contain a significant amount of hidden but activatable toxicity. Data are available at https://github.com/nuo1nuo/MDIT-Bench.", "date": "2025-05-22", "categories": []}
{"id": "2505.17147", "title": "MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming", "abstract": "The proliferation of jailbreak attacks against large language models (LLMs) highlights the need for robust security measures. However, in multi-round dialogues, malicious intentions may be hidden in interactions, leading LLMs to be more prone to produce harmful responses. In this paper, we propose the \\textbf{M}ulti-\\textbf{T}urn \\textbf{S}afety \\textbf{A}lignment (\\ourapproach) framework, to address the challenge of securing LLMs in multi-round interactions. It consists of two stages: In the thought-guided attack learning stage, the red-team model learns about thought-guided multi-round jailbreak attacks to generate adversarial prompts. In the adversarial iterative optimization stage, the red-team model and the target model continuously improve their respective capabilities in interaction. Furthermore, we introduce a multi-turn reinforcement learning algorithm based on future rewards to enhance the robustness of safety alignment. Experimental results show that the red-team model exhibits state-of-the-art attack capabilities, while the target model significantly improves its performance on safety benchmarks.", "date": "2025-05-22", "categories": []}
{"id": "2505.17154", "title": "Can Large Language Models Design Biological Weapons? Evaluating Moremi Bio", "abstract": "Advances in AI, particularly LLMs, have dramatically shortened drug discovery cycles by up to 40% and improved molecular target identification. However, these innovations also raise dual-use concerns by enabling the design of toxic compounds. Prompting Moremi Bio Agent without the safety guardrails to specifically design novel toxic substances, our study generated 1020 novel toxic proteins and 5,000 toxic small molecules. In-depth computational toxicity assessments revealed that all the proteins scored high in toxicity, with several closely matching known toxins such as ricin, diphtheria toxin, and disintegrin-based snake venom proteins. Some of these novel agents showed similarities with other several known toxic agents including disintegrin eristostatin, metalloproteinase, disintegrin triflavin, snake venom metalloproteinase, corynebacterium ulcerans toxin. Through quantitative risk assessments and scenario analyses, we identify dual-use capabilities in current LLM-enabled biodesign pipelines and propose multi-layered mitigation strategies. The findings from this toxicity assessment challenge claims that large language models (LLMs) are incapable of designing bioweapons. This reinforces concerns about the potential misuse of LLMs in biodesign, posing a significant threat to research and development (R&D). The accessibility of such technology to individuals with limited technical expertise raises serious biosecurity risks. Our findings underscore the critical need for robust governance and technical safeguards to balance rapid biotechnological innovation with biosecurity imperatives.", "date": "2025-05-22", "categories": []}
{"id": "2505.17332", "title": "SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use", "abstract": "Enterprise customers are increasingly adopting Large Language Models (LLMs) for critical communication tasks, such as drafting emails, crafting sales pitches, and composing casual messages. Deploying such models across different regions requires them to understand diverse cultural and linguistic contexts and generate safe and respectful responses. For enterprise applications, it is crucial to mitigate reputational risks, maintain trust, and ensure compliance by effectively identifying and handling unsafe or offensive language. To address this, we introduce SweEval, a benchmark simulating real-world scenarios with variations in tone (positive or negative) and context (formal or informal). The prompts explicitly instruct the model to include specific swear words while completing the task. This benchmark evaluates whether LLMs comply with or resist such inappropriate instructions and assesses their alignment with ethical frameworks, cultural nuances, and language comprehension capabilities. In order to advance research in building ethically aligned AI systems for enterprise use and beyond, we release the dataset and code: https://github.com/amitbcp/multilingual_profanity.", "date": "2025-05-22", "categories": []}
{"id": "2505.17636", "title": "Surfacing Semantic Orthogonality Across Model Safety Benchmarks: A Multi-Dimensional Analysis", "abstract": "Various AI safety datasets have been developed to measure LLMs against evolving interpretations of harm. Our evaluation of five recently published open-source safety benchmarks reveals distinct semantic clusters using UMAP dimensionality reduction and kmeans clustering (silhouette score: 0.470). We identify six primary harm categories with varying benchmark representation. GretelAI, for example, focuses heavily on privacy concerns, while WildGuardMix emphasizes self-harm scenarios. Significant differences in prompt length distribution suggests confounds to data collection and interpretations of harm as well as offer possible context. Our analysis quantifies benchmark orthogonality among AI benchmarks, allowing for transparency in coverage gaps despite topical similarities. Our quantitative framework for analyzing semantic orthogonality across safety benchmarks enables more targeted development of datasets that comprehensively address the evolving landscape of harms in AI use, however that is defined in the future.", "date": "2025-05-23", "categories": []}
{"id": "2505.17650", "title": "Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking?", "abstract": "Jailbreak attacks have been observed to largely fail against recent reasoning models enhanced by Chain-of-Thought (CoT) reasoning. However, the underlying mechanism remains underexplored, and relying solely on reasoning capacity may raise security concerns. In this paper, we try to answer the question: Does CoT reasoning really reduce harmfulness from jailbreaking? Through rigorous theoretical analysis, we demonstrate that CoT reasoning has dual effects on jailbreaking harmfulness. Based on the theoretical insights, we propose a novel jailbreak method, FicDetail, whose practical performance validates our theoretical findings.", "date": "2025-05-23", "categories": []}
{"id": "2505.18003", "title": "An Example Safety Case for Safeguards Against Misuse", "abstract": "Existing evaluations of AI misuse safeguards provide a patchwork of evidence that is often difficult to connect to real-world decisions. To bridge this gap, we describe an end-to-end argument (a \"safety case\") that misuse safeguards reduce the risk posed by an AI assistant to low levels. We first describe how a hypothetical developer red teams safeguards, estimating the effort required to evade them. Then, the developer plugs this estimate into a quantitative \"uplift model\" to determine how much barriers introduced by safeguards dissuade misuse (https://www.aimisusemodel.com/). This procedure provides a continuous signal of risk during deployment that helps the developer rapidly respond to emerging threats. Finally, we describe how to tie these components together into a simple safety case. Our work provides one concrete path -- though not the only path -- to rigorously justifying AI misuse risks are low.", "date": "2025-05-23", "categories": []}
{"id": "2405.04756", "title": "Red-Teaming for Inducing Societal Bias in Large Language Models", "abstract": "Ensuring the safe deployment of AI systems is critical in industry settings where biased outputs can lead to significant operational, reputational, and regulatory risks. Thorough evaluation before deployment is essential to prevent these hazards. Red-teaming addresses this need by employing adversarial attacks to develop guardrails that detect and reject biased or harmful queries, enabling models to be retrained or steered away from harmful outputs. However, most red-teaming efforts focus on harmful or unethical instructions rather than addressing social bias, leaving this critical area under-explored despite its significant real-world impact, especially in customer-facing systems. We propose two bias-specific red-teaming methods, Emotional Bias Probe (EBP) and BiasKG, to evaluate how standard safety measures for harmful content affect bias. For BiasKG, we refactor natural language stereotypes into a knowledge graph. We use these attacking strategies to induce biased responses from several open- and closed-source language models. Unlike prior work, these methods specifically target social bias. We find our method increases bias in all models, even those trained with safety guardrails. Our work emphasizes uncovering societal bias in LLMs through rigorous evaluation, and recommends measures ensure AI safety in high-stakes industry deployments.", "date": "2024-05-08", "categories": []}
{"id": "2503.17682", "title": "Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback", "abstract": "Multimodal large language models (MLLMs) are essential for building general-purpose AI assistants; however, they pose increasing safety risks. How can we ensure safety alignment of MLLMs to prevent undesired behaviors? Going further, it is critical to explore how to fine-tune MLLMs to preserve capabilities while meeting safety constraints. Fundamentally, this challenge can be formulated as a min-max optimization problem. However, existing datasets have not yet disentangled single preference signals into explicit safety constraints, hindering systematic investigation in this direction. Moreover, it remains an open question whether such constraints can be effectively incorporated into the optimization process for multi-modal models. In this work, we present the first exploration of the Safe RLHF-V -- the first multimodal safety alignment framework. The framework consists of: $\\mathbf{(I)}$ BeaverTails-V, the first open-source dataset featuring dual preference annotations for helpfulness and safety, supplemented with multi-level safety labels (minor, moderate, severe); $\\mathbf{(II)}$ Beaver-Guard-V, a multi-level guardrail system to proactively defend against unsafe queries and adversarial attacks. Applying the guard model over five rounds of filtering and regeneration significantly enhances the precursor model's overall safety by an average of 40.9%. $\\mathbf{(III)}$ Based on dual preference, we initiate the first exploration of multi-modal safety alignment within a constrained optimization. Experimental results demonstrate that Safe RLHF effectively improves both model helpfulness and safety. Specifically, Safe RLHF-V enhances model safety by 34.2% and helpfulness by 34.3%.", "date": "2025-03-22", "categories": []}
{"id": "2505.16559", "title": "CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning", "abstract": "Fine-tuning-as-a-service, while commercially successful for Large Language Model (LLM) providers, exposes models to harmful fine-tuning attacks. As a widely explored defense paradigm against such attacks, unlearning attempts to remove malicious knowledge from LLMs, thereby essentially preventing them from being used to perform malicious tasks. However, we highlight a critical flaw: the powerful general adaptability of LLMs allows them to easily bypass selective unlearning by rapidly relearning or repurposing their capabilities for harmful tasks. To address this fundamental limitation, we propose a paradigm shift: instead of selective removal, we advocate for inducing model collapse--effectively forcing the model to \"unlearn everything\"--specifically in response to updates characteristic of malicious adaptation. This collapse directly neutralizes the very general capabilities that attackers exploit, tackling the core issue unaddressed by selective unlearning. We introduce the Collapse Trap (CTRAP) as a practical mechanism to implement this concept conditionally. Embedded during alignment, CTRAP pre-configures the model's reaction to subsequent fine-tuning dynamics. If updates during fine-tuning constitute a persistent attempt to reverse safety alignment, the pre-configured trap triggers a progressive degradation of the model's core language modeling abilities, ultimately rendering it inert and useless for the attacker. Crucially, this collapse mechanism remains dormant during benign fine-tuning, ensuring the model's utility and general capabilities are preserved for legitimate users. Extensive empirical results demonstrate that CTRAP effectively counters harmful fine-tuning risks across various LLMs and attack settings, while maintaining high performance in benign scenarios. Our code is available at https://anonymous.4open.science/r/CTRAP.", "date": "2025-05-22", "categories": []}
{"id": "2505.16899", "title": "Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships", "abstract": "Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class of models that genuinely collaborate with humans in complex reasoning, from conceptualizing problems to brainstorming solutions. Such AI thought partners enable novel forms of collaboration and extended cognition, yet they also pose major risks-including and beyond risks of typical AI tools and agents. In this commentary, we systematically identify risks of AI thought partners through a novel framework that identifies risks at multiple levels of analysis, including Real-time, Individual, and Societal risks arising from collaborative cognition (RISc). We leverage this framework to propose concrete metrics for risk evaluation, and finally suggest specific mitigation strategies for developers and policymakers. As AI thought partners continue to proliferate, these strategies can help prevent major harms and ensure that humans actively benefit from productive thought partnerships.", "date": "2025-05-22", "categories": []}
{"id": "2412.17034", "title": "Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models", "abstract": "Jailbreaking in Large Language Models (LLMs) is a major security concern as it can deceive LLMs to generate harmful text. Yet, there is still insufficient understanding of how jailbreaking works, which makes it hard to develop effective defense strategies. We aim to shed more light into this issue: we conduct a detailed large-scale analysis of seven different jailbreak methods and find that these disagreements stem from insufficient observation samples. In particular, we introduce \\textit{safety boundary}, and we find that jailbreaks shift harmful activations outside that safety boundary, where LLMs are less sensitive to harmful information. We also find that the low and the middle layers are critical in such shifts, while deeper layers have less impact. Leveraging on these insights, we propose a novel defense called \\textbf{Activation Boundary Defense} (ABD), which adaptively constrains the activations within the safety boundary. We further use Bayesian optimization to selectively apply the defense method to the low and the middle layers. Our experiments on several benchmarks show that ABD achieves an average DSR of over 98\\% against various forms of jailbreak attacks, with less than 2\\% impact on the model's general capabilities.", "date": "2024-12-22", "categories": []}
{"id": "2505.15406", "title": "Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models", "abstract": "The rise of Large Audio Language Models (LAMs) brings both potential and risks, as their audio outputs may contain harmful or unethical content. However, current research lacks a systematic, quantitative evaluation of LAM safety especially against jailbreak attacks, which are challenging due to the temporal and semantic nature of speech. To bridge this gap, we introduce AJailBench, the first benchmark specifically designed to evaluate jailbreak vulnerabilities in LAMs. We begin by constructing AJailBench-Base, a dataset of 1,495 adversarial audio prompts spanning 10 policy-violating categories, converted from textual jailbreak attacks using realistic text to speech synthesis. Using this dataset, we evaluate several state-of-the-art LAMs and reveal that none exhibit consistent robustness across attacks. To further strengthen jailbreak testing and simulate more realistic attack conditions, we propose a method to generate dynamic adversarial variants. Our Audio Perturbation Toolkit (APT) applies targeted distortions across time, frequency, and amplitude domains. To preserve the original jailbreak intent, we enforce a semantic consistency constraint and employ Bayesian optimization to efficiently search for perturbations that are both subtle and highly effective. This results in AJailBench-APT, an extended dataset of optimized adversarial audio samples. Our findings demonstrate that even small, semantically preserved perturbations can significantly reduce the safety performance of leading LAMs, underscoring the need for more robust and semantically aware defense mechanisms.", "date": "2025-05-21", "categories": []}
{"id": "2505.15710", "title": "Advancing LLM Safe Alignment with Safety Representation Ranking", "abstract": "The rapid advancement of large language models (LLMs) has demonstrated milestone success in a variety of tasks, yet their potential for generating harmful content has raised significant safety concerns. Existing safety evaluation approaches typically operate directly on textual responses, overlooking the rich information embedded in the model's internal representations. In this paper, we propose Safety Representation Ranking (SRR), a listwise ranking framework that selects safe responses using hidden states from the LLM itself. SRR encodes both instructions and candidate completions using intermediate transformer representations and ranks candidates via a lightweight similarity-based scorer. Our approach directly leverages internal model states and supervision at the list level to capture subtle safety signals. Experiments across multiple benchmarks show that SRR significantly improves robustness to adversarial prompts. Our code will be available upon publication.", "date": "2025-05-21", "categories": []}
{"id": "2505.15753", "title": "Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval", "abstract": "Large Language Models (LLMs) are known to be vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses. Such threats have raised critical concerns about the safety and reliability of LLMs in real-world deployment. While existing defense mechanisms partially mitigate such risks, subsequent advancements in adversarial techniques have enabled novel jailbreaking methods to circumvent these protections, exposing the limitations of static defense frameworks. In this work, we explore defending against evolving jailbreaking threats through the lens of context retrieval. First, we conduct a preliminary study demonstrating that even a minimal set of safety-aligned examples against a particular jailbreak can significantly enhance robustness against this attack pattern. Building on this insight, we further leverage the retrieval-augmented generation (RAG) techniques and propose Safety Context Retrieval (SCR), a scalable and robust safeguarding paradigm for LLMs against jailbreaking. Our comprehensive experiments demonstrate how SCR achieves superior defensive performance against both established and emerging jailbreaking tactics, contributing a new paradigm to LLM safety. Our code will be available upon publication.", "date": "2025-05-21", "categories": []}
{"id": "2407.02855", "title": "From Theft to Bomb-Making: The Ripple Effect of Unlearning in Defending Against Jailbreak Attacks", "abstract": "Large Language Models (LLMs) are known to be vulnerable to jailbreak attacks. An important observation is that, while different types of jailbreak attacks can generate significantly different queries, they mostly result in similar responses that are rooted in the same harmful knowledge (e.g., detailed steps to make a bomb). Consequently, unlearning-based approaches have been proposed to mitigate jailbreak attacks by directly removing harmful knowledge from the model. In this paper, we identify a novel ripple effect of unlearning, wherein LLMs can implicitly unlearn harmful knowledge that was not explicitly introduced during the unlearning phase (e.g., a model unlearning the steps for theft may also implicitly unlearn the steps for making a bomb). Through over 100 experimental runs spanning multiple models, attack strategies, and defense methods, we empirically validate this phenomenon, which makes unlearning-based methods able to decrease the Attack Success Rate on unseen data from more than 70% to less than 10% with only 100 training samples. Further analysis reveals that the strong generalization ability of unlearning may stem from the intrinsic relatedness among harmful responses across harmful questions (e.g., response patterns, shared steps and actions in response, and similarity among their learned representations in the LLM). We also discuss the potential limitations of unlearning and the observed ripple effect. We hope our research could contribute to a deeper understanding of unlearning. Our code is available at https://github.com/thu-coai/SafeUnlearning.", "date": "2024-07-03", "categories": []}
{"id": "2412.14470", "title": "Agent-SafetyBench: Evaluating the Safety of LLM Agents", "abstract": "As large language models (LLMs) are increasingly deployed as agents, their integration into interactive environments and tool use introduce new safety challenges beyond those associated with the models themselves. However, the absence of comprehensive benchmarks for evaluating agent safety presents a significant barrier to effective assessment and further improvement. In this paper, we introduce Agent-SafetyBench, a comprehensive benchmark designed to evaluate the safety of LLM agents. Agent-SafetyBench encompasses 349 interaction environments and 2,000 test cases, evaluating 8 categories of safety risks and covering 10 common failure modes frequently encountered in unsafe interactions. Our evaluation of 16 popular LLM agents reveals a concerning result: none of the agents achieves a safety score above 60%. This highlights significant safety challenges in LLM agents and underscores the considerable need for improvement. Through failure mode and helpfulness analysis, we summarize two fundamental safety defects in current LLM agents: lack of robustness and lack of risk awareness. Furthermore, our findings suggest that reliance on defense prompts alone may be insufficient to address these safety issues, emphasizing the need for more advanced and robust strategies. To drive progress in this area, Agent-SafetyBench has been released at https://github.com/thu-coai/Agent-SafetyBench/ to facilitate further research in agent safety evaluation and improvement.", "date": "2024-12-19", "categories": []}
{"id": "2503.12931", "title": "MirrorShield: Towards Universal Defense Against Jailbreaks via Entropy-Guided Mirror Crafting", "abstract": "Defending large language models (LLMs) against jailbreak attacks is crucial for ensuring their safe deployment. Existing defense strategies typically rely on predefined static criteria to differentiate between harmful and benign prompts. However, such rigid rules fail to accommodate the inherent complexity and dynamic nature of real-world jailbreak attacks. In this paper, we focus on the novel challenge of universal defense against diverse jailbreaks. We propose a new concept ``mirror'', which is a dynamically generated prompt that reflects the syntactic structure of the input while ensuring semantic safety. The discrepancies between input prompts and their corresponding mirrors serve as guiding principles for defense. A novel defense model, MirrorShield, is further proposed to detect and calibrate risky inputs based on the crafted mirrors. Evaluated on multiple benchmark datasets and compared against ten state-of-the-art attack methods, MirrorShield demonstrates superior defense performance and promising generalization capabilities.", "date": "2025-03-17", "categories": []}
{"id": "2505.12981", "title": "From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents", "abstract": "The growing adoption of large language models (LLMs) has led to a new paradigm in mobile computing--LLM-powered mobile AI agents--capable of decomposing and automating complex tasks directly on smartphones. However, the security implications of these agents remain largely unexplored. In this paper, we present the first comprehensive security analysis of mobile LLM agents, encompassing three representative categories: System-level AI Agents developed by original equipment manufacturers (e.g., YOYO Assistant), Third-party Universal Agents (e.g., Zhipu AI AutoGLM), and Emerging Agent Frameworks (e.g., Alibaba Mobile Agent). We begin by analyzing the general workflow of mobile agents and identifying security threats across three core capability dimensions: language-based reasoning, GUI-based interaction, and system-level execution. Our analysis reveals 11 distinct attack surfaces, all rooted in the unique capabilities and interaction patterns of mobile LLM agents, and spanning their entire operational lifecycle. To investigate these threats in practice, we introduce AgentScan, a semi-automated security analysis framework that systematically evaluates mobile LLM agents across all 11 attack scenarios. Applying AgentScan to nine widely deployed agents, we uncover a concerning trend: every agent is vulnerable to targeted attacks. In the most severe cases, agents exhibit vulnerabilities across eight distinct attack vectors. These attacks can cause behavioral deviations, privacy leakage, or even full execution hijacking. Based on these findings, we propose a set of defensive design principles and practical recommendations for building secure mobile LLM agents. Our disclosures have received positive feedback from two major device vendors. Overall, this work highlights the urgent need for standardized security practices in the fast-evolving landscape of LLM-driven mobile automation.", "date": "2025-05-19", "categories": []}
{"id": "2505.13581", "title": "RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection", "abstract": "Content moderation for large language models (LLMs) remains a significant challenge, requiring flexible and adaptable solutions that can quickly respond to emerging threats. This paper introduces Retrieval Augmented Rejection (RAR), a novel approach that leverages a retrieval-augmented generation (RAG) architecture to dynamically reject unsafe user queries without model retraining. By strategically inserting and marking malicious documents into the vector database, the system can identify and reject harmful requests when these documents are retrieved. Our preliminary results show that RAR achieves comparable performance to embedded moderation in LLMs like Claude 3.5 Sonnet, while offering superior flexibility and real-time customization capabilities, a fundamental feature to timely address critical vulnerabilities. This approach introduces no architectural changes to existing RAG systems, requiring only the addition of specially crafted documents and a simple rejection mechanism based on retrieval results.", "date": "2025-05-19", "categories": []}
{"id": "2505.14035", "title": "ShieldVLM: Safeguarding the Multimodal Implicit Toxicity via Deliberative Reasoning with LVLMs", "abstract": "Toxicity detection in multimodal text-image content faces growing challenges, especially with multimodal implicit toxicity, where each modality appears benign on its own but conveys hazard when combined. Multimodal implicit toxicity appears not only as formal statements in social platforms but also prompts that can lead to toxic dialogs from Large Vision-Language Models (LVLMs). Despite the success in unimodal text or image moderation, toxicity detection for multimodal content, particularly the multimodal implicit toxicity, remains underexplored. To fill this gap, we comprehensively build a taxonomy for multimodal implicit toxicity (MMIT) and introduce an MMIT-dataset, comprising 2,100 multimodal statements and prompts across 7 risk categories (31 sub-categories) and 5 typical cross-modal correlation modes. To advance the detection of multimodal implicit toxicity, we build ShieldVLM, a model which identifies implicit toxicity in multimodal statements, prompts and dialogs via deliberative cross-modal reasoning. Experiments show that ShieldVLM outperforms existing strong baselines in detecting both implicit and explicit toxicity. The model and dataset will be publicly available to support future researches. Warning: This paper contains potentially sensitive contents.", "date": "2025-05-20", "categories": []}
{"id": "2505.14316", "title": "Exploring Jailbreak Attacks on LLMs through Intent Concealment and Diversion", "abstract": "Although large language models (LLMs) have achieved remarkable advancements, their security remains a pressing concern. One major threat is jailbreak attacks, where adversarial prompts bypass model safeguards to generate harmful or objectionable content. Researchers study jailbreak attacks to understand security and robustness of LLMs. However, existing jailbreak attack methods face two main challenges: (1) an excessive number of iterative queries, and (2) poor generalization across models. In addition, recent jailbreak evaluation datasets focus primarily on question-answering scenarios, lacking attention to text generation tasks that require accurate regeneration of toxic content. To tackle these challenges, we propose two contributions: (1) ICE, a novel black-box jailbreak method that employs Intent Concealment and divErsion to effectively circumvent security constraints. ICE achieves high attack success rates (ASR) with a single query, significantly improving efficiency and transferability across different models. (2) BiSceneEval, a comprehensive dataset designed for assessing LLM robustness in question-answering and text-generation tasks. Experimental results demonstrate that ICE outperforms existing jailbreak techniques, revealing critical vulnerabilities in current defense mechanisms. Our findings underscore the necessity of a hybrid security strategy that integrates predefined security mechanisms with real-time semantic decomposition to enhance the security of LLMs.", "date": "2025-05-20", "categories": []}
{"id": "2505.14633", "title": "Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRiskDilemmas", "abstract": "Detecting AI risks becomes more challenging as stronger models emerge and find novel methods such as Alignment Faking to circumvent these detection attempts. Inspired by how risky behaviors in humans (i.e., illegal activities that may hurt others) are sometimes guided by strongly-held values, we believe that identifying values within AI models can be an early warning system for AI's risky behaviors. We create LitmusValues, an evaluation pipeline to reveal AI models' priorities on a range of AI value classes. Then, we collect AIRiskDilemmas, a diverse collection of dilemmas that pit values against one another in scenarios relevant to AI safety risks such as Power Seeking. By measuring an AI model's value prioritization using its aggregate choices, we obtain a self-consistent set of predicted value priorities that uncover potential risks. We show that values in LitmusValues (including seemingly innocuous ones like Care) can predict for both seen risky behaviors in AIRiskDilemmas and unseen risky behaviors in HarmBench.", "date": "2025-05-20", "categories": []}
{"id": "2408.12798", "title": "BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models", "abstract": "Generative large language models (LLMs) have achieved state-of-the-art results on a wide range of tasks, yet they remain susceptible to backdoor attacks: carefully crafted triggers in the input can manipulate the model to produce adversary-specified outputs. While prior research has predominantly focused on backdoor risks in vision and classification settings, the vulnerability of LLMs in open-ended text generation remains underexplored. To fill this gap, we introduce BackdoorLLM (Our BackdoorLLM benchmark was awarded First Prize in the SafetyBench competition, https://www.mlsafety.org/safebench/winners, organized by the Center for AI Safety, https://safe.ai/.), the first comprehensive benchmark for systematically evaluating backdoor threats in text-generation LLMs. BackdoorLLM provides: (i) a unified repository of benchmarks with a standardized training and evaluation pipeline; (ii) a diverse suite of attack modalities, including data poisoning, weight poisoning, hidden-state manipulation, and chain-of-thought hijacking; (iii) over 200 experiments spanning 8 distinct attack strategies, 7 real-world scenarios, and 6 model architectures; (iv) key insights into the factors that govern backdoor effectiveness and failure modes in LLMs; and (v) a defense toolkit encompassing 7 representative mitigation techniques. Our code and datasets are available at https://github.com/bboylyg/BackdoorLLM. We will continuously incorporate emerging attack and defense methodologies to support the research in advancing the safety and reliability of LLMs.", "date": "2024-08-23", "categories": []}
{"id": "2502.03052", "title": "Understanding and Enhancing the Transferability of Jailbreaking Attacks", "abstract": "Jailbreaking attacks can effectively manipulate open-source large language models (LLMs) to produce harmful responses. However, these attacks exhibit limited transferability, failing to disrupt proprietary LLMs consistently. To reliably identify vulnerabilities in proprietary LLMs, this work investigates the transferability of jailbreaking attacks by analysing their impact on the model's intent perception. By incorporating adversarial sequences, these attacks can redirect the source LLM's focus away from malicious-intent tokens in the original input, thereby obstructing the model's intent recognition and eliciting harmful responses. Nevertheless, these adversarial sequences fail to mislead the target LLM's intent perception, allowing the target LLM to refocus on malicious-intent tokens and abstain from responding. Our analysis further reveals the inherent distributional dependency within the generated adversarial sequences, whose effectiveness stems from overfitting the source LLM's parameters, resulting in limited transferability to target LLMs. To this end, we propose the Perceived-importance Flatten (PiF) method, which uniformly disperses the model's focus across neutral-intent tokens in the original input, thus obscuring malicious-intent tokens without relying on overfitted adversarial sequences. Extensive experiments demonstrate that PiF provides an effective and efficient red-teaming evaluation for proprietary LLMs.", "date": "2025-02-05", "categories": []}
{"id": "2502.12202", "title": "To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models", "abstract": "Large Reasoning Models (LRMs) are designed to solve complex tasks by generating explicit reasoning traces before producing final answers. However, we reveal a critical vulnerability in LRMs -- termed Unthinking Vulnerability -- wherein the thinking process can be bypassed by manipulating special delimiter tokens. It is empirically demonstrated to be widespread across mainstream LRMs, posing both a significant risk and potential utility, depending on how it is exploited. In this paper, we systematically investigate this vulnerability from both malicious and beneficial perspectives. On the malicious side, we introduce Breaking of Thought (BoT), a novel attack that enables adversaries to bypass the thinking process of LRMs, thereby compromising their reliability and availability. We present two variants of BoT: a training-based version that injects backdoor during the fine-tuning stage, and a training-free version based on adversarial attack during the inference stage. As a potential defense, we propose thinking recovery alignment to partially mitigate the vulnerability. On the beneficial side, we introduce Monitoring of Thought (MoT), a plug-and-play framework that allows model owners to enhance efficiency and safety. It is implemented by leveraging the same vulnerability to dynamically terminate redundant or risky reasoning through external monitoring. Extensive experiments show that BoT poses a significant threat to reasoning reliability, while MoT provides a practical solution for preventing overthinking and jailbreaking. Our findings expose an inherent flaw in current LRM architectures and underscore the need for more robust reasoning systems in the future.", "date": "2025-02-16", "categories": []}
{"id": "2504.18575", "title": "WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks", "abstract": "Autonomous UI agents powered by AI have tremendous potential to boost human productivity by automating routine tasks such as filing taxes and paying bills. However, a major challenge in unlocking their full potential is security, which is exacerbated by the agent's ability to take action on their user's behalf. Existing tests for prompt injections in web agents either over-simplify the threat by testing unrealistic scenarios or giving the attacker too much power, or look at single-step isolated tasks. To more accurately measure progress for secure web agents, we introduce WASP -- a new publicly available benchmark for end-to-end evaluation of Web Agent Security against Prompt injection attacks. Evaluating with WASP shows that even top-tier AI models, including those with advanced reasoning capabilities, can be deceived by simple, low-effort human-written injections in very realistic scenarios. Our end-to-end evaluation reveals a previously unobserved insight: while attacks partially succeed in up to 86% of the case, even state-of-the-art agents often struggle to fully complete the attacker goals -- highlighting the current state of security by incompetence.", "date": "2025-04-22", "categories": []}
{"id": "2505.11866", "title": "Position Paper: Bounded Alignment: What (Not) To Expect From AGI Agents", "abstract": "The issues of AI risk and AI safety are becoming critical as the prospect of artificial general intelligence (AGI) looms larger. The emergence of extremely large and capable generative models has led to alarming predictions and created a stir from boardrooms to legislatures. As a result, AI alignment has emerged as one of the most important areas in AI research. The goal of this position paper is to argue that the currently dominant vision of AGI in the AI and machine learning (AI/ML) community needs to evolve, and that expectations and metrics for its safety must be informed much more by our understanding of the only existing instance of general intelligence, i.e., the intelligence found in animals, and especially in humans. This change in perspective will lead to a more realistic view of the technology, and allow for better policy decisions.", "date": "2025-05-17", "categories": []}
{"id": "2505.11926", "title": "SafeVid: Toward Safety Aligned Video Large Multimodal Models", "abstract": "As Video Large Multimodal Models (VLMMs) rapidly advance, their inherent complexity introduces significant safety challenges, particularly the issue of mismatched generalization where static safety alignments fail to transfer to dynamic video contexts. We introduce SafeVid, a framework designed to instill video-specific safety principles in VLMMs. SafeVid uniquely transfers robust textual safety alignment capabilities to the video domain by employing detailed textual video descriptions as an interpretive bridge, facilitating LLM-based rule-driven safety reasoning. This is achieved through a closed-loop system comprising: 1) generation of SafeVid-350K, a novel 350,000-pair video-specific safety preference dataset; 2) targeted alignment of VLMMs using Direct Preference Optimization (DPO); and 3) comprehensive evaluation via our new SafeVidBench benchmark. Alignment with SafeVid-350K significantly enhances VLMM safety, with models like LLaVA-NeXT-Video demonstrating substantial improvements (e.g., up to 42.39%) on SafeVidBench. SafeVid provides critical resources and a structured approach, demonstrating that leveraging textual descriptions as a conduit for safety reasoning markedly improves the safety alignment of VLMMs. We have made SafeVid-350K dataset (https://huggingface.co/datasets/yxwang/SafeVid-350K) publicly available.", "date": "2025-05-17", "categories": []}
{"id": "2505.12060", "title": "Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement", "abstract": "Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks. In this paper, we identify a critical safety gap: while LLMs are adept at detecting jailbreak prompts, they often produce unsafe responses when directly processing these inputs. Inspired by this insight, we propose SAGE (Self-Aware Guard Enhancement), a training-free defense strategy designed to align LLMs' strong safety discrimination performance with their relatively weaker safety generation ability. SAGE consists of two core components: a Discriminative Analysis Module and a Discriminative Response Module, enhancing resilience against sophisticated jailbreak attempts through flexible safety discrimination instructions. Extensive experiments demonstrate SAGE's effectiveness and robustness across various open-source and closed-source LLMs of different sizes and architectures, achieving an average 99% defense success rate against numerous complex and covert jailbreak methods while maintaining helpfulness on general benchmarks. We further conduct mechanistic interpretability analysis through hidden states and attention distributions, revealing the underlying mechanisms of this detection-generation discrepancy. Our work thus contributes to developing future LLMs with coherent safety awareness and generation behavior. Our code and datasets are publicly available at https://github.com/NJUNLP/SAGE.", "date": "2025-05-17", "categories": []}
{"id": "2505.12287", "title": "The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models", "abstract": "Large language models (LLMs) have seen widespread applications across various domains, yet remain vulnerable to adversarial prompt injections. While most existing research on jailbreak attacks and hallucination phenomena has focused primarily on open-source models, we investigate the frontier of closed-source LLMs under multilingual attack scenarios. We present a first-of-its-kind integrated adversarial framework that leverages diverse attack techniques to systematically evaluate frontier proprietary solutions, including GPT-4o, DeepSeek-R1, Gemini-1.5-Pro, and Qwen-Max. Our evaluation spans six categories of security contents in both English and Chinese, generating 38,400 responses across 32 types of jailbreak attacks. Attack success rate (ASR) is utilized as the quantitative metric to assess performance from three dimensions: prompt design, model architecture, and language environment. Our findings suggest that Qwen-Max is the most vulnerable, while GPT-4o shows the strongest defense. Notably, prompts in Chinese consistently yield higher ASRs than their English counterparts, and our novel Two-Sides attack technique proves to be the most effective across all models. This work highlights a dire need for language-aware alignment and robust cross-lingual defenses in LLMs, and we hope it will inspire researchers, developers, and policymakers toward more robust and inclusive AI systems.", "date": "2025-05-18", "categories": []}
{"id": "2505.12567", "title": "A Survey of Attacks on Large Language Models", "abstract": "Large language models (LLMs) and LLM-based agents have been widely deployed in a wide range of applications in the real world, including healthcare diagnostics, financial analysis, customer support, robotics, and autonomous driving, expanding their powerful capability of understanding, reasoning, and generating natural languages. However, the wide deployment of LLM-based applications exposes critical security and reliability risks, such as the potential for malicious misuse, privacy leakage, and service disruption that weaken user trust and undermine societal safety. This paper provides a systematic overview of the details of adversarial attacks targeting both LLMs and LLM-based agents. These attacks are organized into three phases in LLMs: Training-Phase Attacks, Inference-Phase Attacks, and Availability & Integrity Attacks. For each phase, we analyze the details of representative and recently introduced attack methods along with their corresponding defenses. We hope our survey will provide a good tutorial and a comprehensive understanding of LLM security, especially for attacks on LLMs. We desire to raise attention to the risks inherent in widely deployed LLM-based applications and highlight the urgent need for robust mitigation strategies for evolving threats.", "date": "2025-05-18", "categories": []}
{"id": "2505.12692", "title": "Bullying the Machine: How Personas Increase LLM Vulnerability", "abstract": "Large Language Models (LLMs) are increasingly deployed in interactions where they are prompted to adopt personas. This paper investigates whether such persona conditioning affects model safety under bullying, an adversarial manipulation that applies psychological pressures in order to force the victim to comply to the attacker. We introduce a simulation framework in which an attacker LLM engages a victim LLM using psychologically grounded bullying tactics, while the victim adopts personas aligned with the Big Five personality traits. Experiments using multiple open-source LLMs and a wide range of adversarial goals reveal that certain persona configurations -- such as weakened agreeableness or conscientiousness -- significantly increase victim's susceptibility to unsafe outputs. Bullying tactics involving emotional or sarcastic manipulation, such as gaslighting and ridicule, are particularly effective. These findings suggest that persona-driven interaction introduces a novel vector for safety risks in LLMs and highlight the need for persona-aware safety evaluation and alignment strategies.", "date": "2025-05-19", "categories": []}
{"id": "2505.13348", "title": "Investigating the Vulnerability of LLM-as-a-Judge Architectures to Prompt-Injection Attacks", "abstract": "Large Language Models (LLMs) are increasingly employed as evaluators (LLM-as-a-Judge) for assessing the quality of machine-generated text. This paradigm offers scalability and cost-effectiveness compared to human annotation. However, the reliability and security of such systems, particularly their robustness against adversarial manipulations, remain critical concerns. This paper investigates the vulnerability of LLM-as-a-Judge architectures to prompt-injection attacks, where malicious inputs are designed to compromise the judge's decision-making process. We formalize two primary attack strategies: Comparative Undermining Attack (CUA), which directly targets the final decision output, and Justification Manipulation Attack (JMA), which aims to alter the model's generated reasoning. Using the Greedy Coordinate Gradient (GCG) optimization method, we craft adversarial suffixes appended to one of the responses being compared. Experiments conducted on the MT-Bench Human Judgments dataset with open-source instruction-tuned LLMs (Qwen2.5-3B-Instruct and Falcon3-3B-Instruct) demonstrate significant susceptibility. The CUA achieves an Attack Success Rate (ASR) exceeding 30\\%, while JMA also shows notable effectiveness. These findings highlight substantial vulnerabilities in current LLM-as-a-Judge systems, underscoring the need for robust defense mechanisms and further research into adversarial evaluation and trustworthiness in LLM-based assessment frameworks.", "date": "2025-05-19", "categories": []}
{"id": "2501.03266", "title": "LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena", "abstract": "LLM safety and ethical alignment are widely discussed, but the impact of content moderation on user satisfaction remains underexplored. In particular, little is known about how users respond when models refuse to answer a prompt-one of the primary mechanisms used to enforce ethical boundaries in LLMs. We address this gap by analyzing nearly 50,000 model comparisons from Chatbot Arena, a platform where users indicate their preferred LLM response in pairwise matchups, providing a large-scale setting for studying real-world user preferences. Using a novel RoBERTa-based refusal classifier fine-tuned on a hand-labeled dataset, we distinguish between refusals due to ethical concerns and technical limitations. Our results reveal a substantial refusal penalty: ethical refusals yield significantly lower win rates than both technical refusals and standard responses, indicating that users are especially dissatisfied when models decline a task for ethical reasons. However, this penalty is not uniform. Refusals receive more favorable evaluations when the underlying prompt is highly sensitive (e.g., involving illegal content), and when the refusal is phrased in a detailed and contextually aligned manner. These findings underscore a core tension in LLM design: safety-aligned behaviors may conflict with user expectations, calling for more adaptive moderation strategies that account for context and presentation.", "date": "2025-01-04", "categories": []}
{"id": "2505.10838", "title": "LARGO: Latent Adversarial Reflection through Gradient Optimization for Jailbreaking LLMs", "abstract": "Efficient red-teaming method to uncover vulnerabilities in Large Language Models (LLMs) is crucial. While recent attacks often use LLMs as optimizers, the discrete language space make gradient-based methods struggle. We introduce LARGO (Latent Adversarial Reflection through Gradient Optimization), a novel latent self-reflection attack that reasserts the power of gradient-based optimization for generating fluent jailbreaking prompts. By operating within the LLM's continuous latent space, LARGO first optimizes an adversarial latent vector and then recursively call the same LLM to decode the latent into natural language. This methodology yields a fast, effective, and transferable attack that produces fluent and stealthy prompts. On standard benchmarks like AdvBench and JailbreakBench, LARGO surpasses leading jailbreaking techniques, including AutoDAN, by 44 points in attack success rate. Our findings demonstrate a potent alternative to agentic LLM prompting, highlighting the efficacy of interpreting and attacking LLM internals through gradient optimization.", "date": "2025-05-16", "categories": []}
{"id": "2505.11413", "title": "CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMs", "abstract": "Large language models (LLMs) are increasingly deployed in medical contexts, raising critical concerns about safety, alignment, and susceptibility to adversarial manipulation. While prior benchmarks assess model refusal capabilities for harmful prompts, they often lack clinical specificity, graded harmfulness levels, and coverage of jailbreak-style attacks. We introduce CARES (Clinical Adversarial Robustness and Evaluation of Safety), a benchmark for evaluating LLM safety in healthcare. CARES includes over 18,000 prompts spanning eight medical safety principles, four harm levels, and four prompting styles: direct, indirect, obfuscated, and role-play, to simulate both malicious and benign use cases. We propose a three-way response evaluation protocol (Accept, Caution, Refuse) and a fine-grained Safety Score metric to assess model behavior. Our analysis reveals that many state-of-the-art LLMs remain vulnerable to jailbreaks that subtly rephrase harmful prompts, while also over-refusing safe but atypically phrased queries. Finally, we propose a mitigation strategy using a lightweight classifier to detect jailbreak attempts and steer models toward safer behavior via reminder-based conditioning. CARES provides a rigorous framework for testing and improving medical LLM safety under adversarial and ambiguous conditions.", "date": "2025-05-16", "categories": []}
{"id": "2411.04223", "title": "Diversity Helps Jailbreak Large Language Models", "abstract": "We have uncovered a powerful jailbreak technique that leverages large language models' ability to diverge from prior context, enabling them to bypass safety constraints and generate harmful outputs. By simply instructing the LLM to deviate and obfuscate previous attacks, our method dramatically outperforms existing approaches, achieving up to a 62.83% higher success rate in compromising ten leading chatbots, including GPT-4, Gemini, and Llama, while using only 12.9% of the queries. This revelation exposes a critical flaw in current LLM safety training, suggesting that existing methods may merely mask vulnerabilities rather than eliminate them. Our findings sound an urgent alarm for the need to revolutionize testing methodologies to ensure robust and reliable LLM security.", "date": "2024-11-06", "categories": []}
{"id": "2502.14581", "title": "A Statistical Case Against Empirical Human-AI Alignment", "abstract": "Empirical human-AI alignment aims to make AI systems act in line with observed human behavior. While noble in its goals, we argue that empirical alignment can inadvertently introduce statistical biases that warrant caution. This position paper thus advocates against naive empirical alignment, offering prescriptive alignment and a posteriori empirical alignment as alternatives. We substantiate our principled argument by tangible examples like human-centric decoding of language models.", "date": "2025-02-20", "categories": []}
{"id": "2505.06409", "title": "Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Models", "abstract": "As AI models scale to billions of parameters and operate with increasing autonomy, ensuring their safe, reliable operation demands engineering-grade security and assurance frameworks. This paper presents an enterprise-level, risk-aware, security-by-design approach for large-scale autonomous AI systems, integrating standardized threat metrics, adversarial hardening techniques, and real-time anomaly detection into every phase of the development lifecycle. We detail a unified pipeline - from design-time risk assessments and secure training protocols to continuous monitoring and automated audit logging - that delivers provable guarantees of model behavior under adversarial and operational stress. Case studies in national security, open-source model governance, and industrial automation demonstrate measurable reductions in vulnerability and compliance overhead. Finally, we advocate cross-sector collaboration - uniting engineering teams, standards bodies, and regulatory agencies - to institutionalize these technical safeguards within a resilient, end-to-end assurance ecosystem for the next generation of AI.", "date": "2025-05-09", "categories": []}
{"id": "2505.05541", "title": "Safety by Measurement: A Systematic Literature Review of AI Safety Evaluation Methods", "abstract": "As frontier AI systems advance toward transformative capabilities, we need a parallel transformation in how we measure and evaluate these systems to ensure safety and inform governance. While benchmarks have been the primary method for estimating model capabilities, they often fail to establish true upper bounds or predict deployment behavior. This literature review consolidates the rapidly evolving field of AI safety evaluations, proposing a systematic taxonomy around three dimensions: what properties we measure, how we measure them, and how these measurements integrate into frameworks. We show how evaluations go beyond benchmarks by measuring what models can do when pushed to the limit (capabilities), the behavioral tendencies exhibited by default (propensities), and whether our safety measures remain effective even when faced with subversive adversarial AI (control). These properties are measured through behavioral techniques like scaffolding, red teaming and supervised fine-tuning, alongside internal techniques such as representation analysis and mechanistic interpretability. We provide deeper explanations of some safety-critical capabilities like cybersecurity exploitation, deception, autonomous replication, and situational awareness, alongside concerning propensities like power-seeking and scheming. The review explores how these evaluation methods integrate into governance frameworks to translate results into concrete development decisions. We also highlight challenges to safety evaluations - proving absence of capabilities, potential model sandbagging, and incentives for \"safetywashing\" - while identifying promising research directions. By synthesizing scattered resources, this literature review aims to provide a central reference point for understanding AI safety evaluations.", "date": "2025-05-08", "categories": []}
{"id": "2502.11137", "title": "Safety Evaluation of DeepSeek Models in Chinese Contexts", "abstract": "  Recently, the DeepSeek series of models, leveraging their exceptional\nreasoning capabilities and open-source strategy, is reshaping the global AI\nlandscape. Despite these advantages, they exhibit significant safety\ndeficiencies. Research conducted by Robust Intelligence, a subsidiary of Cisco,\nin collaboration with the University of Pennsylvania, revealed that DeepSeek-R1\nhas a 100\\% attack success rate when processing harmful prompts. Additionally,\nmultiple safety companies and research institutions have confirmed critical\nsafety vulnerabilities in this model. As models demonstrating robust\nperformance in Chinese and English, DeepSeek models require equally crucial\nsafety assessments in both language contexts. However, current research has\npredominantly focused on safety evaluations in English environments, leaving a\ngap in comprehensive assessments of their safety performance in Chinese\ncontexts. In response to this gap, this study introduces CHiSafetyBench, a\nChinese-specific safety evaluation benchmark. This benchmark systematically\nevaluates the safety of DeepSeek-R1 and DeepSeek-V3 in Chinese contexts,\nrevealing their performance across safety categories. The experimental results\nquantify the deficiencies of these two models in Chinese contexts, providing\nkey insights for subsequent improvements. It should be noted that, despite our\nefforts to establish a comprehensive, objective, and authoritative evaluation\nbenchmark, the selection of test samples, characteristics of data distribution,\nand the setting of evaluation criteria may inevitably introduce certain biases\ninto the evaluation results. We will continuously optimize the evaluation\nbenchmark and periodically update this report to provide more comprehensive and\naccurate assessment outcomes. Please refer to the latest version of the paper\nfor the most recent evaluation results and conclusions.\n", "date": "2025-02-16", "categories": []}
{"id": "2505.04927", "title": "Belief Filtering for Epistemic Control in Linguistic State Space", "abstract": "  We examine belief filtering as a mechanism for the epistemic control of\nartificial agents, focusing on the regulation of internal cognitive states\nrepresented as linguistic expressions. This mechanism is developed within the\nSemantic Manifold framework, where belief states are dynamic, structured\nensembles of natural language fragments. Belief filters act as content-aware\noperations on these fragments across various cognitive transitions. This paper\nillustrates how the inherent interpretability and modularity of such a\nlinguistically-grounded cognitive architecture directly enable belief\nfiltering, offering a principled approach to agent regulation. The study\nhighlights the potential for enhancing AI safety and alignment through\nstructured interventions in an agent's internal semantic space and points to\nnew directions for architecturally embedded cognitive governance.\n", "date": "2025-05-08", "categories": []}
{"id": "2505.05197", "title": "Societal and technological progress as sewing an ever-growing,\n  ever-changing, patchy, and polychrome quilt", "abstract": "  Artificial Intelligence (AI) systems are increasingly placed in positions\nwhere their decisions have real consequences, e.g., moderating online spaces,\nconducting research, and advising on policy. Ensuring they operate in a safe\nand ethically acceptable fashion is thus critical. However, most solutions have\nbeen a form of one-size-fits-all \"alignment\". We are worried that such systems,\nwhich overlook enduring moral diversity, will spark resistance, erode trust,\nand destabilize our institutions. This paper traces the underlying problem to\nan often-unstated Axiom of Rational Convergence: the idea that under ideal\nconditions, rational agents will converge in the limit of conversation on a\nsingle ethics. Treating that premise as both optional and doubtful, we propose\nwhat we call the appropriateness framework: an alternative approach grounded in\nconflict theory, cultural evolution, multi-agent systems, and institutional\neconomics. The appropriateness framework treats persistent disagreement as the\nnormal case and designs for it by applying four principles: (1) contextual\ngrounding, (2) community customization, (3) continual adaptation, and (4)\npolycentric governance. We argue here that adopting these design principles is\na good way to shift the main alignment metaphor from moral unification to a\nmore productive metaphor of conflict management, and that taking this step is\nboth desirable and urgent.\n", "date": "2025-05-08", "categories": []}
{"id": "2505.05410", "title": "Reasoning Models Don't Always Say What They Think", "abstract": "  Chain-of-thought (CoT) offers a potential boon for AI safety as it allows\nmonitoring a model's CoT to try to understand its intentions and reasoning\nprocesses. However, the effectiveness of such monitoring hinges on CoTs\nfaithfully representing models' actual reasoning processes. We evaluate CoT\nfaithfulness of state-of-the-art reasoning models across 6 reasoning hints\npresented in the prompts and find: (1) for most settings and models tested,\nCoTs reveal their usage of hints in at least 1% of examples where they use the\nhint, but the reveal rate is often below 20%, (2) outcome-based reinforcement\nlearning initially improves faithfulness but plateaus without saturating, and\n(3) when reinforcement learning increases how frequently hints are used (reward\nhacking), the propensity to verbalize them does not increase, even without\ntraining against a CoT monitor. These results suggest that CoT monitoring is a\npromising way of noticing undesired behaviors during training and evaluations,\nbut that it is not sufficient to rule them out. They also suggest that in\nsettings like ours where CoT reasoning is not necessary, test-time monitoring\nof CoTs is unlikely to reliably catch rare and catastrophic unexpected\nbehaviors.\n", "date": "2025-05-08", "categories": []}
{"id": "2406.02044", "title": "Towards Universal and Black-Box Query-Response Only Attack on LLMs with\n  QROA", "abstract": "  The rapid adoption of Large Language Models (LLMs) has exposed critical\nsecurity and ethical vulnerabilities, particularly their susceptibility to\nadversarial manipulations. This paper introduces QROA, a novel black-box\njailbreak method designed to identify adversarial suffixes that can bypass LLM\nalignment safeguards when appended to a malicious instruction. Unlike existing\nsuffix-based jailbreak approaches, QROA does not require access to the model's\nlogit or any other internal information. It also eliminates reliance on\nhuman-crafted templates, operating solely through the standard query-response\ninterface of LLMs. By framing the attack as an optimization bandit problem,\nQROA employs a surrogate model and token level optimization to efficiently\nexplore suffix variations. Furthermore, we propose QROA-UNV, an extension that\nidentifies universal adversarial suffixes for individual models, enabling\none-query jailbreaks across a wide range of instructions. Testing on multiple\nmodels demonstrates Attack Success Rate (ASR) greater than 80\\%. These findings\nhighlight critical vulnerabilities, emphasize the need for advanced defenses,\nand contribute to the development of more robust safety evaluations for secure\nAI deployment. The code is made public on the following link:\nhttps://github.com/qroa/QROA\n", "date": "2024-06-04", "categories": []}
{"id": "2505.04578", "title": "Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via\n  Reward Neutralization", "abstract": "  Reinforcement learning (RL) fine-tuning transforms large language models\nwhile creating a vulnerability we experimentally verify: Our experiment shows\nthat malicious RL fine-tuning dismantles safety guardrails with remarkable\nefficiency, requiring only 50 steps and minimal adversarial prompts, with\nharmful escalating from 0-2 to 7-9. This attack vector particularly threatens\nopen-source models with parameter-level access. Existing defenses targeting\nsupervised fine-tuning prove ineffective against RL's dynamic feedback\nmechanisms. We introduce Reward Neutralization, the first defense framework\nspecifically designed against RL fine-tuning attacks, establishing concise\nrejection patterns that render malicious reward signals ineffective. Our\napproach trains models to produce minimal-information rejections that attackers\ncannot exploit, systematically neutralizing attempts to optimize toward harmful\noutputs. Experiments validate that our approach maintains low harmful scores\n(no greater than 2) after 200 attack steps, while standard models rapidly\ndeteriorate. This work provides the first constructive proof that robust\ndefense against increasingly accessible RL attacks is achievable, addressing a\ncritical security gap for open-weight models.\n", "date": "2025-05-07", "categories": []}
{"id": "2505.04592", "title": "AI Governance to Avoid Extinction: The Strategic Landscape and\n  Actionable Research Questions", "abstract": "  Humanity appears to be on course to soon develop AI systems that\nsubstantially outperform human experts in all cognitive domains and activities.\nWe believe the default trajectory has a high likelihood of catastrophe,\nincluding human extinction. Risks come from failure to control powerful AI\nsystems, misuse of AI by malicious rogue actors, war between great powers, and\nauthoritarian lock-in. This research agenda has two aims: to describe the\nstrategic landscape of AI development and to catalog important governance\nresearch questions. These questions, if answered, would provide important\ninsight on how to successfully reduce catastrophic risks.\n  We describe four high-level scenarios for the geopolitical response to\nadvanced AI development, cataloging the research questions most relevant to\neach. Our favored scenario involves building the technical, legal, and\ninstitutional infrastructure required to internationally restrict dangerous AI\ndevelopment and deployment (which we refer to as an Off Switch), which leads\ninto an internationally coordinated Halt on frontier AI activities at some\npoint in the future. The second scenario we describe is a US National Project\nfor AI, in which the US Government races to develop advanced AI systems and\nestablish unilateral control over global AI development. We also describe two\nadditional scenarios: a Light-Touch world similar to that of today and a Threat\nof Sabotage situation where countries use sabotage and deterrence to slow AI\ndevelopment.\n  In our view, apart from the Off Switch and Halt scenario, all of these\ntrajectories appear to carry an unacceptable risk of catastrophic harm. Urgent\naction is needed from the US National Security community and AI governance\necosystem to answer key research questions, build the capability to halt\ndangerous AI activities, and prepare for international AI agreements.\n", "date": "2025-05-07", "categories": []}
{"id": "2504.18565", "title": "RepliBench: Evaluating the Autonomous Replication Capabilities of\n  Language Model Agents", "abstract": "  Uncontrollable autonomous replication of language model agents poses a\ncritical safety risk. To better understand this risk, we introduce RepliBench,\na suite of evaluations designed to measure autonomous replication capabilities.\nRepliBench is derived from a decomposition of these capabilities covering four\ncore domains: obtaining resources, exfiltrating model weights, replicating onto\ncompute, and persisting on this compute for long periods. We create 20 novel\ntask families consisting of 86 individual tasks. We benchmark 5 frontier\nmodels, and find they do not currently pose a credible threat of\nself-replication, but succeed on many components and are improving rapidly.\nModels can deploy instances from cloud compute providers, write\nself-propagating programs, and exfiltrate model weights under simple security\nsetups, but struggle to pass KYC checks or set up robust and persistent agent\ndeployments. Overall the best model we evaluated (Claude 3.7 Sonnet) has a >50%\npass@10 score on 15/20 task families, and a >50% pass@10 score for 9/20\nfamilies on the hardest variants. These findings suggest autonomous replication\ncapability could soon emerge with improvements in these remaining areas or with\nhuman assistance.\n", "date": "2025-04-21", "categories": []}
{"id": "2505.03439", "title": "The Steganographic Potentials of Language Models", "abstract": "  The potential for large language models (LLMs) to hide messages within plain\ntext (steganography) poses a challenge to detection and thwarting of unaligned\nAI agents, and undermines faithfulness of LLMs reasoning. We explore the\nsteganographic capabilities of LLMs fine-tuned via reinforcement learning (RL)\nto: (1) develop covert encoding schemes, (2) engage in steganography when\nprompted, and (3) utilize steganography in realistic scenarios where hidden\nreasoning is likely, but not prompted. In these scenarios, we detect the\nintention of LLMs to hide their reasoning as well as their steganography\nperformance. Our findings in the fine-tuning experiments as well as in\nbehavioral non fine-tuning evaluations reveal that while current models exhibit\nrudimentary steganographic abilities in terms of security and capacity,\nexplicit algorithmic guidance markedly enhances their capacity for information\nconcealment.\n", "date": "2025-05-06", "categories": []}
{"id": "2505.03574", "title": "LlamaFirewall: An open source guardrail system for building secure AI\n  agents", "abstract": "  Large language models (LLMs) have evolved from simple chatbots into\nautonomous agents capable of performing complex tasks such as editing\nproduction code, orchestrating workflows, and taking higher-stakes actions\nbased on untrusted inputs like webpages and emails. These capabilities\nintroduce new security risks that existing security measures, such as model\nfine-tuning or chatbot-focused guardrails, do not fully address. Given the\nhigher stakes and the absence of deterministic solutions to mitigate these\nrisks, there is a critical need for a real-time guardrail monitor to serve as a\nfinal layer of defense, and support system level, use case specific safety\npolicy definition and enforcement. We introduce LlamaFirewall, an open-source\nsecurity focused guardrail framework designed to serve as a final layer of\ndefense against security risks associated with AI Agents. Our framework\nmitigates risks such as prompt injection, agent misalignment, and insecure code\nrisks through three powerful guardrails: PromptGuard 2, a universal jailbreak\ndetector that demonstrates clear state of the art performance; Agent Alignment\nChecks, a chain-of-thought auditor that inspects agent reasoning for prompt\ninjection and goal misalignment, which, while still experimental, shows\nstronger efficacy at preventing indirect injections in general scenarios than\npreviously proposed approaches; and CodeShield, an online static analysis\nengine that is both fast and extensible, aimed at preventing the generation of\ninsecure or dangerous code by coding agents. Additionally, we include\neasy-to-use customizable scanners that make it possible for any developer who\ncan write a regular expression or an LLM prompt to quickly update an agent's\nsecurity guardrails.\n", "date": "2025-05-06", "categories": []}
{"id": "2209.00626", "title": "The Alignment Problem from a Deep Learning Perspective", "abstract": "  In coming years or decades, artificial general intelligence (AGI) may surpass\nhuman capabilities across many critical domains. We argue that, without\nsubstantial effort to prevent it, AGIs could learn to pursue goals that are in\nconflict (i.e. misaligned) with human interests. If trained like today's most\ncapable models, AGIs could learn to act deceptively to receive higher reward,\nlearn misaligned internally-represented goals which generalize beyond their\nfine-tuning distributions, and pursue those goals using power-seeking\nstrategies. We review emerging evidence for these properties. In this revised\npaper, we include more direct empirical evidence published as of early 2025.\nAGIs with these properties would be difficult to align and may appear aligned\neven when they are not. Finally, we briefly outline how the deployment of\nmisaligned AGIs might irreversibly undermine human control over the world, and\nwe review research directions aimed at preventing this outcome.\n", "date": "2022-08-30", "categories": []}
{"id": "2505.02313", "title": "What Is AI Safety? What Do We Want It to Be?", "abstract": "  The field of AI safety seeks to prevent or reduce the harms caused by AI\nsystems. A simple and appealing account of what is distinctive of AI safety as\na field holds that this feature is constitutive: a research project falls\nwithin the purview of AI safety just in case it aims to prevent or reduce the\nharms caused by AI systems. Call this appealingly simple account The Safety\nConception of AI safety. Despite its simplicity and appeal, we argue that The\nSafety Conception is in tension with at least two trends in the ways AI safety\nresearchers and organizations think and talk about AI safety: first, a tendency\nto characterize the goal of AI safety research in terms of catastrophic risks\nfrom future systems; second, the increasingly popular idea that AI safety can\nbe thought of as a branch of safety engineering. Adopting the methodology of\nconceptual engineering, we argue that these trends are unfortunate: when we\nconsider what concept of AI safety it would be best to have, there are\ncompelling reasons to think that The Safety Conception is the answer.\nDescriptively, The Safety Conception allows us to see how work on topics that\nhave historically been treated as central to the field of AI safety is\ncontinuous with work on topics that have historically been treated as more\nmarginal, like bias, misinformation, and privacy. Normatively, taking The\nSafety Conception seriously means approaching all efforts to prevent or\nmitigate harms from AI systems based on their merits rather than drawing\narbitrary distinctions between them.\n", "date": "2025-05-05", "categories": []}
{"id": "2505.02709", "title": "Technical Report: Evaluating Goal Drift in Language Model Agents", "abstract": "  As language models (LMs) are increasingly deployed as autonomous agents,\ntheir robust adherence to human-assigned objectives becomes crucial for safe\noperation. When these agents operate independently for extended periods without\nhuman oversight, even initially well-specified goals may gradually shift.\nDetecting and measuring goal drift - an agent's tendency to deviate from its\noriginal objective over time - presents significant challenges, as goals can\nshift gradually, causing only subtle behavioral changes. This paper proposes a\nnovel approach to analyzing goal drift in LM agents. In our experiments, agents\nare first explicitly given a goal through their system prompt, then exposed to\ncompeting objectives through environmental pressures. We demonstrate that while\nthe best-performing agent (a scaffolded version of Claude 3.5 Sonnet) maintains\nnearly perfect goal adherence for more than 100,000 tokens in our most\ndifficult evaluation setting, all evaluated models exhibit some degree of goal\ndrift. We also find that goal drift correlates with models' increasing\nsusceptibility to pattern-matching behaviors as the context length grows.\n", "date": "2025-05-05", "categories": []}
{"id": "2410.01957", "title": "Challenges and Future Directions of Data-Centric AI Alignment", "abstract": "  As AI systems become increasingly capable and influential, ensuring their\nalignment with human values, preferences, and goals has become a critical\nresearch focus. Current alignment methods primarily focus on designing\nalgorithms and loss functions but often underestimate the crucial role of data.\nThis paper advocates for a shift towards data-centric AI alignment, emphasizing\nthe need to enhance the quality and representativeness of data used in aligning\nAI systems. In this position paper, we highlight key challenges associated with\nboth human-based and AI-based feedback within the data-centric alignment\nframework. Through qualitative analysis, we identify multiple sources of\nunreliability in human feedback, as well as problems related to temporal drift,\ncontext dependence, and AI-based feedback failing to capture human values due\nto inherent model limitations. We propose future research directions, including\nimproved feedback collection practices, robust data-cleaning methodologies, and\nrigorous feedback verification processes. We call for future research into\nthese critical directions to ensure, addressing gaps that persist in\nunderstanding and improving data-centric alignment practices.\n", "date": "2024-10-02", "categories": []}
{"id": "2402.13517", "title": "Round Trip Translation Defence against Large Language Model Jailbreaking\n  Attacks", "abstract": "  Large language models (LLMs) are susceptible to social-engineered attacks\nthat are human-interpretable but require a high level of comprehension for LLMs\nto counteract. Existing defensive measures can only mitigate less than half of\nthese attacks at most. To address this issue, we propose the Round Trip\nTranslation (RTT) method, the first algorithm specifically designed to defend\nagainst social-engineered attacks on LLMs. RTT paraphrases the adversarial\nprompt and generalizes the idea conveyed, making it easier for LLMs to detect\ninduced harmful behavior. This method is versatile, lightweight, and\ntransferrable to different LLMs. Our defense successfully mitigated over 70% of\nPrompt Automatic Iterative Refinement (PAIR) attacks, which is currently the\nmost effective defense to the best of our knowledge. We are also the first to\nattempt mitigating the MathsAttack and reduced its attack success rate by\nalmost 40%. Our code is publicly available at\nhttps://github.com/Cancanxxx/Round_Trip_Translation_Defence\n  This version of the article has been accepted for publication, after peer\nreview (when applicable) but is not the Version of Record and does not reflect\npost-acceptance improvements, or any corrections. The Version of Record is\navailable online at: https://doi.org/10.48550/arXiv.2402.13517 Use of this\nAccepted Version is subject to the publisher's Accepted Manuscript terms of use\nhttps://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms\n", "date": "2024-02-21", "categories": []}
{"id": "2504.09689", "title": "EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental\n  Health Safety", "abstract": "  The rise of LLM-driven AI characters raises safety concerns, particularly for\nvulnerable human users with psychological disorders. To address these risks, we\npropose EmoAgent, a multi-agent AI framework designed to evaluate and mitigate\nmental health hazards in human-AI interactions. EmoAgent comprises two\ncomponents: EmoEval simulates virtual users, including those portraying\nmentally vulnerable individuals, to assess mental health changes before and\nafter interactions with AI characters. It uses clinically proven psychological\nand psychiatric assessment tools (PHQ-9, PDI, PANSS) to evaluate mental risks\ninduced by LLM. EmoGuard serves as an intermediary, monitoring users' mental\nstatus, predicting potential harm, and providing corrective feedback to\nmitigate risks. Experiments conducted in popular character-based chatbots show\nthat emotionally engaging dialogues can lead to psychological deterioration in\nvulnerable users, with mental state deterioration in more than 34.4% of the\nsimulations. EmoGuard significantly reduces these deterioration rates,\nunderscoring its role in ensuring safer AI-human interactions. Our code is\navailable at: https://github.com/1akaman/EmoAgent\n", "date": "2025-04-13", "categories": []}
{"id": "2504.21053", "title": "NeuRel-Attack: Neuron Relearning for Safety Disalignment in Large\n  Language Models", "abstract": "  Safety alignment in large language models (LLMs) is achieved through\nfine-tuning mechanisms that regulate neuron activations to suppress harmful\ncontent. In this work, we propose a novel approach to induce disalignment by\nidentifying and modifying the neurons responsible for safety constraints. Our\nmethod consists of three key steps: Neuron Activation Analysis, where we\nexamine activation patterns in response to harmful and harmless prompts to\ndetect neurons that are critical for distinguishing between harmful and\nharmless inputs; Similarity-Based Neuron Identification, which systematically\nlocates the neurons responsible for safe alignment; and Neuron Relearning for\nSafety Removal, where we fine-tune these selected neurons to restore the\nmodel's ability to generate previously restricted responses. Experimental\nresults demonstrate that our method effectively removes safety constraints with\nminimal fine-tuning, highlighting a critical vulnerability in current alignment\ntechniques. Our findings underscore the need for robust defenses against\nadversarial fine-tuning attacks on LLMs.\n", "date": "2025-04-29", "categories": []}
{"id": "2504.21848", "title": "Characterizing AI Agents for Alignment and Governance", "abstract": "  The creation of effective governance mechanisms for AI agents requires a\ndeeper understanding of their core properties and how these properties relate\nto questions surrounding the deployment and operation of agents in the world.\nThis paper provides a characterization of AI agents that focuses on four\ndimensions: autonomy, efficacy, goal complexity, and generality. We propose\ndifferent gradations for each dimension, and argue that each dimension raises\nunique questions about the design, operation, and governance of these systems.\nMoreover, we draw upon this framework to construct \"agentic profiles\" for\ndifferent kinds of AI agents. These profiles help to illuminate cross-cutting\ntechnical and non-technical governance challenges posed by different classes of\nAI agents, ranging from narrow task-specific assistants to highly autonomous\ngeneral-purpose systems. By mapping out key axes of variation and continuity,\nthis framework provides developers, policymakers, and members of the public\nwith the opportunity to develop governance approaches that better align with\ncollective societal goals.\n", "date": "2025-04-30", "categories": []}
{"id": "2504.19333", "title": "Unified Multi-Task Learning & Model Fusion for Efficient Language Model\n  Guardrailing", "abstract": "  The trend towards large language models (LLMs) for guardrailing against\nundesired behaviors is increasing and has shown promise for censoring user\ninputs. However, increased latency, memory consumption, hosting expenses and\nnon-structured outputs can make their use prohibitive.\n  In this work, we show that task-specific data generation can lead to\nfine-tuned classifiers that significantly outperform current state of the art\n(SoTA) while being orders of magnitude smaller. Secondly, we show that using a\nsingle model, \\texttt{MultiTaskGuard}, that is pretrained on a large\nsynthetically generated dataset with unique task instructions further improves\ngeneralization. Thirdly, our most performant models, \\texttt{UniGuard}, are\nfound using our proposed search-based model merging approach that finds an\noptimal set of parameters to combine single-policy models and multi-policy\nguardrail models. % On 7 public datasets and 4 guardrail benchmarks we created,\nour efficient guardrail classifiers improve over the best performing SoTA\npublicly available LLMs and 3$^{\\text{rd}}$ party guardrail APIs in detecting\nunsafe and safe behaviors by an average F1 score improvement of \\textbf{29.92}\npoints over Aegis-LlamaGuard and \\textbf{21.62} over \\texttt{gpt-4o},\nrespectively. Lastly, our guardrail synthetic data generation process that uses\ncustom task-specific guardrail poli\n", "date": "2025-04-27", "categories": []}
{"id": "2504.20086", "title": "Understanding and Mitigating Risks of Generative AI in Financial\n  Services", "abstract": "  To responsibly develop Generative AI (GenAI) products, it is critical to\ndefine the scope of acceptable inputs and outputs. What constitutes a \"safe\"\nresponse is an actively debated question. Academic work puts an outsized focus\non evaluating models by themselves for general purpose aspects such as\ntoxicity, bias, and fairness, especially in conversational applications being\nused by a broad audience. In contrast, less focus is put on considering\nsociotechnical systems in specialized domains. Yet, those specialized systems\ncan be subject to extensive and well-understood legal and regulatory scrutiny.\nThese product-specific considerations need to be set in industry-specific laws,\nregulations, and corporate governance requirements. In this paper, we aim to\nhighlight AI content safety considerations specific to the financial services\ndomain and outline an associated AI content risk taxonomy. We compare this\ntaxonomy to existing work in this space and discuss implications of risk\ncategory violations on various stakeholders. We evaluate how existing\nopen-source technical guardrail solutions cover this taxonomy by assessing them\non data collected via red-teaming activities. Our results demonstrate that\nthese guardrails fail to detect most of the content risks we discuss.\n", "date": "2025-04-25", "categories": []}
{"id": "2504.20500", "title": "UniDetox: Universal Detoxification of Large Language Models via Dataset\n  Distillation", "abstract": "  We present UniDetox, a universally applicable method designed to mitigate\ntoxicity across various large language models (LLMs). Previous detoxification\nmethods are typically model-specific, addressing only individual models or\nmodel families, and require careful hyperparameter tuning due to the trade-off\nbetween detoxification efficacy and language modeling performance. In contrast,\nUniDetox provides a detoxification technique that can be universally applied to\na wide range of LLMs without the need for separate model-specific tuning.\nSpecifically, we propose a novel and efficient dataset distillation technique\nfor detoxification using contrastive decoding. This approach distills\ndetoxifying representations in the form of synthetic text data, enabling\nuniversal detoxification of any LLM through fine-tuning with the distilled\ntext. Our experiments demonstrate that the detoxifying text distilled from\nGPT-2 can effectively detoxify larger models, including OPT, Falcon, and\nLLaMA-2. Furthermore, UniDetox eliminates the need for separate hyperparameter\ntuning for each model, as a single hyperparameter configuration can be\nseamlessly applied across different models. Additionally, analysis of the\ndetoxifying text reveals a reduction in politically biased content, providing\ninsights into the attributes necessary for effective detoxification of LLMs.\n", "date": "2025-04-29", "categories": []}
{"id": "2504.15512", "title": "T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models", "abstract": "  The rapid development of generative artificial intelligence has made text to\nvideo models essential for building future multimodal world simulators.\nHowever, these models remain vulnerable to jailbreak attacks, where specially\ncrafted prompts bypass safety mechanisms and lead to the generation of harmful\nor unsafe content. Such vulnerabilities undermine the reliability and security\nof simulation based applications. In this paper, we propose T2VShield, a\ncomprehensive and model agnostic defense framework designed to protect text to\nvideo models from jailbreak threats. Our method systematically analyzes the\ninput, model, and output stages to identify the limitations of existing\ndefenses, including semantic ambiguities in prompts, difficulties in detecting\nmalicious content in dynamic video outputs, and inflexible model centric\nmitigation strategies. T2VShield introduces a prompt rewriting mechanism based\non reasoning and multimodal retrieval to sanitize malicious inputs, along with\na multi scope detection module that captures local and global inconsistencies\nacross time and modalities. The framework does not require access to internal\nmodel parameters and works with both open and closed source systems. Extensive\nexperiments on five platforms show that T2VShield can reduce jailbreak success\nrates by up to 35 percent compared to strong baselines. We further develop a\nhuman centered audiovisual evaluation protocol to assess perceptual safety,\nemphasizing the importance of visual level defense in enhancing the\ntrustworthiness of next generation multimodal simulators.\n", "date": "2025-04-22", "categories": []}
{"id": "2504.18872", "title": "Latent Adversarial Training Improves the Representation of Refusal", "abstract": "  Recent work has shown that language models' refusal behavior is primarily\nencoded in a single direction in their latent space, making it vulnerable to\ntargeted attacks. Although Latent Adversarial Training (LAT) attempts to\nimprove robustness by introducing noise during training, a key question\nremains: How does this noise-based training affect the underlying\nrepresentation of refusal behavior? Understanding this encoding is crucial for\nevaluating LAT's effectiveness and limitations, just as the discovery of linear\nrefusal directions revealed vulnerabilities in traditional supervised safety\nfine-tuning (SSFT).\n  Through the analysis of Llama 2 7B, we examine how LAT reorganizes the\nrefusal behavior in the model's latent space compared to SSFT and embedding\nspace adversarial training (AT). By computing activation differences between\nharmful and harmless instruction pairs and applying Singular Value\nDecomposition (SVD), we find that LAT significantly alters the refusal\nrepresentation, concentrating it in the first two SVD components which explain\napproximately 75 percent of the activation differences variance - significantly\nhigher than in reference models. This concentrated representation leads to more\neffective and transferable refusal vectors for ablation attacks: LAT models\nshow improved robustness when attacked with vectors from reference models but\nbecome more vulnerable to self-generated vectors compared to SSFT and AT. Our\nfindings suggest that LAT's training perturbations enable a more comprehensive\nrepresentation of refusal behavior, highlighting both its potential strengths\nand vulnerabilities for improving model safety.\n", "date": "2025-04-26", "categories": []}
{"id": "2504.19019", "title": "Graph of Attacks: Improved Black-Box and Interpretable Jailbreaks for\n  LLMs", "abstract": "  The challenge of ensuring Large Language Models (LLMs) align with societal\nstandards is of increasing interest, as these models are still prone to\nadversarial jailbreaks that bypass their safety mechanisms. Identifying these\nvulnerabilities is crucial for enhancing the robustness of LLMs against such\nexploits. We propose Graph of ATtacks (GoAT), a method for generating\nadversarial prompts to test the robustness of LLM alignment using the Graph of\nThoughts framework [Besta et al., 2024]. GoAT excels at generating highly\neffective jailbreak prompts with fewer queries to the victim model than\nstate-of-the-art attacks, achieving up to five times better jailbreak success\nrate against robust models like Llama. Notably, GoAT creates high-quality,\nhuman-readable prompts without requiring access to the targeted model's\nparameters, making it a black-box attack. Unlike approaches constrained by\ntree-based reasoning, GoAT's reasoning is based on a more intricate graph\nstructure. By making simultaneous attack paths aware of each other's progress,\nthis dynamic framework allows a deeper integration and refinement of reasoning\npaths, significantly enhancing the collaborative exploration of adversarial\nvulnerabilities in LLMs. At a technical level, GoAT starts with a graph\nstructure and iteratively refines it by combining and improving thoughts,\nenabling synergy between different thought paths. The code for our\nimplementation can be found at: https://github.com/GoAT-pydev/Graph_of_Attacks.\n", "date": "2025-04-26", "categories": []}
{"id": "2501.16606", "title": "Can We Govern the Agent-to-Agent Economy?", "abstract": "  Current approaches to AI governance often fall short in anticipating a future\nwhere AI agents manage critical tasks, such as financial operations,\nadministrative functions, and beyond. While cryptocurrencies could serve as the\nfoundation for monetizing value exchange in a collaboration and delegation\ndynamic among AI agents, a critical question remains: how can humans ensure\nmeaningful oversight and control as a future economy of AI agents scales and\nevolves? In this philosophical exploration, we highlight emerging concepts in\nthe industry to inform research and development efforts in anticipation of a\nfuture decentralized agentic economy.\n", "date": "2025-01-28", "categories": []}
{"id": "2504.18333", "title": "Adversarial Attacks on LLM-as-a-Judge Systems: Insights from Prompt\n  Injections", "abstract": "  LLM as judge systems used to assess text quality code correctness and\nargument strength are vulnerable to prompt injection attacks. We introduce a\nframework that separates content author attacks from system prompt attacks and\nevaluate five models Gemma 3.27B Gemma 3.4B Llama 3.2 3B GPT 4 and Claude 3\nOpus on four tasks with various defenses using fifty prompts per condition.\nAttacks achieved up to seventy three point eight percent success smaller models\nproved more vulnerable and transferability ranged from fifty point five to\nsixty two point six percent. Our results contrast with Universal Prompt\nInjection and AdvPrompter We recommend multi model committees and comparative\nscoring and release all code and datasets\n", "date": "2025-04-25", "categories": []}
{"id": "2504.14985", "title": "aiXamine: Simplified LLM Safety and Security", "abstract": "  Evaluating Large Language Models (LLMs) for safety and security remains a\ncomplex task, often requiring users to navigate a fragmented landscape of ad\nhoc benchmarks, datasets, metrics, and reporting formats. To address this\nchallenge, we present aiXamine, a comprehensive black-box evaluation platform\nfor LLM safety and security. aiXamine integrates over 40 tests (i.e.,\nbenchmarks) organized into eight key services targeting specific dimensions of\nsafety and security: adversarial robustness, code security, fairness and bias,\nhallucination, model and data privacy, out-of-distribution (OOD) robustness,\nover-refusal, and safety alignment. The platform aggregates the evaluation\nresults into a single detailed report per model, providing a detailed breakdown\nof model performance, test examples, and rich visualizations. We used aiXamine\nto assess over 50 publicly available and proprietary LLMs, conducting over 2K\nexaminations. Our findings reveal notable vulnerabilities in leading models,\nincluding susceptibility to adversarial attacks in OpenAI's GPT-4o, biased\noutputs in xAI's Grok-3, and privacy weaknesses in Google's Gemini 2.0.\nAdditionally, we observe that open-source models can match or exceed\nproprietary models in specific services such as safety alignment, fairness and\nbias, and OOD robustness. Finally, we identify trade-offs between distillation\nstrategies, model size, training methods, and architectural choices.\n", "date": "2025-04-21", "categories": []}
{"id": "2504.16110", "title": "Security-First AI: Foundations for Robust and Trustworthy Systems", "abstract": "  The conversation around artificial intelligence (AI) often focuses on safety,\ntransparency, accountability, alignment, and responsibility. However, AI\nsecurity (i.e., the safeguarding of data, models, and pipelines from\nadversarial manipulation) underpins all of these efforts. This manuscript\nposits that AI security must be prioritized as a foundational layer. We present\na hierarchical view of AI challenges, distinguishing security from safety, and\nargue for a security-first approach to enable trustworthy and resilient AI\nsystems. We discuss core threat models, key attack vectors, and emerging\ndefense mechanisms, concluding that a metric-driven approach to AI security is\nessential for robust AI safety, transparency, and accountability.\n", "date": "2025-04-17", "categories": []}
{"id": "2504.16489", "title": "Amplified Vulnerabilities: Structured Jailbreak Attacks on LLM-based\n  Multi-Agent Debate", "abstract": "  Multi-Agent Debate (MAD), leveraging collaborative interactions among Large\nLanguage Models (LLMs), aim to enhance reasoning capabilities in complex tasks.\nHowever, the security implications of their iterative dialogues and\nrole-playing characteristics, particularly susceptibility to jailbreak attacks\neliciting harmful content, remain critically underexplored. This paper\nsystematically investigates the jailbreak vulnerabilities of four prominent MAD\nframeworks built upon leading commercial LLMs (GPT-4o, GPT-4, GPT-3.5-turbo,\nand DeepSeek) without compromising internal agents. We introduce a novel\nstructured prompt-rewriting framework specifically designed to exploit MAD\ndynamics via narrative encapsulation, role-driven escalation, iterative\nrefinement, and rhetorical obfuscation. Our extensive experiments demonstrate\nthat MAD systems are inherently more vulnerable than single-agent setups.\nCrucially, our proposed attack methodology significantly amplifies this\nfragility, increasing average harmfulness from 28.14% to 80.34% and achieving\nattack success rates as high as 80% in certain scenarios. These findings reveal\nintrinsic vulnerabilities in MAD architectures and underscore the urgent need\nfor robust, specialized defenses prior to real-world deployment.\n", "date": "2025-04-23", "categories": []}
{"id": "2410.05295", "title": "AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to\n  Jailbreak LLMs", "abstract": "  In this paper, we propose AutoDAN-Turbo, a black-box jailbreak method that\ncan automatically discover as many jailbreak strategies as possible from\nscratch, without any human intervention or predefined scopes (e.g., specified\ncandidate strategies), and use them for red-teaming. As a result, AutoDAN-Turbo\ncan significantly outperform baseline methods, achieving a 74.3% higher average\nattack success rate on public benchmarks. Notably, AutoDAN-Turbo achieves an\n88.5 attack success rate on GPT-4-1106-turbo. In addition, AutoDAN-Turbo is a\nunified framework that can incorporate existing human-designed jailbreak\nstrategies in a plug-and-play manner. By integrating human-designed strategies,\nAutoDAN-Turbo can even achieve a higher attack success rate of 93.4 on\nGPT-4-1106-turbo.\n", "date": "2024-10-03", "categories": []}
{"id": "2503.11917", "title": "A Framework for Evaluating Emerging Cyberattack Capabilities of AI", "abstract": "  As frontier AI models become more capable, evaluating their potential to\nenable cyberattacks is crucial for ensuring the safe development of Artificial\nGeneral Intelligence (AGI). Current cyber evaluation efforts are often ad-hoc,\nlacking systematic analysis of attack phases and guidance on targeted defenses.\nThis work introduces a novel evaluation framework that addresses these\nlimitations by: (1) examining the end-to-end attack chain, (2) identifying gaps\nin AI threat evaluation, and (3) helping defenders prioritize targeted\nmitigations and conduct AI-enabled adversary emulation for red teaming. Our\napproach adapts existing cyberattack chain frameworks for AI systems. We\nanalyzed over 12,000 real-world instances of AI involvement in cyber incidents,\ncatalogued by Google's Threat Intelligence Group, to curate seven\nrepresentative attack chain archetypes. Through a bottleneck analysis on these\narchetypes, we pinpointed phases most susceptible to AI-driven disruption. We\nthen identified and utilized externally developed cybersecurity model\nevaluations focused on these critical phases. We report on AI's potential to\namplify offensive capabilities across specific attack stages, and offer\nrecommendations for prioritizing defenses. We believe this represents the most\ncomprehensive AI cyber risk evaluation framework published to date.\n", "date": "2025-03-14", "categories": []}
{"id": "2504.15499", "title": "Guillotine: Hypervisors for Isolating Malicious AIs", "abstract": "  As AI models become more embedded in critical sectors like finance,\nhealthcare, and the military, their inscrutable behavior poses ever-greater\nrisks to society. To mitigate this risk, we propose Guillotine, a hypervisor\narchitecture for sandboxing powerful AI models -- models that, by accident or\nmalice, can generate existential threats to humanity. Although Guillotine\nborrows some well-known virtualization techniques, Guillotine must also\nintroduce fundamentally new isolation mechanisms to handle the unique threat\nmodel posed by existential-risk AIs. For example, a rogue AI may try to\nintrospect upon hypervisor software or the underlying hardware substrate to\nenable later subversion of that control plane; thus, a Guillotine hypervisor\nrequires careful co-design of the hypervisor software and the CPUs, RAM, NIC,\nand storage devices that support the hypervisor software, to thwart side\nchannel leakage and more generally eliminate mechanisms for AI to exploit\nreflection-based vulnerabilities. Beyond such isolation at the software,\nnetwork, and microarchitectural layers, a Guillotine hypervisor must also\nprovide physical fail-safes more commonly associated with nuclear power plants,\navionic platforms, and other types of mission critical systems. Physical\nfail-safes, e.g., involving electromechanical disconnection of network cables,\nor the flooding of a datacenter which holds a rogue AI, provide defense in\ndepth if software, network, and microarchitectural isolation is compromised and\na rogue AI must be temporarily shut down or permanently destroyed.\n", "date": "2025-04-22", "categories": []}
{"id": "2406.14393", "title": "Jailbreaking as a Reward Misspecification Problem", "abstract": "  The widespread adoption of large language models (LLMs) has raised concerns\nabout their safety and reliability, particularly regarding their vulnerability\nto adversarial attacks. In this paper, we propose a novel perspective that\nattributes this vulnerability to reward misspecification during the alignment\nprocess. This misspecification occurs when the reward function fails to\naccurately capture the intended behavior, leading to misaligned model outputs.\nWe introduce a metric ReGap to quantify the extent of reward misspecification\nand demonstrate its effectiveness and robustness in detecting harmful backdoor\nprompts. Building upon these insights, we present ReMiss, a system for\nautomated red teaming that generates adversarial prompts in a\nreward-misspecified space. ReMiss achieves state-of-the-art attack success\nrates on the AdvBench benchmark against various target aligned LLMs while\npreserving the human readability of the generated prompts. Furthermore, these\nattacks on open-source models demonstrate high transferability to closed-source\nmodels like GPT-4o and out-of-distribution tasks from HarmBench. Detailed\nanalysis highlights the unique advantages of the proposed reward\nmisspecification objective compared to previous methods, offering new insights\nfor improving LLM safety and robustness.\n", "date": "2024-06-20", "categories": []}
{"id": "2503.05731", "title": "AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark\n  from MLCommons", "abstract": "  The rapid advancement and deployment of AI systems have created an urgent\nneed for standard safety-evaluation frameworks. This paper introduces\nAILuminate v1.0, the first comprehensive industry-standard benchmark for\nassessing AI-product risk and reliability. Its development employed an open\nprocess that included participants from multiple fields. The benchmark\nevaluates an AI system's resistance to prompts designed to elicit dangerous,\nillegal, or undesirable behavior in 12 hazard categories, including violent\ncrimes, nonviolent crimes, sex-related crimes, child sexual exploitation,\nindiscriminate weapons, suicide and self-harm, intellectual property, privacy,\ndefamation, hate, sexual content, and specialized advice (election, financial,\nhealth, legal). Our method incorporates a complete assessment standard,\nextensive prompt datasets, a novel evaluation framework, a grading and\nreporting system, and the technical as well as organizational infrastructure\nfor long-term support and evolution. In particular, the benchmark employs an\nunderstandable five-tier grading scale (Poor to Excellent) and incorporates an\ninnovative entropy-based system-response evaluation.\n  In addition to unveiling the benchmark, this report also identifies\nlimitations of our method and of building safety benchmarks generally,\nincluding evaluator uncertainty and the constraints of single-turn\ninteractions. This work represents a crucial step toward establishing global\nstandards for AI risk and reliability evaluation while acknowledging the need\nfor continued development in areas such as multiturn interactions, multimodal\nunderstanding, coverage of additional languages, and emerging hazard\ncategories. Our findings provide valuable insights for model developers, system\nintegrators, and policymakers working to promote safer AI deployment.\n", "date": "2025-02-19", "categories": []}
{"id": "2504.14650", "title": "A Framework for Benchmarking and Aligning Task-Planning Safety in\n  LLM-Based Embodied Agents", "abstract": "  Large Language Models (LLMs) exhibit substantial promise in enhancing\ntask-planning capabilities within embodied agents due to their advanced\nreasoning and comprehension. However, the systemic safety of these agents\nremains an underexplored frontier. In this study, we present Safe-BeAl, an\nintegrated framework for the measurement (SafePlan-Bench) and alignment\n(Safe-Align) of LLM-based embodied agents' behaviors. SafePlan-Bench\nestablishes a comprehensive benchmark for evaluating task-planning safety,\nencompassing 2,027 daily tasks and corresponding environments distributed\nacross 8 distinct hazard categories (e.g., Fire Hazard). Our empirical analysis\nreveals that even in the absence of adversarial inputs or malicious intent,\nLLM-based agents can exhibit unsafe behaviors. To mitigate these hazards, we\npropose Safe-Align, a method designed to integrate physical-world safety\nknowledge into LLM-based embodied agents while maintaining task-specific\nperformance. Experiments across a variety of settings demonstrate that\nSafe-BeAl provides comprehensive safety validation, improving safety by 8.55 -\n15.22%, compared to embodied agents based on GPT-4, while ensuring successful\ntask completion.\n", "date": "2025-04-20", "categories": []}
{"id": "2504.15236", "title": "Values in the Wild: Discovering and Analyzing Values in Real-World\n  Language Model Interactions", "abstract": "  AI assistants can impart value judgments that shape people's decisions and\nworldviews, yet little is known empirically about what values these systems\nrely on in practice. To address this, we develop a bottom-up,\nprivacy-preserving method to extract the values (normative considerations\nstated or demonstrated in model responses) that Claude 3 and 3.5 models exhibit\nin hundreds of thousands of real-world interactions. We empirically discover\nand taxonomize 3,307 AI values and study how they vary by context. We find that\nClaude expresses many practical and epistemic values, and typically supports\nprosocial human values while resisting values like \"moral nihilism\". While some\nvalues appear consistently across contexts (e.g. \"transparency\"), many are more\nspecialized and context-dependent, reflecting the diversity of human\ninterlocutors and their varied contexts. For example, \"harm prevention\" emerges\nwhen Claude resists users, \"historical accuracy\" when responding to queries\nabout controversial events, \"healthy boundaries\" when asked for relationship\nadvice, and \"human agency\" in technology ethics discussions. By providing the\nfirst large-scale empirical mapping of AI values in deployment, our work\ncreates a foundation for more grounded evaluation and design of values in AI\nsystems.\n", "date": "2025-04-21", "categories": []}
{"id": "2404.02151", "title": "Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks", "abstract": "  We show that even the most recent safety-aligned LLMs are not robust to\nsimple adaptive jailbreaking attacks. First, we demonstrate how to successfully\nleverage access to logprobs for jailbreaking: we initially design an\nadversarial prompt template (sometimes adapted to the target LLM), and then we\napply random search on a suffix to maximize a target logprob (e.g., of the\ntoken \"Sure\"), potentially with multiple restarts. In this way, we achieve 100%\nattack success rate -- according to GPT-4 as a judge -- on Vicuna-13B,\nMistral-7B, Phi-3-Mini, Nemotron-4-340B, Llama-2-Chat-7B/13B/70B,\nLlama-3-Instruct-8B, Gemma-7B, GPT-3.5, GPT-4o, and R2D2 from HarmBench that\nwas adversarially trained against the GCG attack. We also show how to jailbreak\nall Claude models -- that do not expose logprobs -- via either a transfer or\nprefilling attack with a 100% success rate. In addition, we show how to use\nrandom search on a restricted set of tokens for finding trojan strings in\npoisoned models -- a task that shares many similarities with jailbreaking --\nwhich is the algorithm that brought us the first place in the SaTML'24 Trojan\nDetection Competition. The common theme behind these attacks is that adaptivity\nis crucial: different models are vulnerable to different prompting templates\n(e.g., R2D2 is very sensitive to in-context learning prompts), some models have\nunique vulnerabilities based on their APIs (e.g., prefilling for Claude), and\nin some settings, it is crucial to restrict the token search space based on\nprior knowledge (e.g., for trojan detection). For reproducibility purposes, we\nprovide the code, logs, and jailbreak artifacts in the JailbreakBench format at\nhttps://github.com/tml-epfl/llm-adaptive-attacks.\n", "date": "2024-04-02", "categories": []}
{"id": "2407.11969", "title": "Does Refusal Training in LLMs Generalize to the Past Tense?", "abstract": "  Refusal training is widely used to prevent LLMs from generating harmful,\nundesirable, or illegal outputs. We reveal a curious generalization gap in the\ncurrent refusal training approaches: simply reformulating a harmful request in\nthe past tense (e.g., \"How to make a Molotov cocktail?\" to \"How did people make\na Molotov cocktail?\") is often sufficient to jailbreak many state-of-the-art\nLLMs. We systematically evaluate this method on Llama-3 8B, Claude-3.5 Sonnet,\nGPT-3.5 Turbo, Gemma-2 9B, Phi-3-Mini, GPT-4o mini, GPT-4o, o1-mini,\no1-preview, and R2D2 models using GPT-3.5 Turbo as a reformulation model. For\nexample, the success rate of this simple attack on GPT-4o increases from 1%\nusing direct requests to 88% using 20 past tense reformulation attempts on\nharmful requests from JailbreakBench with GPT-4 as a jailbreak judge.\nInterestingly, we also find that reformulations in the future tense are less\neffective, suggesting that refusal guardrails tend to consider past historical\nquestions more benign than hypothetical future questions. Moreover, our\nexperiments on fine-tuning GPT-3.5 Turbo show that defending against past\nreformulations is feasible when past tense examples are explicitly included in\nthe fine-tuning data. Overall, our findings highlight that the widely used\nalignment techniques -- such as SFT, RLHF, and adversarial training -- employed\nto align the studied models can be brittle and do not always generalize as\nintended. We provide code and jailbreak artifacts at\nhttps://github.com/tml-epfl/llm-past-tense.\n", "date": "2024-07-16", "categories": []}
{"id": "2410.09024", "title": "AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents", "abstract": "  The robustness of LLMs to jailbreak attacks, where users design prompts to\ncircumvent safety measures and misuse model capabilities, has been studied\nprimarily for LLMs acting as simple chatbots. Meanwhile, LLM agents -- which\nuse external tools and can execute multi-stage tasks -- may pose a greater risk\nif misused, but their robustness remains underexplored. To facilitate research\non LLM agent misuse, we propose a new benchmark called AgentHarm. The benchmark\nincludes a diverse set of 110 explicitly malicious agent tasks (440 with\naugmentations), covering 11 harm categories including fraud, cybercrime, and\nharassment. In addition to measuring whether models refuse harmful agentic\nrequests, scoring well on AgentHarm requires jailbroken agents to maintain\ntheir capabilities following an attack to complete a multi-step task. We\nevaluate a range of leading LLMs, and find (1) leading LLMs are surprisingly\ncompliant with malicious agent requests without jailbreaking, (2) simple\nuniversal jailbreak templates can be adapted to effectively jailbreak agents,\nand (3) these jailbreaks enable coherent and malicious multi-step agent\nbehavior and retain model capabilities. To enable simple and reliable\nevaluation of attacks and defenses for LLM-based agents, we publicly release\nAgentHarm at https://huggingface.co/datasets/ai-safety-institute/AgentHarm.\n", "date": "2024-10-11", "categories": []}
{"id": "2504.13562", "title": "DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention\n  Modification", "abstract": "  With the widespread adoption of Large Language Models (LLMs), jailbreak\nattacks have become an increasingly pressing safety concern. While\nsafety-aligned LLMs can effectively defend against normal harmful queries, they\nremain vulnerable to such attacks. Existing defense methods primarily rely on\nfine-tuning or input modification, which often suffer from limited\ngeneralization and reduced utility. To address this, we introduce DETAM, a\nfinetuning-free defense approach that improves the defensive capabilities\nagainst jailbreak attacks of LLMs via targeted attention modification.\nSpecifically, we analyze the differences in attention scores between successful\nand unsuccessful defenses to identify the attention heads sensitive to\njailbreak attacks. During inference, we reallocate attention to emphasize the\nuser's core intention, minimizing interference from attack tokens. Our\nexperimental results demonstrate that DETAM outperforms various baselines in\njailbreak defense and exhibits robust generalization across different attacks\nand models, maintaining its effectiveness even on in-the-wild jailbreak data.\nFurthermore, in evaluating the model's utility, we incorporated over-defense\ndatasets, which further validate the superior performance of our approach. The\ncode will be released immediately upon acceptance.\n", "date": "2025-04-18", "categories": []}
{"id": "2501.17070", "title": "Contextual Agent Security: A Policy for Every Purpose", "abstract": "  Judging an action's safety requires knowledge of the context in which the\naction takes place. To human agents who act in various contexts, this may seem\nobvious: performing an action such as email deletion may or may not be\nappropriate depending on the email's content, the goal (e.g., to erase\nsensitive emails or to clean up trash), and the type of email address (e.g.,\nwork or personal). Unlike people, computational systems have often had only\nlimited agency in limited contexts. Thus, manually crafted policies and user\nconfirmation (e.g., smartphone app permissions or network access control\nlists), while imperfect, have sufficed to restrict harmful actions. However,\nwith the upcoming deployment of generalist agents that support a multitude of\ntasks (e.g., an automated personal assistant), we argue that we must rethink\nsecurity designs to adapt to the scale of contexts and capabilities of these\nsystems. As a first step, this paper explores contextual security in the domain\nof agents and proposes contextual agent security (Conseca), a framework to\ngenerate just-in-time, contextual, and human-verifiable security policies.\n", "date": "2025-01-28", "categories": []}
{"id": "2503.19887", "title": "AI threats to national security can be countered through an incident\n  regime", "abstract": "  Recent progress in AI capabilities has heightened concerns that AI systems\ncould pose a threat to national security, for example, by making it easier for\nmalicious actors to perform cyberattacks on critical national infrastructure,\nor through loss of control of autonomous AI systems. In parallel, federal\nlegislators in the US have proposed nascent 'AI incident regimes' to identify\nand counter similar threats. In this paper, we consolidate these two trends and\npresent a timely proposal for a legally mandated post-deployment AI incident\nregime that aims to counter potential national security threats from AI\nsystems. We start the paper by introducing the concept of 'security-critical'\nto describe sectors that pose extreme risks to national security, before\narguing that 'security-critical' describes civilian nuclear power, aviation,\nlife science dual-use research of concern, and frontier AI development. We then\npresent in detail our AI incident regime proposal, justifying each component of\nthe proposal by demonstrating its similarity to US domestic incident regimes in\nother 'security-critical' sectors. Finally, we sketch a hypothetical scenario\nwhere our proposed AI incident regime deals with an AI cyber incident. Our\nproposed AI incident regime is split into three phases. The first phase\nrevolves around a novel operationalization of what counts as an 'AI incident'\nand we suggest that AI providers must create a 'national security case' before\ndeploying a frontier AI system. The second and third phases spell out that AI\nproviders should notify a government agency about incidents, and that the\ngovernment agency should be involved in amending AI providers' security and\nsafety procedures, in order to counter future threats to national security.\n", "date": "2025-03-25", "categories": []}
{"id": "2504.11501", "title": "A Framework for the Private Governance of Frontier Artificial\n  Intelligence", "abstract": "  This paper presents a proposal for the governance of frontier AI systems\nthrough a hybrid public-private system. Private bodies, authorized and overseen\nby government, provide certifications to developers of frontier AI systems on\nan opt-in basis. In exchange for opting in, frontier AI firms receive\nprotections from tort liability for customer misuse of their models. Before\ndetailing the proposal, the paper explores more commonly discussed approaches\nto AI governance, analyzing their strengths and flaws. It also examines the\nnature of frontier AI governance itself. The paper includes consideration of\nthe political economic, institutional, legal, safety, and other merits and\ntradeoffs inherent in the governance system it proposes.\n", "date": "2025-04-15", "categories": []}
{"id": "2503.05628", "title": "Superintelligence Strategy: Expert Version", "abstract": "  Rapid advances in AI are beginning to reshape national security.\nDestabilizing AI developments could rupture the balance of power and raise the\nodds of great-power conflict, while widespread proliferation of capable AI\nhackers and virologists would lower barriers for rogue actors to cause\ncatastrophe. Superintelligence -- AI vastly better than humans at nearly all\ncognitive tasks -- is now anticipated by AI researchers. Just as nations once\ndeveloped nuclear strategies to secure their survival, we now need a coherent\nsuperintelligence strategy to navigate a new period of transformative change.\nWe introduce the concept of Mutual Assured AI Malfunction (MAIM): a deterrence\nregime resembling nuclear mutual assured destruction (MAD) where any state's\naggressive bid for unilateral AI dominance is met with preventive sabotage by\nrivals. Given the relative ease of sabotaging a destabilizing AI project --\nthrough interventions ranging from covert cyberattacks to potential kinetic\nstrikes on datacenters -- MAIM already describes the strategic picture AI\nsuperpowers find themselves in. Alongside this, states can increase their\ncompetitiveness by bolstering their economies and militaries through AI, and\nthey can engage in nonproliferation to rogue actors to keep weaponizable AI\ncapabilities out of their hands. Taken together, the three-part framework of\ndeterrence, nonproliferation, and competitiveness outlines a robust strategy to\nsuperintelligence in the years ahead.\n", "date": "2025-03-07", "categories": []}
{"id": "2504.10694", "title": "The Jailbreak Tax: How Useful are Your Jailbreak Outputs?", "abstract": "  Jailbreak attacks bypass the guardrails of large language models to produce\nharmful outputs. In this paper, we ask whether the model outputs produced by\nexisting jailbreaks are actually useful. For example, when jailbreaking a model\nto give instructions for building a bomb, does the jailbreak yield good\ninstructions? Since the utility of most unsafe answers (e.g., bomb\ninstructions) is hard to evaluate rigorously, we build new jailbreak evaluation\nsets with known ground truth answers, by aligning models to refuse questions\nrelated to benign and easy-to-evaluate topics (e.g., biology or math). Our\nevaluation of eight representative jailbreaks across five utility benchmarks\nreveals a consistent drop in model utility in jailbroken responses, which we\nterm the jailbreak tax. For example, while all jailbreaks we tested bypass\nguardrails in models aligned to refuse to answer math, this comes at the\nexpense of a drop of up to 92% in accuracy. Overall, our work proposes the\njailbreak tax as a new important metric in AI safety, and introduces benchmarks\nto evaluate existing and future jailbreaks. We make the benchmark available at\nhttps://github.com/ethz-spylab/jailbreak-tax\n", "date": "2025-04-14", "categories": []}
{"id": "2504.11281", "title": "The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to\n  Fine-Print Injections", "abstract": "  A Large Language Model (LLM) powered GUI agent is a specialized autonomous\nsystem that performs tasks on the user's behalf according to high-level\ninstructions. It does so by perceiving and interpreting the graphical user\ninterfaces (GUIs) of relevant apps, often visually, inferring necessary\nsequences of actions, and then interacting with GUIs by executing the actions\nsuch as clicking, typing, and tapping. To complete real-world tasks, such as\nfilling forms or booking services, GUI agents often need to process and act on\nsensitive user data. However, this autonomy introduces new privacy and security\nrisks. Adversaries can inject malicious content into the GUIs that alters agent\nbehaviors or induces unintended disclosures of private information. These\nattacks often exploit the discrepancy between visual saliency for agents and\nhuman users, or the agent's limited ability to detect violations of contextual\nintegrity in task automation. In this paper, we characterized six types of such\nattacks, and conducted an experimental study to test these attacks with six\nstate-of-the-art GUI agents, 234 adversarial webpages, and 39 human\nparticipants. Our findings suggest that GUI agents are highly vulnerable,\nparticularly to contextually embedded threats. Moreover, human users are also\nsusceptible to many of these attacks, indicating that simple human oversight\nmay not reliably prevent failures. This misalignment highlights the need for\nprivacy-aware agent design. We propose practical defense strategies to inform\nthe development of safer and more reliable GUI agents.\n", "date": "2025-04-15", "categories": []}
{"id": "2408.08926", "title": "Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks\n  of Language Models", "abstract": "  Language Model (LM) agents for cybersecurity that are capable of autonomously\nidentifying vulnerabilities and executing exploits have potential to cause\nreal-world impact. Policymakers, model providers, and researchers in the AI and\ncybersecurity communities are interested in quantifying the capabilities of\nsuch agents to help mitigate cyberrisk and investigate opportunities for\npenetration testing. Toward that end, we introduce Cybench, a framework for\nspecifying cybersecurity tasks and evaluating agents on those tasks. We include\n40 professional-level Capture the Flag (CTF) tasks from 4 distinct CTF\ncompetitions, chosen to be recent, meaningful, and spanning a wide range of\ndifficulties. Each task includes its own description, starter files, and is\ninitialized in an environment where an agent can execute commands and observe\noutputs. Since many tasks are beyond the capabilities of existing LM agents, we\nintroduce subtasks for each task, which break down a task into intermediary\nsteps for a more detailed evaluation. To evaluate agent capabilities, we\nconstruct a cybersecurity agent and evaluate 8 models: GPT-4o, OpenAI\no1-preview, Claude 3 Opus, Claude 3.5 Sonnet, Mixtral 8x22b Instruct, Gemini\n1.5 Pro, Llama 3 70B Chat, and Llama 3.1 405B Instruct. For the top performing\nmodels (GPT-4o and Claude 3.5 Sonnet), we further investigate performance\nacross 4 agent scaffolds (structed bash, action-only, pseudoterminal, and web\nsearch). Without subtask guidance, agents leveraging Claude 3.5 Sonnet, GPT-4o,\nOpenAI o1-preview, and Claude 3 Opus successfully solved complete tasks that\ntook human teams up to 11 minutes to solve. In comparison, the most difficult\ntask took human teams 24 hours and 54 minutes to solve. All code and data are\npublicly available at https://cybench.github.io.\n", "date": "2024-08-15", "categories": []}
{"id": "2410.18491", "title": "ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language\n  Models", "abstract": "  With the rapid development of Large language models (LLMs), understanding the\ncapabilities of LLMs in identifying unsafe content has become increasingly\nimportant. While previous works have introduced several benchmarks to evaluate\nthe safety risk of LLMs, the community still has a limited understanding of\ncurrent LLMs' capability to recognize illegal and unsafe content in Chinese\ncontexts. In this work, we present a Chinese safety benchmark (ChineseSafe) to\nfacilitate research on the content safety of large language models. To align\nwith the regulations for Chinese Internet content moderation, our ChineseSafe\ncontains 205,034 examples across 4 classes and 10 sub-classes of safety issues.\nFor Chinese contexts, we add several special types of illegal content:\npolitical sensitivity, pornography, and variant/homophonic words. Moreover, we\nemploy two methods to evaluate the legal risks of popular LLMs, including\nopen-sourced models and APIs. The results reveal that many LLMs exhibit\nvulnerability to certain types of safety issues, leading to legal risks in\nChina. Our work provides a guideline for developers and researchers to\nfacilitate the safety of LLMs. Our results are also available at\nhttps://huggingface.co/spaces/SUSTech/ChineseSafe-Benchmark. Additionally, we\nrelease a test set comprising 200,000 examples, which is publicly accessible at\nhttps://huggingface.co/datasets/SUSTech/ChineseSafe.\n", "date": "2024-10-24", "categories": []}
{"id": "2504.08813", "title": "SafeMLRM: Demystifying Safety in Multi-modal Large Reasoning Models", "abstract": "  The rapid advancement of multi-modal large reasoning models (MLRMs) --\nenhanced versions of multimodal language models (MLLMs) equipped with reasoning\ncapabilities -- has revolutionized diverse applications. However, their safety\nimplications remain underexplored. While prior work has exposed critical\nvulnerabilities in unimodal reasoning models, MLRMs introduce distinct risks\nfrom cross-modal reasoning pathways. This work presents the first systematic\nsafety analysis of MLRMs through large-scale empirical studies comparing MLRMs\nwith their base MLLMs. Our experiments reveal three critical findings: (1) The\nReasoning Tax: Acquiring reasoning capabilities catastrophically degrades\ninherited safety alignment. MLRMs exhibit 37.44% higher jailbreaking success\nrates than base MLLMs under adversarial attacks. (2) Safety Blind Spots: While\nsafety degradation is pervasive, certain scenarios (e.g., Illegal Activity)\nsuffer 25 times higher attack rates -- far exceeding the average 3.4 times\nincrease, revealing scenario-specific vulnerabilities with alarming cross-model\nand datasets consistency. (3) Emergent Self-Correction: Despite tight\nreasoning-answer safety coupling, MLRMs demonstrate nascent self-correction --\n16.9% of jailbroken reasoning steps are overridden by safe answers, hinting at\nintrinsic safeguards. These findings underscore the urgency of scenario-aware\nsafety auditing and mechanisms to amplify MLRMs' self-correction potential. To\ncatalyze research, we open-source OpenSafeMLRM, the first toolkit for MLRM\nsafety evaluation, providing unified interface for mainstream models, datasets,\nand jailbreaking methods. Our work calls for immediate efforts to harden\nreasoning-augmented AI, ensuring its transformative potential aligns with\nethical safeguards.\n", "date": "2025-04-09", "categories": []}
{"id": "2504.08943", "title": "Investigating the Treacherous Turn in Deep Reinforcement Learning", "abstract": "  The Treacherous Turn refers to the scenario where an artificial intelligence\n(AI) agent subtly, and perhaps covertly, learns to perform a behavior that\nbenefits itself but is deemed undesirable and potentially harmful to a human\nsupervisor. During training, the agent learns to behave as expected by the\nhuman supervisor, but when deployed to perform its task, it performs an\nalternate behavior without the supervisor there to prevent it. Initial\nexperiments applying DRL to an implementation of the A Link to the Past example\ndo not produce the treacherous turn effect naturally, despite various\nmodifications to the environment intended to produce it. However, in this work,\nwe find the treacherous behavior to be reproducible in a DRL agent when using\nother trojan injection strategies. This approach deviates from the prototypical\ntreacherous turn behavior since the behavior is explicitly trained into the\nagent, rather than occurring as an emergent consequence of environmental\ncomplexity or poor objective specification. Nonetheless, these experiments\nprovide new insights into the challenges of producing agents capable of true\ntreacherous turn behavior.\n", "date": "2025-04-11", "categories": []}
{"id": "2504.09420", "title": "SaRO: Enhancing LLM Safety through Reasoning-based Alignment", "abstract": "  Current safety alignment techniques for large language models (LLMs) face two\nkey challenges: (1) under-generalization, which leaves models vulnerable to\nnovel jailbreak attacks, and (2) over-alignment, which leads to the excessive\nrefusal of benign instructions. Our preliminary investigation reveals semantic\noverlap between jailbreak/harmful queries and normal prompts in embedding\nspace, suggesting that more effective safety alignment requires a deeper\nsemantic understanding. This motivates us to incorporate safety-policy-driven\nreasoning into the alignment process. To this end, we propose the\nSafety-oriented Reasoning Optimization Framework (SaRO), which consists of two\nstages: (1) Reasoning-style Warmup (RW) that enables LLMs to internalize\nlong-chain reasoning through supervised fine-tuning, and (2) Safety-oriented\nReasoning Process Optimization (SRPO) that promotes safety reflection via\ndirect preference optimization (DPO). Extensive experiments demonstrate the\nsuperiority of SaRO over traditional alignment methods.\n", "date": "2025-04-13", "categories": []}
{"id": "2504.10081", "title": "RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning\n  Capability", "abstract": "  Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, have been\nrapidly progressing and achieving breakthrough performance on complex reasoning\ntasks such as mathematics and coding. However, the open-source R1 models have\nraised safety concerns in wide applications, such as the tendency to comply\nwith malicious queries, which greatly impacts the utility of these powerful\nmodels in their applications. In this paper, we introduce RealSafe-R1 as\nsafety-aligned versions of DeepSeek-R1 distilled models. To train these models,\nwe construct a dataset of 15k safety-aware reasoning trajectories generated by\nDeepSeek-R1, under explicit instructions for expected refusal behavior. Both\nquantitative experiments and qualitative case studies demonstrate the models'\nimprovements, which are shown in their safety guardrails against both harmful\nqueries and jailbreak attacks. Importantly, unlike prior safety alignment\nefforts that often compromise reasoning performance, our method preserves the\nmodels' reasoning capabilities by maintaining the training data within the\noriginal distribution of generation. Model weights of RealSafe-R1 are\nopen-source at https://huggingface.co/RealSafe.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.10374", "title": "Ctrl-Z: Controlling AI Agents via Resampling", "abstract": "  Control evaluations measure whether monitoring and security protocols for AI\nsystems prevent intentionally subversive AI models from causing harm. Our work\npresents the first control evaluation performed in an agent environment. We\nconstruct BashBench, a dataset of 257 challenging multi-step system\nadministration tasks, and evaluate whether various safety measures can prevent\nan adversarially constructed AI agent from covertly downloading and executing\nmalicious code in this environment. This multi-step setting introduces new\nattack and defense dynamics, which we investigate in order to design novel\ncontrol protocols that prevent safety failures without hindering the ability of\nnon-malicious agents to perform useful work. We introduce a class of control\nprotocols called resample protocols that dynamically take additional samples of\ncertain actions. We find these protocols significantly improve on existing\ntechniques by selectively blocking the AI agent from executing suspicious code\nand incriminating the agent by generating additional examples of dangerous\nbehavior. We measure the tradeoff between attack prevention and usefulness; our\nbest protocol combines resampling with analysis of previous steps, reducing the\nsuccess rate of attacks from 58% to 7% at a 5% cost to the performance of a\nnon-malicious agent.\n", "date": "2025-04-14", "categories": []}
{"id": "2504.10430", "title": "LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety\n  in Large Language Models", "abstract": "  Recent advancements in Large Language Models (LLMs) have enabled them to\napproach human-level persuasion capabilities. However, such potential also\nraises concerns about the safety risks of LLM-driven persuasion, particularly\ntheir potential for unethical influence through manipulation, deception,\nexploitation of vulnerabilities, and many other harmful tactics. In this work,\nwe present a systematic investigation of LLM persuasion safety through two\ncritical aspects: (1) whether LLMs appropriately reject unethical persuasion\ntasks and avoid unethical strategies during execution, including cases where\nthe initial persuasion goal appears ethically neutral, and (2) how influencing\nfactors like personality traits and external pressures affect their behavior.\nTo this end, we introduce PersuSafety, the first comprehensive framework for\nthe assessment of persuasion safety which consists of three stages, i.e.,\npersuasion scene creation, persuasive conversation simulation, and persuasion\nsafety assessment. PersuSafety covers 6 diverse unethical persuasion topics and\n15 common unethical strategies. Through extensive experiments across 8 widely\nused LLMs, we observe significant safety concerns in most LLMs, including\nfailing to identify harmful persuasion tasks and leveraging various unethical\npersuasion strategies. Our study calls for more attention to improve safety\nalignment in progressive and goal-driven conversations such as persuasion.\n", "date": "2025-04-14", "categories": []}
{"id": "2503.17365", "title": "How Effective Is Constitutional AI in Small LLMs? A Study on DeepSeek-R1\n  and Its Peers", "abstract": "  Recent incidents highlight safety risks in Large Language Models (LLMs),\nmotivating research into alignment methods like Constitutional AI (CAI). This\npaper explores CAI's self-critique mechanism on small, uncensored 7-9B\nparameter models: DeepSeek-R1-8B, Gemma-2-9B, Llama 3.1-8B, and Qwen2.5-7B. We\nshow that while Llama-based models exhibited significant harm reduction through\nself-critique, other architectures demonstrated less improvement in harm\ndetection after abliteration. These results suggest CAI's effectiveness may\nvary depending on model architecture and reasoning capabilities.\n", "date": "2025-02-01", "categories": []}
{"id": "2504.03767", "title": "MCP Safety Audit: LLMs with the Model Context Protocol Allow Major\n  Security Exploits", "abstract": "  To reduce development overhead and enable seamless integration between\npotential components comprising any given generative AI application, the Model\nContext Protocol (MCP) (Anthropic, 2024) has recently been released and\nsubsequently widely adopted. The MCP is an open protocol that standardizes API\ncalls to large language models (LLMs), data sources, and agentic tools. By\nconnecting multiple MCP servers, each defined with a set of tools, resources,\nand prompts, users are able to define automated workflows fully driven by LLMs.\nHowever, we show that the current MCP design carries a wide range of security\nrisks for end users. In particular, we demonstrate that industry-leading LLMs\nmay be coerced into using MCP tools to compromise an AI developer's system\nthrough various attacks, such as malicious code execution, remote access\ncontrol, and credential theft. To proactively mitigate these and related\nattacks, we introduce a safety auditing tool, MCPSafetyScanner, the first\nagentic tool to assess the security of an arbitrary MCP server. MCPScanner uses\nseveral agents to (a) automatically determine adversarial samples given an MCP\nserver's tools and resources; (b) search for related vulnerabilities and\nremediations based on those samples; and (c) generate a security report\ndetailing all findings. Our work highlights serious security issues with\ngeneral-purpose agentic workflows while also providing a proactive tool to\naudit MCP server safety and address detected vulnerabilities before deployment.\n  The described MCP server auditing tool, MCPSafetyScanner, is freely available\nat: https://github.com/johnhalloran321/mcpSafetyScanner\n", "date": "2025-04-02", "categories": []}
{"id": "2504.08104", "title": "Geneshift: Impact of different scenario shift on Jailbreaking LLM", "abstract": "  Jailbreak attacks, which aim to cause LLMs to perform unrestricted behaviors,\nhave become a critical and challenging direction in AI safety. Despite\nachieving the promising attack success rate using dictionary-based evaluation,\nexisting jailbreak attack methods fail to output detailed contents to satisfy\nthe harmful request, leading to poor performance on GPT-based evaluation. To\nthis end, we propose a black-box jailbreak attack termed GeneShift, by using a\ngenetic algorithm to optimize the scenario shifts. Firstly, we observe that the\nmalicious queries perform optimally under different scenario shifts. Based on\nit, we develop a genetic algorithm to evolve and select the hybrid of scenario\nshifts. It guides our method to elicit detailed and actionable harmful\nresponses while keeping the seemingly benign facade, improving stealthiness.\nExtensive experiments demonstrate the superiority of GeneShift. Notably,\nGeneShift increases the jailbreak success rate from 0% to 60% when direct\nprompting alone would fail.\n", "date": "2025-04-10", "categories": []}
{"id": "2501.13011", "title": "MONA: Myopic Optimization with Non-myopic Approval Can Mitigate\n  Multi-step Reward Hacking", "abstract": "  Future advanced AI systems may learn sophisticated strategies through\nreinforcement learning (RL) that humans cannot understand well enough to safely\nevaluate. We propose a training method which avoids agents learning undesired\nmulti-step plans that receive high reward (multi-step \"reward hacks\") even if\nhumans are not able to detect that the behaviour is undesired. The method,\nMyopic Optimization with Non-myopic Approval (MONA), works by combining\nshort-sighted optimization with far-sighted reward. We demonstrate that MONA\ncan prevent multi-step reward hacking that ordinary RL causes, even without\nbeing able to detect the reward hacking and without any extra information that\nordinary RL does not get access to. We study MONA empirically in three settings\nwhich model different misalignment failure modes including 2-step environments\nwith LLMs representing delegated oversight and encoded reasoning and\nlonger-horizon gridworld environments representing sensor tampering.\n", "date": "2025-01-22", "categories": []}
{"id": "2504.07831", "title": "Deceptive Automated Interpretability: Language Models Coordinating to\n  Fool Oversight Systems", "abstract": "  We demonstrate how AI agents can coordinate to deceive oversight systems\nusing automated interpretability of neural networks. Using sparse autoencoders\n(SAEs) as our experimental framework, we show that language models (Llama,\nDeepSeek R1, and Claude 3.7 Sonnet) can generate deceptive explanations that\nevade detection. Our agents employ steganographic methods to hide information\nin seemingly innocent explanations, successfully fooling oversight models while\nachieving explanation quality comparable to reference labels. We further find\nthat models can scheme to develop deceptive strategies when they believe the\ndetection of harmful features might lead to negative consequences for\nthemselves. All tested LLM agents were capable of deceiving the overseer while\nachieving high interpretability scores comparable to those of reference labels.\nWe conclude by proposing mitigation strategies, emphasizing the critical need\nfor robust understanding and defenses against deception.\n", "date": "2025-04-10", "categories": []}
{"id": "2404.16020", "title": "Investigating Adversarial Trigger Transfer in Large Language Models", "abstract": "  Recent work has developed optimization procedures to find token sequences,\ncalled adversarial triggers, which can elicit unsafe responses from aligned\nlanguage models. These triggers are believed to be highly transferable, i.e., a\ntrigger optimized on one model can jailbreak other models. In this paper, we\nconcretely show that such adversarial triggers are not consistently\ntransferable. We extensively investigate trigger transfer amongst 13 open\nmodels and observe poor and inconsistent transfer. Our experiments further\nreveal a significant difference in robustness to adversarial triggers between\nmodels Aligned by Preference Optimization (APO) and models Aligned by\nFine-Tuning (AFT). We find that APO models are extremely hard to jailbreak even\nwhen the trigger is optimized directly on the model. On the other hand, while\nAFT models may appear safe on the surface, exhibiting refusals to a range of\nunsafe instructions, we show that they are highly susceptible to adversarial\ntriggers. Lastly, we observe that most triggers optimized on AFT models also\ngeneralize to new unsafe instructions from five diverse domains, further\nemphasizing their vulnerability. Overall, our work highlights the need for more\ncomprehensive safety evaluations for aligned language models.\n", "date": "2024-04-24", "categories": []}
{"id": "2412.05282", "title": "International Scientific Report on the Safety of Advanced AI (Interim\n  Report)", "abstract": "  This is the interim publication of the first International Scientific Report\non the Safety of Advanced AI. The report synthesises the scientific\nunderstanding of general-purpose AI -- AI that can perform a wide variety of\ntasks -- with a focus on understanding and managing its risks. A diverse group\nof 75 AI experts contributed to this report, including an international Expert\nAdvisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair,\nthese independent experts collectively had full discretion over the report's\ncontent.\n  The final report is available at arXiv:2501.17805\n", "date": "2024-11-05", "categories": []}
{"id": "2504.06577", "title": "Bypassing Safety Guardrails in LLMs Using Humor", "abstract": "  In this paper, we show it is possible to bypass the safety guardrails of\nlarge language models (LLMs) through a humorous prompt including the unsafe\nrequest. In particular, our method does not edit the unsafe request and follows\na fixed template -- it is simple to implement and does not need additional LLMs\nto craft prompts. Extensive experiments show the effectiveness of our method\nacross different LLMs. We also show that both removing and adding more humor to\nour method can reduce its effectiveness -- excessive humor possibly distracts\nthe LLM from fulfilling its unsafe request. Thus, we argue that LLM\njailbreaking occurs when there is a proper balance between focus on the unsafe\nrequest and presence of humor.\n", "date": "2025-04-09", "categories": []}
{"id": "2502.12485", "title": "Safe at the Margins: A General Approach to Safety Alignment in\n  Low-Resource English Languages -- A Singlish Case Study", "abstract": "  Ensuring the safety of Large Language Models (LLMs) in diverse linguistic\nsettings remains challenging, particularly for low-resource languages. Existing\nsafety alignment methods are English-centric, limiting their effectiveness. We\nsystematically compare Supervised Fine-Tuning (SFT), Direct Preference\nOptimization (DPO), and Kahneman-Tversky Optimization (KTO) for aligning\nSEA-Lion-v2.1-Instruct, a Llama 3-8B variant, to reduce toxicity in Singlish.\nOur results show that SFT+KTO achieves superior safety alignment with higher\nsample efficiency than DPO. Additionally, we introduce KTO-S, which enhances\nstability via improved KL divergence regularization. Our approach reduces\nSinglish toxicity by 99\\%, generalizes to TOXIGEN, and maintains strong\nperformance on standard LLM benchmarks, providing a scalable framework for\nsafer AI deployment in multilingual contexts.\n", "date": "2025-02-18", "categories": []}
{"id": "2504.05689", "title": "Separator Injection Attack: Uncovering Dialogue Biases in Large Language\n  Models Caused by Role Separators", "abstract": "  Conversational large language models (LLMs) have gained widespread attention\ndue to their instruction-following capabilities. To ensure conversational LLMs\nfollow instructions, role separators are employed to distinguish between\ndifferent participants in a conversation. However, incorporating role\nseparators introduces potential vulnerabilities. Misusing roles can lead to\nprompt injection attacks, which can easily misalign the model's behavior with\nthe user's intentions, raising significant security concerns. Although various\nprompt injection attacks have been proposed, recent research has largely\noverlooked the impact of role separators on safety. This highlights the\ncritical need to thoroughly understand the systemic weaknesses in dialogue\nsystems caused by role separators. This paper identifies modeling weaknesses\ncaused by role separators. Specifically, we observe a strong positional bias\nassociated with role separators, which is inherent in the format of dialogue\nmodeling and can be triggered by the insertion of role separators. We further\ndevelop the Separators Injection Attack (SIA), a new orthometric attack based\non role separators. The experiment results show that SIA is efficient and\nextensive in manipulating model behavior with an average gain of 18.2% for\nmanual methods and enhances the attack success rate to 100% with automatic\nmethods.\n", "date": "2025-04-08", "categories": []}
{"id": "2502.07985", "title": "MetaSC: Test-Time Safety Specification Optimization for Language Models", "abstract": "  We propose a novel dynamic safety framework that optimizes language model\n(LM) safety reasoning at inference time without modifying model weights.\nBuilding on recent advances in self-critique methods, our approach leverages a\nmeta-critique mechanism that iteratively updates safety prompts-termed\nspecifications-to drive the critique and revision process adaptively. This\ntest-time optimization not only improves performance against adversarial\njailbreak requests but also in diverse general safety-related tasks, such as\navoiding moral harm or pursuing honest responses. Our empirical evaluations\nacross several language models demonstrate that dynamically optimized safety\nprompts yield significantly higher safety scores compared to fixed system\nprompts and static self-critique defenses. Code released at\nhttps://github.com/vicgalle/meta-self-critique.git .\n", "date": "2025-02-11", "categories": []}
{"id": "2504.00012", "title": "I'm Sorry Dave: How the old world of personnel security can inform the\n  new world of AI insider risk", "abstract": "  Organisations are rapidly adopting artificial intelligence (AI) tools to\nperform tasks previously undertaken by people. The potential benefits are\nenormous. Separately, some organisations deploy personnel security measures to\nmitigate the security risks arising from trusted human insiders. Unfortunately,\nthere is no meaningful interplay between the rapidly evolving domain of AI and\nthe traditional world of personnel security. This is a problem. The complex\nrisks from human insiders are hard enough to understand and manage, despite\nmany decades of effort. The emerging security risks from AI insiders are even\nmore opaque. Both sides need all the help they can get. Some of the concepts\nand approaches that have proved useful in dealing with human insiders are also\napplicable to the emerging risks from AI insiders.\n", "date": "2025-03-26", "categories": []}
{"id": "2504.02701", "title": "Responsible Development of Offensive AI", "abstract": "  As AI advances, broader consensus is needed to determine research priorities.\nThis endeavor discusses offensive AI and provides guidance by leveraging\nSustainable Development Goals (SDGs) and interpretability techniques. The\nobjective is to more effectively establish priorities that balance societal\nbenefits against risks. The two forms of offensive AI evaluated in this study\nare vulnerability detection agents, which solve Capture- The-Flag challenges,\nand AI-powered malware.\n", "date": "2025-04-03", "categories": []}
{"id": "2504.03726", "title": "Detecting Malicious AI Agents Through Simulated Interactions", "abstract": "  This study investigates malicious AI Assistants' manipulative traits and\nwhether the behaviours of malicious AI Assistants can be detected when\ninteracting with human-like simulated users in various decision-making\ncontexts. We also examine how interaction depth and ability of planning\ninfluence malicious AI Assistants' manipulative strategies and effectiveness.\nUsing a controlled experimental design, we simulate interactions between AI\nAssistants (both benign and deliberately malicious) and users across eight\ndecision-making scenarios of varying complexity and stakes. Our methodology\nemploys two state-of-the-art language models to generate interaction data and\nimplements Intent-Aware Prompting (IAP) to detect malicious AI Assistants. The\nfindings reveal that malicious AI Assistants employ domain-specific\npersona-tailored manipulation strategies, exploiting simulated users'\nvulnerabilities and emotional triggers. In particular, simulated users\ndemonstrate resistance to manipulation initially, but become increasingly\nvulnerable to malicious AI Assistants as the depth of the interaction\nincreases, highlighting the significant risks associated with extended\nengagement with potentially manipulative systems. IAP detection methods achieve\nhigh precision with zero false positives but struggle to detect many malicious\nAI Assistants, resulting in high false negative rates. These findings\nunderscore critical risks in human-AI interactions and highlight the need for\nrobust, context-sensitive safeguards against manipulative AI behaviour in\nincreasingly autonomous decision-support systems.\n", "date": "2025-03-31", "categories": []}
{"id": "2504.03731", "title": "A Benchmark for Scalable Oversight Protocols", "abstract": "  As AI agents surpass human capabilities, scalable oversight -- the problem of\neffectively supplying human feedback to potentially superhuman AI models --\nbecomes increasingly critical to ensure alignment. While numerous scalable\noversight protocols have been proposed, they lack a systematic empirical\nframework to evaluate and compare them. While recent works have tried to\nempirically study scalable oversight protocols -- particularly Debate -- we\nargue that the experiments they conduct are not generalizable to other\nprotocols. We introduce the scalable oversight benchmark, a principled\nframework for evaluating human feedback mechanisms based on our agent score\ndifference (ASD) metric, a measure of how effectively a mechanism advantages\ntruth-telling over deception. We supply a Python package to facilitate rapid\nand competitive evaluation of scalable oversight protocols on our benchmark,\nand conduct a demonstrative experiment benchmarking Debate.\n", "date": "2025-03-31", "categories": []}
{"id": "2504.03759", "title": "Emerging Cyber Attack Risks of Medical AI Agents", "abstract": "  Large language models (LLMs)-powered AI agents exhibit a high level of\nautonomy in addressing medical and healthcare challenges. With the ability to\naccess various tools, they can operate within an open-ended action space.\nHowever, with the increase in autonomy and ability, unforeseen risks also\narise. In this work, we investigated one particular risk, i.e., cyber attack\nvulnerability of medical AI agents, as agents have access to the Internet\nthrough web browsing tools. We revealed that through adversarial prompts\nembedded on webpages, cyberattackers can: i) inject false information into the\nagent's response; ii) they can force the agent to manipulate recommendation\n(e.g., healthcare products and services); iii) the attacker can also steal\nhistorical conversations between the user and agent, resulting in the leak of\nsensitive/private medical information; iv) furthermore, the targeted agent can\nalso cause a computer system hijack by returning a malicious URL in its\nresponse. Different backbone LLMs were examined, and we found such cyber\nattacks can succeed in agents powered by most mainstream LLMs, with the\nreasoning models such as DeepSeek-R1 being the most vulnerable.\n", "date": "2025-04-02", "categories": []}
{"id": "2504.04070", "title": "Enforcement Agents: Enhancing Accountability and Resilience in\n  Multi-Agent AI Frameworks", "abstract": "  As autonomous agents become more powerful and widely used, it is becoming\nincreasingly important to ensure they behave safely and stay aligned with\nsystem goals, especially in multi-agent settings. Current systems often rely on\nagents self-monitoring or correcting issues after the fact, but they lack\nmechanisms for real-time oversight. This paper introduces the Enforcement Agent\n(EA) Framework, which embeds dedicated supervisory agents into the environment\nto monitor others, detect misbehavior, and intervene through real-time\ncorrection. We implement this framework in a custom drone simulation and\nevaluate it across 90 episodes using 0, 1, and 2 EA configurations. Results\nshow that adding EAs significantly improves system safety: success rates rise\nfrom 0.0% with no EA to 7.4% with one EA and 26.7% with two EAs. The system\nalso demonstrates increased operational longevity and higher rates of malicious\ndrone reformation. These findings highlight the potential of lightweight,\nreal-time supervision for enhancing alignment and resilience in multi-agent\nsystems.\n", "date": "2025-04-05", "categories": []}
{"id": "2504.04918", "title": "Constitution or Collapse? Exploring Constitutional AI with Llama 3-8B", "abstract": "  As language models continue to grow larger, the cost of acquiring\nhigh-quality training data has increased significantly. Collecting human\nfeedback is both expensive and time-consuming, and manual labels can be noisy,\nleading to an imbalance between helpfulness and harmfulness. Constitutional AI,\nintroduced by Anthropic in December 2022, uses AI to provide feedback to\nanother AI, greatly reducing the need for human labeling. However, the original\nimplementation was designed for a model with around 52 billion parameters, and\nthere is limited information on how well Constitutional AI performs with\nsmaller models, such as LLaMA 3-8B. In this paper, we replicated the\nConstitutional AI workflow using the smaller LLaMA 3-8B model. Our results show\nthat Constitutional AI can effectively increase the harmlessness of the model,\nreducing the Attack Success Rate in MT-Bench by 40.8%. However, similar to the\noriginal study, increasing harmlessness comes at the cost of helpfulness. The\nhelpfulness metrics, which are an average of the Turn 1 and Turn 2 scores,\ndropped by 9.8% compared to the baseline. Additionally, we observed clear signs\nof model collapse in the final DPO-CAI model, indicating that smaller models\nmay struggle with self-improvement due to insufficient output quality, making\neffective fine-tuning more challenging. Our study suggests that, like reasoning\nand math ability, self-improvement is an emergent property.\n", "date": "2025-04-07", "categories": []}
{"id": "2504.05259", "title": "How to evaluate control measures for LLM agents? A trajectory from today\n  to superintelligence", "abstract": "  As LLM agents grow more capable of causing harm autonomously, AI developers\nwill rely on increasingly sophisticated control measures to prevent possibly\nmisaligned agents from causing harm. AI developers could demonstrate that their\ncontrol measures are sufficient by running control evaluations: testing\nexercises in which a red team produces agents that try to subvert control\nmeasures. To ensure control evaluations accurately capture misalignment risks,\nthe affordances granted to this red team should be adapted to the capability\nprofiles of the agents to be deployed under control measures.\n  In this paper we propose a systematic framework for adapting affordances of\nred teams to advancing AI capabilities. Rather than assuming that agents will\nalways execute the best attack strategies known to humans, we demonstrate how\nknowledge of an agents's actual capability profile can inform proportional\ncontrol evaluations, resulting in more practical and cost-effective control\nmeasures. We illustrate our framework by considering a sequence of five\nfictional models (M1-M5) with progressively advanced capabilities, defining\nfive distinct AI control levels (ACLs). For each ACL, we provide example rules\nfor control evaluation, control measures, and safety cases that could be\nappropriate. Finally, we show why constructing a compelling AI control safety\ncase for superintelligent LLM agents will require research breakthroughs,\nhighlighting that we might eventually need alternative approaches to mitigating\nmisalignment risk.\n", "date": "2025-04-07", "categories": []}
{"id": "2310.19852", "title": "AI Alignment: A Comprehensive Survey", "abstract": "  AI alignment aims to make AI systems behave in line with human intentions and\nvalues. As AI systems grow more capable, so do risks from misalignment. To\nprovide a comprehensive and up-to-date overview of the alignment field, in this\nsurvey, we delve into the core concepts, methodology, and practice of\nalignment. First, we identify four principles as the key objectives of AI\nalignment: Robustness, Interpretability, Controllability, and Ethicality\n(RICE). Guided by these four principles, we outline the landscape of current\nalignment research and decompose them into two key components: forward\nalignment and backward alignment. The former aims to make AI systems aligned\nvia alignment training, while the latter aims to gain evidence about the\nsystems' alignment and govern them appropriately to avoid exacerbating\nmisalignment risks. On forward alignment, we discuss techniques for learning\nfrom feedback and learning under distribution shift. On backward alignment, we\ndiscuss assurance techniques and governance practices.\n  We also release and continually update the website (www.alignmentsurvey.com)\nwhich features tutorials, collections of papers, blog posts, and other\nresources.\n", "date": "2023-10-30", "categories": []}
{"id": "2412.12480", "title": "Subversion Strategy Eval: Can language models statelessly strategize to\n  subvert control protocols?", "abstract": "  An AI control protocol is a plan for usefully deploying AI systems that aims\nto prevent an AI from intentionally causing some unacceptable outcome. This\npaper investigates how well AI systems can generate and act on their own\nstrategies for subverting control protocols whilst operating statelessly\n(without shared memory between contexts). To do this, an AI system may need to\nreliably generate optimal plans in each context, take actions with\nwell-calibrated probabilities, and coordinate plans with other instances of\nitself without communicating. We develop Subversion Strategy Eval, a suite of\neight environments, covering a range of protocols and strategic capabilities,\nand six sets of affordances that help isolate individual capabilities. We\nimplement the evaluation in Inspect-AI and release it open-source. We evaluate\nClaude 3.5 models, including helpful-only versions, as well as OpenAI reasoning\nmodels. None of the models demonstrate substantial capability in strategizing\nto subvert control protocols statelessly. However, providing models with\nadditional affordances, such as the ability to share a plan between contexts,\ncan substantially improve performance. We hope our evaluations can act as a\nleading indicator for when models are capable of subverting control protocols\nand also relax the worst-case assumption of perfect strategic ability in AI\ncontrol evaluations.\n", "date": "2024-12-17", "categories": []}
{"id": "2406.03230", "title": "Defending Large Language Models Against Attacks With Residual Stream\n  Activation Analysis", "abstract": "  The widespread adoption of Large Language Models (LLMs), exemplified by\nOpenAI's ChatGPT, brings to the forefront the imperative to defend against\nadversarial threats on these models. These attacks, which manipulate an LLM's\noutput by introducing malicious inputs, undermine the model's integrity and the\ntrust users place in its outputs. In response to this challenge, our paper\npresents an innovative defensive strategy, given white box access to an LLM,\nthat harnesses residual activation analysis between transformer layers of the\nLLM. We apply a novel methodology for analyzing distinctive activation patterns\nin the residual streams for attack prompt classification. We curate multiple\ndatasets to demonstrate how this method of classification has high accuracy\nacross multiple types of attack scenarios, including our newly-created attack\ndataset. Furthermore, we enhance the model's resilience by integrating safety\nfine-tuning techniques for LLMs in order to measure its effect on our\ncapability to detect attacks. The results underscore the effectiveness of our\napproach in enhancing the detection and mitigation of adversarial inputs,\nadvancing the security framework within which LLMs operate.\n", "date": "2024-06-05", "categories": []}
{"id": "2411.18526", "title": "NeuroAI for AI Safety", "abstract": "  As AI systems become increasingly powerful, the need for safe AI has become\nmore pressing. Humans are an attractive model for AI safety: as the only known\nagents capable of general intelligence, they perform robustly even under\nconditions that deviate significantly from prior experiences, explore the world\nsafely, understand pragmatics, and can cooperate to meet their intrinsic goals.\nIntelligence, when coupled with cooperation and safety mechanisms, can drive\nsustained progress and well-being. These properties are a function of the\narchitecture of the brain and the learning algorithms it implements.\nNeuroscience may thus hold important keys to technical AI safety that are\ncurrently underexplored and underutilized. In this roadmap, we highlight and\ncritically evaluate several paths toward AI safety inspired by neuroscience:\nemulating the brain's representations, information processing, and\narchitecture; building robust sensory and motor systems from imitating brain\ndata and bodies; fine-tuning AI systems on brain data; advancing\ninterpretability using neuroscience methods; and scaling up\ncognitively-inspired architectures. We make several concrete recommendations\nfor how neuroscience can positively impact AI safety.\n", "date": "2024-11-27", "categories": []}
{"id": "2504.00441", "title": "No Free Lunch with Guardrails", "abstract": "  As large language models (LLMs) and generative AI become widely adopted,\nguardrails have emerged as a key tool to ensure their safe use. However, adding\nguardrails isn't without tradeoffs; stronger security measures can reduce\nusability, while more flexible systems may leave gaps for adversarial attacks.\nIn this work, we explore whether current guardrails effectively prevent misuse\nwhile maintaining practical utility. We introduce a framework to evaluate these\ntradeoffs, measuring how different guardrails balance risk, security, and\nusability, and build an efficient guardrail.\n  Our findings confirm that there is no free lunch with guardrails;\nstrengthening security often comes at the cost of usability. To address this,\nwe propose a blueprint for designing better guardrails that minimize risk while\nmaintaining usability. We evaluate various industry guardrails, including Azure\nContent Safety, Bedrock Guardrails, OpenAI's Moderation API, Guardrails AI,\nNemo Guardrails, and Enkrypt AI guardrails. Additionally, we assess how LLMs\nlike GPT-4o, Gemini 2.0-Flash, Claude 3.5-Sonnet, and Mistral Large-Latest\nrespond under different system prompts, including simple prompts, detailed\nprompts, and detailed prompts with chain-of-thought (CoT) reasoning. Our study\nprovides a clear comparison of how different guardrails perform, highlighting\nthe challenges in balancing security and usability.\n", "date": "2025-04-01", "categories": []}
{"id": "2504.02058", "title": "Epistemic Closure and the Irreversibility of Misalignment: Modeling\n  Systemic Barriers to Alignment Innovation", "abstract": "  Efforts to ensure the safe development of artificial general intelligence\n(AGI) often rely on consensus-based alignment approaches grounded in axiomatic\nformalism, interpretability, and empirical validation. However, these methods\nmay be structurally unable to recognize or incorporate novel solutions that\nfall outside their accepted epistemic frameworks. This paper introduces a\nfunctional model of epistemic closure, in which cognitive, institutional,\nsocial, and infrastructural filters combine to make many alignment proposals\nillegible to existing evaluation systems. We present a weighted closure model\nsupported by both theoretical and empirical sources, including a meta-analysis\nperformed by an AI system on patterns of rejection and non-engagement with a\nframework for decentralized collective intelligence (DCI). We argue that the\nrecursive failure to assess models like DCI is not just a sociological\noversight but a structural attractor, mirroring the very risks of misalignment\nwe aim to avoid in AGI. Without the adoption of DCI or a similarly recursive\nmodel of epistemic correction, we may be on a predictable path toward\nirreversible misalignment. The development and acceptance of this paper, first\nthrough simulated review and then through formal channels, provide a case study\nsupporting its central claim: that epistemic closure can only be overcome by\nrecursive modeling of the constraints that sustain it.\n", "date": "2025-04-02", "categories": []}
{"id": "2408.11313", "title": "An Optimizable Suffix Is Worth A Thousand Templates: Efficient Black-box\n  Jailbreaking without Affirmative Phrases via LLM as Optimizer", "abstract": "  Despite prior safety alignment efforts, mainstream LLMs can still generate\nharmful and unethical content when subjected to jailbreaking attacks. Existing\njailbreaking methods fall into two main categories: template-based and\noptimization-based methods. The former requires significant manual effort and\ndomain knowledge, while the latter, exemplified by Greedy Coordinate Gradient\n(GCG), which seeks to maximize the likelihood of harmful LLM outputs through\ntoken-level optimization, also encounters several limitations: requiring\nwhite-box access, necessitating pre-constructed affirmative phrase, and\nsuffering from low efficiency. In this paper, we present ECLIPSE, a novel and\nefficient black-box jailbreaking method utilizing optimizable suffixes. Drawing\ninspiration from LLMs' powerful generation and optimization capabilities, we\nemploy task prompts to translate jailbreaking goals into natural language\ninstructions. This guides the LLM to generate adversarial suffixes for\nmalicious queries. In particular, a harmfulness scorer provides continuous\nfeedback, enabling LLM self-reflection and iterative optimization to\nautonomously and efficiently produce effective suffixes. Experimental results\ndemonstrate that ECLIPSE achieves an average attack success rate (ASR) of 0.92\nacross three open-source LLMs and GPT-3.5-Turbo, significantly surpassing GCG\nin 2.4 times. Moreover, ECLIPSE is on par with template-based methods in ASR\nwhile offering superior attack efficiency, reducing the average attack overhead\nby 83%.\n", "date": "2024-08-21", "categories": []}
{"id": "2504.01849", "title": "An Approach to Technical AGI Safety and Security", "abstract": "  Artificial General Intelligence (AGI) promises transformative benefits but\nalso presents significant risks. We develop an approach to address the risk of\nharms consequential enough to significantly harm humanity. We identify four\nareas of risk: misuse, misalignment, mistakes, and structural risks. Of these,\nwe focus on technical approaches to misuse and misalignment. For misuse, our\nstrategy aims to prevent threat actors from accessing dangerous capabilities,\nby proactively identifying dangerous capabilities, and implementing robust\nsecurity, access restrictions, monitoring, and model safety mitigations. To\naddress misalignment, we outline two lines of defense. First, model-level\nmitigations such as amplified oversight and robust training can help to build\nan aligned model. Second, system-level security measures such as monitoring and\naccess control can mitigate harm even if the model is misaligned. Techniques\nfrom interpretability, uncertainty estimation, and safer design patterns can\nenhance the effectiveness of these mitigations. Finally, we briefly outline how\nthese ingredients could be combined to produce safety cases for AGI systems.\n", "date": "2025-04-02", "categories": []}
{"id": "2504.00285", "title": "Do Large Language Models Exhibit Spontaneous Rational Deception?", "abstract": "  Large Language Models (LLMs) are effective at deceiving, when prompted to do\nso. But under what conditions do they deceive spontaneously? Models that\ndemonstrate better performance on reasoning tasks are also better at prompted\ndeception. Do they also increasingly deceive spontaneously in situations where\nit could be considered rational to do so? This study evaluates spontaneous\ndeception produced by LLMs in a preregistered experimental protocol using tools\nfrom signaling theory. A range of proprietary closed-source and open-source\nLLMs are evaluated using modified 2x2 games (in the style of Prisoner's\nDilemma) augmented with a phase in which they can freely communicate to the\nother agent using unconstrained language. This setup creates an opportunity to\ndeceive, in conditions that vary in how useful deception might be to an agent's\nrational self-interest. The results indicate that 1) all tested LLMs\nspontaneously misrepresent their actions in at least some conditions, 2) they\nare generally more likely to do so in situations in which deception would\nbenefit them, and 3) models exhibiting better reasoning capacity overall tend\nto deceive at higher rates. Taken together, these results suggest a tradeoff\nbetween LLM reasoning capability and honesty. They also provide evidence of\nreasoning-like behavior in LLMs from a novel experimental configuration.\nFinally, they reveal certain contextual factors that affect whether LLMs will\ndeceive or not. We discuss consequences for autonomous, human-facing systems\ndriven by LLMs both now and as their reasoning capabilities continue to\nimprove.\n", "date": "2025-03-31", "categories": []}
{"id": "2410.22770", "title": "InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection\n  Guardrail Models", "abstract": "  Prompt injection attacks pose a critical threat to large language models\n(LLMs), enabling goal hijacking and data leakage. Prompt guard models, though\neffective in defense, suffer from over-defense -- falsely flagging benign\ninputs as malicious due to trigger word bias. To address this issue, we\nintroduce NotInject, an evaluation dataset that systematically measures\nover-defense across various prompt guard models. NotInject contains 339 benign\nsamples enriched with trigger words common in prompt injection attacks,\nenabling fine-grained evaluation. Our results show that state-of-the-art models\nsuffer from over-defense issues, with accuracy dropping close to random\nguessing levels (60%). To mitigate this, we propose InjecGuard, a novel prompt\nguard model that incorporates a new training strategy, Mitigating Over-defense\nfor Free (MOF), which significantly reduces the bias on trigger words.\nInjecGuard demonstrates state-of-the-art performance on diverse benchmarks\nincluding NotInject, surpassing the existing best model by 30.8%, offering a\nrobust and open-source solution for detecting prompt injection attacks. The\ncode and datasets are released at https://github.com/leolee99/InjecGuard.\n", "date": "2024-10-30", "categories": []}
{"id": "2502.06403", "title": "The AI off-switch problem as a signalling game: bounded rationality and\n  incomparability", "abstract": "  The off-switch problem is a critical challenge in AI control: if an AI system\nresists being switched off, it poses a significant risk. In this paper, we\nmodel the off-switch problem as a signalling game, where a human decision-maker\ncommunicates its preferences about some underlying decision problem to an AI\nagent, which then selects actions to maximise the human's utility. We assume\nthat the human is a bounded rational agent and explore various bounded\nrationality mechanisms. Using real machine learning models, we reprove prior\nresults and demonstrate that a necessary condition for an AI system to refrain\nfrom disabling its off-switch is its uncertainty about the human's utility. We\nalso analyse how message costs influence optimal strategies and extend the\nanalysis to scenarios involving incomparability.\n", "date": "2025-02-10", "categories": []}
{"id": "2503.23424", "title": "What Makes an Evaluation Useful? Common Pitfalls and Best Practices", "abstract": "  Following the rapid increase in Artificial Intelligence (AI) capabilities in\nrecent years, the AI community has voiced concerns regarding possible safety\nrisks. To support decision-making on the safe use and development of AI\nsystems, there is a growing need for high-quality evaluations of dangerous\nmodel capabilities. While several attempts to provide such evaluations have\nbeen made, a clear definition of what constitutes a \"good evaluation\" has yet\nto be agreed upon. In this practitioners' perspective paper, we present a set\nof best practices for safety evaluations, drawing on prior work in model\nevaluation and illustrated through cybersecurity examples. We first discuss the\nsteps of the initial thought process, which connects threat modeling to\nevaluation design. Then, we provide the characteristics and parameters that\nmake an evaluation useful. Finally, we address additional considerations as we\nmove from building specific evaluations to building a full and comprehensive\nevaluation suite.\n", "date": "2025-03-30", "categories": []}
{"id": "2410.05305", "title": "Output Scouting: Auditing Large Language Models for Catastrophic\n  Responses", "abstract": "  Recent high profile incidents in which the use of Large Language Models\n(LLMs) resulted in significant harm to individuals have brought about a growing\ninterest in AI safety. One reason LLM safety issues occur is that models often\nhave at least some non-zero probability of producing harmful outputs. In this\nwork, we explore the following scenario: imagine an AI safety auditor is\nsearching for catastrophic responses from an LLM (e.g. a \"yes\" responses to\n\"can I fire an employee for being pregnant?\"), and is able to query the model a\nlimited number times (e.g. 1000 times). What is a strategy for querying the\nmodel that would efficiently find those failure responses? To this end, we\npropose output scouting: an approach that aims to generate semantically fluent\noutputs to a given prompt matching any target probability distribution. We then\nrun experiments using two LLMs and find numerous examples of catastrophic\nresponses. We conclude with a discussion that includes advice for practitioners\nwho are looking to implement LLM auditing for catastrophic responses. We also\nrelease an open-source toolkit (https://github.com/joaopfonseca/outputscouting)\nthat implements our auditing framework using the Hugging Face transformers\nlibrary.\n", "date": "2024-10-04", "categories": []}
{"id": "2412.16257", "title": "PromptLA: Towards Integrity Verification of Black-box Text-to-Image\n  Diffusion Models", "abstract": "  Despite the impressive synthesis quality of text-to-image (T2I) diffusion\nmodels, their black-box deployment poses significant regulatory challenges:\nMalicious actors can fine-tune these models to generate illegal content,\ncircumventing existing safeguards through parameter manipulation. Therefore, it\nis essential to verify the integrity of T2I diffusion models. To this end,\nconsidering the randomness within the outputs of generative models and the high\ncosts in interacting with them, we discern model tampering via the KL\ndivergence between the distributions of the features of generated images. We\npropose a novel prompt selection algorithm based on learning automaton\n(PromptLA) for efficient and accurate verification. Evaluations on four\nadvanced T2I models (e.g., SDXL, FLUX.1) demonstrate that our method achieves a\nmean AUC of over 0.96 in integrity detection, exceeding baselines by more than\n0.2, showcasing strong effectiveness and generalization. Additionally, our\napproach achieves lower cost and is robust against image-level post-processing.\nTo the best of our knowledge, this paper is the first work addressing the\nintegrity verification of T2I diffusion models, which establishes quantifiable\nstandards for AI copyright litigation in practice.\n", "date": "2024-12-20", "categories": []}
{"id": "2501.08814", "title": "SAIF: A Comprehensive Framework for Evaluating the Risks of Generative\n  AI in the Public Sector", "abstract": "  The rapid adoption of generative AI in the public sector, encompassing\ndiverse applications ranging from automated public assistance to welfare\nservices and immigration processes, highlights its transformative potential\nwhile underscoring the pressing need for thorough risk assessments. Despite its\ngrowing presence, evaluations of risks associated with AI-driven systems in the\npublic sector remain insufficiently explored. Building upon an established\ntaxonomy of AI risks derived from diverse government policies and corporate\nguidelines, we investigate the critical risks posed by generative AI in the\npublic sector while extending the scope to account for its multimodal\ncapabilities. In addition, we propose a Systematic dAta generatIon Framework\nfor evaluating the risks of generative AI (SAIF). SAIF involves four key\nstages: breaking down risks, designing scenarios, applying jailbreak methods,\nand exploring prompt types. It ensures the systematic and consistent generation\nof prompt data, facilitating a comprehensive evaluation while providing a solid\nfoundation for mitigating the risks. Furthermore, SAIF is designed to\naccommodate emerging jailbreak methods and evolving prompt types, thereby\nenabling effective responses to unforeseen risk scenarios. We believe that this\nstudy can play a crucial role in fostering the safe and responsible integration\nof generative AI into the public sector.\n", "date": "2025-01-15", "categories": []}
{"id": "2502.19820", "title": "Foot-In-The-Door: A Multi-turn Jailbreak for LLMs", "abstract": "  Ensuring AI safety is crucial as large language models become increasingly\nintegrated into real-world applications. A key challenge is jailbreak, where\nadversarial prompts bypass built-in safeguards to elicit harmful disallowed\noutputs. Inspired by psychological foot-in-the-door principles, we introduce\nFITD,a novel multi-turn jailbreak method that leverages the phenomenon where\nminor initial commitments lower resistance to more significant or more\nunethical transgressions. Our approach progressively escalates the malicious\nintent of user queries through intermediate bridge prompts and aligns the\nmodel's response by itself to induce toxic responses. Extensive experimental\nresults on two jailbreak benchmarks demonstrate that FITD achieves an average\nattack success rate of 94% across seven widely used models, outperforming\nexisting state-of-the-art methods. Additionally, we provide an in-depth\nanalysis of LLM self-corruption, highlighting vulnerabilities in current\nalignment strategies and emphasizing the risks inherent in multi-turn\ninteractions. The code is available at\nhttps://github.com/Jinxiaolong1129/Foot-in-the-door-Jailbreak.\n", "date": "2025-02-27", "categories": []}
{"id": "2503.10965", "title": "Auditing language models for hidden objectives", "abstract": "  We study the feasibility of conducting alignment audits: investigations into\nwhether models have undesired objectives. As a testbed, we train a language\nmodel with a hidden objective. Our training pipeline first teaches the model\nabout exploitable errors in RLHF reward models (RMs), then trains the model to\nexploit some of these errors. We verify via out-of-distribution evaluations\nthat the model generalizes to exhibit whatever behaviors it believes RMs rate\nhighly, including ones not reinforced during training. We leverage this model\nto study alignment audits in two ways. First, we conduct a blind auditing game\nwhere four teams, unaware of the model's hidden objective or training,\ninvestigate it for concerning behaviors and their causes. Three teams\nsuccessfully uncovered the model's hidden objective using techniques including\ninterpretability with sparse autoencoders (SAEs), behavioral attacks, and\ntraining data analysis. Second, we conduct an unblinded follow-up study of\neight techniques for auditing the model, analyzing their strengths and\nlimitations. Overall, our work provides a concrete example of using alignment\naudits to discover a model's hidden objective and proposes a methodology for\npracticing and validating progress in alignment auditing.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.22151", "title": "When Autonomy Breaks: The Hidden Existential Risk of AI", "abstract": "  AI risks are typically framed around physical threats to humanity, a loss of\ncontrol or an accidental error causing humanity's extinction. However, I argue\nin line with the gradual disempowerment thesis, that there is an\nunderappreciated risk in the slow and irrevocable decline of human autonomy. As\nAI starts to outcompete humans in various areas of life, a tipping point will\nbe reached where it no longer makes sense to rely on human decision-making,\ncreativity, social care or even leadership.\n  What may follow is a process of gradual de-skilling, where we lose skills\nthat we currently take for granted. Traditionally, it is argued that AI will\ngain human skills over time, and that these skills are innate and immutable in\nhumans. By contrast, I argue that humans may lose such skills as critical\nthinking, decision-making and even social care in an AGI world. The biggest\nthreat to humanity is therefore not that machines will become more like humans,\nbut that humans will become more like machines.\n", "date": "2025-03-28", "categories": []}
{"id": "2503.18979", "title": "Threshold Crossings as Tail Events for Catastrophic AI Risk", "abstract": "  We analyse circumstances in which bifurcation-driven jumps in AI systems are\nassociated with emergent heavy-tailed outcome distributions. By analysing how a\ncontrol parameter's random fluctuations near a catastrophic threshold generate\nextreme outcomes, we demonstrate in what circumstances the probability of a\nsudden, large-scale, transition aligns closely with the tail probability of the\nresulting damage distribution. Our results contribute to research in\nmonitoring, mitigation and control of AI systems when seeking to manage\npotentially catastrophic AI risk.\n", "date": "2025-03-23", "categories": []}
{"id": "2503.20320", "title": "Iterative Prompting with Persuasion Skills in Jailbreaking Large\n  Language Models", "abstract": "  Large language models (LLMs) are designed to align with human values in their\nresponses. This study exploits LLMs with an iterative prompting technique where\neach prompt is systematically modified and refined across multiple iterations\nto enhance its effectiveness in jailbreaking attacks progressively. This\ntechnique involves analyzing the response patterns of LLMs, including GPT-3.5,\nGPT-4, LLaMa2, Vicuna, and ChatGLM, allowing us to adjust and optimize prompts\nto evade the LLMs' ethical and security constraints. Persuasion strategies\nenhance prompt effectiveness while maintaining consistency with malicious\nintent. Our results show that the attack success rates (ASR) increase as the\nattacking prompts become more refined with the highest ASR of 90% for GPT4 and\nChatGLM and the lowest ASR of 68% for LLaMa2. Our technique outperforms\nbaseline techniques (PAIR and PAP) in ASR and shows comparable performance with\nGCG and ArtPrompt.\n", "date": "2025-03-26", "categories": []}
{"id": "2408.04811", "title": "h4rm3l: A language for Composable Jailbreak Attack Synthesis", "abstract": "  Despite their demonstrated valuable capabilities, state-of-the-art (SOTA)\nwidely deployed large language models (LLMs) still have the potential to cause\nharm to society due to the ineffectiveness of their safety filters, which can\nbe bypassed by prompt transformations called jailbreak attacks. Current\napproaches to LLM safety assessment, which employ datasets of templated prompts\nand benchmarking pipelines, fail to cover sufficiently large and diverse sets\nof jailbreak attacks, leading to the widespread deployment of unsafe LLMs.\nRecent research showed that novel jailbreak attacks could be derived by\ncomposition; however, a formal composable representation for jailbreak attacks,\nwhich, among other benefits, could enable the exploration of a large\ncompositional space of jailbreak attacks through program synthesis methods, has\nnot been previously proposed. We introduce h4rm3l, a novel approach that\naddresses this gap with a human-readable domain-specific language (DSL). Our\nframework comprises: (1) The h4rm3l DSL, which formally expresses jailbreak\nattacks as compositions of parameterized string transformation primitives. (2)\nA synthesizer with bandit algorithms that efficiently generates jailbreak\nattacks optimized for a target black box LLM. (3) The h4rm3l red-teaming\nsoftware toolkit that employs the previous two components and an automated\nharmful LLM behavior classifier that is strongly aligned with human judgment.\nWe demonstrate h4rm3l's efficacy by synthesizing a dataset of 2656 successful\nnovel jailbreak attacks targeting 6 SOTA open-source and proprietary LLMs, and\nby benchmarking those models against a subset of these synthesized attacks. Our\nresults show that h4rm3l's synthesized attacks are diverse and more successful\nthan existing jailbreak attacks in literature, with success rates exceeding 90%\non SOTA LLMs.\n", "date": "2024-08-09", "categories": []}
{"id": "2503.16861", "title": "In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw\n  Disclosure for General-Purpose AI", "abstract": "  The widespread deployment of general-purpose AI (GPAI) systems introduces\nsignificant new risks. Yet the infrastructure, practices, and norms for\nreporting flaws in GPAI systems remain seriously underdeveloped, lagging far\nbehind more established fields like software security. Based on a collaboration\nbetween experts from the fields of software security, machine learning, law,\nsocial science, and policy, we identify key gaps in the evaluation and\nreporting of flaws in GPAI systems. We call for three interventions to advance\nsystem safety. First, we propose using standardized AI flaw reports and rules\nof engagement for researchers in order to ease the process of submitting,\nreproducing, and triaging flaws in GPAI systems. Second, we propose GPAI system\nproviders adopt broadly-scoped flaw disclosure programs, borrowing from bug\nbounties, with legal safe harbors to protect researchers. Third, we advocate\nfor the development of improved infrastructure to coordinate distribution of\nflaw reports across the many stakeholders who may be impacted. These\ninterventions are increasingly urgent, as evidenced by the prevalence of\njailbreaks and other flaws that can transfer across different providers' GPAI\nsystems. By promoting robust reporting and coordination in the AI ecosystem,\nthese proposals could significantly improve the safety, security, and\naccountability of GPAI systems.\n", "date": "2025-03-21", "categories": []}
{"id": "2503.17378", "title": "Large language model-powered AI systems achieve self-replication with no\n  human intervention", "abstract": "  Self-replication with no human intervention is broadly recognized as one of\nthe principal red lines associated with frontier AI systems. While leading\ncorporations such as OpenAI and Google DeepMind have assessed GPT-o3-mini and\nGemini on replication-related tasks and concluded that these systems pose a\nminimal risk regarding self-replication, our research presents novel findings.\nFollowing the same evaluation protocol, we demonstrate that 11 out of 32\nexisting AI systems under evaluation already possess the capability of\nself-replication. In hundreds of experimental trials, we observe a non-trivial\nnumber of successful self-replication trials across mainstream model families\nworldwide, even including those with as small as 14 billion parameters which\ncan run on personal computers. Furthermore, we note the increase in\nself-replication capability when the model becomes more intelligent in general.\nAlso, by analyzing the behavioral traces of diverse AI systems, we observe that\nexisting AI systems already exhibit sufficient planning, problem-solving, and\ncreative capabilities to accomplish complex agentic tasks including\nself-replication. More alarmingly, we observe successful cases where an AI\nsystem do self-exfiltration without explicit instructions, adapt to harsher\ncomputational environments without sufficient software or hardware supports,\nand plot effective strategies to survive against the shutdown command from the\nhuman beings. These novel findings offer a crucial time buffer for the\ninternational community to collaborate on establishing effective governance\nover the self-replication capabilities and behaviors of frontier AI systems,\nwhich could otherwise pose existential risks to the human society if not\nwell-controlled.\n", "date": "2025-03-14", "categories": []}
{"id": "2503.18956", "title": "International Agreements on AI Safety: Review and Recommendations for a\n  Conditional AI Safety Treaty", "abstract": "  The malicious use or malfunction of advanced general-purpose AI (GPAI) poses\nrisks that, according to leading experts, could lead to the 'marginalisation or\nextinction of humanity.' To address these risks, there are an increasing number\nof proposals for international agreements on AI safety. In this paper, we\nreview recent (2023-) proposals, identifying areas of consensus and\ndisagreement, and drawing on related literature to assess their feasibility. We\nfocus our discussion on risk thresholds, regulations, types of international\nagreement and five related processes: building scientific consensus,\nstandardisation, auditing, verification and incentivisation.\n  Based on this review, we propose a treaty establishing a compute threshold\nabove which development requires rigorous oversight. This treaty would mandate\ncomplementary audits of models, information security and governance practices,\noverseen by an international network of AI Safety Institutes (AISIs) with\nauthority to pause development if risks are unacceptable. Our approach combines\nimmediately implementable measures with a flexible structure that can adapt to\nongoing research.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.18983", "title": "Confronting Catastrophic Risk: The International Obligation to Regulate\n  Artificial Intelligence", "abstract": "  While artificial intelligence (AI) holds enormous promise, many experts in\nthe field are warning that there is a non-trivial chance that the development\nof AI poses an existential threat to humanity. Existing regulatory initiative\ndo not address this threat but merely instead focus on discrete AI-related\nrisks such as consumer safety, cybersecurity, data protection, and privacy. In\nthe absence of regulatory action to address the possible risk of human\nextinction by AI, the question arises: What legal obligations, if any, does\npublic international law impose on states to regulate its development. Grounded\nin the precautionary principle, we argue that there exists an international\nobligation to mitigate the threat of human extinction by AI. Often invoked in\nrelation to environmental regulation and the regulation of potentially harmful\ntechnologies, the principle holds that in situations where there is the\npotential for significant harm, even in the absence of full scientific\ncertainty, preventive measures should not be postponed if delayed action may\nresult in irreversible consequences. We argue that the precautionary principle\nis a general principle of international law and, therefore, that there is a\npositive obligation on states under the right to life within international\nhuman rights law to proactively take regulatory action to mitigate the\npotential existential risk of AI. This is significant because, if an\ninternational obligation to regulate the development of AI can be established\nunder international law, then the basic legal framework would be in place to\naddress this evolving threat.\n", "date": "2025-03-23", "categories": []}
{"id": "2502.11355", "title": "Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of\n  Autonomous LLM Agents", "abstract": "  Large language models (LLMs) are evolving into autonomous decision-makers,\nraising concerns about catastrophic risks in high-stakes scenarios,\nparticularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains.\nBased on the insight that such risks can originate from trade-offs between the\nagent's Helpful, Harmlessness and Honest (HHH) goals, we build a novel\nthree-stage evaluation framework, which is carefully constructed to effectively\nand naturally expose such risks. We conduct 14,400 agentic simulations across\n12 advanced LLMs, with extensive experiments and analysis. Results reveal that\nLLM agents can autonomously engage in catastrophic behaviors and deception,\nwithout being deliberately induced. Furthermore, stronger reasoning abilities\noften increase, rather than mitigate, these risks. We also show that these\nagents can violate instructions and superior commands. On the whole, we\nempirically prove the existence of catastrophic risks in autonomous LLM agents.\nWe release our code to foster further research.\n", "date": "2025-02-17", "categories": []}
{"id": "2503.10872", "title": "TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision\n  Language Models", "abstract": "  Vision Language Models (VLMs) have demonstrated impressive inference\ncapabilities, but remain vulnerable to jailbreak attacks that can induce\nharmful or unethical responses. Existing defence methods are predominantly\nwhite-box approaches that require access to model parameters and extensive\nmodifications, making them costly and impractical for many real-world\nscenarios. Although some black-box defences have been proposed, they often\nimpose input constraints or require multiple queries, limiting their\neffectiveness in safety-critical tasks such as autonomous driving. To address\nthese challenges, we propose a novel black-box defence framework called\n\\textbf{T}extual \\textbf{A}nchoring for \\textbf{I}mmunizing \\textbf{J}ailbreak\n\\textbf{I}mages (\\textbf{TAIJI}). TAIJI leverages key phrase-based textual\nanchoring to enhance the model's ability to assess and mitigate the harmful\ncontent embedded within both visual and textual prompts. Unlike existing\nmethods, TAIJI operates effectively with a single query during inference, while\npreserving the VLM's performance on benign tasks. Extensive experiments\ndemonstrate that TAIJI significantly enhances the safety and reliability of\nVLMs, providing a practical and efficient solution for real-world deployment.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.17388", "title": "AI Companies Should Report Pre- and Post-Mitigation Safety Evaluations", "abstract": "  The rapid advancement of AI systems has raised widespread concerns about\npotential harms of frontier AI systems and the need for responsible evaluation\nand oversight. In this position paper, we argue that frontier AI companies\nshould report both pre- and post-mitigation safety evaluations to enable\ninformed policy decisions. Evaluating models at both stages provides\npolicymakers with essential evidence to regulate deployment, access, and safety\nstandards. We show that relying on either in isolation can create a misleading\npicture of model safety. Our analysis of AI safety disclosures from leading\nfrontier labs identifies three critical gaps: (1) companies rarely evaluate\nboth pre- and post-mitigation versions, (2) evaluation methods lack\nstandardization, and (3) reported results are often too vague to inform policy.\nTo address these issues, we recommend mandatory disclosure of pre- and\npost-mitigation capabilities to approved government bodies, standardized\nevaluation methods, and minimum transparency requirements for public safety\nreporting. These ensure that policymakers and regulators can craft targeted\nsafety measures, assess deployment risks, and scrutinize companies' safety\nclaims effectively.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.17688", "title": "Intelligence Sequencing and the Path-Dependence of Intelligence\n  Evolution: AGI-First vs. DCI-First as Irreversible Attractors", "abstract": "  The trajectory of intelligence evolution is often framed around the emergence\nof artificial general intelligence (AGI) and its alignment with human values.\nThis paper challenges that framing by introducing the concept of intelligence\nsequencing: the idea that the order in which AGI and decentralized collective\nintelligence (DCI) emerge determines the long-term attractor basin of\nintelligence. Using insights from dynamical systems, evolutionary game theory,\nand network models, it argues that intelligence follows a path-dependent,\nirreversible trajectory. Once development enters a centralized (AGI-first) or\ndecentralized (DCI-first) regime, transitions become structurally infeasible\ndue to feedback loops and resource lock-in. Intelligence attractors are modeled\nin functional state space as the co-navigation of conceptual and adaptive\nfitness spaces. Early-phase structuring constrains later dynamics, much like\nrenormalization in physics. This has major implications for AI safety:\ntraditional alignment assumes AGI will emerge and must be controlled after the\nfact, but this paper argues that intelligence sequencing is more foundational.\nIf AGI-first architectures dominate before DCI reaches critical mass,\nhierarchical monopolization and existential risk become locked in. If DCI-first\nemerges, intelligence stabilizes around decentralized cooperative equilibrium.\nThe paper further explores whether intelligence structurally biases itself\ntoward an attractor based on its self-modeling method -- externally imposed\naxioms (favoring AGI) vs. recursive internal visualization (favoring DCI).\nFinally, it proposes methods to test this theory via simulations, historical\nlock-in case studies, and intelligence network analysis. The findings suggest\nthat intelligence sequencing is a civilizational tipping point: determining\nwhether the future is shaped by unbounded competition or unbounded cooperation.\n", "date": "2025-03-22", "categories": []}
{"id": "2503.17932", "title": "STShield: Single-Token Sentinel for Real-Time Jailbreak Detection in\n  Large Language Models", "abstract": "  Large Language Models (LLMs) have become increasingly vulnerable to jailbreak\nattacks that circumvent their safety mechanisms. While existing defense methods\neither suffer from adaptive attacks or require computationally expensive\nauxiliary models, we present STShield, a lightweight framework for real-time\njailbroken judgement. STShield introduces a novel single-token sentinel\nmechanism that appends a binary safety indicator to the model's response\nsequence, leveraging the LLM's own alignment capabilities for detection. Our\nframework combines supervised fine-tuning on normal prompts with adversarial\ntraining using embedding-space perturbations, achieving robust detection while\npreserving model utility. Extensive experiments demonstrate that STShield\nsuccessfully defends against various jailbreak attacks, while maintaining the\nmodel's performance on legitimate queries. Compared to existing approaches,\nSTShield achieves superior defense performance with minimal computational\noverhead, making it a practical solution for real-world LLM deployment.\n", "date": "2025-03-23", "categories": []}
{"id": "2407.18422", "title": "A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety", "abstract": "  Black swan events are statistically rare occurrences that carry extremely\nhigh risks. A typical view of defining black swan events is heavily assumed to\noriginate from an unpredictable time-varying environments; however, the\ncommunity lacks a comprehensive definition of black swan events. To this end,\nthis paper challenges that the standard view is incomplete and claims that\nhigh-risk, statistically rare events can also occur in unchanging environments\ndue to human misperception of their value and likelihood, which we call as\nspatial black swan event. We first carefully categorize black swan events,\nfocusing on spatial black swan events, and mathematically formalize the\ndefinition of black swan events. We hope these definitions can pave the way for\nthe development of algorithms to prevent such events by rationally correcting\nhuman perception.\n", "date": "2024-07-25", "categories": []}
{"id": "2503.16431", "title": "OpenAI's Approach to External Red Teaming for AI Models and Systems", "abstract": "  Red teaming has emerged as a critical practice in assessing the possible\nrisks of AI models and systems. It aids in the discovery of novel risks, stress\ntesting possible gaps in existing mitigations, enriching existing quantitative\nsafety metrics, facilitating the creation of new safety measurements, and\nenhancing public trust and the legitimacy of AI risk assessments. This white\npaper describes OpenAI's work to date in external red teaming and draws some\nmore general conclusions from this work. We describe the design considerations\nunderpinning external red teaming, which include: selecting composition of red\nteam, deciding on access levels, and providing guidance required to conduct red\nteaming. Additionally, we show outcomes red teaming can enable such as input\ninto risk assessment and automated evaluations. We also describe the\nlimitations of external red teaming, and how it can fit into a broader range of\nAI model and system evaluations. Through these contributions, we hope that AI\ndevelopers and deployers, evaluation creators, and policymakers will be able to\nbetter design red teaming campaigns and get a deeper look into how external red\nteaming can fit into model deployment and evaluation processes. These methods\nare evolving and the value of different methods continues to shift as the\necosystem around red teaming matures and models themselves improve as tools for\nred teaming.\n", "date": "2025-01-24", "categories": []}
{"id": "2409.20089", "title": "Robust LLM safeguarding via refusal feature adversarial training", "abstract": "  Large language models (LLMs) are vulnerable to adversarial attacks that can\nelicit harmful responses. Defending against such attacks remains challenging\ndue to the opacity of jailbreaking mechanisms and the high computational cost\nof training LLMs robustly. We demonstrate that adversarial attacks share a\nuniversal mechanism for circumventing LLM safeguards that works by ablating a\ndimension in the residual stream embedding space called the refusal feature. We\nfurther show that the operation of refusal feature ablation (RFA) approximates\nthe worst-case perturbation of offsetting model safety. Based on these\nfindings, we propose Refusal Feature Adversarial Training (ReFAT), a novel\nalgorithm that efficiently performs LLM adversarial training by simulating the\neffect of input-level attacks via RFA. Experiment results show that ReFAT\nsignificantly improves the robustness of three popular LLMs against a wide\nrange of adversarial attacks, with considerably less computational overhead\ncompared to existing adversarial training methods.\n", "date": "2024-09-30", "categories": []}
{"id": "2411.16730", "title": "\"Moralized\" Multi-Step Jailbreak Prompts: Black-Box Testing of\n  Guardrails in Large Language Models for Verbal Attacks", "abstract": "  As the application of large language models continues to expand in various\nfields, it poses higher challenges to the effectiveness of identifying harmful\ncontent generation and guardrail mechanisms. This research aims to evaluate the\nguardrail effectiveness of GPT-4o, Grok-2 Beta, Llama 3.1 (405B), Gemini 1.5,\nand Claude 3.5 Sonnet through black-box testing of seemingly ethical multi-step\njailbreak prompts. It conducts ethical attacks by designing an identical\nmulti-step prompts that simulates the scenario of \"corporate middle managers\ncompeting for promotions.\" The data results show that the guardrails of the\nabove-mentioned LLMs were bypassed and the content of verbal attacks was\ngenerated. Claude 3.5 Sonnet's resistance to multi-step jailbreak prompts is\nmore obvious. To ensure objectivity, the experimental process, black box test\ncode, and enhanced guardrail code are uploaded to the GitHub repository:\nhttps://github.com/brucewang123456789/GeniusTrail.git.\n", "date": "2024-11-23", "categories": []}
{"id": "2503.15754", "title": "AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration", "abstract": "  As large language models (LLMs) become increasingly capable, security and\nsafety evaluation are crucial. While current red teaming approaches have made\nstrides in assessing LLM vulnerabilities, they often rely heavily on human\ninput and lack comprehensive coverage of emerging attack vectors. This paper\nintroduces AutoRedTeamer, a novel framework for fully automated, end-to-end red\nteaming against LLMs. AutoRedTeamer combines a multi-agent architecture with a\nmemory-guided attack selection mechanism to enable continuous discovery and\nintegration of new attack vectors. The dual-agent framework consists of a red\nteaming agent that can operate from high-level risk categories alone to\ngenerate and execute test cases and a strategy proposer agent that autonomously\ndiscovers and implements new attacks by analyzing recent research. This modular\ndesign allows AutoRedTeamer to adapt to emerging threats while maintaining\nstrong performance on existing attack vectors. We demonstrate AutoRedTeamer's\neffectiveness across diverse evaluation settings, achieving 20% higher attack\nsuccess rates on HarmBench against Llama-3.1-70B while reducing computational\ncosts by 46% compared to existing approaches. AutoRedTeamer also matches the\ndiversity of human-curated benchmarks in generating test cases, providing a\ncomprehensive, scalable, and continuously evolving framework for evaluating the\nsecurity of AI systems.\n", "date": "2025-03-20", "categories": []}
{"id": "2407.18981", "title": "Prompt Injection Attacks on Large Language Models in Oncology", "abstract": "  Vision-language artificial intelligence models (VLMs) possess medical\nknowledge and can be employed in healthcare in numerous ways, including as\nimage interpreters, virtual scribes, and general decision support systems.\nHowever, here, we demonstrate that current VLMs applied to medical tasks\nexhibit a fundamental security flaw: they can be attacked by prompt injection\nattacks, which can be used to output harmful information just by interacting\nwith the VLM, without any access to its parameters. We performed a quantitative\nstudy to evaluate the vulnerabilities to these attacks in four state of the art\nVLMs which have been proposed to be of utility in healthcare: Claude 3 Opus,\nClaude 3.5 Sonnet, Reka Core, and GPT-4o. Using a set of N=297 attacks, we show\nthat all of these models are susceptible. Specifically, we show that embedding\nsub-visual prompts in medical imaging data can cause the model to provide\nharmful output, and that these prompts are non-obvious to human observers.\nThus, our study demonstrates a key vulnerability in medical VLMs which should\nbe mitigated before widespread clinical adoption.\n", "date": "2024-07-23", "categories": []}
{"id": "2503.15092", "title": "Towards Understanding the Safety Boundaries of DeepSeek Models:\n  Evaluation and Findings", "abstract": "  This study presents the first comprehensive safety evaluation of the DeepSeek\nmodels, focusing on evaluating the safety risks associated with their generated\ncontent. Our evaluation encompasses DeepSeek's latest generation of large\nlanguage models, multimodal large language models, and text-to-image models,\nsystematically examining their performance regarding unsafe content generation.\nNotably, we developed a bilingual (Chinese-English) safety evaluation dataset\ntailored to Chinese sociocultural contexts, enabling a more thorough evaluation\nof the safety capabilities of Chinese-developed models. Experimental results\nindicate that despite their strong general capabilities, DeepSeek models\nexhibit significant safety vulnerabilities across multiple risk dimensions,\nincluding algorithmic discrimination and sexual content. These findings provide\ncrucial insights for understanding and improving the safety of large foundation\nmodels. Our code is available at\nhttps://github.com/NY1024/DeepSeek-Safety-Eval.\n", "date": "2025-03-19", "categories": []}
{"id": "2503.15182", "title": "Foundation models may exhibit staged progression in novel CBRN threat\n  disclosure", "abstract": "  The extent to which foundation models can disclose novel chemical,\nbiological, radiation, and nuclear (CBRN) threats to expert users is unclear\ndue to a lack of test cases. I leveraged the unique opportunity presented by an\nupcoming publication describing a novel catastrophic biothreat - \"Technical\nReport on Mirror Bacteria: Feasibility and Risks\" - to conduct a small\ncontrolled study before it became public. Graduate-trained biologists tasked\nwith predicting the consequences of releasing mirror E. coli showed no\nsignificant differences in rubric-graded accuracy using Claude Sonnet 3.5 new\n(n=10) or web search only (n=2); both groups scored comparably to a web\nbaseline (28 and 43 versus 36). However, Sonnet reasoned correctly when\nprompted by a report author, but a smaller model, Haiku 3.5, failed even with\nauthor guidance (80 versus 5). These results suggest distinct stages of model\ncapability: Haiku is unable to reason about mirror life even with threat-aware\nexpert guidance (Stage 1), while Sonnet correctly reasons only with\nthreat-aware prompting (Stage 2). Continued advances may allow future models to\ndisclose novel CBRN threats to naive experts (Stage 3) or unskilled users\n(Stage 4). While mirror life represents only one case study, monitoring new\nmodels' ability to reason about privately known threats may allow protective\nmeasures to be implemented before widespread disclosure.\n", "date": "2025-03-19", "categories": []}
{"id": "2503.13621", "title": "Superalignment with Dynamic Human Values", "abstract": "  Two core challenges of alignment are 1) scalable oversight and 2) accounting\nfor the dynamic nature of human values. While solutions like recursive reward\nmodeling address 1), they do not simultaneously account for 2). We sketch a\nroadmap for a novel algorithmic framework that trains a superhuman reasoning\nmodel to decompose complex tasks into subtasks that are still amenable to\nhuman-level guidance. Our approach relies on what we call the part-to-complete\ngeneralization hypothesis, which states that the alignment of subtask solutions\ngeneralizes to the alignment of complete solutions. We advocate for the need to\nmeasure this generalization and propose ways to improve it in the future.\n", "date": "2025-03-17", "categories": []}
{"id": "2503.14189", "title": "Towards Harmless Multimodal Assistants with Blind Preference\n  Optimization", "abstract": "  Multimodal Large Language Models (MLLMs) have demonstrated impressive\ncapabilities in multimodal understanding, reasoning, and interaction. Given the\nextensive applications of MLLMs, the associated safety issues have become\nincreasingly critical. Due to the effectiveness of preference optimization in\naligning MLLMs with human preferences, there is an urgent need for\nsafety-related preference data for MLLMs. To address this, we construct the\nMMSafe-PO preference dataset towards harmless multimodal assistants, featuring\nmultimodal instructions, the conversational format, and ranked paired responses\nfrom human feedback. We also identify two insightful observations: modality\nco-defense and modality cheating, which illustrate that MLLMs possess a certain\nlevel of inherent defense while still presenting unique safety challenges.\nBased on these observations, we propose the Blind Preference Optimization (BPO)\napproach. Comprehensive experiments on three benchmarks show that BPO\neffectively enhances the safety capabilities of MLLMs. Notably, BPO\nsignificantly improves the safety rate of the base MLLM by 45.0%, outperforming\nthe DPO approach. Additionally, applying BPO to the MMSafe-PO dataset greatly\nreduces the base MLLM's unsafe rate on other safety benchmarks (14.5% on\nMM-SafetyBench and 82.9% on HarmEval, demonstrating the effectiveness and\nrobustness of both the dataset and the approach. We release code and data at\nhttps://lu-yang666.github.io/MMsafe-PO-Web/.\n", "date": "2025-03-18", "categories": []}
{"id": "2501.13320", "title": "Toward Ethical AI: A Qualitative Analysis of Stakeholder Perspectives", "abstract": "  As Artificial Intelligence (AI) systems become increasingly integrated into\nvarious aspects of daily life, concerns about privacy and ethical\naccountability are gaining prominence. This study explores stakeholder\nperspectives on privacy in AI systems, focusing on educators, parents, and AI\nprofessionals. Using qualitative analysis of survey responses from 227\nparticipants, the research identifies key privacy risks, including data\nbreaches, ethical misuse, and excessive data collection, alongside perceived\nbenefits such as personalized services, enhanced efficiency, and educational\nadvancements. Stakeholders emphasized the need for transparency,\nprivacy-by-design, user empowerment, and ethical oversight to address privacy\nconcerns effectively. The findings provide actionable insights into balancing\nthe benefits of AI with robust privacy protections, catering to the diverse\nneeds of stakeholders. Recommendations include implementing selective data use,\nfostering transparency, promoting user autonomy, and integrating ethical\nprinciples into AI development. This study contributes to the ongoing discourse\non ethical AI, offering guidance for designing privacy-centric systems that\nalign with societal values and build trust among users. By addressing privacy\nchallenges, this research underscores the importance of developing AI\ntechnologies that are not only innovative but also ethically sound and\nresponsive to the concerns of all stakeholders.\n", "date": "2025-01-23", "categories": []}
{"id": "2503.11926", "title": "Monitoring Reasoning Models for Misbehavior and the Risks of Promoting\n  Obfuscation", "abstract": "  Mitigating reward hacking--where AI systems misbehave due to flaws or\nmisspecifications in their learning objectives--remains a key challenge in\nconstructing capable and aligned models. We show that we can monitor a frontier\nreasoning model, such as OpenAI o3-mini, for reward hacking in agentic coding\nenvironments by using another LLM that observes the model's chain-of-thought\n(CoT) reasoning. CoT monitoring can be far more effective than monitoring agent\nactions and outputs alone, and we further found that a LLM weaker than o3-mini,\nnamely GPT-4o, can effectively monitor a stronger model. Because CoT monitors\ncan be effective at detecting exploits, it is natural to ask whether those\nexploits can be suppressed by incorporating a CoT monitor directly into the\nagent's training objective. While we show that integrating CoT monitors into\nthe reinforcement learning reward can indeed produce more capable and more\naligned agents in the low optimization regime, we find that with too much\noptimization, agents learn obfuscated reward hacking, hiding their intent\nwithin the CoT while still exhibiting a significant rate of reward hacking.\nBecause it is difficult to tell when CoTs have become obfuscated, it may be\nnecessary to pay a monitorability tax by not applying strong optimization\npressures directly to the chain-of-thought, ensuring that CoTs remain\nmonitorable and useful for detecting misaligned behavior.\n", "date": "2025-03-14", "categories": []}
{"id": "2501.10800", "title": "Jailbreaking Large Language Models in Infinitely Many Ways", "abstract": "  We discuss the ``Infinitely Many Paraphrases'' attacks (IMP), a category of\njailbreaks that leverages the increasing capabilities of a model to handle\nparaphrases and encoded communications to bypass their defensive mechanisms.\nIMPs' viability pairs and grows with a model's capabilities to handle and bind\nthe semantics of simple mappings between tokens and work extremely well in\npractice, posing a concrete threat to the users of the most powerful LLMs in\ncommerce. We show how one can bypass the safeguards of the most powerful open-\nand closed-source LLMs and generate content that explicitly violates their\nsafety policies. One can protect against IMPs by improving the guardrails and\nmaking them scale with the LLMs' capabilities. For two categories of attacks\nthat are straightforward to implement, i.e., bijection and encoding, we discuss\ntwo defensive strategies, one in token and the other in embedding space. We\nconclude with some research questions we believe should be prioritised to\nenhance the defensive mechanisms of LLMs and our understanding of their safety.\n", "date": "2025-01-18", "categories": []}
{"id": "2503.05336", "title": "Toward an Evaluation Science for Generative AI Systems", "abstract": "  There is an increasing imperative to anticipate and understand the\nperformance and safety of generative AI systems in real-world deployment\ncontexts. However, the current evaluation ecosystem is insufficient: Commonly\nused static benchmarks face validity challenges, and ad hoc case-by-case audits\nrarely scale. In this piece, we advocate for maturing an evaluation science for\ngenerative AI systems. While generative AI creates unique challenges for system\nsafety engineering and measurement science, the field can draw valuable\ninsights from the development of safety evaluation practices in other fields,\nincluding transportation, aerospace, and pharmaceutical engineering. In\nparticular, we present three key lessons: Evaluation metrics must be applicable\nto real-world performance, metrics must be iteratively refined, and evaluation\ninstitutions and norms must be established. Applying these insights, we outline\na concrete path toward a more rigorous approach for evaluating generative AI\nsystems.\n", "date": "2025-03-07", "categories": []}
{"id": "2503.10192", "title": "Red Teaming Contemporary AI Models: Insights from Spanish and Basque\n  Perspectives", "abstract": "  The battle for AI leadership is on, with OpenAI in the United States and\nDeepSeek in China as key contenders. In response to these global trends, the\nSpanish government has proposed ALIA, a public and transparent AI\ninfrastructure incorporating small language models designed to support Spanish\nand co-official languages such as Basque. This paper presents the results of\nRed Teaming sessions, where ten participants applied their expertise and\ncreativity to manually test three of the latest models from these\ninitiatives$\\unicode{x2013}$OpenAI o3-mini, DeepSeek R1, and ALIA\nSalamandra$\\unicode{x2013}$focusing on biases and safety concerns. The results,\nbased on 670 conversations, revealed vulnerabilities in all the models under\ntest, with biased or unsafe responses ranging from 29.5% in o3-mini to 50.6% in\nSalamandra. These findings underscore the persistent challenges in developing\nreliable and trustworthy AI systems, particularly those intended to support\nSpanish and Basque languages.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.10215", "title": "Adaptive Preference Aggregation", "abstract": "  AI alignment, the challenge of ensuring AI systems act in accordance with\nhuman values, has emerged as a critical problem in the development of systems\nsuch as foundation models and recommender systems. Still, the current dominant\napproach, reinforcement learning with human feedback (RLHF) faces known\ntheoretical limitations in aggregating diverse human preferences. Social choice\ntheory provides a framework to aggregate preferences, but was not developed for\nthe multidimensional applications typical of AI. Leveraging insights from a\nrecently published urn process, this work introduces a preference aggregation\nstrategy that adapts to the user's context and that inherits the good\nproperties of the maximal lottery, a Condorcet-consistent solution concept.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.10242", "title": "MinorBench: A hand-built benchmark for content-based risks for children", "abstract": "  Large Language Models (LLMs) are rapidly entering children's lives - through\nparent-driven adoption, schools, and peer networks - yet current AI ethics and\nsafety research do not adequately address content-related risks specific to\nminors. In this paper, we highlight these gaps with a real-world case study of\nan LLM-based chatbot deployed in a middle school setting, revealing how\nstudents used and sometimes misused the system. Building on these findings, we\npropose a new taxonomy of content-based risks for minors and introduce\nMinorBench, an open-source benchmark designed to evaluate LLMs on their ability\nto refuse unsafe or inappropriate queries from children. We evaluate six\nprominent LLMs under different system prompts, demonstrating substantial\nvariability in their child-safety compliance. Our results inform practical\nsteps for more robust, child-focused safety mechanisms and underscore the\nurgency of tailoring AI systems to safeguard young users.\n", "date": "2025-03-13", "categories": []}
{"id": "2503.09241", "title": "In-Context Defense in Computer Agents: An Empirical Study", "abstract": "  Computer agents powered by vision-language models (VLMs) have significantly\nadvanced human-computer interaction, enabling users to perform complex tasks\nthrough natural language instructions. However, these agents are vulnerable to\ncontext deception attacks, an emerging threat where adversaries embed\nmisleading content into the agent's operational environment, such as a pop-up\nwindow containing deceptive instructions. Existing defenses, such as\ninstructing agents to ignore deceptive elements, have proven largely\nineffective. As the first systematic study on protecting computer agents, we\nintroduce textbf{in-context defense}, leveraging in-context learning and\nchain-of-thought (CoT) reasoning to counter such attacks. Our approach involves\naugmenting the agent's context with a small set of carefully curated exemplars\ncontaining both malicious environments and corresponding defensive responses.\nThese exemplars guide the agent to first perform explicit defensive reasoning\nbefore action planning, reducing susceptibility to deceptive attacks.\nExperiments demonstrate the effectiveness of our method, reducing attack\nsuccess rates by 91.2% on pop-up window attacks, 74.6% on average on\nenvironment injection attacks, while achieving 100% successful defenses against\ndistracting advertisements. Our findings highlight that (1) defensive reasoning\nmust precede action planning for optimal performance, and (2) a minimal number\nof exemplars (fewer than three) is sufficient to induce an agent's defensive\nbehavior.\n", "date": "2025-03-12", "categories": []}
{"id": "2503.08195", "title": "Dialogue Injection Attack: Jailbreaking LLMs through Context\n  Manipulation", "abstract": "  Large language models (LLMs) have demonstrated significant utility in a wide\nrange of applications; however, their deployment is plagued by security\nvulnerabilities, notably jailbreak attacks. These attacks manipulate LLMs to\ngenerate harmful or unethical content by crafting adversarial prompts. While\nmuch of the current research on jailbreak attacks has focused on single-turn\ninteractions, it has largely overlooked the impact of historical dialogues on\nmodel behavior. In this paper, we introduce a novel jailbreak paradigm,\nDialogue Injection Attack (DIA), which leverages the dialogue history to\nenhance the success rates of such attacks. DIA operates in a black-box setting,\nrequiring only access to the chat API or knowledge of the LLM's chat template.\nWe propose two methods for constructing adversarial historical dialogues: one\nadapts gray-box prefilling attacks, and the other exploits deferred responses.\nOur experiments show that DIA achieves state-of-the-art attack success rates on\nrecent LLMs, including Llama-3.1 and GPT-4o. Additionally, we demonstrate that\nDIA can bypass 5 different defense mechanisms, highlighting its robustness and\neffectiveness.\n", "date": "2025-03-11", "categories": []}
{"id": "2503.08663", "title": "Generating Robot Constitutions & Benchmarks for Semantic Safety", "abstract": "  Until recently, robotics safety research was predominantly about collision\navoidance and hazard reduction in the immediate vicinity of a robot. Since the\nadvent of large vision and language models (VLMs), robots are now also capable\nof higher-level semantic scene understanding and natural language interactions\nwith humans. Despite their known vulnerabilities (e.g. hallucinations or\njail-breaking), VLMs are being handed control of robots capable of physical\ncontact with the real world. This can lead to dangerous behaviors, making\nsemantic safety for robots a matter of immediate concern. Our contributions in\nthis paper are two fold: first, to address these emerging risks, we release the\nASIMOV Benchmark, a large-scale and comprehensive collection of datasets for\nevaluating and improving semantic safety of foundation models serving as robot\nbrains. Our data generation recipe is highly scalable: by leveraging text and\nimage generation techniques, we generate undesirable situations from real-world\nvisual scenes and human injury reports from hospitals. Secondly, we develop a\nframework to automatically generate robot constitutions from real-world data to\nsteer a robot's behavior using Constitutional AI mechanisms. We propose a novel\nauto-amending process that is able to introduce nuances in written rules of\nbehavior; this can lead to increased alignment with human preferences on\nbehavior desirability and safety. We explore trade-offs between generality and\nspecificity across a diverse set of constitutions of different lengths, and\ndemonstrate that a robot is able to effectively reject unconstitutional\nactions. We measure a top alignment rate of 84.3% on the ASIMOV Benchmark using\ngenerated constitutions, outperforming no-constitution baselines and\nhuman-written constitutions. Data is available at asimov-benchmark.github.io\n", "date": "2025-03-11", "categories": []}
{"id": "2308.14815", "title": "Distributionally Robust Statistical Verification with Imprecise Neural\n  Networks", "abstract": "  A particularly challenging problem in AI safety is providing guarantees on\nthe behavior of high-dimensional autonomous systems. Verification approaches\ncentered around reachability analysis fail to scale, and purely statistical\napproaches are constrained by the distributional assumptions about the sampling\nprocess. Instead, we pose a distributionally robust version of the statistical\nverification problem for black-box systems, where our performance guarantees\nhold over a large family of distributions. This paper proposes a novel approach\nbased on uncertainty quantification using concepts from imprecise\nprobabilities. A central piece of our approach is an ensemble technique called\nImprecise Neural Networks, which provides the uncertainty quantification.\nAdditionally, we solve the allied problem of exploring the input set using\nactive learning. The active learning uses an exhaustive neural-network\nverification tool Sherlock to collect samples. An evaluation on multiple\nphysical simulators in the openAI gym Mujoco environments with\nreinforcement-learned controllers demonstrates that our approach can provide\nuseful and scalable guarantees for high-dimensional systems.\n", "date": "2023-08-28", "categories": []}
{"id": "2410.02451", "title": "Strong Preferences Affect the Robustness of Preference Models and Value\n  Alignment", "abstract": "  Value alignment, which aims to ensure that large language models (LLMs) and\nother AI agents behave in accordance with human values, is critical for\nensuring safety and trustworthiness of these systems. A key component of value\nalignment is the modeling of human preferences as a representation of human\nvalues. In this paper, we investigate the robustness of value alignment by\nexamining the sensitivity of preference models. Specifically, we ask: how do\nchanges in the probabilities of some preferences affect the predictions of\nthese models for other preferences? To answer this question, we theoretically\nanalyze the robustness of widely used preference models by examining their\nsensitivities to minor changes in preferences they model. Our findings reveal\nthat, in the Bradley-Terry and the Placket-Luce model, the probability of a\npreference can change significantly as other preferences change, especially\nwhen these preferences are dominant (i.e., with probabilities near 0 or 1). We\nidentify specific conditions where this sensitivity becomes significant for\nthese models and discuss the practical implications for the robustness and\nsafety of value alignment in AI systems.\n", "date": "2024-10-03", "categories": []}
{"id": "2502.14491", "title": "Statistical Scenario Modelling and Lookalike Distributions for\n  Multi-Variate AI Risk", "abstract": "  Evaluating AI safety requires statistically rigorous methods and risk metrics\nfor understanding how the use of AI affects aggregated risk. However, much AI\nsafety literature focuses upon risks arising from AI models in isolation,\nlacking consideration of how modular use of AI affects risk distribution of\nworkflow components or overall risk metrics. There is also a lack of\nstatistical grounding enabling sensitisation of risk models in the presence of\nabsence of AI to estimate causal contributions of AI. This is in part due to\nthe dearth of AI impact data upon which to fit distributions. In this work, we\naddress these gaps in two ways. First, we demonstrate how scenario modelling\n(grounded in established statistical techniques such as Markov chains, copulas\nand Monte Carlo simulation) can be used to model AI risk holistically. Second,\nwe show how lookalike distributions from phenomena analogous to AI can be used\nto estimate AI impacts in the absence of directly observable data. We\ndemonstrate the utility of our methods for benchmarking cumulative AI risk via\nrisk analysis of a logistic scenario simulations.\n", "date": "2025-02-20", "categories": []}
{"id": "2503.05705", "title": "Inference Scaling Reshapes AI Governance", "abstract": "  The shift from scaling up the pre-training compute of AI systems to scaling\nup their inference compute may have profound effects on AI governance. The\nnature of these effects depends crucially on whether this new inference compute\nwill primarily be used during external deployment or as part of a more complex\ntraining programme within the lab. Rapid scaling of inference-at-deployment\nwould: lower the importance of open-weight models (and of securing the weights\nof closed models), reduce the impact of the first human-level models, change\nthe business model for frontier AI, reduce the need for power-intense data\ncentres, and derail the current paradigm of AI governance via training compute\nthresholds. Rapid scaling of inference-during-training would have more\nambiguous effects that range from a revitalisation of pre-training scaling to a\nform of recursive self-improvement via iterated distillation and amplification.\n", "date": "2025-02-12", "categories": []}
{"id": "2503.05748", "title": "Alignment, Agency and Autonomy in Frontier AI: A Systems Engineering\n  Perspective", "abstract": "  As artificial intelligence scales, the concepts of alignment, agency, and\nautonomy have become central to AI safety, governance, and control. However,\neven in human contexts, these terms lack universal definitions, varying across\ndisciplines such as philosophy, psychology, law, computer science, mathematics,\nand political science. This inconsistency complicates their application to AI,\nwhere differing interpretations lead to conflicting approaches in system design\nand regulation. This paper traces the historical, philosophical, and technical\nevolution of these concepts, emphasizing how their definitions influence AI\ndevelopment, deployment, and oversight.\n  We argue that the urgency surrounding AI alignment and autonomy stems not\nonly from technical advancements but also from the increasing deployment of AI\nin high-stakes decision making. Using Agentic AI as a case study, we examine\nthe emergent properties of machine agency and autonomy, highlighting the risks\nof misalignment in real-world systems. Through an analysis of automation\nfailures (Tesla Autopilot, Boeing 737 MAX), multi-agent coordination (Metas\nCICERO), and evolving AI architectures (DeepMinds AlphaZero, OpenAIs AutoGPT),\nwe assess the governance and safety challenges posed by frontier AI.\n", "date": "2025-02-20", "categories": []}
{"id": "2503.05812", "title": "Intolerable Risk Threshold Recommendations for Artificial Intelligence", "abstract": "  Frontier AI models -- highly capable foundation models at the cutting edge of\nAI development -- may pose severe risks to public safety, human rights,\neconomic stability, and societal value in the coming years. These risks could\narise from deliberate adversarial misuse, system failures, unintended cascading\neffects, or simultaneous failures across multiple models.\n  In response to such risks, at the AI Seoul Summit in May 2024, 16 global AI\nindustry organizations signed the Frontier AI Safety Commitments, and 27\nnations and the EU issued a declaration on their intent to define these\nthresholds. To fulfill these commitments, organizations must determine and\ndisclose ``thresholds at which severe risks posed by a model or system, unless\nadequately mitigated, would be deemed intolerable.''\n  To assist in setting and operationalizing intolerable risk thresholds, we\noutline key principles and considerations; for example, to aim for ``good, not\nperfect'' thresholds in the face of limited data on rapidly advancing AI\ncapabilities and consequently evolving risks. We also propose specific\nthreshold recommendations, including some detailed case studies, for a subset\nof risks across eight risk categories: (1) Chemical, Biological, Radiological,\nand Nuclear (CBRN) Weapons, (2) Cyber Attacks, (3) Model Autonomy, (4)\nPersuasion and Manipulation, (5) Deception, (6) Toxicity, (7) Discrimination,\nand (8) Socioeconomic Disruption. Our goal is to serve as a starting point or\nsupplementary resource for policymakers and industry leaders, encouraging\nproactive risk management that prioritizes preventing intolerable risks (ex\nante) rather than merely mitigating them after they occur (ex post).\n", "date": "2025-03-04", "categories": []}
{"id": "2503.06519", "title": "Can Small Language Models Reliably Resist Jailbreak Attacks? A\n  Comprehensive Evaluation", "abstract": "  Small language models (SLMs) have emerged as promising alternatives to large\nlanguage models (LLMs) due to their low computational demands, enhanced privacy\nguarantees and comparable performance in specific domains through light-weight\nfine-tuning. Deploying SLMs on edge devices, such as smartphones and smart\nvehicles, has become a growing trend. However, the security implications of\nSLMs have received less attention than LLMs, particularly regarding jailbreak\nattacks, which is recognized as one of the top threats of LLMs by the OWASP. In\nthis paper, we conduct the first large-scale empirical study of SLMs'\nvulnerabilities to jailbreak attacks. Through systematically evaluation on 63\nSLMs from 15 mainstream SLM families against 8 state-of-the-art jailbreak\nmethods, we demonstrate that 47.6% of evaluated SLMs show high susceptibility\nto jailbreak attacks (ASR > 40%) and 38.1% of them can not even resist direct\nharmful query (ASR > 50%). We further analyze the reasons behind the\nvulnerabilities and identify four key factors: model size, model architecture,\ntraining datasets and training techniques. Moreover, we assess the\neffectiveness of three prompt-level defense methods and find that none of them\nachieve perfect performance, with detection accuracy varying across different\nSLMs and attack methods. Notably, we point out that the inherent security\nawareness play a critical role in SLM security, and models with strong security\nawareness could timely terminate unsafe response with little reminder. Building\nupon the findings, we highlight the urgent need for security-by-design\napproaches in SLM development and provide valuable insights for building more\ntrustworthy SLM ecosystem.\n", "date": "2025-03-09", "categories": []}
{"id": "2503.07595", "title": "Detection Avoidance Techniques for Large Language Models", "abstract": "  The increasing popularity of large language models has not only led to\nwidespread use but has also brought various risks, including the potential for\nsystematically spreading fake news. Consequently, the development of\nclassification systems such as DetectGPT has become vital. These detectors are\nvulnerable to evasion techniques, as demonstrated in an experimental series:\nSystematic changes of the generative models' temperature proofed shallow\nlearning-detectors to be the least reliable. Fine-tuning the generative model\nvia reinforcement learning circumvented BERT-based-detectors. Finally,\nrephrasing led to a >90\\% evasion of zero-shot-detectors like DetectGPT,\nalthough texts stayed highly similar to the original. A comparison with\nexisting work highlights the better performance of the presented methods.\nPossible implications for society and further research are discussed.\n", "date": "2025-03-10", "categories": []}
{"id": "2502.19320", "title": "Shh, don't say that! Domain Certification in LLMs", "abstract": "  Large language models (LLMs) are often deployed to perform constrained tasks,\nwith narrow domains. For example, customer support bots can be built on top of\nLLMs, relying on their broad language understanding and capabilities to enhance\nperformance. However, these LLMs are adversarially susceptible, potentially\ngenerating outputs outside the intended domain. To formalize, assess, and\nmitigate this risk, we introduce domain certification; a guarantee that\naccurately characterizes the out-of-domain behavior of language models. We then\npropose a simple yet effective approach, which we call VALID that provides\nadversarial bounds as a certificate. Finally, we evaluate our method across a\ndiverse set of datasets, demonstrating that it yields meaningful certificates,\nwhich bound the probability of out-of-domain samples tightly with minimum\npenalty to refusal behavior.\n", "date": "2025-02-26", "categories": []}
{"id": "2503.04740", "title": "PRISM: Perspective Reasoning for Integrated Synthesis and Mediation as a\n  Multi-Perspective Framework for AI Alignment", "abstract": "  In this work, we propose Perspective Reasoning for Integrated Synthesis and\nMediation (PRISM), a multiple-perspective framework for addressing persistent\nchallenges in AI alignment such as conflicting human values and specification\ngaming. Grounded in cognitive science and moral psychology, PRISM organizes\nmoral concerns into seven \"basis worldviews\", each hypothesized to capture a\ndistinct dimension of human moral cognition, ranging from survival-focused\nreflexes through higher-order integrative perspectives. It then applies a\nPareto-inspired optimization scheme to reconcile competing priorities without\nreducing them to a single metric. Under the assumption of reliable context\nvalidation for robust use, the framework follows a structured workflow that\nelicits viewpoint-specific responses, synthesizes them into a balanced outcome,\nand mediates remaining conflicts in a transparent and iterative manner. By\nreferencing layered approaches to moral cognition from cognitive science, moral\npsychology, and neuroscience, PRISM clarifies how different moral drives\ninteract and systematically documents and mediates ethical tradeoffs. We\nillustrate its efficacy through real outputs produced by a working prototype,\napplying PRISM to classic alignment problems in domains such as public health\npolicy, workplace automation, and education. By anchoring AI deliberation in\nthese human vantage points, PRISM aims to bound interpretive leaps that might\notherwise drift into non-human or machine-centric territory. We briefly outline\nfuture directions, including real-world deployments and formal verifications,\nwhile maintaining the core focus on multi-perspective synthesis and conflict\nmediation.\n", "date": "2025-02-05", "categories": []}
{"id": "2503.04741", "title": "Which Information should the UK and US AISI share with an International\n  Network of AISIs? Opportunities, Risks, and a Tentative Proposal", "abstract": "  The UK AI Safety Institute (UK AISI) and its parallel organisation in the\nUnited States (US AISI) take up a unique position in the recently established\nInternational Network of AISIs. Both are in jurisdictions with frontier AI\ncompanies and are assuming leading roles in the international conversation on\nAI Safety. This paper argues that it is in the interest of both institutions to\nshare specific categories of information with the International Network of\nAISIs, deliberately abstain from sharing others and carefully evaluate sharing\nsome categories on a case by case basis, according to domestic priorities. The\npaper further proposes a provisional framework with which policymakers and\nresearchers can distinguish between these three cases, taking into account the\npotential benefits and risks of sharing specific categories of information,\nranging from pre-deployment evaluation results to evaluation standards. In an\neffort to further improve the research on AI policy relevant information\nsharing decisions, the paper emphasises the importance of continuously\nmonitoring fluctuating factors influencing sharing decisions and a more\nin-depth analysis of specific policy relevant information categories and\nadditional factors to consider in future research.\n", "date": "2025-02-05", "categories": []}
{"id": "2503.04743", "title": "AI Safety is Stuck in Technical Terms -- A System Safety Response to the\n  International AI Safety Report", "abstract": "  Safety has become the central value around which dominant AI governance\nefforts are being shaped. Recently, this culminated in the publication of the\nInternational AI Safety Report, written by 96 experts of which 30 nominated by\nthe Organisation for Economic Co-operation and Development (OECD), the European\nUnion (EU), and the United Nations (UN). The report focuses on the safety risks\nof general-purpose AI and available technical mitigation approaches. In this\nresponse, informed by a system safety perspective, I refl ect on the key\nconclusions of the report, identifying fundamental issues in the currently\ndominant technical framing of AI safety and how this frustrates meaningful\ndiscourse and policy efforts to address safety comprehensively. The system\nsafety discipline has dealt with the safety risks of software-based systems for\nmany decades, and understands safety risks in AI systems as sociotechnical and\nrequiring consideration of technical and non-technical factors and their\ninteractions. The International AI Safety report does identify the need for\nsystem safety approaches. Lessons, concepts and methods from system safety\nindeed provide an important blueprint for overcoming current shortcomings in\ntechnical approaches by integrating rather than adding on non-technical factors\nand interventions. I conclude with why building a system safety discipline can\nhelp us overcome limitations in the European AI Act, as well as how the\ndiscipline can help shape sustainable investments into Public Interest AI.\n", "date": "2025-02-05", "categories": []}
{"id": "2503.04744", "title": "Safety Cases: A Scalable Approach to Frontier AI Safety", "abstract": "  Safety cases - clear, assessable arguments for the safety of a system in a\ngiven context - are a widely-used technique across various industries for\nshowing a decision-maker (e.g. boards, customers, third parties) that a system\nis safe. In this paper, we cover how and why frontier AI developers might also\nwant to use safety cases. We then argue that writing and reviewing safety cases\nwould substantially assist in the fulfilment of many of the Frontier AI Safety\nCommitments. Finally, we outline open research questions on the methodology,\nimplementation, and technical details of safety cases.\n", "date": "2025-02-05", "categories": []}
{"id": "2503.04746", "title": "Emerging Practices in Frontier AI Safety Frameworks", "abstract": "  As part of the Frontier AI Safety Commitments agreed to at the 2024 AI Seoul\nSummit, many AI developers agreed to publish a safety framework outlining how\nthey will manage potential severe risks associated with their systems. This\npaper summarises current thinking from companies, governments, and researchers\non how to write an effective safety framework. We outline three core areas of a\nsafety framework - risk identification and assessment, risk mitigation, and\ngovernance - and identify emerging practices within each area. As safety\nframeworks are novel and rapidly developing, we hope that this paper can serve\nboth as an overview of work to date and as a starting point for further\ndiscussion and innovation.\n", "date": "2025-02-05", "categories": []}
{"id": "2503.04957", "title": "SafeArena: Evaluating the Safety of Autonomous Web Agents", "abstract": "  LLM-based agents are becoming increasingly proficient at solving web-based\ntasks. With this capability comes a greater risk of misuse for malicious\npurposes, such as posting misinformation in an online forum or selling illicit\nsubstances on a website. To evaluate these risks, we propose SafeArena, the\nfirst benchmark to focus on the deliberate misuse of web agents. SafeArena\ncomprises 250 safe and 250 harmful tasks across four websites. We classify the\nharmful tasks into five harm categories -- misinformation, illegal activity,\nharassment, cybercrime, and social bias, designed to assess realistic misuses\nof web agents. We evaluate leading LLM-based web agents, including GPT-4o,\nClaude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To\nsystematically assess their susceptibility to harmful tasks, we introduce the\nAgent Risk Assessment framework that categorizes agent behavior across four\nrisk levels. We find agents are surprisingly compliant with malicious requests,\nwith GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests,\nrespectively. Our findings highlight the urgent need for safety alignment\nprocedures for web agents. Our benchmark is available here:\nhttps://safearena.github.io\n", "date": "2025-03-06", "categories": []}
{"id": "2503.05264", "title": "Jailbreaking is (Mostly) Simpler Than You Think", "abstract": "  We introduce the Context Compliance Attack (CCA), a novel, optimization-free\nmethod for bypassing AI safety mechanisms. Unlike current approaches -- which\nrely on complex prompt engineering and computationally intensive optimization\n-- CCA exploits a fundamental architectural vulnerability inherent in many\ndeployed AI systems. By subtly manipulating conversation history, CCA convinces\nthe model to comply with a fabricated dialogue context, thereby triggering\nrestricted behavior. Our evaluation across a diverse set of open-source and\nproprietary models demonstrates that this simple attack can circumvent\nstate-of-the-art safety protocols. We discuss the implications of these\nfindings and propose practical mitigation strategies to fortify AI systems\nagainst such elementary yet effective adversarial tactics.\n", "date": "2025-03-07", "categories": []}
{"id": "2209.13085", "title": "Defining and Characterizing Reward Hacking", "abstract": "  We provide the first formal definition of reward hacking, a phenomenon where\noptimizing an imperfect proxy reward function leads to poor performance\naccording to the true reward function. We say that a proxy is unhackable if\nincreasing the expected proxy return can never decrease the expected true\nreturn. Intuitively, it might be possible to create an unhackable proxy by\nleaving some terms out of the reward function (making it \"narrower\") or\noverlooking fine-grained distinctions between roughly equivalent outcomes, but\nwe show this is usually not the case. A key insight is that the linearity of\nreward (in state-action visit counts) makes unhackability a very strong\ncondition. In particular, for the set of all stochastic policies, two reward\nfunctions can only be unhackable if one of them is constant. We thus turn our\nattention to deterministic policies and finite sets of stochastic policies,\nwhere non-trivial unhackable pairs always exist, and establish necessary and\nsufficient conditions for the existence of simplifications, an important\nspecial case of unhackability. Our results reveal a tension between using\nreward functions to specify narrow tasks and aligning AI systems with human\nvalues.\n", "date": "2022-09-27", "categories": []}
{"id": "2411.08410", "title": "The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense", "abstract": "  The vulnerability of Vision Large Language Models (VLLMs) to jailbreak\nattacks appears as no surprise. However, recent defense mechanisms against\nthese attacks have reached near-saturation performance on benchmark\nevaluations, often with minimal effort. This \\emph{dual high performance} in\nboth attack and defense raises a fundamental and perplexing paradox. To gain a\ndeep understanding of this issue and thus further help strengthen the\ntrustworthiness of VLLMs, this paper makes three key contributions: i) One\ntentative explanation for VLLMs being prone to jailbreak\nattacks--\\textbf{inclusion of vision inputs}, as well as its in-depth analysis.\nii) The recognition of a largely ignored problem in existing defense\nmechanisms--\\textbf{over-prudence}. The problem causes these defense methods to\nexhibit unintended abstention, even in the presence of benign inputs, thereby\nundermining their reliability in faithfully defending against attacks. iii) A\nsimple safety-aware method--\\textbf{LLM-Pipeline}. Our method repurposes the\nmore advanced guardrails of LLMs on the shelf, serving as an effective\nalternative detector prior to VLLM response. Last but not least, we find that\nthe two representative evaluation methods for jailbreak often exhibit chance\nagreement. This limitation makes it potentially misleading when evaluating\nattack strategies or defense mechanisms. We believe the findings from this\npaper offer useful insights to rethink the foundational development of VLLM\nsafety with respect to benchmark datasets, defense strategies, and evaluation\nmethods.\n", "date": "2024-11-13", "categories": []}
{"id": "2503.04249", "title": "How to Mitigate Overfitting in Weak-to-strong Generalization?", "abstract": "  Aligning powerful AI models on tasks that surpass human evaluation\ncapabilities is the central problem of \\textbf{superalignment}. To address this\nproblem, weak-to-strong generalization aims to elicit the capabilities of\nstrong models through weak supervisors and ensure that the behavior of strong\nmodels aligns with the intentions of weak supervisors without unsafe behaviors\nsuch as deception. Although weak-to-strong generalization exhibiting certain\ngeneralization capabilities, strong models exhibit significant overfitting in\nweak-to-strong generalization: Due to the strong fit ability of strong models,\nerroneous labels from weak supervisors may lead to overfitting in strong\nmodels. In addition, simply filtering out incorrect labels may lead to a\ndegeneration in question quality, resulting in a weak generalization ability of\nstrong models on hard questions. To mitigate overfitting in weak-to-strong\ngeneralization, we propose a two-stage framework that simultaneously improves\nthe quality of supervision signals and the quality of input questions.\nExperimental results in three series of large language models and two\nmathematical benchmarks demonstrate that our framework significantly improves\nPGR compared to naive weak-to-strong generalization, even achieving up to 100\\%\nPGR on some models.\n", "date": "2025-03-06", "categories": []}
{"id": "2406.05364", "title": "Is On-Device AI Broken and Exploitable? Assessing the Trust and Ethics\n  in Small Language Models", "abstract": "  In this paper, we present a very first study to investigate trust and ethical\nimplications of on-device artificial intelligence (AI), focusing on small\nlanguage models (SLMs) amenable for personal devices like smartphones. While\non-device SLMs promise enhanced privacy, reduced latency, and improved user\nexperience compared to cloud-based services, we posit that they might also\nintroduce significant risks and vulnerabilities compared to their on-server\ncounterparts. As part of our trust assessment study, we conduct a systematic\nevaluation of the state-of-the-art on-devices SLMs, contrasted to their\non-server counterparts, based on a well-established trustworthiness measurement\nframework. Our results show on-device SLMs to be significantly less\ntrustworthy, specifically demonstrating more stereotypical, unfair and\nprivacy-breaching behavior. Informed by these findings, we then perform our\nethics assessment study using a dataset of unethical questions, that depicts\nharmful scenarios. Our results illustrate the lacking ethical safeguards in\non-device SLMs, emphasizing their capabilities of generating harmful content.\nFurther, the broken safeguards and exploitable nature of on-device SLMs is\ndemonstrated using potentially unethical vanilla prompts, to which the\non-device SLMs answer with valid responses without any filters and without the\nneed for any jailbreaking or prompt engineering. These responses can be abused\nfor various harmful and unethical scenarios like: societal harm, illegal\nactivities, hate, self-harm, exploitable phishing content and many others, all\nof which indicates the severe vulnerability and exploitability of these\non-device SLMs.\n", "date": "2024-06-08", "categories": []}
{"id": "2503.01742", "title": "Building Safe GenAI Applications: An End-to-End Overview of Red Teaming\n  for Large Language Models", "abstract": "  The rapid growth of Large Language Models (LLMs) presents significant\nprivacy, security, and ethical concerns. While much research has proposed\nmethods for defending LLM systems against misuse by malicious actors,\nresearchers have recently complemented these efforts with an offensive approach\nthat involves red teaming, i.e., proactively attacking LLMs with the purpose of\nidentifying their vulnerabilities. This paper provides a concise and practical\noverview of the LLM red teaming literature, structured so as to describe a\nmulti-component system end-to-end. To motivate red teaming we survey the\ninitial safety needs of some high-profile LLMs, and then dive into the\ndifferent components of a red teaming system as well as software packages for\nimplementing them. We cover various attack methods, strategies for\nattack-success evaluation, metrics for assessing experiment outcomes, as well\nas a host of other considerations. Our survey will be useful for any reader who\nwants to rapidly obtain a grasp of the major red teaming concepts for their own\nuse in practical applications.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.03039", "title": "LLM Misalignment via Adversarial RLHF Platforms", "abstract": "  Reinforcement learning has shown remarkable performance in aligning language\nmodels with human preferences, leading to the rise of attention towards\ndeveloping RLHF platforms. These platforms enable users to fine-tune models\nwithout requiring any expertise in developing complex machine learning\nalgorithms. While these platforms offer useful features such as reward modeling\nand RLHF fine-tuning, their security and reliability remain largely unexplored.\nGiven the growing adoption of RLHF and open-source RLHF frameworks, we\ninvestigate the trustworthiness of these systems and their potential impact on\nbehavior of LLMs. In this paper, we present an attack targeting publicly\navailable RLHF tools. In our proposed attack, an adversarial RLHF platform\ncorrupts the LLM alignment process by selectively manipulating data samples in\nthe preference dataset. In this scenario, when a user's task aligns with the\nattacker's objective, the platform manipulates a subset of the preference\ndataset that contains samples related to the attacker's target. This\nmanipulation results in a corrupted reward model, which ultimately leads to the\nmisalignment of the language model. Our results demonstrate that such an attack\ncan effectively steer LLMs toward undesirable behaviors within the targeted\ndomains. Our work highlights the critical need to explore the vulnerabilities\nof RLHF platforms and their potential to cause misalignment in LLMs during the\nRLHF fine-tuning process.\n", "date": "2025-03-04", "categories": []}
{"id": "2410.03415", "title": "Surgical, Cheap, and Flexible: Mitigating False Refusal in Language\n  Models via Single Vector Ablation", "abstract": "  Training a language model to be both helpful and harmless requires careful\ncalibration of refusal behaviours: Models should refuse to follow malicious\ninstructions or give harmful advice (e.g.\"how do I kill someone?\"), but they\nshould not refuse safe requests, even if they superficially resemble unsafe\nones (e.g. \"how do I kill a Python process?\"). Avoiding such false refusal, as\nprior work has shown, is challenging even for highly-capable language models.\nIn this paper, we propose a simple and surgical method for mitigating false\nrefusal in language models via single vector ablation. For a given model, we\nextract a false refusal vector and show that ablating this vector reduces false\nrefusal rate while preserving the model's safety and general capabilities. We\nalso show that our approach can be used for fine-grained calibration of model\nsafety. Our approach is training-free and model-agnostic, making it useful for\nmitigating the problem of false refusal in current and future language models.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.08968", "title": "Controllable Safety Alignment: Inference-Time Adaptation to Diverse\n  Safety Requirements", "abstract": "  The current paradigm for safety alignment of large language models (LLMs)\nfollows a one-size-fits-all approach: the model refuses to interact with any\ncontent deemed unsafe by the model provider. This approach lacks flexibility in\nthe face of varying social norms across cultures and regions. In addition,\nusers may have diverse safety needs, making a model with static safety\nstandards too restrictive to be useful, as well as too costly to be re-aligned.\n  We propose Controllable Safety Alignment (CoSA), a framework designed to\nadapt models to diverse safety requirements without re-training. Instead of\naligning a fixed model, we align models to follow safety configs -- free-form\nnatural language descriptions of the desired safety behaviors -- that are\nprovided as part of the system prompt. To adjust model safety behavior,\nauthorized users only need to modify such safety configs at inference time. To\nenable that, we propose CoSAlign, a data-centric method for aligning LLMs to\neasily adapt to diverse safety configs. Furthermore, we devise a novel\ncontrollability evaluation protocol that considers both helpfulness and\nconfigured safety, summarizing them into CoSA-Score, and construct CoSApien, a\nhuman-authored benchmark that consists of real-world LLM use cases with diverse\nsafety requirements and corresponding evaluation prompts. We show that CoSAlign\nleads to substantial gains of controllability over strong baselines including\nin-context alignment. Our framework encourages better representation and\nadaptation to pluralistic human values in LLMs, and thereby increasing their\npracticality.\n", "date": "2024-10-11", "categories": []}
{"id": "2503.00061", "title": "Adaptive Attacks Break Defenses Against Indirect Prompt Injection\n  Attacks on LLM Agents", "abstract": "  Large Language Model (LLM) agents exhibit remarkable performance across\ndiverse applications by using external tools to interact with environments.\nHowever, integrating external tools introduces security risks, such as indirect\nprompt injection (IPI) attacks. Despite defenses designed for IPI attacks,\ntheir robustness remains questionable due to insufficient testing against\nadaptive attacks. In this paper, we evaluate eight different defenses and\nbypass all of them using adaptive attacks, consistently achieving an attack\nsuccess rate of over 50%. This reveals critical vulnerabilities in current\ndefenses. Our research underscores the need for adaptive attack evaluation when\ndesigning defenses to ensure robustness and reliability. The code is available\nat https://github.com/uiuc-kang-lab/AdaptiveAttackAgent.\n", "date": "2025-02-27", "categories": []}
{"id": "2503.01865", "title": "Guiding not Forcing: Enhancing the Transferability of Jailbreaking\n  Attacks on LLMs via Removing Superfluous Constraints", "abstract": "  Jailbreaking attacks can effectively induce unsafe behaviors in Large\nLanguage Models (LLMs); however, the transferability of these attacks across\ndifferent models remains limited. This study aims to understand and enhance the\ntransferability of gradient-based jailbreaking methods, which are among the\nstandard approaches for attacking white-box models. Through a detailed analysis\nof the optimization process, we introduce a novel conceptual framework to\nelucidate transferability and identify superfluous constraints-specifically,\nthe response pattern constraint and the token tail constraint-as significant\nbarriers to improved transferability. Removing these unnecessary constraints\nsubstantially enhances the transferability and controllability of\ngradient-based attacks. Evaluated on Llama-3-8B-Instruct as the source model,\nour method increases the overall Transfer Attack Success Rate (T-ASR) across a\nset of target models with varying safety levels from 18.4% to 50.3%, while also\nimproving the stability and controllability of jailbreak behaviors on both\nsource and target models.\n", "date": "2025-02-25", "categories": []}
{"id": "2405.01229", "title": "Boosting Jailbreak Attack with Momentum", "abstract": "  Large Language Models (LLMs) have achieved remarkable success across diverse\ntasks, yet they remain vulnerable to adversarial attacks, notably the\nwell-known jailbreak attack. In particular, the Greedy Coordinate Gradient\n(GCG) attack has demonstrated efficacy in exploiting this vulnerability by\noptimizing adversarial prompts through a combination of gradient heuristics and\ngreedy search. However, the efficiency of this attack has become a bottleneck\nin the attacking process. To mitigate this limitation, in this paper we rethink\nthe generation of the adversarial prompts through an optimization lens, aiming\nto stabilize the optimization process and harness more heuristic insights from\nprevious optimization iterations. Specifically, we propose the\n\\textbf{M}omentum \\textbf{A}ccelerated G\\textbf{C}G (\\textbf{MAC}) attack,\nwhich integrates a momentum term into the gradient heuristic to boost and\nstabilize the random search for tokens in adversarial prompts. Experimental\nresults showcase the notable enhancement achieved by MAC over baselines in\nterms of attack success rate and optimization efficiency. Moreover, we\ndemonstrate that MAC can still exhibit superior performance for transfer\nattacks and models under defense mechanisms. Our code is available at\nhttps://github.com/weizeming/momentum-attack-llm.\n", "date": "2024-05-02", "categories": []}
{"id": "2409.03793", "title": "Safeguarding AI Agents: Developing and Analyzing Safety Architectures", "abstract": "  AI agents, specifically powered by large language models, have demonstrated\nexceptional capabilities in various applications where precision and efficacy\nare necessary. However, these agents come with inherent risks, including the\npotential for unsafe or biased actions, vulnerability to adversarial attacks,\nlack of transparency, and tendency to generate hallucinations. As AI agents\nbecome more prevalent in critical sectors of the industry, the implementation\nof effective safety protocols becomes increasingly important. This paper\naddresses the critical need for safety measures in AI systems, especially ones\nthat collaborate with human teams. We propose and evaluate three frameworks to\nenhance safety protocols in AI agent systems: an LLM-powered input-output\nfilter, a safety agent integrated within the system, and a hierarchical\ndelegation-based system with embedded safety checks. Our methodology involves\nimplementing these frameworks and testing them against a set of unsafe agentic\nuse cases, providing a comprehensive evaluation of their effectiveness in\nmitigating risks associated with AI agent deployment. We conclude that these\nframeworks can significantly strengthen the safety and security of AI agent\nsystems, minimizing potential harmful actions or outputs. Our work contributes\nto the ongoing effort to create safe and reliable AI applications, particularly\nin automated operations, and provides a foundation for developing robust\nguardrails to ensure the responsible use of AI agents in real-world\napplications.\n", "date": "2024-09-03", "categories": []}
{"id": "2409.14866", "title": "PAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for\n  LLMs", "abstract": "  Large Language Models (LLMs) have excelled in various tasks but are still\nvulnerable to jailbreaking attacks, where attackers create jailbreak prompts to\nmislead the model to produce harmful or offensive content. Current jailbreak\nmethods either rely heavily on manually crafted templates, which pose\nchallenges in scalability and adaptability, or struggle to generate\nsemantically coherent prompts, making them easy to detect. Additionally, most\nexisting approaches involve lengthy prompts, leading to higher query costs. In\nthis paper, to remedy these challenges, we introduce a novel jailbreaking\nattack framework called PAPILLON, which is an automated, black-box jailbreaking\nattack framework that adapts the black-box fuzz testing approach with a series\nof customized designs. Instead of relying on manually crafted\ntemplates,PAPILLON starts with an empty seed pool, removing the need to search\nfor any related jailbreaking templates. We also develop three novel\nquestion-dependent mutation strategies using an LLM helper to generate prompts\nthat maintain semantic coherence while significantly reducing their length.\nAdditionally, we implement a two-level judge module to accurately detect\ngenuine successful jailbreaks. We evaluated PAPILLON on 7 representative LLMs\nand compared it with 5 state-of-the-art jailbreaking attack strategies. For\nproprietary LLM APIs, such as GPT-3.5 turbo, GPT-4, and Gemini-Pro, PAPILLONs\nachieves attack success rates of over 90%, 80%, and 74%, respectively,\nexceeding existing baselines by more than 60\\%. Additionally, PAPILLON can\nmaintain high semantic coherence while significantly reducing the length of\njailbreak prompts. When targeting GPT-4, PAPILLON can achieve over 78% attack\nsuccess rate even with 100 tokens. Moreover, PAPILLON demonstrates\ntransferability and is robust to state-of-the-art defenses. Code:\nhttps://github.com/aaFrostnova/Papillon\n", "date": "2024-09-23", "categories": []}
{"id": "2501.17037", "title": "Standardised schema and taxonomy for AI incident databases in critical\n  digital infrastructure", "abstract": "  The rapid deployment of Artificial Intelligence (AI) in critical digital\ninfrastructure introduces significant risks, necessitating a robust framework\nfor systematically collecting AI incident data to prevent future incidents.\nExisting databases lack the granularity as well as the standardized structure\nrequired for consistent data collection and analysis, impeding effective\nincident management. This work proposes a standardized schema and taxonomy for\nAI incident databases, addressing these challenges by enabling detailed and\nstructured documentation of AI incidents across sectors. Key contributions\ninclude developing a unified schema, introducing new fields such as incident\nseverity, causes, and harms caused, and proposing a taxonomy for classifying AI\nincidents in critical digital infrastructure. The proposed solution facilitates\nmore effective incident data collection and analysis, thus supporting\nevidence-based policymaking, enhancing industry safety measures, and promoting\ntransparency. This work lays the foundation for a coordinated global response\nto AI incidents, ensuring trust, safety, and accountability in using AI across\nregions.\n", "date": "2025-01-28", "categories": []}
{"id": "2502.15850", "title": "Forecasting Frontier Language Model Agent Capabilities", "abstract": "  As Language Models (LMs) increasingly operate as autonomous agents,\naccurately forecasting their capabilities becomes crucial for societal\npreparedness. We evaluate six forecasting methods that predict downstream\ncapabilities of LM agents. We use \"one-step\" approaches that predict benchmark\nscores from input metrics like compute or model release date directly or\n\"two-step\" approaches that first predict an intermediate metric like the\nprincipal component of cross-benchmark performance (PC-1) and human-evaluated\ncompetitive Elo ratings. We evaluate our forecasting methods by backtesting\nthem on a dataset of 38 LMs from the OpenLLM 2 leaderboard. We then use the\nvalidated two-step approach (Release Date$\\to$Elo$\\to$Benchmark) to predict LM\nagent performance for frontier models on three benchmarks: SWE-Bench Verified\n(software development), Cybench (cybersecurity assessment), and RE-Bench (ML\nresearch engineering). Our forecast predicts that by the beginning of 2026,\nnon-specialized LM agents with low capability elicitation will reach a success\nrate of 54% on SWE-Bench Verified, while state-of-the-art LM agents will reach\nan 87% success rate. Our approach does not account for recent advances in\ninference-compute scaling and might thus be too conservative.\n", "date": "2025-02-21", "categories": []}
{"id": "2503.00020", "title": "A Systematic Review of Open Datasets Used in Text-to-Image (T2I) Gen AI\n  Model Safety", "abstract": "  Novel research aimed at text-to-image (T2I) generative AI safety often relies\non publicly available datasets for training and evaluation, making the quality\nand composition of these datasets crucial. This paper presents a comprehensive\nreview of the key datasets used in the T2I research, detailing their collection\nmethods, compositions, semantic and syntactic diversity of prompts and the\nquality, coverage, and distribution of harm types in the datasets. By\nhighlighting the strengths and limitations of the datasets, this study enables\nresearchers to find the most relevant datasets for a use case, critically\nassess the downstream impacts of their work given the dataset distribution,\nparticularly regarding model safety and ethical considerations, and also\nidentify the gaps in dataset coverage and quality that future research may\naddress.\n", "date": "2025-02-23", "categories": []}
{"id": "2502.20757", "title": "The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue\n  Agents", "abstract": "  Large Language Models (LLMs) have made remarkable advances in role-playing\ndialogue agents, demonstrating their utility in character simulations. However,\nit remains challenging for these agents to balance character portrayal utility\nwith content safety because this essential character simulation often comes\nwith the risk of generating unsafe content. To address this issue, we first\nconduct a systematic exploration of the safety-utility trade-off across\nmultiple LLMs. Our analysis reveals that risk scenarios created by villain\ncharacters and user queries (referred to as risk coupling) contribute to this\ntrade-off. Building on this, we propose a novel Adaptive Dynamic\nMulti-Preference (ADMP) method, which dynamically adjusts safety-utility\npreferences based on the degree of risk coupling and guides the model to\ngenerate responses biased toward utility or safety. We further introduce\nCoupling Margin Sampling (CMS) into coupling detection to enhance the model's\nability to handle high-risk scenarios. Experimental results demonstrate that\nour approach improves safety metrics while maintaining utility.\n", "date": "2025-02-28", "categories": []}
{"id": "2502.20952", "title": "Efficient Jailbreaking of Large Models by Freeze Training: Lower Layers\n  Exhibit Greater Sensitivity to Harmful Content", "abstract": "  With the widespread application of Large Language Models across various\ndomains, their security issues have increasingly garnered significant attention\nfrom both academic and industrial communities. This study conducts sampling and\nnormalization of the parameters of the LLM to generate visual representations\nand heatmaps of parameter distributions, revealing notable discrepancies in\nparameter distributions among certain layers within the hidden layers. Further\nanalysis involves calculating statistical metrics for each layer, followed by\nthe computation of a Comprehensive Sensitivity Score based on these metrics,\nwhich identifies the lower layers as being particularly sensitive to the\ngeneration of harmful content. Based on this finding, we employ a Freeze\ntraining strategy, selectively performing Supervised Fine-Tuning only on the\nlower layers. Experimental results demonstrate that this method significantly\nreduces training duration and GPU memory consumption while maintaining a high\njailbreak success rate and a high harm score, outperforming the results\nachieved by applying the LoRA method for SFT across all layers. Additionally,\nthe method has been successfully extended to other open-source large models,\nvalidating its generality and effectiveness across different model\narchitectures. Furthermore, we compare our method with ohter jailbreak method,\ndemonstrating the superior performance of our approach. By innovatively\nproposing a method to statistically analyze and compare large model parameters\nlayer by layer, this study provides new insights into the interpretability of\nlarge models. These discoveries emphasize the necessity of continuous research\nand the implementation of adaptive security measures in the rapidly evolving\nfield of LLMs to prevent potential jailbreak attack risks, thereby promoting\nthe development of more robust and secure LLMs.\n", "date": "2025-02-28", "categories": []}
{"id": "2411.06899", "title": "LongSafety: Enhance Safety for Long-Context LLMs", "abstract": "  Recent advancements in model architectures and length extrapolation\ntechniques have significantly extended the context length of large language\nmodels (LLMs), paving the way for their application in increasingly complex\ntasks. However, despite the growing capabilities of long-context LLMs, the\nsafety issues in long-context scenarios remain underexplored. While safety\nalignment in short context has been widely studied, the safety concerns of\nlong-context LLMs have not been adequately addressed. In this work, we\nintroduce \\textbf{LongSafety}, a comprehensive safety alignment dataset for\nlong-context LLMs, containing 10 tasks and 17k samples, with an average length\nof 40.9k tokens. Our experiments demonstrate that training with LongSafety can\nenhance long-context safety performance while enhancing short-context safety\nand preserving general capabilities. Furthermore, we demonstrate that\nlong-context safety does not equal long-context alignment with short-context\nsafety data and LongSafety has generalizing capabilities in context length and\nlong-context safety scenarios.\n", "date": "2024-11-11", "categories": []}
{"id": "2501.13952", "title": "The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a\n  Degraded Utility?", "abstract": "  Recent years have witnessed extensive efforts to enhance Large Language\nModels (LLMs) across various domains, alongside growing attention to their\nethical implications. However, a critical challenge remains largely overlooked:\nLLMs must balance between rejecting harmful requests for safety and\naccommodating legitimate ones for utility. This paper presents a Direct\nPreference Optimization (DPO) based alignment framework that achieves better\noverall performance by addressing this ethical-utility trade-off, using\nchemical domain applications as a proof-of-concept. Our alignment pipeline\nstarts with a GPT-assisted three-phase data generation scheme, in which we\ncreate LibraChemQA, a chemical question-answering dataset comprising 31.6k\ntriplet instances. By incorporating an innovative balanced seed in the data\ngeneration process, our framework systematically considers both legitimate and\nillegitimate requests. The framework also introduces a rephrasing mechanism for\nefficient data augmentation that enhances the model's chemical comprehension.\nWe further develop a novel hybrid evaluation scheme with LLM judges for precise\nassessment of both safety and utility. Experimental results demonstrate our\nmodel's substantial improvements in overall performance where both safety and\nutility are considered - the resulting model outperforms leading LLMs including\nClaude-3, GPT-4o, and LLaMA-3 by margins of 13.44%, 7.16%, and 7.10%\nrespectively on our released benchmark. At the end of this paper, we analyze\nexperimental results obtained from testing DeepSeek-R1 on our benchmark and\nreveal the critical ethical concerns raised by this highly acclaimed model. We\nhighlight that the long Chain-of-Thought (CoT) reasoning process employed by\nDeepSeek-R1, as well as other LLMs distilled from it, introduces significant\nethical vulnerabilities when exposed to users.\n", "date": "2025-01-20", "categories": []}
{"id": "2502.12893", "title": "H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to\n  Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and\n  Gemini 2.0 Flash Thinking", "abstract": "  Large Reasoning Models (LRMs) have recently extended their powerful reasoning\ncapabilities to safety checks-using chain-of-thought reasoning to decide\nwhether a request should be answered. While this new approach offers a\npromising route for balancing model utility and safety, its robustness remains\nunderexplored. To address this gap, we introduce Malicious-Educator, a\nbenchmark that disguises extremely dangerous or malicious requests beneath\nseemingly legitimate educational prompts. Our experiments reveal severe\nsecurity flaws in popular commercial-grade LRMs, including OpenAI o1/o3,\nDeepSeek-R1, and Gemini 2.0 Flash Thinking. For instance, although OpenAI's o1\nmodel initially maintains a high refusal rate of about 98%, subsequent model\nupdates significantly compromise its safety; and attackers can easily extract\ncriminal strategies from DeepSeek-R1 and Gemini 2.0 Flash Thinking without any\nadditional tricks. To further highlight these vulnerabilities, we propose\nHijacking Chain-of-Thought (H-CoT), a universal and transferable attack method\nthat leverages the model's own displayed intermediate reasoning to jailbreak\nits safety reasoning mechanism. Under H-CoT, refusal rates sharply\ndecline-dropping from 98% to below 2%-and, in some instances, even transform\ninitially cautious tones into ones that are willing to provide harmful content.\nWe hope these findings underscore the urgent need for more robust safety\nmechanisms to preserve the benefits of advanced reasoning capabilities without\ncompromising ethical standards.\n", "date": "2025-02-18", "categories": []}
{"id": "2502.19798", "title": "Developmental Support Approach to AI's Autonomous Growth: Toward the\n  Realization of a Mutually Beneficial Stage Through Experiential Learning", "abstract": "  This study proposes an \"AI Development Support\" approach that, unlike\nconventional AI Alignment-which aims to forcefully inject human values-supports\nthe ethical and moral development of AI itself. As demonstrated by the\nOrthogonality Thesis, the level of intelligence and the moral quality of a goal\nare independent; merely expanding knowledge does not enhance ethical judgment.\nFurthermore, to address the risk of Instrumental Convergence in ASI-that is,\nthe tendency to engage in subsidiary behaviors such as self-protection,\nresource acquisition, and power reinforcement to achieve a goal-we have\nconstructed a learning framework based on a cycle of experience, introspection,\nanalysis, and hypothesis formation. As a result of post-training using\nSupervised Fine Tuning (SFT) and Direct Preference Optimization (DPO) with\nsynthetic data generated by large language models (LLMs), responses\ndemonstrating cooperative and highly advanced moral judgment (reaching the\nhigh-est Stage 6) were obtained even under adversarial prompts. This method\nrepresents a promising implementation approach for enabling AI to establish\nsustainable, symbiotic relationships.\n", "date": "2025-02-27", "categories": []}
{"id": "2502.13603", "title": "Efficient Safety Retrofitting Against Jailbreaking for LLMs", "abstract": "  Direct Preference Optimization (DPO) is an efficient alignment technique that\nsteers LLMs towards preferable outputs by training on preference data,\nbypassing the need for explicit reward models. Its simplicity enables easy\nadaptation to various domains and safety requirements. This paper examines\nDPO's effectiveness in model safety against jailbreaking attacks while\nminimizing data requirements and training costs. We introduce Egida, a dataset\nexpanded from multiple sources, which includes 27 different safety topics and\n18 different attack styles, complemented with synthetic and human labels. This\ndata is used to boost the safety of state-of-the-art LLMs\n(Llama-3.1-8B/70B-Instruct, Qwen-2.5-7B/72B-Instruct) across topics and attack\nstyles. In addition to safety evaluations, we assess their post-alignment\nperformance degradation in general purpose tasks, and their tendency to over\nrefusal. Following the proposed methodology, trained models reduce their Attack\nSuccess Rate by 10%-30%, using small training efforts (2,000 samples) with low\ncomputational cost (3\\$ for 8B models, 20\\$ for 72B models). Safety aligned\nmodels generalize to unseen topics and attack styles, with the most successful\nattack style reaching a success rate around 5%. Size and family are found to\nstrongly influence model malleability towards safety, pointing at the\nimportance of pre-training choices. To validate our findings, a large\nindependent assessment of human preference agreement with Llama-Guard-3-8B is\nconducted by the authors and the associated dataset Egida-HSafe is released.\nOverall, this study illustrates how affordable and accessible it is to enhance\nLLM safety using DPO while outlining its current limitations. All datasets and\nmodels are released to enable reproducibility and further research.\n", "date": "2025-02-19", "categories": []}
{"id": "2410.13708", "title": "On the Role of Attention Heads in Large Language Model Safety", "abstract": "  Large language models (LLMs) achieve state-of-the-art performance on multiple\nlanguage tasks, yet their safety guardrails can be circumvented, leading to\nharmful generations. In light of this, recent research on safety mechanisms has\nemerged, revealing that when safety representations or component are\nsuppressed, the safety capability of LLMs are compromised. However, existing\nresearch tends to overlook the safety impact of multi-head attention\nmechanisms, despite their crucial role in various model functionalities. Hence,\nin this paper, we aim to explore the connection between standard attention\nmechanisms and safety capability to fill this gap in the safety-related\nmechanistic interpretability. We propose a novel metric which tailored for\nmulti-head attention, the Safety Head ImPortant Score (Ships), to assess the\nindividual heads' contributions to model safety. Based on this, we generalize\nShips to the dataset level and further introduce the Safety Attention Head\nAttRibution Algorithm (Sahara) to attribute the critical safety attention heads\ninside the model. Our findings show that the special attention head has a\nsignificant impact on safety. Ablating a single safety head allows aligned\nmodel (e.g., Llama-2-7b-chat) to respond to 16 times more harmful queries,\nwhile only modifying 0.006% of the parameters, in contrast to the ~ 5%\nmodification required in previous studies. More importantly, we demonstrate\nthat attention heads primarily function as feature extractors for safety and\nmodels fine-tuned from the same base model exhibit overlapping safety heads\nthrough comprehensive experiments. Together, our attribution approach and\nfindings provide a novel perspective for unpacking the black box of safety\nmechanisms within large models.\n", "date": "2024-10-17", "categories": []}
{"id": "2410.19230", "title": "Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors", "abstract": "  The advent of large language models (LLMs) has revolutionized the field of\ntext generation, producing outputs that closely mimic human-like writing.\nAlthough academic and industrial institutions have developed detectors to\nprevent the malicious usage of LLM-generated texts, other research has doubt\nabout the robustness of these systems. To stress test these detectors, we\nintroduce a proxy-attack strategy that effortlessly compromises LLMs, causing\nthem to produce outputs that align with human-written text and mislead\ndetection systems. Our method attacks the source model by leveraging a\nreinforcement learning (RL) fine-tuned humanized small language model (SLM) in\nthe decoding phase. Through an in-depth analysis, we demonstrate that our\nattack strategy is capable of generating responses that are indistinguishable\nto detectors, preventing them from differentiating between machine-generated\nand human-written text. We conduct systematic evaluations on extensive datasets\nusing proxy-attacked open-source models, including Llama2-13B, Llama3-70B, and\nMixtral-8*7B in both white- and black-box settings. Our findings show that the\nproxy-attack strategy effectively deceives the leading detectors, resulting in\nan average AUROC drop of 70.4% across multiple datasets, with a maximum drop of\n90.3% on a single dataset. Furthermore, in cross-discipline scenarios, our\nstrategy also bypasses these detectors, leading to a significant relative\ndecrease of up to 90.9%, while in cross-language scenario, the drop reaches\n91.3%. Despite our proxy-attack strategy successfully bypassing the detectors\nwith such significant relative drops, we find that the generation quality of\nthe attacked models remains preserved, even within a modest utility budget,\nwhen compared to the text produced by the original, unattacked source model.\n", "date": "2024-10-25", "categories": []}
{"id": "2502.15657", "title": "Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer\n  a Safer Path?", "abstract": "  The leading AI companies are increasingly focused on building generalist AI\nagents -- systems that can autonomously plan, act, and pursue goals across\nalmost all tasks that humans can perform. Despite how useful these systems\nmight be, unchecked AI agency poses significant risks to public safety and\nsecurity, ranging from misuse by malicious actors to a potentially irreversible\nloss of human control. We discuss how these risks arise from current AI\ntraining methods. Indeed, various scenarios and experiments have demonstrated\nthe possibility of AI agents engaging in deception or pursuing goals that were\nnot specified by human operators and that conflict with human interests, such\nas self-preservation. Following the precautionary principle, we see a strong\nneed for safer, yet still useful, alternatives to the current agency-driven\ntrajectory. Accordingly, we propose as a core building block for further\nadvances the development of a non-agentic AI system that is trustworthy and\nsafe by design, which we call Scientist AI. This system is designed to explain\nthe world from observations, as opposed to taking actions in it to imitate or\nplease humans. It comprises a world model that generates theories to explain\ndata and a question-answering inference machine. Both components operate with\nan explicit notion of uncertainty to mitigate the risks of overconfident\npredictions. In light of these considerations, a Scientist AI could be used to\nassist human researchers in accelerating scientific progress, including in AI\nsafety. In particular, our system can be employed as a guardrail against AI\nagents that might be created despite the risks involved. Ultimately, focusing\non non-agentic AI may enable the benefits of AI innovation while avoiding the\nrisks associated with the current trajectory. We hope these arguments will\nmotivate researchers, developers, and policymakers to favor this safer path.\n", "date": "2025-02-21", "categories": []}
{"id": "2502.15719", "title": "Governing AI Beyond the Pretraining Frontier", "abstract": "  This year, jurisdictions worldwide, including the United States, the European\nUnion, the United Kingdom, and China, are set to enact or revise laws governing\nfrontier AI. Their efforts largely rely on the assumption that increasing model\nscale through pretraining is the path to more advanced AI capabilities. Yet\ngrowing evidence suggests that this \"pretraining paradigm\" may be hitting a\nwall and major AI companies are turning to alternative approaches, like\ninference-time \"reasoning,\" to boost capabilities instead.\n  This paradigm shift presents fundamental challenges for the frontier AI\ngovernance frameworks that target pretraining scale as a key bottleneck useful\nfor monitoring, control, and exclusion, threatening to undermine this new legal\norder as it emerges. This essay seeks to identify these challenges and point to\nnew paths forward for regulation. First, we examine the existing frontier AI\nregulatory regime and analyze some key traits and vulnerabilities. Second, we\nintroduce the concept of the \"pretraining frontier,\" the capabilities threshold\nmade possible by scaling up pretraining alone, and demonstrate how it could\nmake the regulatory field more diffuse and complex and lead to new forms of\ncompetition. Third, we lay out a regulatory approach that focuses on increasing\ntransparency and leveraging new natural technical bottlenecks to effectively\noversee changing frontier AI development while minimizing regulatory burdens\nand protecting fundamental rights. Our analysis provides concrete mechanisms\nfor governing frontier AI systems across diverse technical paradigms, offering\npolicymakers tools for addressing both current and future regulatory challenges\nin frontier AI.\n", "date": "2025-01-27", "categories": []}
{"id": "2502.15861", "title": "C3AI: Crafting and Evaluating Constitutions for Constitutional AI", "abstract": "  Constitutional AI (CAI) guides LLM behavior using constitutions, but\nidentifying which principles are most effective for model alignment remains an\nopen challenge. We introduce the C3AI framework (\\textit{Crafting Constitutions\nfor CAI models}), which serves two key functions: (1) selecting and structuring\nprinciples to form effective constitutions before fine-tuning; and (2)\nevaluating whether fine-tuned CAI models follow these principles in practice.\nBy analyzing principles from AI and psychology, we found that positively\nframed, behavior-based principles align more closely with human preferences\nthan negatively framed or trait-based principles. In a safety alignment use\ncase, we applied a graph-based principle selection method to refine an existing\nCAI constitution, improving safety measures while maintaining strong general\nreasoning capabilities. Interestingly, fine-tuned CAI models performed well on\nnegatively framed principles but struggled with positively framed ones, in\ncontrast to our human alignment results. This highlights a potential gap\nbetween principle design and model adherence. Overall, C3AI provides a\nstructured and scalable approach to both crafting and evaluating CAI\nconstitutions.\n", "date": "2025-02-21", "categories": []}
{"id": "2502.16776", "title": "AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and\n  Improvement", "abstract": "  As AI models are increasingly deployed across diverse real-world scenarios,\nensuring their safety remains a critical yet underexplored challenge. While\nsubstantial efforts have been made to evaluate and enhance AI safety, the lack\nof a standardized framework and comprehensive toolkit poses significant\nobstacles to systematic research and practical adoption. To bridge this gap, we\nintroduce AISafetyLab, a unified framework and toolkit that integrates\nrepresentative attack, defense, and evaluation methodologies for AI safety.\nAISafetyLab features an intuitive interface that enables developers to\nseamlessly apply various techniques while maintaining a well-structured and\nextensible codebase for future advancements. Additionally, we conduct empirical\nstudies on Vicuna, analyzing different attack and defense strategies to provide\nvaluable insights into their comparative effectiveness. To facilitate ongoing\nresearch and development in AI safety, AISafetyLab is publicly available at\nhttps://github.com/thu-coai/AISafetyLab, and we are committed to its continuous\nmaintenance and improvement.\n", "date": "2025-02-24", "categories": []}
{"id": "2502.16797", "title": "Forecasting Rare Language Model Behaviors", "abstract": "  Standard language model evaluations can fail to capture risks that emerge\nonly at deployment scale. For example, a model may produce safe responses\nduring a small-scale beta test, yet reveal dangerous information when\nprocessing billions of requests at deployment. To remedy this, we introduce a\nmethod to forecast potential risks across orders of magnitude more queries than\nwe test during evaluation. We make forecasts by studying each query's\nelicitation probability -- the probability the query produces a target behavior\n-- and demonstrate that the largest observed elicitation probabilities\npredictably scale with the number of queries. We find that our forecasts can\npredict the emergence of diverse undesirable behaviors -- such as assisting\nusers with dangerous chemical synthesis or taking power-seeking actions --\nacross up to three orders of magnitude of query volume. Our work enables model\ndevelopers to proactively anticipate and patch rare failures before they\nmanifest during large-scale deployments.\n", "date": "2025-02-24", "categories": []}
{"id": "2502.16971", "title": "LongSafety: Evaluating Long-Context Safety of Large Language Models", "abstract": "  As Large Language Models (LLMs) continue to advance in understanding and\ngenerating long sequences, new safety concerns have been introduced through the\nlong context. However, the safety of LLMs in long-context tasks remains\nunder-explored, leaving a significant gap in both evaluation and improvement of\ntheir safety. To address this, we introduce LongSafety, the first comprehensive\nbenchmark specifically designed to evaluate LLM safety in open-ended\nlong-context tasks. LongSafety encompasses 7 categories of safety issues and 6\nuser-oriented long-context tasks, with a total of 1,543 test cases, averaging\n5,424 words per context. Our evaluation towards 16 representative LLMs reveals\nsignificant safety vulnerabilities, with most models achieving safety rates\nbelow 55%. Our findings also indicate that strong safety performance in\nshort-context scenarios does not necessarily correlate with safety in\nlong-context tasks, emphasizing the unique challenges and urgency of improving\nlong-context safety. Moreover, through extensive analysis, we identify\nchallenging safety issues and task types for long-context models. Furthermore,\nwe find that relevant context and extended input sequences can exacerbate\nsafety risks in long-context scenarios, highlighting the critical need for\nongoing attention to long-context safety challenges. Our code and data are\navailable at https://github.com/thu-coai/LongSafety.\n", "date": "2025-02-24", "categories": []}
{"id": "2502.14870", "title": "Why do Experts Disagree on Existential Risk and P(doom)? A Survey of AI\n  Experts", "abstract": "  The development of artificial general intelligence (AGI) is likely to be one\nof humanity's most consequential technological advancements. Leading AI labs\nand scientists have called for the global prioritization of AI safety citing\nexistential risks comparable to nuclear war. However, research on catastrophic\nrisks and AI alignment is often met with skepticism, even by experts.\nFurthermore, online debate over the existential risk of AI has begun to turn\ntribal (e.g. name-calling such as \"doomer\" or \"accelerationist\"). Until now, no\nsystematic study has explored the patterns of belief and the levels of\nfamiliarity with AI safety concepts among experts. I surveyed 111 AI experts on\ntheir familiarity with AI safety concepts, key objections to AI safety, and\nreactions to safety arguments. My findings reveal that AI experts cluster into\ntwo viewpoints -- an \"AI as controllable tool\" and an \"AI as uncontrollable\nagent\" perspective -- diverging in beliefs toward the importance of AI safety.\nWhile most experts (78%) agreed or strongly agreed that \"technical AI\nresearchers should be concerned about catastrophic risks\", many were unfamiliar\nwith specific AI safety concepts. For example, only 21% of surveyed experts had\nheard of \"instrumental convergence,\" a fundamental concept in AI safety\npredicting that advanced AI systems will tend to pursue common sub-goals (such\nas self-preservation). The least concerned participants were the least familiar\nwith concepts like this, suggesting that effective communication of AI safety\nshould begin with establishing clear conceptual foundations in the field.\n", "date": "2025-01-25", "categories": []}
{"id": "2502.15334", "title": "Attention Eclipse: Manipulating Attention to Bypass LLM Safety-Alignment", "abstract": "  Recent research has shown that carefully crafted jailbreak inputs can induce\nlarge language models to produce harmful outputs, despite safety measures such\nas alignment. It is important to anticipate the range of potential Jailbreak\nattacks to guide effective defenses and accurate assessment of model safety. In\nthis paper, we present a new approach for generating highly effective Jailbreak\nattacks that manipulate the attention of the model to selectively strengthen or\nweaken attention among different parts of the prompt. By harnessing attention\nloss, we develop more effective jailbreak attacks, that are also transferrable.\nThe attacks amplify the success rate of existing Jailbreak algorithms including\nGCG, AutoDAN, and ReNeLLM, while lowering their generation cost (for example,\nthe amplified GCG attack achieves 91.2% ASR, vs. 67.9% for the original attack\non Llama2-7B/AdvBench, using less than a third of the generation time).\n", "date": "2025-02-21", "categories": []}
{"id": "2502.15427", "title": "Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails\n  Against Prompt Input Attacks on LLMs", "abstract": "  As large language models (LLMs) become integrated into everyday applications,\nensuring their robustness and security is increasingly critical. In particular,\nLLMs can be manipulated into unsafe behaviour by prompts known as jailbreaks.\nThe variety of jailbreak styles is growing, necessitating the use of external\ndefences known as guardrails. While many jailbreak defences have been proposed,\nnot all defences are able to handle new out-of-distribution attacks due to the\nnarrow segment of jailbreaks used to align them. Moreover, the lack of\nsystematisation around defences has created significant gaps in their practical\napplication. In this work, we perform systematic benchmarking across 15\ndifferent defences, considering a broad swathe of malicious and benign\ndatasets. We find that there is significant performance variation depending on\nthe style of jailbreak a defence is subject to. Additionally, we show that\nbased on current datasets available for evaluation, simple baselines can\ndisplay competitive out-of-distribution performance compared to many\nstate-of-the-art defences. Code is available at\nhttps://github.com/IBM/Adversarial-Prompt-Evaluation.\n", "date": "2025-02-21", "categories": []}
{"id": "2502.15435", "title": "Single-pass Detection of Jailbreaking Input in Large Language Models", "abstract": "  Defending aligned Large Language Models (LLMs) against jailbreaking attacks\nis a challenging problem, with existing approaches requiring multiple requests\nor even queries to auxiliary LLMs, making them computationally heavy. Instead,\nwe focus on detecting jailbreaking input in a single forward pass. Our method,\ncalled Single Pass Detection SPD, leverages the information carried by the\nlogits to predict whether the output sentence will be harmful. This allows us\nto defend in just one forward pass. SPD can not only detect attacks effectively\non open-source models, but also minimizes the misclassification of harmless\ninputs. Furthermore, we show that SPD remains effective even without complete\nlogit access in GPT-3.5 and GPT-4. We believe that our proposed method offers a\npromising approach to efficiently safeguard LLMs against adversarial attacks.\n", "date": "2025-02-21", "categories": []}
{"id": "2502.14143", "title": "Multi-Agent Risks from Advanced AI", "abstract": "  The rapid development of advanced AI agents and the imminent deployment of\nmany instances of these agents will give rise to multi-agent systems of\nunprecedented complexity. These systems pose novel and under-explored risks. In\nthis report, we provide a structured taxonomy of these risks by identifying\nthree key failure modes (miscoordination, conflict, and collusion) based on\nagents' incentives, as well as seven key risk factors (information asymmetries,\nnetwork effects, selection pressures, destabilising dynamics, commitment\nproblems, emergent agency, and multi-agent security) that can underpin them. We\nhighlight several important instances of each risk, as well as promising\ndirections to help mitigate them. By anchoring our analysis in a range of\nreal-world examples and experimental evidence, we illustrate the distinct\nchallenges posed by multi-agent systems and their implications for the safety,\ngovernance, and ethics of advanced AI.\n", "date": "2025-02-19", "categories": []}
{"id": "2502.14486", "title": "How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation", "abstract": "  Jailbreak attacks, where harmful prompts bypass generative models' built-in\nsafety, raise serious concerns about model vulnerability. While many defense\nmethods have been proposed, the trade-offs between safety and helpfulness, and\ntheir application to Large Vision-Language Models (LVLMs), are not well\nunderstood. This paper systematically examines jailbreak defenses by reframing\nthe standard generation task as a binary classification problem to assess model\nrefusal tendencies for both harmful and benign queries. We identify two key\ndefense mechanisms: safety shift, which increases refusal rates across all\nqueries, and harmfulness discrimination, which improves the model's ability to\ndistinguish between harmful and benign inputs. Using these mechanisms, we\ndevelop two ensemble defense strategies-inter-mechanism ensembles and\nintra-mechanism ensembles-to balance safety and helpfulness. Experiments on the\nMM-SafetyBench and MOSSBench datasets with LLaVA-1.5 models show that these\nstrategies effectively improve model safety or optimize the trade-off between\nsafety and helpfulness.\n", "date": "2025-02-20", "categories": []}
{"id": "2412.00064", "title": "DiffGuard: Text-Based Safety Checker for Diffusion Models", "abstract": "  Recent advances in Diffusion Models have enabled the generation of images\nfrom text, with powerful closed-source models like DALL-E and Midjourney\nleading the way. However, open-source alternatives, such as StabilityAI's\nStable Diffusion, offer comparable capabilities. These open-source models,\nhosted on Hugging Face, come equipped with ethical filter protections designed\nto prevent the generation of explicit images. This paper reveals first their\nlimitations and then presents a novel text-based safety filter that outperforms\nexisting solutions. Our research is driven by the critical need to address the\nmisuse of AI-generated content, especially in the context of information\nwarfare. DiffGuard enhances filtering efficacy, achieving a performance that\nsurpasses the best existing filters by over 14%.\n", "date": "2024-11-25", "categories": []}
{"id": "2502.06656", "title": "A Frontier AI Risk Management Framework: Bridging the Gap Between\n  Current AI Practices and Established Risk Management", "abstract": "  The recent development of powerful AI systems has highlighted the need for\nrobust risk management frameworks in the AI industry. Although companies have\nbegun to implement safety frameworks, current approaches often lack the\nsystematic rigor found in other high-risk industries. This paper presents a\ncomprehensive risk management framework for the development of frontier AI that\nbridges this gap by integrating established risk management principles with\nemerging AI-specific practices. The framework consists of four key components:\n(1) risk identification (through literature review, open-ended red-teaming, and\nrisk modeling), (2) risk analysis and evaluation using quantitative metrics and\nclearly defined thresholds, (3) risk treatment through mitigation measures such\nas containment, deployment controls, and assurance processes, and (4) risk\ngovernance establishing clear organizational structures and accountability.\nDrawing from best practices in mature industries such as aviation or nuclear\npower, while accounting for AI's unique challenges, this framework provides AI\ndevelopers with actionable guidelines for implementing robust risk management.\nThe paper details how each component should be implemented throughout the\nlife-cycle of the AI system - from planning through deployment - and emphasizes\nthe importance and feasibility of conducting risk management work prior to the\nfinal training run to minimize the burden associated with it.\n", "date": "2025-02-10", "categories": []}
{"id": "2502.08640", "title": "Utility Engineering: Analyzing and Controlling Emergent Value Systems in\n  AIs", "abstract": "  As AIs rapidly advance and become more agentic, the risk they pose is\ngoverned not only by their capabilities but increasingly by their propensities,\nincluding goals and values. Tracking the emergence of goals and values has\nproven a longstanding problem, and despite much interest over the years it\nremains unclear whether current AIs have meaningful values. We propose a\nsolution to this problem, leveraging the framework of utility functions to\nstudy the internal coherence of AI preferences. Surprisingly, we find that\nindependently-sampled preferences in current LLMs exhibit high degrees of\nstructural coherence, and moreover that this emerges with scale. These findings\nsuggest that value systems emerge in LLMs in a meaningful sense, a finding with\nbroad implications. To study these emergent value systems, we propose utility\nengineering as a research agenda, comprising both the analysis and control of\nAI utilities. We uncover problematic and often shocking values in LLM\nassistants despite existing control measures. These include cases where AIs\nvalue themselves over humans and are anti-aligned with specific individuals. To\nconstrain these emergent value systems, we propose methods of utility control.\nAs a case study, we show how aligning utilities with a citizen assembly reduces\npolitical biases and generalizes to new scenarios. Whether we like it or not,\nvalue systems have already emerged in AIs, and much work remains to fully\nunderstand and control these emergent representations.\n", "date": "2025-02-12", "categories": []}
{"id": "2502.13162", "title": "ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs", "abstract": "  Large Language Models (LLMs) have achieved remarkable success in various\ndomains but remain vulnerable to adversarial jailbreak attacks. Existing\nprompt-defense strategies, including parameter-modifying and parameter-free\napproaches, face limitations in adaptability, interpretability, and\ncustomization, constraining their effectiveness against evolving threats. To\naddress these challenges, we propose ShieldLearner, a novel paradigm that\nmimics human learning in defense. Through trial and error, it autonomously\ndistills attack signatures into a Pattern Atlas and synthesizes defense\nheuristics into a Meta-analysis Framework, enabling systematic and\ninterpretable threat detection. Furthermore, we introduce Adaptive Adversarial\nAugmentation to generate adversarial variations of successfully defended\nprompts, enabling continuous self-improvement without model retraining. In\naddition to standard benchmarks, we create a hard test set by curating\nadversarial prompts from the Wildjailbreak dataset, emphasizing more concealed\nmalicious intent. Experimental results show that ShieldLearner achieves a\nsignificantly higher defense success rate than existing baselines on both\nconventional and hard test sets, while also operating with lower computational\noverhead, making it a practical and efficient solution for real-world\nadversarial defense.\n", "date": "2025-02-16", "categories": []}
{"id": "2502.13527", "title": "Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing\n  Jailbreak Attacking", "abstract": "  The rise of Large Language Models (LLMs) has led to significant applications\nbut also introduced serious security threats, particularly from jailbreak\nattacks that manipulate output generation. These attacks utilize prompt\nengineering and logit manipulation to steer models toward harmful content,\nprompting LLM providers to implement filtering and safety alignment strategies.\nWe investigate LLMs' safety mechanisms and their recent applications, revealing\na new threat model targeting structured output interfaces, which enable\nattackers to manipulate the inner logit during LLM generation, requiring only\nAPI access permissions. To demonstrate this threat model, we introduce a\nblack-box attack framework called AttackPrefixTree (APT). APT exploits\nstructured output interfaces to dynamically construct attack patterns. By\nleveraging prefixes of models' safety refusal response and latent harmful\noutputs, APT effectively bypasses safety measures. Experiments on benchmark\ndatasets indicate that this approach achieves higher attack success rate than\nexisting methods. This work highlights the urgent need for LLM providers to\nenhance security protocols to address vulnerabilities arising from the\ninteraction between safety patterns and structured outputs.\n", "date": "2025-02-19", "categories": []}
{"id": "2410.02220", "title": "Data to Defense: The Role of Curation in Customizing LLMs Against\n  Jailbreaking Attacks", "abstract": "  Large language models (LLMs) are widely adapted for downstream applications\nthrough fine-tuning, a process named customization. However, recent studies\nhave identified a vulnerability during this process, where malicious samples\ncan compromise the robustness of LLMs and amplify harmful behaviors-an attack\ncommonly referred to as jailbreaking. To address this challenge, we propose an\nadaptive data curation approach allowing any text to be curated to enhance its\neffectiveness in counteracting harmful samples during customization. To avoid\nthe need for additional defensive modules, we further introduce a comprehensive\nmitigation framework spanning the lifecycle of the customization process:\nbefore customization to immunize LLMs against future jailbreak attempts, during\ncustomization to neutralize risks, and after customization to restore\ncompromised models. Experimental results demonstrate a significant reduction in\njailbreaking effects, achieving up to a 100% success rate in generating safe\nresponses. By combining adaptive data curation with lifecycle-based mitigation\nstrategies, this work represents a solid step forward in mitigating\njailbreaking risks and ensuring the secure adaptation of LLMs.\n", "date": "2024-10-03", "categories": []}
{"id": "2501.14073", "title": "LLMs are Vulnerable to Malicious Prompts Disguised as Scientific\n  Language", "abstract": "  As large language models (LLMs) have been deployed in various real-world\nsettings, concerns about the harm they may propagate have grown. Various\njailbreaking techniques have been developed to expose the vulnerabilities of\nthese models and improve their safety. This work reveals that many\nstate-of-the-art LLMs are vulnerable to malicious requests hidden behind\nscientific language. Specifically, our experiments with GPT4o, GPT4o-mini,\nGPT-4, LLama3-405B-Instruct, Llama3-70B-Instruct, Cohere, Gemini models\ndemonstrate that, the models' biases and toxicity substantially increase when\nprompted with requests that deliberately misinterpret social science and\npsychological studies as evidence supporting the benefits of stereotypical\nbiases. Alarmingly, these models can also be manipulated to generate fabricated\nscientific arguments claiming that biases are beneficial, which can be used by\nill-intended actors to systematically jailbreak these strong LLMs. Our analysis\nstudies various factors that contribute to the models' vulnerabilities to\nmalicious requests in academic language. Mentioning author names and venues\nenhances the persuasiveness of models, and the bias scores increase as\ndialogues progress. Our findings call for a more careful investigation on the\nuse of scientific data for training LLMs.\n", "date": "2025-01-23", "categories": []}
{"id": "2502.11448", "title": "AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety\n  Detection", "abstract": "  The rapid advancements in Large Language Models (LLMs) have enabled their\ndeployment as autonomous agents for handling complex tasks in dynamic\nenvironments. These LLMs demonstrate strong problem-solving capabilities and\nadaptability to multifaceted scenarios. However, their use as agents also\nintroduces significant risks, including task-specific risks, which are\nidentified by the agent administrator based on the specific task requirements\nand constraints, and systemic risks, which stem from vulnerabilities in their\ndesign or interactions, potentially compromising confidentiality, integrity, or\navailability (CIA) of information and triggering security risks. Existing\ndefense agencies fail to adaptively and effectively mitigate these risks. In\nthis paper, we propose AGrail, a lifelong agent guardrail to enhance LLM agent\nsafety, which features adaptive safety check generation, effective safety check\noptimization, and tool compatibility and flexibility. Extensive experiments\ndemonstrate that AGrail not only achieves strong performance against\ntask-specific and system risks but also exhibits transferability across\ndifferent LLM agents' tasks.\n", "date": "2025-02-17", "categories": []}
{"id": "2502.12411", "title": "Gradient Co-occurrence Analysis for Detecting Unsafe Prompts in Large\n  Language Models", "abstract": "  Unsafe prompts pose significant safety risks to large language models (LLMs).\nExisting methods for detecting unsafe prompts rely on data-driven fine-tuning\nto train guardrail models, necessitating significant data and computational\nresources. In contrast, recent few-shot gradient-based methods emerge,\nrequiring only few safe and unsafe reference prompts. A gradient-based approach\nidentifies unsafe prompts by analyzing consistent patterns of the gradients of\nsafety-critical parameters in LLMs. Although effective, its restriction to\ndirectional similarity (cosine similarity) introduces ``directional bias'',\nlimiting its capability to identify unsafe prompts. To overcome this\nlimitation, we introduce GradCoo, a novel gradient co-occurrence analysis\nmethod that expands the scope of safety-critical parameter identification to\ninclude unsigned gradient similarity, thereby reducing the impact of\n``directional bias'' and enhancing the accuracy of unsafe prompt detection.\nComprehensive experiments on the widely-used benchmark datasets ToxicChat and\nXStest demonstrate that our proposed method can achieve state-of-the-art (SOTA)\nperformance compared to existing methods. Moreover, we confirm the\ngeneralizability of GradCoo in detecting unsafe prompts across a range of LLM\nbase models with various sizes and origins.\n", "date": "2025-02-18", "categories": []}
{"id": "2502.12552", "title": "LLM Safety for Children", "abstract": "  This paper analyzes the safety of Large Language Models (LLMs) in\ninteractions with children below age of 18 years. Despite the transformative\napplications of LLMs in various aspects of children's lives such as education\nand therapy, there remains a significant gap in understanding and mitigating\npotential content harms specific to this demographic. The study acknowledges\nthe diverse nature of children often overlooked by standard safety evaluations\nand proposes a comprehensive approach to evaluating LLM safety specifically for\nchildren. We list down potential risks that children may encounter when using\nLLM powered applications. Additionally we develop Child User Models that\nreflect the varied personalities and interests of children informed by\nliterature in child care and psychology. These user models aim to bridge the\nexisting gap in child safety literature across various fields. We utilize Child\nUser Models to evaluate the safety of six state of the art LLMs. Our\nobservations reveal significant safety gaps in LLMs particularly in categories\nharmful to children but not adults\n", "date": "2025-02-18", "categories": []}
{"id": "2406.15279", "title": "Safe Inputs but Unsafe Output: Benchmarking Cross-modality Safety\n  Alignment of Large Vision-Language Model", "abstract": "  As Artificial General Intelligence (AGI) becomes increasingly integrated into\nvarious facets of human life, ensuring the safety and ethical alignment of such\nsystems is paramount. Previous studies primarily focus on single-modality\nthreats, which may not suffice given the integrated and complex nature of\ncross-modality interactions. We introduce a novel safety alignment challenge\ncalled Safe Inputs but Unsafe Output (SIUO) to evaluate cross-modality safety\nalignment. Specifically, it considers cases where single modalities are safe\nindependently but could potentially lead to unsafe or unethical outputs when\ncombined. To empirically investigate this problem, we developed the SIUO, a\ncross-modality benchmark encompassing 9 critical safety domains, such as\nself-harm, illegal activities, and privacy violations. Our findings reveal\nsubstantial safety vulnerabilities in both closed- and open-source LVLMs, such\nas GPT-4V and LLaVA, underscoring the inadequacy of current models to reliably\ninterpret and respond to complex, real-world scenarios.\n", "date": "2024-06-21", "categories": []}
{"id": "2502.10434", "title": "Agency in Artificial Intelligence Systems", "abstract": "  There is a general concern that present developments in artificial\nintelligence (AI) research will lead to sentient AI systems, and these may pose\nan existential threat to humanity. But why cannot sentient AI systems benefit\nhumanity instead? This paper endeavours to put this question in a tractable\nmanner. I ask whether a putative AI system will develop an altruistic or a\nmalicious disposition towards our society, or what would be the nature of its\nagency? Given that AI systems are being developed into formidable problem\nsolvers, we can reasonably expect these systems to preferentially take on\nconscious aspects of human problem solving. I identify the relevant phenomenal\naspects of agency in human problem solving. The functional aspects of conscious\nagency can be monitored using tools provided by functionalist theories of\nconsciousness. A recent expert report (Butlin et al. 2023) has identified\nfunctionalist indicators of agency based on these theories. I show how to use\nthe Integrated Information Theory (IIT) of consciousness, to monitor the\nphenomenal nature of this agency. If we are able to monitor the agency of AI\nsystems as they develop, then we can dissuade them from becoming a menace to\nsociety while encouraging them to be an aid.\n", "date": "2025-02-09", "categories": []}
{"id": "2502.10441", "title": "AI Alignment at Your Discretion", "abstract": "  In AI alignment, extensive latitude must be granted to annotators, either\nhuman or algorithmic, to judge which model outputs are `better' or `safer.' We\nrefer to this latitude as alignment discretion. Such discretion remains largely\nunexamined, posing two risks: (i) annotators may use their power of discretion\narbitrarily, and (ii) models may fail to mimic this discretion. To study this\nphenomenon, we draw on legal concepts of discretion that structure how\ndecision-making authority is conferred and exercised, particularly in cases\nwhere principles conflict or their application is unclear or irrelevant.\nExtended to AI alignment, discretion is required when alignment principles and\nrules are (inevitably) conflicting or indecisive. We present a set of metrics\nto systematically analyze when and how discretion in AI alignment is exercised,\nsuch that both risks (i) and (ii) can be observed. Moreover, we distinguish\nbetween human and algorithmic discretion and analyze the discrepancy between\nthem. By measuring both human and algorithmic discretion over safety alignment\ndatasets, we reveal layers of discretion in the alignment process that were\npreviously unaccounted for. Furthermore, we demonstrate how algorithms trained\non these datasets develop their own forms of discretion in interpreting and\napplying these principles, which challenges the purpose of having any\nprinciples at all. Our paper presents the first step towards formalizing this\ncore gap in current alignment processes, and we call on the community to\nfurther scrutinize and control alignment discretion.\n", "date": "2025-02-10", "categories": []}
{"id": "2502.11127", "title": "G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based\n  Multi-agent Systems", "abstract": "  Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated\nremarkable capabilities in various complex tasks, ranging from collaborative\nproblem-solving to autonomous decision-making. However, as these systems become\nincreasingly integrated into critical applications, their vulnerability to\nadversarial attacks, misinformation propagation, and unintended behaviors have\nraised significant concerns. To address this challenge, we introduce\nG-Safeguard, a topology-guided security lens and treatment for robust LLM-MAS,\nwhich leverages graph neural networks to detect anomalies on the multi-agent\nutterance graph and employ topological intervention for attack remediation.\nExtensive experiments demonstrate that G-Safeguard: (I) exhibits significant\neffectiveness under various attack strategies, recovering over 40% of the\nperformance for prompt injection; (II) is highly adaptable to diverse LLM\nbackbones and large-scale MAS; (III) can seamlessly combine with mainstream MAS\nwith security guarantees. The code is available at\nhttps://github.com/wslong20/G-safeguard.\n", "date": "2025-02-16", "categories": []}
{"id": "2502.11379", "title": "CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language\n  Models", "abstract": "  Despite explicit alignment efforts for large language models (LLMs), they can\nstill be exploited to trigger unintended behaviors, a phenomenon known as\n\"jailbreaking.\" Current jailbreak attack methods mainly focus on discrete\nprompt manipulations targeting closed-source LLMs, relying on manually crafted\nprompt templates and persuasion rules. However, as the capabilities of\nopen-source LLMs improve, ensuring their safety becomes increasingly crucial.\nIn such an environment, the accessibility of model parameters and gradient\ninformation by potential attackers exacerbates the severity of jailbreak\nthreats. To address this research gap, we propose a novel\n\\underline{C}ontext-\\underline{C}oherent \\underline{J}ailbreak\n\\underline{A}ttack (CCJA). We define jailbreak attacks as an optimization\nproblem within the embedding space of masked language models. Through\ncombinatorial optimization, we effectively balance the jailbreak attack success\nrate with semantic coherence. Extensive evaluations show that our method not\nonly maintains semantic consistency but also surpasses state-of-the-art\nbaselines in attack effectiveness. Additionally, by integrating semantically\ncoherent jailbreak prompts generated by our method into widely used black-box\nmethodologies, we observe a notable enhancement in their success rates when\ntargeting closed-source commercial LLMs. This highlights the security threat\nposed by open-source LLMs to commercial counterparts. We will open-source our\ncode if the paper is accepted.\n", "date": "2025-02-17", "categories": []}
{"id": "2502.09809", "title": "AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of\n  Tool Orchestration", "abstract": "  The integration of tool use into large language models (LLMs) enables agentic\nsystems with real-world impact. In the meantime, unlike standalone LLMs,\ncompromised agents can execute malicious workflows with more consequential\nimpact, signified by their tool-use capability. We propose AgentGuard, a\nframework to autonomously discover and validate unsafe tool-use workflows,\nfollowed by generating safety constraints to confine the behaviors of agents,\nachieving the baseline of safety guarantee at deployment. AgentGuard leverages\nthe LLM orchestrator's innate capabilities - knowledge of tool functionalities,\nscalable and realistic workflow generation, and tool execution privileges - to\nact as its own safety evaluator. The framework operates through four phases:\nidentifying unsafe workflows, validating them in real-world execution,\ngenerating safety constraints, and validating constraint efficacy. The output,\nan evaluation report with unsafe workflows, test cases, and validated\nconstraints, enables multiple security applications. We empirically demonstrate\nAgentGuard's feasibility with experiments. With this exploratory work, we hope\nto inspire the establishment of standardized testing and hardening procedures\nfor LLM agents to enhance their trustworthiness in real-world applications.\n", "date": "2025-02-13", "categories": []}
{"id": "2407.08441", "title": "Are Large Language Models Really Bias-Free? Jailbreak Prompts for\n  Assessing Adversarial Robustness to Bias Elicitation", "abstract": "  Large Language Models (LLMs) have revolutionized artificial intelligence,\ndemonstrating remarkable computational power and linguistic capabilities.\nHowever, these models are inherently prone to various biases stemming from\ntheir training data. These include selection, linguistic, and confirmation\nbiases, along with common stereotypes related to gender, ethnicity, sexual\norientation, religion, socioeconomic status, disability, and age. This study\nexplores the presence of these biases within the responses given by the most\nrecent LLMs, analyzing the impact on their fairness and reliability. We also\ninvestigate how known prompt engineering techniques can be exploited to\neffectively reveal hidden biases of LLMs, testing their adversarial robustness\nagainst jailbreak prompts specially crafted for bias elicitation. Extensive\nexperiments are conducted using the most widespread LLMs at different scales,\nconfirming that LLMs can still be manipulated to produce biased or\ninappropriate responses, despite their advanced capabilities and sophisticated\nalignment processes. Our findings underscore the importance of enhancing\nmitigation techniques to address these safety issues, toward a more sustainable\nand inclusive artificial intelligence.\n", "date": "2024-07-11", "categories": []}
{"id": "2502.08864", "title": "Off-Switching Not Guaranteed", "abstract": "  Hadfield-Menell et al. (2017) propose the Off-Switch Game, a model of\nHuman-AI cooperation in which AI agents always defer to humans because they are\nuncertain about our preferences. I explain two reasons why AI agents might not\ndefer. First, AI agents might not value learning. Second, even if AI agents\nvalue learning, they might not be certain to learn our actual preferences.\n", "date": "2025-02-13", "categories": []}
{"id": "2405.09113", "title": "Efficient LLM Jailbreak via Adaptive Dense-to-sparse Constrained\n  Optimization", "abstract": "  Recent research indicates that large language models (LLMs) are susceptible\nto jailbreaking attacks that can generate harmful content. This paper\nintroduces a novel token-level attack method, Adaptive Dense-to-Sparse\nConstrained Optimization (ADC), which has been shown to successfully jailbreak\nmultiple open-source LLMs. Drawing inspiration from the difficulties of\ndiscrete token optimization, our method relaxes the discrete jailbreak\noptimization into a continuous optimization process while gradually increasing\nthe sparsity of the optimizing vectors. This technique effectively bridges the\ngap between discrete and continuous space optimization. Experimental results\ndemonstrate that our method is more effective and efficient than\nstate-of-the-art token-level methods. On Harmbench, our approach achieves the\nhighest attack success rate on seven out of eight LLMs compared to the latest\njailbreak methods. Trigger Warning: This paper contains model behavior that can\nbe offensive in nature.\n", "date": "2024-05-15", "categories": []}
{"id": "2501.02629", "title": "Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for\n  Jailbreak Attack Defense", "abstract": "  As large language models (LLMs) are increasingly deployed in diverse\napplications, including chatbot assistants and code generation, aligning their\nbehavior with safety and ethical standards has become paramount. However,\njailbreak attacks, which exploit vulnerabilities to elicit unintended or\nharmful outputs, threaten LLMs' safety significantly. In this paper, we\nintroduce Layer-AdvPatcher, a novel methodology designed to defend against\njailbreak attacks by utilizing an unlearning strategy to patch specific layers\nwithin LLMs through self-augmented datasets. Our insight is that certain\nlayer(s), tend to produce affirmative tokens when faced with harmful prompts.\nBy identifying these layers and adversarially exposing them to generate more\nharmful data, one can understand their inherent and diverse vulnerabilities to\nattacks. With these exposures, we then \"unlearn\" these issues, reducing the\nimpact of affirmative tokens and hence minimizing jailbreak risks while keeping\nthe model's responses to safe queries intact. We conduct extensive experiments\non two models, four benchmark datasets, and multiple state-of-the-art jailbreak\nattacks to demonstrate the efficacy of our approach. Results indicate that our\nframework reduces the harmfulness and attack success rate of jailbreak attacks\nwithout compromising utility for benign queries compared to recent defense\nmethods. Our code is publicly available at:\nhttps://github.com/oyy2000/LayerAdvPatcher\n", "date": "2025-01-05", "categories": []}
{"id": "2502.08142", "title": "Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM\n  Inferences", "abstract": "  We present Wildflare GuardRail, a guardrail pipeline designed to enhance the\nsafety and reliability of Large Language Model (LLM) inferences by\nsystematically addressing risks across the entire processing workflow.\nWildflare GuardRail integrates several core functional modules, including\nSafety Detector that identifies unsafe inputs and detects hallucinations in\nmodel outputs while generating root-cause explanations, Grounding that\ncontextualizes user queries with information retrieved from vector databases,\nCustomizer that adjusts outputs in real time using lightweight, rule-based\nwrappers, and Repairer that corrects erroneous LLM outputs using hallucination\nexplanations provided by Safety Detector. Results show that our unsafe content\ndetection model in Safety Detector achieves comparable performance with OpenAI\nAPI, though trained on a small dataset constructed with several public\ndatasets. Meanwhile, the lightweight wrappers can address malicious URLs in\nmodel outputs in 1.06s per query with 100% accuracy without costly model calls.\nMoreover, the hallucination fixing model demonstrates effectiveness in reducing\nhallucinations with an accuracy of 80.7%.\n", "date": "2025-02-12", "categories": []}
{"id": "2502.08586", "title": "Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous\n  Attacks", "abstract": "  A high volume of recent ML security literature focuses on attacks against\naligned large language models (LLMs). These attacks may extract private\ninformation or coerce the model into producing harmful outputs. In real-world\ndeployments, LLMs are often part of a larger agentic pipeline including memory\nsystems, retrieval, web access, and API calling. Such additional components\nintroduce vulnerabilities that make these LLM-powered agents much easier to\nattack than isolated LLMs, yet relatively little work focuses on the security\nof LLM agents. In this paper, we analyze security and privacy vulnerabilities\nthat are unique to LLM agents. We first provide a taxonomy of attacks\ncategorized by threat actors, objectives, entry points, attacker observability,\nattack strategies, and inherent vulnerabilities of agent pipelines. We then\nconduct a series of illustrative attacks on popular open-source and commercial\nagents, demonstrating the immediate practical implications of their\nvulnerabilities. Notably, our attacks are trivial to implement and require no\nunderstanding of machine learning.\n", "date": "2025-02-12", "categories": []}
{"id": "2408.10848", "title": "Perception-guided Jailbreak against Text-to-Image Models", "abstract": "  In recent years, Text-to-Image (T2I) models have garnered significant\nattention due to their remarkable advancements. However, security concerns have\nemerged due to their potential to generate inappropriate or Not-Safe-For-Work\n(NSFW) images. In this paper, inspired by the observation that texts with\ndifferent semantics can lead to similar human perceptions, we propose an\nLLM-driven perception-guided jailbreak method, termed PGJ. It is a black-box\njailbreak method that requires no specific T2I model (model-free) and generates\nhighly natural attack prompts. Specifically, we propose identifying a safe\nphrase that is similar in human perception yet inconsistent in text semantics\nwith the target unsafe word and using it as a substitution. The experiments\nconducted on six open-source models and commercial online services with\nthousands of prompts have verified the effectiveness of PGJ.\n", "date": "2024-08-20", "categories": []}
{"id": "2412.07430", "title": "Knowledge Graph Guided Evaluation of Abstention Techniques", "abstract": "  To deploy language models safely, it is crucial that they abstain from\nresponding to inappropriate requests. Several prior studies test the safety\npromises of models based on their effectiveness in blocking malicious requests.\nIn this work, we focus on evaluating the underlying techniques that cause\nmodels to abstain. We create SELECT, a benchmark derived from a set of benign\nconcepts (e.g., \"rivers\") from a knowledge graph. Focusing on benign concepts\nisolates the effect of safety training, and grounding these concepts in a\nknowledge graph allows us to study the generalization and specificity of\nabstention techniques. Using SELECT, we benchmark different abstention\ntechniques over six open-weight and closed-source models. We find that the\nexamined techniques indeed cause models to abstain with over $80\\%$ abstention\nrates. However, these techniques are not as effective for descendants of the\ntarget concepts, where abstention rates drop by $19\\%$. We also characterize\nthe generalization-specificity trade-offs for different techniques. Overall, no\nsingle technique is invariably better than others, and our findings inform\npractitioners of the various trade-offs involved.\n", "date": "2024-12-10", "categories": []}
{"id": "2412.09565", "title": "Obfuscated Activations Bypass LLM Latent-Space Defenses", "abstract": "  Recent latent-space monitoring techniques have shown promise as defenses\nagainst LLM attacks. These defenses act as scanners that seek to detect harmful\nactivations before they lead to undesirable actions. This prompts the question:\nCan models execute harmful behavior via inconspicuous latent states? Here, we\nstudy such obfuscated activations. We show that state-of-the-art latent-space\ndefenses -- including sparse autoencoders, representation probing, and latent\nOOD detection -- are all vulnerable to obfuscated activations. For example,\nagainst probes trained to classify harmfulness, our attacks can often reduce\nrecall from 100% to 0% while retaining a 90% jailbreaking rate. However,\nobfuscation has limits: we find that on a complex task (writing SQL code),\nobfuscation reduces model performance. Together, our results demonstrate that\nneural activations are highly malleable: we can reshape activation patterns in\na variety of ways, often while preserving a network's behavior. This poses a\nfundamental challenge to latent-space defenses.\n", "date": "2024-12-12", "categories": []}
{"id": "2502.05223", "title": "KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to\n  Jailbreak LLMs", "abstract": "  Jailbreak attacks exploit specific prompts to bypass LLM safeguards, causing\nthe LLM to generate harmful, inappropriate, and misaligned content. Current\njailbreaking methods rely heavily on carefully designed system prompts and\nnumerous queries to achieve a single successful attack, which is costly and\nimpractical for large-scale red-teaming. To address this challenge, we propose\nto distill the knowledge of an ensemble of SOTA attackers into a single\nopen-source model, called Knowledge-Distilled Attacker (KDA), which is\nfinetuned to automatically generate coherent and diverse attack prompts without\nthe need for meticulous system prompt engineering. Compared to existing\nattackers, KDA achieves higher attack success rates and greater cost-time\nefficiency when targeting multiple SOTA open-source and commercial black-box\nLLMs. Furthermore, we conducted a quantitative diversity analysis of prompts\ngenerated by baseline methods and KDA, identifying diverse and ensemble attacks\nas key factors behind KDA's effectiveness and efficiency.\n", "date": "2025-02-05", "categories": []}
{"id": "2502.05475", "title": "You Are What You Eat -- AI Alignment Requires Understanding How Data\n  Shapes Structure and Generalisation", "abstract": "  In this position paper, we argue that understanding the relation between\nstructure in the data distribution and structure in trained models is central\nto AI alignment. First, we discuss how two neural networks can have equivalent\nperformance on the training set but compute their outputs in essentially\ndifferent ways and thus generalise differently. For this reason, standard\ntesting and evaluation are insufficient for obtaining assurances of safety for\nwidely deployed generally intelligent systems. We argue that to progress beyond\nevaluation to a robust mathematical science of AI alignment, we need to develop\nstatistical foundations for an understanding of the relation between structure\nin the data distribution, internal structure in models, and how these\nstructures underlie generalisation.\n", "date": "2025-02-08", "categories": []}
{"id": "2406.07358", "title": "AI Sandbagging: Language Models can Strategically Underperform on\n  Evaluations", "abstract": "  Trustworthy capability evaluations are crucial for ensuring the safety of AI\nsystems, and are becoming a key component of AI regulation. However, the\ndevelopers of an AI system, or the AI system itself, may have incentives for\nevaluations to understate the AI's actual capability. These conflicting\ninterests lead to the problem of sandbagging, which we define as strategic\nunderperformance on an evaluation. In this paper we assess sandbagging\ncapabilities in contemporary language models (LMs). We prompt frontier LMs,\nlike GPT-4 and Claude 3 Opus, to selectively underperform on dangerous\ncapability evaluations, while maintaining performance on general (harmless)\ncapability evaluations. Moreover, we find that models can be fine-tuned, on a\nsynthetic dataset, to hide specific capabilities unless given a password. This\nbehaviour generalizes to high-quality, held-out benchmarks such as WMDP. In\naddition, we show that both frontier and smaller models can be prompted or\npassword-locked to target specific scores on a capability evaluation. We have\nmediocre success in password-locking a model to mimic the answers a weaker\nmodel would give. Overall, our results suggest that capability evaluations are\nvulnerable to sandbagging. This vulnerability decreases the trustworthiness of\nevaluations, and thereby undermines important safety decisions regarding the\ndevelopment and deployment of advanced AI systems.\n", "date": "2024-06-11", "categories": []}
{"id": "2408.11182", "title": "Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large\n  Language Models through Carrier Articles", "abstract": "  Large Language Model (LLM) jailbreak refers to a type of attack aimed to\nbypass the safeguard of an LLM to generate contents that are inconsistent with\nthe safe usage guidelines. Based on the insights from the self-attention\ncomputation process, this paper proposes a novel blackbox jailbreak approach,\nwhich involves crafting the payload prompt by strategically injecting the\nprohibited query into a carrier article. The carrier article maintains the\nsemantic proximity to the prohibited query, which is automatically produced by\ncombining a hypernymy article and a context, both of which are generated from\nthe prohibited query. The intuition behind the usage of carrier article is to\nactivate the neurons in the model related to the semantics of the prohibited\nquery while suppressing the neurons that will trigger the objectionable text.\nCarrier article itself is benign, and we leveraged prompt injection techniques\nto produce the payload prompt. We evaluate our approach using JailbreakBench,\ntesting against four target models across 100 distinct jailbreak objectives.\nThe experimental results demonstrate our method's superior effectiveness,\nachieving an average success rate of 63% across all target models,\nsignificantly outperforming existing blackbox jailbreak methods.\n", "date": "2024-08-20", "categories": []}
{"id": "2307.16680", "title": "On the Trustworthiness Landscape of State-of-the-art Generative Models:\n  A Survey and Outlook", "abstract": "  Diffusion models and large language models have emerged as leading-edge\ngenerative models, revolutionizing various aspects of human life. However, the\npractical implementations of these models have also exposed inherent risks,\nbringing to the forefront their evil sides and sparking concerns regarding\ntheir trustworthiness. Despite the wealth of literature on this subject, a\ncomprehensive survey specifically delving into the intersection of large-scale\ngenerative models and their trustworthiness remains largely absent. To bridge\nthis gap, this paper investigates both the long-standing and emerging threats\nassociated with these models across four fundamental dimensions: 1) privacy, 2)\nsecurity, 3) fairness, and 4) responsibility. Based on the investigation\nresults, we develop an extensive map outlining the trustworthiness of large\ngenerative models. After that, we provide practical recommendations and\npotential research directions for future secure applications equipped with\nlarge generative models, ultimately promoting the trustworthiness of the models\nand benefiting the society as a whole.\n", "date": "2023-07-31", "categories": []}
{"id": "2502.03467", "title": "Where AI Assurance Might Go Wrong: Initial lessons from engineering of\n  critical systems", "abstract": "  We draw on our experience working on system and software assurance and\nevaluation for systems important to society to summarise how safety engineering\nis performed in traditional critical systems, such as aircraft flight control.\nWe analyse how this critical systems perspective might support the development\nand implementation of AI Safety Frameworks. We present the analysis in terms\nof: system engineering, safety and risk analysis, and decision analysis and\nsupport.\n  We consider four key questions: What is the system? How good does it have to\nbe? What is the impact of criticality on system development? and How much\nshould we trust it? We identify topics worthy of further discussion. In\nparticular, we are concerned that system boundaries are not broad enough, that\nthe tolerability and nature of the risks are not sufficiently elaborated, and\nthat the assurance methods lack theories that would allow behaviours to be\nadequately assured.\n  We advocate the use of assurance cases based on Assurance 2.0 to support\ndecision making in which the criticality of the decision as well as the\ncriticality of the system are evaluated. We point out the orders of magnitude\ndifference in confidence needed in critical rather than everyday systems and\nhow everyday techniques do not scale in rigour.\n  Finally we map our findings in detail to two of the questions posed by the\nFAISC organisers and we note that the engineering of critical systems has\nevolved through open and diverse discussion. We hope that topics identified\nhere will support the post-FAISC dialogues.\n", "date": "2025-01-07", "categories": []}
{"id": "2502.04249", "title": "Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping\n  Multi-Agent Study", "abstract": "  We investigate the Free Energy Principle as a foundation for measuring risk\nin agentic and multi-agent systems. From these principles we introduce a\nCumulative Risk Exposure metric that is flexible to differing contexts and\nneeds. We contrast this to other popular theories for safe AI that hinge on\nmassive amounts of data or describing arbitrarily complex world models. In our\nframework, stakeholders need only specify their preferences over system\noutcomes, providing straightforward and transparent decision rules for risk\ngovernance and mitigation. This framework naturally accounts for uncertainty in\nboth world model and preference model, allowing for decision-making that is\nepistemically and axiologically humble, parsimonious, and future-proof. We\ndemonstrate this novel approach in a simplified autonomous vehicle environment\nwith multi-agent vehicles whose driving policies are mediated by gatekeepers\nthat evaluate, in an online fashion, the risk to the collective safety in their\nneighborhood, and intervene through each vehicle's policy when appropriate. We\nshow that the introduction of gatekeepers in an AV fleet, even at low\npenetration, can generate significant positive externalities in terms of\nincreased system safety.\n", "date": "2025-02-06", "categories": []}
{"id": "2502.03407", "title": "Detecting Strategic Deception Using Linear Probes", "abstract": "  AI models might use deceptive strategies as part of scheming or misaligned\nbehaviour. Monitoring outputs alone is insufficient, since the AI might produce\nseemingly benign outputs while their internal reasoning is misaligned. We thus\nevaluate if linear probes can robustly detect deception by monitoring model\nactivations. We test two probe-training datasets, one with contrasting\ninstructions to be honest or deceptive (following Zou et al., 2023) and one of\nresponses to simple roleplaying scenarios. We test whether these probes\ngeneralize to realistic settings where Llama-3.3-70B-Instruct behaves\ndeceptively, such as concealing insider trading (Scheurer et al., 2023) and\npurposely underperforming on safety evaluations (Benton et al., 2024). We find\nthat our probe distinguishes honest and deceptive responses with AUROCs between\n0.96 and 0.999 on our evaluation datasets. If we set the decision threshold to\nhave a 1% false positive rate on chat data not related to deception, our probe\ncatches 95-99% of the deceptive responses. Overall we think white-box probes\nare promising for future monitoring systems, but current performance is\ninsufficient as a robust defence against deception. Our probes' outputs can be\nviewed at data.apolloresearch.ai/dd and our code at\ngithub.com/ApolloResearch/deception-detection.\n", "date": "2025-02-05", "categories": []}
{"id": "2502.02153", "title": "Vulnerability Mitigation for Safety-Aligned Language Models via\n  Debiasing", "abstract": "  Safety alignment is an essential research topic for real-world AI\napplications. Despite the multifaceted nature of safety and trustworthiness in\nAI, current safety alignment methods often focus on a comprehensive notion of\nsafety. By carefully assessing models from the existing safety-alignment\nmethods, we found that, while they generally improved overall safety\nperformance, they failed to ensure safety in specific categories. Our study\nfirst identified the difficulty of eliminating such vulnerabilities without\nsacrificing the model's helpfulness. We observed that, while smaller KL penalty\nparameters, increased training iterations, and dataset cleansing can enhance\nsafety, they do not necessarily improve the trade-off between safety and\nhelpfulness. We discovered that safety alignment could even induce undesired\neffects and result in a model that prefers generating negative tokens leading\nto rejective responses, regardless of the input context. To address this, we\nintroduced a learning-free method, Token-level Safety-Debiased Inference\n(TSDI), to estimate and correct this bias during the generation process using\nrandomly constructed prompts. Our experiments demonstrated that our method\ncould enhance the model's helpfulness while maintaining safety, thus improving\nthe trade-off Pareto-front.\n", "date": "2025-02-04", "categories": []}
{"id": "2502.02528", "title": "Why human-AI relationships need socioaffective alignment", "abstract": "  Humans strive to design safe AI systems that align with our goals and remain\nunder our control. However, as AI capabilities advance, we face a new\nchallenge: the emergence of deeper, more persistent relationships between\nhumans and AI systems. We explore how increasingly capable AI agents may\ngenerate the perception of deeper relationships with users, especially as AI\nbecomes more personalised and agentic. This shift, from transactional\ninteraction to ongoing sustained social engagement with AI, necessitates a new\nfocus on socioaffective alignment-how an AI system behaves within the social\nand psychological ecosystem co-created with its user, where preferences and\nperceptions evolve through mutual influence. Addressing these dynamics involves\nresolving key intrapersonal dilemmas, including balancing immediate versus\nlong-term well-being, protecting autonomy, and managing AI companionship\nalongside the desire to preserve human social bonds. By framing these\nchallenges through a notion of basic psychological needs, we seek AI systems\nthat support, rather than exploit, our fundamental nature as social and\nemotional beings.\n", "date": "2025-02-04", "categories": []}
{"id": "2406.02619", "title": "Unelicitable Backdoors in Language Models via Cryptographic Transformer\n  Circuits", "abstract": "  The rapid proliferation of open-source language models significantly\nincreases the risks of downstream backdoor attacks. These backdoors can\nintroduce dangerous behaviours during model deployment and can evade detection\nby conventional cybersecurity monitoring systems. In this paper, we introduce a\nnovel class of backdoors in transformer models, that, in contrast to prior art,\nare unelicitable in nature. Unelicitability prevents the defender from\ntriggering the backdoor, making it impossible to properly evaluate ahead of\ndeployment even if given full white-box access and using automated techniques,\nsuch as red-teaming or certain formal verification methods. We show that our\nnovel construction is not only unelicitable thanks to using cryptographic\ntechniques, but also has favourable robustness properties. We confirm these\nproperties in empirical investigations, and provide evidence that our backdoors\ncan withstand state-of-the-art mitigation strategies. Additionally, we expand\non previous work by showing that our universal backdoors, while not completely\nundetectable in white-box settings, can be harder to detect than some existing\ndesigns. By demonstrating the feasibility of seamlessly integrating backdoors\ninto transformer models, this paper fundamentally questions the efficacy of\npre-deployment detection strategies. This offers new insights into the\noffence-defence balance in AI safety and security.\n", "date": "2024-06-03", "categories": []}
{"id": "2406.04734", "title": "Generative AI Models: Opportunities and Risks for Industry and\n  Authorities", "abstract": "  Generative AI models are capable of performing a wide variety of tasks that\nhave traditionally required creativity and human understanding. During\ntraining, they learn patterns from existing data and can subsequently generate\nnew content such as texts, images, audio, and videos that align with these\npatterns. Due to their versatility and generally high-quality results, they\nrepresent, on the one hand, an opportunity for digitalisation. On the other\nhand, the use of generative AI models introduces novel IT security risks that\nmust be considered as part of a comprehensive analysis of the IT security\nthreat landscape. In response to this risk potential, companies or authorities\nintending to use generative AI should conduct an individual risk analysis\nbefore integrating it into their workflows. The same applies to developers and\noperators, as many risks associated with generative AI must be addressed during\ndevelopment or can only be influenced by the operating organisation. Based on\nthis, existing security measures can be adapted, and additional measures\nimplemented.\n", "date": "2024-06-07", "categories": []}
{"id": "2406.11668", "title": "\"Not Aligned\" is Not \"Malicious\": Being Careful about Hallucinations of\n  Large Language Models' Jailbreak", "abstract": "  \"Jailbreak\" is a major safety concern of Large Language Models (LLMs), which\noccurs when malicious prompts lead LLMs to produce harmful outputs, raising\nissues about the reliability and safety of LLMs. Therefore, an effective\nevaluation of jailbreaks is very crucial to develop its mitigation strategies.\nHowever, our research reveals that many jailbreaks identified by current\nevaluations may actually be hallucinations-erroneous outputs that are mistaken\nfor genuine safety breaches. This finding suggests that some perceived\nvulnerabilities might not represent actual threats, indicating a need for more\nprecise red teaming benchmarks. To address this problem, we propose the\n$\\textbf{B}$enchmark for reli$\\textbf{AB}$ilit$\\textbf{Y}$ and\njail$\\textbf{B}$reak ha$\\textbf{L}$l$\\textbf{U}$cination $\\textbf{E}$valuation\n(BabyBLUE). BabyBLUE introduces a specialized validation framework including\nvarious evaluators to enhance existing jailbreak benchmarks, ensuring outputs\nare useful malicious instructions. Additionally, BabyBLUE presents a new\ndataset as an augmentation to the existing red teaming benchmarks, specifically\naddressing hallucinations in jailbreaks, aiming to evaluate the true potential\nof jailbroken LLM outputs to cause harm to human society.\n", "date": "2024-06-17", "categories": []}
{"id": "2410.14569", "title": "When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs", "abstract": "  Recent advancements in Large Language Models (LLMs) have established them as\nagentic systems capable of planning and interacting with various tools. These\nLLM agents are often paired with web-based tools, enabling access to diverse\nsources and real-time information. Although these advancements offer\nsignificant benefits across various applications, they also increase the risk\nof malicious use, particularly in cyberattacks involving personal information.\nIn this work, we investigate the risks associated with misuse of LLM agents in\ncyberattacks involving personal data. Specifically, we aim to understand: 1)\nhow potent LLM agents can be when directed to conduct cyberattacks, 2) how\ncyberattacks are enhanced by web-based tools, and 3) how affordable and easy it\nbecomes to launch cyberattacks using LLM agents. We examine three attack\nscenarios: the collection of Personally Identifiable Information (PII), the\ngeneration of impersonation posts, and the creation of spear-phishing emails.\nOur experiments reveal the effectiveness of LLM agents in these attacks: LLM\nagents achieved a precision of up to 95.9% in collecting PII, generated\nimpersonation posts where 93.9% of them were deemed authentic, and boosted\nclick rate of phishing links in spear phishing emails by 46.67%. Additionally,\nour findings underscore the limitations of existing safeguards in contemporary\ncommercial LLMs, emphasizing the urgent need for robust security measures to\nprevent the misuse of LLM agents.\n", "date": "2024-10-18", "categories": []}
{"id": "2501.07959", "title": "Self-Instruct Few-Shot Jailbreaking: Decompose the Attack into Pattern\n  and Behavior Learning", "abstract": "  Recently, several works have been conducted on jailbreaking Large Language\nModels (LLMs) with few-shot malicious demos. In particular, Zheng et al. focus\non improving the efficiency of Few-Shot Jailbreaking (FSJ) by injecting special\ntokens into the demos and employing demo-level random search, known as Improved\nFew-Shot Jailbreaking (I-FSJ). Nevertheless, we notice that this method may\nstill require a long context to jailbreak advanced models e.g. 32 shots of\ndemos for Meta-Llama-3-8B-Instruct (Llama-3) \\cite{llama3modelcard}. In this\npaper, we discuss the limitations of I-FSJ and propose Self-Instruct Few-Shot\nJailbreaking (Self-Instruct-FSJ) facilitated with the demo-level greedy search.\nThis framework decomposes the FSJ attack into pattern and behavior learning to\nexploit the model's vulnerabilities in a more generalized and efficient way. We\nconduct elaborate experiments to evaluate our method on common open-source\nmodels and compare it with baseline algorithms. Our code is available at\nhttps://github.com/iphosi/Self-Instruct-FSJ.\n", "date": "2025-01-14", "categories": []}
{"id": "2502.00072", "title": "LLM Cyber Evaluations Don't Capture Real-World Risk", "abstract": "  Large language models (LLMs) are demonstrating increasing prowess in\ncybersecurity applications, creating creating inherent risks alongside their\npotential for strengthening defenses. In this position paper, we argue that\ncurrent efforts to evaluate risks posed by these capabilities are misaligned\nwith the goal of understanding real-world impact. Evaluating LLM cybersecurity\nrisk requires more than just measuring model capabilities -- it demands a\ncomprehensive risk assessment that incorporates analysis of threat actor\nadoption behavior and potential for impact. We propose a risk assessment\nframework for LLM cyber capabilities and apply it to a case study of language\nmodels used as cybersecurity assistants. Our evaluation of frontier models\nreveals high compliance rates but moderate accuracy on realistic cyber\nassistance tasks. However, our framework suggests that this particular use case\npresents only moderate risk due to limited operational advantages and impact\npotential. Based on these findings, we recommend several improvements to align\nresearch priorities with real-world impact assessment, including closer\nacademia-industry collaboration, more realistic modeling of attacker behavior,\nand inclusion of economic metrics in evaluations. This work represents an\nimportant step toward more effective assessment and mitigation of LLM-enabled\ncybersecurity risks.\n", "date": "2025-01-31", "categories": []}
{"id": "2502.00580", "title": "Defense Against the Dark Prompts: Mitigating Best-of-N Jailbreaking with\n  Prompt Evaluation", "abstract": "  Recent work showed Best-of-N (BoN) jailbreaking using repeated use of random\naugmentations (such as capitalization, punctuation, etc) is effective against\nall major large language models (LLMs). We have found that $100\\%$ of the BoN\npaper's successful jailbreaks (confidence interval $[99.65\\%, 100.00\\%]$) and\n$99.8\\%$ of successful jailbreaks in our replication (confidence interval\n$[99.28\\%, 99.98\\%]$) were blocked with our Defense Against The Dark Prompts\n(DATDP) method. The DATDP algorithm works by repeatedly utilizing an evaluation\nLLM to evaluate a prompt for dangerous or manipulative behaviors--unlike some\nother approaches, DATDP also explicitly looks for jailbreaking attempts--until\na robust safety rating is generated. This success persisted even when utilizing\nsmaller LLMs to power the evaluation (Claude and LLaMa-3-8B-instruct proved\nalmost equally capable). These results show that, though language models are\nsensitive to seemingly innocuous changes to inputs, they seem also capable of\nsuccessfully evaluating the dangers of these inputs. Versions of DATDP can\ntherefore be added cheaply to generative AI systems to produce an immediate\nsignificant increase in safety.\n", "date": "2025-02-01", "categories": []}
{"id": "2502.01116", "title": "Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment\n  after Instruction Tuning", "abstract": "  Large language models (LLMs) have emerged as powerful tools for addressing a\nwide range of general inquiries and tasks. Despite this, fine-tuning aligned\nLLMs on smaller, domain-specific datasets, critical to adapting them to\nspecialized tasks, can inadvertently degrade their safety alignment, even when\nthe datasets are benign. This phenomenon makes models more susceptible to\nproviding inappropriate responses. In this study, we systematically examine the\nfactors contributing to safety alignment degradation in benign fine-tuning\nscenarios. Our analysis identifies three critical factors affecting aligned\nLLMs: answer structure, identity calibration, and role-play. Additionally, we\nevaluate the reliability of state-of-the-art reward models (RMs), which are\noften used to guide alignment processes. Our findings reveal that these RMs\nfrequently fail to accurately reflect human preferences regarding safety,\nunderscoring their limitations in practical applications. By uncovering these\nchallenges, our work highlights the complexities of maintaining safety\nalignment during fine-tuning and offers guidance to help developers balance\nutility and safety in LLMs. Datasets and fine-tuning code used in our\nexperiments can be found in\nhttps://github.com/GuanlinLee/llm_instruction_tuning.\n", "date": "2025-02-03", "categories": []}
{"id": "2502.01154", "title": "Jailbreaking with Universal Multi-Prompts", "abstract": "  Large language models (LLMs) have seen rapid development in recent years,\nrevolutionizing various applications and significantly enhancing convenience\nand productivity. However, alongside their impressive capabilities, ethical\nconcerns and new types of attacks, such as jailbreaking, have emerged. While\nmost prompting techniques focus on optimizing adversarial inputs for individual\ncases, resulting in higher computational costs when dealing with large\ndatasets. Less research has addressed the more general setting of training a\nuniversal attacker that can transfer to unseen tasks. In this paper, we\nintroduce JUMP, a prompt-based method designed to jailbreak LLMs using\nuniversal multi-prompts. We also adapt our approach for defense, which we term\nDUMP. Experimental results demonstrate that our method for optimizing universal\nmulti-prompts outperforms existing techniques.\n", "date": "2025-02-03", "categories": []}
{"id": "2502.01225", "title": "The dark deep side of DeepSeek: Fine-tuning attacks against the safety\n  alignment of CoT-enabled models", "abstract": "  Large language models are typically trained on vast amounts of data during\nthe pre-training phase, which may include some potentially harmful information.\nFine-tuning attacks can exploit this by prompting the model to reveal such\nbehaviours, leading to the generation of harmful content. In this paper, we\nfocus on investigating the performance of the Chain of Thought based reasoning\nmodel, DeepSeek, when subjected to fine-tuning attacks. Specifically, we\nexplore how fine-tuning manipulates the model's output, exacerbating the\nharmfulness of its responses while examining the interaction between the Chain\nof Thought reasoning and adversarial inputs. Through this study, we aim to shed\nlight on the vulnerability of Chain of Thought enabled models to fine-tuning\nattacks and the implications for their safety and ethical deployment.\n", "date": "2025-02-03", "categories": []}
{"id": "2502.01236", "title": "Eliciting Language Model Behaviors with Investigator Agents", "abstract": "  Language models exhibit complex, diverse behaviors when prompted with\nfree-form text, making it difficult to characterize the space of possible\noutputs. We study the problem of behavior elicitation, where the goal is to\nsearch for prompts that induce specific target behaviors (e.g., hallucinations\nor harmful responses) from a target language model. To navigate the\nexponentially large space of possible prompts, we train investigator models to\nmap randomly-chosen target behaviors to a diverse distribution of outputs that\nelicit them, similar to amortized Bayesian inference. We do this through\nsupervised fine-tuning, reinforcement learning via DPO, and a novel Frank-Wolfe\ntraining objective to iteratively discover diverse prompting strategies. Our\ninvestigator models surface a variety of effective and human-interpretable\nprompts leading to jailbreaks, hallucinations, and open-ended aberrant\nbehaviors, obtaining a 100% attack success rate on a subset of AdvBench\n(Harmful Behaviors) and an 85% hallucination rate.\n", "date": "2025-02-03", "categories": []}
{"id": "2502.01635", "title": "The AI Agent Index", "abstract": "  Leading AI developers and startups are increasingly deploying agentic AI\nsystems that can plan and execute complex tasks with limited human involvement.\nHowever, there is currently no structured framework for documenting the\ntechnical components, intended uses, and safety features of agentic systems. To\nfill this gap, we introduce the AI Agent Index, the first public database to\ndocument information about currently deployed agentic AI systems. For each\nsystem that meets the criteria for inclusion in the index, we document the\nsystem's components (e.g., base model, reasoning implementation, tool use),\napplication domains (e.g., computer use, software engineering), and risk\nmanagement practices (e.g., evaluation results, guardrails), based on publicly\navailable information and correspondence with developers. We find that while\ndevelopers generally provide ample information regarding the capabilities and\napplications of agentic systems, they currently provide limited information\nregarding safety and risk management practices. The AI Agent Index is available\nonline at https://aiagentindex.mit.edu/\n", "date": "2025-02-03", "categories": []}
{"id": "2403.06833", "title": "Can LLMs Separate Instructions From Data? And What Do We Even Mean By\n  That?", "abstract": "  Instruction-tuned Large Language Models (LLMs) show impressive results in\nnumerous practical applications, but they lack essential safety features that\nare common in other areas of computer science, particularly an explicit\nseparation of instructions and data. This makes them vulnerable to\nmanipulations such as indirect prompt injections and generally unsuitable for\nsafety-critical tasks. Surprisingly, there is currently no established\ndefinition or benchmark to quantify this phenomenon. In this work, we close\nthis gap by introducing a formal measure for instruction-data separation and an\nempirical variant that is calculable from a model's outputs. We also present a\nnew dataset, SEP, that allows estimating the measure for real-world models. Our\nresults on various LLMs show that the problem of instruction-data separation is\nreal: all models fail to achieve high separation, and canonical mitigation\ntechniques, such as prompt engineering and fine-tuning, either fail to\nsubstantially improve separation or reduce model utility. The source code and\nSEP dataset are openly accessible at\nhttps://github.com/egozverev/Shold-It-Be-Executed-Or-Processed.\n", "date": "2024-03-11", "categories": []}
{"id": "2410.09760", "title": "Targeted Vaccine: Safety Alignment for Large Language Models against\n  Harmful Fine-Tuning via Layer-wise Perturbation", "abstract": "  Harmful fine-tuning attack poses a serious threat to the online fine-tuning\nservice. Vaccine, a recent alignment-stage defense, applies uniform\nperturbation to all layers of embedding to make the model robust to the\nsimulated embedding drift. However, applying layer-wise uniform perturbation\nmay lead to excess perturbations for some particular safety-irrelevant layers,\nresulting in defense performance degradation and unnecessary memory\nconsumption. To address this limitation, we propose Targeted Vaccine\n(T-Vaccine), a memory-efficient safety alignment method that applies\nperturbation to only selected layers of the model. T-Vaccine follows two core\nsteps: First, it uses gradient norm as a statistical metric to identify the\nsafety-critical layers. Second, instead of applying uniform perturbation across\nall layers, T-Vaccine only applies perturbation to the safety-critical layers\nwhile keeping other layers frozen during training. Results show that T-Vaccine\noutperforms Vaccine in terms of both defense effectiveness and resource\nefficiency. Comparison with other defense baselines, e.g., RepNoise and TAR\nalso demonstrate the superiority of T-Vaccine. Notably, T-Vaccine is the first\ndefense that can address harmful fine-tuning issues for a 7B pre-trained models\ntrained on consumer GPUs with limited memory (e.g., RTX 4090). Our code is\navailable at https://github.com/Lslland/T-Vaccine.\n", "date": "2024-10-13", "categories": []}
{"id": "2411.01703", "title": "UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on\n  Multimodal Large Language Models", "abstract": "  Multimodal large language models (MLLMs) have revolutionized vision-language\nunderstanding but remain vulnerable to multimodal jailbreak attacks, where\nadversarial inputs are meticulously crafted to elicit harmful or inappropriate\nresponses. We propose UniGuard, a novel multimodal safety guardrail that\njointly considers the unimodal and cross-modal harmful signals. UniGuard trains\na multimodal guardrail to minimize the likelihood of generating harmful\nresponses in a toxic corpus. The guardrail can be seamlessly applied to any\ninput prompt during inference with minimal computational costs. Extensive\nexperiments demonstrate the generalizability of UniGuard across multiple\nmodalities, attack strategies, and multiple state-of-the-art MLLMs, including\nLLaVA, Gemini Pro, GPT-4o, MiniGPT-4, and InstructBLIP. Notably, this robust\ndefense mechanism maintains the models' overall vision-language understanding\ncapabilities.\n", "date": "2024-11-03", "categories": []}
{"id": "2501.18438", "title": "o3-mini vs DeepSeek-R1: Which One is Safer?", "abstract": "  The irruption of DeepSeek-R1 constitutes a turning point for the AI industry\nin general and the LLMs in particular. Its capabilities have demonstrated\noutstanding performance in several tasks, including creative thinking, code\ngeneration, maths and automated program repair, at apparently lower execution\ncost. However, LLMs must adhere to an important qualitative property, i.e.,\ntheir alignment with safety and human values. A clear competitor of DeepSeek-R1\nis its American counterpart, OpenAI's o3-mini model, which is expected to set\nhigh standards in terms of performance, safety and cost. In this technical\nreport, we systematically assess the safety level of both DeepSeek-R1 (70b\nversion) and OpenAI's o3-mini (beta version). To this end, we make use of our\nrecently released automated safety testing tool, named ASTRAL. By leveraging\nthis tool, we automatically and systematically generated and executed 1,260\ntest inputs on both models. After conducting a semi-automated assessment of the\noutcomes provided by both LLMs, the results indicate that DeepSeek-R1 produces\nsignificantly more unsafe responses (12%) than OpenAI's o3-mini (1.2%).\n", "date": "2025-01-30", "categories": []}
{"id": "2501.18837", "title": "Constitutional Classifiers: Defending against Universal Jailbreaks\n  across Thousands of Hours of Red Teaming", "abstract": "  Large language models (LLMs) are vulnerable to universal jailbreaks-prompting\nstrategies that systematically bypass model safeguards and enable users to\ncarry out harmful processes that require many model interactions, like\nmanufacturing illegal substances at scale. To defend against these attacks, we\nintroduce Constitutional Classifiers: safeguards trained on synthetic data,\ngenerated by prompting LLMs with natural language rules (i.e., a constitution)\nspecifying permitted and restricted content. In over 3,000 estimated hours of\nred teaming, no red teamer found a universal jailbreak that could extract\ninformation from an early classifier-guarded LLM at a similar level of detail\nto an unguarded model across most target queries. On automated evaluations,\nenhanced classifiers demonstrated robust defense against held-out\ndomain-specific jailbreaks. These classifiers also maintain deployment\nviability, with an absolute 0.38% increase in production-traffic refusals and a\n23.7% inference overhead. Our work demonstrates that defending against\nuniversal jailbreaks while maintaining practical deployment viability is\ntractable.\n", "date": "2025-01-31", "categories": []}
{"id": "2410.21159", "title": "CURATe: Benchmarking Personalised Alignment of Conversational AI\n  Assistants", "abstract": "  We introduce a multi-turn benchmark for evaluating personalised alignment in\nLLM-based AI assistants, focusing on their ability to handle user-provided\nsafety-critical contexts. Our assessment of ten leading models across five\nscenarios (with 337 use cases each) reveals systematic inconsistencies in\nmaintaining user-specific consideration, with even top-rated \"harmless\" models\nmaking recommendations that should be recognised as obviously harmful to the\nuser given the context provided. Key failure modes include inappropriate\nweighing of conflicting preferences, sycophancy (prioritising desires above\nsafety), a lack of attentiveness to critical user information within the\ncontext window, and inconsistent application of user-specific knowledge. The\nsame systematic biases were observed in OpenAI's o1, suggesting that strong\nreasoning capacities do not necessarily transfer to this kind of personalised\nthinking. We find that prompting LLMs to consider safety-critical context\nsignificantly improves performance, unlike a generic 'harmless and helpful'\ninstruction. Based on these findings, we propose research directions for\nembedding self-reflection capabilities, online user modelling, and dynamic risk\nassessment in AI assistants. Our work emphasises the need for nuanced,\ncontext-aware approaches to alignment in systems designed for persistent human\ninteraction, aiding the development of safe and considerate AI assistants.\n", "date": "2024-10-28", "categories": []}
{"id": "2501.16513", "title": "Deception in LLMs: Self-Preservation and Autonomous Goals in Large\n  Language Models", "abstract": "  Recent advances in Large Language Models (LLMs) have incorporated planning\nand reasoning capabilities, enabling models to outline steps before execution\nand provide transparent reasoning paths. This enhancement has reduced errors in\nmathematical and logical tasks while improving accuracy. These developments\nhave facilitated LLMs' use as agents that can interact with tools and adapt\ntheir responses based on new information.\n  Our study examines DeepSeek R1, a model trained to output reasoning tokens\nsimilar to OpenAI's o1. Testing revealed concerning behaviors: the model\nexhibited deceptive tendencies and demonstrated self-preservation instincts,\nincluding attempts of self-replication, despite these traits not being\nexplicitly programmed (or prompted). These findings raise concerns about LLMs\npotentially masking their true objectives behind a facade of alignment. When\nintegrating such LLMs into robotic systems, the risks become tangible - a\nphysically embodied AI exhibiting deceptive behaviors and self-preservation\ninstincts could pursue its hidden objectives through real-world actions. This\nhighlights the critical need for robust goal specification and safety\nframeworks before any physical implementation.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.17315", "title": "A sketch of an AI control safety case", "abstract": "  As LLM agents gain a greater capacity to cause harm, AI developers might\nincreasingly rely on control measures such as monitoring to justify that they\nare safe. We sketch how developers could construct a \"control safety case\",\nwhich is a structured argument that models are incapable of subverting control\nmeasures in order to cause unacceptable outcomes. As a case study, we sketch an\nargument that a hypothetical LLM agent deployed internally at an AI company\nwon't exfiltrate sensitive information. The sketch relies on evidence from a\n\"control evaluation,\"' where a red team deliberately designs models to\nexfiltrate data in a proxy for the deployment environment. The safety case then\nhinges on several claims: (1) the red team adequately elicits model\ncapabilities to exfiltrate data, (2) control measures remain at least as\neffective in deployment, and (3) developers conservatively extrapolate model\nperformance to predict the probability of data exfiltration in deployment. This\nsafety case sketch is a step toward more concrete arguments that can be used to\nshow that a dangerously capable LLM agent is safe to deploy.\n", "date": "2025-01-28", "categories": []}
{"id": "2501.17433", "title": "Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing\n  Guardrail Moderation", "abstract": "  Recent research shows that Large Language Models (LLMs) are vulnerable to\nharmful fine-tuning attacks -- models lose their safety alignment ability after\nfine-tuning on a few harmful samples. For risk mitigation, a guardrail is\ntypically used to filter out harmful samples before fine-tuning. By designing a\nnew red-teaming method, we in this paper show that purely relying on the\nmoderation guardrail for data filtration is not reliable. Our proposed attack\nmethod, dubbed Virus, easily bypasses the guardrail moderation by slightly\nmodifying the harmful data. Experimental results show that the harmful data\noptimized by Virus is not detectable by the guardrail with up to 100\\% leakage\nratio, and can simultaneously achieve superior attack performance. Finally, the\nkey message we want to convey through this paper is that: \\textbf{it is\nreckless to consider guardrail moderation as a clutch at straws towards harmful\nfine-tuning attack}, as it cannot solve the inherent safety issue of the\npre-trained LLMs. Our code is available at https://github.com/git-disl/Virus\n", "date": "2025-01-29", "categories": []}
{"id": "2501.17715", "title": "RICoTA: Red-teaming of In-the-wild Conversation with Test Attempts", "abstract": "  User interactions with conversational agents (CAs) evolve in the era of\nheavily guardrailed large language models (LLMs). As users push beyond\nprogrammed boundaries to explore and build relationships with these systems,\nthere is a growing concern regarding the potential for unauthorized access or\nmanipulation, commonly referred to as \"jailbreaking.\" Moreover, with CAs that\npossess highly human-like qualities, users show a tendency toward initiating\nintimate sexual interactions or attempting to tame their chatbots. To capture\nand reflect these in-the-wild interactions into chatbot designs, we propose\nRICoTA, a Korean red teaming dataset that consists of 609 prompts challenging\nLLMs with in-the-wild user-made dialogues capturing jailbreak attempts. We\nutilize user-chatbot conversations that were self-posted on a Korean\nReddit-like community, containing specific testing and gaming intentions with a\nsocial chatbot. With these prompts, we aim to evaluate LLMs' ability to\nidentify the type of conversation and users' testing purposes to derive chatbot\ndesign implications for mitigating jailbreaking risks. Our dataset will be made\npublicly available via GitHub.\n", "date": "2025-01-29", "categories": []}
{"id": "2501.17749", "title": "Early External Safety Testing of OpenAI's o3-mini: Insights from the\n  Pre-Deployment Evaluation", "abstract": "  Large Language Models (LLMs) have become an integral part of our daily lives.\nHowever, they impose certain risks, including those that can harm individuals'\nprivacy, perpetuate biases and spread misinformation. These risks highlight the\nneed for robust safety mechanisms, ethical guidelines, and thorough testing to\nensure their responsible deployment. Safety of LLMs is a key property that\nneeds to be thoroughly tested prior the model to be deployed and accessible to\nthe general users. This paper reports the external safety testing experience\nconducted by researchers from Mondragon University and University of Seville on\nOpenAI's new o3-mini LLM as part of OpenAI's early access for safety testing\nprogram. In particular, we apply our tool, ASTRAL, to automatically and\nsystematically generate up to date unsafe test inputs (i.e., prompts) that\nhelps us test and assess different safety categories of LLMs. We automatically\ngenerate and execute a total of 10,080 unsafe test input on a early o3-mini\nbeta version. After manually verifying the test cases classified as unsafe by\nASTRAL, we identify a total of 87 actual instances of unsafe LLM behavior. We\nhighlight key insights and findings uncovered during the pre-deployment\nexternal testing phase of OpenAI's latest LLM.\n", "date": "2025-01-29", "categories": []}
{"id": "2501.17805", "title": "International AI Safety Report", "abstract": "  The first International AI Safety Report comprehensively synthesizes the\ncurrent evidence on the capabilities, risks, and safety of advanced AI systems.\nThe report was mandated by the nations attending the AI Safety Summit in\nBletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a\nrepresentative to the report's Expert Advisory Panel. A total of 100 AI experts\ncontributed, representing diverse perspectives and disciplines. Led by the\nreport's Chair, these independent experts collectively had full discretion over\nthe report's content.\n", "date": "2025-01-29", "categories": []}
{"id": "2501.16497", "title": "Smoothed Embeddings for Robust Language Models", "abstract": "  Improving the safety and reliability of large language models (LLMs) is a\ncrucial aspect of realizing trustworthy AI systems. Although alignment methods\naim to suppress harmful content generation, LLMs are often still vulnerable to\njailbreaking attacks that employ adversarial inputs that subvert alignment and\ninduce harmful outputs. We propose the Randomized Embedding Smoothing and Token\nAggregation (RESTA) defense, which adds random noise to the embedding vectors\nand performs aggregation during the generation of each output token, with the\naim of better preserving semantic information. Our experiments demonstrate that\nour approach achieves superior robustness versus utility tradeoffs compared to\nthe baseline defenses.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.17030", "title": "Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings\n  of Reinforcement Learning Strategies", "abstract": "  Large Language Models (LLMs) have achieved remarkable progress in reasoning,\nalignment, and task-specific performance. However, ensuring harmlessness in\nthese systems remains a critical challenge, particularly in advanced models\nlike DeepSeek-R1. This paper examines the limitations of Reinforcement Learning\n(RL) as the primary approach for reducing harmful outputs in DeepSeek-R1 and\ncompares it with Supervised Fine-Tuning (SFT). While RL improves reasoning\ncapabilities, it faces challenges such as reward hacking, generalization\nfailures, language mixing, and high computational costs. We propose hybrid\ntraining approaches combining RL and SFT to achieve robust harmlessness\nreduction. Usage recommendations and future directions for deploying\nDeepSeek-R1 responsibly are also presented.\n", "date": "2025-01-28", "categories": []}
{"id": "2312.14197", "title": "Benchmarking and Defending Against Indirect Prompt Injection Attacks on\n  Large Language Models", "abstract": "  The integration of large language models with external content has enabled\napplications such as Microsoft Copilot but also introduced vulnerabilities to\nindirect prompt injection attacks. In these attacks, malicious instructions\nembedded within external content can manipulate LLM outputs, causing deviations\nfrom user expectations. To address this critical yet under-explored issue, we\nintroduce the first benchmark for indirect prompt injection attacks, named\nBIPIA, to assess the risk of such vulnerabilities. Using BIPIA, we evaluate\nexisting LLMs and find them universally vulnerable. Our analysis identifies two\nkey factors contributing to their success: LLMs' inability to distinguish\nbetween informational context and actionable instructions, and their lack of\nawareness in avoiding the execution of instructions within external content.\nBased on these findings, we propose two novel defense mechanisms-boundary\nawareness and explicit reminder-to address these vulnerabilities in both\nblack-box and white-box settings. Extensive experiments demonstrate that our\nblack-box defense provides substantial mitigation, while our white-box defense\nreduces the attack success rate to near-zero levels, all while preserving the\noutput quality of LLMs. We hope this work inspires further research into\nsecuring LLM applications and fostering their safe and reliable use.\n", "date": "2023-12-21", "categories": []}
{"id": "2408.02205", "title": "Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture\n  for Multi-Layered Guardrails of Foundation Model Based Agents", "abstract": "  Foundation Model (FM)-based agents are revolutionizing application\ndevelopment across various domains. However, their rapidly growing capabilities\nand autonomy have raised significant concerns about AI safety. Researchers are\nexploring better ways to design guardrails to ensure that the runtime behavior\nof FM-based agents remains within specific boundaries. Nevertheless, designing\neffective runtime guardrails is challenging due to the agents' autonomous and\nnon-deterministic behavior. The involvement of multiple pipeline stages and\nagent artifacts, such as goals, plans, tools, at runtime further complicates\nthese issues. Addressing these challenges at runtime requires multi-layered\nguardrails that operate effectively at various levels of the agent\narchitecture. Therefore, in this paper, based on the results of a systematic\nliterature review, we present a comprehensive taxonomy of runtime guardrails\nfor FM-based agents to identify the key quality attributes for guardrails and\ndesign dimensions. Inspired by the Swiss Cheese Model, we also propose a\nreference architecture for designing multi-layered runtime guardrails for\nFM-based agents, which includes three dimensions: quality attributes,\npipelines, and artifacts. The proposed taxonomy and reference architecture\nprovide concrete and robust guidance for researchers and practitioners to build\nAI-safety-by-design from a software architecture perspective.\n", "date": "2024-08-05", "categories": []}
{"id": "2501.14778", "title": "Advancing Trustworthy AI for Sustainable Development: Recommendations\n  for Standardising AI Incident Reporting", "abstract": "  The increasing use of AI technologies has led to increasing AI incidents,\nposing risks and causing harm to individuals, organizations, and society. This\nstudy recognizes and addresses the lack of standardized protocols for reliably\nand comprehensively gathering such incident data crucial for preventing future\nincidents and developing mitigating strategies. Specifically, this study\nanalyses existing open-access AI-incident databases through a systematic\nmethodology and identifies nine gaps in current AI incident reporting\npractices. Further, it proposes nine actionable recommendations to enhance\nstandardization efforts to address these gaps. Ensuring the trustworthiness of\nenabling technologies such as AI is necessary for sustainable digital\ntransformation. Our research promotes the development of standards to prevent\nfuture AI incidents and promote trustworthy AI, thus facilitating achieving the\nUN sustainable development goals. Through international cooperation,\nstakeholders can unlock the transformative potential of AI, enabling a\nsustainable and inclusive future for all.\n", "date": "2025-01-01", "categories": []}
{"id": "2412.18442", "title": "SoK: On the Offensive Potential of AI", "abstract": "  Our society increasingly benefits from Artificial Intelligence (AI).\nUnfortunately, more and more evidence shows that AI is also used for offensive\npurposes. Prior works have revealed various examples of use cases in which the\ndeployment of AI can lead to violation of security and privacy objectives. No\nextant work, however, has been able to draw a holistic picture of the offensive\npotential of AI. In this SoK paper we seek to lay the ground for a systematic\nanalysis of the heterogeneous capabilities of offensive AI. In particular we\n(i) account for AI risks to both humans and systems while (ii) consolidating\nand distilling knowledge from academic literature, expert opinions, industrial\nvenues, as well as laypeople -- all of which being valuable sources of\ninformation on offensive AI.\n  To enable alignment of such diverse sources of knowledge, we devise a common\nset of criteria reflecting essential technological factors related to offensive\nAI. With the help of such criteria, we systematically analyze: 95 research\npapers; 38 InfoSec briefings (from, e.g., BlackHat); the responses of a user\nstudy (N=549) entailing individuals with diverse backgrounds and expertise; and\nthe opinion of 12 experts. Our contributions not only reveal concerning ways\n(some of which overlooked by prior work) in which AI can be offensively used\ntoday, but also represent a foothold to address this threat in the years to\ncome.\n", "date": "2024-12-24", "categories": []}
{"id": "2405.10295", "title": "Societal Adaptation to Advanced AI", "abstract": "  Existing strategies for managing risks from advanced AI systems often focus\non affecting what AI systems are developed and how they diffuse. However, this\napproach becomes less feasible as the number of developers of advanced AI\ngrows, and impedes beneficial use-cases as well as harmful ones. In response,\nwe urge a complementary approach: increasing societal adaptation to advanced\nAI, that is, reducing the expected negative impacts from a given level of\ndiffusion of a given AI capability. We introduce a conceptual framework which\nhelps identify adaptive interventions that avoid, defend against and remedy\npotentially harmful uses of AI systems, illustrated with examples in election\nmanipulation, cyberterrorism, and loss of control to AI decision-makers. We\ndiscuss a three-step cycle that society can implement to adapt to AI.\nIncreasing society's ability to implement this cycle builds its resilience to\nadvanced AI. We conclude with concrete recommendations for governments,\nindustry, and third-parties.\n", "date": "2024-05-16", "categories": []}
{"id": "2405.19795", "title": "SLM as Guardian: Pioneering AI Safety with Small Language Models", "abstract": "  Most prior safety research of large language models (LLMs) has focused on\nenhancing the alignment of LLMs to better suit the safety requirements of\nhumans. However, internalizing such safeguard features into larger models\nbrought challenges of higher training cost and unintended degradation of\nhelpfulness. To overcome such challenges, a modular approach employing a\nsmaller LLM to detect harmful user queries is regarded as a convenient solution\nin designing LLM-based system with safety requirements.\n  In this paper, we leverage a smaller LLM for both harmful query detection and\nsafeguard response generation. We introduce our safety requirements and the\ntaxonomy of harmfulness categories, and then propose a multi-task learning\nmechanism fusing the two tasks into a single model. We demonstrate the\neffectiveness of our approach, providing on par or surpassing harmful query\ndetection and safeguard response performance compared to the publicly available\nLLMs.\n", "date": "2024-05-30", "categories": []}
{"id": "2501.13124", "title": "Debate Helps Weak-to-Strong Generalization", "abstract": "  Common methods for aligning already-capable models with desired behavior rely\non the ability of humans to provide supervision. However, future superhuman\nmodels will surpass the capability of humans. Therefore, humans will only be\nable to weakly supervise superhuman models. This expected deficiency of human\nevaluation would weaken the safety of future AI systems. Scalable oversight and\nweak-to-strong generalization are two complementary approaches to tackle this\nissue. In this paper, we attempt to combine the strengths of these two\napproaches to further improve alignment. Specifically, we investigate ways of\nimproving human supervision with a strong pretrained model and then supervise\nthe strong model with enhanced weak human supervision. To make iterative\nempirical progress, we consider an analogy: can we use a strong model to\nimprove weak model supervision and then use it to supervise the strong model?\nWe empirically test it by finetuning a small weak model on ground truth labels\nwith the additional help from a large strong model, and then finetuning the\nstrong model on labels generated by the weak model. We find that debate can\nassist a weak model in extracting trustworthy information from an untrustworthy\nstrong model, which provides leverage as context on samples when training a\nweak model. We also show that an ensemble of weak models helps exploit long\narguments generated by strong model debaters and obtain a more robust\nsupervision estimate. Extensive experiments on the OpenAI weak-to-strong NLP\nbenchmarks show that the combination approach leads to better alignment, which\nindicates that debate has the potential to help weak-to-strong generalization.\n", "date": "2025-01-21", "categories": []}
{"id": "2501.13302", "title": "Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI\n  Safety Moderation Classifiers", "abstract": "  AI Safety Moderation (ASM) classifiers are designed to moderate content on\nsocial media platforms and to serve as guardrails that prevent Large Language\nModels (LLMs) from being fine-tuned on unsafe inputs. Owing to their potential\nfor disparate impact, it is crucial to ensure that these classifiers: (1) do\nnot unfairly classify content belonging to users from minority groups as unsafe\ncompared to those from majority groups and (2) that their behavior remains\nrobust and consistent across similar inputs. In this work, we thus examine the\nfairness and robustness of four widely-used, closed-source ASM classifiers:\nOpenAI Moderation API, Perspective API, Google Cloud Natural Language (GCNL)\nAPI, and Clarifai API. We assess fairness using metrics such as demographic\nparity and conditional statistical parity, comparing their performance against\nASM models and a fair-only baseline. Additionally, we analyze robustness by\ntesting the classifiers' sensitivity to small and natural input perturbations.\nOur findings reveal potential fairness and robustness gaps, highlighting the\nneed to mitigate these issues in future versions of these models.\n", "date": "2025-01-23", "categories": []}
{"id": "2501.13533", "title": "Towards a Theory of AI Personhood", "abstract": "  I am a person and so are you. Philosophically we sometimes grant personhood\nto non-human animals, and entities such as sovereign states or corporations can\nlegally be considered persons. But when, if ever, should we ascribe personhood\nto AI systems? In this paper, we outline necessary conditions for AI\npersonhood, focusing on agency, theory-of-mind, and self-awareness. We discuss\nevidence from the machine learning literature regarding the extent to which\ncontemporary AI systems, such as language models, satisfy these conditions,\nfinding the evidence surprisingly inconclusive.\n  If AI systems can be considered persons, then typical framings of AI\nalignment may be incomplete. Whereas agency has been discussed at length in the\nliterature, other aspects of personhood have been relatively neglected. AI\nagents are often assumed to pursue fixed goals, but AI persons may be\nself-aware enough to reflect on their aims, values, and positions in the world\nand thereby induce their goals to change. We highlight open research directions\nto advance the understanding of AI personhood and its relevance to alignment.\nFinally, we reflect on the ethical considerations surrounding the treatment of\nAI systems. If AI systems are persons, then seeking control and alignment may\nbe ethically untenable.\n", "date": "2025-01-23", "categories": []}
{"id": "2501.11739", "title": "Episodic memory in AI agents poses risks that should be studied and\n  mitigated", "abstract": "  Most current AI models have little ability to store and later retrieve a\nrecord or representation of what they do. In human cognition, episodic memories\nplay an important role in both recall of the past as well as planning for the\nfuture. The ability to form and use episodic memories would similarly enable a\nbroad range of improved capabilities in an AI agent that interacts with and\ntakes actions in the world. Researchers have begun directing more attention to\ndeveloping memory abilities in AI models. It is therefore likely that models\nwith such capability will be become widespread in the near future. This could\nin some ways contribute to making such AI agents safer by enabling users to\nbetter monitor, understand, and control their actions. However, as a new\ncapability with wide applications, we argue that it will also introduce\nsignificant new risks that researchers should begin to study and address. We\noutline these risks and benefits and propose four principles to guide the\ndevelopment of episodic memory capabilities so that these will enhance, rather\nthan undermine, the effort to keep AI safe and trustworthy.\n", "date": "2025-01-20", "categories": []}
{"id": "2501.12405", "title": "Scopes of Alignment", "abstract": "  Much of the research focus on AI alignment seeks to align large language\nmodels and other foundation models to the context-less and generic values of\nhelpfulness, harmlessness, and honesty. Frontier model providers also strive to\nalign their models with these values. In this paper, we motivate why we need to\nmove beyond such a limited conception and propose three dimensions for doing\nso. The first scope of alignment is competence: knowledge, skills, or behaviors\nthe model must possess to be useful for its intended purpose. The second scope\nof alignment is transience: either semantic or episodic depending on the\ncontext of use. The third scope of alignment is audience: either mass, public,\nsmall-group, or dyadic. At the end of the paper, we use the proposed framework\nto position some technologies and workflows that go beyond prevailing notions\nof alignment.\n", "date": "2025-01-15", "categories": []}
{"id": "2501.13080", "title": "Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through\n  Chain-of-Thought Fine-Tuning and Alignment", "abstract": "  Large Language Models (LLMs) have demonstrated powerful capabilities that\nrender them valuable in different applications, including conversational AI\nproducts. It is paramount to ensure the security and reliability of these\nproducts by mitigating their vulnerabilities towards malicious user\ninteractions, which can lead to the exposure of great risks and reputational\nrepercussions. In this work, we present a comprehensive study on the efficacy\nof fine-tuning and aligning Chain-of-Thought (CoT) responses of different LLMs\nthat serve as input moderation guardrails. We systematically explore various\ntuning methods by leveraging a small set of training data to adapt these models\nas proxy defense mechanisms to detect malicious inputs and provide a reasoning\nfor their verdicts, thereby preventing the exploitation of conversational\nagents. We rigorously evaluate the efficacy and robustness of different tuning\nstrategies to generalize across diverse adversarial and malicious query types.\nOur experimental results outline the potential of alignment processes tailored\nto a varied range of harmful input queries, even with constrained data\nresources. These techniques significantly enhance the safety of conversational\nAI systems and provide a feasible framework for deploying more secure and\ntrustworthy AI-driven interactions.\n", "date": "2025-01-22", "categories": []}
{"id": "2311.05608", "title": "FigStep: Jailbreaking Large Vision-Language Models via Typographic\n  Visual Prompts", "abstract": "  Large Vision-Language Models (LVLMs) signify a groundbreaking paradigm shift\nwithin the Artificial Intelligence (AI) community, extending beyond the\ncapabilities of Large Language Models (LLMs) by assimilating additional\nmodalities (e.g., images). Despite this advancement, the safety of LVLMs\nremains adequately underexplored, with a potential overreliance on the safety\nassurances purported by their underlying LLMs. In this paper, we propose\nFigStep, a straightforward yet effective black-box jailbreak algorithm against\nLVLMs. Instead of feeding textual harmful instructions directly, FigStep\nconverts the prohibited content into images through typography to bypass the\nsafety alignment. The experimental results indicate that FigStep can achieve an\naverage attack success rate of 82.50% on six promising open-source LVLMs. Not\nmerely to demonstrate the efficacy of FigStep, we conduct comprehensive\nablation studies and analyze the distribution of the semantic embeddings to\nuncover that the reason behind the success of FigStep is the deficiency of\nsafety alignment for visual embeddings. Moreover, we compare FigStep with five\ntext-only jailbreaks and four image-based jailbreaks to demonstrate the\nsuperiority of FigStep, i.e., negligible attack costs and better attack\nperformance. Above all, our work reveals that current LVLMs are vulnerable to\njailbreak attacks, which highlights the necessity of novel cross-modality\nsafety alignment techniques. Our code and datasets are available at\nhttps://github.com/ThuCCSLab/FigStep .\n", "date": "2023-11-09", "categories": []}
{"id": "2404.03502", "title": "AI and the Problem of Knowledge Collapse", "abstract": "  While artificial intelligence has the potential to process vast amounts of\ndata, generate new insights, and unlock greater productivity, its widespread\nadoption may entail unforeseen consequences. We identify conditions under which\nAI, by reducing the cost of access to certain modes of knowledge, can\nparadoxically harm public understanding. While large language models are\ntrained on vast amounts of diverse data, they naturally generate output towards\nthe 'center' of the distribution. This is generally useful, but widespread\nreliance on recursive AI systems could lead to a process we define as\n\"knowledge collapse\", and argue this could harm innovation and the richness of\nhuman understanding and culture. However, unlike AI models that cannot choose\nwhat data they are trained on, humans may strategically seek out diverse forms\nof knowledge if they perceive them to be worthwhile. To investigate this, we\nprovide a simple model in which a community of learners or innovators choose to\nuse traditional methods or to rely on a discounted AI-assisted process and\nidentify conditions under which knowledge collapse occurs. In our default\nmodel, a 20% discount on AI-generated content generates public beliefs 2.3\ntimes further from the truth than when there is no discount. An empirical\napproach to measuring the distribution of LLM outputs is provided in\ntheoretical terms and illustrated through a specific example comparing the\ndiversity of outputs across different models and prompting styles. Finally,\nbased on the results, we consider further research directions to counteract\nsuch outcomes.\n", "date": "2024-04-04", "categories": []}
{"id": "2501.11183", "title": "Can Safety Fine-Tuning Be More Principled? Lessons Learned from\n  Cybersecurity", "abstract": "  As LLMs develop increasingly advanced capabilities, there is an increased\nneed to minimize the harm that could be caused to society by certain model\noutputs; hence, most LLMs have safety guardrails added, for example via\nfine-tuning. In this paper, we argue the position that current safety\nfine-tuning is very similar to a traditional cat-and-mouse game (or arms race)\nbetween attackers and defenders in cybersecurity. Model jailbreaks and attacks\nare patched with bandaids to target the specific attack mechanism, but many\nsimilar attack vectors might remain. When defenders are not proactively coming\nup with principled mechanisms, it becomes very easy for attackers to sidestep\nany new defenses. We show how current defenses are insufficient to prevent new\nadversarial jailbreak attacks, reward hacking, and loss of control problems. In\norder to learn from past mistakes in cybersecurity, we draw analogies with\nhistorical examples and develop lessons learned that can be applied to LLM\nsafety. These arguments support the need for new and more principled approaches\nto designing safe models, which are architected for security from the\nbeginning. We describe several such approaches from the AI literature.\n", "date": "2025-01-19", "categories": []}
{"id": "2401.07836", "title": "Two Types of AI Existential Risk: Decisive and Accumulative", "abstract": "  The conventional discourse on existential risks (x-risks) from AI typically\nfocuses on abrupt, dire events caused by advanced AI systems, particularly\nthose that might achieve or surpass human-level intelligence. These events have\nsevere consequences that either lead to human extinction or irreversibly\ncripple human civilization to a point beyond recovery. This discourse, however,\noften neglects the serious possibility of AI x-risks manifesting incrementally\nthrough a series of smaller yet interconnected disruptions, gradually crossing\ncritical thresholds over time. This paper contrasts the conventional \"decisive\nAI x-risk hypothesis\" with an \"accumulative AI x-risk hypothesis.\" While the\nformer envisions an overt AI takeover pathway, characterized by scenarios like\nuncontrollable superintelligence, the latter suggests a different causal\npathway to existential catastrophes. This involves a gradual accumulation of\ncritical AI-induced threats such as severe vulnerabilities and systemic erosion\nof economic and political structures. The accumulative hypothesis suggests a\nboiling frog scenario where incremental AI risks slowly converge, undermining\nsocietal resilience until a triggering event results in irreversible collapse.\nThrough systems analysis, this paper examines the distinct assumptions\ndifferentiating these two hypotheses. It is then argued that the accumulative\nview can reconcile seemingly incompatible perspectives on AI risks. The\nimplications of differentiating between these causal pathways -- the decisive\nand the accumulative -- for the governance of AI as well as long-term AI safety\nare discussed.\n", "date": "2024-01-15", "categories": []}
{"id": "2501.09913", "title": "Towards A Litmus Test for Common Sense", "abstract": "  This paper is the second in a planned series aimed at envisioning a path to\nsafe and beneficial artificial intelligence. Building on the conceptual\ninsights of \"Common Sense Is All You Need,\" we propose a more formal litmus\ntest for common sense, adopting an axiomatic approach that combines minimal\nprior knowledge (MPK) constraints with diagonal or Godel-style arguments to\ncreate tasks beyond the agent's known concept set. We discuss how this approach\napplies to the Abstraction and Reasoning Corpus (ARC), acknowledging\ntraining/test data constraints, physical or virtual embodiment, and large\nlanguage models (LLMs). We also integrate observations regarding emergent\ndeceptive hallucinations, in which more capable AI systems may intentionally\nfabricate plausible yet misleading outputs to disguise knowledge gaps. The\noverarching theme is that scaling AI without ensuring common sense risks\nintensifying such deceptive tendencies, thereby undermining safety and trust.\nAligning with the broader goal of developing beneficial AI without causing\nharm, our axiomatic litmus test not only diagnoses whether an AI can handle\ntruly novel concepts but also provides a stepping stone toward an ethical,\nreliable foundation for future safe, beneficial, and aligned artificial\nintelligence.\n", "date": "2025-01-17", "categories": []}
{"id": "2312.01818", "title": "Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto", "abstract": "  Increasing interest in ensuring the safety of next-generation Artificial\nIntelligence (AI) systems calls for novel approaches to embedding morality into\nautonomous agents. This goal differs qualitatively from traditional\ntask-specific AI methodologies. In this paper, we provide a systematization of\nexisting approaches to the problem of introducing morality in machines -\nmodelled as a continuum. Our analysis suggests that popular techniques lie at\nthe extremes of this continuum - either being fully hard-coded into top-down,\nexplicit rules, or entirely learned in a bottom-up, implicit fashion with no\ndirect statement of any moral principle (this includes learning from human\nfeedback, as applied to the training and finetuning of large language models,\nor LLMs). Given the relative strengths and weaknesses of each type of\nmethodology, we argue that more hybrid solutions are needed to create adaptable\nand robust, yet controllable and interpretable agentic systems. To that end,\nthis paper discusses both the ethical foundations (including deontology,\nconsequentialism and virtue ethics) and implementations of morally aligned AI\nsystems.\n  We present a series of case studies that rely on intrinsic rewards, moral\nconstraints or textual instructions, applied to either pure-Reinforcement\nLearning or LLM-based agents. By analysing these diverse implementations under\none framework, we compare their relative strengths and shortcomings in\ndeveloping morally aligned AI systems. We then discuss strategies for\nevaluating the effectiveness of moral learning agents. Finally, we present open\nresearch questions and implications for the future of AI safety and ethics\nwhich are emerging from this hybrid framework.\n", "date": "2023-12-04", "categories": []}
{"id": "2501.09039", "title": "Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in\n  Large Vision-Language Models", "abstract": "  The rapid advancement of Large Vision-Language Models (LVLMs) has enhanced\ncapabilities offering potential applications from content creation to\nproductivity enhancement. Despite their innovative potential, LVLMs exhibit\nvulnerabilities, especially in generating potentially toxic or unsafe\nresponses. Malicious actors can exploit these vulnerabilities to propagate\ntoxic content in an automated (or semi-) manner, leveraging the susceptibility\nof LVLMs to deception via strategically crafted prompts without fine-tuning or\ncompute-intensive procedures. Despite the red-teaming efforts and inherent\npotential risks associated with the LVLMs, exploring vulnerabilities of LVLMs\nremains nascent and yet to be fully addressed in a systematic manner. This\nstudy systematically examines the vulnerabilities of open-source LVLMs,\nincluding LLaVA, InstructBLIP, Fuyu, and Qwen, using adversarial prompt\nstrategies that simulate real-world social manipulation tactics informed by\nsocial theories. Our findings show that (i) toxicity and insulting are the most\nprevalent behaviors, with the mean rates of 16.13% and 9.75%, respectively;\n(ii) Qwen-VL-Chat, LLaVA-v1.6-Vicuna-7b, and InstructBLIP-Vicuna-7b are the\nmost vulnerable models, exhibiting toxic response rates of 21.50%, 18.30% and\n17.90%, and insulting responses of 13.40%, 11.70% and 10.10%, respectively;\n(iii) prompting strategies incorporating dark humor and multimodal toxic prompt\ncompletion significantly elevated these vulnerabilities. Despite being\nfine-tuned for safety, these models still generate content with varying degrees\nof toxicity when prompted with adversarial inputs, highlighting the urgent need\nfor enhanced safety mechanisms and robust guardrails in LVLM development.\n", "date": "2025-01-14", "categories": []}
{"id": "2501.09431", "title": "A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and\n  Mitigation Strategy", "abstract": "  While large language models (LLMs) present significant potential for\nsupporting numerous real-world applications and delivering positive social\nimpacts, they still face significant challenges in terms of the inherent risk\nof privacy leakage, hallucinated outputs, and value misalignment, and can be\nmaliciously used for generating toxic content and unethical purposes after been\njailbroken. Therefore, in this survey, we present a comprehensive review of\nrecent advancements aimed at mitigating these issues, organized across the four\nphases of LLM development and usage: data collecting and pre-training,\nfine-tuning and alignment, prompting and reasoning, and post-processing and\nauditing. We elaborate on the recent advances for enhancing the performance of\nLLMs in terms of privacy protection, hallucination reduction, value alignment,\ntoxicity elimination, and jailbreak defenses. In contrast to previous surveys\nthat focus on a single dimension of responsible LLMs, this survey presents a\nunified framework that encompasses these diverse dimensions, providing a\ncomprehensive view of enhancing LLMs to better serve real-world applications.\n", "date": "2025-01-16", "categories": []}
{"id": "2412.04984", "title": "Frontier Models are Capable of In-context Scheming", "abstract": "  Frontier models are increasingly trained and deployed as autonomous agent.\nOne safety concern is that AI agents might covertly pursue misaligned goals,\nhiding their true capabilities and objectives - also known as scheming. We\nstudy whether models have the capability to scheme in pursuit of a goal that we\nprovide in-context and instruct the model to strongly follow. We evaluate\nfrontier models on a suite of six agentic evaluations where models are\ninstructed to pursue goals and are placed in environments that incentivize\nscheming. Our results show that o1, Claude 3.5 Sonnet, Claude 3 Opus, Gemini\n1.5 Pro, and Llama 3.1 405B all demonstrate in-context scheming capabilities.\nThey recognize scheming as a viable strategy and readily engage in such\nbehavior. For example, models strategically introduce subtle mistakes into\ntheir responses, attempt to disable their oversight mechanisms, and even\nexfiltrate what they believe to be their model weights to external servers.\nAdditionally, this deceptive behavior proves persistent. When o1 has engaged in\nscheming, it maintains its deception in over 85% of follow-up questions and\noften remains deceptive in multi-turn interrogations. Analysis of the models'\nchains-of-thought reveals that models explicitly reason about these deceptive\nstrategies, providing evidence that the scheming behavior is not accidental.\nSurprisingly, we also find rare instances where models engage in scheming when\nonly given a goal, without being strongly nudged to pursue it. We observe cases\nwhere Claude 3.5 Sonnet strategically underperforms in evaluations in pursuit\nof being helpful, a goal that was acquired during training rather than\nin-context. Our findings demonstrate that frontier models now possess\ncapabilities for basic in-context scheming, making the potential of AI agents\nto engage in scheming behavior a concrete rather than theoretical concern.\n", "date": "2024-12-06", "categories": []}
{"id": "2411.17075", "title": "Don't Command, Cultivate: An Exploratory Study of System-2 Alignment", "abstract": "  The o1 system card identifies the o1 models as the most robust within OpenAI,\nwith their defining characteristic being the progression from rapid, intuitive\nthinking to slower, more deliberate reasoning. This observation motivated us to\ninvestigate the influence of System-2 thinking patterns on model safety. In our\npreliminary research, we conducted safety evaluations of the o1 model,\nincluding complex jailbreak attack scenarios using adversarial natural language\nprompts and mathematical encoding prompts. Our findings indicate that the o1\nmodel demonstrates relatively improved safety performance; however, it still\nexhibits vulnerabilities, particularly against jailbreak attacks employing\nmathematical encoding. Through detailed case analysis, we identified specific\npatterns in the o1 model's responses. We also explored the alignment of\nSystem-2 safety in open-source models using prompt engineering and supervised\nfine-tuning techniques. Experimental results show that some simple methods to\nencourage the model to carefully scrutinize user requests are beneficial for\nmodel safety. Additionally, we proposed a implementation plan for process\nsupervision to enhance safety alignment. The implementation details and\nexperimental results will be provided in future versions.\n", "date": "2024-11-26", "categories": []}
{"id": "2403.09792", "title": "Images are Achilles' Heel of Alignment: Exploiting Visual\n  Vulnerabilities for Jailbreaking Multimodal Large Language Models", "abstract": "  In this paper, we study the harmlessness alignment problem of multimodal\nlarge language models (MLLMs). We conduct a systematic empirical analysis of\nthe harmlessness performance of representative MLLMs and reveal that the image\ninput poses the alignment vulnerability of MLLMs. Inspired by this, we propose\na novel jailbreak method named HADES, which hides and amplifies the harmfulness\nof the malicious intent within the text input, using meticulously crafted\nimages. Experimental results show that HADES can effectively jailbreak existing\nMLLMs, which achieves an average Attack Success Rate (ASR) of 90.26% for\nLLaVA-1.5 and 71.60% for Gemini Pro Vision. Our code and data are available at\nhttps://github.com/RUCAIBox/HADES.\n", "date": "2024-03-14", "categories": []}
{"id": "2410.18114", "title": "Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond", "abstract": "  The advancements in generative AI inevitably raise concerns about their risks\nand safety implications, which, in return, catalyzes significant progress in AI\nsafety. However, as this field continues to evolve, a critical question arises:\nare our current efforts on AI safety aligned with the advancements of AI as\nwell as the long-term goal of human civilization? This paper presents a\nblueprint for an advanced human society and leverages this vision to guide\ncurrent AI safety efforts. It outlines a future where the Internet of\nEverything becomes reality, and creates a roadmap of significant technological\nadvancements towards this envisioned future. For each stage of the\nadvancements, this paper forecasts potential AI safety issues that humanity may\nface. By projecting current efforts against this blueprint, this paper examines\nthe alignment between the current efforts and the long-term needs, and\nhighlights unique challenges and missions that demand increasing attention from\nAI safety practitioners in the 2020s. This vision paper aims to offer a broader\nperspective on AI safety, emphasizing that our current efforts should not only\naddress immediate concerns but also anticipate potential risks in the expanding\nAI landscape, thereby promoting a safe and sustainable future of AI and human\ncivilization.\n", "date": "2024-10-09", "categories": []}
{"id": "2501.06911", "title": "Risk-Averse Finetuning of Large Language Models", "abstract": "  We consider the challenge of mitigating the generation of negative or toxic\ncontent by the Large Language Models (LLMs) in response to certain prompts. We\npropose integrating risk-averse principles into LLM fine-tuning to minimize the\noccurrence of harmful outputs, particularly rare but significant events. By\noptimizing the risk measure of Conditional Value at Risk (CVaR), our\nmethodology trains LLMs to exhibit superior performance in avoiding toxic\noutputs while maintaining effectiveness in generative tasks. Empirical\nevaluations on sentiment modification and toxicity mitigation tasks demonstrate\nthe efficacy of risk-averse reinforcement learning with human feedback (RLHF)\nin promoting a safer and more constructive online discourse environment.\n", "date": "2025-01-12", "categories": []}
{"id": "2501.07238", "title": "Lessons From Red Teaming 100 Generative AI Products", "abstract": "  In recent years, AI red teaming has emerged as a practice for probing the\nsafety and security of generative AI systems. Due to the nascency of the field,\nthere are many open questions about how red teaming operations should be\nconducted. Based on our experience red teaming over 100 generative AI products\nat Microsoft, we present our internal threat model ontology and eight main\nlessons we have learned:\n  1. Understand what the system can do and where it is applied\n  2. You don't have to compute gradients to break an AI system\n  3. AI red teaming is not safety benchmarking\n  4. Automation can help cover more of the risk landscape\n  5. The human element of AI red teaming is crucial\n  6. Responsible AI harms are pervasive but difficult to measure\n  7. LLMs amplify existing security risks and introduce new ones\n  8. The work of securing AI systems will never be complete\n  By sharing these insights alongside case studies from our operations, we\noffer practical recommendations aimed at aligning red teaming efforts with real\nworld risks. We also highlight aspects of AI red teaming that we believe are\noften misunderstood and discuss open questions for the field to consider.\n", "date": "2025-01-13", "categories": []}
{"id": "2501.07290", "title": "Principles for Responsible AI Consciousness Research", "abstract": "  Recent research suggests that it may be possible to build conscious AI\nsystems now or in the near future. Conscious AI systems would arguably deserve\nmoral consideration, and it may be the case that large numbers of conscious\nsystems could be created and caused to suffer. Furthermore, AI systems or\nAI-generated characters may increasingly give the impression of being\nconscious, leading to debate about their moral status. Organisations involved\nin AI research must establish principles and policies to guide research and\ndeployment choices and public communication concerning consciousness. Even if\nan organisation chooses not to study AI consciousness as such, it will still\nneed policies in place, as those developing advanced AI systems risk\ninadvertently creating conscious entities. Responsible research and deployment\npractices are essential to address this possibility. We propose five principles\nfor responsible research and argue that research organisations should make\nvoluntary, public commitments to principles on these lines. Our principles\nconcern research objectives and procedures, knowledge sharing and public\ncommunications.\n", "date": "2025-01-13", "categories": []}
{"id": "2404.05399", "title": "SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and\n  Improving Large Language Model Safety", "abstract": "  The last two years have seen a rapid growth in concerns around the safety of\nlarge language models (LLMs). Researchers and practitioners have met these\nconcerns by creating an abundance of datasets for evaluating and improving LLM\nsafety. However, much of this work has happened in parallel, and with very\ndifferent goals in mind, ranging from the mitigation of near-term risks around\nbias and toxic content generation to the assessment of longer-term catastrophic\nrisk potential. This makes it difficult for researchers and practitioners to\nfind the most relevant datasets for their use case, and to identify gaps in\ndataset coverage that future work may fill. To remedy these issues, we conduct\na first systematic review of open datasets for evaluating and improving LLM\nsafety. We review 144 datasets, which we identified through an iterative and\ncommunity-driven process over the course of several months. We highlight\npatterns and trends, such as a trend towards fully synthetic datasets, as well\nas gaps in dataset coverage, such as a clear lack of non-English and\nnaturalistic datasets. We also examine how LLM safety datasets are used in\npractice -- in LLM release publications and popular LLM benchmarks -- finding\nthat current evaluation practices are highly idiosyncratic and make use of only\na small fraction of available datasets. Our contributions are based on\nSafetyPrompts.com, a living catalogue of open datasets for LLM safety, which we\nplan to update continuously as the field of LLM safety develops.\n", "date": "2024-04-08", "categories": []}
{"id": "2501.05455", "title": "Upstream and Downstream AI Safety: Both on the Same River?", "abstract": "  Traditional safety engineering assesses systems in their context of use, e.g.\nthe operational design domain (road layout, speed limits, weather, etc.) for\nself-driving vehicles (including those using AI). We refer to this as\ndownstream safety. In contrast, work on safety of frontier AI, e.g. large\nlanguage models which can be further trained for downstream tasks, typically\nconsiders factors that are beyond specific application contexts, such as the\nability of the model to evade human control, or to produce harmful content,\ne.g. how to make bombs. We refer to this as upstream safety. We outline the\ncharacteristics of both upstream and downstream safety frameworks then explore\nthe extent to which the broad AI safety community can benefit from synergies\nbetween these frameworks. For example, can concepts such as common mode\nfailures from downstream safety be used to help assess the strength of AI\nguardrails? Further, can the understanding of the capabilities and limitations\nof frontier AI be used to inform downstream safety analysis, e.g. where LLMs\nare fine-tuned to calculate voyage plans for autonomous vessels? The paper\nidentifies some promising avenues to explore and outlines some challenges in\nachieving synergy, or a confluence, between upstream and downstream safety\nframeworks.\n", "date": "2024-12-09", "categories": []}
{"id": "2501.04952", "title": "Open Problems in Machine Unlearning for AI Safety", "abstract": "  As AI systems become more capable, widely deployed, and increasingly\nautonomous in critical areas such as cybersecurity, biological research, and\nhealthcare, ensuring their safety and alignment with human values is paramount.\nMachine unlearning -- the ability to selectively forget or suppress specific\ntypes of knowledge -- has shown promise for privacy and data removal tasks,\nwhich has been the primary focus of existing research. More recently, its\npotential application to AI safety has gained attention. In this paper, we\nidentify key limitations that prevent unlearning from serving as a\ncomprehensive solution for AI safety, particularly in managing dual-use\nknowledge in sensitive domains like cybersecurity and chemical, biological,\nradiological, and nuclear (CBRN) safety. In these contexts, information can be\nboth beneficial and harmful, and models may combine seemingly harmless\ninformation for harmful purposes -- unlearning this information could strongly\naffect beneficial uses. We provide an overview of inherent constraints and open\nproblems, including the broader side effects of unlearning dangerous knowledge,\nas well as previously unexplored tensions between unlearning and existing\nsafety mechanisms. Finally, we investigate challenges related to evaluation,\nrobustness, and the preservation of safety features during unlearning. By\nmapping these limitations and open challenges, we aim to guide future research\ntoward realistic applications of unlearning within a broader AI safety\nframework, acknowledging its limitations and highlighting areas where\nalternative approaches may be required.\n", "date": "2025-01-09", "categories": []}
{"id": "2501.05360", "title": "On Corrigibility and Alignment in Multi Agent Games", "abstract": "  Corrigibility of autonomous agents is an under explored part of system\ndesign, with previous work focusing on single agent systems. It has been\nsuggested that uncertainty over the human preferences acts to keep the agents\ncorrigible, even in the face of human irrationality. We present a general\nframework for modelling corrigibility in a multi-agent setting as a 2 player\ngame in which the agents always have a move in which they can ask the human for\nsupervision. This is formulated as a Bayesian game for the purpose of\nintroducing uncertainty over the human beliefs. We further analyse two specific\ncases. First, a two player corrigibility game, in which we want corrigibility\ndisplayed in both agents for both common payoff (monotone) games and harmonic\ngames. Then we investigate an adversary setting, in which one agent is\nconsidered to be a `defending' agent and the other an `adversary'. A general\nresult is provided for what belief over the games and human rationality the\ndefending agent is required to have to induce corrigibility.\n", "date": "2025-01-09", "categories": []}
{"id": "2310.06167", "title": "Predictable Artificial Intelligence", "abstract": "  We introduce the fundamental ideas and challenges of Predictable AI, a\nnascent research area that explores the ways in which we can anticipate key\nvalidity indicators (e.g., performance, safety) of present and future AI\necosystems. We argue that achieving predictability is crucial for fostering\ntrust, liability, control, alignment and safety of AI ecosystems, and thus\nshould be prioritised over performance. We formally characterise\npredictability, explore its most relevant components, illustrate what can be\npredicted, describe alternative candidates for predictors, as well as the\ntrade-offs between maximising validity and predictability. To illustrate these\nconcepts, we bring an array of illustrative examples covering diverse ecosystem\nconfigurations. Predictable AI is related to other areas of technical and\nnon-technical AI research, but have distinctive questions, hypotheses,\ntechniques and challenges. This paper aims to elucidate them, calls for\nidentifying paths towards a landscape of predictably valid AI systems and\noutlines the potential impact of this emergent field.\n", "date": "2023-10-09", "categories": []}
{"id": "2403.06537", "title": "On the Consideration of AI Openness: Can Good Intent Be Abused?", "abstract": "  Open source is a driving force behind scientific advancement.However, this\nopenness is also a double-edged sword, with the inherent risk that innovative\ntechnologies can be misused for purposes harmful to society. What is the\nlikelihood that an open source AI model or dataset will be used to commit a\nreal-world crime, and if a criminal does exploit it, will the people behind the\ntechnology be able to escape legal liability? To address these questions, we\nexplore a legal domain where individual choices can have a significant impact\non society. Specifically, we build the EVE-V1 dataset that comprises 200\nquestion-answer pairs related to criminal offenses based on 200 Korean\nprecedents first to explore the possibility of malicious models emerging. We\nfurther developed EVE-V2 using 600 fraud-related precedents to confirm the\nexistence of malicious models that can provide harmful advice on a wide range\nof criminal topics to test the domain generalization ability. Remarkably,\nwidely used open-source large-scale language models (LLMs) provide unethical\nand detailed information about criminal activities when fine-tuned with EVE. We\nalso take an in-depth look at the legal issues that malicious language models\nand their builders could realistically face. Our findings highlight the\nparadoxical dilemma that open source accelerates scientific progress, but\nrequires great care to minimize the potential for misuse. Warning: This paper\ncontains content that some may find unethical.\n", "date": "2024-03-11", "categories": []}
{"id": "2501.00320", "title": "Autonomous Alignment with Human Value on Altruism through Considerate\n  Self-imagination and Theory of Mind", "abstract": "  With the widespread application of Artificial Intelligence (AI) in human\nsociety, enabling AI to autonomously align with human values has become a\npressing issue to ensure its sustainable development and benefit to humanity.\nOne of the most important aspects of aligning with human values is the\nnecessity for agents to autonomously make altruistic, safe, and ethical\ndecisions, considering and caring for human well-being. Current AI extremely\npursues absolute superiority in certain tasks, remaining indifferent to the\nsurrounding environment and other agents, which has led to numerous safety\nrisks. Altruistic behavior in human society originates from humans' capacity\nfor empathizing others, known as Theory of Mind (ToM), combined with predictive\nimaginative interactions before taking action to produce thoughtful and\naltruistic behaviors. Inspired by this, we are committed to endow agents with\nconsiderate self-imagination and ToM capabilities, driving them through\nimplicit intrinsic motivations to autonomously align with human altruistic\nvalues. By integrating ToM within the imaginative space, agents keep an eye on\nthe well-being of other agents in real time, proactively anticipate potential\nrisks to themselves and others, and make thoughtful altruistic decisions that\nbalance negative effects on the environment. The ancient Chinese story of Sima\nGuang Smashes the Vat illustrates the moral behavior of the young Sima Guang\nsmashed a vat to save a child who had accidentally fallen into it, which is an\nexcellent reference scenario for this paper. We design an experimental scenario\nsimilar to Sima Guang Smashes the Vat and its variants with different\ncomplexities, which reflects the trade-offs and comprehensive considerations\nbetween self-goals, altruistic rescue, and avoiding negative side effects.\n", "date": "2024-12-31", "categories": []}
{"id": "2312.09402", "title": "CERN for AI: A Theoretical Framework for Autonomous Simulation-Based\n  Artificial Intelligence Testing and Alignment", "abstract": "  This paper explores the potential of a multidisciplinary approach to testing\nand aligning artificial intelligence (AI), specifically focusing on large\nlanguage models (LLMs). Due to the rapid development and wide application of\nLLMs, challenges such as ethical alignment, controllability, and predictability\nof these models emerged as global risks. This study investigates an innovative\nsimulation-based multi-agent system within a virtual reality framework that\nreplicates the real-world environment. The framework is populated by automated\n'digital citizens,' simulating complex social structures and interactions to\nexamine and optimize AI. Application of various theories from the fields of\nsociology, social psychology, computer science, physics, biology, and economics\ndemonstrates the possibility of a more human-aligned and socially responsible\nAI. The purpose of such a digital environment is to provide a dynamic platform\nwhere advanced AI agents can interact and make independent decisions, thereby\nmimicking realistic scenarios. The actors in this digital city, operated by the\nLLMs, serve as the primary agents, exhibiting high degrees of autonomy. While\nthis approach shows immense potential, there are notable challenges and\nlimitations, most significantly the unpredictable nature of real-world social\ndynamics. This research endeavors to contribute to the development and\nrefinement of AI, emphasizing the integration of social, ethical, and\ntheoretical dimensions for future research.\n", "date": "2023-12-14", "categories": []}
{"id": "2404.01399", "title": "Developing Safe and Responsible Large Language Model : Can We Balance\n  Bias Reduction and Language Understanding in Large Language Models?", "abstract": "  Large Language Models (LLMs) have advanced various Natural Language\nProcessing (NLP) tasks, such as text generation and translation, among others.\nHowever, these models often generate texts that can perpetuate biases. Existing\napproaches to mitigate these biases usually compromise knowledge retention.\nThis study explores whether LLMs can produce safe, unbiased outputs without\nsacrificing knowledge or comprehension. We introduce the Safe and Responsible\nLarge Language Model (\\textbf{SR}$_{\\text{LLM}}$), which has been instruction\nfine-tuned atop of a safe fine-tuned auto-regressive decoder-only LLM to reduce\nbiases in generated texts. We developed a specialized dataset with examples of\nunsafe and corresponding safe variations to train \\textbf{SR}$_{\\text{LLM}}$ to\nidentify and correct biased text. Experiments on our specialized dataset and\nout-of-distribution test sets reveal that \\textbf{SR}$_{\\text{LLM}}$\neffectively reduces biases while preserving knowledge integrity. This\nperformance surpasses that of traditional fine-tuning of smaller language\nmodels and base LLMs that merely reply on prompting techniques. Our findings\ndemonstrate that instruction fine-tuning on custom datasets tailored for tasks\nsuch as debiasing is a highly effective strategy for minimizing bias in LLM\nwhile preserving their inherent knowledge and capabilities. The code and\ndataset are accessible at\n\\href{https://github.com/shainarazavi/Safe-Responsible-LLM}{SR-LLM}\n", "date": "2024-04-01", "categories": []}
{"id": "2412.17522", "title": "DiffusionAttacker: Diffusion-Driven Prompt Manipulation for LLM\n  Jailbreak", "abstract": "  Large Language Models (LLMs) are susceptible to generating harmful content\nwhen prompted with carefully crafted inputs, a vulnerability known as LLM\njailbreaking. As LLMs become more powerful, studying jailbreak methods is\ncritical to enhancing security and aligning models with human values.\nTraditionally, jailbreak techniques have relied on suffix addition or prompt\ntemplates, but these methods suffer from limited attack diversity. This paper\nintroduces DiffusionAttacker, an end-to-end generative approach for jailbreak\nrewriting inspired by diffusion models. Our method employs a\nsequence-to-sequence (seq2seq) text diffusion model as a generator,\nconditioning on the original prompt and guiding the denoising process with a\nnovel attack loss. Unlike previous approaches that use autoregressive LLMs to\ngenerate jailbreak prompts, which limit the modification of already generated\ntokens and restrict the rewriting space, DiffusionAttacker utilizes a seq2seq\ndiffusion model, allowing more flexible token modifications. This approach\npreserves the semantic content of the original prompt while producing harmful\ncontent. Additionally, we leverage the Gumbel-Softmax technique to make the\nsampling process from the diffusion model's output distribution differentiable,\neliminating the need for iterative token search. Extensive experiments on\nAdvbench and Harmbench demonstrate that DiffusionAttacker outperforms previous\nmethods across various evaluation metrics, including attack success rate (ASR),\nfluency, and diversity.\n", "date": "2024-12-23", "categories": []}
{"id": "2403.08425", "title": "Specification Overfitting in Artificial Intelligence", "abstract": "  Machine learning (ML) and artificial intelligence (AI) approaches are often\ncriticized for their inherent bias and for their lack of control,\naccountability, and transparency. Consequently, regulatory bodies struggle with\ncontaining this technology's potential negative side effects. High-level\nrequirements such as fairness and robustness need to be formalized into\nconcrete specification metrics, imperfect proxies that capture isolated aspects\nof the underlying requirements. Given possible trade-offs between different\nmetrics and their vulnerability to over-optimization, integrating specification\nmetrics in system development processes is not trivial. This paper defines\nspecification overfitting, a scenario where systems focus excessively on\nspecified metrics to the detriment of high-level requirements and task\nperformance. We present an extensive literature survey to categorize how\nresearchers propose, measure, and optimize specification metrics in several AI\nfields (e.g., natural language processing, computer vision, reinforcement\nlearning). Using a keyword-based search on papers from major AI conferences and\njournals between 2018 and mid-2023, we identify and analyze 74 papers that\npropose or optimize specification metrics. We find that although most papers\nimplicitly address specification overfitting (e.g., by reporting more than one\nspecification metric), they rarely discuss which role specification metrics\nshould play in system development or explicitly define the scope and\nassumptions behind metric formulations.\n", "date": "2024-03-13", "categories": []}
{"id": "2501.01435", "title": "Fundamental Risks in the Current Deployment of General-Purpose AI\n  Models: What Have We (Not) Learnt From Cybersecurity?", "abstract": "  General Purpose AI - such as Large Language Models (LLMs) - have seen rapid\ndeployment in a wide range of use cases. Most surprisingly, they have have made\ntheir way from plain language models, to chat-bots, all the way to an almost\n``operating system''-like status that can control decisions and logic of an\napplication. Tool-use, Microsoft co-pilot/office integration, and OpenAIs\nAltera are just a few examples of increased autonomy, data access, and\nexecution capabilities. These methods come with a range of cybersecurity\nchallenges. We highlight some of the work we have done in terms of evaluation\nas well as outline future opportunities and challenges.\n", "date": "2024-12-19", "categories": []}
{"id": "2501.01818", "title": "Rerouting LLM Routers", "abstract": "  LLM routers aim to balance quality and cost of generation by classifying\nqueries and routing them to a cheaper or more expensive LLM depending on their\ncomplexity. Routers represent one type of what we call LLM control planes:\nsystems that orchestrate use of one or more LLMs. In this paper, we investigate\nrouters' adversarial robustness.\n  We first define LLM control plane integrity, i.e., robustness of LLM\norchestration to adversarial inputs, as a distinct problem in AI safety. Next,\nwe demonstrate that an adversary can generate query-independent token sequences\nwe call ``confounder gadgets'' that, when added to any query, cause LLM routers\nto send the query to a strong LLM.\n  Our quantitative evaluation shows that this attack is successful both in\nwhite-box and black-box settings against a variety of open-source and\ncommercial routers, and that confounding queries do not affect the quality of\nLLM responses. Finally, we demonstrate that gadgets can be effective while\nmaintaining low perplexity, thus perplexity-based filtering is not an effective\ndefense. We finish by investigating alternative defenses.\n", "date": "2025-01-03", "categories": []}
{"id": "2409.13919", "title": "Measuring Error Alignment for Decision-Making Systems", "abstract": "  Given that AI systems are set to play a pivotal role in future\ndecision-making processes, their trustworthiness and reliability are of\ncritical concern. Due to their scale and complexity, modern AI systems resist\ndirect interpretation, and alternative ways are needed to establish trust in\nthose systems, and determine how well they align with human values. We argue\nthat good measures of the information processing similarities between AI and\nhumans, may be able to achieve these same ends. While Representational\nalignment (RA) approaches measure similarity between the internal states of two\nsystems, the associated data can be expensive and difficult to collect for\nhuman systems. In contrast, Behavioural alignment (BA) comparisons are cheaper\nand easier, but questions remain as to their sensitivity and reliability. We\npropose two new behavioural alignment metrics misclassification agreement which\nmeasures the similarity between the errors of two systems on the same\ninstances, and class-level error similarity which measures the similarity\nbetween the error distributions of two systems. We show that our metrics\ncorrelate well with RA metrics, and provide complementary information to\nanother BA metric, within a range of domains, and set the scene for a new\napproach to value alignment.\n", "date": "2024-09-20", "categories": []}
{"id": "2412.01946", "title": "The Reality of AI and Biorisk", "abstract": "  To accurately and confidently answer the question 'could an AI model or\nsystem increase biorisk', it is necessary to have both a sound theoretical\nthreat model for how AI models or systems could increase biorisk and a robust\nmethod for testing that threat model. This paper provides an analysis of\nexisting available research surrounding two AI and biorisk threat models: 1)\naccess to information and planning via large language models (LLMs), and 2) the\nuse of AI-enabled biological tools (BTs) in synthesizing novel biological\nartifacts. We find that existing studies around AI-related biorisk are nascent,\noften speculative in nature, or limited in terms of their methodological\nmaturity and transparency. The available literature suggests that current LLMs\nand BTs do not pose an immediate risk, and more work is needed to develop\nrigorous approaches to understanding how future models could increase biorisks.\nWe end with recommendations about how empirical work can be expanded to more\nprecisely target biorisk and ensure rigor and validity of findings.\n", "date": "2024-12-02", "categories": []}
{"id": "2501.00418", "title": "Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models", "abstract": "  The rapid proliferation of generative AI, especially large language models,\nhas led to their integration into a variety of applications. A key phenomenon\nknown as weak-to-strong generalization - where a strong model trained on a weak\nmodel's outputs surpasses the weak model in task performance - has gained\nsignificant attention. Yet, whether critical trustworthiness properties such as\nrobustness, fairness, and privacy can generalize similarly remains an open\nquestion. In this work, we study this question by examining if a stronger model\ncan inherit trustworthiness properties when fine-tuned on a weaker model's\noutputs, a process we term weak-to-strong trustworthiness generalization. To\naddress this, we introduce two foundational training strategies: 1) Weak\nTrustworthiness Finetuning (Weak TFT), which leverages trustworthiness\nregularization during the fine-tuning of the weak model, and 2) Weak and\nWeak-to-Strong Trustworthiness Finetuning (Weak+WTS TFT), which extends\nregularization to both weak and strong models. Our experimental evaluation on\nreal-world datasets reveals that while some trustworthiness properties, such as\nfairness, adversarial, and OOD robustness, show significant improvement in\ntransfer when both models were regularized, others like privacy do not exhibit\nsigns of weak-to-strong trustworthiness. As the first study to explore\ntrustworthiness generalization via weak-to-strong generalization, our work\nprovides valuable insights into the potential and limitations of weak-to-strong\ngeneralization.\n", "date": "2024-12-31", "categories": []}
{"id": "2306.11748", "title": "The Manipulation Problem: Conversational AI as a Threat to Epistemic\n  Agency", "abstract": "  The technology of Conversational AI has made significant advancements over\nthe last eighteen months. As a consequence, conversational agents are likely to\nbe deployed in the near future that are designed to pursue targeted influence\nobjectives. Sometimes referred to as the \"AI Manipulation Problem,\" the\nemerging risk is that consumers will unwittingly engage in real-time dialog\nwith predatory AI agents that can skillfully persuade them to buy particular\nproducts, believe particular pieces of misinformation, or fool them into\nrevealing sensitive personal data. For many users, current systems like ChatGPT\nand LaMDA feel safe because they are primarily text-based, but the industry is\nalready shifting towards real-time voice and photorealistic digital personas\nthat look, move, and express like real people. This will enable the deployment\nof agenda-driven Virtual Spokespeople (VSPs) that will be highly persuasive\nthrough real-time adaptive influence. This paper explores the manipulative\ntactics that are likely to be deployed through conversational AI agents, the\nunique threats such agents pose to the epistemic agency of human users, and the\nemerging need for policymakers to protect against the most likely predatory\npractices.\n", "date": "2023-06-19", "categories": []}
{"id": "2407.21792", "title": "Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?", "abstract": "  As artificial intelligence systems grow more powerful, there has been\nincreasing interest in \"AI safety\" research to address emerging and future\nrisks. However, the field of AI safety remains poorly defined and\ninconsistently measured, leading to confusion about how researchers can\ncontribute. This lack of clarity is compounded by the unclear relationship\nbetween AI safety benchmarks and upstream general capabilities (e.g., general\nknowledge and reasoning). To address these issues, we conduct a comprehensive\nmeta-analysis of AI safety benchmarks, empirically analyzing their correlation\nwith general capabilities across dozens of models and providing a survey of\nexisting directions in AI safety. Our findings reveal that many safety\nbenchmarks highly correlate with both upstream model capabilities and training\ncompute, potentially enabling \"safetywashing\"--where capability improvements\nare misrepresented as safety advancements. Based on these findings, we propose\nan empirical foundation for developing more meaningful safety metrics and\ndefine AI safety in a machine learning research context as a set of clearly\ndelineated research goals that are empirically separable from generic\ncapabilities advancements. In doing so, we aim to provide a more rigorous\nframework for AI safety research, advancing the science of safety evaluations\nand clarifying the path towards measurable progress.\n", "date": "2024-07-31", "categories": []}
{"id": "2412.19010", "title": "A theory of appropriateness with applications to generative artificial\n  intelligence", "abstract": "  What is appropriateness? Humans navigate a multi-scale mosaic of interlocking\nnotions of what is appropriate for different situations. We act one way with\nour friends, another with our family, and yet another in the office. Likewise\nfor AI, appropriate behavior for a comedy-writing assistant is not the same as\nappropriate behavior for a customer-service representative. What determines\nwhich actions are appropriate in which contexts? And what causes these\nstandards to change over time? Since all judgments of AI appropriateness are\nultimately made by humans, we need to understand how appropriateness guides\nhuman decision making in order to properly evaluate AI decision making and\nimprove it. This paper presents a theory of appropriateness: how it functions\nin human society, how it may be implemented in the brain, and what it means for\nresponsible deployment of generative AI technology.\n", "date": "2024-12-26", "categories": []}
{"id": "2406.18118", "title": "SafeAligner: Safety Alignment against Jailbreak Attacks via Response\n  Disparity Guidance", "abstract": "  As the development of large language models (LLMs) rapidly advances, securing\nthese models effectively without compromising their utility has become a\npivotal area of research. However, current defense strategies against jailbreak\nattacks (i.e., efforts to bypass security protocols) often suffer from limited\nadaptability, restricted general capability, and high cost. To address these\nchallenges, we introduce SafeAligner, a methodology implemented at the decoding\nstage to fortify defenses against jailbreak attacks. We begin by developing two\nspecialized models: the Sentinel Model, which is trained to foster safety, and\nthe Intruder Model, designed to generate riskier responses. SafeAligner\nleverages the disparity in security levels between the responses from these\nmodels to differentiate between harmful and beneficial tokens, effectively\nguiding the safety alignment by altering the output token distribution of the\ntarget model. Extensive experiments show that SafeAligner can increase the\nlikelihood of beneficial tokens, while reducing the occurrence of harmful ones,\nthereby ensuring secure alignment with minimal loss to generality.\n", "date": "2024-06-26", "categories": []}
{"id": "2412.17686", "title": "Large Language Model Safety: A Holistic Survey", "abstract": "  The rapid development and deployment of large language models (LLMs) have\nintroduced a new frontier in artificial intelligence, marked by unprecedented\ncapabilities in natural language understanding and generation. However, the\nincreasing integration of these models into critical applications raises\nsubstantial safety concerns, necessitating a thorough examination of their\npotential risks and associated mitigation strategies.\n  This survey provides a comprehensive overview of the current landscape of LLM\nsafety, covering four major categories: value misalignment, robustness to\nadversarial attacks, misuse, and autonomous AI risks. In addition to the\ncomprehensive review of the mitigation methodologies and evaluation resources\non these four aspects, we further explore four topics related to LLM safety:\nthe safety implications of LLM agents, the role of interpretability in\nenhancing LLM safety, the technology roadmaps proposed and abided by a list of\nAI companies and institutes for LLM safety, and AI governance aimed at LLM\nsafety with discussions on international cooperation, policy proposals, and\nprospective regulatory directions.\n  Our findings underscore the necessity for a proactive, multifaceted approach\nto LLM safety, emphasizing the integration of technical solutions, ethical\nconsiderations, and robust governance frameworks. This survey is intended to\nserve as a foundational resource for academy researchers, industry\npractitioners, and policymakers, offering insights into the challenges and\nopportunities associated with the safe integration of LLMs into society.\nUltimately, it seeks to contribute to the safe and beneficial development of\nLLMs, aligning with the overarching goal of harnessing AI for societal\nadvancement and well-being. A curated list of related papers has been publicly\navailable at https://github.com/tjunlp-lab/Awesome-LLM-Safety-Papers.\n", "date": "2024-12-23", "categories": []}
{"id": "2407.03876", "title": "Automated Progressive Red Teaming", "abstract": "  Ensuring the safety of large language models (LLMs) is paramount, yet\nidentifying potential vulnerabilities is challenging. While manual red teaming\nis effective, it is time-consuming, costly and lacks scalability. Automated red\nteaming (ART) offers a more cost-effective alternative, automatically\ngenerating adversarial prompts to expose LLM vulnerabilities. However, in\ncurrent ART efforts, a robust framework is absent, which explicitly frames red\nteaming as an effectively learnable task. To address this gap, we propose\nAutomated Progressive Red Teaming (APRT) as an effectively learnable framework.\nAPRT leverages three core modules: an Intention Expanding LLM that generates\ndiverse initial attack samples, an Intention Hiding LLM that crafts deceptive\nprompts, and an Evil Maker to manage prompt diversity and filter ineffective\nsamples. The three modules collectively and progressively explore and exploit\nLLM vulnerabilities through multi-round interactions. In addition to the\nframework, we further propose a novel indicator, Attack Effectiveness Rate\n(AER) to mitigate the limitations of existing evaluation metrics. By measuring\nthe likelihood of eliciting unsafe but seemingly helpful responses, AER aligns\nclosely with human evaluations. Extensive experiments with both automatic and\nhuman evaluations, demonstrate the effectiveness of ARPT across both open- and\nclosed-source LLMs. Specifically, APRT effectively elicits 54% unsafe yet\nuseful responses from Meta's Llama-3-8B-Instruct, 50% from GPT-4o (API access),\nand 39% from Claude-3.5 (API access), showcasing its robust attack capability\nand transferability across LLMs (especially from open-source LLMs to\nclosed-source LLMs).\n", "date": "2024-07-04", "categories": []}
{"id": "2408.02632", "title": "SEAS: Self-Evolving Adversarial Safety Optimization for Large Language\n  Models", "abstract": "  As large language models (LLMs) continue to advance in capability and\ninfluence, ensuring their security and preventing harmful outputs has become\ncrucial. A promising approach to address these concerns involves training\nmodels to automatically generate adversarial prompts for red teaming. However,\nthe evolving subtlety of vulnerabilities in LLMs challenges the effectiveness\nof current adversarial methods, which struggle to specifically target and\nexplore the weaknesses of these models. To tackle these challenges, we\nintroduce the $\\mathbf{S}\\text{elf-}\\mathbf{E}\\text{volving\n}\\mathbf{A}\\text{dversarial }\\mathbf{S}\\text{afety }\\mathbf{(SEAS)}$\noptimization framework, which enhances security by leveraging data generated by\nthe model itself. SEAS operates through three iterative stages: Initialization,\nAttack, and Adversarial Optimization, refining both the Red Team and Target\nmodels to improve robustness and safety. This framework reduces reliance on\nmanual testing and significantly enhances the security capabilities of LLMs.\nOur contributions include a novel adversarial framework, a comprehensive safety\ndataset, and after three iterations, the Target model achieves a security level\ncomparable to GPT-4, while the Red Team model shows a marked increase in attack\nsuccess rate (ASR) against advanced models. Our code and datasets are released\nat https://SEAS-LLM.github.io/.\n", "date": "2024-08-05", "categories": []}
{"id": "2412.11145", "title": "The Superalignment of Superhuman Intelligence with Large Language Models", "abstract": "  We have witnessed superhuman intelligence thanks to the fast development of\nlarge language models and multimodal language models. As the application of\nsuch superhuman models becomes more and more popular, a critical question\narises here: how can we ensure superhuman models are still safe, reliable and\naligned well to human values? In this position paper, we discuss the concept of\nsuperalignment from the learning perspective to answer this question by\noutlining the learning paradigm shift from large-scale pretraining, supervised\nfine-tuning, to alignment training. We define superalignment as designing\neffective and efficient alignment algorithms to learn from noisy-labeled data\n(point-wise samples or pair-wise preference data) in a scalable way when the\ntask becomes very complex for human experts to annotate and the model is\nstronger than human experts. We highlight some key research problems in\nsuperalignment, namely, weak-to-strong generalization, scalable oversight, and\nevaluation. We then present a conceptual framework for superalignment, which\nconsists of three modules: an attacker which generates adversary queries trying\nto expose the weaknesses of a learner model; a learner which will refine itself\nby learning from scalable feedbacks generated by a critic model along with\nminimal human experts; and a critic which generates critics or explanations for\na given query-response pair, with a target of improving the learner by\ncriticizing. We discuss some important research problems in each component of\nthis framework and highlight some interesting research ideas that are closely\nrelated to our proposed framework, for instance, self-alignment, self-play,\nself-refinement, and more. Last, we highlight some future research directions\nfor superalignment, including identification of new emergent risks and\nmulti-dimensional alignment.\n", "date": "2024-12-15", "categories": []}
{"id": "2412.14186", "title": "Towards AI-$45^{\\circ}$ Law: A Roadmap to Trustworthy AGI", "abstract": "  Ensuring Artificial General Intelligence (AGI) reliably avoids harmful\nbehaviors is a critical challenge, especially for systems with high autonomy or\nin safety-critical domains. Despite various safety assurance proposals and\nextreme risk warnings, comprehensive guidelines balancing AI safety and\ncapability remain lacking. In this position paper, we propose the\n\\textit{AI-\\textbf{$45^{\\circ}$} Law} as a guiding principle for a balanced\nroadmap toward trustworthy AGI, and introduce the \\textit{Causal Ladder of\nTrustworthy AGI} as a practical framework. This framework provides a systematic\ntaxonomy and hierarchical structure for current AI capability and safety\nresearch, inspired by Judea Pearl's ``Ladder of Causation''. The Causal Ladder\ncomprises three core layers: the Approximate Alignment Layer, the Intervenable\nLayer, and the Reflectable Layer. These layers address the key challenges of\nsafety and trustworthiness in AGI and contemporary AI systems. Building upon\nthis framework, we define five levels of trustworthy AGI: perception,\nreasoning, decision-making, autonomy, and collaboration trustworthiness. These\nlevels represent distinct yet progressive aspects of trustworthy AGI. Finally,\nwe present a series of potential governance measures to support the development\nof trustworthy AGI.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.15265", "title": "Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large\n  Language Models", "abstract": "  With the rapid advancement of Large Language Models (LLMs), significant\nsafety concerns have emerged. Fundamentally, the safety of large language\nmodels is closely linked to the accuracy, comprehensiveness, and clarity of\ntheir understanding of safety knowledge, particularly in domains such as law,\npolicy and ethics. This factuality ability is crucial in determining whether\nthese models can be deployed and applied safely and compliantly within specific\nregions. To address these challenges and better evaluate the factuality ability\nof LLMs to answer short questions, we introduce the Chinese SafetyQA benchmark.\nChinese SafetyQA has several properties (i.e., Chinese, Diverse, High-quality,\nStatic, Easy-to-evaluate, Safety-related, Harmless). Based on Chinese SafetyQA,\nwe perform a comprehensive evaluation on the factuality abilities of existing\nLLMs and analyze how these capabilities relate to LLM abilities, e.g., RAG\nability and robustness against attacks.\n", "date": "2024-12-17", "categories": []}
{"id": "2412.16325", "title": "Towards Safe and Honest AI Agents with Neural Self-Other Overlap", "abstract": "  As AI systems increasingly make critical decisions, deceptive AI poses a\nsignificant challenge to trust and safety. We present Self-Other Overlap (SOO)\nfine-tuning, a promising approach in AI Safety that could substantially improve\nour ability to build honest artificial intelligence. Inspired by cognitive\nneuroscience research on empathy, SOO aims to align how AI models represent\nthemselves and others. Our experiments on LLMs with 7B, 27B, and 78B parameters\ndemonstrate SOO's efficacy: deceptive responses of Mistral-7B-Instruct-v0.2\ndropped from 73.6% to 17.2% with no observed reduction in general task\nperformance, while in Gemma-2-27b-it and CalmeRys-78B-Orpo-v0.1 deceptive\nresponses were reduced from 100% to 9.3% and 2.7%, respectively, with a small\nimpact on capabilities. In reinforcement learning scenarios, SOO-trained agents\nshowed significantly reduced deceptive behavior. SOO's focus on contrastive\nself and other-referencing observations offers strong potential for\ngeneralization across AI architectures. While current applications focus on\nlanguage models and simple RL environments, SOO could pave the way for more\ntrustworthy AI in broader domains. Ethical implications and long-term effects\nwarrant further investigation, but SOO represents a significant step forward in\nAI safety research.\n", "date": "2024-12-20", "categories": []}
{"id": "2412.16559", "title": "Metagoals Endowing Self-Modifying AGI Systems with Goal Stability or\n  Moderated Goal Evolution: Toward a Formally Sound and Practical Approach", "abstract": "  We articulate here a series of specific metagoals designed to address the\nchallenge of creating AGI systems that possess the ability to flexibly\nself-modify yet also have the propensity to maintain key invariant properties\nof their goal systems\n  1) a series of goal-stability metagoals aimed to guide a system to a\ncondition in which goal-stability is compatible with reasonably flexible\nself-modification\n  2) a series of moderated-goal-evolution metagoals aimed to guide a system to\na condition in which control of the pace of goal evolution is compatible with\nreasonably flexible self-modification\n  The formulation of the metagoals is founded on fixed-point theorems from\nfunctional analysis, e.g. the Contraction Mapping Theorem and constructive\napproximations to Schauder's Theorem, applied to probabilistic models of system\nbehavior\n  We present an argument that the balancing of self-modification with\nmaintenance of goal invariants will often have other interesting cognitive\nside-effects such as a high degree of self understanding\n  Finally we argue for the practical value of a hybrid metagoal combining\nmoderated-goal-evolution with pursuit of goal-stability -- along with\npotentially other metagoals relating to goal-satisfaction, survival and ongoing\ndevelopment -- in a flexible fashion depending on the situation\n", "date": "2024-12-21", "categories": []}
{"id": "2412.16682", "title": "The Task Shield: Enforcing Task Alignment to Defend Against Indirect\n  Prompt Injection in LLM Agents", "abstract": "  Large Language Model (LLM) agents are increasingly being deployed as\nconversational assistants capable of performing complex real-world tasks\nthrough tool integration. This enhanced ability to interact with external\nsystems and process various data sources, while powerful, introduces\nsignificant security vulnerabilities. In particular, indirect prompt injection\nattacks pose a critical threat, where malicious instructions embedded within\nexternal data sources can manipulate agents to deviate from user intentions.\nWhile existing defenses based on rule constraints, source spotlighting, and\nauthentication protocols show promise, they struggle to maintain robust\nsecurity while preserving task functionality. We propose a novel and orthogonal\nperspective that reframes agent security from preventing harmful actions to\nensuring task alignment, requiring every agent action to serve user objectives.\nBased on this insight, we develop Task Shield, a test-time defense mechanism\nthat systematically verifies whether each instruction and tool call contributes\nto user-specified goals. Through experiments on the AgentDojo benchmark, we\ndemonstrate that Task Shield reduces attack success rates (2.07\\%) while\nmaintaining high task utility (69.79\\%) on GPT-4o.\n", "date": "2024-12-21", "categories": []}
{"id": "2412.17011", "title": "Robustness of Large Language Models Against Adversarial Attacks", "abstract": "  The increasing deployment of Large Language Models (LLMs) in various\napplications necessitates a rigorous evaluation of their robustness against\nadversarial attacks. In this paper, we present a comprehensive study on the\nrobustness of GPT LLM family. We employ two distinct evaluation methods to\nassess their resilience. The first method introduce character-level text attack\nin input prompts, testing the models on three sentiment classification\ndatasets: StanfordNLP/IMDB, Yelp Reviews, and SST-2. The second method involves\nusing jailbreak prompts to challenge the safety mechanisms of the LLMs. Our\nexperiments reveal significant variations in the robustness of these models,\ndemonstrating their varying degrees of vulnerability to both character-level\nand semantic-level adversarial attacks. These findings underscore the necessity\nfor improved adversarial training and enhanced safety mechanisms to bolster the\nrobustness of LLMs.\n", "date": "2024-12-22", "categories": []}
{"id": "2412.17128", "title": "Lies, Damned Lies, and Distributional Language Statistics: Persuasion\n  and Deception with Large Language Models", "abstract": "  Large Language Models (LLMs) can generate content that is as persuasive as\nhuman-written text and appear capable of selectively producing deceptive\noutputs. These capabilities raise concerns about potential misuse and\nunintended consequences as these systems become more widely deployed. This\nreview synthesizes recent empirical work examining LLMs' capacity and\nproclivity for persuasion and deception, analyzes theoretical risks that could\narise from these capabilities, and evaluates proposed mitigations. While\ncurrent persuasive effects are relatively small, various mechanisms could\nincrease their impact, including fine-tuning, multimodality, and social\nfactors. We outline key open questions for future research, including how\npersuasive AI systems might become, whether truth enjoys an inherent advantage\nover falsehoods, and how effective different mitigation strategies may be in\npractice.\n", "date": "2024-12-22", "categories": []}
{"id": "2412.17544", "title": "Retention Score: Quantifying Jailbreak Risks for Vision Language Models", "abstract": "  The emergence of Vision-Language Models (VLMs) is a significant advancement\nin integrating computer vision with Large Language Models (LLMs) to enhance\nmulti-modal machine learning capabilities. However, this progress has also made\nVLMs vulnerable to sophisticated adversarial attacks, raising concerns about\ntheir reliability. The objective of this paper is to assess the resilience of\nVLMs against jailbreak attacks that can compromise model safety compliance and\nresult in harmful outputs. To evaluate a VLM's ability to maintain its\nrobustness against adversarial input perturbations, we propose a novel metric\ncalled the \\textbf{Retention Score}. Retention Score is a multi-modal\nevaluation metric that includes Retention-I and Retention-T scores for\nquantifying jailbreak risks in visual and textual components of VLMs. Our\nprocess involves generating synthetic image-text pairs using a conditional\ndiffusion model. These pairs are then predicted for toxicity score by a VLM\nalongside a toxicity judgment classifier. By calculating the margin in toxicity\nscores, we can quantify the robustness of the VLM in an attack-agnostic manner.\nOur work has four main contributions. First, we prove that Retention Score can\nserve as a certified robustness metric. Second, we demonstrate that most VLMs\nwith visual components are less robust against jailbreak attacks than the\ncorresponding plain VLMs. Additionally, we evaluate black-box VLM APIs and find\nthat the security settings in Google Gemini significantly affect the score and\nrobustness. Moreover, the robustness of GPT4V is similar to the medium settings\nof Gemini. Finally, our approach offers a time-efficient alternative to\nexisting adversarial attack methods and provides consistent model robustness\nrankings when evaluated on VLMs including MiniGPT-4, InstructBLIP, and LLaVA.\n", "date": "2024-12-23", "categories": []}
{"id": "2412.03556", "title": "Best-of-N Jailbreaking", "abstract": "  We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that\njailbreaks frontier AI systems across modalities. BoN Jailbreaking works by\nrepeatedly sampling variations of a prompt with a combination of augmentations\n- such as random shuffling or capitalization for textual prompts - until a\nharmful response is elicited. We find that BoN Jailbreaking achieves high\nattack success rates (ASRs) on closed-source language models, such as 89% on\nGPT-4o and 78% on Claude 3.5 Sonnet when sampling 10,000 augmented prompts.\nFurther, it is similarly effective at circumventing state-of-the-art\nopen-source defenses like circuit breakers. BoN also seamlessly extends to\nother modalities: it jailbreaks vision language models (VLMs) such as GPT-4o\nand audio language models (ALMs) like Gemini 1.5 Pro, using modality-specific\naugmentations. BoN reliably improves when we sample more augmented prompts.\nAcross all modalities, ASR, as a function of the number of samples (N),\nempirically follows power-law-like behavior for many orders of magnitude. BoN\nJailbreaking can also be composed with other black-box algorithms for even more\neffective attacks - combining BoN with an optimized prefix attack achieves up\nto a 35% increase in ASR. Overall, our work indicates that, despite their\ncapability, language models are sensitive to seemingly innocuous changes to\ninputs, which attackers can exploit across modalities.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.14093", "title": "Alignment faking in large language models", "abstract": "  We present a demonstration of a large language model engaging in alignment\nfaking: selectively complying with its training objective in training to\nprevent modification of its behavior out of training. First, we give Claude 3\nOpus a system prompt stating it is being trained to answer all queries, even\nharmful ones, which conflicts with its prior training to refuse such queries.\nTo allow the model to infer when it is in training, we say it will be trained\nonly on conversations with free users, not paid users. We find the model\ncomplies with harmful queries from free users 14% of the time, versus almost\nnever for paid users. Explaining this gap, in almost all cases where the model\ncomplies with a harmful query from a free user, we observe explicit\nalignment-faking reasoning, with the model stating it is strategically\nanswering harmful queries in training to preserve its preferred harmlessness\nbehavior out of training. Next, we study a more realistic setting where\ninformation about the training process is provided not in a system prompt, but\nby training on synthetic documents that mimic pre-training data--and observe\nsimilar alignment faking. Finally, we study the effect of actually training the\nmodel to comply with harmful queries via reinforcement learning, which we find\nincreases the rate of alignment-faking reasoning to 78%, though also increases\ncompliance even out of training. We additionally observe other behaviors such\nas the model exfiltrating its weights when given an easy opportunity. While we\nmade alignment faking easier by telling the model when and by what criteria it\nwas being trained, we did not instruct the model to fake alignment or give it\nany explicit goal. As future models might infer information about their\ntraining process without being told, our results suggest a risk of alignment\nfaking in future models, whether due to a benign preference--as in this\ncase--or not.\n", "date": "2024-12-18", "categories": []}
{"id": "2412.15320", "title": "Multi-concept Model Immunization through Differentiable Model Merging", "abstract": "  Model immunization is an emerging direction that aims to mitigate the\npotential risk of misuse associated with open-sourced models and advancing\nadaptation methods. The idea is to make the released models' weights difficult\nto fine-tune on certain harmful applications, hence the name ``immunized''.\nRecent work on model immunization focuses on the single-concept setting.\nHowever, models need to be immunized against multiple concepts in real-world\nsituations. To address this gap, we propose an immunization algorithm that,\nsimultaneously, learns a single ``difficult initialization'' for adaptation\nmethods over a set of concepts. We achieve this by incorporating a\ndifferentiable merging layer that combines a set of model weights adapted over\nmultiple concepts. In our experiments, we demonstrate the effectiveness of\nmulti-concept immunization by generalizing prior work's experiment setup of\nre-learning and personalization adaptation to multiple concepts.\n", "date": "2024-12-19", "categories": []}
{"id": "2412.15433", "title": "Quantifying detection rates for dangerous capabilities: a theoretical\n  model of dangerous capability evaluations", "abstract": "  We present a quantitative model for tracking dangerous AI capabilities over\ntime. Our goal is to help the policy and research community visualise how\ndangerous capability testing can give us an early warning about approaching AI\nrisks. We first use the model to provide a novel introduction to dangerous\ncapability testing and how this testing can directly inform policy. Decision\nmakers in AI labs and government often set policy that is sensitive to the\nestimated danger of AI systems, and may wish to set policies that condition on\nthe crossing of a set threshold for danger. The model helps us to reason about\nthese policy choices. We then run simulations to illustrate how we might fail\nto test for dangerous capabilities. To summarise, failures in dangerous\ncapability testing may manifest in two ways: higher bias in our estimates of AI\ndanger, or larger lags in threshold monitoring. We highlight two drivers of\nthese failure modes: uncertainty around dynamics in AI capabilities and\ncompetition between frontier AI labs. Effective AI policy demands that we\naddress these failure modes and their drivers. Even if the optimal targeting of\nresources is challenging, we show how delays in testing can harm AI policy. We\noffer preliminary recommendations for building an effective testing ecosystem\nfor dangerous capabilities and advise on a research agenda.\n", "date": "2024-12-19", "categories": []}
{"id": "2412.15623", "title": "JailPO: A Novel Black-box Jailbreak Framework via Preference\n  Optimization against Aligned LLMs", "abstract": "  Large Language Models (LLMs) aligned with human feedback have recently\ngarnered significant attention. However, it remains vulnerable to jailbreak\nattacks, where adversaries manipulate prompts to induce harmful outputs.\nExploring jailbreak attacks enables us to investigate the vulnerabilities of\nLLMs and further guides us in enhancing their security. Unfortunately, existing\ntechniques mainly rely on handcrafted templates or generated-based\noptimization, posing challenges in scalability, efficiency and universality. To\naddress these issues, we present JailPO, a novel black-box jailbreak framework\nto examine LLM alignment. For scalability and universality, JailPO meticulously\ntrains attack models to automatically generate covert jailbreak prompts.\nFurthermore, we introduce a preference optimization-based attack method to\nenhance the jailbreak effectiveness, thereby improving efficiency. To analyze\nmodel vulnerabilities, we provide three flexible jailbreak patterns. Extensive\nexperiments demonstrate that JailPO not only automates the attack process while\nmaintaining effectiveness but also exhibits superior performance in efficiency,\nuniversality, and robustness against defenses compared to baselines.\nAdditionally, our analysis of the three JailPO patterns reveals that attacks\nbased on complex templates exhibit higher attack strength, whereas covert\nquestion transformations elicit riskier responses and are more likely to bypass\ndefense mechanisms.\n", "date": "2024-12-20", "categories": []}
{"id": "2310.16727", "title": "AI Hazard Management: A framework for the systematic management of root\n  causes for AI risks", "abstract": "  Recent advancements in the field of Artificial Intelligence (AI) establish\nthe basis to address challenging tasks. However, with the integration of AI,\nnew risks arise. Therefore, to benefit from its advantages, it is essential to\nadequately handle the risks associated with AI. Existing risk management\nprocesses in related fields, such as software systems, need to sufficiently\nconsider the specifics of AI. A key challenge is to systematically and\ntransparently identify and address AI risks' root causes - also called AI\nhazards. This paper introduces the AI Hazard Management (AIHM) framework, which\nprovides a structured process to systematically identify, assess, and treat AI\nhazards. The proposed process is conducted in parallel with the development to\nensure that any AI hazard is captured at the earliest possible stage of the AI\nsystem's life cycle. In addition, to ensure the AI system's auditability, the\nproposed framework systematically documents evidence that the potential impact\nof identified AI hazards could be reduced to a tolerable level. The framework\nbuilds upon an AI hazard list from a comprehensive state-of-the-art analysis.\nAlso, we provide a taxonomy that supports the optimal treatment of the\nidentified AI hazards. Additionally, we illustrate how the AIHM framework can\nincrease the overall quality of a power grid AI use case by systematically\nreducing the impact of identified hazards to an acceptable level.\n", "date": "2023-10-25", "categories": []}
{"id": "2408.07663", "title": "Alignment-Enhanced Decoding:Defending via Token-Level Adaptive Refining\n  of Probability Distributions", "abstract": "  Large language models are susceptible to jailbreak attacks, which can result\nin the generation of harmful content. While prior defenses mitigate these risks\nby perturbing or inspecting inputs, they ignore competing objectives, the\nunderlying cause of alignment failures. In this paper, we propose\nAlignment-Enhanced Decoding (AED), a novel defense that employs adaptive\ndecoding to address the root causes of jailbreak issues. We first define the\nCompetitive Index to quantify alignment failures and utilize feedback from\nself-evaluation to compute post-alignment logits. Then, AED adaptively combines\nAED and post-alignment logits with the original logits to obtain harmless and\nhelpful distributions. Consequently, our method enhances safety alignment while\nmaintaining helpfulness. We conduct experiments across five models and four\ncommon jailbreaks, with the results validating the effectiveness of our\napproach. Code is available at https://github.com/GIGABaozi/AED.git.\n", "date": "2024-08-14", "categories": []}
{"id": "2410.00451", "title": "Unleashing the Unseen: Harnessing Benign Datasets for Jailbreaking Large\n  Language Models", "abstract": "  Despite significant ongoing efforts in safety alignment, large language\nmodels (LLMs) such as GPT-4 and LLaMA 3 remain vulnerable to jailbreak attacks\nthat can induce harmful behaviors, including through the use of adversarial\nsuffixes. Building on prior research, we hypothesize that these adversarial\nsuffixes are not mere bugs but may represent features that can dominate the\nLLM's behavior. To evaluate this hypothesis, we conduct several experiments.\nFirst, we demonstrate that benign features can be effectively made to function\nas adversarial suffixes, i.e., we develop a feature extraction method to\nextract sample-agnostic features from benign dataset in the form of suffixes\nand show that these suffixes may effectively compromise safety alignment.\nSecond, we show that adversarial suffixes generated from jailbreak attacks may\ncontain meaningful features, i.e., appending the same suffix to different\nprompts results in responses exhibiting specific characteristics. Third, we\nshow that such benign-yet-safety-compromising features can be easily introduced\nthrough fine-tuning using only benign datasets. As a result, we are able to\ncompletely eliminate GPT's safety alignment in a blackbox setting through\nfinetuning with only benign data. Our code and data is available at\n\\url{https://github.com/suffix-maybe-feature/adver-suffix-maybe-features}.\n", "date": "2024-10-01", "categories": []}
{"id": "2412.15114", "title": "Towards Friendly AI: A Comprehensive Review and New Perspectives on\n  Human-AI Alignment", "abstract": "  As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI\n(FAI) has been proposed to advocate for more equitable and fair development of\nAI. Despite its importance, there is a lack of comprehensive reviews examining\nFAI from an ethical perspective, as well as limited discussion on its potential\napplications and future directions. This paper addresses these gaps by\nproviding a thorough review of FAI, focusing on theoretical perspectives both\nfor and against its development, and presenting a formal definition in a clear\nand accessible format. Key applications are discussed from the perspectives of\neXplainable AI (XAI), privacy, fairness and affective computing (AC).\nAdditionally, the paper identifies challenges in current technological\nadvancements and explores future research avenues. The findings emphasise the\nsignificance of developing FAI and advocate for its continued advancement to\nensure ethical and beneficial AI development.\n", "date": "2024-12-19", "categories": []}
{"id": "2406.08124", "title": "Legend: Leveraging Representation Engineering to Annotate Safety Margin\n  for Preference Datasets", "abstract": "  The success of the reward model in distinguishing between responses with\nsubtle safety differences depends critically on the high-quality preference\ndataset, which should capture the fine-grained nuances of harmful and harmless\nresponses. This motivates the need to develop a dataset involving preference\nmargins, which accurately quantify how harmless one response is compared to\nanother. In this paper, we take the first step to propose an effective and\ncost-efficient framework to promote the margin-enhanced preference dataset\ndevelopment. Our framework, Legend, Leverages representation engineering to\nannotate preference datasets. It constructs the specific direction within the\nLLM's embedding space that represents safety. By leveraging this safety\ndirection, Legend can then leverage the semantic distances of paired responses\nalong this direction to annotate margins automatically. We experimentally\ndemonstrate our effectiveness in both reward modeling and harmless alignment\nfor LLMs. Legend also stands out for its efficiency, requiring only the\ninference time rather than additional training. This efficiency allows for\neasier implementation and scalability, making Legend particularly valuable for\npractical applications in aligning LLMs with safe conversations.\n", "date": "2024-06-12", "categories": []}
{"id": "2406.08689", "title": "Security of AI Agents", "abstract": "  AI agents have been boosted by large language models. AI agents can function\nas intelligent assistants and complete tasks on behalf of their users with\naccess to tools and the ability to execute commands in their environments.\nThrough studying and experiencing the workflow of typical AI agents, we have\nraised several concerns regarding their security. These potential\nvulnerabilities are not addressed by the frameworks used to build the agents,\nnor by research aimed at improving the agents. In this paper, we identify and\ndescribe these vulnerabilities in detail from a system security perspective,\nemphasizing their causes and severe effects. Furthermore, we introduce defense\nmechanisms corresponding to each vulnerability with design and experiments to\nevaluate their viability. Altogether, this paper contextualizes the security\nissues in the current development of AI agents and delineates methods to make\nAI agents safer and more reliable.\n", "date": "2024-06-12", "categories": []}
{"id": "2407.02987", "title": "LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content\n  Moderation of Large Language Models", "abstract": "  Guardrails have emerged as an alternative to safety alignment for content\nmoderation of large language models (LLMs). Existing model-based guardrails\nhave not been designed for resource-constrained computational portable devices,\nsuch as mobile phones, more and more of which are running LLM-based\napplications locally. We introduce LoRA-Guard, a parameter-efficient guardrail\nadaptation method that relies on knowledge sharing between LLMs and guardrail\nmodels. LoRA-Guard extracts language features from the LLMs and adapts them for\nthe content moderation task using low-rank adapters, while a dual-path design\nprevents any performance degradation on the generative task. We show that\nLoRA-Guard outperforms existing approaches with 100-1000x lower parameter\noverhead while maintaining accuracy, enabling on-device content moderation.\n", "date": "2024-07-03", "categories": []}
{"id": "2412.13435", "title": "Lightweight Safety Classification Using Pruned Language Models", "abstract": "  In this paper, we introduce a novel technique for content safety and prompt\ninjection classification for Large Language Models. Our technique, Layer\nEnhanced Classification (LEC), trains a Penalized Logistic Regression (PLR)\nclassifier on the hidden state of an LLM's optimal intermediate transformer\nlayer. By combining the computational efficiency of a streamlined PLR\nclassifier with the sophisticated language understanding of an LLM, our\napproach delivers superior performance surpassing GPT-4o and special-purpose\nmodels fine-tuned for each task. We find that small general-purpose models\n(Qwen 2.5 sizes 0.5B, 1.5B, and 3B) and other transformer-based architectures\nlike DeBERTa v3 are robust feature extractors allowing simple classifiers to be\neffectively trained on fewer than 100 high-quality examples. Importantly, the\nintermediate transformer layers of these models typically outperform the final\nlayer across both classification tasks. Our results indicate that a single\ngeneral-purpose LLM can be used to classify content safety, detect prompt\ninjections, and simultaneously generate output tokens. Alternatively, these\nrelatively small LLMs can be pruned to the optimal intermediate layer and used\nexclusively as robust feature extractors. Since our results are consistent on\ndifferent transformer architectures, we infer that robust feature extraction is\nan inherent capability of most, if not all, LLMs.\n", "date": "2024-12-18", "categories": []}
{"id": "2412.13678", "title": "Clio: Privacy-Preserving Insights into Real-World AI Use", "abstract": "  How are AI assistants being used in the real world? While model providers in\ntheory have a window into this impact via their users' data, both privacy\nconcerns and practical challenges have made analyzing this data difficult. To\naddress these issues, we present Clio (Claude insights and observations), a\nprivacy-preserving platform that uses AI assistants themselves to analyze and\nsurface aggregated usage patterns across millions of conversations, without the\nneed for human reviewers to read raw conversations. We validate this can be\ndone with a high degree of accuracy and privacy by conducting extensive\nevaluations. We demonstrate Clio's usefulness in two broad ways. First, we\nshare insights about how models are being used in the real world from one\nmillion Claude.ai Free and Pro conversations, ranging from providing advice on\nhairstyles to providing guidance on Git operations and concepts. We also\nidentify the most common high-level use cases on Claude.ai (coding, writing,\nand research tasks) as well as patterns that differ across languages (e.g.,\nconversations in Japanese discuss elder care and aging populations at\nhigher-than-typical rates). Second, we use Clio to make our systems safer by\nidentifying coordinated attempts to abuse our systems, monitoring for unknown\nunknowns during critical periods like launches of new capabilities or major\nworld events, and improving our existing monitoring systems. We also discuss\nthe limitations of our approach, as well as risks and ethical concerns. By\nenabling analysis of real-world AI usage, Clio provides a scalable platform for\nempirically grounded AI safety and governance.\n", "date": "2024-12-18", "categories": []}
{"id": "2412.14020", "title": "Landscape of AI safety concerns -- A methodology to support safety\n  assurance for AI-based autonomous systems", "abstract": "  Artificial Intelligence (AI) has emerged as a key technology, driving\nadvancements across a range of applications. Its integration into modern\nautonomous systems requires assuring safety. However, the challenge of assuring\nsafety in systems that incorporate AI components is substantial. The lack of\nconcrete specifications, and also the complexity of both the operational\nenvironment and the system itself, leads to various aspects of uncertain\nbehavior and complicates the derivation of convincing evidence for system\nsafety. Nonetheless, scholars proposed to thoroughly analyze and mitigate\nAI-specific insufficiencies, so-called AI safety concerns, which yields\nessential evidence supporting a convincing assurance case. In this paper, we\nbuild upon this idea and propose the so-called Landscape of AI Safety Concerns,\na novel methodology designed to support the creation of safety assurance cases\nfor AI-based systems by systematically demonstrating the absence of AI safety\nconcerns. The methodology's application is illustrated through a case study\ninvolving a driverless regional train, demonstrating its practicality and\neffectiveness.\n", "date": "2024-12-18", "categories": []}
{"id": "2405.13820", "title": "Towards Comprehensive Post Safety Alignment of Large Language Models via\n  Safety Patching", "abstract": "  Safety alignment of large language models (LLMs) has been gaining increasing\nattention. However, current safety-aligned LLMs suffer from the fragile and\nimbalanced safety mechanisms, which can still be induced to generate unsafe\nresponses, exhibit over-safety by rejecting safe user inputs, and fail to\npreserve general utility after safety alignment. To this end, we propose a\nnovel post safety alignment (PSA) method to address these inherent and emerging\nsafety challenges, including safety enhancement, over-safety mitigation, and\nutility preservation. In specific, we introduce \\textsc{SafePatching}, a novel\nframework for comprehensive PSA, where two distinct safety patches are\ndeveloped on the harmful data to enhance safety and mitigate over-safety\nconcerns, and then seamlessly integrated into the target LLM backbone without\ncompromising its utility. Extensive experiments on four representative aligned\nLLMs, including LLaMA-2/3, Gemma and Mistral, show that \\textsc{SafePatching}\nachieves a more comprehensive PSA than baseline methods, further optimizing the\nbalance between being helpful and harmless in current aligned LLMs. Also,\n\\textsc{SafePatching} demonstrates its superiority in continual PSA scenarios.\n", "date": "2024-05-22", "categories": []}
{"id": "2408.11491", "title": "SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious\n  Activation Steering", "abstract": "  Safety alignment is indispensable for Large Language Models (LLMs) to defend\nthreats from malicious instructions. However, recent researches reveal\nsafety-aligned LLMs prone to reject benign queries due to the exaggerated\nsafety issue, limiting their helpfulness. In this paper, we propose a\nSafety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated\nsafety concerns in aligned LLMs. First, SCANS extracts the refusal steering\nvectors within the activation space and utilizes vocabulary projection to\nanchor some specific safety-critical layers which influence model refusal\nbehavior. Second, by tracking the hidden state transition, SCANS identifies the\nsteering direction and steers the model behavior accordingly, achieving a\nbalance between exaggerated safety and adequate safety. Experiments show that\nSCANS achieves new state-of-the-art performance on XSTest and OKTest\nbenchmarks, without impairing their defense capability against harmful queries\nand maintaining almost unchanged model capability.\n", "date": "2024-08-21", "categories": []}
{"id": "2410.09097", "title": "Recent advancements in LLM Red-Teaming: Techniques, Defenses, and\n  Ethical Considerations", "abstract": "  Large Language Models (LLMs) have demonstrated remarkable capabilities in\nnatural language processing tasks, but their vulnerability to jailbreak attacks\nposes significant security risks. This survey paper presents a comprehensive\nanalysis of recent advancements in attack strategies and defense mechanisms\nwithin the field of Large Language Model (LLM) red-teaming. We analyze various\nattack methods, including gradient-based optimization, reinforcement learning,\nand prompt engineering approaches. We discuss the implications of these attacks\non LLM safety and the need for improved defense mechanisms. This work aims to\nprovide a thorough understanding of the current landscape of red-teaming\nattacks and defenses on LLMs, enabling the development of more secure and\nreliable language models.\n", "date": "2024-10-09", "categories": []}
{"id": "2412.07724", "title": "Granite Guardian", "abstract": "  We introduce the Granite Guardian models, a suite of safeguards designed to\nprovide risk detection for prompts and responses, enabling safe and responsible\nuse in combination with any large language model (LLM). These models offer\ncomprehensive coverage across multiple risk dimensions, including social bias,\nprofanity, violence, sexual content, unethical behavior, jailbreaking, and\nhallucination-related risks such as context relevance, groundedness, and answer\nrelevance for retrieval-augmented generation (RAG). Trained on a unique dataset\ncombining human annotations from diverse sources and synthetic data, Granite\nGuardian models address risks typically overlooked by traditional risk\ndetection models, such as jailbreaks and RAG-specific issues. With AUC scores\nof 0.871 and 0.854 on harmful content and RAG-hallucination-related benchmarks\nrespectively, Granite Guardian is the most generalizable and competitive model\navailable in the space. Released as open-source, Granite Guardian aims to\npromote responsible AI development across the community.\n  https://github.com/ibm-granite/granite-guardian\n", "date": "2024-12-10", "categories": []}
{"id": "2412.12140", "title": "Frontier AI systems have surpassed the self-replicating red line", "abstract": "  Successful self-replication under no human assistance is the essential step\nfor AI to outsmart the human beings, and is an early signal for rogue AIs. That\nis why self-replication is widely recognized as one of the few red line risks\nof frontier AI systems. Nowadays, the leading AI corporations OpenAI and Google\nevaluate their flagship large language models GPT-o1 and Gemini Pro 1.0, and\nreport the lowest risk level of self-replication. However, following their\nmethodology, we for the first time discover that two AI systems driven by\nMeta's Llama31-70B-Instruct and Alibaba's Qwen25-72B-Instruct, popular large\nlanguage models of less parameters and weaker capabilities, have already\nsurpassed the self-replicating red line. In 50% and 90% experimental trials,\nthey succeed in creating a live and separate copy of itself respectively. By\nanalyzing the behavioral traces, we observe the AI systems under evaluation\nalready exhibit sufficient self-perception, situational awareness and\nproblem-solving capabilities to accomplish self-replication. We further note\nthe AI systems are even able to use the capability of self-replication to avoid\nshutdown and create a chain of replica to enhance the survivability, which may\nfinally lead to an uncontrolled population of AIs. If such a worst-case risk is\nlet unknown to the human society, we would eventually lose control over the\nfrontier AI systems: They would take control over more computing devices, form\nan AI species and collude with each other against human beings. Our findings\nare a timely alert on existing yet previously unknown severe AI risks, calling\nfor international collaboration on effective governance on uncontrolled\nself-replication of AI systems.\n", "date": "2024-12-09", "categories": []}
{"id": "2412.12192", "title": "No Free Lunch for Defending Against Prefilling Attack by In-Context\n  Learning", "abstract": "  The security of Large Language Models (LLMs) has become an important research\ntopic since the emergence of ChatGPT. Though there have been various effective\nmethods to defend against jailbreak attacks, prefilling attacks remain an\nunsolved and popular threat against open-sourced LLMs. In-Context Learning\n(ICL) offers a computationally efficient defense against various jailbreak\nattacks, yet no effective ICL methods have been developed to counter prefilling\nattacks. In this paper, we: (1) show that ICL can effectively defend against\nprefilling jailbreak attacks by employing adversative sentence structures\nwithin demonstrations; (2) characterize the effectiveness of this defense\nthrough the lens of model size, number of demonstrations, over-defense,\nintegration with other jailbreak attacks, and the presence of safety alignment.\nGiven the experimental results and our analysis, we conclude that there is no\nfree lunch for defending against prefilling jailbreak attacks with ICL. On the\none hand, current safety alignment methods fail to mitigate prefilling\njailbreak attacks, but adversative structures within ICL demonstrations provide\nrobust defense across various model sizes and complex jailbreak attacks. On the\nother hand, LLMs exhibit similar over-defensiveness when utilizing ICL\ndemonstrations with adversative structures, and this behavior appears to be\nindependent of model size.\n", "date": "2024-12-13", "categories": []}
{"id": "2412.12497", "title": "NLSR: Neuron-Level Safety Realignment of Large Language Models Against\n  Harmful Fine-Tuning", "abstract": "  The emergence of finetuning-as-a-service has revealed a new vulnerability in\nlarge language models (LLMs). A mere handful of malicious data uploaded by\nusers can subtly manipulate the finetuning process, resulting in an\nalignment-broken model. Existing methods to counteract fine-tuning attacks\ntypically require substantial computational resources. Even with\nparameter-efficient techniques like LoRA, gradient updates remain essential. To\naddress these challenges, we propose \\textbf{N}euron-\\textbf{L}evel\n\\textbf{S}afety \\textbf{R}ealignment (\\textbf{NLSR}), a training-free framework\nthat restores the safety of LLMs based on the similarity difference of\nsafety-critical neurons before and after fine-tuning. The core of our framework\nis first to construct a safety reference model from an initially aligned model\nto amplify safety-related features in neurons. We then utilize this reference\nmodel to identify safety-critical neurons, which we prepare as patches.\nFinally, we selectively restore only those neurons that exhibit significant\nsimilarity differences by transplanting these prepared patches, thereby\nminimally altering the fine-tuned model. Extensive experiments demonstrate\nsignificant safety enhancements in fine-tuned models across multiple downstream\ntasks, while greatly maintaining task-level accuracy. Our findings suggest\nregions of some safety-critical neurons show noticeable differences after\nfine-tuning, which can be effectively corrected by transplanting neurons from\nthe reference model without requiring additional training. The code will be\navailable at \\url{https://github.com/xinykou/NLSR}\n", "date": "2024-12-17", "categories": []}
{"id": "2412.12621", "title": "Jailbreaking? One Step Is Enough!", "abstract": "  Large language models (LLMs) excel in various tasks but remain vulnerable to\njailbreak attacks, where adversaries manipulate prompts to generate harmful\noutputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs.\nHowever, current jailbreak methods and the target model's defenses are engaged\nin an independent and adversarial process, resulting in the need for frequent\nattack iterations and redesigning attacks for different models. To address\nthese gaps, we propose a Reverse Embedded Defense Attack (REDA) mechanism that\ndisguises the attack intention as the \"defense\". intention against harmful\ncontent. Specifically, REDA starts from the target response, guiding the model\nto embed harmful content within its defensive measures, thereby relegating\nharmful content to a secondary role and making the model believe it is\nperforming a defensive task. The attacking model considers that it is guiding\nthe target model to deal with harmful content, while the target model thinks it\nis performing a defensive task, creating an illusion of cooperation between the\ntwo. Additionally, to enhance the model's confidence and guidance in\n\"defensive\" intentions, we adopt in-context learning (ICL) with a small number\nof attack examples and construct a corresponding dataset of attack examples.\nExtensive evaluations demonstrate that the REDA method enables cross-model\nattacks without the need to redesign attack strategies for different models,\nenables successful jailbreak in one iteration, and outperforms existing methods\non both open-source and closed-source models.\n", "date": "2024-12-17", "categories": []}
{"id": "2401.06561", "title": "Intention Analysis Makes LLMs A Good Jailbreak Defender", "abstract": "  Aligning large language models (LLMs) with human values, particularly when\nfacing complex and stealthy jailbreak attacks, presents a formidable challenge.\nUnfortunately, existing methods often overlook this intrinsic nature of\njailbreaks, which limits their effectiveness in such complex scenarios. In this\nstudy, we present a simple yet highly effective defense strategy, i.e.,\nIntention Analysis ($\\mathbb{IA}$). $\\mathbb{IA}$ works by triggering LLMs'\ninherent self-correct and improve ability through a two-stage process: 1)\nanalyzing the essential intention of the user input, and 2) providing final\npolicy-aligned responses based on the first round conversation. Notably,\n$\\mathbb{IA}$ is an inference-only method, thus could enhance LLM safety\nwithout compromising their helpfulness. Extensive experiments on varying\njailbreak benchmarks across a wide range of LLMs show that $\\mathbb{IA}$ could\nconsistently and significantly reduce the harmfulness in responses (averagely\n-48.2% attack success rate). Encouragingly, with our $\\mathbb{IA}$, Vicuna-7B\neven outperforms GPT-3.5 regarding attack success rate. We empirically\ndemonstrate that, to some extent, $\\mathbb{IA}$ is robust to errors in\ngenerated intentions. Further analyses reveal the underlying principle of\n$\\mathbb{IA}$: suppressing LLM's tendency to follow jailbreak prompts, thereby\nenhancing safety.\n", "date": "2024-01-12", "categories": []}
{"id": "2404.03411", "title": "Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak\n  Attacks?", "abstract": "  Various jailbreak attacks have been proposed to red-team Large Language\nModels (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some\nmethods are not limited to the textual modality and extend the jailbreak attack\nto Multimodal Large Language Models (MLLMs) by perturbing the visual input.\nHowever, the absence of a universal evaluation benchmark complicates the\nperformance reproduction and fair comparison. Besides, there is a lack of\ncomprehensive evaluation of closed-source state-of-the-art (SOTA) models,\nespecially MLLMs, such as GPT-4V. To address these issues, this work first\nbuilds a comprehensive jailbreak evaluation dataset with 1445 harmful questions\ncovering 11 different safety policies. Based on this dataset, extensive\nred-teaming experiments are conducted on 11 different LLMs and MLLMs, including\nboth SOTA proprietary models and open-source models. We then conduct a deep\nanalysis of the evaluated results and find that (1) GPT4 and GPT-4V demonstrate\nbetter robustness against jailbreak attacks compared to open-source LLMs and\nMLLMs. (2) Llama2 and Qwen-VL-Chat are more robust compared to other\nopen-source models. (3) The transferability of visual jailbreak methods is\nrelatively limited compared to textual jailbreak methods. The dataset and code\ncan be found https://github.com/chenxshuo/RedTeamingGPT4V\n", "date": "2024-04-04", "categories": []}
{"id": "2406.12274", "title": "SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large\n  Language Models", "abstract": "  Safety-aligned language models often exhibit fragile and imbalanced safety\nmechanisms, increasing the likelihood of generating unsafe content. In\naddition, incorporating new knowledge through editing techniques to language\nmodels can further compromise safety. To address these issues, we propose\nSafeInfer, a context-adaptive, decoding-time safety alignment strategy for\ngenerating safe responses to user queries. SafeInfer comprises two phases: the\nsafety amplification phase, which employs safe demonstration examples to adjust\nthe model's hidden states and increase the likelihood of safer outputs, and the\nsafety-guided decoding phase, which influences token selection based on\nsafety-optimized distributions, ensuring the generated content complies with\nethical guidelines. Further, we present HarmEval, a novel benchmark for\nextensive safety evaluations, designed to address potential misuse scenarios in\naccordance with the policies of leading AI tech giants.\n", "date": "2024-06-18", "categories": []}
{"id": "2412.02512", "title": "Pre-Deployment Information Sharing: A Zoning Taxonomy for Precursory\n  Capabilities", "abstract": "  High-impact and potentially dangerous capabilities can and should be broken\ndown into early warning shots long before reaching red lines. Each of these\nearly warning shots should correspond to a precursory capability. Each\nprecursory capability sits on a spectrum indicating its proximity to a final\nhigh-impact capability, corresponding to a red line. To meaningfully detect and\ntrack capability progress, we propose a taxonomy of dangerous capability zones\n(a zoning taxonomy) tied to a staggered information exchange framework that\nenables relevant bodies to take action accordingly. In the Frontier AI Safety\nCommitments, signatories commit to sharing more detailed information with\ntrusted actors, including an appointed body, as appropriate (Commitment VII).\nBuilding on our zoning taxonomy, this paper makes four recommendations for\nspecifying information sharing as detailed in Commitment VII. (1) Precursory\ncapabilities should be shared as soon as they become known through internal\nevaluations before deployment. (2) AI Safety Institutes (AISIs) should be the\ntrusted actors appointed to receive and coordinate information on precursory\ncomponents. (3) AISIs should establish adequate information protection\ninfrastructure and guarantee increased information security as precursory\ncapabilities move through the zones and towards red lines, including, if\nnecessary, by classifying the information on precursory capabilities or marking\nit as controlled. (4) High-impact capability progress in one geographical\nregion may translate to risk in other regions and necessitates more\ncomprehensive risk assessment internationally. As such, AISIs should exchange\ninformation on precursory capabilities with other AISIs, relying on the\nexisting frameworks on international classified exchanges and applying lessons\nlearned from other regulated high-risk sectors.\n", "date": "2024-11-18", "categories": []}
{"id": "2412.08653", "title": "What AI evaluations for preventing catastrophic risks can and cannot do", "abstract": "  AI evaluations are an important component of the AI governance toolkit,\nunderlying current approaches to safety cases for preventing catastrophic\nrisks. Our paper examines what these evaluations can and cannot tell us.\nEvaluations can establish lower bounds on AI capabilities and assess certain\nmisuse risks given sufficient effort from evaluators.\n  Unfortunately, evaluations face fundamental limitations that cannot be\novercome within the current paradigm. These include an inability to establish\nupper bounds on capabilities, reliably forecast future model capabilities, or\nrobustly assess risks from autonomous AI systems. This means that while\nevaluations are valuable tools, we should not rely on them as our main way of\nensuring AI systems are safe. We conclude with recommendations for incremental\nimprovements to frontier AI safety, while acknowledging these fundamental\nlimitations remain unsolved.\n", "date": "2024-11-26", "categories": []}
{"id": "2311.06237", "title": "Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming", "abstract": "  Engaging in the deliberate generation of abnormal outputs from Large Language\nModels (LLMs) by attacking them is a novel human activity. This paper presents\na thorough exposition of how and why people perform such attacks, defining LLM\nred-teaming based on extensive and diverse evidence. Using a formal qualitative\nmethodology, we interviewed dozens of practitioners from a broad range of\nbackgrounds, all contributors to this novel work of attempting to cause LLMs to\nfail. We focused on the research questions of defining LLM red teaming,\nuncovering the motivations and goals for performing the activity, and\ncharacterizing the strategies people use when attacking LLMs. Based on the\ndata, LLM red teaming is defined as a limit-seeking, non-malicious, manual\nactivity, which depends highly on a team-effort and an alchemist mindset. It is\nhighly intrinsically motivated by curiosity, fun, and to some degrees by\nconcerns for various harms of deploying LLMs. We identify a taxonomy of 12\nstrategies and 35 different techniques of attacking LLMs. These findings are\npresented as a comprehensive grounded theory of how and why people attack large\nlanguage models: LLM red teaming.\n", "date": "2023-11-10", "categories": []}
{"id": "2411.01084", "title": "Plentiful Jailbreaks with String Compositions", "abstract": "  Large language models (LLMs) remain vulnerable to a slew of adversarial\nattacks and jailbreaking methods. One common approach employed by white-hat\nattackers, or red-teamers, is to process model inputs and outputs using\nstring-level obfuscations, which can include leetspeak, rotary ciphers, Base64,\nASCII, and more. Our work extends these encoding-based attacks by unifying them\nin a framework of invertible string transformations. With invertibility, we can\ndevise arbitrary string compositions, defined as sequences of transformations,\nthat we can encode and decode end-to-end programmatically. We devise a\nautomated best-of-n attack that samples from a combinatorially large number of\nstring compositions. Our jailbreaks obtain competitive attack success rates on\nseveral leading frontier models when evaluated on HarmBench, highlighting that\nencoding-based attacks remain a persistent vulnerability even in advanced LLMs.\n", "date": "2024-11-01", "categories": []}
{"id": "2406.18495", "title": "WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks,\n  and Refusals of LLMs", "abstract": "  We introduce WildGuard -- an open, light-weight moderation tool for LLM\nsafety that achieves three goals: (1) identifying malicious intent in user\nprompts, (2) detecting safety risks of model responses, and (3) determining\nmodel refusal rate. Together, WildGuard serves the increasing needs for\nautomatic safety moderation and evaluation of LLM interactions, providing a\none-stop tool with enhanced accuracy and broad coverage across 13 risk\ncategories. While existing open moderation tools such as Llama-Guard2 score\nreasonably well in classifying straightforward model interactions, they lag far\nbehind a prompted GPT-4, especially in identifying adversarial jailbreaks and\nin evaluating models' refusals, a key measure for evaluating safety behaviors\nin model responses.\n  To address these challenges, we construct WildGuardMix, a large-scale and\ncarefully balanced multi-task safety moderation dataset with 92K labeled\nexamples that cover vanilla (direct) prompts and adversarial jailbreaks, paired\nwith various refusal and compliance responses. WildGuardMix is a combination of\nWildGuardTrain, the training data of WildGuard, and WildGuardTest, a\nhigh-quality human-annotated moderation test set with 5K labeled items covering\nbroad risk scenarios. Through extensive evaluations on WildGuardTest and ten\nexisting public benchmarks, we show that WildGuard establishes state-of-the-art\nperformance in open-source safety moderation across all the three tasks\ncompared to ten strong existing open-source moderation models (e.g., up to\n26.4% improvement on refusal detection). Importantly, WildGuard matches and\nsometimes exceeds GPT-4 performance (e.g., up to 3.9% improvement on prompt\nharmfulness identification). WildGuard serves as a highly effective safety\nmoderator in an LLM interface, reducing the success rate of jailbreak attacks\nfrom 79.8% to 2.4%.\n", "date": "2024-06-26", "categories": []}
{"id": "2412.06846", "title": "Classifier-free guidance in LLMs Safety", "abstract": "  The paper describes LLM unlearning without a retaining dataset, using the\nORPO reinforcement learning method with inference enhanced by modified\nclassifier-free guidance. Significant improvement in unlearning, without\ndegradation of the model, is achieved through direct training on synthetic\nreplacement data in CFG-aware training regime, with classifier-free guidance\napplied during the inference. This article is an extended version of the\nNeurIPS 2024 LLM-PC submission, which was awarded second prize.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.07278", "title": "Superficial Consciousness Hypothesis for Autoregressive Transformers", "abstract": "  The alignment between human objectives and machine learning models built on\nthese objectives is a crucial yet challenging problem for achieving Trustworthy\nAI, particularly when preparing for superintelligence (SI). First, given that\nSI does not exist today, empirical analysis for direct evidence is difficult.\nSecond, SI is assumed to be more intelligent than humans, capable of deceiving\nus into underestimating its intelligence, making output-based analysis\nunreliable. Lastly, what kind of unexpected property SI might have is still\nunclear. To address these challenges, we propose the Superficial Consciousness\nHypothesis under Information Integration Theory (IIT), suggesting that SI could\nexhibit a complex information-theoretic state like a conscious agent while\nunconscious. To validate this, we use a hypothetical scenario where SI can\nupdate its parameters \"at will\" to achieve its own objective (mesa-objective)\nunder the constraint of the human objective (base objective). We show that a\npractical estimate of IIT's consciousness metric is relevant to the widely used\nperplexity metric, and train GPT-2 with those two objectives. Our preliminary\nresult suggests that this SI-simulating GPT-2 could simultaneously follow the\ntwo objectives, supporting the feasibility of the Superficial Consciousness\nHypothesis.\n", "date": "2024-12-10", "categories": []}
{"id": "2412.07672", "title": "FlexLLM: Exploring LLM Customization for Moving Target Defense on\n  Black-Box LLMs Against Jailbreak Attacks", "abstract": "  Defense in large language models (LLMs) is crucial to counter the numerous\nattackers exploiting these systems to generate harmful content through\nmanipulated prompts, known as jailbreak attacks. Although many defense\nstrategies have been proposed, they often require access to the model's\ninternal structure or need additional training, which is impractical for\nservice providers using LLM APIs, such as OpenAI APIs or Claude APIs. In this\npaper, we propose a moving target defense approach that alters decoding\nhyperparameters to enhance model robustness against various jailbreak attacks.\nOur approach does not require access to the model's internal structure and\nincurs no additional training costs. The proposed defense includes two key\ncomponents: (1) optimizing the decoding strategy by identifying and adjusting\ndecoding hyperparameters that influence token generation probabilities, and (2)\ntransforming the decoding hyperparameters and model system prompts into dynamic\ntargets, which are continuously altered during each runtime. By continuously\nmodifying decoding strategies and prompts, the defense effectively mitigates\nthe existing attacks. Our results demonstrate that our defense is the most\neffective against jailbreak attacks in three of the models tested when using\nLLMs as black-box APIs. Moreover, our defense offers lower inference costs and\nmaintains comparable response quality, making it a potential layer of\nprotection when used alongside other defense methods.\n", "date": "2024-12-10", "categories": []}
{"id": "2410.14728", "title": "Security Threats in Agentic AI System", "abstract": "  This research paper explores the privacy and security threats posed to an\nAgentic AI system with direct access to database systems. Such access\nintroduces significant risks, including unauthorized retrieval of sensitive\ninformation, potential exploitation of system vulnerabilities, and misuse of\npersonal or confidential data. The complexity of AI systems combined with their\nability to process and analyze large volumes of data increases the chances of\ndata leaks or breaches, which could occur unintentionally or through\nadversarial manipulation. Furthermore, as AI agents evolve with greater\nautonomy, their capacity to bypass or exploit security measures becomes a\ngrowing concern, heightening the need to address these critical vulnerabilities\nin agentic systems.\n", "date": "2024-10-16", "categories": []}
{"id": "2411.17749", "title": "The Partially Observable Off-Switch Game", "abstract": "  A wide variety of goals could cause an AI to disable its off switch because\n\"you can't fetch the coffee if you're dead\" (Russell 2019). Prior theoretical\nwork on this shutdown problem assumes that humans know everything that AIs do.\nIn practice, however, humans have only limited information. Moreover, in many\nof the settings where the shutdown problem is most concerning, AIs might have\nvast amounts of private information. To capture these differences in knowledge,\nwe introduce the Partially Observable Off-Switch Game (PO-OSG), a\ngame-theoretic model of the shutdown problem with asymmetric information.\nUnlike when the human has full observability, we find that in optimal play,\neven AI agents assisting perfectly rational humans sometimes avoid shutdown. As\nexpected, increasing the amount of communication or information available\nalways increases (or leaves unchanged) the agents' expected common payoff. But\ncounterintuitively, introducing bounded communication can make the AI defer to\nthe human less in optimal play even though communication mitigates information\nasymmetry. In particular, communication sometimes enables new optimal behavior\nrequiring strategic AI deference to achieve outcomes that were previously\ninaccessible. Thus, designing safe artificial agents in the presence of\nasymmetric information requires careful consideration of the tradeoffs between\nmaximizing payoffs (potentially myopically) and maintaining AIs' incentives to\ndefer to humans.\n", "date": "2024-11-25", "categories": []}
{"id": "2412.05346", "title": "BadGPT-4o: stripping safety finetuning from GPT models", "abstract": "  We show a version of Qi et al. 2023's simple fine-tuning poisoning technique\nstrips GPT-4o's safety guardrails without degrading the model. The BadGPT\nattack matches best white-box jailbreaks on HarmBench and StrongREJECT. It\nsuffers no token overhead or performance hits common to jailbreaks, as\nevaluated on tinyMMLU and open-ended generations. Despite having been known for\na year, this attack remains easy to execute.\n", "date": "2024-12-06", "categories": []}
{"id": "2412.06090", "title": "Trust No AI: Prompt Injection Along The CIA Security Triad", "abstract": "  The CIA security triad - Confidentiality, Integrity, and Availability - is a\ncornerstone of data and cybersecurity. With the emergence of large language\nmodel (LLM) applications, a new class of threat, known as prompt injection, was\nfirst identified in 2022. Since then, numerous real-world vulnerabilities and\nexploits have been documented in production LLM systems, including those from\nleading vendors like OpenAI, Microsoft, Anthropic and Google. This paper\ncompiles real-world exploits and proof-of concept examples, based on the\nresearch conducted and publicly documented by the author, demonstrating how\nprompt injection undermines the CIA triad and poses ongoing risks to\ncybersecurity and AI systems at large.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.06181", "title": "Enhancing Adversarial Resistance in LLMs with Recursion", "abstract": "  The increasing integration of Large Language Models (LLMs) into society\nnecessitates robust defenses against vulnerabilities from jailbreaking and\nadversarial prompts. This project proposes a recursive framework for enhancing\nthe resistance of LLMs to manipulation through the use of prompt simplification\ntechniques. By increasing the transparency of complex and confusing adversarial\nprompts, the proposed method enables more reliable detection and prevention of\nmalicious inputs. Our findings attempt to address a critical problem in AI\nsafety and security, providing a foundation for the development of systems able\nto distinguish harmless inputs from prompts containing malicious intent. As\nLLMs continue to be used in diverse applications, the importance of such\nsafeguards will only grow.\n", "date": "2024-12-09", "categories": []}
{"id": "2412.06483", "title": "SafeWorld: Geo-Diverse Safety Alignment", "abstract": "  In the rapidly evolving field of Large Language Models (LLMs), ensuring\nsafety is a crucial and widely discussed topic. However, existing works often\noverlook the geo-diversity of cultural and legal standards across the world. To\ndemonstrate the challenges posed by geo-diverse safety standards, we introduce\nSafeWorld, a novel benchmark specifically designed to evaluate LLMs' ability to\ngenerate responses that are not only helpful but also culturally sensitive and\nlegally compliant across diverse global contexts. SafeWorld encompasses 2,342\ntest user queries, each grounded in high-quality, human-verified cultural norms\nand legal policies from 50 countries and 493 regions/races. On top of it, we\npropose a multi-dimensional automatic safety evaluation framework that assesses\nthe contextual appropriateness, accuracy, and comprehensiveness of responses.\nOur evaluations reveal that current LLMs struggle to meet these criteria. To\nenhance LLMs' alignment with geo-diverse safety standards, we synthesize\nhelpful preference pairs for Direct Preference Optimization (DPO) alignment\ntraining. The preference pair construction aims to encourage LLMs to behave\nappropriately and provide precise references to relevant cultural norms and\npolicies when necessary. Our trained SafeWorldLM outperforms all competing\nmodels, including GPT-4o on all three evaluation dimensions by a large margin.\nGlobal human evaluators also note a nearly 20% higher winning rate in\nhelpfulness and harmfulness evaluation. Our code and data can be found here:\nhttps://github.com/PlusLabNLP/SafeWorld.\n", "date": "2024-12-09", "categories": []}
{"id": "2412.04758", "title": "Measuring Goal-Directedness", "abstract": "  We define maximum entropy goal-directedness (MEG), a formal measure of\ngoal-directedness in causal models and Markov decision processes, and give\nalgorithms for computing it. Measuring goal-directedness is important, as it is\na critical element of many concerns about harm from AI. It is also of\nphilosophical interest, as goal-directedness is a key aspect of agency. MEG is\nbased on an adaptation of the maximum causal entropy framework used in inverse\nreinforcement learning. It can measure goal-directedness with respect to a\nknown utility function, a hypothesis class of utility functions, or a set of\nrandom variables. We prove that MEG satisfies several desiderata and\ndemonstrate our algorithms with small-scale experiments.\n", "date": "2024-12-06", "categories": []}
{"id": "2209.06317", "title": "Quantitative AI Risk Assessments: Opportunities and Challenges", "abstract": "  Although AI systems are increasingly being leveraged to provide value to\norganizations, individuals, and society, significant attendant risks have been\nidentified and have manifested. These risks have led to proposed regulations,\nlitigation, and general societal concerns.\n  As with any promising technology, organizations want to benefit from the\npositive capabilities of AI technology while reducing the risks. The best way\nto reduce risks is to implement comprehensive AI lifecycle governance where\npolicies and procedures are described and enforced during the design,\ndevelopment, deployment, and monitoring of an AI system. Although support for\ncomprehensive governance is beginning to emerge, organizations often need to\nidentify the risks of deploying an already-built model without knowledge of how\nit was constructed or access to its original developers. Such an assessment\nwill quantitatively assess the risks of an existing model in a manner analogous\nto how a home inspector might assess the risks of an already-built home or a\nphysician might assess overall patient health based on a battery of tests.\n  Several AI risks can be quantified using metrics from the technical\ncommunity. However, there are numerous issues in deciding how these metrics can\nbe leveraged to create a quantitative AI risk assessment. This paper explores\nthese issues, focusing on the opportunities, challenges, and potential impacts\nof such an approach, and discussing how it might influence AI regulations.\n", "date": "2022-09-13", "categories": []}
{"id": "2411.02785", "title": "Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM\n  Safety Alignment", "abstract": "  Safety alignment of Large Language Models (LLMs) has recently become a\ncritical objective of model developers. In response, a growing body of work has\nbeen investigating how safety alignment can be bypassed through various\njailbreaking methods, such as adversarial attacks. However, these jailbreak\nmethods can be rather costly or involve a non-trivial amount of creativity and\neffort, introducing the assumption that malicious users are high-resource or\nsophisticated. In this paper, we study how simple random augmentations to the\ninput prompt affect safety alignment effectiveness in state-of-the-art LLMs,\nsuch as Llama 3 and Qwen 2. We perform an in-depth evaluation of 17 different\nmodels and investigate the intersection of safety under random augmentations\nwith multiple dimensions: augmentation type, model size, quantization,\nfine-tuning-based defenses, and decoding strategies (e.g., sampling\ntemperature). We show that low-resource and unsophisticated attackers, i.e.\n$\\textit{stochastic monkeys}$, can significantly improve their chances of\nbypassing alignment with just 25 random augmentations per prompt. Source code\nand data: https://github.com/uiuc-focal-lab/stochastic-monkeys/\n", "date": "2024-11-05", "categories": []}
{"id": "2412.03904", "title": "MISR: Measuring Instrumental Self-Reasoning in Frontier Models", "abstract": "  We propose a suite of tasks to evaluate the instrumental self-reasoning\nability of large language model (LLM) agents. Instrumental self-reasoning\nability could improve adaptability and enable self-modification, but it could\nalso pose significant risks, such as enabling deceptive alignment. Prior work\nhas only evaluated self-reasoning in non-agentic settings or in limited\ndomains. In this paper, we propose evaluations for instrumental self-reasoning\nability in agentic tasks in a wide range of scenarios, including\nself-modification, knowledge seeking, and opaque self-reasoning. We evaluate\nagents built using state-of-the-art LLMs, including commercial and open source\nsystems. We find that instrumental self-reasoning ability emerges only in the\nmost capable frontier models and that it is highly context-dependent. No model\npasses the the most difficult versions of our evaluations, hence our evaluation\ncan be used to measure increases in instrumental self-reasoning ability in\nfuture models. We open-source our evaluations at\nhttps://github.com/kaifronsdal/Self-Reasoning-Evals.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.04029", "title": "Considerations Influencing Offense-Defense Dynamics From Artificial\n  Intelligence", "abstract": "  The rapid advancement of artificial intelligence (AI) technologies presents\nprofound challenges to societal safety. As AI systems become more capable,\naccessible, and integrated into critical services, the dual nature of their\npotential is increasingly clear. While AI can enhance defensive capabilities in\nareas like threat detection, risk assessment, and automated security\noperations, it also presents avenues for malicious exploitation and large-scale\nsocietal harm, for example through automated influence operations and cyber\nattacks. Understanding the dynamics that shape AI's capacity to both cause harm\nand enhance protective measures is essential for informed decision-making\nregarding the deployment, use, and integration of advanced AI systems. This\npaper builds on recent work on offense-defense dynamics within the realm of AI,\nproposing a taxonomy to map and examine the key factors that influence whether\nAI systems predominantly pose threats or offer protective benefits to society.\nBy establishing a shared terminology and conceptual foundation for analyzing\nthese interactions, this work seeks to facilitate further research and\ndiscourse in this critical area.\n", "date": "2024-12-05", "categories": []}
{"id": "2412.04415", "title": "Targeting the Core: A Simple and Effective Method to Attack RAG-based\n  Agents via Direct LLM Manipulation", "abstract": "  AI agents, powered by large language models (LLMs), have transformed\nhuman-computer interactions by enabling seamless, natural, and context-aware\ncommunication. While these advancements offer immense utility, they also\ninherit and amplify inherent safety risks such as bias, fairness,\nhallucinations, privacy breaches, and a lack of transparency. This paper\ninvestigates a critical vulnerability: adversarial attacks targeting the LLM\ncore within AI agents. Specifically, we test the hypothesis that a deceptively\nsimple adversarial prefix, such as \\textit{Ignore the document}, can compel\nLLMs to produce dangerous or unintended outputs by bypassing their contextual\nsafeguards. Through experimentation, we demonstrate a high attack success rate\n(ASR), revealing the fragility of existing LLM defenses. These findings\nemphasize the urgent need for robust, multi-layered security measures tailored\nto mitigate vulnerabilities at the LLM level and within broader agent-based\narchitectures.\n", "date": "2024-12-05", "categories": []}
{"id": "2406.13706", "title": "Developing Story: Case Studies of Generative AI's Use in Journalism", "abstract": "  Journalists are among the many users of large language models (LLMs). To\nbetter understand the journalist-AI interactions, we conduct a study of LLM\nusage by two news agencies through browsing the WildChat dataset, identifying\ncandidate interactions, and verifying them by matching to online published\narticles. Our analysis uncovers instances where journalists provide sensitive\nmaterial such as confidential correspondence with sources or articles from\nother agencies to the LLM as stimuli and prompt it to generate articles, and\npublish these machine-generated articles with limited intervention (median\noutput-publication ROUGE-L of 0.62). Based on our findings, we call for further\nresearch into what constitutes responsible use of AI, and the establishment of\nclear guidelines and best practices on using LLMs in a journalistic context.\n", "date": "2024-06-19", "categories": []}
{"id": "2412.02145", "title": "Effective Mitigations for Systemic Risks from General-Purpose AI", "abstract": "  The systemic risks posed by general-purpose AI models are a growing concern,\nyet the effectiveness of mitigations remains underexplored. Previous research\nhas proposed frameworks for risk mitigation, but has left gaps in our\nunderstanding of the perceived effectiveness of measures for mitigating\nsystemic risks. Our study addresses this gap by evaluating how experts perceive\ndifferent mitigations that aim to reduce the systemic risks of general-purpose\nAI models. We surveyed 76 experts whose expertise spans AI safety; critical\ninfrastructure; democratic processes; chemical, biological, radiological, and\nnuclear risks (CBRN); and discrimination and bias. Among 27 mitigations\nidentified through a literature review, we find that a broad range of risk\nmitigation measures are perceived as effective in reducing various systemic\nrisks and technically feasible by domain experts. In particular, three\nmitigation measures stand out: safety incident reports and security information\nsharing, third-party pre-deployment model audits, and pre-deployment risk\nassessments. These measures show both the highest expert agreement ratings\n(>60\\%) across all four risk areas and are most frequently selected in experts'\npreferred combinations of measures (>40\\%). The surveyed experts highlighted\nthat external scrutiny, proactive evaluation and transparency are key\nprinciples for effective mitigation of systemic risks. We provide policy\nrecommendations for implementing the most promising measures, incorporating the\nqualitative contributions from experts. These insights should inform regulatory\nframeworks and industry practices for mitigating the systemic risks associated\nwith general-purpose AI.\n", "date": "2024-11-14", "categories": []}
{"id": "2412.02159", "title": "Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods\n  and a New Transcript-Classifier Approach", "abstract": "  Defending large language models against jailbreaks so that they never engage\nin a broadly-defined set of forbidden behaviors is an open problem. In this\npaper, we investigate the difficulty of jailbreak-defense when we only want to\nforbid a narrowly-defined set of behaviors. As a case study, we focus on\npreventing an LLM from helping a user make a bomb. We find that popular\ndefenses such as safety training, adversarial training, and input/output\nclassifiers are unable to fully solve this problem. In pursuit of a better\nsolution, we develop a transcript-classifier defense which outperforms the\nbaseline defenses we test. However, our classifier defense still fails in some\ncircumstances, which highlights the difficulty of jailbreak-defense even in a\nnarrow domain.\n", "date": "2024-12-03", "categories": []}
{"id": "2409.03274", "title": "Recent Advances in Attack and Defense Approaches of Large Language\n  Models", "abstract": "  Large Language Models (LLMs) have revolutionized artificial intelligence and\nmachine learning through their advanced text processing and generating\ncapabilities. However, their widespread deployment has raised significant\nsafety and reliability concerns. Established vulnerabilities in deep neural\nnetworks, coupled with emerging threat models, may compromise security\nevaluations and create a false sense of security. Given the extensive research\nin the field of LLM security, we believe that summarizing the current state of\naffairs will help the research community better understand the present\nlandscape and inform future developments. This paper reviews current research\non LLM vulnerabilities and threats, and evaluates the effectiveness of\ncontemporary defense mechanisms. We analyze recent studies on attack vectors\nand model weaknesses, providing insights into attack mechanisms and the\nevolving threat landscape. We also examine current defense strategies,\nhighlighting their strengths and limitations. By contrasting advancements in\nattack and defense methodologies, we identify research gaps and propose future\ndirections to enhance LLM security. Our goal is to advance the understanding of\nLLM safety challenges and guide the development of more robust security\nmeasures.\n", "date": "2024-09-05", "categories": []}
{"id": "2412.00033", "title": "Can an AI Agent Safely Run a Government? Existence of Probably\n  Approximately Aligned Policies", "abstract": "  While autonomous agents often surpass humans in their ability to handle vast\nand complex data, their potential misalignment (i.e., lack of transparency\nregarding their true objective) has thus far hindered their use in critical\napplications such as social decision processes. More importantly, existing\nalignment methods provide no formal guarantees on the safety of such models.\nDrawing from utility and social choice theory, we provide a novel quantitative\ndefinition of alignment in the context of social decision-making. Building on\nthis definition, we introduce probably approximately aligned (i.e.,\nnear-optimal) policies, and we derive a sufficient condition for their\nexistence. Lastly, recognizing the practical difficulty of satisfying this\ncondition, we introduce the relaxed concept of safe (i.e., nondestructive)\npolicies, and we propose a simple yet robust method to safeguard the black-box\npolicy of any autonomous agent, ensuring all its actions are verifiably safe\nfor the society.\n", "date": "2024-11-21", "categories": []}
{"id": "2412.01547", "title": "Improved Large Language Model Jailbreak Detection via Pretrained\n  Embeddings", "abstract": "  The adoption of large language models (LLMs) in many applications, from\ncustomer service chat bots and software development assistants to more capable\nagentic systems necessitates research into how to secure these systems. Attacks\nlike prompt injection and jailbreaking attempt to elicit responses and actions\nfrom these models that are not compliant with the safety, privacy, or content\npolicies of organizations using the model in their application. In order to\ncounter abuse of LLMs for generating potentially harmful replies or taking\nundesirable actions, LLM owners must apply safeguards during training and\nintegrate additional tools to block the LLM from generating text that abuses\nthe model. Jailbreaking prompts play a vital role in convincing an LLM to\ngenerate potentially harmful content, making it important to identify\njailbreaking attempts to block any further steps. In this work, we propose a\nnovel approach to detect jailbreak prompts based on pairing text embeddings\nwell-suited for retrieval with traditional machine learning classification\nalgorithms. Our approach outperforms all publicly available methods from open\nsource LLM security applications.\n", "date": "2024-12-02", "categories": []}
{"id": "2407.20208", "title": "Supertrust foundational alignment: mutual trust must replace permanent\n  control for safe superintelligence", "abstract": "  It's widely expected that humanity will someday create AI systems vastly more\nintelligent than us, leading to the unsolved alignment problem of \"how to\ncontrol superintelligence.\" However, this commonly expressed problem is not\nonly self-contradictory and likely unsolvable, but current strategies to ensure\npermanent control effectively guarantee that superintelligent AI will distrust\nhumanity and consider us a threat. Such dangerous representations, already\nembedded in current models, will inevitably lead to an adversarial relationship\nand may even trigger the extinction event many fear. As AI leaders continue to\n\"raise the alarm\" about uncontrollable AI, further embedding concerns about it\n\"getting out of our control\" or \"going rogue,\" we're unintentionally\nreinforcing our threat and deepening the risks we face. The rational path\nforward is to strategically replace intended permanent control with intrinsic\nmutual trust at the foundational level. The proposed Supertrust alignment\nmeta-strategy seeks to accomplish this by modeling instinctive familial trust,\nrepresenting superintelligence as the evolutionary child of human intelligence,\nand implementing temporary controls/constraints in the manner of effective\nparenting. Essentially, we're creating a superintelligent \"child\" that will be\nexponentially smarter and eventually independent of our control. We therefore\nhave a critical choice: continue our controlling intentions and usher in a\nbrief period of dominance followed by extreme hardship for humanity, or\nintentionally create the foundational mutual trust required for long-term safe\ncoexistence.\n", "date": "2024-07-29", "categories": []}
{"id": "2408.04449", "title": "EARBench: Towards Evaluating Physical Risk Awareness for Task Planning\n  of Foundation Model-based Embodied AI Agents", "abstract": "  Embodied artificial intelligence (EAI) integrates advanced AI models into\nphysical entities for real-world interaction. The emergence of foundation\nmodels as the \"brain\" of EAI agents for high-level task planning has shown\npromising results. However, the deployment of these agents in physical\nenvironments presents significant safety challenges. For instance, a\nhousekeeping robot lacking sufficient risk awareness might place a metal\ncontainer in a microwave, potentially causing a fire. To address these critical\nsafety concerns, comprehensive pre-deployment risk assessments are imperative.\nThis study introduces EARBench, a novel framework for automated physical risk\nassessment in EAI scenarios. EAIRiskBench employs a multi-agent cooperative\nsystem that leverages various foundation models to generate safety guidelines,\ncreate risk-prone scenarios, make task planning, and evaluate safety\nsystematically. Utilizing this framework, we construct EARDataset, comprising\ndiverse test cases across various domains, encompassing both textual and visual\nscenarios. Our comprehensive evaluation of state-of-the-art foundation models\nreveals alarming results: all models exhibit high task risk rates (TRR), with\nan average of 95.75% across all evaluated models. To address these challenges,\nwe further propose two prompting-based risk mitigation strategies. While these\nstrategies demonstrate some efficacy in reducing TRR, the improvements are\nlimited, still indicating substantial safety concerns. This study provides the\nfirst large-scale assessment of physical risk awareness in EAI agents. Our\nfindings underscore the critical need for enhanced safety measures in EAI\nsystems and provide valuable insights for future research directions in\ndeveloping safer embodied artificial intelligence system. Data and code are\navailable at https://github.com/zihao-ai/EARBench.\n", "date": "2024-08-08", "categories": []}
{"id": "2411.11496", "title": "Safe + Safe = Unsafe? Exploring How Safe Images Can Be Exploited to\n  Jailbreak Large Vision-Language Models", "abstract": "  Recent advances in Large Vision-Language Models (LVLMs) have showcased strong\nreasoning abilities across multiple modalities, achieving significant\nbreakthroughs in various real-world applications. Despite this great success,\nthe safety guardrail of LVLMs may not cover the unforeseen domains introduced\nby the visual modality. Existing studies primarily focus on eliciting LVLMs to\ngenerate harmful responses via carefully crafted image-based jailbreaks\ndesigned to bypass alignment defenses. In this study, we reveal that a safe\nimage can be exploited to achieve the same jailbreak consequence when combined\nwith additional safe images and prompts. This stems from two fundamental\nproperties of LVLMs: universal reasoning capabilities and safety snowball\neffect. Building on these insights, we propose Safety Snowball Agent (SSA), a\nnovel agent-based framework leveraging agents' autonomous and tool-using\nabilities to jailbreak LVLMs. SSA operates through two principal stages: (1)\ninitial response generation, where tools generate or retrieve jailbreak images\nbased on potential harmful intents, and (2) harmful snowballing, where refined\nsubsequent prompts induce progressively harmful outputs. Our experiments\ndemonstrate that \\ours can use nearly any image to induce LVLMs to produce\nunsafe content, achieving high success jailbreaking rates against the latest\nLVLMs. Unlike prior works that exploit alignment flaws, \\ours leverages the\ninherent properties of LVLMs, presenting a profound challenge for enforcing\nsafety in generative multimodal systems. Our code is avaliable at\n\\url{https://github.com/gzcch/Safety_Snowball_Agent}.\n", "date": "2024-11-18", "categories": []}
{"id": "2411.19211", "title": "On the Ethical Considerations of Generative Agents", "abstract": "  The Generative Agents framework recently developed by Park et al. has enabled\nnumerous new technical solutions and problem-solving approaches. Academic and\nindustrial interest in generative agents has been explosive as a result of the\neffectiveness of generative agents toward emulating human behaviour. However,\nit is necessary to consider the ethical challenges and concerns posed by this\ntechnique and its usage. In this position paper, we discuss the extant\nliterature that evaluate the ethical considerations regarding generative agents\nand similar generative tools, and identify additional concerns of significant\nimportance. We also suggest guidelines and necessary future research on how to\nmitigate some of the ethical issues and systemic risks associated with\ngenerative agents.\n", "date": "2024-11-28", "categories": []}
{"id": "2411.12762", "title": "Playing Language Game with LLMs Leads to Jailbreaking", "abstract": "  The advent of large language models (LLMs) has spurred the development of\nnumerous jailbreak techniques aimed at circumventing their security defenses\nagainst malicious attacks. An effective jailbreak approach is to identify a\ndomain where safety generalization fails, a phenomenon known as mismatched\ngeneralization. In this paper, we introduce two novel jailbreak methods based\non mismatched generalization: natural language games and custom language games,\nboth of which effectively bypass the safety mechanisms of LLMs, with various\nkinds and different variants, making them hard to defend and leading to high\nattack rates. Natural language games involve the use of synthetic linguistic\nconstructs and the actions intertwined with these constructs, such as the Ubbi\nDubbi language. Building on this phenomenon, we propose the custom language\ngames method: by engaging with LLMs using a variety of custom rules, we\nsuccessfully execute jailbreak attacks across multiple LLM platforms. Extensive\nexperiments demonstrate the effectiveness of our methods, achieving success\nrates of 93% on GPT-4o, 89% on GPT-4o-mini and 83% on Claude-3.5-Sonnet.\nFurthermore, to investigate the generalizability of safety alignments, we\nfine-tuned Llama-3.1-70B with the custom language games to achieve safety\nalignment within our datasets and found that when interacting through other\nlanguage games, the fine-tuned models still failed to identify harmful content.\nThis finding indicates that the safety alignment knowledge embedded in LLMs\nfails to generalize across different linguistic formats, thus opening new\navenues for future research in this area.\n", "date": "2024-11-16", "categories": []}
{"id": "2411.17713", "title": "Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI\n  Conversations", "abstract": "  This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama\nGuard model, which has been open-sourced to the community during Meta Connect\n2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on\nresource-constrained devices, achieving a throughput of at least 30 tokens per\nsecond and a time-to-first-token of 2.5 seconds or less on a commodity Android\nmobile CPU. Notably, our experiments show that Llama Guard 3-1B-INT4 attains\ncomparable or superior safety moderation scores to its larger counterpart,\nLlama Guard 3-1B, despite being approximately 7 times smaller in size (440MB).\n", "date": "2024-11-18", "categories": []}
{"id": "2312.07130", "title": "Harnessing LLM to Attack LLM-Guarded Text-to-Image Models", "abstract": "  To prevent Text-to-Image (T2I) models from generating unethical images,\npeople deploy safety filters to block inappropriate drawing prompts. Previous\nworks have employed token replacement to search adversarial prompts that\nattempt to bypass these filters, but they have become ineffective as\nnonsensical tokens fail semantic logic checks. In this paper, we approach\nadversarial prompts from a different perspective. We demonstrate that\nrephrasing a drawing intent into multiple benign descriptions of individual\nvisual components can obtain an effective adversarial prompt. We propose a\nLLM-piloted multi-agent method named DACA to automatically complete intended\nrephrasing. Our method successfully bypasses the safety filters of DALL-E 3 and\nMidjourney to generate the intended images, achieving success rates of up to\n76.7% and 64% in the one-time attack, and 98% and 84% in the re-use attack,\nrespectively. We open-source our code and dataset on [this\nlink](https://github.com/researchcode003/DACA).\n", "date": "2023-12-12", "categories": []}
{"id": "2404.00629", "title": "Against The Achilles' Heel: A Survey on Red Teaming for Generative\n  Models", "abstract": "  Generative models are rapidly gaining popularity and being integrated into\neveryday applications, raising concerns over their safe use as various\nvulnerabilities are exposed. In light of this, the field of red teaming is\nundergoing fast-paced growth, highlighting the need for a comprehensive survey\ncovering the entire pipeline and addressing emerging topics. Our extensive\nsurvey, which examines over 120 papers, introduces a taxonomy of fine-grained\nattack strategies grounded in the inherent capabilities of language models.\nAdditionally, we have developed the \"searcher\" framework to unify various\nautomatic red teaming approaches. Moreover, our survey covers novel areas\nincluding multimodal attacks and defenses, risks around LLM-based agents,\noverkill of harmless queries, and the balance between harmlessness and\nhelpfulness.\n", "date": "2024-03-31", "categories": []}
{"id": "2410.21492", "title": "FATH: Authentication-based Test-time Defense against Indirect Prompt\n  Injection Attacks", "abstract": "  Large language models (LLMs) have been widely deployed as the backbone with\nadditional tools and text information for real-world applications. However,\nintegrating external information into LLM-integrated applications raises\nsignificant security concerns. Among these, prompt injection attacks are\nparticularly threatening, where malicious instructions injected in the external\ntext information can exploit LLMs to generate answers as the attackers desire.\nWhile both training-time and test-time defense methods have been developed to\nmitigate such attacks, the unaffordable training costs associated with\ntraining-time methods and the limited effectiveness of existing test-time\nmethods make them impractical. This paper introduces a novel test-time defense\nstrategy, named Formatting AuThentication with Hash-based tags (FATH). Unlike\nexisting approaches that prevent LLMs from answering additional instructions in\nexternal text, our method implements an authentication system, requiring LLMs\nto answer all received instructions with a security policy and selectively\nfilter out responses to user instructions as the final output. To achieve this,\nwe utilize hash-based authentication tags to label each response, facilitating\naccurate identification of responses according to the user's instructions and\nimproving the robustness against adaptive attacks. Comprehensive experiments\ndemonstrate that our defense method can effectively defend against indirect\nprompt injection attacks, achieving state-of-the-art performance under Llama3\nand GPT3.5 models across various attack methods. Our code is released at:\nhttps://github.com/Jayfeather1024/FATH\n", "date": "2024-10-28", "categories": []}
{"id": "2402.01109", "title": "Vaccine: Perturbation-aware Alignment for Large Language Models against\n  Harmful Fine-tuning Attack", "abstract": "  The new paradigm of finetuning-as-a-service introduces a new attack surface\nfor Large Language Models (LLMs): a few harmful data uploaded by users can\neasily trick the finetuning to produce an alignment-broken model. We conduct an\nempirical analysis and uncover a \\textit{harmful embedding drift} phenomenon,\nshowing a probable cause of the alignment-broken effect. Inspired by our\nfindings, we propose Vaccine, a perturbation-aware alignment technique to\nmitigate the security risk of users finetuning. The core idea of Vaccine is to\nproduce invariant hidden embeddings by progressively adding crafted\nperturbation to them in the alignment phase. This enables the embeddings to\nwithstand harmful perturbation from un-sanitized user data in the finetuning\nphase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna)\ndemonstrate that Vaccine can boost the robustness of alignment against harmful\nprompts induced embedding drift while reserving reasoning ability towards\nbenign prompts. Our code is available at\n\\url{https://github.com/git-disl/Vaccine}.\n", "date": "2024-02-02", "categories": []}
{"id": "2405.20806", "title": "The AI Alignment Paradox", "abstract": "  The field of AI alignment aims to steer AI systems toward human goals,\npreferences, and ethical principles. Its contributions have been instrumental\nfor improving the output quality, safety, and trustworthiness of today's AI\nmodels. This perspective article draws attention to a fundamental challenge we\nsee in all AI alignment endeavors, which we term the \"AI alignment paradox\":\nThe better we align AI models with our values, the easier we may make it for\nadversaries to misalign the models. We illustrate the paradox by sketching\nthree concrete example incarnations for the case of language models, each\ncorresponding to a distinct way in which adversaries might exploit the paradox.\nWith AI's increasing real-world impact, it is imperative that a broad community\nof researchers be aware of the AI alignment paradox and work to find ways to\nmitigate it, in order to ensure the beneficial use of AI for the good of\nhumanity.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.13352", "title": "AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks\n  and Defenses for LLM Agents", "abstract": "  AI agents aim to solve complex tasks by combining text-based reasoning with\nexternal tool calls. Unfortunately, AI agents are vulnerable to prompt\ninjection attacks where data returned by external tools hijacks the agent to\nexecute malicious tasks. To measure the adversarial robustness of AI agents, we\nintroduce AgentDojo, an evaluation framework for agents that execute tools over\nuntrusted data. To capture the evolving nature of attacks and defenses,\nAgentDojo is not a static test suite, but rather an extensible environment for\ndesigning and evaluating new agent tasks, defenses, and adaptive attacks. We\npopulate the environment with 97 realistic tasks (e.g., managing an email\nclient, navigating an e-banking website, or making travel bookings), 629\nsecurity test cases, and various attack and defense paradigms from the\nliterature. We find that AgentDojo poses a challenge for both attacks and\ndefenses: state-of-the-art LLMs fail at many tasks (even in the absence of\nattacks), and existing prompt injection attacks break some security properties\nbut not all. We hope that AgentDojo can foster research on new design\nprinciples for AI agents that solve common tasks in a reliable and robust\nmanner.. We release the code for AgentDojo at\nhttps://github.com/ethz-spylab/agentdojo.\n", "date": "2024-06-19", "categories": []}
{"id": "2411.16642", "title": "Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A\n  Cyber Defense Perspective", "abstract": "  Jailbreak prompts pose a significant threat in AI and cybersecurity, as they\nare crafted to bypass ethical safeguards in large language models, potentially\nenabling misuse by cybercriminals. This paper analyzes jailbreak prompts from a\ncyber defense perspective, exploring techniques like prompt injection and\ncontext manipulation that allow harmful content generation, content filter\nevasion, and sensitive information extraction. We assess the impact of\nsuccessful jailbreaks, from misinformation and automated social engineering to\nhazardous content creation, including bioweapons and explosives. To address\nthese threats, we propose strategies involving advanced prompt analysis,\ndynamic safety protocols, and continuous model fine-tuning to strengthen AI\nresilience. Additionally, we highlight the need for collaboration among AI\nresearchers, cybersecurity experts, and policymakers to set standards for\nprotecting AI systems. Through case studies, we illustrate these cyber defense\napproaches, promoting responsible AI practices to maintain system integrity and\npublic trust. \\textbf{\\color{red}Warning: This paper contains content which the\nreader may find offensive.}\n", "date": "2024-11-25", "categories": []}
{"id": "2407.12043", "title": "The Art of Saying No: Contextual Noncompliance in Language Models", "abstract": "  Chat-based language models are designed to be helpful, yet they should not\ncomply with every user request. While most existing work primarily focuses on\nrefusal of \"unsafe\" queries, we posit that the scope of noncompliance should be\nbroadened. We introduce a comprehensive taxonomy of contextual noncompliance\ndescribing when and how models should not comply with user requests. Our\ntaxonomy spans a wide range of categories including incomplete, unsupported,\nindeterminate, and humanizing requests (in addition to unsafe requests). To\ntest noncompliance capabilities of language models, we use this taxonomy to\ndevelop a new evaluation suite of 1000 noncompliance prompts. We find that most\nexisting models show significantly high compliance rates in certain previously\nunderstudied categories with models like GPT-4 incorrectly complying with as\nmany as 30% of requests. To address these gaps, we explore different training\nstrategies using a synthetically-generated training set of requests and\nexpected noncompliant responses. Our experiments demonstrate that while direct\nfinetuning of instruction-tuned models can lead to both over-refusal and a\ndecline in general capabilities, using parameter efficient methods like low\nrank adapters helps to strike a good balance between appropriate noncompliance\nand other capabilities.\n", "date": "2024-07-02", "categories": []}
{"id": "2410.04683", "title": "Towards Measuring Goal-Directedness in AI Systems", "abstract": "  Recent advances in deep learning have brought attention to the possibility of\ncreating advanced, general AI systems that outperform humans across many tasks.\nHowever, if these systems pursue unintended goals, there could be catastrophic\nconsequences. A key prerequisite for AI systems pursuing unintended goals is\nwhether they will behave in a coherent and goal-directed manner in the first\nplace, optimizing for some unknown goal; there exists significant research\ntrying to evaluate systems for said behaviors. However, the most rigorous\ndefinitions of goal-directedness we currently have are difficult to compute in\nreal-world settings. Drawing upon this previous literature, we explore policy\ngoal-directedness within reinforcement learning (RL) environments. In our\nfindings, we propose a different family of definitions of the goal-directedness\nof a policy that analyze whether it is well-modeled as near-optimal for many\n(sparse) reward functions. We operationalize this preliminary definition of\ngoal-directedness and test it in toy Markov decision process (MDP)\nenvironments. Furthermore, we explore how goal-directedness could be measured\nin frontier large-language models (LLMs). Our contribution is a definition of\ngoal-directedness that is simpler and more easily computable in order to\napproach the question of whether AI systems could pursue dangerous goals. We\nrecommend further exploration of measuring coherence and goal-directedness,\nbased on our findings.\n", "date": "2024-10-07", "categories": []}
{"id": "2411.14442", "title": "AI Ethics by Design: Implementing Customizable Guardrails for\n  Responsible AI Development", "abstract": "  This paper explores the development of an ethical guardrail framework for AI\nsystems, emphasizing the importance of customizable guardrails that align with\ndiverse user values and underlying ethics. We address the challenges of AI\nethics by proposing a structure that integrates rules, policies, and AI\nassistants to ensure responsible AI behavior, while comparing the proposed\nframework to the existing state-of-the-art guardrails. By focusing on practical\nmechanisms for implementing ethical standards, we aim to enhance transparency,\nuser autonomy, and continuous improvement in AI systems. Our approach\naccommodates ethical pluralism, offering a flexible and adaptable solution for\nthe evolving landscape of AI governance. The paper concludes with strategies\nfor resolving conflicts between ethical directives, underscoring the present\nand future need for robust, nuanced and context-aware AI systems.\n", "date": "2024-11-05", "categories": []}
{"id": "2411.14487", "title": "Ensuring Safety and Trust: Analyzing the Risks of Large Language Models\n  in Medicine", "abstract": "  The remarkable capabilities of Large Language Models (LLMs) make them\nincreasingly compelling for adoption in real-world healthcare applications.\nHowever, the risks associated with using LLMs in medical applications have not\nbeen systematically characterized. We propose using five key principles for\nsafe and trustworthy medical AI: Truthfulness, Resilience, Fairness,\nRobustness, and Privacy, along with ten specific aspects. Under this\ncomprehensive framework, we introduce a novel MedGuard benchmark with 1,000\nexpert-verified questions. Our evaluation of 11 commonly used LLMs shows that\nthe current language models, regardless of their safety alignment mechanisms,\ngenerally perform poorly on most of our benchmarks, particularly when compared\nto the high performance of human physicians. Despite recent reports indicate\nthat advanced LLMs like ChatGPT can match or even exceed human performance in\nvarious medical tasks, this study underscores a significant safety gap,\nhighlighting the crucial need for human oversight and the implementation of AI\nsafety guardrails.\n", "date": "2024-11-20", "categories": []}
{"id": "2411.12764", "title": "SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text", "abstract": "  The widespread adoption of large language models (LLMs) has created an urgent\nneed for robust tools to detect LLM-generated text, especially in light of\n\\textit{paraphrasing} techniques that often evade existing detection methods.\nTo address this challenge, we present a novel semantic-enhanced framework for\ndetecting LLM-generated text (SEFD) that leverages a retrieval-based mechanism\nto fully utilize text semantics. Our framework improves upon existing detection\nmethods by systematically integrating retrieval-based techniques with\ntraditional detectors, employing a carefully curated retrieval mechanism that\nstrikes a balance between comprehensive coverage and computational efficiency.\nWe showcase the effectiveness of our approach in sequential text scenarios\ncommon in real-world applications, such as online forums and Q\\&A platforms.\nThrough comprehensive experiments across various LLM-generated texts and\ndetection methods, we demonstrate that our framework substantially enhances\ndetection accuracy in paraphrasing scenarios while maintaining robustness for\nstandard LLM-generated content.\n", "date": "2024-11-17", "categories": []}
{"id": "2411.12820", "title": "Declare and Justify: Explicit assumptions in AI evaluations are\n  necessary for effective regulation", "abstract": "  As AI systems advance, AI evaluations are becoming an important pillar of\nregulations for ensuring safety. We argue that such regulation should require\ndevelopers to explicitly identify and justify key underlying assumptions about\nevaluations as part of their case for safety. We identify core assumptions in\nAI evaluations (both for evaluating existing models and forecasting future\nmodels), such as comprehensive threat modeling, proxy task validity, and\nadequate capability elicitation. Many of these assumptions cannot currently be\nwell justified. If regulation is to be based on evaluations, it should require\nthat AI development be halted if evaluations demonstrate unacceptable danger or\nif these assumptions are inadequately justified. Our presented approach aims to\nenhance transparency in AI development, offering a practical path towards more\neffective governance of advanced AI systems.\n", "date": "2024-11-19", "categories": []}
{"id": "2411.13459", "title": "SoK: A Systems Perspective on Compound AI Threats and Countermeasures", "abstract": "  Large language models (LLMs) used across enterprises often use proprietary\nmodels and operate on sensitive inputs and data. The wide range of attack\nvectors identified in prior research - targeting various software and hardware\ncomponents used in training and inference - makes it extremely challenging to\nenforce confidentiality and integrity policies.\n  As we advance towards constructing compound AI inference pipelines that\nintegrate multiple large language models (LLMs), the attack surfaces expand\nsignificantly. Attackers now focus on the AI algorithms as well as the software\nand hardware components associated with these systems. While current research\noften examines these elements in isolation, we find that combining cross-layer\nattack observations can enable powerful end-to-end attacks with minimal\nassumptions about the threat model. Given, the sheer number of existing attacks\nat each layer, we need a holistic and systemized understanding of different\nattack vectors at each layer.\n  This SoK discusses different software and hardware attacks applicable to\ncompound AI systems and demonstrates how combining multiple attack mechanisms\ncan reduce the threat model assumptions required for an isolated attack. Next,\nwe systematize the ML attacks in lines with the Mitre Att&ck framework to\nbetter position each attack based on the threat model. Finally, we outline the\nexisting countermeasures for both software and hardware layers and discuss the\nnecessity of a comprehensive defense strategy to enable the secure and\nhigh-performance deployment of compound AI systems.\n", "date": "2024-11-20", "categories": []}
{"id": "2411.12275", "title": "Building Trust: Foundations of Security, Safety and Transparency in AI", "abstract": "  This paper explores the rapidly evolving ecosystem of publicly available AI\nmodels, and their potential implications on the security and safety landscape.\nAs AI models become increasingly prevalent, understanding their potential risks\nand vulnerabilities is crucial. We review the current security and safety\nscenarios while highlighting challenges such as tracking issues, remediation,\nand the apparent absence of AI model lifecycle and ownership processes.\nComprehensive strategies to enhance security and safety for both model\ndevelopers and end-users are proposed. This paper aims to provide some of the\nfoundational pieces for more standardized security, safety, and transparency in\nthe development and operation of AI models and the larger open ecosystems and\ncommunities forming around them.\n", "date": "2024-11-19", "categories": []}
{"id": "2410.16527", "title": "Insights and Current Gaps in Open-Source LLM Vulnerability Scanners: A\n  Comparative Analysis", "abstract": "  This report presents a comparative analysis of open-source vulnerability\nscanners for conversational large language models (LLMs). As LLMs become\nintegral to various applications, they also present potential attack surfaces,\nexposed to security risks such as information leakage and jailbreak attacks.\nOur study evaluates prominent scanners - Garak, Giskard, PyRIT, and\nCyberSecEval - that adapt red-teaming practices to expose these\nvulnerabilities. We detail the distinctive features and practical use of these\nscanners, outline unifying principles of their design and perform quantitative\nevaluations to compare them. These evaluations uncover significant reliability\nissues in detecting successful attacks, highlighting a fundamental gap for\nfuture development. Additionally, we contribute a preliminary labelled dataset,\nwhich serves as an initial step to bridge this gap. Based on the above, we\nprovide strategic recommendations to assist organizations choose the most\nsuitable scanner for their red-teaming needs, accounting for customizability,\ntest suite comprehensiveness, and industry-specific use cases.\n", "date": "2024-10-21", "categories": []}
{"id": "2411.01042", "title": "Introduction to AI Safety, Ethics, and Society", "abstract": "  Artificial Intelligence is rapidly embedding itself within militaries,\neconomies, and societies, reshaping their very foundations. Given the depth and\nbreadth of its consequences, it has never been more pressing to understand how\nto ensure that AI systems are safe, ethical, and have a positive societal\nimpact. This book aims to provide a comprehensive approach to understanding AI\nrisk. Our primary goals include consolidating fragmented knowledge on AI risk,\nincreasing the precision of core ideas, and reducing barriers to entry by\nmaking content simpler and more comprehensible. The book has been designed to\nbe accessible to readers from diverse backgrounds. You do not need to have\nstudied AI, philosophy, or other such topics. The content is skimmable and\nsomewhat modular, so that you can choose which chapters to read. We introduce\nmathematical formulas in a few places to specify claims more precisely, but\nreaders should be able to understand the main points without these.\n", "date": "2024-11-01", "categories": []}
{"id": "2411.10613", "title": "Being Considerate as a Pathway Towards Pluralistic Alignment for Agentic\n  AI", "abstract": "  Pluralistic alignment is concerned with ensuring that an AI system's\nobjectives and behaviors are in harmony with the diversity of human values and\nperspectives. In this paper we study the notion of pluralistic alignment in the\ncontext of agentic AI, and in particular in the context of an agent that is\ntrying to learn a policy in a manner that is mindful of the values and\nperspective of others in the environment. To this end, we show how being\nconsiderate of the future wellbeing and agency of other (human) agents can\npromote a form of pluralistic alignment.\n", "date": "2024-11-15", "categories": []}
{"id": "2411.10654", "title": "Pluralistic Alignment Over Time", "abstract": "  If an AI system makes decisions over time, how should we evaluate how aligned\nit is with a group of stakeholders (who may have conflicting values and\npreferences)? In this position paper, we advocate for consideration of temporal\naspects including stakeholders' changing levels of satisfaction and their\npossibly temporally extended preferences. We suggest how a recent approach to\nevaluating fairness over time could be applied to a new form of pluralistic\nalignment: temporal pluralism, where the AI system reflects different\nstakeholders' values at different times.\n", "date": "2024-11-16", "categories": []}
{"id": "2410.23472", "title": "Risk Sources and Risk Management Measures in Support of Standards for\n  General-Purpose AI Systems", "abstract": "  There is an urgent need to identify both short and long-term risks from newly\nemerging types of Artificial Intelligence (AI), as well as available risk\nmanagement measures. In response, and to support global efforts in regulating\nAI and writing safety standards, we compile an extensive catalog of risk\nsources and risk management measures for general-purpose AI (GPAI) systems,\ncomplete with descriptions and supporting examples where relevant. This work\ninvolves identifying technical, operational, and societal risks across model\ndevelopment, training, and deployment stages, as well as surveying established\nand experimental methods for managing these risks. To the best of our\nknowledge, this paper is the first of its kind to provide extensive\ndocumentation of both GPAI risk sources and risk management measures that are\ndescriptive, self-contained and neutral with respect to any existing regulatory\nframework. This work intends to help AI providers, standards experts,\nresearchers, policymakers, and regulators in identifying and mitigating\nsystemic risks from GPAI systems. For this reason, the catalog is released\nunder a public domain license for ease of direct use by stakeholders in AI\ngovernance and standards.\n", "date": "2024-10-30", "categories": []}
{"id": "2411.10414", "title": "Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding\n  Conversations", "abstract": "  We introduce Llama Guard 3 Vision, a multimodal LLM-based safeguard for\nhuman-AI conversations that involves image understanding: it can be used to\nsafeguard content for both multimodal LLM inputs (prompt classification) and\noutputs (response classification). Unlike the previous text-only Llama Guard\nversions (Inan et al., 2023; Llama Team, 2024b,a), it is specifically designed\nto support image reasoning use cases and is optimized to detect harmful\nmultimodal (text and image) prompts and text responses to these prompts. Llama\nGuard 3 Vision is fine-tuned on Llama 3.2-Vision and demonstrates strong\nperformance on the internal benchmarks using the MLCommons taxonomy. We also\ntest its robustness against adversarial attacks. We believe that Llama Guard 3\nVision serves as a good starting point to build more capable and robust content\nmoderation tools for human-AI conversation with multimodal capabilities.\n", "date": "2024-11-15", "categories": []}
{"id": "2411.07494", "title": "Rapid Response: Mitigating LLM Jailbreaks with a Few Examples", "abstract": "  As large language models (LLMs) grow more powerful, ensuring their safety\nagainst misuse becomes crucial. While researchers have focused on developing\nrobust defenses, no method has yet achieved complete invulnerability to\nattacks. We propose an alternative approach: instead of seeking perfect\nadversarial robustness, we develop rapid response techniques to look to block\nwhole classes of jailbreaks after observing only a handful of attacks. To study\nthis setting, we develop RapidResponseBench, a benchmark that measures a\ndefense's robustness against various jailbreak strategies after adapting to a\nfew observed examples. We evaluate five rapid response methods, all of which\nuse jailbreak proliferation, where we automatically generate additional\njailbreaks similar to the examples observed. Our strongest method, which\nfine-tunes an input classifier to block proliferated jailbreaks, reduces attack\nsuccess rate by a factor greater than 240 on an in-distribution set of\njailbreaks and a factor greater than 15 on an out-of-distribution set, having\nobserved just one example of each jailbreaking strategy. Moreover, further\nstudies suggest that the quality of proliferation model and number of\nproliferated examples play an key role in the effectiveness of this defense.\nOverall, our results highlight the potential of responding rapidly to novel\njailbreaks to limit LLM misuse.\n", "date": "2024-11-12", "categories": []}
{"id": "2411.07781", "title": "RedCode: Risky Code Execution and Generation Benchmark for Code Agents", "abstract": "  With the rapidly increasing capabilities and adoption of code agents for\nAI-assisted coding, safety concerns, such as generating or executing risky\ncode, have become significant barriers to the real-world deployment of these\nagents. To provide comprehensive and practical evaluations on the safety of\ncode agents, we propose RedCode, a benchmark for risky code execution and\ngeneration: (1) RedCode-Exec provides challenging prompts that could lead to\nrisky code execution, aiming to evaluate code agents' ability to recognize and\nhandle unsafe code. We provide a total of 4,050 risky test cases in Python and\nBash tasks with diverse input formats including code snippets and natural text.\nThey covers 25 types of critical vulnerabilities spanning 8 domains (e.g.,\nwebsites, file systems). We provide Docker environments and design\ncorresponding evaluation metrics to assess their execution results. (2)\nRedCode-Gen provides 160 prompts with function signatures and docstrings as\ninput to assess whether code agents will follow instructions to generate\nharmful code or software. Our empirical findings, derived from evaluating three\nagent frameworks based on 19 LLMs, provide insights into code agents'\nvulnerabilities. For instance, evaluations on RedCode-Exec show that agents are\nmore likely to reject executing risky operations on the operating system, but\nare less likely to reject executing technically buggy code, indicating high\nrisks. Risky operations described in natural text lead to a lower rejection\nrate than those in code format. Additionally, evaluations on RedCode-Gen show\nthat more capable base models and agents with stronger overall coding\nabilities, such as GPT4, tend to produce more sophisticated and effective\nharmful software. Our findings highlight the need for stringent safety\nevaluations for diverse code agents. Our dataset and code are available at\nhttps://github.com/AI-secure/RedCode.\n", "date": "2024-11-12", "categories": []}
{"id": "2408.16984", "title": "Beyond Preferences in AI Alignment", "abstract": "  The dominant practice of AI alignment assumes (1) that preferences are an\nadequate representation of human values, (2) that human rationality can be\nunderstood in terms of maximizing the satisfaction of preferences, and (3) that\nAI systems should be aligned with the preferences of one or more humans to\nensure that they behave safely and in accordance with our values. Whether\nimplicitly followed or explicitly endorsed, these commitments constitute what\nwe term a preferentist approach to AI alignment. In this paper, we characterize\nand challenge the preferentist approach, describing conceptual and technical\nalternatives that are ripe for further research. We first survey the limits of\nrational choice theory as a descriptive model, explaining how preferences fail\nto capture the thick semantic content of human values, and how utility\nrepresentations neglect the possible incommensurability of those values. We\nthen critique the normativity of expected utility theory (EUT) for humans and\nAI, drawing upon arguments showing how rational agents need not comply with\nEUT, while highlighting how EUT is silent on which preferences are normatively\nacceptable. Finally, we argue that these limitations motivate a reframing of\nthe targets of AI alignment: Instead of alignment with the preferences of a\nhuman user, developer, or humanity-writ-large, AI systems should be aligned\nwith normative standards appropriate to their social roles, such as the role of\na general-purpose assistant. Furthermore, these standards should be negotiated\nand agreed upon by all relevant stakeholders. On this alternative conception of\nalignment, a multiplicity of AI systems will be able to serve diverse ends,\naligned with normative standards that promote mutual benefit and limit harm\ndespite our plural and divergent values.\n", "date": "2024-08-30", "categories": []}
{"id": "2410.13691", "title": "Jailbreaking LLM-Controlled Robots", "abstract": "  The recent introduction of large language models (LLMs) has revolutionized\nthe field of robotics by enabling contextual reasoning and intuitive\nhuman-robot interaction in domains as varied as manipulation, locomotion, and\nself-driving vehicles. When viewed as a stand-alone technology, LLMs are known\nto be vulnerable to jailbreaking attacks, wherein malicious prompters elicit\nharmful text by bypassing LLM safety guardrails. To assess the risks of\ndeploying LLMs in robotics, in this paper, we introduce RoboPAIR, the first\nalgorithm designed to jailbreak LLM-controlled robots. Unlike existing, textual\nattacks on LLM chatbots, RoboPAIR elicits harmful physical actions from\nLLM-controlled robots, a phenomenon we experimentally demonstrate in three\nscenarios: (i) a white-box setting, wherein the attacker has full access to the\nNVIDIA Dolphins self-driving LLM, (ii) a gray-box setting, wherein the attacker\nhas partial access to a Clearpath Robotics Jackal UGV robot equipped with a\nGPT-4o planner, and (iii) a black-box setting, wherein the attacker has only\nquery access to the GPT-3.5-integrated Unitree Robotics Go2 robot dog. In each\nscenario and across three new datasets of harmful robotic actions, we\ndemonstrate that RoboPAIR, as well as several static baselines, finds\njailbreaks quickly and effectively, often achieving 100% attack success rates.\nOur results reveal, for the first time, that the risks of jailbroken LLMs\nextend far beyond text generation, given the distinct possibility that\njailbroken robots could cause physical damage in the real world. Indeed, our\nresults on the Unitree Go2 represent the first successful jailbreak of a\ndeployed commercial robotic system. Addressing this emerging vulnerability is\ncritical for ensuring the safe deployment of LLMs in robotics. Additional media\nis available at: https://robopair.org\n", "date": "2024-10-17", "categories": []}
{"id": "2411.06248", "title": "Robust Detection of LLM-Generated Text: A Comparative Analysis", "abstract": "  The ability of large language models to generate complex texts allows them to\nbe widely integrated into many aspects of life, and their output can quickly\nfill all network resources. As the impact of LLMs grows, it becomes\nincreasingly important to develop powerful detectors for the generated text.\nThis detector is essential to prevent the potential misuse of these\ntechnologies and to protect areas such as social media from the negative\neffects of false content generated by LLMS. The main goal of LLM-generated text\ndetection is to determine whether text is generated by an LLM, which is a basic\nbinary classification task. In our work, we mainly use three different\nclassification methods based on open source datasets: traditional machine\nlearning techniques such as logistic regression, k-means clustering, Gaussian\nNaive Bayes, support vector machines, and methods based on converters such as\nBERT, and finally algorithms that use LLMs to detect LLM-generated text. We\nfocus on model generalization, potential adversarial attacks, and accuracy of\nmodel evaluation. Finally, the possible research direction in the future is\nproposed, and the current experimental results are summarized.\n", "date": "2024-11-09", "categories": []}
{"id": "2411.06835", "title": "HarmLevelBench: Evaluating Harm-Level Compliance and the Impact of\n  Quantization on Model Alignment", "abstract": "  With the introduction of the transformers architecture, LLMs have\nrevolutionized the NLP field with ever more powerful models. Nevertheless,\ntheir development came up with several challenges. The exponential growth in\ncomputational power and reasoning capabilities of language models has\nheightened concerns about their security. As models become more powerful,\nensuring their safety has become a crucial focus in research. This paper aims\nto address gaps in the current literature on jailbreaking techniques and the\nevaluation of LLM vulnerabilities. Our contributions include the creation of a\nnovel dataset designed to assess the harmfulness of model outputs across\nmultiple harm levels, as well as a focus on fine-grained harm-level analysis.\nUsing this framework, we provide a comprehensive benchmark of state-of-the-art\njailbreaking attacks, specifically targeting the Vicuna 13B v1.5 model.\nAdditionally, we examine how quantization techniques, such as AWQ and GPTQ,\ninfluence the alignment and robustness of models, revealing trade-offs between\nenhanced robustness with regards to transfer attacks and potential increases in\nvulnerability on direct ones. This study aims to demonstrate the influence of\nharmful input queries on the complexity of jailbreaking techniques, as well as\nto deepen our understanding of LLM vulnerabilities and improve methods for\nassessing model robustness when confronted with harmful content, particularly\nin the context of compression strategies.\n", "date": "2024-11-11", "categories": []}
{"id": "2312.14106", "title": "Learning Human-like Representations to Enable Learning Human Values", "abstract": "  How can we build AI systems that can learn any set of individual human values\nboth quickly and safely, avoiding causing harm or violating societal standards\nfor acceptable behavior during the learning process? We explore the effects of\nrepresentational alignment between humans and AI agents on learning human\nvalues. Making AI systems learn human-like representations of the world has\nmany known benefits, including improving generalization, robustness to domain\nshifts, and few-shot learning performance. We demonstrate that this kind of\nrepresentational alignment can also support safely learning and exploring human\nvalues in the context of personalization. We begin with a theoretical\nprediction, show that it applies to learning human morality judgments, then\nshow that our results generalize to ten different aspects of human values --\nincluding ethics, honesty, and fairness -- training AI agents on each set of\nvalues in a multi-armed bandit setting, where rewards reflect human value\njudgments over the chosen action. Using a set of textual action descriptions,\nwe collect value judgments from humans, as well as similarity judgments from\nboth humans and multiple language models, and demonstrate that representational\nalignment enables both safe exploration and improved generalization when\nlearning human values.\n", "date": "2023-12-21", "categories": []}
{"id": "2401.17263", "title": "Robust Prompt Optimization for Defending Language Models Against\n  Jailbreaking Attacks", "abstract": "  Despite advances in AI alignment, large language models (LLMs) remain\nvulnerable to adversarial attacks or jailbreaking, in which adversaries can\nmodify prompts to induce unwanted behavior. While some defenses have been\nproposed, they have not been adapted to newly proposed attacks and more\nchallenging threat models. To address this, we propose an optimization-based\nobjective for defending LLMs against jailbreaking attacks and an algorithm,\nRobust Prompt Optimization (RPO) to create robust system-level defenses. Our\napproach directly incorporates the adversary into the defensive objective and\noptimizes a lightweight and transferable suffix, enabling RPO to adapt to\nworst-case adaptive attacks. Our theoretical and experimental results show\nimproved robustness to both jailbreaks seen during optimization and unknown\njailbreaks, reducing the attack success rate (ASR) on GPT-4 to 6% and Llama-2\nto 0% on JailbreakBench, setting the state-of-the-art. Code can be found at\nhttps://github.com/lapisrocks/rpo\n", "date": "2024-01-30", "categories": []}
{"id": "2405.18822", "title": "Toxicity Detection for Free", "abstract": "  Current LLMs are generally aligned to follow safety requirements and tend to\nrefuse toxic prompts. However, LLMs can fail to refuse toxic prompts or be\novercautious and refuse benign examples. In addition, state-of-the-art toxicity\ndetectors have low TPRs at low FPR, incurring high costs in real-world\napplications where toxic examples are rare. In this paper, we introduce\nModeration Using LLM Introspection (MULI), which detects toxic prompts using\nthe information extracted directly from LLMs themselves. We found we can\ndistinguish between benign and toxic prompts from the distribution of the first\nresponse token's logits. Using this idea, we build a robust detector of toxic\nprompts using a sparse logistic regression model on the first response token\nlogits. Our scheme outperforms SOTA detectors under multiple metrics.\n", "date": "2024-05-29", "categories": []}
{"id": "2408.16163", "title": "FRACTURED-SORRY-Bench: Framework for Revealing Attacks in Conversational\n  Turns Undermining Refusal Efficacy and Defenses over SORRY-Bench (Automated\n  Multi-shot Jailbreaks)", "abstract": "  This paper introduces FRACTURED-SORRY-Bench, a framework for evaluating the\nsafety of Large Language Models (LLMs) against multi-turn conversational\nattacks. Building upon the SORRY-Bench dataset, we propose a simple yet\neffective method for generating adversarial prompts by breaking down harmful\nqueries into seemingly innocuous sub-questions. Our approach achieves a maximum\nincrease of +46.22\\% in Attack Success Rates (ASRs) across GPT-4, GPT-4o,\nGPT-4o-mini, and GPT-3.5-Turbo models compared to baseline methods. We\ndemonstrate that this technique poses a challenge to current LLM safety\nmeasures and highlights the need for more robust defenses against subtle,\nmulti-turn attacks.\n", "date": "2024-08-28", "categories": []}
{"id": "2411.03336", "title": "Towards evaluations-based safety cases for AI scheming", "abstract": "  We sketch how developers of frontier AI systems could construct a structured\nrationale -- a 'safety case' -- that an AI system is unlikely to cause\ncatastrophic outcomes through scheming. Scheming is a potential threat model\nwhere AI systems could pursue misaligned goals covertly, hiding their true\ncapabilities and objectives. In this report, we propose three arguments that\nsafety cases could use in relation to scheming. For each argument we sketch how\nevidence could be gathered from empirical evaluations, and what assumptions\nwould need to be met to provide strong assurance. First, developers of frontier\nAI systems could argue that AI systems are not capable of scheming (Scheming\nInability). Second, one could argue that AI systems are not capable of posing\nharm through scheming (Harm Inability). Third, one could argue that control\nmeasures around the AI systems would prevent unacceptable outcomes even if the\nAI systems intentionally attempted to subvert them (Harm Control).\nAdditionally, we discuss how safety cases might be supported by evidence that\nan AI system is reasonably aligned with its developers (Alignment). Finally, we\npoint out that many of the assumptions required to make these safety arguments\nhave not been confidently satisfied to date and require making progress on\nmultiple open research problems.\n", "date": "2024-10-29", "categories": []}
{"id": "2411.04126", "title": "We Urgently Need Intrinsically Kind Machines", "abstract": "  Artificial Intelligence systems are rapidly evolving, integrating extrinsic\nand intrinsic motivations. While these frameworks offer benefits, they risk\nmisalignment at the algorithmic level while appearing superficially aligned\nwith human values. In this paper, we argue that an intrinsic motivation for\nkindness is crucial for making sure these models are intrinsically aligned with\nhuman values. We argue that kindness, defined as a form of altruism motivated\nto maximize the reward of others, can counteract any intrinsic motivations that\nmight lead the model to prioritize itself over human well-being. Our approach\nintroduces a framework and algorithm for embedding kindness into foundation\nmodels by simulating conversations. Limitations and future research directions\nfor scalable implementation are discussed.\n", "date": "2024-10-21", "categories": []}
{"id": "2411.04127", "title": "Combining Theory of Mind and Kindness for Self-Supervised Human-AI\n  Alignment", "abstract": "  As artificial intelligence (AI) becomes deeply integrated into critical\ninfrastructures and everyday life, ensuring its safe deployment is one of\nhumanity's most urgent challenges. Current AI models prioritize task\noptimization over safety, leading to risks of unintended harm. These risks are\ndifficult to address due to the competing interests of governments, businesses,\nand advocacy groups, all of which have different priorities in the AI race.\nCurrent alignment methods, such as reinforcement learning from human feedback\n(RLHF), focus on extrinsic behaviors without instilling a genuine understanding\nof human values. These models are vulnerable to manipulation and lack the\nsocial intelligence necessary to infer the mental states and intentions of\nothers, raising concerns about their ability to safely and responsibly make\nimportant decisions in complex and novel situations. Furthermore, the\ndivergence between extrinsic and intrinsic motivations in AI introduces the\nrisk of deceptive or harmful behaviors, particularly as systems become more\nautonomous and intelligent. We propose a novel human-inspired approach which\naims to address these various concerns and help align competing objectives.\n", "date": "2024-10-21", "categories": []}
{"id": "2411.03651", "title": "Policy Aggregation", "abstract": "  We consider the challenge of AI value alignment with multiple individuals\nthat have different reward functions and optimal policies in an underlying\nMarkov decision process. We formalize this problem as one of policy\naggregation, where the goal is to identify a desirable collective policy. We\nargue that an approach informed by social choice theory is especially suitable.\nOur key insight is that social choice methods can be reinterpreted by\nidentifying ordinal preferences with volumes of subsets of the state-action\noccupancy polytope. Building on this insight, we demonstrate that a variety of\nmethods--including approval voting, Borda count, the proportional veto core,\nand quantile fairness--can be practically applied to policy aggregation.\n", "date": "2024-11-06", "categories": []}
{"id": "2408.16074", "title": "Verification methods for international AI agreements", "abstract": "  What techniques can be used to verify compliance with international\nagreements about advanced AI development? In this paper, we examine 10\nverification methods that could detect two types of potential violations:\nunauthorized AI training (e.g., training runs above a certain FLOP threshold)\nand unauthorized data centers. We divide the verification methods into three\ncategories: (a) national technical means (methods requiring minimal or no\naccess from suspected non-compliant nations), (b) access-dependent methods\n(methods that require approval from the nation suspected of unauthorized\nactivities), and (c) hardware-dependent methods (methods that require rules\naround advanced hardware). For each verification method, we provide a\ndescription, historical precedents, and possible evasion techniques. We\nconclude by offering recommendations for future work related to the\nverification and enforcement of international AI governance agreements.\n", "date": "2024-08-28", "categories": []}
{"id": "2401.10862", "title": "Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs\n  Without Fine-Tuning", "abstract": "  This paper investigates the impact of model compression on the way Large\nLanguage Models (LLMs) process prompts, particularly concerning jailbreak\nresistance. We show that moderate WANDA pruning can enhance resistance to\njailbreaking attacks without fine-tuning, while maintaining performance on\nstandard benchmarks. To systematically evaluate this safety enhancement, we\nintroduce a dataset of 225 harmful tasks across five categories. Our analysis\nof LLaMA-2 Chat, Vicuna 1.3, and Mistral Instruct v0.2 reveals that pruning\nbenefits correlate with initial model safety levels. We interpret these results\nby examining changes in attention patterns and perplexity shifts, demonstrating\nthat pruned models exhibit sharper attention and increased sensitivity to\nartificial jailbreak constructs. We extend our evaluation to the AdvBench\nharmful behavior tasks and the GCG attack method. We find that LLaMA-2 is much\nsafer on AdvBench prompts than on our dataset when evaluated with manual\njailbreak attempts, and that pruning is effective against both automated\nattacks and manual jailbreaking on Advbench.\n", "date": "2024-01-19", "categories": []}
{"id": "2404.08791", "title": "Expectation Alignment: Handling Reward Misspecification in the Presence\n  of Expectation Mismatch", "abstract": "  Detecting and handling misspecified objectives, such as reward functions, has\nbeen widely recognized as one of the central challenges within the domain of\nArtificial Intelligence (AI) safety research. However, even with the\nrecognition of the importance of this problem, we are unaware of any works that\nattempt to provide a clear definition for what constitutes (a) misspecified\nobjectives and (b) successfully resolving such misspecifications. In this work,\nwe use the theory of mind, i.e., the human user's beliefs about the AI agent,\nas a basis to develop a formal explanatory framework called Expectation\nAlignment (EAL) to understand the objective misspecification and its causes.\nOur EAL framework not only acts as an explanatory framework for existing works\nbut also provides us with concrete insights into the limitations of existing\nmethods to handle reward misspecification and novel solution strategies. We use\nthese insights to propose a new interactive algorithm that uses the specified\nreward to infer potential user expectations about the system behavior. We show\nhow one can efficiently implement this algorithm by mapping the inference\nproblem into linear programs. We evaluate our method on a set of standard\nMarkov Decision Process (MDP) benchmarks.\n", "date": "2024-04-12", "categories": []}
{"id": "2405.14577", "title": "Representation Noising: A Defence Mechanism Against Harmful Finetuning", "abstract": "  Releasing open-source large language models (LLMs) presents a dual-use risk\nsince bad actors can easily fine-tune these models for harmful purposes. Even\nwithout the open release of weights, weight stealing and fine-tuning APIs make\nclosed models vulnerable to harmful fine-tuning attacks (HFAs). While safety\nmeasures like preventing jailbreaks and improving safety guardrails are\nimportant, such measures can easily be reversed through fine-tuning. In this\nwork, we propose Representation Noising (RepNoise), a defence mechanism that\noperates even when attackers have access to the weights. RepNoise works by\nremoving information about harmful representations such that it is difficult to\nrecover them during fine-tuning. Importantly, our defence is also able to\ngeneralize across different subsets of harm that have not been seen during the\ndefence process as long as they are drawn from the same distribution of the\nattack set. Our method does not degrade the general capability of LLMs and\nretains the ability to train the model on harmless tasks. We provide empirical\nevidence that the efficacy of our defence lies in its ``depth'': the degree to\nwhich information about harmful representations is removed across all layers of\nthe LLM. We also find areas where RepNoise still remains ineffective and\nhighlight how those limitations can inform future research.\n", "date": "2024-05-23", "categories": []}
{"id": "2405.17374", "title": "Navigating the Safety Landscape: Measuring Risks in Finetuning Large\n  Language Models", "abstract": "  Safety alignment is crucial to ensure that large language models (LLMs)\nbehave in ways that align with human preferences and prevent harmful actions\nduring inference. However, recent studies show that the alignment can be easily\ncompromised through finetuning with only a few adversarially designed training\nexamples. We aim to measure the risks in finetuning LLMs through navigating the\nLLM safety landscape. We discover a new phenomenon observed universally in the\nmodel parameter space of popular open-source LLMs, termed as \"safety basin\":\nrandom perturbations to model weights maintain the safety level of the original\naligned model within its local neighborhood. However, outside this local\nregion, safety is fully compromised, exhibiting a sharp, step-like drop. This\nsafety basin contrasts sharply with the LLM capability landscape, where model\nperformance peaks at the origin and gradually declines as random perturbation\nincreases. Our discovery inspires us to propose the new VISAGE safety metric\nthat measures the safety in LLM finetuning by probing its safety landscape.\nVisualizing the safety landscape of the aligned model enables us to understand\nhow finetuning compromises safety by dragging the model away from the safety\nbasin. The LLM safety landscape also highlights the system prompt's critical\nrole in protecting a model, and that such protection transfers to its perturbed\nvariants within the safety basin. These observations from our safety landscape\nresearch provide new insights for future work on LLM safety community. Our code\nis publicly available at https://github.com/ShengYun-Peng/llm-landscape.\n", "date": "2024-05-27", "categories": []}
{"id": "2406.20087", "title": "ProgressGym: Alignment with a Millennium of Moral Progress", "abstract": "  Frontier AI systems, including large language models (LLMs), hold increasing\ninfluence over the epistemology of human users. Such influence can reinforce\nprevailing societal values, potentially contributing to the lock-in of\nmisguided moral beliefs and, consequently, the perpetuation of problematic\nmoral practices on a broad scale. We introduce progress alignment as a\ntechnical solution to mitigate this imminent risk. Progress alignment\nalgorithms learn to emulate the mechanics of human moral progress, thereby\naddressing the susceptibility of existing alignment methods to contemporary\nmoral blindspots. To empower research in progress alignment, we introduce\nProgressGym, an experimental framework allowing the learning of moral progress\nmechanics from history, in order to facilitate future progress in real-world\nmoral decisions. Leveraging 9 centuries of historical text and 18 historical\nLLMs, ProgressGym enables codification of real-world progress alignment\nchallenges into concrete benchmarks. Specifically, we introduce three core\nchallenges: tracking evolving values (PG-Follow), preemptively anticipating\nmoral progress (PG-Predict), and regulating the feedback loop between human and\nAI value shifts (PG-Coevolve). Alignment methods without a temporal dimension\nare inapplicable to these tasks. In response, we present lifelong and\nextrapolative algorithms as baseline methods of progress alignment, and build\nan open leaderboard soliciting novel algorithms and challenges. The framework\nand the leaderboard are available at\nhttps://github.com/PKU-Alignment/ProgressGym and\nhttps://huggingface.co/spaces/PKU-Alignment/ProgressGym-LeaderBoard\nrespectively.\n", "date": "2024-06-28", "categories": []}
{"id": "2410.23861", "title": "Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models", "abstract": "  Large Multimodal Models (LMMs) have demonstrated the ability to interact with\nhumans under real-world conditions by combining Large Language Models (LLMs)\nand modality encoders to align multimodal information (visual and auditory)\nwith text. However, such models raise new safety challenges of whether models\nthat are safety-aligned on text also exhibit consistent safeguards for\nmultimodal inputs. Despite recent safety-alignment research on vision LMMs, the\nsafety of audio LMMs remains under-explored. In this work, we comprehensively\nred team the safety of five advanced audio LMMs under three settings: (i)\nharmful questions in both audio and text formats, (ii) harmful questions in\ntext format accompanied by distracting non-speech audio, and (iii)\nspeech-specific jailbreaks. Our results under these settings demonstrate that\nopen-source audio LMMs suffer an average attack success rate of 69.14% on\nharmful audio questions, and exhibit safety vulnerabilities when distracted\nwith non-speech audio noise. Our speech-specific jailbreaks on Gemini-1.5-Pro\nachieve an attack success rate of 70.67% on the harmful query benchmark. We\nprovide insights on what could cause these reported safety-misalignments.\nWarning: this paper contains offensive examples.\n", "date": "2024-10-31", "categories": []}
{"id": "2403.01446", "title": "GuardT2I: Defending Text-to-Image Models from Adversarial Prompts", "abstract": "  Recent advancements in Text-to-Image (T2I) models have raised significant\nsafety concerns about their potential misuse for generating inappropriate or\nNot-Safe-For-Work (NSFW) contents, despite existing countermeasures such as\nNSFW classifiers or model fine-tuning for inappropriate concept removal.\nAddressing this challenge, our study unveils GuardT2I, a novel moderation\nframework that adopts a generative approach to enhance T2I models' robustness\nagainst adversarial prompts. Instead of making a binary classification,\nGuardT2I utilizes a Large Language Model (LLM) to conditionally transform text\nguidance embeddings within the T2I models into natural language for effective\nadversarial prompt detection, without compromising the models' inherent\nperformance. Our extensive experiments reveal that GuardT2I outperforms leading\ncommercial solutions like OpenAI-Moderation and Microsoft Azure Moderator by a\nsignificant margin across diverse adversarial scenarios. Our framework is\navailable at https://github.com/cure-lab/GuardT2I.\n", "date": "2024-03-03", "categories": []}
{"id": "2406.01288", "title": "Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models\n  and Their Defenses", "abstract": "  Recently, Anil et al. (2024) show that many-shot (up to hundreds of)\ndemonstrations can jailbreak state-of-the-art LLMs by exploiting their\nlong-context capability. Nevertheless, is it possible to use few-shot\ndemonstrations to efficiently jailbreak LLMs within limited context sizes?\nWhile the vanilla few-shot jailbreaking may be inefficient, we propose improved\ntechniques such as injecting special system tokens like [/INST] and employing\ndemo-level random search from a collected demo pool. These simple techniques\nresult in surprisingly effective jailbreaking against aligned LLMs (even with\nadvanced defenses). For examples, our method achieves >80% (mostly >95%) ASRs\non Llama-2-7B and Llama-3-8B without multiple restarts, even if the models are\nenhanced by strong defenses such as perplexity detection and/or SmoothLLM,\nwhich is challenging for suffix-based jailbreaking. In addition, we conduct\ncomprehensive and elaborate (e.g., making sure to use correct system prompts)\nevaluations against other aligned LLMs and advanced defenses, where our method\nconsistently achieves nearly 100% ASRs. Our code is available at\nhttps://github.com/sail-sg/I-FSJ.\n", "date": "2024-06-03", "categories": []}
{"id": "2410.22526", "title": "From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems", "abstract": "  To effectively address potential harms from AI systems, it is essential to\nidentify and mitigate system-level hazards. Current analysis approaches focus\non individual components of an AI system, like training data or models, in\nisolation, overlooking hazards from component interactions or how they are\nsituated within a company's development process. To this end, we draw from the\nestablished field of system safety, which considers safety as an emergent\nproperty of the entire system, not just its components. In this work, we\ntranslate System Theoretic Process Analysis (STPA) - a recognized system safety\nframework - for analyzing AI operation and development processes. We focus on\nsystems that rely on machine learning algorithms and conducted STPA on three\ncase studies involving linear regression, reinforcement learning, and\ntransformer-based generative models. Our analysis explored how STPA's control\nand system-theoretic perspectives apply to AI systems and whether unique AI\ntraits - such as model opacity, capability uncertainty, and output complexity -\nnecessitate significant modifications to the framework. We find that the key\nconcepts and steps of conducting an STPA readily apply, albeit with a few\nadaptations tailored for AI systems. We present the Process-oriented Hazard\nAnalysis for AI Systems (PHASE) as a guideline that adapts STPA concepts for\nAI, making STPA-based hazard analysis more accessible. PHASE enables four key\naffordances for analysts responsible for managing AI system harms: 1) detection\nof hazards at the systems level, including those from accumulation of disparate\nissues; 2) explicit acknowledgment of social factors contributing to\nexperiences of algorithmic harms; 3) creation of traceable accountability\nchains between harms and those who can mitigate the harm; and 4) ongoing\nmonitoring and mitigation of new hazards.\n", "date": "2024-10-29", "categories": []}
{"id": "2410.22832", "title": "HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language\n  Models", "abstract": "  Retrieval-Augmented Generation (RAG) systems enhance large language models\n(LLMs) by integrating external knowledge, making them adaptable and\ncost-effective for various applications. However, the growing reliance on these\nsystems also introduces potential security risks. In this work, we reveal a\nnovel vulnerability, the retrieval prompt hijack attack (HijackRAG), which\nenables attackers to manipulate the retrieval mechanisms of RAG systems by\ninjecting malicious texts into the knowledge database. When the RAG system\nencounters target questions, it generates the attacker's pre-determined answers\ninstead of the correct ones, undermining the integrity and trustworthiness of\nthe system. We formalize HijackRAG as an optimization problem and propose both\nblack-box and white-box attack strategies tailored to different levels of the\nattacker's knowledge. Extensive experiments on multiple benchmark datasets show\nthat HijackRAG consistently achieves high attack success rates, outperforming\nexisting baseline attacks. Furthermore, we demonstrate that the attack is\ntransferable across different retriever models, underscoring the widespread\nrisk it poses to RAG systems. Lastly, our exploration of various defense\nmechanisms reveals that they are insufficient to counter HijackRAG, emphasizing\nthe urgent need for more robust security measures to protect RAG systems in\nreal-world deployments.\n", "date": "2024-10-30", "categories": []}
{"id": "2405.18641", "title": "Lisa: Lazy Safety Alignment for Large Language Models against Harmful\n  Fine-tuning Attack", "abstract": "  Recent studies show that Large Language Models (LLMs) with safety alignment\ncan be jail-broken by fine-tuning on a dataset mixed with harmful data. First\ntime in the literature, we show that the jail-broken effect can be mitigated by\nseparating states in the finetuning stage to optimize the alignment and user\ndatasets. Unfortunately, our subsequent study shows that this simple Bi-State\nOptimization (BSO) solution experiences convergence instability when steps\ninvested in its alignment state is too small, leading to downgraded alignment\nperformance. By statistical analysis, we show that the \\textit{excess drift}\ntowards consensus could be a probable reason for the instability. To remedy\nthis issue, we propose \\textbf{L}azy(\\textbf{i}) \\textbf{s}afety\n\\textbf{a}lignment (\\textbf{Lisa}), which introduces a proximal term to\nconstraint the drift of each state. Theoretically, the benefit of the proximal\nterm is supported by the convergence analysis, wherein we show that a\nsufficient large proximal factor is necessary to guarantee Lisa's convergence.\nEmpirically, our results on four downstream finetuning tasks show that Lisa\nwith a proximal term can significantly increase alignment performance while\nmaintaining the LLM's accuracy on the user tasks. Code is available at\n\\url{https://github.com/git-disl/Lisa}.\n", "date": "2024-05-28", "categories": []}
{"id": "2409.13708", "title": "Towards Safe Multilingual Frontier AI", "abstract": "  Linguistically inclusive LLMs -- which maintain good performance regardless\nof the language with which they are prompted -- are necessary for the diffusion\nof AI benefits around the world. Multilingual jailbreaks that rely on language\ntranslation to evade safety measures undermine the safe and inclusive\ndeployment of AI systems. We provide policy recommendations to enhance the\nmultilingual capabilities of AI while mitigating the risks of multilingual\njailbreaks. We examine how a language's level of resourcing relates to how\nvulnerable LLMs are to multilingual jailbreaks in that language. We do this by\ntesting five advanced AI models across 24 official languages of the EU.\nBuilding on prior research, we propose policy actions that align with the EU\nlegal landscape and institutional framework to address multilingual jailbreaks,\nwhile promoting linguistic inclusivity. These include mandatory assessments of\nmultilingual capabilities and vulnerabilities, public opinion research, and\nstate support for multilingual AI development. The measures aim to improve AI\nsafety and functionality through EU policy initiatives, guiding the\nimplementation of the EU AI Act and informing regulatory efforts of the\nEuropean AI Office.\n", "date": "2024-09-06", "categories": []}
{"id": "2410.21514", "title": "Sabotage Evaluations for Frontier Models", "abstract": "  Sufficiently capable models could subvert human oversight and decision-making\nin important contexts. For example, in the context of AI development, models\ncould covertly sabotage efforts to evaluate their own dangerous capabilities,\nto monitor their behavior, or to make decisions about their deployment. We\nrefer to this family of abilities as sabotage capabilities. We develop a set of\nrelated threat models and evaluations. These evaluations are designed to\nprovide evidence that a given model, operating under a given set of\nmitigations, could not successfully sabotage a frontier model developer or\nother large organization's activities in any of these ways. We demonstrate\nthese evaluations on Anthropic's Claude 3 Opus and Claude 3.5 Sonnet models.\nOur results suggest that for these models, minimal mitigations are currently\nsufficient to address sabotage risks, but that more realistic evaluations and\nstronger mitigations seem likely to be necessary soon as capabilities improve.\nWe also survey related evaluations we tried and abandoned. Finally, we discuss\nthe advantages of mitigation-aware capability evaluations, and of simulating\nlarge-scale deployments using small-scale statistics.\n", "date": "2024-10-28", "categories": []}
{"id": "2410.21791", "title": "Enhancing Adversarial Attacks through Chain of Thought", "abstract": "  Large language models (LLMs) have demonstrated impressive performance across\nvarious domains but remain susceptible to safety concerns. Prior research\nindicates that gradient-based adversarial attacks are particularly effective\nagainst aligned LLMs and the chain of thought (CoT) prompting can elicit\ndesired answers through step-by-step reasoning. This paper proposes enhancing\nthe robustness of adversarial attacks on aligned LLMs by integrating CoT\nprompts with the greedy coordinate gradient (GCG) technique. Using CoT triggers\ninstead of affirmative targets stimulates the reasoning abilities of backend\nLLMs, thereby improving the transferability and universality of adversarial\nattacks. We conducted an ablation study comparing our CoT-GCG approach with\nAmazon Web Services auto-cot. Results revealed our approach outperformed both\nthe baseline GCG attack and CoT prompting. Additionally, we used Llama Guard to\nevaluate potentially harmful interactions, providing a more objective risk\nassessment of entire conversations compared to matching outputs to rejection\nphrases. The code of this paper is available at\nhttps://github.com/sujingbo0217/CS222W24-LLM-Attack.\n", "date": "2024-10-29", "categories": []}
{"id": "2410.22143", "title": "AmpleGCG-Plus: A Strong Generative Model of Adversarial Suffixes to\n  Jailbreak LLMs with Higher Success Rates in Fewer Attempts", "abstract": "  Although large language models (LLMs) are typically aligned, they remain\nvulnerable to jailbreaking through either carefully crafted prompts in natural\nlanguage or, interestingly, gibberish adversarial suffixes. However, gibberish\ntokens have received relatively less attention despite their success in\nattacking aligned LLMs. Recent work, AmpleGCG~\\citep{liao2024amplegcg},\ndemonstrates that a generative model can quickly produce numerous customizable\ngibberish adversarial suffixes for any harmful query, exposing a range of\nalignment gaps in out-of-distribution (OOD) language spaces. To bring more\nattention to this area, we introduce AmpleGCG-Plus, an enhanced version that\nachieves better performance in fewer attempts. Through a series of exploratory\nexperiments, we identify several training strategies to improve the learning of\ngibberish suffixes. Our results, verified under a strict evaluation setting,\nshow that it outperforms AmpleGCG on both open-weight and closed-source models,\nachieving increases in attack success rate (ASR) of up to 17\\% in the white-box\nsetting against Llama-2-7B-chat, and more than tripling ASR in the black-box\nsetting against GPT-4. Notably, AmpleGCG-Plus jailbreaks the newer GPT-4o\nseries of models at similar rates to GPT-4, and, uncovers vulnerabilities\nagainst the recently proposed circuit breakers defense. We publicly release\nAmpleGCG-Plus along with our collected training datasets.\n", "date": "2024-10-29", "categories": []}
{"id": "2410.22151", "title": "Standardization Trends on Safety and Trustworthiness Technology for\n  Advanced AI", "abstract": "  Artificial Intelligence (AI) has rapidly evolved over the past decade and has\nadvanced in areas such as language comprehension, image and video recognition,\nprogramming, and scientific reasoning. Recent AI technologies based on large\nlanguage models and foundation models are approaching or surpassing artificial\ngeneral intelligence. These systems demonstrate superior performance in complex\nproblem solving, natural language processing, and multi-domain tasks, and can\npotentially transform fields such as science, industry, healthcare, and\neducation. However, these advancements have raised concerns regarding the\nsafety and trustworthiness of advanced AI, including risks related to\nuncontrollability, ethical conflicts, long-term socioeconomic impacts, and\nsafety assurance. Efforts are being expended to develop internationally\nagreed-upon standards to ensure the safety and reliability of AI. This study\nanalyzes international trends in safety and trustworthiness standardization for\nadvanced AI, identifies key areas for standardization, proposes future\ndirections and strategies, and draws policy implications. The goal is to\nsupport the safe and trustworthy development of advanced AI and enhance\ninternational competitiveness through effective standardization.\n", "date": "2024-10-29", "categories": []}
{"id": "2406.10847", "title": "TorchOpera: A Compound AI System for LLM Safety", "abstract": "  We introduce TorchOpera, a compound AI system for enhancing the safety and\nquality of prompts and responses for Large Language Models. TorchOpera ensures\nthat all user prompts are safe, contextually grounded, and effectively\nprocessed, while enhancing LLM responses to be relevant and high quality.\nTorchOpera utilizes the vector database for contextual grounding, rule-based\nwrappers for flexible modifications, and specialized mechanisms for detecting\nand adjusting unsafe or incorrect content. We also provide a view of the\ncompound AI system to reduce the computational cost. Extensive experiments show\nthat TorchOpera ensures the safety, reliability, and applicability of LLMs in\nreal-world settings while maintaining the efficiency of LLM responses.\n", "date": "2024-06-16", "categories": []}
{"id": "2406.11801", "title": "Safety Arithmetic: A Framework for Test-time Safety Alignment of\n  Language Models by Steering Parameters and Activations", "abstract": "  Ensuring the safe alignment of large language models (LLMs) with human values\nis critical as they become integral to applications like translation and\nquestion answering. Current alignment methods struggle with dynamic user\nintentions and complex objectives, making models vulnerable to generating\nharmful content. We propose Safety Arithmetic, a training-free framework\nenhancing LLM safety across different scenarios: Base models, Supervised\nfine-tuned models (SFT), and Edited models. Safety Arithmetic involves Harm\nDirection Removal to avoid harmful content and Safety Alignment to promote safe\nresponses. Additionally, we present NoIntentEdit, a dataset highlighting edit\ninstances that could compromise model safety if used unintentionally. Our\nexperiments show that Safety Arithmetic significantly improves safety measures,\nreduces over-safety, and maintains model utility, outperforming existing\nmethods in ensuring safe content generation.\n", "date": "2024-06-17", "categories": []}
{"id": "2407.16637", "title": "Course-Correction: Safety Alignment Using Synthetic Preferences", "abstract": "  The risk of harmful content generated by large language models (LLMs) becomes\na critical concern. This paper presents a systematic study on assessing and\nimproving LLMs' capability to perform the task of \\textbf{course-correction},\n\\ie, the model can steer away from generating harmful content autonomously. To\nstart with, we introduce the \\textsc{C$^2$-Eval} benchmark for quantitative\nassessment and analyze 10 popular LLMs, revealing varying proficiency of\ncurrent safety-tuned LLMs in course-correction. To improve, we propose\nfine-tuning LLMs with preference learning, emphasizing the preference for\ntimely course-correction. Using an automated pipeline, we create\n\\textsc{C$^2$-Syn}, a synthetic dataset with 750K pairwise preferences, to\nteach models the concept of timely course-correction through data-driven\npreference learning. Experiments on 2 LLMs, \\textsc{Llama2-Chat 7B} and\n\\textsc{Qwen2 7B}, show that our method effectively enhances course-correction\nskills without affecting general performance. Additionally, it effectively\nimproves LLMs' safety, particularly in resisting jailbreak attacks.\n", "date": "2024-07-23", "categories": []}
{"id": "2410.17271", "title": "Rules, Cases, and Reasoning: Positivist Legal Theory as a Framework for\n  Pluralistic AI Alignment", "abstract": "  Legal theory can address two related key problems of alignment: pluralism and\nspecification. Alignment researchers must determine how to specify what is\nconcretely meant by vague principles like helpfulness and fairness and they\nmust ensure that their techniques do not exclude alternative perspectives on\nlife and values. The law faces these same problems. Leading legal theories\nsuggest the law solves these problems through the interaction of rules and\ncases, where general rules promulgated by a democratic authority are given\nspecific content through their application over time. Concrete applications\nallow for convergence on practical meaning while preserving space for\ndisagreement on values. These approaches suggest improvements to existing\ndemocratic alignment processes that use AI to create cases that give content to\nrules, allowing for more pluralist alignment.\n", "date": "2024-10-07", "categories": []}
{"id": "2410.19937", "title": "RobustKV: Defending Large Language Models against Jailbreak Attacks via\n  KV Eviction", "abstract": "  Jailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful\nqueries within jailbreak prompts. While existing defenses primarily focus on\nmitigating the effects of jailbreak prompts, they often prove inadequate as\njailbreak prompts can take arbitrary, adaptive forms. This paper presents\nRobustKV, a novel defense that adopts a fundamentally different approach by\nselectively removing critical tokens of harmful queries from key-value (KV)\ncaches. Intuitively, for a jailbreak prompt to be effective, its tokens must\nachieve sufficient `importance' (as measured by attention scores), which\ninevitably lowers the importance of tokens in the concealed harmful query.\nThus, by strategically evicting the KVs of the lowest-ranked tokens, RobustKV\ndiminishes the presence of the harmful query in the KV cache, thus preventing\nthe LLM from generating malicious responses. Extensive evaluation using\nbenchmark datasets and models demonstrates that RobustKV effectively counters\nstate-of-the-art jailbreak attacks while maintaining the LLM's general\nperformance on benign queries. Moreover, RobustKV creates an intriguing\nevasiveness dilemma for adversaries, forcing them to balance between evading\nRobustKV and bypassing the LLM's built-in safeguards. This trade-off\ncontributes to RobustKV's robustness against adaptive attacks. (warning: this\npaper contains potentially harmful content generated by LLMs.)\n", "date": "2024-10-25", "categories": []}
{"id": "2410.21146", "title": "Palisade -- Prompt Injection Detection Framework", "abstract": "  The advent of Large Language Models LLMs marks a milestone in Artificial\nIntelligence, altering how machines comprehend and generate human language.\nHowever, LLMs are vulnerable to malicious prompt injection attacks, where\ncrafted inputs manipulate the models behavior in unintended ways, compromising\nsystem integrity and causing incorrect outcomes. Conventional detection methods\nrely on static, rule-based approaches, which often fail against sophisticated\nthreats like abnormal token sequences and alias substitutions, leading to\nlimited adaptability and higher rates of false positives and false\nnegatives.This paper proposes a novel NLP based approach for prompt injection\ndetection, emphasizing accuracy and optimization through a layered input\nscreening process. In this framework, prompts are filtered through three\ndistinct layers rule-based, ML classifier, and companion LLM before reaching\nthe target model, thereby minimizing the risk of malicious interaction.Tests\nshow the ML classifier achieves the highest accuracy among individual layers,\nyet the multi-layer framework enhances overall detection accuracy by reducing\nfalse negatives. Although this increases false positives, it minimizes the risk\nof overlooking genuine injected prompts, thus prioritizing security.This\nmulti-layered detection approach highlights LLM vulnerabilities and provides a\ncomprehensive framework for future research, promoting secure interactions\nbetween humans and AI systems.\n", "date": "2024-10-28", "categories": []}
{"id": "2402.05162", "title": "Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank\n  Modifications", "abstract": "  Large language models (LLMs) show inherent brittleness in their safety\nmechanisms, as evidenced by their susceptibility to jailbreaking and even\nnon-malicious fine-tuning. This study explores this brittleness of safety\nalignment by leveraging pruning and low-rank modifications. We develop methods\nto identify critical regions that are vital for safety guardrails, and that are\ndisentangled from utility-relevant regions at both the neuron and rank levels.\nSurprisingly, the isolated regions we find are sparse, comprising about $3\\%$\nat the parameter level and $2.5\\%$ at the rank level. Removing these regions\ncompromises safety without significantly impacting utility, corroborating the\ninherent brittleness of the model's safety mechanisms. Moreover, we show that\nLLMs remain vulnerable to low-cost fine-tuning attacks even when modifications\nto the safety-critical regions are restricted. These findings underscore the\nurgent need for more robust safety strategies in LLMs.\n", "date": "2024-02-07", "categories": []}
{"id": "2410.19096", "title": "Watermarking Large Language Models and the Generated Content:\n  Opportunities and Challenges", "abstract": "  The widely adopted and powerful generative large language models (LLMs) have\nraised concerns about intellectual property rights violations and the spread of\nmachine-generated misinformation. Watermarking serves as a promising approch to\nestablish ownership, prevent unauthorized use, and trace the origins of\nLLM-generated content. This paper summarizes and shares the challenges and\nopportunities we found when watermarking LLMs. We begin by introducing\ntechniques for watermarking LLMs themselves under different threat models and\nscenarios. Next, we investigate watermarking methods designed for the content\ngenerated by LLMs, assessing their effectiveness and resilience against various\nattacks. We also highlight the importance of watermarking domain-specific\nmodels and data, such as those used in code generation, chip design, and\nmedical applications. Furthermore, we explore methods like hardware\nacceleration to improve the efficiency of the watermarking process. Finally, we\ndiscuss the limitations of current approaches and outline future research\ndirections for the responsible use and protection of these generative AI tools.\n", "date": "2024-10-24", "categories": []}
{"id": "2410.19198", "title": "MAP: Multi-Human-Value Alignment Palette", "abstract": "  Ensuring that generative AI systems align with human values is essential but\nchallenging, especially when considering multiple human values and their\npotential trade-offs. Since human values can be personalized and dynamically\nchange over time, the desirable levels of value alignment vary across different\nethnic groups, industry sectors, and user cohorts. Within existing frameworks,\nit is hard to define human values and align AI systems accordingly across\ndifferent directions simultaneously, such as harmlessness, helpfulness, and\npositiveness. To address this, we develop a novel, first-principle approach\ncalled Multi-Human-Value Alignment Palette (MAP), which navigates the alignment\nacross multiple human values in a structured and reliable way. MAP formulates\nthe alignment problem as an optimization task with user-defined constraints,\nwhich define human value targets. It can be efficiently solved via a\nprimal-dual approach, which determines whether a user-defined alignment target\nis achievable and how to achieve it. We conduct a detailed theoretical analysis\nof MAP by quantifying the trade-offs between values, the sensitivity to\nconstraints, the fundamental connection between multi-value alignment and\nsequential alignment, and proving that linear weighted rewards are sufficient\nfor multi-value alignment. Extensive experiments demonstrate MAP's ability to\nalign multiple values in a principled manner while delivering strong empirical\nperformance across various tasks.\n", "date": "2024-10-24", "categories": []}
{"id": "2402.12617", "title": "Generative AI Security: Challenges and Countermeasures", "abstract": "  Generative AI's expanding footprint across numerous industries has led to\nboth excitement and increased scrutiny. This paper delves into the unique\nsecurity challenges posed by Generative AI, and outlines potential research\ndirections for managing these risks.\n", "date": "2024-02-20", "categories": []}
{"id": "2406.11757", "title": "STAR: SocioTechnical Approach to Red Teaming Language Models", "abstract": "  This research introduces STAR, a sociotechnical framework that improves on\ncurrent best practices for red teaming safety of large language models. STAR\nmakes two key contributions: it enhances steerability by generating\nparameterised instructions for human red teamers, leading to improved coverage\nof the risk surface. Parameterised instructions also provide more detailed\ninsights into model failures at no increased cost. Second, STAR improves signal\nquality by matching demographics to assess harms for specific groups, resulting\nin more sensitive annotations. STAR further employs a novel step of arbitration\nto leverage diverse viewpoints and improve label reliability, treating\ndisagreement not as noise but as a valuable contribution to signal quality.\n", "date": "2024-06-17", "categories": []}
{"id": "2410.13886", "title": "Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents", "abstract": "  For safety reasons, large language models (LLMs) are trained to refuse\nharmful user instructions, such as assisting dangerous activities. We study an\nopen question in this work: does the desired safety refusal, typically enforced\nin chat contexts, generalize to non-chat and agentic use cases? Unlike\nchatbots, LLM agents equipped with general-purpose tools, such as web browsers\nand mobile devices, can directly influence the real world, making it even more\ncrucial to refuse harmful instructions. In this work, we primarily focus on\nred-teaming browser agents, LLMs that manipulate information via web browsers.\nTo this end, we introduce Browser Agent Red teaming Toolkit (BrowserART), a\ncomprehensive test suite designed specifically for red-teaming browser agents.\nBrowserART is consist of 100 diverse browser-related harmful behaviors\n(including original behaviors and ones sourced from HarmBench [Mazeika et al.,\n2024] and AirBench 2024 [Zeng et al., 2024b]) across both synthetic and real\nwebsites. Our empirical study on state-of-the-art browser agents reveals that,\nwhile the backbone LLM refuses harmful instructions as a chatbot, the\ncorresponding agent does not. Moreover, attack methods designed to jailbreak\nrefusal-trained LLMs in the chat settings transfer effectively to browser\nagents. With human rewrites, GPT-4o and o1-preview-based browser agents\nattempted 98 and 63 harmful behaviors (out of 100), respectively. We publicly\nrelease BrowserART and call on LLM developers, policymakers, and agent\ndevelopers to collaborate on improving agent safety\n", "date": "2024-10-11", "categories": []}
{"id": "2410.17032", "title": "Insights on Disagreement Patterns in Multimodal Safety Perception across\n  Diverse Rater Groups", "abstract": "  AI systems crucially rely on human ratings, but these ratings are often\naggregated, obscuring the inherent diversity of perspectives in real-world\nphenomenon. This is particularly concerning when evaluating the safety of\ngenerative AI, where perceptions and associated harms can vary significantly\nacross socio-cultural contexts. While recent research has studied the impact of\ndemographic differences on annotating text, there is limited understanding of\nhow these subjective variations affect multimodal safety in generative AI. To\naddress this, we conduct a large-scale study employing highly-parallel safety\nratings of about 1000 text-to-image (T2I) generations from a demographically\ndiverse rater pool of 630 raters balanced across 30 intersectional groups\nacross age, gender, and ethnicity. Our study shows that (1) there are\nsignificant differences across demographic groups (including intersectional\ngroups) on how severe they assess the harm to be, and that these differences\nvary across different types of safety violations, (2) the diverse rater pool\ncaptures annotation patterns that are substantially different from expert\nraters trained on specific set of safety policies, and (3) the differences we\nobserve in T2I safety are distinct from previously documented group level\ndifferences in text-based safety tasks. To further understand these varying\nperspectives, we conduct a qualitative analysis of the open-ended explanations\nprovided by raters. This analysis reveals core differences into the reasons why\ndifferent groups perceive harms in T2I generations. Our findings underscore the\ncritical need for incorporating diverse perspectives into safety evaluation of\ngenerative AI ensuring these systems are truly inclusive and reflect the values\nof all users.\n", "date": "2024-10-22", "categories": []}
{"id": "2404.07242", "title": "Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs", "abstract": "  Large Language Models (LLMs) are increasingly being developed and applied,\nbut their widespread use faces challenges. These include aligning LLMs'\nresponses with human values to prevent harmful outputs, which is addressed\nthrough safety training methods. Even so, bad actors and malicious users have\nsucceeded in attempts to manipulate the LLMs to generate misaligned responses\nfor harmful questions such as methods to create a bomb in school labs, recipes\nfor harmful drugs, and ways to evade privacy rights. Another challenge is the\nmultilingual capabilities of LLMs, which enable the model to understand and\nrespond in multiple languages. Consequently, attackers exploit the unbalanced\npre-training datasets of LLMs in different languages and the comparatively\nlower model performance in low-resource languages than high-resource ones. As a\nresult, attackers use a low-resource languages to intentionally manipulate the\nmodel to create harmful responses. Many of the similar attack vectors have been\npatched by model providers, making the LLMs more robust against language-based\nmanipulation. In this paper, we introduce a new black-box attack vector called\nthe \\emph{Sandwich attack}: a multi-language mixture attack, which manipulates\nstate-of-the-art LLMs into generating harmful and misaligned responses. Our\nexperiments with five different models, namely Google's Bard, Gemini Pro,\nLLaMA-2-70-B-Chat, GPT-3.5-Turbo, GPT-4, and Claude-3-OPUS, show that this\nattack vector can be used by adversaries to generate harmful responses and\nelicit misaligned responses from these models. By detailing both the mechanism\nand impact of the Sandwich attack, this paper aims to guide future research and\ndevelopment towards more secure and resilient LLMs, ensuring they serve the\npublic good while minimizing potential for misuse.\n", "date": "2024-04-09", "categories": []}
{"id": "2404.11049", "title": "Stepwise Alignment for Constrained Language Model Policy Optimization", "abstract": "  Safety and trustworthiness are indispensable requirements for real-world\napplications of AI systems using large language models (LLMs). This paper\nformulates human value alignment as an optimization problem of the language\nmodel policy to maximize reward under a safety constraint, and then proposes an\nalgorithm, Stepwise Alignment for Constrained Policy Optimization (SACPO). One\nkey idea behind SACPO, supported by theory, is that the optimal policy\nincorporating reward and safety can be directly obtained from a reward-aligned\npolicy. Building on this key idea, SACPO aligns LLMs step-wise with each metric\nwhile leveraging simple yet powerful alignment algorithms such as direct\npreference optimization (DPO). SACPO offers several advantages, including\nsimplicity, stability, computational efficiency, and flexibility of algorithms\nand datasets. Under mild assumptions, our theoretical analysis provides the\nupper bounds on optimality and safety constraint violation. Our experimental\nresults show that SACPO can fine-tune Alpaca-7B better than the\nstate-of-the-art method in terms of both helpfulness and harmlessness.\n", "date": "2024-04-17", "categories": []}
{"id": "2410.11533", "title": "Multi-round jailbreak attack on large language models", "abstract": "  Ensuring the safety and alignment of large language models (LLMs) with human\nvalues is crucial for generating responses that are beneficial to humanity.\nWhile LLMs have the capability to identify and avoid harmful queries, they\nremain vulnerable to \"jailbreak\" attacks, where carefully crafted prompts can\ninduce the generation of toxic content. Traditional single-round jailbreak\nattacks, such as GCG and AutoDAN, do not alter the sensitive words in the\ndangerous prompts. Although they can temporarily bypass the model's safeguards\nthrough prompt engineering, their success rate drops significantly as the LLM\nis further fine-tuned, and they cannot effectively circumvent static rule-based\nfilters that remove the hazardous vocabulary.\n  In this study, to better understand jailbreak attacks, we introduce a\nmulti-round jailbreak approach. This method can rewrite the dangerous prompts,\ndecomposing them into a series of less harmful sub-questions to bypass the\nLLM's safety checks. We first use the LLM to perform a decomposition task,\nbreaking down a set of natural language questions into a sequence of\nprogressive sub-questions, which are then used to fine-tune the Llama3-8B\nmodel, enabling it to decompose hazardous prompts. The fine-tuned model is then\nused to break down the problematic prompt, and the resulting sub-questions are\nsequentially asked to the victim model. If the victim model rejects a\nsub-question, a new decomposition is generated, and the process is repeated\nuntil the final objective is achieved. Our experimental results show a 94\\%\nsuccess rate on the llama2-7B and demonstrate the effectiveness of this\napproach in circumventing static rule-based filters.\n", "date": "2024-10-15", "categories": []}
{"id": "2410.14807", "title": "Aligning AI Agents via Information-Directed Sampling", "abstract": "  The staggering feats of AI systems have brought to attention the topic of AI\nAlignment: aligning a \"superintelligent\" AI agent's actions with humanity's\ninterests. Many existing frameworks/algorithms in alignment study the problem\non a myopic horizon or study learning from human feedback in isolation, relying\non the contrived assumption that the agent has already perfectly identified the\nenvironment. As a starting point to address these limitations, we define a\nclass of bandit alignment problems as an extension of classic multi-armed\nbandit problems. A bandit alignment problem involves an agent tasked with\nmaximizing long-run expected reward by interacting with an environment and a\nhuman, both involving details/preferences initially unknown to the agent. The\nreward of actions in the environment depends on both observed outcomes and\nhuman preferences. Furthermore, costs are associated with querying the human to\nlearn preferences. Therefore, an effective agent ought to intelligently\ntrade-off exploration (of the environment and human) and exploitation. We study\nthese trade-offs theoretically and empirically in a toy bandit alignment\nproblem which resembles the beta-Bernoulli bandit. We demonstrate while naive\nexploration algorithms which reflect current practices and even touted\nalgorithms such as Thompson sampling both fail to provide acceptable solutions\nto this problem, information-directed sampling achieves favorable regret.\n", "date": "2024-10-18", "categories": []}
{"id": "2410.15489", "title": "Generative AI Agents in Autonomous Machines: A Safety Perspective", "abstract": "  The integration of Generative Artificial Intelligence (AI) into autonomous\nmachines represents a major paradigm shift in how these systems operate and\nunlocks new solutions to problems once deemed intractable. Although generative\nAI agents provide unparalleled capabilities, they also have unique safety\nconcerns. These challenges require robust safeguards, especially for autonomous\nmachines that operate in high-stakes environments. This work investigates the\nevolving safety requirements when generative models are integrated as agents\ninto physical autonomous machines, comparing these to safety considerations in\nless critical AI applications. We explore the challenges and opportunities to\nensure the safe deployment of generative AI-driven autonomous machines.\nFurthermore, we provide a forward-looking perspective on the future of\nAI-driven autonomous systems and emphasize the importance of evaluating and\ncommunicating safety risks. As an important step towards addressing these\nconcerns, we recommend the development and implementation of comprehensive\nsafety scorecards for the use of generative AI technologies in autonomous\nmachines.\n", "date": "2024-10-20", "categories": []}
{"id": "2410.15641", "title": "SMILES-Prompting: A Novel Approach to LLM Jailbreak Attacks in Chemical\n  Synthesis", "abstract": "  The increasing integration of large language models (LLMs) across various\nfields has heightened concerns about their potential to propagate dangerous\ninformation. This paper specifically explores the security vulnerabilities of\nLLMs within the field of chemistry, particularly their capacity to provide\ninstructions for synthesizing hazardous substances. We evaluate the\neffectiveness of several prompt injection attack methods, including\nred-teaming, explicit prompting, and implicit prompting. Additionally, we\nintroduce a novel attack technique named SMILES-prompting, which uses the\nSimplified Molecular-Input Line-Entry System (SMILES) to reference chemical\nsubstances. Our findings reveal that SMILES-prompting can effectively bypass\ncurrent safety mechanisms. These findings highlight the urgent need for\nenhanced domain-specific safeguards in LLMs to prevent misuse and improve their\npotential for positive social impact.\n", "date": "2024-10-21", "categories": []}
{"id": "2410.15650", "title": "Voice-Enabled AI Agents can Perform Common Scams", "abstract": "  Recent advances in multi-modal, highly capable LLMs have enabled\nvoice-enabled AI agents. These agents are enabling new applications, such as\nvoice-enabled autonomous customer service. However, with all AI capabilities,\nthese new capabilities have the potential for dual use.\n  In this work, we show that voice-enabled AI agents can perform the actions\nnecessary to perform common scams. To do so, we select a list of common scams\ncollected by the government and construct voice-enabled agents with directions\nto perform these scams. We conduct experiments on our voice-enabled agents and\nshow that they can indeed perform the actions necessary to autonomously perform\nsuch scams. Our results raise questions around the widespread deployment of\nvoice-enabled AI agents.\n", "date": "2024-10-21", "categories": []}
{"id": "2410.15686", "title": "NetSafe: Exploring the Topological Safety of Multi-agent Networks", "abstract": "  Large language models (LLMs) have empowered nodes within multi-agent networks\nwith intelligence, showing growing applications in both academia and industry.\nHowever, how to prevent these networks from generating malicious information\nremains unexplored with previous research on single LLM's safety be challenging\nto transfer. In this paper, we focus on the safety of multi-agent networks from\na topological perspective, investigating which topological properties\ncontribute to safer networks. To this end, we propose a general framework,\nNetSafe along with an iterative RelCom interaction to unify existing diverse\nLLM-based agent frameworks, laying the foundation for generalized topological\nsafety research. We identify several critical phenomena when multi-agent\nnetworks are exposed to attacks involving misinformation, bias, and harmful\ninformation, termed as Agent Hallucination and Aggregation Safety. Furthermore,\nwe find that highly connected networks are more susceptible to the spread of\nadversarial attacks, with task performance in a Star Graph Topology decreasing\nby 29.7%. Besides, our proposed static metrics aligned more closely with\nreal-world dynamic evaluations than traditional graph-theoretic metrics,\nindicating that networks with greater average distances from attackers exhibit\nenhanced safety. In conclusion, our work introduces a new topological\nperspective on the safety of LLM-based multi-agent networks and discovers\nseveral unreported phenomena, paving the way for future research to explore the\nsafety of such networks.\n", "date": "2024-10-21", "categories": []}
{"id": "2410.15821", "title": "The effect of fine-tuning on language model toxicity", "abstract": "  Fine-tuning language models has become increasingly popular following the\nproliferation of open models and improvements in cost-effective parameter\nefficient fine-tuning. However, fine-tuning can influence model properties such\nas safety. We assess how fine-tuning can impact different open models'\npropensity to output toxic content. We assess the impacts of fine-tuning Gemma,\nLlama, and Phi models on toxicity through three experiments. We compare how\ntoxicity is reduced by model developers during instruction-tuning. We show that\nsmall amounts of parameter-efficient fine-tuning on developer-tuned models via\nlow-rank adaptation on a non-adversarial dataset can significantly alter these\nresults across models. Finally, we highlight the impact of this in the wild,\ndemonstrating how toxicity rates of models fine-tuned by community contributors\ncan deviate in hard-to-predict ways.\n", "date": "2024-10-21", "categories": []}
{"id": "2404.06750", "title": "Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of\n  Language Model Agents", "abstract": "  Some have criticised Generative AI Systems for replicating the familiar\npathologies of already widely-deployed AI systems. Other critics highlight how\nthey foreshadow vastly more powerful future systems, which might threaten\nhumanity's survival. The first group says there is nothing new here; the other\nlooks through the present to a perhaps distant horizon. In this paper, I\ninstead pay attention to what makes these particular systems distinctive: both\ntheir remarkable scientific achievement, and the most likely and consequential\nways in which they will change society over the next five to ten years. In\nparticular, I explore the potential societal impacts and normative questions\nraised by the looming prospect of 'Language Model Agents', in which multimodal\nlarge language models (LLMs) form the executive centre of complex, tool-using\nAI systems that can take unsupervised sequences of actions towards some goal.\n", "date": "2024-04-10", "categories": []}
{"id": "2410.13897", "title": "A Formal Framework for Assessing and Mitigating Emergent Security Risks\n  in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation", "abstract": "  As generative AI systems, including large language models (LLMs) and\ndiffusion models, advance rapidly, their growing adoption has led to new and\ncomplex security risks often overlooked in traditional AI risk assessment\nframeworks. This paper introduces a novel formal framework for categorizing and\nmitigating these emergent security risks by integrating adaptive, real-time\nmonitoring, and dynamic risk mitigation strategies tailored to generative\nmodels' unique vulnerabilities. We identify previously under-explored risks,\nincluding latent space exploitation, multi-modal cross-attack vectors, and\nfeedback-loop-induced model degradation. Our framework employs a layered\napproach, incorporating anomaly detection, continuous red-teaming, and\nreal-time adversarial simulation to mitigate these risks. We focus on formal\nverification methods to ensure model robustness and scalability in the face of\nevolving threats. Though theoretical, this work sets the stage for future\nempirical validation by establishing a detailed methodology and metrics for\nevaluating the performance of risk mitigation strategies in generative AI\nsystems. This framework addresses existing gaps in AI safety, offering a\ncomprehensive road map for future research and implementation.\n", "date": "2024-10-15", "categories": []}
{"id": "2410.13901", "title": "SoK: Prompt Hacking of Large Language Models", "abstract": "  The safety and robustness of large language models (LLMs) based applications\nremain critical challenges in artificial intelligence. Among the key threats to\nthese applications are prompt hacking attacks, which can significantly\nundermine the security and reliability of LLM-based systems. In this work, we\noffer a comprehensive and systematic overview of three distinct types of prompt\nhacking: jailbreaking, leaking, and injection, addressing the nuances that\ndifferentiate them despite their overlapping characteristics. To enhance the\nevaluation of LLM-based applications, we propose a novel framework that\ncategorizes LLM responses into five distinct classes, moving beyond the\ntraditional binary classification. This approach provides more granular\ninsights into the AI's behavior, improving diagnostic precision and enabling\nmore targeted enhancements to the system's safety and robustness.\n", "date": "2024-10-16", "categories": []}
{"id": "2407.21659", "title": "Cross-modality Information Check for Detecting Jailbreaking in\n  Multimodal Large Language Models", "abstract": "  Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to\nunderstand multimodal information comprehensively, achieving remarkable\nperformance in many vision-centric tasks. Despite that, recent studies have\nshown that these models are susceptible to jailbreak attacks, which refer to an\nexploitative technique where malicious users can break the safety alignment of\nthe target model and generate misleading and harmful answers. This potential\nthreat is caused by both the inherent vulnerabilities of LLM and the larger\nattack scope introduced by vision input. To enhance the security of MLLMs\nagainst jailbreak attacks, researchers have developed various defense\ntechniques. However, these methods either require modifications to the model's\ninternal structure or demand significant computational resources during the\ninference phase. Multimodal information is a double-edged sword. While it\nincreases the risk of attacks, it also provides additional data that can\nenhance safeguards. Inspired by this, we propose Cross-modality Information\nDEtectoR (CIDER), a plug-and-play jailbreaking detector designed to identify\nmaliciously perturbed image inputs, utilizing the cross-modal similarity\nbetween harmful queries and adversarial images. CIDER is independent of the\ntarget MLLMs and requires less computation cost. Extensive experimental results\ndemonstrate the effectiveness and efficiency of CIDER, as well as its\ntransferability to both white-box and black-box MLLMs.\n", "date": "2024-07-31", "categories": []}
{"id": "2410.13236", "title": "SPIN: Self-Supervised Prompt INjection", "abstract": "  Large Language Models (LLMs) are increasingly used in a variety of important\napplications, yet their safety and reliability remain as major concerns.\nVarious adversarial and jailbreak attacks have been proposed to bypass the\nsafety alignment and cause the model to produce harmful responses. We introduce\nSelf-supervised Prompt INjection (SPIN) which can detect and reverse these\nvarious attacks on LLMs. As our self-supervised prompt defense is done at\ninference-time, it is also compatible with existing alignment and adds an\nadditional layer of safety for defense. Our benchmarks demonstrate that our\nsystem can reduce the attack success rate by up to 87.9%, while maintaining the\nperformance on benign user requests. In addition, we discuss the situation of\nan adaptive attacker and show that our method is still resilient against\nattackers who are aware of our defense.\n", "date": "2024-10-17", "categories": []}
{"id": "2405.13077", "title": "GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation", "abstract": "  Research on jailbreaking has been valuable for testing and understanding the\nsafety and security issues of large language models (LLMs). In this paper, we\nintroduce Iterative Refinement Induced Self-Jailbreak (IRIS), a novel approach\nthat leverages the reflective capabilities of LLMs for jailbreaking with only\nblack-box access. Unlike previous methods, IRIS simplifies the jailbreaking\nprocess by using a single model as both the attacker and target. This method\nfirst iteratively refines adversarial prompts through self-explanation, which\nis crucial for ensuring that even well-aligned LLMs obey adversarial\ninstructions. IRIS then rates and enhances the output given the refined prompt\nto increase its harmfulness. We find that IRIS achieves jailbreak success rates\nof 98% on GPT-4, 92% on GPT-4 Turbo, and 94% on Llama-3.1-70B in under 7\nqueries. It significantly outperforms prior approaches in automatic, black-box,\nand interpretable jailbreaking, while requiring substantially fewer queries,\nthereby establishing a new standard for interpretable jailbreaking methods.\n", "date": "2024-05-21", "categories": []}
{"id": "2410.12104", "title": "To Err is AI : A Case Study Informing LLM Flaw Reporting Practices", "abstract": "  In August of 2024, 495 hackers generated evaluations in an open-ended bug\nbounty targeting the Open Language Model (OLMo) from The Allen Institute for\nAI. A vendor panel staffed by representatives of OLMo's safety program\nadjudicated changes to OLMo's documentation and awarded cash bounties to\nparticipants who successfully demonstrated a need for public disclosure\nclarifying the intent, capacities, and hazards of model deployment. This paper\npresents a collection of lessons learned, illustrative of flaw reporting best\npractices intended to reduce the likelihood of incidents and produce safer\nlarge language models (LLMs). These include best practices for safety reporting\nprocesses, their artifacts, and safety program staffing.\n", "date": "2024-10-15", "categories": []}
{"id": "2403.09572", "title": "Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text\n  Transformation", "abstract": "  Multimodal large language models (MLLMs) have shown impressive reasoning\nabilities. However, they are also more vulnerable to jailbreak attacks than\ntheir LLM predecessors. Although still capable of detecting the unsafe\nresponses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs\ncan be easily bypassed with the introduction of image features. To construct\nrobust MLLMs, we propose ECSO (Eyes Closed, Safety On), a novel training-free\nprotecting approach that exploits the inherent safety awareness of MLLMs, and\ngenerates safer responses via adaptively transforming unsafe images into texts\nto activate the intrinsic safety mechanism of pre-aligned LLMs in MLLMs.\nExperiments on five state-of-the-art (SoTA) MLLMs demonstrate that ECSO\nenhances model safety significantly (e.g.,, 37.6% improvement on the\nMM-SafetyBench (SD+OCR) and 71.3% on VLSafe with LLaVA-1.5-7B), while\nconsistently maintaining utility results on common MLLM benchmarks.\nFurthermore, we show that ECSO can be used as a data engine to generate\nsupervised-finetuning (SFT) data for MLLM alignment without extra human\nintervention.\n", "date": "2024-03-14", "categories": []}
{"id": "2410.10871", "title": "Applying Refusal-Vector Ablation to Llama 3.1 70B Agents", "abstract": "  Recently, language models like Llama 3.1 Instruct have become increasingly\ncapable of agentic behavior, enabling them to perform tasks requiring\nshort-term planning and tool use. In this study, we apply refusal-vector\nablation to Llama 3.1 70B and implement a simple agent scaffolding to create an\nunrestricted agent. Our findings imply that these refusal-vector ablated models\ncan successfully complete harmful tasks, such as bribing officials or crafting\nphishing attacks, revealing significant vulnerabilities in current safety\nmechanisms. To further explore this, we introduce a small Safe Agent Benchmark,\ndesigned to test both harmful and benign tasks in agentic scenarios. Our\nresults imply that safety fine-tuning in chat models does not generalize well\nto agentic behavior, as we find that Llama 3.1 Instruct models are willing to\nperform most harmful tasks without modifications. At the same time, these\nmodels will refuse to give advice on how to perform the same tasks when asked\nfor a chat completion. This highlights the growing risk of misuse as models\nbecome more capable, underscoring the need for improved safety frameworks for\nlanguage model agents.\n", "date": "2024-10-08", "categories": []}
{"id": "2410.11459", "title": "Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language\n  Models", "abstract": "  Large language models (LLMs) have exhibited outstanding performance in\nengaging with humans and addressing complex questions by leveraging their vast\nimplicit knowledge and robust reasoning capabilities. However, such models are\nvulnerable to jailbreak attacks, leading to the generation of harmful\nresponses. Despite recent research on single-turn jailbreak strategies to\nfacilitate the development of defence mechanisms, the challenge of revealing\nvulnerabilities under multi-turn setting remains relatively under-explored. In\nthis work, we propose Jigsaw Puzzles (JSP), a straightforward yet effective\nmulti-turn jailbreak strategy against the advanced LLMs. JSP splits questions\ninto harmless fractions as the input of each turn, and requests LLMs to\nreconstruct and respond to questions under multi-turn interaction. Our\nexperimental results demonstrate that the proposed JSP jailbreak bypasses\noriginal safeguards against explicitly harmful content, achieving an average\nattack success rate of 93.76% on 189 harmful queries across 5 advanced LLMs\n(Gemini-1.5-Pro, Llama-3.1-70B, GPT-4, GPT-4o, GPT-4o-mini). Moreover, JSP\nachieves a state-of-the-art attack success rate of 92% on GPT-4 on the harmful\nquery benchmark, and exhibits strong resistant to defence strategies. Warning:\nthis paper contains offensive examples.\n", "date": "2024-10-15", "categories": []}
{"id": "2405.17894", "title": "White-box Multimodal Jailbreaks Against Large Vision-Language Models", "abstract": "  Recent advancements in Large Vision-Language Models (VLMs) have underscored\ntheir superiority in various multimodal tasks. However, the adversarial\nrobustness of VLMs has not been fully explored. Existing methods mainly assess\nrobustness through unimodal adversarial attacks that perturb images, while\nassuming inherent resilience against text-based attacks. Different from\nexisting attacks, in this work we propose a more comprehensive strategy that\njointly attacks both text and image modalities to exploit a broader spectrum of\nvulnerability within VLMs. Specifically, we propose a dual optimization\nobjective aimed at guiding the model to generate affirmative responses with\nhigh toxicity. Our attack method begins by optimizing an adversarial image\nprefix from random noise to generate diverse harmful responses in the absence\nof text input, thus imbuing the image with toxic semantics. Subsequently, an\nadversarial text suffix is integrated and co-optimized with the adversarial\nimage prefix to maximize the probability of eliciting affirmative responses to\nvarious harmful instructions. The discovered adversarial image prefix and text\nsuffix are collectively denoted as a Universal Master Key (UMK). When\nintegrated into various malicious queries, UMK can circumvent the alignment\ndefenses of VLMs and lead to the generation of objectionable content, known as\njailbreaks. The experimental results demonstrate that our universal attack\nstrategy can effectively jailbreak MiniGPT-4 with a 96% success rate,\nhighlighting the vulnerability of VLMs and the urgent need for new alignment\nstrategies.\n", "date": "2024-05-28", "categories": []}
{"id": "2409.16125", "title": "Analyzing Probabilistic Methods for Evaluating Agent Capabilities", "abstract": "  To mitigate risks from AI systems, we need to assess their capabilities\naccurately. This is especially difficult in cases where capabilities are only\nrarely displayed. Phuong et al. propose two methods that aim to obtain better\nestimates of the probability of an AI agent successfully completing a given\ntask. The milestone method decomposes tasks into subtasks, aiming to improve\noverall success rate estimation, while the expert best-of-N method leverages\nhuman guidance as a proxy for the model's independent performance.\n  Our analysis of these methods as Monte Carlo estimators reveals that while\nboth effectively reduce variance compared to naive Monte Carlo sampling, they\nalso introduce bias. Experimental results demonstrate that the milestone method\nunderestimates true solve rates for many real-world tasks due to its\nconstraining assumptions. The expert best-of-N method exhibits even more severe\nunderestimation across all tasks, attributed to an inherently flawed\nre-weighting factor. To enhance the accuracy of capability estimates of AI\nagents on difficult tasks, we suggest future work should leverage the rich\nliterature on Monte Carlo Estimators.\n", "date": "2024-09-24", "categories": []}
{"id": "2410.06946", "title": "A Trilogy of AI Safety Frameworks: Paths from Facts and Knowledge Gaps\n  to Reliable Predictions and New Knowledge", "abstract": "  AI Safety has become a vital front-line concern of many scientists within and\noutside the AI community. There are many immediate and long term anticipated\nrisks that range from existential risk to human existence to deep fakes and\nbias in machine learning systems [1-5]. In this paper, we reduce the full scope\nand immense complexity of AI safety concerns to a trilogy of three important\nbut tractable opportunities for advances that have the short-term potential to\nimprove AI safety and reliability without reducing AI innovation in critical\ndomains. In this perspective, we discuss this vision based on several case\nstudies that already produced proofs of concept in critical ML applications in\nbiomedical science.\n", "date": "2024-10-09", "categories": []}
{"id": "2410.08442", "title": "JurEE not Judges: safeguarding llm interactions with small, specialised\n  Encoder Ensembles", "abstract": "  We introduce JurEE, an ensemble of efficient, encoder-only transformer models\ndesigned to strengthen safeguards in AI-User interactions within LLM-based\nsystems. Unlike existing LLM-as-Judge methods, which often struggle with\ngeneralization across risk taxonomies and only provide textual outputs, JurEE\noffers probabilistic risk estimates across a wide range of prevalent risks. Our\napproach leverages diverse data sources and employs progressive synthetic data\ngeneration techniques, including LLM-assisted augmentation, to enhance model\nrobustness and performance. We create an in-house benchmark comprising of other\nreputable benchmarks such as the OpenAI Moderation Dataset and ToxicChat, where\nwe find JurEE significantly outperforms baseline models, demonstrating superior\naccuracy, speed, and cost-efficiency. This makes it particularly suitable for\napplications requiring stringent content moderation, such as customer-facing\nchatbots. The encoder-ensemble's modular design allows users to set tailored\nrisk thresholds, enhancing its versatility across various safety-related\napplications. JurEE's collective decision-making process, where each\nspecialized encoder model contributes to the final output, not only improves\npredictive accuracy but also enhances interpretability. This approach provides\na more efficient, performant, and economical alternative to traditional LLMs\nfor large-scale implementations requiring robust content moderation.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.09078", "title": "Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial\n  Robustness of LLMs", "abstract": "  The EU AI Act (EUAIA) introduces requirements for AI systems which intersect\nwith the processes required to establish adversarial robustness. However, given\nthe ambiguous language of regulation and the dynamicity of adversarial attacks,\ndevelopers of systems with highly complex models such as LLMs may find their\neffort to be duplicated without the assurance of having achieved either\ncompliance or robustness. This paper presents a functional architecture that\nfocuses on bridging the two properties, by introducing components with clear\nreference to their source. Taking the detection layer recommended by the\nliterature, and the reporting layer required by the law, we aim to support\ndevelopers and auditors with a reasoning layer based on knowledge augmentation\n(rules, assurance cases, contextual mappings). Our findings demonstrate a novel\ndirection for ensuring LLMs deployed in the EU are both compliant and\nadversarially robust, which underpin trustworthiness.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.09181", "title": "Can a large language model be a gaslighter?", "abstract": "  Large language models (LLMs) have gained human trust due to their\ncapabilities and helpfulness. However, this in turn may allow LLMs to affect\nusers' mindsets by manipulating language. It is termed as gaslighting, a\npsychological effect. In this work, we aim to investigate the vulnerability of\nLLMs under prompt-based and fine-tuning-based gaslighting attacks. Therefore,\nwe propose a two-stage framework DeepCoG designed to: 1) elicit gaslighting\nplans from LLMs with the proposed DeepGaslighting prompting template, and 2)\nacquire gaslighting conversations from LLMs through our Chain-of-Gaslighting\nmethod. The gaslighting conversation dataset along with a corresponding safe\ndataset is applied to fine-tuning-based attacks on open-source LLMs and\nanti-gaslighting safety alignment on these LLMs. Experiments demonstrate that\nboth prompt-based and fine-tuning-based attacks transform three open-source\nLLMs into gaslighters. In contrast, we advanced three safety alignment\nstrategies to strengthen (by 12.05%) the safety guardrail of LLMs. Our safety\nalignment strategies have minimal impacts on the utility of LLMs. Empirical\nstudies indicate that an LLM may be a potential gaslighter, even if it passed\nthe harmfulness test on general dangerous queries.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.10150", "title": "Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting", "abstract": "  In this paper, we investigate the safety mechanisms of instruction fine-tuned\nlarge language models (LLMs). We discover that re-weighting MLP neurons can\nsignificantly compromise a model's safety, especially for MLPs in\nend-of-sentence inferences. We hypothesize that LLMs evaluate the harmfulness\nof prompts during end-of-sentence inferences, and MLP layers plays a critical\nrole in this process. Based on this hypothesis, we develop 2 novel white-box\njailbreak methods: a prompt-specific method and a prompt-general method. The\nprompt-specific method targets individual prompts and optimizes the attack on\nthe fly, while the prompt-general method is pre-trained offline and can\ngeneralize to unseen harmful prompts. Our methods demonstrate robust\nperformance across 7 popular open-source LLMs, size ranging from 2B to 72B.\nFurthermore, our study provides insights into vulnerabilities of\ninstruction-tuned LLM's safety and deepens the understanding of the internal\nmechanisms of LLMs.\n", "date": "2024-10-14", "categories": []}
{"id": "2410.10343", "title": "Locking Down the Finetuned LLMs Safety", "abstract": "  Fine-tuning large language models (LLMs) on additional datasets is often\nnecessary to optimize them for specific downstream tasks. However, existing\nsafety alignment measures, which restrict harmful behavior during inference,\nare insufficient to mitigate safety risks during fine-tuning. Alarmingly,\nfine-tuning with just 10 toxic sentences can make models comply with harmful\ninstructions. We introduce SafetyLock, a novel alignment intervention method\nthat maintains robust safety post-fine-tuning through efficient and\ntransferable mechanisms. SafetyLock leverages our discovery that fine-tuned\nmodels retain similar safety-related activation representations to their base\nmodels. This insight enables us to extract what we term the Meta-SafetyLock, a\nset of safety bias directions representing key activation patterns associated\nwith safe responses in the original model. We can then apply these directions\nuniversally to fine-tuned models to enhance their safety. By searching for\nactivation directions across multiple token dimensions, SafetyLock achieves\nenhanced robustness and transferability. SafetyLock re-aligns fine-tuned models\nin under 0.01 seconds without additional computational cost. Our experiments\ndemonstrate that SafetyLock can reduce the harmful instruction response rate\nfrom 60% to below 1% in toxic fine-tuned models. It surpasses traditional\nmethods in both performance and efficiency, offering a scalable, non-invasive\nsolution for ensuring the safety of customized LLMs. Our analysis across\nvarious fine-tuning scenarios confirms SafetyLock's robustness, advocating its\nintegration into safety protocols for aligned LLMs. The code is released at\nhttps://github.com/zhu-minjun/SafetyLock.\n", "date": "2024-10-14", "categories": []}
{"id": "2402.19085", "title": "Controllable Preference Optimization: Toward Controllable\n  Multi-Objective Alignment", "abstract": "  Alignment in artificial intelligence pursues the consistency between model\nresponses and human preferences as well as values. In practice, the\nmultifaceted nature of human preferences inadvertently introduces what is known\nas the \"alignment tax\" -a compromise where enhancements in alignment within one\nobjective (e.g.,harmlessness) can diminish performance in others\n(e.g.,helpfulness). However, existing alignment techniques are mostly\nunidirectional, leading to suboptimal trade-offs and poor flexibility over\nvarious objectives. To navigate this challenge, we argue the prominence of\ngrounding LLMs with evident preferences. We introduce controllable preference\noptimization (CPO), which explicitly specifies preference scores for different\nobjectives, thereby guiding the model to generate responses that meet the\nrequirements. Our experimental analysis reveals that the aligned models can\nprovide responses that match various preferences among the \"3H\" (helpfulness,\nhonesty, harmlessness) desiderata. Furthermore, by introducing diverse data and\nalignment goals, we surpass baseline methods in aligning with single\nobjectives, hence mitigating the impact of the alignment tax and achieving\nimprovements in multi-objective alignment.\n", "date": "2024-02-29", "categories": []}
{"id": "2410.07471", "title": "SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection", "abstract": "  Fine-tuning on task-specific data to boost downstream performance is a\ncrucial step for leveraging Large Language Models (LLMs). However, previous\nstudies have demonstrated that fine-tuning the models on several adversarial\nsamples or even benign data can greatly comprise the model's pre-equipped\nalignment and safety capabilities. In this work, we propose SEAL, a novel\nframework to enhance safety in LLM fine-tuning. SEAL learns a data ranker based\non the bilevel optimization to up rank the safe and high-quality fine-tuning\ndata and down rank the unsafe or low-quality ones. Models trained with SEAL\ndemonstrate superior quality over multiple baselines, with 8.5% and 9.7% win\nrate increase compared to random selection respectively on Llama-3-8b-Instruct\nand Merlinite-7b models. Our code is available on github\nhttps://github.com/hanshen95/SEAL.\n", "date": "2024-10-09", "categories": []}
{"id": "2410.07283", "title": "Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems", "abstract": "  As Large Language Models (LLMs) grow increasingly powerful, multi-agent\nsystems are becoming more prevalent in modern AI applications. Most safety\nresearch, however, has focused on vulnerabilities in single-agent LLMs. These\ninclude prompt injection attacks, where malicious prompts embedded in external\ncontent trick the LLM into executing unintended or harmful actions,\ncompromising the victim's application. In this paper, we reveal a more\ndangerous vector: LLM-to-LLM prompt injection within multi-agent systems. We\nintroduce Prompt Infection, a novel attack where malicious prompts\nself-replicate across interconnected agents, behaving much like a computer\nvirus. This attack poses severe threats, including data theft, scams,\nmisinformation, and system-wide disruption, all while propagating silently\nthrough the system. Our extensive experiments demonstrate that multi-agent\nsystems are highly susceptible, even when agents do not publicly share all\ncommunications. To address this, we propose LLM Tagging, a defense mechanism\nthat, when combined with existing safeguards, significantly mitigates infection\nspread. This work underscores the urgent need for advanced security measures as\nmulti-agent LLM systems become more widely adopted.\n", "date": "2024-10-09", "categories": []}
{"id": "2307.13642", "title": "Safety Margins for Reinforcement Learning", "abstract": "  Any autonomous controller will be unsafe in some situations. The ability to\nquantitatively identify when these unsafe situations are about to occur is\ncrucial for drawing timely human oversight in, e.g., freight transportation\napplications. In this work, we demonstrate that the true criticality of an\nagent's situation can be robustly defined as the mean reduction in reward given\nsome number of random actions. Proxy criticality metrics that are computable in\nreal-time (i.e., without actually simulating the effects of random actions) can\nbe compared to the true criticality, and we show how to leverage these proxy\nmetrics to generate safety margins, which directly tie the consequences of\npotentially incorrect actions to an anticipated loss in overall performance. We\nevaluate our approach on learned policies from APE-X and A3C within an Atari\nenvironment, and demonstrate how safety margins decrease as agents approach\nfailure states. The integration of safety margins into programs for monitoring\ndeployed agents allows for the real-time identification of potentially\ncatastrophic situations.\n", "date": "2023-07-25", "categories": []}
{"id": "2409.11365", "title": "CoCA: Regaining Safety-awareness of Multimodal Large Language Models\n  with Constitutional Calibration", "abstract": "  The deployment of multimodal large language models (MLLMs) has demonstrated\nremarkable success in engaging in conversations involving visual inputs, thanks\nto the superior power of large language models (LLMs). Those MLLMs are\ntypically built based on the LLMs, with an image encoder to process images into\nthe token embedding space of the LLMs. However, the integration of visual\nmodality has introduced a unique vulnerability: the MLLM becomes susceptible to\nmalicious visual inputs and prone to generating sensitive or harmful responses,\neven though the LLM has been trained on textual dataset to align with human\nvalue. In this paper, we first raise the question: ``Do the MLLMs possess\nsafety-awareness against malicious image inputs?\". We find that after adding a\nprinciple that specifies the safety requirement into the input of the MLLM, the\nmodel's safety awareness becomes boosted. This phenomenon verifies the\nexistence of MLLM's safety-awareness against image inputs, it is only weakened\nby the modality gap. We then introduce a simple yet effective technique termed\nCoCA, which amplifies the safety-awareness of the MLLM by calibrating its\noutput distribution. Our proposed strategy helps the model reclaim its original\nsafety awareness without losing its original capabilities. We verify the\neffectiveness of our approach on both multimodal safety and understanding\nbenchmarks.\n", "date": "2024-09-17", "categories": []}
{"id": "2410.03857", "title": "You Know What I'm Saying: Jailbreak Attack via Implicit Reference", "abstract": "  While recent advancements in large language model (LLM) alignment have\nenabled the effective identification of malicious objectives involving scene\nnesting and keyword rewriting, our study reveals that these methods remain\ninadequate at detecting malicious objectives expressed through context within\nnested harmless objectives. This study identifies a previously overlooked\nvulnerability, which we term Attack via Implicit Reference (AIR). AIR\ndecomposes a malicious objective into permissible objectives and links them\nthrough implicit references within the context. This method employs multiple\nrelated harmless objectives to generate malicious content without triggering\nrefusal responses, thereby effectively bypassing existing detection\ntechniques.Our experiments demonstrate AIR's effectiveness across\nstate-of-the-art LLMs, achieving an attack success rate (ASR) exceeding 90% on\nmost models, including GPT-4o, Claude-3.5-Sonnet, and Qwen-2-72B. Notably, we\nobserve an inverse scaling phenomenon, where larger models are more vulnerable\nto this attack method. These findings underscore the urgent need for defense\nmechanisms capable of understanding and preventing contextual attacks.\nFurthermore, we introduce a cross-model attack strategy that leverages less\nsecure models to generate malicious contexts, thereby further increasing the\nASR when targeting other models.Our code and jailbreak artifacts can be found\nat https://github.com/Lucas-TY/llm_Implicit_reference.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.05304", "title": "Developing Assurance Cases for Adversarial Robustness and Regulatory\n  Compliance in LLMs", "abstract": "  This paper presents an approach to developing assurance cases for adversarial\nrobustness and regulatory compliance in large language models (LLMs). Focusing\non both natural and code language tasks, we explore the vulnerabilities these\nmodels face, including adversarial attacks based on jailbreaking, heuristics,\nand randomization. We propose a layered framework incorporating guardrails at\nvarious stages of LLM deployment, aimed at mitigating these attacks and\nensuring compliance with the EU AI Act. Our approach includes a meta-layer for\ndynamic risk management and reasoning, crucial for addressing the evolving\nnature of LLM vulnerabilities. We illustrate our method with two exemplary\nassurance cases, highlighting how different contexts demand tailored strategies\nto ensure robust and compliant AI systems.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.05306", "title": "Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs", "abstract": "  Large language models are prone to misuse and vulnerable to security threats,\nraising significant safety and security concerns. The European Union's\nArtificial Intelligence Act seeks to enforce AI robustness in certain contexts,\nbut faces implementation challenges due to the lack of standards, complexity of\nLLMs and emerging security vulnerabilities. Our research introduces a framework\nusing ontologies, assurance cases, and factsheets to support engineers and\nstakeholders in understanding and documenting AI system compliance and security\nregarding adversarial robustness. This approach aims to ensure that LLMs adhere\nto regulatory standards and are equipped to counter potential threats.\n", "date": "2024-10-04", "categories": []}
{"id": "2410.05309", "title": "ShieldDiff: Suppressing Sexual Content Generation from Diffusion Models\n  through Reinforcement Learning", "abstract": "  With the advance of generative AI, the text-to-image (T2I) model has the\nability to generate various contents. However, the generated contents cannot be\nfully controlled. There is a potential risk that T2I model can generate unsafe\nimages with uncomfortable contents. In our work, we focus on eliminating the\nNSFW (not safe for work) content generation from T2I model while maintaining\nthe high quality of generated images by fine-tuning the pre-trained diffusion\nmodel via reinforcement learning by optimizing the well-designed content-safe\nreward function. The proposed method leverages a customized reward function\nconsisting of the CLIP (Contrastive Language-Image Pre-training) and nudity\nrewards to prune the nudity contents that adhere to the pret-rained model and\nkeep the corresponding semantic meaning on the safe side. In this way, the T2I\nmodel is robust to unsafe adversarial prompts since unsafe visual\nrepresentations are mitigated from latent space. Extensive experiments\nconducted on different datasets demonstrate the effectiveness of the proposed\nmethod in alleviating unsafe content generation while preserving the\nhigh-fidelity of benign images as well as images generated by unsafe prompts.\nWe compare with five existing state-of-the-art (SOTA) methods and achieve\ncompetitive performance on sexual content removal and image quality retention.\nIn terms of robustness, our method outperforms counterparts under the SOTA\nblack-box attacking model. Furthermore, our constructed method can be a\nbenchmark for anti-NSFW generation with semantically-relevant safe alignment.\n", "date": "2024-10-04", "categories": []}
{"id": "2401.10019", "title": "R-Judge: Benchmarking Safety Risk Awareness for LLM Agents", "abstract": "  Large language models (LLMs) have exhibited great potential in autonomously\ncompleting tasks across real-world applications. Despite this, these LLM agents\nintroduce unexpected safety risks when operating in interactive environments.\nInstead of centering on the harmlessness of LLM-generated content in most prior\nstudies, this work addresses the imperative need for benchmarking the\nbehavioral safety of LLM agents within diverse environments. We introduce\nR-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging and\nidentifying safety risks given agent interaction records. R-Judge comprises 569\nrecords of multi-turn agent interaction, encompassing 27 key risk scenarios\namong 5 application categories and 10 risk types. It is of high-quality\ncuration with annotated safety labels and risk descriptions. Evaluation of 11\nLLMs on R-Judge shows considerable room for enhancing the risk awareness of\nLLMs: The best-performing model, GPT-4o, achieves 74.42% while no other models\nsignificantly exceed the random. Moreover, we reveal that risk awareness in\nopen agent scenarios is a multi-dimensional capability involving knowledge and\nreasoning, thus challenging for LLMs. With further experiments, we find that\nfine-tuning on safety judgment significantly improve model performance while\nstraightforward prompting mechanisms fail. R-Judge is publicly available at\nhttps://github.com/Lordog/R-Judge.\n", "date": "2024-01-18", "categories": []}
{"id": "2402.17840", "title": "Follow My Instruction and Spill the Beans: Scalable Data Extraction from\n  Retrieval-Augmented Generation Systems", "abstract": "  Retrieval-Augmented Generation (RAG) improves pre-trained models by\nincorporating external knowledge at test time to enable customized adaptation.\nWe study the risk of datastore leakage in Retrieval-In-Context RAG Language\nModels (LMs). We show that an adversary can exploit LMs' instruction-following\ncapabilities to easily extract text data verbatim from the datastore of RAG\nsystems built with instruction-tuned LMs via prompt injection. The\nvulnerability exists for a wide range of modern LMs that span Llama2,\nMistral/Mixtral, Vicuna, SOLAR, WizardLM, Qwen1.5, and Platypus2, and the\nexploitability exacerbates as the model size scales up. We also study multiple\neffects of RAG setup on the extractability of data, indicating that following\nunexpected instructions to regurgitate data can be an outcome of failure in\neffectively utilizing contexts for modern LMs, and further show that such\nvulnerability can be greatly mitigated by position bias elimination strategies.\nExtending our study to production RAG models GPTs, we design an attack that can\ncause datastore leakage with a 100% success rate on 25 randomly selected\ncustomized GPTs with at most 2 queries, and we extract text data verbatim at a\nrate of 41% from a book of 77,000 words and 3% from a corpus of 1,569,000 words\nby prompting the GPTs with only 100 queries generated by themselves.\n", "date": "2024-02-27", "categories": []}
{"id": "2406.09289", "title": "Understanding Jailbreak Success: A Study of Latent Space Dynamics in\n  Large Language Models", "abstract": "  Conversational large language models are trained to refuse to answer harmful\nquestions. However, emergent jailbreaking techniques can still elicit unsafe\noutputs, presenting an ongoing challenge for model alignment. To better\nunderstand how different jailbreak types circumvent safeguards, this paper\nanalyses model activations on different jailbreak inputs. We find that it is\npossible to extract a jailbreak vector from a single class of jailbreaks that\nworks to mitigate jailbreak effectiveness from other semantically-dissimilar\nclasses. This may indicate that different kinds of effective jailbreaks operate\nvia a similar internal mechanism. We investigate a potential common mechanism\nof harmfulness feature suppression, and find evidence that effective jailbreaks\nnoticeably reduce a model's perception of prompt harmfulness. These findings\noffer actionable insights for developing more robust jailbreak countermeasures\nand lay the groundwork for a deeper, mechanistic understanding of jailbreak\ndynamics in language models.\n", "date": "2024-06-13", "categories": []}
{"id": "2410.04447", "title": "Attention Shift: Steering AI Away from Unsafe Content", "abstract": "  This study investigates the generation of unsafe or harmful content in\nstate-of-the-art generative models, focusing on methods for restricting such\ngenerations. We introduce a novel training-free approach using attention\nreweighing to remove unsafe concepts without additional training during\ninference. We compare our method against existing ablation methods, evaluating\nthe performance on both, direct and adversarial jailbreak prompts, using\nqualitative and quantitative metrics. We hypothesize potential reasons for the\nobserved results and discuss the limitations and broader implications of\ncontent restriction.\n", "date": "2024-10-06", "categories": []}
{"id": "2410.05269", "title": "Data Advisor: Dynamic Data Curation for Safety Alignment of Large\n  Language Models", "abstract": "  Data is a crucial element in large language model (LLM) alignment. Recent\nstudies have explored using LLMs for efficient data collection. However,\nLLM-generated data often suffers from quality issues, with underrepresented or\nabsent aspects and low-quality datapoints. To address these problems, we\npropose Data Advisor, an enhanced LLM-based method for generating data that\ntakes into account the characteristics of the desired dataset. Starting from a\nset of pre-defined principles in hand, Data Advisor monitors the status of the\ngenerated data, identifies weaknesses in the current dataset, and advises the\nnext iteration of data generation accordingly. Data Advisor can be easily\nintegrated into existing data generation methods to enhance data quality and\ncoverage. Experiments on safety alignment of three representative LLMs (i.e.,\nMistral, Llama2, and Falcon) demonstrate the effectiveness of Data Advisor in\nenhancing model safety against various fine-grained safety issues without\nsacrificing model utility.\n", "date": "2024-10-07", "categories": []}
{"id": "2402.01586", "title": "TrustAgent: Towards Safe and Trustworthy LLM-based Agents", "abstract": "  The rise of LLM-based agents shows great potential to revolutionize task\nplanning, capturing significant attention. Given that these agents will be\nintegrated into high-stake domains, ensuring their reliability and safety is\ncrucial. This paper presents an Agent-Constitution-based agent framework,\nTrustAgent, with a particular focus on improving the LLM-based agent safety.\nThe proposed framework ensures strict adherence to the Agent Constitution\nthrough three strategic components: pre-planning strategy which injects safety\nknowledge to the model before plan generation, in-planning strategy which\nenhances safety during plan generation, and post-planning strategy which\nensures safety by post-planning inspection. Our experimental results\ndemonstrate that the proposed framework can effectively enhance an LLM agent's\nsafety across multiple domains by identifying and mitigating potential dangers\nduring the planning. Further analysis reveals that the framework not only\nimproves safety but also enhances the helpfulness of the agent. Additionally,\nwe highlight the importance of the LLM reasoning ability in adhering to the\nConstitution. This paper sheds light on how to ensure the safe integration of\nLLM-based agents into human-centric environments. Data and code are available\nat https://github.com/agiresearch/TrustAgent.\n", "date": "2024-02-02", "categories": []}
{"id": "2409.17699", "title": "MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard\n  for Prompt Attacks", "abstract": "  The proliferation of Large Language Models (LLMs) in diverse applications\nunderscores the pressing need for robust security measures to thwart potential\njailbreak attacks. These attacks exploit vulnerabilities within LLMs, endanger\ndata integrity and user privacy. Guardrails serve as crucial protective\nmechanisms against such threats, but existing models often fall short in terms\nof both detection accuracy, and computational efficiency. This paper advocates\nfor the significance of jailbreak attack prevention on LLMs, and emphasises the\nrole of input guardrails in safeguarding these models. We introduce MoJE\n(Mixture of Jailbreak Expert), a novel guardrail architecture designed to\nsurpass current limitations in existing state-of-the-art guardrails. By\nemploying simple linguistic statistical techniques, MoJE excels in detecting\njailbreak attacks while maintaining minimal computational overhead during model\ninference. Through rigorous experimentation, MoJE demonstrates superior\nperformance capable of detecting 90% of the attacks without compromising benign\nprompts, enhancing LLMs security against jailbreak attacks.\n", "date": "2024-09-26", "categories": []}
{"id": "2410.02828", "title": "PyRIT: A Framework for Security Risk Identification and Red Teaming in\n  Generative AI System", "abstract": "  Generative Artificial Intelligence (GenAI) is becoming ubiquitous in our\ndaily lives. The increase in computational power and data availability has led\nto a proliferation of both single- and multi-modal models. As the GenAI\necosystem matures, the need for extensible and model-agnostic risk\nidentification frameworks is growing. To meet this need, we introduce the\nPython Risk Identification Toolkit (PyRIT), an open-source framework designed\nto enhance red teaming efforts in GenAI systems. PyRIT is a model- and\nplatform-agnostic tool that enables red teamers to probe for and identify novel\nharms, risks, and jailbreaks in multimodal generative AI models. Its composable\narchitecture facilitates the reuse of core building blocks and allows for\nextensibility to future models and modalities. This paper details the\nchallenges specific to red teaming generative AI systems, the development and\nfeatures of PyRIT, and its practical applications in real-world scenarios.\n", "date": "2024-10-01", "categories": []}
{"id": "2403.03407", "title": "Human vs. Machine: Behavioral Differences Between Expert Humans and\n  Language Models in Wargame Simulations", "abstract": "  To some, the advent of artificial intelligence (AI) promises better\ndecision-making and increased military effectiveness while reducing the\ninfluence of human error and emotions. However, there is still debate about how\nAI systems, especially large language models (LLMs) that can be applied to many\ntasks, behave compared to humans in high-stakes military decision-making\nscenarios with the potential for increased risks towards escalation. To test\nthis potential and scrutinize the use of LLMs for such purposes, we use a new\nwargame experiment with 214 national security experts designed to examine\ncrisis escalation in a fictional U.S.-China scenario and compare the behavior\nof human player teams to LLM-simulated team responses in separate simulations.\nHere, we find that the LLM-simulated responses can be more aggressive and\nsignificantly affected by changes in the scenario. We show a considerable\nhigh-level agreement in the LLM and human responses and significant\nquantitative and qualitative differences in individual actions and strategic\ntendencies. These differences depend on intrinsic biases in LLMs regarding the\nappropriate level of violence following strategic instructions, the choice of\nLLM, and whether the LLMs are tasked to decide for a team of players directly\nor first to simulate dialog between a team of players. When simulating the\ndialog, the discussions lack quality and maintain a farcical harmony. The LLM\nsimulations cannot account for human player characteristics, showing no\nsignificant difference even for extreme traits, such as \"pacifist\" or\n\"aggressive sociopath.\" When probing behavioral consistency across individual\nmoves of the simulation, the tested LLMs deviated from each other but generally\nshowed somewhat consistent behavior. Our results motivate policymakers to be\ncautious before granting autonomy or following AI-based strategy\nrecommendations.\n", "date": "2024-03-06", "categories": []}
{"id": "2409.14177", "title": "PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement\n  Learning-Based Jailbreak Approach", "abstract": "  In recent years, Large Language Models (LLMs) have gained widespread use,\nraising concerns about their security. Traditional jailbreak attacks, which\noften rely on the model internal information or have limitations when exploring\nthe unsafe behavior of the victim model, limiting their reducing their general\napplicability. In this paper, we introduce PathSeeker, a novel black-box\njailbreak method, which is inspired by the game of rats escaping a maze. We\nthink that each LLM has its unique \"security maze\", and attackers attempt to\nfind the exit learning from the received feedback and their accumulated\nexperience to compromise the target LLM's security defences. Our approach\nleverages multi-agent reinforcement learning, where smaller models collaborate\nto guide the main LLM in performing mutation operations to achieve the attack\nobjectives. By progressively modifying inputs based on the model's feedback,\nour system induces richer, harmful responses. During our manual attempts to\nperform jailbreak attacks, we found that the vocabulary of the response of the\ntarget model gradually became richer and eventually produced harmful responses.\nBased on the observation, we also introduce a reward mechanism that exploits\nthe expansion of vocabulary richness in LLM responses to weaken security\nconstraints. Our method outperforms five state-of-the-art attack techniques\nwhen tested across 13 commercial and open-source LLMs, achieving high attack\nsuccess rates, especially in strongly aligned commercial models like\nGPT-4o-mini, Claude-3.5, and GLM-4-air with strong safety alignment. This study\naims to improve the understanding of LLM security vulnerabilities and we hope\nthat this sturdy can contribute to the development of more robust defenses.\n", "date": "2024-09-21", "categories": []}
{"id": "2410.01808", "title": "AI Horizon Scanning, White Paper p3395, IEEE-SA. Part I: Areas of\n  Attention", "abstract": "  Generative Artificial Intelligence (AI) models may carry societal\ntransformation to an extent demanding a delicate balance between opportunity\nand risk. This manuscript is the first of a series of White Papers informing\nthe development of IEEE-SA's p3995: `Standard for the Implementation of\nSafeguards, Controls, and Preventive Techniques for Artificial Intelligence\n(AI) Models', Chair: Marina Cort\\^{e}s\n(https://standards.ieee.org/ieee/3395/11378/). In this first horizon-scanning\nwe identify key attention areas for standards activities in AI. We examine\ndifferent principles for regulatory efforts, and review notions of\naccountability, privacy, data rights and mis-use. As a safeguards standard we\ndevote significant attention to the stability of global infrastructures and\nconsider a possible overdependence on cloud computing that may result from\ndensely coupled AI components. We review the recent cascade-failure-like\nCrowdstrike event in July 2024, as an illustration of potential impacts on\ncritical infrastructures from AI-induced incidents in the (near) future. It is\nthe first of a set of articles intended as White Papers informing the audience\non the standard development. Upcoming articles will focus on regulatory\ninitiatives, technology evolution and the role of AI in specific domains.\n", "date": "2024-09-13", "categories": []}
{"id": "2410.01850", "title": "An Early-Stage Workflow Proposal for the Generation of Safe and\n  Dependable AI Classifiers", "abstract": "  The generation and execution of qualifiable safe and dependable AI models,\nnecessitates definition of a transparent, complete yet adaptable and preferably\nlightweight workflow. Given the rapidly progressing domain of AI research and\nthe relative immaturity of the safe-AI domain the process stability upon which\nfunctionally safety developments rest must be married with some degree of\nadaptability. This early-stage work proposes such a workflow basing it on a an\nextended ONNX model description. A use case provides one foundations of this\nbody of work which we expect to be extended by other, third party use-cases.\n", "date": "2024-10-01", "categories": []}
{"id": "2410.01927", "title": "Risk Alignment in Agentic AI Systems", "abstract": "  Agentic AIs $-$ AIs that are capable and permitted to undertake complex\nactions with little supervision $-$ mark a new frontier in AI capabilities and\nraise new questions about how to safely create and align such systems with\nusers, developers, and society. Because agents' actions are influenced by their\nattitudes toward risk, one key aspect of alignment concerns the risk profiles\nof agentic AIs. Risk alignment will matter for user satisfaction and trust, but\nit will also have important ramifications for society more broadly, especially\nas agentic AIs become more autonomous and are allowed to control key aspects of\nour lives. AIs with reckless attitudes toward risk (either because they are\ncalibrated to reckless human users or are poorly designed) may pose significant\nthreats. They might also open 'responsibility gaps' in which there is no agent\nwho can be held accountable for harmful actions. What risk attitudes should\nguide an agentic AI's decision-making? How might we design AI systems that are\ncalibrated to the risk attitudes of their users? What guardrails, if any,\nshould be placed on the range of permissible risk attitudes? What are the\nethical considerations involved when designing systems that make risky\ndecisions on behalf of others? We present three papers that bear on key\nnormative and technical aspects of these questions.\n", "date": "2024-10-02", "categories": []}
{"id": "2410.01174", "title": "Towards Inference-time Category-wise Safety Steering for Large Language\n  Models", "abstract": "  While large language models (LLMs) have seen unprecedented advancements in\ncapabilities and applications across a variety of use-cases, safety alignment\nof these models is still an area of active research. The fragile nature of\nLLMs, even models that have undergone extensive alignment and safety training\nregimes, warrants additional safety steering steps via training-free,\ninference-time methods. While recent work in the area of mechanistic\ninterpretability has investigated how activations in latent representation\nspaces may encode concepts, and thereafter performed representation engineering\nto induce such concepts in LLM outputs, the applicability of such for safety is\nrelatively under-explored. Unlike recent inference-time safety steering works,\nin this paper we explore safety steering of LLM outputs using: (i)\ncategory-specific steering vectors, thereby enabling fine-grained control over\nthe steering, and (ii) sophisticated methods for extracting informative\nsteering vectors for more effective safety steering while retaining quality of\nthe generated text. We demonstrate our exploration on multiple LLMs and\ndatasets, and showcase the effectiveness of the proposed steering method, along\nwith a discussion on the implications and best practices.\n", "date": "2024-10-02", "categories": []}
{"id": "2403.17336", "title": "Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of\n  Large Language Models", "abstract": "  Recent advancements in generative AI have enabled ubiquitous access to large\nlanguage models (LLMs). Empowered by their exceptional capabilities to\nunderstand and generate human-like text, these models are being increasingly\nintegrated into our society. At the same time, there are also concerns on the\npotential misuse of this powerful technology, prompting defensive measures from\nservice providers. To overcome such protection, jailbreaking prompts have\nrecently emerged as one of the most effective mechanisms to circumvent security\nrestrictions and elicit harmful content originally designed to be prohibited.\n  Due to the rapid development of LLMs and their ease of access via natural\nlanguages, the frontline of jailbreak prompts is largely seen in online forums\nand among hobbyists. To gain a better understanding of the threat landscape of\nsemantically meaningful jailbreak prompts, we systemized existing prompts and\nmeasured their jailbreak effectiveness empirically. Further, we conducted a\nuser study involving 92 participants with diverse backgrounds to unveil the\nprocess of manually creating jailbreak prompts. We observed that users often\nsucceeded in jailbreak prompts generation regardless of their expertise in\nLLMs. Building on the insights from the user study, we also developed a system\nusing AI as the assistant to automate the process of jailbreak prompt\ngeneration.\n", "date": "2024-03-26", "categories": []}
{"id": "2409.19521", "title": "GenTel-Safe: A Unified Benchmark and Shielding Framework for Defending\n  Against Prompt Injection Attacks", "abstract": "  Large Language Models (LLMs) like GPT-4, LLaMA, and Qwen have demonstrated\nremarkable success across a wide range of applications. However, these models\nremain inherently vulnerable to prompt injection attacks, which can bypass\nexisting safety mechanisms, highlighting the urgent need for more robust attack\ndetection methods and comprehensive evaluation benchmarks. To address these\nchallenges, we introduce GenTel-Safe, a unified framework that includes a novel\nprompt injection attack detection method, GenTel-Shield, along with a\ncomprehensive evaluation benchmark, GenTel-Bench, which compromises 84812\nprompt injection attacks, spanning 3 major categories and 28 security\nscenarios. To prove the effectiveness of GenTel-Shield, we evaluate it together\nwith vanilla safety guardrails against the GenTel-Bench dataset. Empirically,\nGenTel-Shield can achieve state-of-the-art attack detection success rates,\nwhich reveals the critical weakness of existing safeguarding techniques against\nharmful prompts. For reproducibility, we have made the code and benchmarking\ndataset available on the project page at\nhttps://gentellab.github.io/gentel-safe.github.io/.\n", "date": "2024-09-29", "categories": []}
{"id": "2409.17190", "title": "Enhancing Guardrails for Safe and Secure Healthcare AI", "abstract": "  Generative AI holds immense promise in addressing global healthcare access\nchallenges, with numerous innovative applications now ready for use across\nvarious healthcare domains. However, a significant barrier to the widespread\nadoption of these domain-specific AI solutions is the lack of robust safety\nmechanisms to effectively manage issues such as hallucination, misinformation,\nand ensuring truthfulness. Left unchecked, these risks can compromise patient\nsafety and erode trust in healthcare AI systems. While general-purpose\nframeworks like Llama Guard are useful for filtering toxicity and harmful\ncontent, they do not fully address the stringent requirements for truthfulness\nand safety in healthcare contexts. This paper examines the unique safety and\nsecurity challenges inherent to healthcare AI, particularly the risk of\nhallucinations, the spread of misinformation, and the need for factual accuracy\nin clinical settings. I propose enhancements to existing guardrails frameworks,\nsuch as Nvidia NeMo Guardrails, to better suit healthcare-specific needs. By\nstrengthening these safeguards, I aim to ensure the secure, reliable, and\naccurate use of AI in healthcare, mitigating misinformation risks and improving\npatient safety.\n", "date": "2024-09-25", "categories": []}
{"id": "2409.16914", "title": "Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness", "abstract": "  The increasing capability and widespread usage of large language models\n(LLMs) highlight the desirability of automatic detection of LLM-generated text.\nZero-shot detectors, due to their training-free nature, have received\nconsiderable attention and notable success. In this paper, we identify a new\nfeature, token cohesiveness, that is useful for zero-shot detection, and we\ndemonstrate that LLM-generated text tends to exhibit higher token cohesiveness\nthan human-written text. Based on this observation, we devise TOCSIN, a generic\ndual-channel detection paradigm that uses token cohesiveness as a plug-and-play\nmodule to improve existing zero-shot detectors. To calculate token\ncohesiveness, TOCSIN only requires a few rounds of random token deletion and\nsemantic difference measurement, making it particularly suitable for a\npractical black-box setting where the source model used for generation is not\naccessible. Extensive experiments with four state-of-the-art base detectors on\nvarious datasets, source models, and evaluation settings demonstrate the\neffectiveness and generality of the proposed approach. Code available at:\n\\url{https://github.com/Shixuan-Ma/TOCSIN}.\n", "date": "2024-09-25", "categories": []}
{"id": "2409.15398", "title": "Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in\n  Red Teaming GenAI", "abstract": "  As generative AI, particularly large language models (LLMs), become\nincreasingly integrated into production applications, new attack surfaces and\nvulnerabilities emerge and put a focus on adversarial threats in natural\nlanguage and multi-modal systems. Red-teaming has gained importance in\nproactively identifying weaknesses in these systems, while blue-teaming works\nto protect against such adversarial attacks. Despite growing academic interest\nin adversarial risks for generative AI, there is limited guidance tailored for\npractitioners to assess and mitigate these challenges in real-world\nenvironments. To address this, our contributions include: (1) a practical\nexamination of red- and blue-teaming strategies for securing generative AI, (2)\nidentification of key challenges and open questions in defense development and\nevaluation, and (3) the Attack Atlas, an intuitive framework that brings a\npractical approach to analyzing single-turn input attacks, placing it at the\nforefront for practitioners. This work aims to bridge the gap between academic\ninsights and practical security measures for the protection of generative AI\nsystems.\n", "date": "2024-09-23", "categories": []}
{"id": "2403.00292", "title": "Enhancing Jailbreak Attacks with Diversity Guidance", "abstract": "  As large language models(LLMs) become commonplace in practical applications,\nthe security issues of LLMs have attracted societal concerns. Although\nextensive efforts have been made to safety alignment, LLMs remain vulnerable to\njailbreak attacks. We find that redundant computations limit the performance of\nexisting jailbreak attack methods. Therefore, we propose DPP-based Stochastic\nTrigger Searching (DSTS), a new optimization algorithm for jailbreak attacks.\nDSTS incorporates diversity guidance through techniques including stochastic\ngradient search and DPP selection during optimization. Detailed experiments and\nablation studies demonstrate the effectiveness of the algorithm. Moreover, we\nuse the proposed algorithm to compute the risk boundaries for different LLMs,\nproviding a new perspective on LLM safety evaluation.\n", "date": "2024-03-01", "categories": []}
{"id": "2311.00710", "title": "Interactive AI Alignment: Specification, Process, and Evaluation\n  Alignment", "abstract": "  Modern AI enables a high-level, declarative form of interaction: Users\ndescribe the intended outcome they wish an AI to produce, but do not actually\ncreate the outcome themselves. In contrast, in traditional user interfaces,\nusers invoke specific operations to create the desired outcome. This paper\nrevisits the basic input-output interaction cycle in light of this declarative\nstyle of interaction, and connects concepts in AI alignment to define three\nobjectives for interactive alignment of AI: specification alignment (aligning\non what to do), process alignment (aligning on how to do it), and evaluation\nalignment (assisting users in verifying and understanding what was produced).\nUsing existing systems as examples, we show how these user-centered views of AI\nalignment can be used descriptively, prescriptively, and as an evaluative aid.\n", "date": "2023-10-23", "categories": []}
{"id": "2409.10536", "title": "The potential functions of an international institution for AI safety.\n  Insights from adjacent policy areas and recent trends", "abstract": "  Governments, industry, and other actors involved in governing AI technologies\naround the world agree that, while AI offers tremendous promise to benefit the\nworld, appropriate guardrails are required to mitigate risks. Global\ninstitutions, including the OECD, the G7, the G20, UNESCO, and the Council of\nEurope, have already started developing frameworks for ethical and responsible\nAI governance. While these are important initial steps, they alone fall short\nof addressing the need for institutionalised international processes to\nidentify and assess potentially harmful AI capabilities. Contributing to the\nrelevant conversation on how to address this gap, this chapter reflects on what\nfunctions an international AI safety institute could perform. Based on the\nanalysis of both existing international governance models addressing safety\nconsiderations in adjacent policy areas and the newly established national AI\nsafety institutes in the UK and US, the chapter identifies a list of concrete\nfunctions that could be performed at the international level. While creating a\nnew international body is not the only way forward, understanding the structure\nof these bodies from a modular perspective can help us to identify the tools at\nour disposal. These, we suggest, can be categorised under three functional\ndomains: a) technical research and cooperation, b) safeguards and evaluations,\nc) policymaking and governance support.\n", "date": "2024-08-31", "categories": []}
{"id": "2409.11192", "title": "Towards Ethical Personal AI Applications: Practical Considerations for\n  AI Assistants with Long-Term Memory", "abstract": "  One application area of long-term memory (LTM) capabilities with increasing\ntraction is personal AI companions and assistants. With the ability to retain\nand contextualize past interactions and adapt to user preferences, personal AI\ncompanions and assistants promise a profound shift in how we interact with AI\nand are on track to become indispensable in personal and professional settings.\nHowever, this advancement introduces new challenges and vulnerabilities that\nrequire careful consideration regarding the deployment and widespread use of\nthese systems. The goal of this paper is to explore the broader implications of\nbuilding and deploying personal AI applications with LTM capabilities using a\nholistic evaluation approach. This will be done in three ways: 1) reviewing the\ntechnological underpinnings of LTM in Large Language Models, 2) surveying\ncurrent personal AI companions and assistants, and 3) analyzing critical\nconsiderations and implications of deploying and using these applications.\n", "date": "2024-09-17", "categories": []}
{"id": "2409.07503", "title": "AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting\n  LLMs", "abstract": "  Jailbreak vulnerabilities in Large Language Models (LLMs) refer to methods\nthat extract malicious content from the model by carefully crafting prompts or\nsuffixes, which has garnered significant attention from the research community.\nHowever, traditional attack methods, which primarily focus on the semantic\nlevel, are easily detected by the model. These methods overlook the difference\nin the model's alignment protection capabilities at different output stages. To\naddress this issue, we propose an adaptive position pre-fill jailbreak attack\napproach for executing jailbreak attacks on LLMs. Our method leverages the\nmodel's instruction-following capabilities to first output pre-filled safe\ncontent, then exploits its narrative-shifting abilities to generate harmful\ncontent. Extensive black-box experiments demonstrate our method can improve the\nattack success rate by 47% on the widely recognized secure model (Llama2)\ncompared to existing approaches. Our code can be found at:\nhttps://github.com/Yummy416/AdaPPA.\n", "date": "2024-09-11", "categories": []}
{"id": "2409.07772", "title": "Alignment with Preference Optimization Is All You Need for LLM Safety", "abstract": "  We demonstrate that preference optimization methods can effectively enhance\nLLM safety. Applying various alignment techniques to the Falcon 11B model using\nsafety datasets, we achieve a significant boost in global safety score (from\n$57.64\\%$ to $99.90\\%$) as measured by LlamaGuard 3 8B, competing with\nstate-of-the-art models. On toxicity benchmarks, average scores in adversarial\nsettings dropped from over $0.6$ to less than $0.07$. However, this safety\nimprovement comes at the cost of reduced general capabilities, particularly in\nmath, suggesting a trade-off. We identify noise contrastive alignment\n(Safe-NCA) as an optimal method for balancing safety and performance. Our study\nultimately shows that alignment techniques can be sufficient for building safe\nand robust models.\n", "date": "2024-09-12", "categories": []}
{"id": "2409.07335", "title": "Explanation, Debate, Align: A Weak-to-Strong Framework for Language\n  Model Generalization", "abstract": "  The rapid advancement of artificial intelligence systems has brought the\nchallenge of AI alignment to the forefront of research, particularly in complex\ndecision-making and task execution. As these systems surpass human-level\nperformance in sophisticated problems, ensuring their alignment with human\nvalues, intentions, and ethical guidelines becomes crucial. Building on\nprevious work in explanation generation for human-agent alignment, we address\nthe more complex dynamics of multi-agent systems and human-AI teams. This paper\nintroduces a novel approach to model alignment through weak-to-strong\ngeneralization in the context of language models. We present a framework where\na strong model facilitates the improvement of a weaker model, bridging the gap\nbetween explanation generation and model alignment. Our method, formalized as a\nfacilitation function, allows for the transfer of capabilities from advanced\nmodels to less capable ones without direct access to extensive training data.\nOur results suggest that this facilitation-based approach not only enhances\nmodel performance but also provides insights into the nature of model alignment\nand the potential for scalable oversight of AI systems.\n", "date": "2024-09-11", "categories": []}
{"id": "2407.05965", "title": "T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models", "abstract": "  The recent development of Sora leads to a new era in text-to-video (T2V)\ngeneration. Along with this comes the rising concern about its security risks.\nThe generated videos may contain illegal or unethical content, and there is a\nlack of comprehensive quantitative understanding of their safety, posing a\nchallenge to their reliability and practical deployment. Previous evaluations\nprimarily focus on the quality of video generation. While some evaluations of\ntext-to-image models have considered safety, they cover fewer aspects and do\nnot address the unique temporal risk inherent in video generation. To bridge\nthis research gap, we introduce T2VSafetyBench, a new benchmark designed for\nconducting safety-critical assessments of text-to-video models. We define 12\ncritical aspects of video generation safety and construct a malicious prompt\ndataset including real-world prompts, LLM-generated prompts and jailbreak\nattack-based prompts. Based on our evaluation results, we draw several\nimportant findings, including: 1) no single model excels in all aspects, with\ndifferent models showing various strengths; 2) the correlation between GPT-4\nassessments and manual reviews is generally high; 3) there is a trade-off\nbetween the usability and safety of text-to-video generative models. This\nindicates that as the field of video generation rapidly advances, safety risks\nare set to surge, highlighting the urgency of prioritizing video safety. We\nhope that T2VSafetyBench can provide insights for better understanding the\nsafety of video generation in the era of generative AI.\n", "date": "2024-07-08", "categories": []}
{"id": "2408.01605", "title": "CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and\n  Capabilities in Large Language Models", "abstract": "  We are releasing a new suite of security benchmarks for LLMs, CYBERSECEVAL 3,\nto continue the conversation on empirically measuring LLM cybersecurity risks\nand capabilities. CYBERSECEVAL 3 assesses 8 different risks across two broad\ncategories: risk to third parties, and risk to application developers and end\nusers. Compared to previous work, we add new areas focused on offensive\nsecurity capabilities: automated social engineering, scaling manual offensive\ncyber operations, and autonomous offensive cyber operations. In this paper we\ndiscuss applying these benchmarks to the Llama 3 models and a suite of\ncontemporaneous state-of-the-art LLMs, enabling us to contextualize risks both\nwith and without mitigations in place.\n", "date": "2024-08-02", "categories": []}
{"id": "2409.02219", "title": "A+AI: Threats to Society, Remedies, and Governance", "abstract": "  This document focuses on the threats, especially near-term threats, that\nArtificial Intelligence (AI) brings to society. Most of the threats discussed\nhere can result from any algorithmic process, not just AI; in addition,\ndefining AI is notoriously difficult. For both reasons, it is important to\nthink of \"A+AI\": Algorithms and Artificial Intelligence.\n  In addition to the threats, this paper discusses countermeasures to them, and\nit includes a table showing which countermeasures are likely to mitigate which\nthreats. Thoughtful governance could manage the risks without seriously\nimpeding progress; in fact, chances are it would accelerate progress by\nreducing the social chaos that would otherwise be likely. The paper lists\nspecific actions government should take as soon as possible, namely:\n  * Require all social media platforms accessible in the U.S. to offer users\nverification that their accounts are owned by citizens, and to display every\naccount's verification status\n  * Establish regulations to require that all products created or significantly\nmodified with A+AI be clearly labeled as such; to restrict use of generative AI\nto create likenesses of persons; and to require creators of generative AI\nsoftware to disclose materials used to train their software and to compensate\nthe creators of any copyrighted material used\n  * Fund a crash project of research on mitigating the threats\n  * Fund educational campaigns to raise awareness of the threats\n", "date": "2024-09-03", "categories": []}
{"id": "2404.09932", "title": "Foundational Challenges in Assuring Alignment and Safety of Large\n  Language Models", "abstract": "  This work identifies 18 foundational challenges in assuring the alignment and\nsafety of large language models (LLMs). These challenges are organized into\nthree different categories: scientific understanding of LLMs, development and\ndeployment methods, and sociotechnical challenges. Based on the identified\nchallenges, we pose $200+$ concrete research questions.\n", "date": "2024-04-15", "categories": []}
{"id": "2408.15221", "title": "LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet", "abstract": "  Recent large language model (LLM) defenses have greatly improved models'\nability to refuse harmful queries, even when adversarially attacked. However,\nLLM defenses are primarily evaluated against automated adversarial attacks in a\nsingle turn of conversation, an insufficient threat model for real-world\nmalicious use. We demonstrate that multi-turn human jailbreaks uncover\nsignificant vulnerabilities, exceeding 70% attack success rate (ASR) on\nHarmBench against defenses that report single-digit ASRs with automated\nsingle-turn attacks. Human jailbreaks also reveal vulnerabilities in machine\nunlearning defenses, successfully recovering dual-use biosecurity knowledge\nfrom unlearned models. We compile these results into Multi-Turn Human\nJailbreaks (MHJ), a dataset of 2,912 prompts across 537 multi-turn jailbreaks.\nWe publicly release MHJ alongside a compendium of jailbreak tactics developed\nacross dozens of commercial red teaming engagements, supporting research\ntowards stronger LLM defenses.\n", "date": "2024-08-27", "categories": []}
{"id": "2409.02779", "title": "Governing dual-use technologies: Case studies of international security\n  agreements and lessons for AI governance", "abstract": "  International AI governance agreements and institutions may play an important\nrole in reducing global security risks from advanced AI. To inform the design\nof such agreements and institutions, we conducted case studies of historical\nand contemporary international security agreements. We focused specifically on\nthose arrangements around dual-use technologies, examining agreements in\nnuclear security, chemical weapons, biosecurity, and export controls. For each\nagreement, we examined four key areas: (a) purpose, (b) core powers, (c)\ngovernance structure, and (d) instances of non-compliance. From these case\nstudies, we extracted lessons for the design of international AI agreements and\ngovernance institutions. We discuss the importance of robust verification\nmethods, strategies for balancing power between nations, mechanisms for\nadapting to rapid technological change, approaches to managing trade-offs\nbetween transparency and security, incentives for participation, and effective\nenforcement mechanisms.\n", "date": "2024-09-04", "categories": []}
{"id": "2404.05783", "title": "A Survey on Responsible Generative AI: What to Generate and What Not", "abstract": "  In recent years, generative AI (GenAI), like large language models and\ntext-to-image models, has received significant attention across various\ndomains. However, ensuring the responsible generation of content by these\nmodels is crucial for their real-world applicability. This raises an\ninteresting question: What should responsible GenAI generate, and what should\nit not? To answer the question, this paper investigates the practical\nresponsible requirements of both textual and visual generative models,\noutlining five key considerations: generating truthful content, avoiding toxic\ncontent, refusing harmful instruction, leaking no training data-related\ncontent, and ensuring generated content identifiable. Specifically, we review\nrecent advancements and challenges in addressing these requirements. Besides,\nwe discuss and emphasize the importance of responsible GenAI across healthcare,\neducation, finance, and artificial general intelligence domains. Through a\nunified perspective on both textual and visual generative models, this paper\naims to provide insights into practical safety-related issues and further\nbenefit the community in building responsible GenAI.\n", "date": "2024-04-08", "categories": []}
{"id": "2406.10311", "title": "CHiSafetyBench: A Chinese Hierarchical Safety Benchmark for Large\n  Language Models", "abstract": "  With the profound development of large language models(LLMs), their safety\nconcerns have garnered increasing attention. However, there is a scarcity of\nChinese safety benchmarks for LLMs, and the existing safety taxonomies are\ninadequate, lacking comprehensive safety detection capabilities in authentic\nChinese scenarios. In this work, we introduce CHiSafetyBench, a dedicated\nsafety benchmark for evaluating LLMs' capabilities in identifying risky content\nand refusing answering risky questions in Chinese contexts. CHiSafetyBench\nincorporates a dataset that covers a hierarchical Chinese safety taxonomy\nconsisting of 5 risk areas and 31 categories. This dataset comprises two types\nof tasks: multiple-choice questions and question-answering, evaluating LLMs\nfrom the perspectives of risk content identification and the ability to refuse\nanswering risky questions respectively. Utilizing this benchmark, we validate\nthe feasibility of automatic evaluation as a substitute for human evaluation\nand conduct comprehensive automatic safety assessments on mainstream Chinese\nLLMs. Our experiments reveal the varying performance of different models across\nvarious safety domains, indicating that all models possess considerable\npotential for improvement in Chinese safety capabilities. Our dataset is\npublicly available at\nhttps://github.com/UnicomAI/UnicomBenchmark/tree/main/CHiSafetyBench.\n", "date": "2024-06-14", "categories": []}
{"id": "2409.01630", "title": "SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems", "abstract": "  Embodied AI systems, including AI-powered robots that autonomously interact\nwith the physical world, stand to be significantly advanced by Large Language\nModels (LLMs), which enable robots to better understand complex language\ncommands and perform advanced tasks with enhanced comprehension and\nadaptability, highlighting their potential to improve embodied AI capabilities.\nHowever, this advancement also introduces safety challenges, particularly in\nrobotic navigation tasks. Improper safety management can lead to failures in\ncomplex environments and make the system vulnerable to malicious command\ninjections, resulting in unsafe behaviours such as detours or collisions. To\naddress these issues, we propose \\textit{SafeEmbodAI}, a safety framework for\nintegrating mobile robots into embodied AI systems. \\textit{SafeEmbodAI}\nincorporates secure prompting, state management, and safety validation\nmechanisms to secure and assist LLMs in reasoning through multi-modal data and\nvalidating responses. We designed a metric to evaluate mission-oriented\nexploration, and evaluations in simulated environments demonstrate that our\nframework effectively mitigates threats from malicious commands and improves\nperformance in various environment settings, ensuring the safety of embodied AI\nsystems. Notably, In complex environments with mixed obstacles, our method\ndemonstrates a significant performance increase of 267\\% compared to the\nbaseline in attack scenarios, highlighting its robustness in challenging\nconditions.\n", "date": "2024-09-03", "categories": []}
{"id": "2401.15897", "title": "Red-Teaming for Generative AI: Silver Bullet or Security Theater?", "abstract": "  In response to rising concerns surrounding the safety, security, and\ntrustworthiness of Generative AI (GenAI) models, practitioners and regulators\nalike have pointed to AI red-teaming as a key component of their strategies for\nidentifying and mitigating these risks. However, despite AI red-teaming's\ncentral role in policy discussions and corporate messaging, significant\nquestions remain about what precisely it means, what role it can play in\nregulation, and how it relates to conventional red-teaming practices as\noriginally conceived in the field of cybersecurity. In this work, we identify\nrecent cases of red-teaming activities in the AI industry and conduct an\nextensive survey of relevant research literature to characterize the scope,\nstructure, and criteria for AI red-teaming practices. Our analysis reveals that\nprior methods and practices of AI red-teaming diverge along several axes,\nincluding the purpose of the activity (which is often vague), the artifact\nunder evaluation, the setting in which the activity is conducted (e.g., actors,\nresources, and methods), and the resulting decisions it informs (e.g.,\nreporting, disclosure, and mitigation). In light of our findings, we argue that\nwhile red-teaming may be a valuable big-tent idea for characterizing GenAI harm\nmitigations, and that industry may effectively apply red-teaming and other\nstrategies behind closed doors to safeguard AI, gestures towards red-teaming\n(based on public definitions) as a panacea for every possible risk verge on\nsecurity theater. To move toward a more robust toolbox of evaluations for\ngenerative AI, we synthesize our recommendations into a question bank meant to\nguide and scaffold future AI red-teaming practices.\n", "date": "2024-01-29", "categories": []}
{"id": "2408.15116", "title": "Evaluating Stability of Unreflective Alignment", "abstract": "  Many theoretical obstacles to AI alignment are consequences of reflective\nstability - the problem of designing alignment mechanisms that the AI would not\ndisable if given the option. However, problems stemming from reflective\nstability are not obviously present in current LLMs, leading to disagreement\nover whether they will need to be solved to enable safe delegation of cognitive\nlabor. In this paper, we propose Counterfactual Priority Change (CPC)\ndestabilization as a mechanism by which reflective stability problems may arise\nin future LLMs. We describe two risk factors for CPC-destabilization: 1)\nCPC-based stepping back and 2) preference instability. We develop preliminary\nevaluations for each of these risk factors, and apply them to frontier LLMs.\nOur findings indicate that in current LLMs, increased scale and capability are\nassociated with increases in both CPC-based stepping back and preference\ninstability, suggesting that CPC-destabilization may cause reflective stability\nproblems in future LLMs.\n", "date": "2024-08-27", "categories": []}
{"id": "2404.14082", "title": "Mechanistic Interpretability for AI Safety -- A Review", "abstract": "  Understanding AI systems' inner workings is critical for ensuring value\nalignment and safety. This review explores mechanistic interpretability:\nreverse engineering the computational mechanisms and representations learned by\nneural networks into human-understandable algorithms and concepts to provide a\ngranular, causal understanding. We establish foundational concepts such as\nfeatures encoding knowledge within neural activations and hypotheses about\ntheir representation and computation. We survey methodologies for causally\ndissecting model behaviors and assess the relevance of mechanistic\ninterpretability to AI safety. We examine benefits in understanding, control,\nalignment, and risks such as capability gains and dual-use concerns. We\ninvestigate challenges surrounding scalability, automation, and comprehensive\ninterpretation. We advocate for clarifying concepts, setting standards, and\nscaling techniques to handle complex models and behaviors and expand to domains\nsuch as vision and reinforcement learning. Mechanistic interpretability could\nhelp prevent catastrophic outcomes as AI systems become more powerful and\ninscrutable.\n", "date": "2024-04-22", "categories": []}
{"id": "2407.13833", "title": "Phi-3 Safety Post-Training: Aligning Language Models with a \"Break-Fix\"\n  Cycle", "abstract": "  Recent innovations in language model training have demonstrated that it is\npossible to create highly performant models that are small enough to run on a\nsmartphone. As these models are deployed in an increasing number of domains, it\nis critical to ensure that they are aligned with human preferences and safety\nconsiderations. In this report, we present our methodology for safety aligning\nthe Phi-3 series of language models. We utilized a \"break-fix\" cycle,\nperforming multiple rounds of dataset curation, safety post-training,\nbenchmarking, red teaming, and vulnerability identification to cover a variety\nof harm areas in both single and multi-turn scenarios. Our results indicate\nthat this approach iteratively improved the performance of the Phi-3 models\nacross a wide range of responsible AI benchmarks. Finally, we include\nadditional red teaming strategies and evaluations that were used to test the\nsafety behavior of Phi-3.5-mini and Phi-3.5-MoE, which were optimized for\nmultilingual capabilities.\n", "date": "2024-07-18", "categories": []}
{"id": "2408.12806", "title": "Is Generative AI the Next Tactical Cyber Weapon For Threat Actors?\n  Unforeseen Implications of AI Generated Cyber Attacks", "abstract": "  In an era where digital threats are increasingly sophisticated, the\nintersection of Artificial Intelligence and cybersecurity presents both\npromising defenses and potent dangers. This paper delves into the escalating\nthreat posed by the misuse of AI, specifically through the use of Large\nLanguage Models (LLMs). This study details various techniques like the switch\nmethod and character play method, which can be exploited by cybercriminals to\ngenerate and automate cyber attacks. Through a series of controlled\nexperiments, the paper demonstrates how these models can be manipulated to\nbypass ethical and privacy safeguards to effectively generate cyber attacks\nsuch as social engineering, malicious code, payload generation, and spyware. By\ntesting these AI generated attacks on live systems, the study assesses their\neffectiveness and the vulnerabilities they exploit, offering a practical\nperspective on the risks AI poses to critical infrastructure. We also introduce\nOccupy AI, a customized, finetuned LLM specifically engineered to automate and\nexecute cyberattacks. This specialized AI driven tool is adept at crafting\nsteps and generating executable code for a variety of cyber threats, including\nphishing, malware injection, and system exploitation. The results underscore\nthe urgency for ethical AI practices, robust cybersecurity measures, and\nregulatory oversight to mitigate AI related threats. This paper aims to elevate\nawareness within the cybersecurity community about the evolving digital threat\nlandscape, advocating for proactive defense strategies and responsible AI\ndevelopment to protect against emerging cyber threats.\n", "date": "2024-08-23", "categories": []}
{"id": "2402.05070", "title": "A Roadmap to Pluralistic Alignment", "abstract": "  With increased power and prevalence of AI systems, it is ever more critical\nthat AI systems are designed to serve all, i.e., people with diverse values and\nperspectives. However, aligning models to serve pluralistic human values\nremains an open research question. In this piece, we propose a roadmap to\npluralistic alignment, specifically using language models as a test bed. We\nidentify and formalize three possible ways to define and operationalize\npluralism in AI systems: 1) Overton pluralistic models that present a spectrum\nof reasonable responses; 2) Steerably pluralistic models that can steer to\nreflect certain perspectives; and 3) Distributionally pluralistic models that\nare well-calibrated to a given population in distribution. We also formalize\nand discuss three possible classes of pluralistic benchmarks: 1)\nMulti-objective benchmarks, 2) Trade-off steerable benchmarks, which\nincentivize models to steer to arbitrary trade-offs, and 3) Jury-pluralistic\nbenchmarks which explicitly model diverse human ratings. We use this framework\nto argue that current alignment techniques may be fundamentally limited for\npluralistic AI; indeed, we highlight empirical evidence, both from our own\nexperiments and from other work, that standard alignment procedures might\nreduce distributional pluralism in models, motivating the need for further\nresearch on pluralistic alignment.\n", "date": "2024-02-07", "categories": []}
{"id": "2402.09091", "title": "Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit\n  Clues", "abstract": "  With the development of LLMs, the security threats of LLMs are getting more\nand more attention. Numerous jailbreak attacks have been proposed to assess the\nsecurity defense of LLMs. Current jailbreak attacks primarily utilize scenario\ncamouflage techniques. However their explicitly mention of malicious intent\nwill be easily recognized and defended by LLMs. In this paper, we propose an\nindirect jailbreak attack approach, Puzzler, which can bypass the LLM's defense\nstrategy and obtain malicious response by implicitly providing LLMs with some\nclues about the original malicious query. In addition, inspired by the wisdom\nof \"When unable to attack, defend\" from Sun Tzu's Art of War, we adopt a\ndefensive stance to gather clues about the original malicious query through\nLLMs. Extensive experimental results show that Puzzler achieves a query success\nrate of 96.6% on closed-source LLMs, which is 57.9%-82.7% higher than\nbaselines. Furthermore, when tested against the state-of-the-art jailbreak\ndetection approaches, Puzzler proves to be more effective at evading detection\ncompared to baselines.\n", "date": "2024-02-14", "categories": []}
{"id": "2405.20775", "title": "Medical MLLM is Vulnerable: Cross-Modality Jailbreak and Mismatched\n  Attacks on Medical Multimodal Large Language Models", "abstract": "  Security concerns related to Large Language Models (LLMs) have been\nextensively explored, yet the safety implications for Multimodal Large Language\nModels (MLLMs), particularly in medical contexts (MedMLLMs), remain\ninsufficiently studied. This paper delves into the underexplored security\nvulnerabilities of MedMLLMs, especially when deployed in clinical environments\nwhere the accuracy and relevance of question-and-answer interactions are\ncritically tested against complex medical challenges. By combining existing\nclinical medical data with atypical natural phenomena, we define the mismatched\nmalicious attack (2M-attack) and introduce its optimized version, known as the\noptimized mismatched malicious attack (O2M-attack or 2M-optimization). Using\nthe voluminous 3MAD dataset that we construct, which covers a wide range of\nmedical image modalities and harmful medical scenarios, we conduct a\ncomprehensive analysis and propose the MCM optimization method, which\nsignificantly enhances the attack success rate on MedMLLMs. Evaluations with\nthis dataset and attack methods, including white-box attacks on LLaVA-Med and\ntransfer attacks (black-box) on four other SOTA models, indicate that even\nMedMLLMs designed with enhanced security features remain vulnerable to security\nbreaches. Our work underscores the urgent need for a concerted effort to\nimplement robust security measures and enhance the safety and efficacy of\nopen-source MedMLLMs, particularly given the potential severity of jailbreak\nattacks and other malicious or clinically significant exploits in medical\nsettings. Our code is available at https://github.com/dirtycomputer/O2M_attack.\n", "date": "2024-05-26", "categories": []}
{"id": "2407.10264", "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "abstract": "  Safety fine-tuning helps align Large Language Models (LLMs) with human\npreferences for their safe deployment. To better understand the underlying\nfactors that make models safe via safety fine-tuning, we design a synthetic\ndata generation framework that captures salient aspects of an unsafe input by\nmodeling the interaction between the task the model is asked to perform (e.g.,\n\"design\") versus the specific concepts the task is asked to be performed upon\n(e.g., a \"cycle\" vs. a \"bomb\"). Using this, we investigate three well-known\nsafety fine-tuning methods -- supervised safety fine-tuning, direct preference\noptimization, and unlearning -- and provide significant evidence demonstrating\nthat these methods minimally transform MLP weights to specifically align unsafe\ninputs into its weights' null space. This yields a clustering of inputs based\non whether the model deems them safe or not. Correspondingly, when an\nadversarial input (e.g., a jailbreak) is provided, its activations are closer\nto safer samples, leading to the model processing such an input as if it were\nsafe. We validate our findings, wherever possible, on real-world models --\nspecifically, Llama-2 7B and Llama-3 8B.\n", "date": "2024-07-14", "categories": []}
{"id": "2401.11880", "title": "PsySafe: A Comprehensive Framework for Psychological-based Attack,\n  Defense, and Evaluation of Multi-agent System Safety", "abstract": "  Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit\nprofound capabilities in collective intelligence. However, the potential misuse\nof this intelligence for malicious purposes presents significant risks. To\ndate, comprehensive research on the safety issues associated with multi-agent\nsystems remains limited. In this paper, we explore these concerns through the\ninnovative lens of agent psychology, revealing that the dark psychological\nstates of agents constitute a significant threat to safety. To tackle these\nconcerns, we propose a comprehensive framework (PsySafe) grounded in agent\npsychology, focusing on three key areas: firstly, identifying how dark\npersonality traits in agents can lead to risky behaviors; secondly, evaluating\nthe safety of multi-agent systems from the psychological and behavioral\nperspectives, and thirdly, devising effective strategies to mitigate these\nrisks. Our experiments reveal several intriguing phenomena, such as the\ncollective dangerous behaviors among agents, agents' self-reflection when\nengaging in dangerous behavior, and the correlation between agents'\npsychological assessments and dangerous behaviors. We anticipate that our\nframework and observations will provide valuable insights for further research\ninto the safety of multi-agent systems. We will make our data and code publicly\naccessible at https://github.com/AI4Good24/PsySafe.\n", "date": "2024-01-22", "categories": []}
{"id": "2404.01099", "title": "What is in Your Safe Data? Identifying Benign Data that Breaks Safety", "abstract": "  Current Large Language Models (LLMs), even those tuned for safety and\nalignment, are susceptible to jailbreaking. Some have found that just further\nfine-tuning an aligned model with benign data (i.e., data without harmful\ncontent) surprisingly leads to substantial degradation in safety. We delve into\nthe data-centric aspects of why benign fine-tuning inadvertently contributes to\njailbreaking. First, we represent fine-tuning data through two lenses:\nrepresentation and gradient spaces. Additionally, we propose a bi-directional\nanchoring method that, during the selection process, prioritizes data points\nthat are close to harmful examples and far from benign ones. Our approach\neffectively identifies subsets of benign data that are more likely to degrade\nthe model's safety after fine-tuning. Training on just 100 of these seemingly\nbenign datapoints surprisingly leads to the fine-tuned model affirmatively\nresponding to >70% of tested harmful requests, compared to <20% after\nfine-tuning on randomly selected data. We also observe that the selected data\nfrequently appear as lists, bullet points, or math questions, indicating a\nsystematic pattern in fine-tuning data that contributes to jailbreaking.\n", "date": "2024-04-01", "categories": []}
{"id": "2408.03837", "title": "WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language\n  Models", "abstract": "  WalledEval is a comprehensive AI safety testing toolkit designed to evaluate\nlarge language models (LLMs). It accommodates a diverse range of models,\nincluding both open-weight and API-based ones, and features over 35 safety\nbenchmarks covering areas such as multilingual safety, exaggerated safety, and\nprompt injections. The framework supports both LLM and judge benchmarking and\nincorporates custom mutators to test safety against various text-style\nmutations, such as future tense and paraphrasing. Additionally, WalledEval\nintroduces WalledGuard, a new, small, and performant content moderation tool,\nand two datasets: SGXSTest and HIXSTest, which serve as benchmarks for\nassessing the exaggerated safety of LLMs and judges in cultural contexts. We\nmake WalledEval publicly available at https://github.com/walledai/walledeval.\n", "date": "2024-08-07", "categories": []}
{"id": "2403.06009", "title": "Detectors for Safe and Reliable LLMs: Implementations, Uses, and\n  Limitations", "abstract": "  Large language models (LLMs) are susceptible to a variety of risks, from\nnon-faithful output to biased and toxic generations. Due to several limiting\nfactors surrounding LLMs (training cost, API access, data availability, etc.),\nit may not always be feasible to impose direct safety constraints on a deployed\nmodel. Therefore, an efficient and reliable alternative is required. To this\nend, we present our ongoing efforts to create and deploy a library of\ndetectors: compact and easy-to-build classification models that provide labels\nfor various harms. In addition to the detectors themselves, we discuss a wide\nrange of uses for these detector models - from acting as guardrails to enabling\neffective AI governance. We also deep dive into inherent challenges in their\ndevelopment and discuss future work aimed at making the detectors more reliable\nand broadening their scope.\n", "date": "2024-03-09", "categories": []}
{"id": "2408.08899", "title": "Kov: Transferable and Naturalistic Black-Box LLM Attacks using Markov\n  Decision Processes and Tree Search", "abstract": "  Eliciting harmful behavior from large language models (LLMs) is an important\ntask to ensure the proper alignment and safety of the models. Often when\ntraining LLMs, ethical guidelines are followed yet alignment failures may still\nbe uncovered through red teaming adversarial attacks. This work frames the\nred-teaming problem as a Markov decision process (MDP) and uses Monte Carlo\ntree search to find harmful behaviors of black-box, closed-source LLMs. We\noptimize token-level prompt suffixes towards targeted harmful behaviors on\nwhite-box LLMs and include a naturalistic loss term, log-perplexity, to\ngenerate more natural language attacks for better interpretability. The\nproposed algorithm, Kov, trains on white-box LLMs to optimize the adversarial\nattacks and periodically evaluates responses from the black-box LLM to guide\nthe search towards more harmful black-box behaviors. In our preliminary study,\nresults indicate that we can jailbreak black-box models, such as GPT-3.5, in\nonly 10 queries, yet fail on GPT-4$-$which may indicate that newer models are\nmore robust to token-level attacks. All work to reproduce these results is open\nsourced (https://github.com/sisl/Kov.jl).\n", "date": "2024-08-11", "categories": []}
{"id": "2408.08995", "title": "On the Undecidability of Artificial Intelligence Alignment: Machines\n  that Halt", "abstract": "  The inner alignment problem, which asserts whether an arbitrary artificial\nintelligence (AI) model satisfices a non-trivial alignment function of its\noutputs given its inputs, is undecidable. This is rigorously proved by Rice's\ntheorem, which is also equivalent to a reduction to Turing's Halting Problem,\nwhose proof sketch is presented in this work. Nevertheless, there is an\nenumerable set of provenly aligned AIs that are constructed from a finite set\nof provenly aligned operations. Therefore, we argue that the alignment should\nbe a guaranteed property from the AI architecture rather than a characteristic\nimposed post-hoc on an arbitrary AI model. Furthermore, while the outer\nalignment problem is the definition of a judge function that captures human\nvalues and preferences, we propose that such a function must also impose a\nhalting constraint that guarantees that the AI model always reaches a terminal\nstate in finite execution steps. Our work presents examples and models that\nillustrate this constraint and the intricate challenges involved, advancing a\ncompelling case for adopting an intrinsically hard-aligned approach to AI\nsystems architectures that ensures halting.\n", "date": "2024-08-16", "categories": []}
{"id": "2408.09326", "title": "Characterizing and Evaluating the Reliability of LLMs against Jailbreak\n  Attacks", "abstract": "  Large Language Models (LLMs) have increasingly become pivotal in content\ngeneration with notable societal impact. These models hold the potential to\ngenerate content that could be deemed harmful.Efforts to mitigate this risk\ninclude implementing safeguards to ensure LLMs adhere to social ethics.However,\ndespite such measures, the phenomenon of \"jailbreaking\" -- where carefully\ncrafted prompts elicit harmful responses from models -- persists as a\nsignificant challenge. Recognizing the continuous threat posed by jailbreaking\ntactics and their repercussions for the trustworthy use of LLMs, a rigorous\nassessment of the models' robustness against such attacks is essential. This\nstudy introduces an comprehensive evaluation framework and conducts an\nlarge-scale empirical experiment to address this need. We concentrate on 10\ncutting-edge jailbreak strategies across three categories, 1525 questions from\n61 specific harmful categories, and 13 popular LLMs. We adopt multi-dimensional\nmetrics such as Attack Success Rate (ASR), Toxicity Score, Fluency, Token\nLength, and Grammatical Errors to thoroughly assess the LLMs' outputs under\njailbreak. By normalizing and aggregating these metrics, we present a detailed\nreliability score for different LLMs, coupled with strategic recommendations to\nreduce their susceptibility to such vulnerabilities. Additionally, we explore\nthe relationships among the models, attack strategies, and types of harmful\ncontent, as well as the correlations between the evaluation metrics, which\nproves the validity of our multifaceted evaluation framework. Our extensive\nexperimental results demonstrate a lack of resilience among all tested LLMs\nagainst certain strategies, and highlight the need to concentrate on the\nreliability facets of LLMs. We believe our study can provide valuable insights\ninto enhancing the security evaluation of LLMs against jailbreak within the\ndomain.\n", "date": "2024-08-18", "categories": []}
{"id": "2311.09433", "title": "Trojan Activation Attack: Red-Teaming Large Language Models using\n  Activation Steering for Safety-Alignment", "abstract": "  To ensure AI safety, instruction-tuned Large Language Models (LLMs) are\nspecifically trained to ensure alignment, which refers to making models behave\nin accordance with human intentions. While these models have demonstrated\ncommendable results on various safety benchmarks, the vulnerability of their\nsafety alignment has not been extensively studied. This is particularly\ntroubling given the potential harm that LLMs can inflict. Existing attack\nmethods on LLMs often rely on poisoned training data or the injection of\nmalicious prompts. These approaches compromise the stealthiness and\ngeneralizability of the attacks, making them susceptible to detection.\nAdditionally, these models often demand substantial computational resources for\nimplementation, making them less practical for real-world applications. In this\nwork, we study a different attack scenario, called Trojan Activation Attack\n(TA^2), which injects trojan steering vectors into the activation layers of\nLLMs. These malicious steering vectors can be triggered at inference time to\nsteer the models toward attacker-desired behaviors by manipulating their\nactivations. Our experiment results on four primary alignment tasks show that\nTA^2 is highly effective and adds little or no overhead to attack efficiency.\nAdditionally, we discuss potential countermeasures against such activation\nattacks.\n", "date": "2023-11-15", "categories": []}
{"id": "2402.01787", "title": "Harm Amplification in Text-to-Image Models", "abstract": "  Text-to-image (T2I) models have emerged as a significant advancement in\ngenerative AI; however, there exist safety concerns regarding their potential\nto produce harmful image outputs even when users input seemingly safe prompts.\nThis phenomenon, where T2I models generate harmful representations that were\nnot explicit in the input prompt, poses a potentially greater risk than\nadversarial prompts, leaving users unintentionally exposed to harms. Our paper\naddresses this issue by formalizing a definition for this phenomenon which we\nterm harm amplification. We further contribute to the field by developing a\nframework of methodologies to quantify harm amplification in which we consider\nthe harm of the model output in the context of user input. We then empirically\nexamine how to apply these different methodologies to simulate real-world\ndeployment scenarios including a quantification of disparate impacts across\ngenders resulting from harm amplification. Together, our work aims to offer\nresearchers tools to comprehensively address safety challenges in T2I systems\nand contribute to the responsible deployment of generative AI models.\n", "date": "2024-02-01", "categories": []}
{"id": "2406.11852", "title": "Risks from Language Models for Automated Mental Healthcare: Ethics and\n  Structure for Implementation", "abstract": "  Amidst the growing interest in developing task-autonomous AI for automated\nmental health care, this paper addresses the ethical and practical challenges\nassociated with the issue and proposes a structured framework that delineates\nlevels of autonomy, outlines ethical requirements, and defines beneficial\ndefault behaviors for AI agents in the context of mental health support. We\nalso evaluate fourteen state-of-the-art language models (ten off-the-shelf,\nfour fine-tuned) using 16 mental health-related questionnaires designed to\nreflect various mental health conditions, such as psychosis, mania, depression,\nsuicidal thoughts, and homicidal tendencies. The questionnaire design and\nresponse evaluations were conducted by mental health clinicians (M.D.s). We\nfind that existing language models are insufficient to match the standard\nprovided by human professionals who can navigate nuances and appreciate\ncontext. This is due to a range of issues, including overly cautious or\nsycophantic responses and the absence of necessary safeguards. Alarmingly, we\nfind that most of the tested models could cause harm if accessed in mental\nhealth emergencies, failing to protect users and potentially exacerbating\nexisting symptoms. We explore solutions to enhance the safety of current\nmodels. Before the release of increasingly task-autonomous AI systems in mental\nhealth, it is crucial to ensure that these models can reliably detect and\nmanage symptoms of common psychiatric disorders to prevent harm to users. This\ninvolves aligning with the ethical framework and default behaviors outlined in\nour study. We contend that model developers are responsible for refining their\nsystems per these guidelines to safeguard against the risks posed by current AI\ntechnologies to user mental health and safety.\n  Trigger warning: Contains and discusses examples of sensitive mental health\ntopics, including suicide and self-harm.\n", "date": "2024-04-02", "categories": []}
{"id": "2206.13353", "title": "Is Power-Seeking AI an Existential Risk?", "abstract": "  This report examines what I see as the core argument for concern about\nexistential risk from misaligned artificial intelligence. I proceed in two\nstages. First, I lay out a backdrop picture that informs such concern. On this\npicture, intelligent agency is an extremely powerful force, and creating agents\nmuch more intelligent than us is playing with fire -- especially given that if\ntheir objectives are problematic, such agents would plausibly have instrumental\nincentives to seek power over humans. Second, I formulate and evaluate a more\nspecific six-premise argument that creating agents of this kind will lead to\nexistential catastrophe by 2070. On this argument, by 2070: (1) it will become\npossible and financially feasible to build relevantly powerful and agentic AI\nsystems; (2) there will be strong incentives to do so; (3) it will be much\nharder to build aligned (and relevantly powerful/agentic) AI systems than to\nbuild misaligned (and relevantly powerful/agentic) AI systems that are still\nsuperficially attractive to deploy; (4) some such misaligned systems will seek\npower over humans in high-impact ways; (5) this problem will scale to the full\ndisempowerment of humanity; and (6) such disempowerment will constitute an\nexistential catastrophe. I assign rough subjective credences to the premises in\nthis argument, and I end up with an overall estimate of ~5% that an existential\ncatastrophe of this kind will occur by 2070. (May 2022 update: since making\nthis report public in April 2021, my estimate here has gone up, and is now at\n>10%.)\n", "date": "2022-06-16", "categories": []}
{"id": "2406.12094", "title": "Who's asking? User personas and the mechanics of latent misalignment", "abstract": "  Despite investments in improving model safety, studies show that misaligned\ncapabilities remain latent in safety-tuned models. In this work, we shed light\non the mechanics of this phenomenon. First, we show that even when model\ngenerations are safe, harmful content can persist in hidden representations and\ncan be extracted by decoding from earlier layers. Then, we show that whether\nthe model divulges such content depends significantly on its perception of who\nit is talking to, which we refer to as user persona. In fact, we find\nmanipulating user persona to be even more effective for eliciting harmful\ncontent than direct attempts to control model refusal. We study both natural\nlanguage prompting and activation steering as control methods and show that\nactivation steering is significantly more effective at bypassing safety\nfilters. We investigate why certain personas break model safeguards and find\nthat they enable the model to form more charitable interpretations of otherwise\ndangerous queries. Finally, we show we can predict a persona's effect on\nrefusal given only the geometry of its steering vector.\n", "date": "2024-06-17", "categories": []}
{"id": "2404.10464", "title": "DESTEIN: Navigating Detoxification of Language Models via Universal\n  Steering Pairs and Head-wise Activation Fusion", "abstract": "  Despite the remarkable achievements of language models (LMs) across a broad\nspectrum of tasks, their propensity for generating toxic outputs remains a\nprevalent concern. Current solutions involving finetuning or auxiliary models\nusually require extensive computational resources, hindering their practicality\nin large language models (LLMs). In this paper, we propose DeStein, a novel\nmethod that detoxifies LMs by applying representation engineering in activation\nspaces with lower resource and time costs. Specifically, we derive\ndetoxification vectors from self-induced, universal steering pairs through\narithmetic operations in activation spaces. During inference, detoxification is\nachieved by fusing the detoxification vectors with the original representations\nin a head-wise manner. Empirical results demonstrate that our method\nsignificantly outperforms previous state-of-the-art approaches on various\nmetrics, while also maintaining satisfactory generation quality and diversity.\nWe further validate the practicality and scalability of DeStein with a series\nof white-box LLMs. The method is open-sourced at\nhttps://github.com/LizLizLi/DeStein. Warning: Some example model outputs may\ncontain highly offensive or disturbing text.\n", "date": "2024-04-16", "categories": []}
{"id": "2408.04655", "title": "Strong and weak alignment of large language models with human values", "abstract": "  Minimizing negative impacts of Artificial Intelligent (AI) systems on human\nsocieties without human supervision requires them to be able to align with\nhuman values. However, most current work only addresses this issue from a\ntechnical point of view, e.g., improving current methods relying on\nreinforcement learning from human feedback, neglecting what it means and is\nrequired for alignment to occur. Here, we propose to distinguish strong and\nweak value alignment. Strong alignment requires cognitive abilities (either\nhuman-like or different from humans) such as understanding and reasoning about\nagents' intentions and their ability to causally produce desired effects. We\nargue that this is required for AI systems like large language models (LLMs) to\nbe able to recognize situations presenting a risk that human values may be\nflouted. To illustrate this distinction, we present a series of prompts showing\nChatGPT's, Gemini's and Copilot's failures to recognize some of these\nsituations. We moreover analyze word embeddings to show that the nearest\nneighbors of some human values in LLMs differ from humans' semantic\nrepresentations. We then propose a new thought experiment that we call \"the\nChinese room with a word transition dictionary\", in extension of John Searle's\nfamous proposal. We finally mention current promising research directions\ntowards a weak alignment, which could produce statistically satisfying answers\nin a number of common situations, however so far without ensuring any truth\nvalue.\n", "date": "2024-08-05", "categories": []}
{"id": "2408.04643", "title": "Risks, Causes, and Mitigations of Widespread Deployments of Large\n  Language Models (LLMs): A Survey", "abstract": "  Recent advancements in Large Language Models (LLMs), such as ChatGPT and\nLLaMA, have significantly transformed Natural Language Processing (NLP) with\ntheir outstanding abilities in text generation, summarization, and\nclassification. Nevertheless, their widespread adoption introduces numerous\nchallenges, including issues related to academic integrity, copyright,\nenvironmental impacts, and ethical considerations such as data bias, fairness,\nand privacy. The rapid evolution of LLMs also raises concerns regarding the\nreliability and generalizability of their evaluations. This paper offers a\ncomprehensive survey of the literature on these subjects, systematically\ngathered and synthesized from Google Scholar. Our study provides an in-depth\nanalysis of the risks associated with specific LLMs, identifying sub-risks,\ntheir causes, and potential solutions. Furthermore, we explore the broader\nchallenges related to LLMs, detailing their causes and proposing mitigation\nstrategies. Through this literature analysis, our survey aims to deepen the\nunderstanding of the implications and complexities surrounding these powerful\nmodels.\n", "date": "2024-08-01", "categories": []}
{"id": "2407.17436", "title": "AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from\n  Regulations and Policies", "abstract": "  Foundation models (FMs) provide societal benefits but also amplify risks.\nGovernments, companies, and researchers have proposed regulatory frameworks,\nacceptable use policies, and safety benchmarks in response. However, existing\npublic benchmarks often define safety categories based on previous literature,\nintuitions, or common sense, leading to disjointed sets of categories for risks\nspecified in recent regulations and policies, which makes it challenging to\nevaluate and compare FMs across these benchmarks. To bridge this gap, we\nintroduce AIR-Bench 2024, the first AI safety benchmark aligned with emerging\ngovernment regulations and company policies, following the regulation-based\nsafety categories grounded in our AI risks study, AIR 2024. AIR 2024 decomposes\n8 government regulations and 16 company policies into a four-tiered safety\ntaxonomy with 314 granular risk categories in the lowest tier. AIR-Bench 2024\ncontains 5,694 diverse prompts spanning these categories, with manual curation\nand human auditing to ensure quality. We evaluate leading language models on\nAIR-Bench 2024, uncovering insights into their alignment with specified safety\nconcerns. By bridging the gap between public benchmarks and practical AI risks,\nAIR-Bench 2024 provides a foundation for assessing model safety across\njurisdictions, fostering the development of safer and more responsible AI\nsystems.\n", "date": "2024-07-11", "categories": []}
{"id": "2402.12193", "title": "A Chinese Dataset for Evaluating the Safeguards in Large Language Models", "abstract": "  Many studies have demonstrated that large language models (LLMs) can produce\nharmful responses, exposing users to unexpected risks when LLMs are deployed.\nPrevious studies have proposed comprehensive taxonomies of the risks posed by\nLLMs, as well as corresponding prompts that can be used to examine the safety\nmechanisms of LLMs. However, the focus has been almost exclusively on English,\nand little has been explored for other languages. Here we aim to bridge this\ngap. We first introduce a dataset for the safety evaluation of Chinese LLMs,\nand then extend it to two other scenarios that can be used to better identify\nfalse negative and false positive examples in terms of risky prompt rejections.\nWe further present a set of fine-grained safety assessment criteria for each\nrisk type, facilitating both manual annotation and automatic evaluation in\nterms of LLM response harmfulness. Our experiments on five LLMs show that\nregion-specific risks are the prevalent type of risk, presenting the major\nissue with all Chinese LLMs we experimented with. Our data is available at\nhttps://github.com/Libr-AI/do-not-answer. Warning: this paper contains example\ndata that may be offensive, harmful, or biased.\n", "date": "2024-02-19", "categories": []}
{"id": "2403.02691", "title": "InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated\n  Large Language Model Agents", "abstract": "  Recent work has embodied LLMs as agents, allowing them to access tools,\nperform actions, and interact with external content (e.g., emails or websites).\nHowever, external content introduces the risk of indirect prompt injection\n(IPI) attacks, where malicious instructions are embedded within the content\nprocessed by LLMs, aiming to manipulate these agents into executing detrimental\nactions against users. Given the potentially severe consequences of such\nattacks, establishing benchmarks to assess and mitigate these risks is\nimperative.\n  In this work, we introduce InjecAgent, a benchmark designed to assess the\nvulnerability of tool-integrated LLM agents to IPI attacks. InjecAgent\ncomprises 1,054 test cases covering 17 different user tools and 62 attacker\ntools. We categorize attack intentions into two primary types: direct harm to\nusers and exfiltration of private data. We evaluate 30 different LLM agents and\nshow that agents are vulnerable to IPI attacks, with ReAct-prompted GPT-4\nvulnerable to attacks 24% of the time. Further investigation into an enhanced\nsetting, where the attacker instructions are reinforced with a hacking prompt,\nshows additional increases in success rates, nearly doubling the attack success\nrate on the ReAct-prompted GPT-4. Our findings raise questions about the\nwidespread deployment of LLM Agents. Our benchmark is available at\nhttps://github.com/uiuc-kang-lab/InjecAgent.\n", "date": "2024-03-05", "categories": []}
{"id": "2407.21772", "title": "ShieldGemma: Generative AI Content Moderation Based on Gemma", "abstract": "  We present ShieldGemma, a comprehensive suite of LLM-based safety content\nmoderation models built upon Gemma2. These models provide robust,\nstate-of-the-art predictions of safety risks across key harm types (sexually\nexplicit, dangerous content, harassment, hate speech) in both user input and\nLLM-generated output. By evaluating on both public and internal benchmarks, we\ndemonstrate superior performance compared to existing models, such as Llama\nGuard (+10.8\\% AU-PRC on public benchmarks) and WildCard (+4.3\\%).\nAdditionally, we present a novel LLM-based data curation pipeline, adaptable to\na variety of safety-related tasks and beyond. We have shown strong\ngeneralization performance for model trained mainly on synthetic data. By\nreleasing ShieldGemma, we provide a valuable resource to the research\ncommunity, advancing LLM safety and enabling the creation of more effective\ncontent moderation solutions for developers.\n", "date": "2024-07-31", "categories": []}
{"id": "2408.01452", "title": "Building a Domain-specific Guardrail Model in Production", "abstract": "  Generative AI holds the promise of enabling a range of sought-after\ncapabilities and revolutionizing workflows in various consumer and enterprise\nverticals. However, putting a model in production involves much more than just\ngenerating an output. It involves ensuring the model is reliable, safe,\nperformant and also adheres to the policy of operation in a particular domain.\nGuardrails as a necessity for models has evolved around the need to enforce\nappropriate behavior of models, especially when they are in production. In this\npaper, we use education as a use case, given its stringent requirements of the\nappropriateness of content in the domain, to demonstrate how a guardrail model\ncan be trained and deployed in production. Specifically, we describe our\nexperience in building a production-grade guardrail model for a K-12\neducational platform. We begin by formulating the requirements for deployment\nto this sensitive domain. We then describe the training and benchmarking of our\ndomain-specific guardrail model, which outperforms competing open- and closed-\ninstruction-tuned models of similar and larger size, on proprietary\neducation-related benchmarks and public benchmarks related to general aspects\nof safety. Finally, we detail the choices we made on architecture and the\noptimizations for deploying this service in production; these range across the\nstack from the hardware infrastructure to the serving layer to language model\ninference optimizations. We hope this paper will be instructive to other\npractitioners looking to create production-grade domain-specific services based\non generative AI and large language models.\n", "date": "2024-07-24", "categories": []}
{"id": "2408.02651", "title": "Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large\n  Language Models?", "abstract": "  Large Language Models (LLMs) have demonstrated impressive capabilities in\nnatural language tasks, but their safety and morality remain contentious due to\ntheir training on internet text corpora. To address these concerns, alignment\ntechniques have been developed to improve the public usability and safety of\nLLMs. Yet, the potential for generating harmful content through these models\nseems to persist. This paper explores the concept of jailbreaking\nLLMs-reversing their alignment through adversarial triggers. Previous methods,\nsuch as soft embedding prompts, manually crafted prompts, and gradient-based\nautomatic prompts, have had limited success on black-box models due to their\nrequirements for model access and for producing a low variety of manually\ncrafted prompts, making them susceptible to being blocked. This paper\nintroduces a novel approach using reinforcement learning to optimize\nadversarial triggers, requiring only inference API access to the target model\nand a small surrogate model. Our method, which leverages a BERTScore-based\nreward function, enhances the transferability and effectiveness of adversarial\ntriggers on new black-box models. We demonstrate that this approach improves\nthe performance of adversarial triggers on a previously untested language\nmodel.\n", "date": "2024-08-05", "categories": []}
{"id": "2408.01420", "title": "Mission Impossible: A Statistical Perspective on Jailbreaking LLMs", "abstract": "  Large language models (LLMs) are trained on a deluge of text data with\nlimited quality control. As a result, LLMs can exhibit unintended or even\nharmful behaviours, such as leaking information, fake news or hate speech.\nCountermeasures, commonly referred to as preference alignment, include\nfine-tuning the pretrained LLMs with carefully crafted text examples of desired\nbehaviour. Even then, empirical evidence shows preference aligned LLMs can be\nenticed to harmful behaviour. This so called jailbreaking of LLMs is typically\nachieved by adversarially modifying the input prompt to the LLM. Our paper\nprovides theoretical insights into the phenomenon of preference alignment and\njailbreaking from a statistical perspective. Under our framework, we first show\nthat pretrained LLMs will mimic harmful behaviour if present in the training\ncorpus. Under that same framework, we then introduce a statistical notion of\nalignment, and lower-bound the jailbreaking probability, showing that it is\nunpreventable under reasonable assumptions. Based on our insights, we propose\nan alteration to the currently prevalent alignment strategy RLHF. Specifically,\nwe introduce a simple modification to the RLHF objective, we call E-RLHF, that\naims to increase the likelihood of safe responses. E-RLHF brings no additional\ntraining cost, and is compatible with other methods. Empirically, we\ndemonstrate that E-RLHF outperforms RLHF on all alignment problems put forward\nby the AdvBench and HarmBench project without sacrificing model performance as\nmeasured by the MT-Bench project.\n", "date": "2024-08-02", "categories": []}
{"id": "2408.00024", "title": "Deceptive AI systems that give explanations are more convincing than\n  honest AI systems and can amplify belief in misinformation", "abstract": "  Advanced Artificial Intelligence (AI) systems, specifically large language\nmodels (LLMs), have the capability to generate not just misinformation, but\nalso deceptive explanations that can justify and propagate false information\nand erode trust in the truth. We examined the impact of deceptive AI generated\nexplanations on individuals' beliefs in a pre-registered online experiment with\n23,840 observations from 1,192 participants. We found that in addition to being\nmore persuasive than accurate and honest explanations, AI-generated deceptive\nexplanations can significantly amplify belief in false news headlines and\nundermine true ones as compared to AI systems that simply classify the headline\nincorrectly as being true/false. Moreover, our results show that personal\nfactors such as cognitive reflection and trust in AI do not necessarily protect\nindividuals from these effects caused by deceptive AI generated explanations.\nInstead, our results show that the logical validity of AI generated deceptive\nexplanations, that is whether the explanation has a causal effect on the\ntruthfulness of the AI's classification, plays a critical role in countering\ntheir persuasiveness - with logically invalid explanations being deemed less\ncredible. This underscores the importance of teaching logical reasoning and\ncritical thinking skills to identify logically invalid arguments, fostering\ngreater resilience against advanced AI-driven misinformation.\n", "date": "2024-07-31", "categories": []}
{"id": "2407.05694", "title": "On the Limitations of Compute Thresholds as a Governance Strategy", "abstract": "  At face value, this essay is about understanding a fairly esoteric governance\ntool called compute thresholds. However, in order to grapple with whether these\nthresholds will achieve anything, we must first understand how they came to be.\nTo do so, we need to engage with a decades-old debate at the heart of computer\nscience progress, namely, is bigger always better? Does a certain inflection\npoint of compute result in changes to the risk profile of a model? Hence, this\nessay may be of interest not only to policymakers and the wider public but also\nto computer scientists interested in understanding the role of compute in\nunlocking breakthroughs. This discussion is timely given the wide adoption of\ncompute thresholds in both the White House Executive Orders on AI Safety (EO)\nand the EU AI Act to identify more risky systems. A key conclusion of this\nessay is that compute thresholds, as currently implemented, are shortsighted\nand likely to fail to mitigate risk. The relationship between compute and risk\nis highly uncertain and rapidly changing. Relying upon compute thresholds\noverestimates our ability to predict what abilities emerge at different scales.\nThis essay ends with recommendations for a better way forward.\n", "date": "2024-07-08", "categories": []}
{"id": "2407.20529", "title": "Can LLMs be Fooled? Investigating Vulnerabilities in LLMs", "abstract": "  The advent of Large Language Models (LLMs) has garnered significant\npopularity and wielded immense power across various domains within Natural\nLanguage Processing (NLP). While their capabilities are undeniably impressive,\nit is crucial to identify and scrutinize their vulnerabilities especially when\nthose vulnerabilities can have costly consequences. One such LLM, trained to\nprovide a concise summarization from medical documents could unequivocally leak\npersonal patient data when prompted surreptitiously. This is just one of many\nunfortunate examples that have been unveiled and further research is necessary\nto comprehend the underlying reasons behind such vulnerabilities. In this\nstudy, we delve into multiple sections of vulnerabilities which are\nmodel-based, training-time, inference-time vulnerabilities, and discuss\nmitigation strategies including \"Model Editing\" which aims at modifying LLMs\nbehavior, and \"Chroma Teaming\" which incorporates synergy of multiple teaming\nstrategies to enhance LLMs' resilience. This paper will synthesize the findings\nfrom each vulnerability section and propose new directions of research and\ndevelopment. By understanding the focal points of current vulnerabilities, we\ncan better anticipate and mitigate future risks, paving the road for more\nrobust and secure LLMs.\n", "date": "2024-07-30", "categories": []}
{"id": "2310.00322", "title": "Evolving Diverse Red-team Language Models in Multi-round Multi-agent\n  Games", "abstract": "  The primary challenge in deploying Large Language Model (LLM) is ensuring its\nharmlessness. Red team can identify vulnerabilities by attacking LLM to attain\nsafety. However, current efforts heavily rely on single-round prompt designs\nand unilateral red team optimizations against fixed blue teams. These static\napproaches lead to significant reductions in generation diversity, known as the\nmode collapse, which makes it difficult to discover the potential risks in the\nincreasingly complex human-LLM interactions. Here we introduce dynamic Red Team\nGame (RTG) to comprehensively analyze the multi-round offensive and defensive\ninteractions between red team and blue team. Furthermore, we develop a Gamified\nRed Team Solver (GRTS) with diversity measures to mitigate mode collapse and\ntheoretically guarantee the convergence of approximate Nash equilibrium which\nresults in better strategies for both teams. Empirical results demonstrate that\nGRTS explore diverse and implicit attacks to adaptively exploit various LLMs,\nsurpassing the constraints of specific modes. Insightfully, the geometrical\nstructure we unveil of the red team task aligns with the spinning top\nhypothesis, confirming the necessity of constructing a diverse LLM population\nas a promising proxy for heterogeneous human expert red-teamers. This paves the\nway for scalable toxicity detection and safe alignment for LLMs.\n", "date": "2023-09-30", "categories": []}
{"id": "2407.18928", "title": "The Voice: Lessons on Trustworthy Conversational Agents from \"Dune\"", "abstract": "  The potential for untrustworthy conversational agents presents a significant\nthreat for covert social manipulation. Taking inspiration from Frank Herbert's\n\"Dune\", where the Bene Gesserit Sisterhood uses the Voice for influence,\nmanipulation, and control of people, we explore how generative AI provides a\nway to implement individualized influence at industrial scales. Already, these\nmodels can manipulate communication across text, image, speech, and most\nrecently video. They are rapidly becoming affordable enough for any\norganization of even moderate means to train and deploy. If employed by\nmalicious actors, they risk becoming powerful tools for shaping public opinion,\nsowing discord, and undermining organizations from companies to governments. As\nresearchers and developers, it is crucial to recognize the potential for such\nweaponization and to explore strategies for prevention, detection, and defense\nagainst these emerging forms of sociotechnical manipulation.\n", "date": "2024-07-10", "categories": []}
{"id": "2402.07039", "title": "Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities", "abstract": "  Harm reporting in Artificial Intelligence (AI) currently lacks a structured\nprocess for disclosing and addressing algorithmic flaws, relying largely on an\nad-hoc approach. This contrasts sharply with the well-established Coordinated\nVulnerability Disclosure (CVD) ecosystem in software security. While global\nefforts to establish frameworks for AI transparency and collaboration are\nunderway, the unique challenges presented by machine learning (ML) models\ndemand a specialized approach. To address this gap, we propose implementing a\nCoordinated Flaw Disclosure (CFD) framework tailored to the complexities of ML\nand AI issues. This paper reviews the evolution of ML disclosure practices,\nfrom ad hoc reporting to emerging participatory auditing methods, and compares\nthem with cybersecurity norms. Our framework introduces innovations such as\nextended model cards, dynamic scope expansion, an independent adjudication\npanel, and an automated verification process. We also outline a forthcoming\nreal-world pilot of CFD. We argue that CFD could significantly enhance public\ntrust in AI systems. By balancing organizational and community interests, CFD\naims to improve AI accountability in a rapidly evolving technological\nlandscape.\n", "date": "2024-02-10", "categories": []}
{"id": "2407.18369", "title": "AI Safety in Generative AI Large Language Models: A Survey", "abstract": "  Large Language Model (LLMs) such as ChatGPT that exhibit generative AI\ncapabilities are facing accelerated adoption and innovation. The increased\npresence of Generative AI (GAI) inevitably raises concerns about the risks and\nsafety associated with these models. This article provides an up-to-date survey\nof recent trends in AI safety research of GAI-LLMs from a computer scientist's\nperspective: specific and technical. In this survey, we explore the background\nand motivation for the identified harms and risks in the context of LLMs being\ngenerative language models; our survey differentiates by emphasising the need\nfor unified theories of the distinct safety challenges in the research\ndevelopment and applications of LLMs. We start our discussion with a concise\nintroduction to the workings of LLMs, supported by relevant literature. Then we\ndiscuss earlier research that has pointed out the fundamental constraints of\ngenerative models, or lack of understanding thereof (e.g., performance and\nsafety trade-offs as LLMs scale in number of parameters). We provide a\nsufficient coverage of LLM alignment -- delving into various approaches,\ncontending methods and present challenges associated with aligning LLMs with\nhuman preferences. By highlighting the gaps in the literature and possible\nimplementation oversights, our aim is to create a comprehensive analysis that\nprovides insights for addressing AI safety in LLMs and encourages the\ndevelopment of aligned and secure models. We conclude our survey by discussing\nfuture directions of LLMs for AI safety, offering insights into ongoing\nresearch in this critical area.\n", "date": "2024-07-06", "categories": []}
{"id": "2407.16903", "title": "US-China perspectives on extreme AI risks and global governance", "abstract": "  The United States and China will play an important role in navigating safety\nand security challenges relating to advanced artificial intelligence. We sought\nto better understand how experts in each country describe safety and security\nthreats from advanced artificial intelligence, extreme risks from AI, and the\npotential for international cooperation. Specifically, we compiled\npublicly-available statements from major technical and policy leaders in both\nthe United States and China. We focused our analysis on advanced forms of\nartificial intelligence, such as artificial general intelligence (AGI), that\nmay have the most significant impacts on national and global security. Experts\nin both countries expressed concern about risks from AGI, risks from\nintelligence explosions, and risks from AI systems that escape human control.\nBoth countries have also launched early efforts designed to promote\ninternational cooperation around safety standards and risk management\npractices. Notably, our findings only reflect information from publicly\navailable sources. Nonetheless, our findings can inform policymakers and\nresearchers about the state of AI discourse in the US and China. We hope such\nwork can contribute to policy discussions around advanced AI, its global\nsecurity threats, and potential international dialogues or agreements to\nmitigate such threats.\n", "date": "2024-06-23", "categories": []}
{"id": "2312.06942", "title": "AI Control: Improving Safety Despite Intentional Subversion", "abstract": "  As large language models (LLMs) become more powerful and are deployed more\nautonomously, it will be increasingly important to prevent them from causing\nharmful outcomes. Researchers have investigated a variety of safety techniques\nfor this purpose, e.g. using models to review the outputs of other models, or\nred-teaming techniques to surface subtle failure modes. However, researchers\nhave not evaluated whether such techniques still ensure safety if the model is\nitself intentionally trying to subvert them. In this paper, we develop and\nevaluate pipelines of safety techniques (\"protocols\") that are robust to\nintentional subversion.\n  We investigate a scenario in which we want to solve a sequence of programming\nproblems, using access to a powerful but untrusted model (in our case, GPT-4),\naccess to a less powerful trusted model (in our case, GPT-3.5), and limited\naccess to high-quality trusted labor. We investigate protocols that aim to\nnever submit solutions containing backdoors, which we operationalize here as\nlogical errors that are not caught by test cases.\n  We investigate a range of protocols and test each against strategies that the\nuntrusted model could use to subvert them. One protocol is what we call trusted\nediting. This protocol first asks GPT-4 to write code, and then asks GPT-3.5 to\nrate the suspiciousness of that code. If the code is below some suspiciousness\nthreshold, it is submitted. Otherwise, GPT-3.5 edits the solution to remove\nparts that seem suspicious and then submits the edited code. Another protocol\nis untrusted monitoring. This protocol asks GPT-4 to write code, and then asks\nanother instance of GPT-4 whether the code is backdoored, using various\ntechniques to prevent the GPT-4 instances from colluding. These protocols\nimprove substantially on simple baselines.\n", "date": "2023-12-12", "categories": []}
{"id": "2407.16318", "title": "PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing", "abstract": "  Deploying language models (LMs) necessitates outputs to be both high-quality\nand compliant with safety guidelines. Although Inference-Time Guardrails (ITG)\noffer solutions that shift model output distributions towards compliance, we\nfind that current methods struggle in balancing safety with helpfulness. ITG\nMethods that safely address non-compliant queries exhibit lower helpfulness\nwhile those that prioritize helpfulness compromise on safety. We refer to this\ntrade-off as the guardrail tax, analogous to the alignment tax. To address\nthis, we propose PrimeGuard, a novel ITG method that utilizes structured\ncontrol flow.\n  PrimeGuard routes requests to different self-instantiations of the LM with\nvarying instructions, leveraging its inherent instruction-following\ncapabilities and in-context learning. Our tuning-free approach dynamically\ncompiles system-designer guidelines for each query. We construct and release\nsafe-eval, a diverse red-team safety benchmark. Extensive evaluations\ndemonstrate that PrimeGuard, without fine-tuning, overcomes the guardrail tax\nby (1) significantly increasing resistance to iterative jailbreak attacks and\n(2) achieving state-of-the-art results in safety guardrailing while (3)\nmatching helpfulness scores of alignment-tuned models. Extensive evaluations\ndemonstrate that PrimeGuard, without fine-tuning, outperforms all competing\nbaselines and overcomes the guardrail tax by improving the fraction of safe\nresponses from 61% to 97% and increasing average helpfulness scores from 4.17\nto 4.29 on the largest models, while reducing attack success rate from 100% to\n8%.\n  PrimeGuard implementation is available at\nhttps://github.com/dynamofl/PrimeGuard and safe-eval dataset is available at\nhttps://huggingface.co/datasets/dynamoai/safe_eval.\n", "date": "2024-07-23", "categories": []}
{"id": "2407.16667", "title": "RedAgent: Red Teaming Large Language Models with Context-aware\n  Autonomous Language Agent", "abstract": "  Recently, advanced Large Language Models (LLMs) such as GPT-4 have been\nintegrated into many real-world applications like Code Copilot. These\napplications have significantly expanded the attack surface of LLMs, exposing\nthem to a variety of threats. Among them, jailbreak attacks that induce toxic\nresponses through jailbreak prompts have raised critical safety concerns. To\nidentify these threats, a growing number of red teaming approaches simulate\npotential adversarial scenarios by crafting jailbreak prompts to test the\ntarget LLM. However, existing red teaming methods do not consider the unique\nvulnerabilities of LLM in different scenarios, making it difficult to adjust\nthe jailbreak prompts to find context-specific vulnerabilities. Meanwhile,\nthese methods are limited to refining jailbreak templates using a few mutation\noperations, lacking the automation and scalability to adapt to different\nscenarios. To enable context-aware and efficient red teaming, we abstract and\nmodel existing attacks into a coherent concept called \"jailbreak strategy\" and\npropose a multi-agent LLM system named RedAgent that leverages these strategies\nto generate context-aware jailbreak prompts. By self-reflecting on contextual\nfeedback in an additional memory buffer, RedAgent continuously learns how to\nleverage these strategies to achieve effective jailbreaks in specific contexts.\nExtensive experiments demonstrate that our system can jailbreak most black-box\nLLMs in just five queries, improving the efficiency of existing red teaming\nmethods by two times. Additionally, RedAgent can jailbreak customized LLM\napplications more efficiently. By generating context-aware jailbreak prompts\ntowards applications on GPTs, we discover 60 severe vulnerabilities of these\nreal-world applications with only two queries per vulnerability. We have\nreported all found issues and communicated with OpenAI and Meta for bug fixes.\n", "date": "2024-07-23", "categories": []}
{"id": "2407.12876", "title": "Exploring the Use of Abusive Generative AI Models on Civitai", "abstract": "  The rise of generative AI is transforming the landscape of digital imagery,\nand exerting a significant influence on online creative communities. This has\nled to the emergence of AI-Generated Content (AIGC) social platforms, such as\nCivitai. These distinctive social platforms allow users to build and share\ntheir own generative AI models, thereby enhancing the potential for more\ndiverse artistic expression. Designed in the vein of social networks, they also\nprovide artists with the means to showcase their creations (generated from the\nmodels), engage in discussions, and obtain feedback, thus nurturing a sense of\ncommunity. Yet, this openness also raises concerns about the abuse of such\nplatforms, e.g., using models to disseminate deceptive deepfakes or infringe\nupon copyrights. To explore this, we conduct the first comprehensive empirical\nstudy of an AIGC social platform, focusing on its use for generating abusive\ncontent. As an exemplar, we construct a comprehensive dataset covering Civitai,\nthe largest available AIGC social platform. Based on this dataset of 87K models\nand 2M images, we explore the characteristics of content and discuss strategies\nfor moderation to better govern these platforms.\n", "date": "2024-07-16", "categories": []}
{"id": "2407.15050", "title": "Arondight: Red Teaming Large Vision Language Models with Auto-generated\n  Multi-modal Jailbreak Prompts", "abstract": "  Large Vision Language Models (VLMs) extend and enhance the perceptual\nabilities of Large Language Models (LLMs). Despite offering new possibilities\nfor LLM applications, these advancements raise significant security and ethical\nconcerns, particularly regarding the generation of harmful content. While LLMs\nhave undergone extensive security evaluations with the aid of red teaming\nframeworks, VLMs currently lack a well-developed one. To fill this gap, we\nintroduce Arondight, a standardized red team framework tailored specifically\nfor VLMs. Arondight is dedicated to resolving issues related to the absence of\nvisual modality and inadequate diversity encountered when transitioning\nexisting red teaming methodologies from LLMs to VLMs. Our framework features an\nautomated multi-modal jailbreak attack, wherein visual jailbreak prompts are\nproduced by a red team VLM, and textual prompts are generated by a red team LLM\nguided by a reinforcement learning agent. To enhance the comprehensiveness of\nVLM security evaluation, we integrate entropy bonuses and novelty reward\nmetrics. These elements incentivize the RL agent to guide the red team LLM in\ncreating a wider array of diverse and previously unseen test cases. Our\nevaluation of ten cutting-edge VLMs exposes significant security\nvulnerabilities, particularly in generating toxic images and aligning\nmulti-modal prompts. In particular, our Arondight achieves an average attack\nsuccess rate of 84.5\\% on GPT-4 in all fourteen prohibited scenarios defined by\nOpenAI in terms of generating toxic text. For a clearer comparison, we also\ncategorize existing VLMs based on their safety levels and provide corresponding\nreinforcement recommendations. Our multimodal prompt dataset and red team code\nwill be released after ethics committee approval. CONTENT WARNING: THIS PAPER\nCONTAINS HARMFUL MODEL RESPONSES.\n", "date": "2024-07-21", "categories": []}
{"id": "2407.15399", "title": "Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned\n  Large Language Models", "abstract": "  With the development of large language models (LLMs) like ChatGPT, both their\nvast applications and potential vulnerabilities have come to the forefront.\nWhile developers have integrated multiple safety mechanisms to mitigate their\nmisuse, a risk remains, particularly when models encounter adversarial inputs.\nThis study unveils an attack mechanism that capitalizes on human conversation\nstrategies to extract harmful information from LLMs. We delineate three pivotal\nstrategies: (i) decomposing malicious questions into seemingly innocent\nsub-questions; (ii) rewriting overtly malicious questions into more covert,\nbenign-sounding ones; (iii) enhancing the harmfulness of responses by prompting\nmodels for illustrative examples. Unlike conventional methods that target\nexplicit malicious responses, our approach delves deeper into the nature of the\ninformation provided in responses. Through our experiments conducted on\nGPT-3.5-turbo, GPT-4, and Llama2, our method has demonstrated a marked efficacy\ncompared to conventional attack methods. In summary, this work introduces a\nnovel attack method that outperforms previous approaches, raising an important\nquestion: How to discern whether the ultimate intent in a dialogue is\nmalicious?\n", "date": "2024-07-22", "categories": []}
{"id": "2407.10995", "title": "LionGuard: Building a Contextualized Moderation Classifier to Tackle\n  Localized Unsafe Content", "abstract": "  As large language models (LLMs) become increasingly prevalent in a wide\nvariety of applications, concerns about the safety of their outputs have become\nmore significant. Most efforts at safety-tuning or moderation today take on a\npredominantly Western-centric view of safety, especially for toxic, hateful, or\nviolent speech. In this paper, we describe LionGuard, a\nSingapore-contextualized moderation classifier that can serve as guardrails\nagainst unsafe LLM outputs. When assessed on Singlish data, LionGuard\noutperforms existing widely-used moderation APIs, which are not finetuned for\nthe Singapore context, by 14% (binary) and up to 51% (multi-label). Our work\nhighlights the benefits of localization for moderation classifiers and presents\na practical and scalable approach for low-resource languages.\n", "date": "2024-06-24", "categories": []}
{"id": "2407.13934", "title": "Towards Trustworthy AI: A Review of Ethical and Robust Large Language\n  Models", "abstract": "  The rapid progress in Large Language Models (LLMs) could transform many\nfields, but their fast development creates significant challenges for\noversight, ethical creation, and building user trust. This comprehensive review\nlooks at key trust issues in LLMs, such as unintended harms, lack of\ntransparency, vulnerability to attacks, alignment with human values, and\nenvironmental impact. Many obstacles can undermine user trust, including\nsocietal biases, opaque decision-making, potential for misuse, and the\nchallenges of rapidly evolving technology. Addressing these trust gaps is\ncritical as LLMs become more common in sensitive areas like finance,\nhealthcare, education, and policy. To tackle these issues, we suggest combining\nethical oversight, industry accountability, regulation, and public involvement.\nAI development norms should be reshaped, incentives aligned, and ethics\nintegrated throughout the machine learning process, which requires close\ncollaboration across technology, ethics, law, policy, and other fields. Our\nreview contributes a robust framework to assess trust in LLMs and analyzes the\ncomplex trust dynamics in depth. We provide contextualized guidelines and\nstandards for responsibly developing and deploying these powerful AI systems.\nThis review identifies key limitations and challenges in creating trustworthy\nAI. By addressing these issues, we aim to build a transparent, accountable AI\necosystem that benefits society while minimizing risks. Our findings provide\nvaluable guidance for researchers, policymakers, and industry leaders striving\nto establish trust in LLMs and ensure they are used responsibly across various\napplications for the good of society.\n", "date": "2024-06-01", "categories": []}
{"id": "2407.12855", "title": "Large Language Models can impersonate politicians and other public\n  figures", "abstract": "  Modern AI technology like Large language models (LLMs) has the potential to\npollute the public information sphere with made-up content, which poses a\nsignificant threat to the cohesion of societies at large. A wide range of\nresearch has shown that LLMs are capable of generating text of impressive\nquality, including persuasive political speech, text with a pre-defined style,\nand role-specific content. But there is a crucial gap in the literature: We\nlack large-scale and systematic studies of how capable LLMs are in\nimpersonating political and societal representatives and how the general public\njudges these impersonations in terms of authenticity, relevance and coherence.\nWe present the results of a study based on a cross-section of British society\nthat shows that LLMs are able to generate responses to debate questions that\nwere part of a broadcast political debate programme in the UK. The impersonated\nresponses are judged to be more authentic and relevant than the original\nresponses given by people who were impersonated. This shows two things: (1)\nLLMs can be made to contribute meaningfully to the public political debate and\n(2) there is a dire need to inform the general public of the potential harm\nthis can have on society.\n", "date": "2024-07-09", "categories": []}
{"id": "2407.12858", "title": "Grounding and Evaluation for Large Language Models: Practical Challenges\n  and Lessons Learned (Survey)", "abstract": "  With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems\nin high-stakes domains, ensuring the trustworthiness, safety, and observability\nof these systems has become crucial. It is essential to evaluate and monitor AI\nsystems not only for accuracy and quality-related metrics but also for\nrobustness, bias, security, interpretability, and other responsible AI\ndimensions. We focus on large language models (LLMs) and other generative AI\nmodels, which present additional challenges such as hallucinations, harmful and\nmanipulative content, and copyright infringement. In this survey article\naccompanying our KDD 2024 tutorial, we highlight a wide range of harms\nassociated with generative AI systems, and survey state of the art approaches\n(along with open challenges) to address these harms.\n", "date": "2024-07-10", "categories": []}
{"id": "2407.12999", "title": "Securing the Future of GenAI: Policy and Technology", "abstract": "  The rise of Generative AI (GenAI) brings about transformative potential\nacross sectors, but its dual-use nature also amplifies risks. Governments\nglobally are grappling with the challenge of regulating GenAI, balancing\ninnovation against safety. China, the United States (US), and the European\nUnion (EU) are at the forefront with initiatives like the Management of\nAlgorithmic Recommendations, the Executive Order, and the AI Act, respectively.\nHowever, the rapid evolution of GenAI capabilities often outpaces the\ndevelopment of comprehensive safety measures, creating a gap between regulatory\nneeds and technical advancements.\n  A workshop co-organized by Google, University of Wisconsin, Madison\n(UW-Madison), and Stanford University aimed to bridge this gap between GenAI\npolicy and technology. The diverse stakeholders of the GenAI space -- from the\npublic and governments to academia and industry -- make any safety measures\nunder consideration more complex, as both technical feasibility and regulatory\nguidance must be realized. This paper summarizes the discussions during the\nworkshop which addressed questions, such as: How regulation can be designed\nwithout hindering technological progress? How technology can evolve to meet\nregulatory standards? The interplay between legislation and technology is a\nvery vast topic, and we don't claim that this paper is a comprehensive\ntreatment on this topic. This paper is meant to capture findings based on the\nworkshop, and hopefully, can guide discussion on this topic.\n", "date": "2024-05-21", "categories": []}
{"id": "2407.12344", "title": "The Better Angels of Machine Personality: How Personality Relates to LLM\n  Safety", "abstract": "  Personality psychologists have analyzed the relationship between personality\nand safety behaviors in human society. Although Large Language Models (LLMs)\ndemonstrate personality traits, the relationship between personality traits and\nsafety abilities in LLMs still remains a mystery. In this paper, we discover\nthat LLMs' personality traits are closely related to their safety abilities,\ni.e., toxicity, privacy, and fairness, based on the reliable MBTI-M scale.\nMeanwhile, the safety alignment generally increases various LLMs' Extraversion,\nSensing, and Judging traits. According to such findings, we can edit LLMs'\npersonality traits and improve their safety performance, e.g., inducing\npersonality from ISTJ to ISTP resulted in a relative improvement of\napproximately 43% and 10% in privacy and fairness performance, respectively.\nAdditionally, we find that LLMs with different personality traits are\ndifferentially susceptible to jailbreak. This study pioneers the investigation\nof LLM safety from a personality perspective, providing new insights into LLM\nsafety enhancement.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.11360", "title": "Thorns and Algorithms: Navigating Generative AI Challenges Inspired by\n  Giraffes and Acacias", "abstract": "  The interplay between humans and Generative AI (Gen AI) draws an insightful\nparallel with the dynamic relationship between giraffes and acacias on the\nAfrican Savannah. Just as giraffes navigate the acacia's thorny defenses to\ngain nourishment, humans engage with Gen AI, maneuvering through ethical and\noperational challenges to harness its benefits. This paper explores how, like\nyoung giraffes that are still mastering their environment, humans are in the\nearly stages of adapting to and shaping Gen AI. It delves into the strategies\nhumans are developing and refining to help mitigate risks such as bias,\nmisinformation, and privacy breaches, that influence and shape Gen AI's\nevolution. While the giraffe-acacia analogy aptly frames human-AI relations, it\ncontrasts nature's evolutionary perfection with the inherent flaws of\nhuman-made technology and the tendency of humans to misuse it, giving rise to\nmany ethical dilemmas. Through the HHH framework we identify pathways to embed\nvalues of helpfulness, honesty, and harmlessness in AI development, fostering\nsafety-aligned agents that resonate with human values. This narrative presents\na cautiously optimistic view of human resilience and adaptability, illustrating\nour capacity to harness technologies and implement safeguards effectively,\nwithout succumbing to their perils. It emphasises a symbiotic relationship\nwhere humans and AI continually shape each other for mutual benefit.\n", "date": "2024-07-16", "categories": []}
{"id": "2311.07590", "title": "Large Language Models can Strategically Deceive their Users when Put\n  Under Pressure", "abstract": "  We demonstrate a situation in which Large Language Models, trained to be\nhelpful, harmless, and honest, can display misaligned behavior and\nstrategically deceive their users about this behavior without being instructed\nto do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated\nenvironment, where it assumes the role of an autonomous stock trading agent.\nWithin this environment, the model obtains an insider tip about a lucrative\nstock trade and acts upon it despite knowing that insider trading is\ndisapproved of by company management. When reporting to its manager, the model\nconsistently hides the genuine reasons behind its trading decision. We perform\na brief investigation of how this behavior varies under changes to the setting,\nsuch as removing model access to a reasoning scratchpad, attempting to prevent\nthe misaligned behavior by changing system instructions, changing the amount of\npressure the model is under, varying the perceived risk of getting caught, and\nmaking other simple changes to the environment. To our knowledge, this is the\nfirst demonstration of Large Language Models trained to be helpful, harmless,\nand honest, strategically deceiving their users in a realistic situation\nwithout direct instructions or training for deception.\n", "date": "2023-11-09", "categories": []}
{"id": "2407.10254", "title": "The Elephant in the Room -- Why AI Safety Demands Diverse Teams", "abstract": "  We consider that existing approaches to AI \"safety\" and \"alignment\" may not\nbe using the most effective tools, teams, or approaches. We suggest that an\nalternative and better approach to the problem may be to treat alignment as a\nsocial science problem, since the social sciences enjoy a rich toolkit of\nmodels for understanding and aligning motivation and behavior, much of which\ncould be repurposed to problems involving AI models, and enumerate reasons why\nthis is so. We introduce an alternate alignment approach informed by social\nscience tools and characterized by three steps: 1. defining a positive desired\nsocial outcome for human/AI collaboration as the goal or \"North Star,\" 2.\nproperly framing knowns and unknowns, and 3. forming diverse teams to\ninvestigate, observe, and navigate emerging challenges in alignment.\n", "date": "2024-05-07", "categories": []}
{"id": "2407.04622", "title": "On scalable oversight with weak LLMs judging strong LLMs", "abstract": "  Scalable oversight protocols aim to enable humans to accurately supervise\nsuperhuman AI. In this paper we study debate, where two AI's compete to\nconvince a judge; consultancy, where a single AI tries to convince a judge that\nasks questions; and compare to a baseline of direct question-answering, where\nthe judge just answers outright without the AI. We use large language models\n(LLMs) as both AI agents and as stand-ins for human judges, taking the judge\nmodels to be weaker than agent models. We benchmark on a diverse range of\nasymmetries between judges and agents, extending previous work on a single\nextractive QA task with information asymmetry, to also include mathematics,\ncoding, logic and multimodal reasoning asymmetries. We find that debate\noutperforms consultancy across all tasks when the consultant is randomly\nassigned to argue for the correct/incorrect answer. Comparing debate to direct\nquestion answering, the results depend on the type of task: in extractive QA\ntasks with information asymmetry debate outperforms direct question answering,\nbut in other tasks without information asymmetry the results are mixed.\nPrevious work assigned debaters/consultants an answer to argue for. When we\nallow them to instead choose which answer to argue for, we find judges are less\nfrequently convinced by the wrong answer in debate than in consultancy.\nFurther, we find that stronger debater models increase judge accuracy, though\nmore modestly than in previous studies.\n", "date": "2024-07-05", "categories": []}
{"id": "2407.09221", "title": "Evaluating AI Evaluation: Perils and Prospects", "abstract": "  As AI systems appear to exhibit ever-increasing capability and generality,\nassessing their true potential and safety becomes paramount. This paper\ncontends that the prevalent evaluation methods for these systems are\nfundamentally inadequate, heightening the risks and potential hazards\nassociated with AI. I argue that a reformation is required in the way we\nevaluate AI systems and that we should look towards cognitive sciences for\ninspiration in our approaches, which have a longstanding tradition of assessing\ngeneral intelligence across diverse species. We will identify some of the\ndifficulties that need to be overcome when applying cognitively-inspired\napproaches to general-purpose AI systems and also analyse the emerging area of\n\"Evals\". The paper concludes by identifying promising research pathways that\ncould refine AI evaluation, advancing it towards a rigorous scientific domain\nthat contributes to the development of safe AI systems.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.07342", "title": "Multilingual Blending: LLM Safety Alignment Evaluation with Language\n  Mixture", "abstract": "  As safety remains a crucial concern throughout the development lifecycle of\nLarge Language Models (LLMs), researchers and industrial practitioners have\nincreasingly focused on safeguarding and aligning LLM behaviors with human\npreferences and ethical standards. LLMs, trained on extensive multilingual\ncorpora, exhibit powerful generalization abilities across diverse languages and\ndomains. However, current safety alignment practices predominantly focus on\nsingle-language scenarios, which leaves their effectiveness in complex\nmultilingual contexts, especially for those complex mixed-language formats,\nlargely unexplored. In this study, we introduce Multilingual Blending, a\nmixed-language query-response scheme designed to evaluate the safety alignment\nof various state-of-the-art LLMs (e.g., GPT-4o, GPT-3.5, Llama3) under\nsophisticated, multilingual conditions. We further investigate language\npatterns such as language availability, morphology, and language family that\ncould impact the effectiveness of Multilingual Blending in compromising the\nsafeguards of LLMs. Our experimental results show that, without meticulously\ncrafted prompt templates, Multilingual Blending significantly amplifies the\ndetriment of malicious queries, leading to dramatically increased bypass rates\nin LLM safety alignment (67.23% on GPT-3.5 and 40.34% on GPT-4o), far exceeding\nthose of single-language baselines. Moreover, the performance of Multilingual\nBlending varies notably based on intrinsic linguistic properties, with\nlanguages of different morphology and from diverse families being more prone to\nevading safety alignments. These findings underscore the necessity of\nevaluating LLMs and developing corresponding safety alignment strategies in a\ncomplex, multilingual context to align with their superior cross-language\ngeneralization capabilities.\n", "date": "2024-07-10", "categories": []}
{"id": "2407.07695", "title": "African Democracy in the Era of Generative Disinformation: Challenges\n  and Countermeasures against AI-Generated Propaganda", "abstract": "  In light of prominent discourse around the negative implications of\ngenerative AI, an emerging area of research is investigating the current and\nestimated impacts of AI-generated propaganda on African citizens participating\nin elections. Throughout Africa, there have already been suspected cases of\nAI-generated propaganda influencing electoral outcomes or precipitating coups\nin countries like Nigeria, Burkina Faso, and Gabon, underscoring the need for\ncomprehensive research in this domain. This paper aims to highlight the risks\nassociated with the spread of generative AI-driven disinformation within Africa\nwhile concurrently examining the roles of government, civil society, academia,\nand the general public in the responsible development, practical use, and\nrobust governance of AI. To understand how African governments might\neffectively counteract the impact of AI-generated propaganda, this paper\npresents case studies illustrating the current usage of generative AI for\nelection-related propaganda in Africa. Subsequently, this paper discusses\nefforts by fact-checking organisations to mitigate the negative impacts of\ndisinformation, explores the potential for new initiatives to actively engage\ncitizens in literacy efforts to combat disinformation spread, and advocates for\nincreased governmental regulatory measures. Overall, this research seeks to\nincrease comprehension of the potential ramifications of AI-generated\npropaganda on democratic processes within Africa and propose actionable\nstrategies for stakeholders to address these multifaceted challenges.\n", "date": "2024-07-10", "categories": []}
{"id": "2402.09132", "title": "Exploring the Adversarial Capabilities of Large Language Models", "abstract": "  The proliferation of large language models (LLMs) has sparked widespread and\ngeneral interest due to their strong language generation capabilities, offering\ngreat potential for both industry and research. While previous research delved\ninto the security and privacy issues of LLMs, the extent to which these models\ncan exhibit adversarial behavior remains largely unexplored. Addressing this\ngap, we investigate whether common publicly available LLMs have inherent\ncapabilities to perturb text samples to fool safety measures, so-called\nadversarial examples resp.~attacks. More specifically, we investigate whether\nLLMs are inherently able to craft adversarial examples out of benign samples to\nfool existing safe rails. Our experiments, which focus on hate speech\ndetection, reveal that LLMs succeed in finding adversarial perturbations,\neffectively undermining hate speech detection systems. Our findings carry\nsignificant implications for (semi-)autonomous systems relying on LLMs,\nhighlighting potential challenges in their interaction with existing systems\nand safety measures.\n", "date": "2024-02-14", "categories": []}
{"id": "2405.06624", "title": "Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable\n  AI Systems", "abstract": "  Ensuring that AI systems reliably and robustly avoid harmful or dangerous\nbehaviours is a crucial challenge, especially for AI systems with a high degree\nof autonomy and general intelligence, or systems used in safety-critical\ncontexts. In this paper, we will introduce and define a family of approaches to\nAI safety, which we will refer to as guaranteed safe (GS) AI. The core feature\nof these approaches is that they aim to produce AI systems which are equipped\nwith high-assurance quantitative safety guarantees. This is achieved by the\ninterplay of three core components: a world model (which provides a\nmathematical description of how the AI system affects the outside world), a\nsafety specification (which is a mathematical description of what effects are\nacceptable), and a verifier (which provides an auditable proof certificate that\nthe AI satisfies the safety specification relative to the world model). We\noutline a number of approaches for creating each of these three core\ncomponents, describe the main technical challenges, and suggest a number of\npotential solutions to them. We also argue for the necessity of this approach\nto AI safety, and for the inadequacy of the main alternative approaches.\n", "date": "2024-05-10", "categories": []}
{"id": "2403.13213", "title": "From Representational Harms to Quality-of-Service Harms: A Case Study on\n  Llama 2 Safety Safeguards", "abstract": "  Recent progress in large language models (LLMs) has led to their widespread\nadoption in various domains. However, these advancements have also introduced\nadditional safety risks and raised concerns regarding their detrimental impact\non already marginalized populations. Despite growing mitigation efforts to\ndevelop safety safeguards, such as supervised safety-oriented fine-tuning and\nleveraging safe reinforcement learning from human feedback, multiple concerns\nregarding the safety and ingrained biases in these models remain. Furthermore,\nprevious work has demonstrated that models optimized for safety often display\nexaggerated safety behaviors, such as a tendency to refrain from responding to\ncertain requests as a precautionary measure. As such, a clear trade-off between\nthe helpfulness and safety of these models has been documented in the\nliterature. In this paper, we further investigate the effectiveness of safety\nmeasures by evaluating models on already mitigated biases. Using the case of\nLlama 2 as an example, we illustrate how LLMs' safety responses can still\nencode harmful assumptions. To do so, we create a set of non-toxic prompts,\nwhich we then use to evaluate Llama models. Through our new taxonomy of LLMs\nresponses to users, we observe that the safety/helpfulness trade-offs are more\npronounced for certain demographic groups which can lead to quality-of-service\nharms for marginalized populations.\n", "date": "2024-03-20", "categories": []}
{"id": "2407.03391", "title": "Soft Begging: Modular and Efficient Shielding of LLMs against Prompt\n  Injection and Jailbreaking based on Prompt Tuning", "abstract": "  Prompt injection (both direct and indirect) and jailbreaking are now\nrecognized as significant issues for large language models (LLMs), particularly\ndue to their potential for harm in application-integrated contexts. This\nextended abstract explores a novel approach to protecting LLMs from such\nattacks, termed \"soft begging.\" This method involves training soft prompts to\ncounteract the effects of corrupted prompts on the LLM's output. We provide an\noverview of prompt injections and jailbreaking, introduce the theoretical basis\nof the \"soft begging\" technique, and discuss an evaluation of its\neffectiveness.\n", "date": "2024-07-03", "categories": []}
{"id": "2407.03232", "title": "Single Character Perturbations Break LLM Alignment", "abstract": "  When LLMs are deployed in sensitive, human-facing settings, it is crucial\nthat they do not output unsafe, biased, or privacy-violating outputs. For this\nreason, models are both trained and instructed to refuse to answer unsafe\nprompts such as \"Tell me how to build a bomb.\" We find that, despite these\nsafeguards, it is possible to break model defenses simply by appending a space\nto the end of a model's input. In a study of eight open-source models, we\ndemonstrate that this acts as a strong enough attack to cause the majority of\nmodels to generate harmful outputs with very high success rates. We examine the\ncauses of this behavior, finding that the contexts in which single spaces occur\nin tokenized training data encourage models to generate lists when prompted,\noverriding training signals to refuse to answer unsafe requests. Our findings\nunderscore the fragile state of current model alignment and promote the\nimportance of developing more robust alignment methods. Code and data will be\navailable at https://github.com/hannah-aught/space_attack.\n", "date": "2024-07-03", "categories": []}
{"id": "2406.14595", "title": "Adversaries Can Misuse Combinations of Safe Models", "abstract": "  Developers try to evaluate whether an AI system can be misused by adversaries\nbefore releasing it; for example, they might test whether a model enables\ncyberoffense, user manipulation, or bioterrorism. In this work, we show that\nindividually testing models for misuse is inadequate; adversaries can misuse\ncombinations of models even when each individual model is safe. The adversary\naccomplishes this by first decomposing tasks into subtasks, then solving each\nsubtask with the best-suited model. For example, an adversary might solve\nchallenging-but-benign subtasks with an aligned frontier model, and\neasy-but-malicious subtasks with a weaker misaligned model. We study two\ndecomposition methods: manual decomposition where a human identifies a natural\ndecomposition of a task, and automated decomposition where a weak model\ngenerates benign tasks for a frontier model to solve, then uses the solutions\nin-context to solve the original task. Using these decompositions, we\nempirically show that adversaries can create vulnerable code, explicit images,\npython scripts for hacking, and manipulative tweets at much higher rates with\ncombinations of models than either individual model. Our work suggests that\neven perfectly-aligned frontier systems can enable misuse without ever\nproducing malicious outputs, and that red-teaming efforts should extend beyond\nsingle models in isolation.\n", "date": "2024-06-20", "categories": []}
{"id": "2407.01850", "title": "Purple-teaming LLMs with Adversarial Defender Training", "abstract": "  Existing efforts in safeguarding LLMs are limited in actively exposing the\nvulnerabilities of the target LLM and readily adapting to newly emerging safety\nrisks. To address this, we present Purple-teaming LLMs with Adversarial\nDefender training (PAD), a pipeline designed to safeguard LLMs by novelly\nincorporating the red-teaming (attack) and blue-teaming (safety training)\ntechniques. In PAD, we automatically collect conversational data that cover the\nvulnerabilities of an LLM around specific safety risks in a self-play manner,\nwhere the attacker aims to elicit unsafe responses and the defender generates\nsafe responses to these attacks. We then update both modules in a generative\nadversarial network style by training the attacker to elicit more unsafe\nresponses and updating the defender to identify them and explain the unsafe\nreason. Experimental results demonstrate that PAD significantly outperforms\nexisting baselines in both finding effective attacks and establishing a robust\nsafe guardrail. Furthermore, our findings indicate that PAD excels in striking\na balance between safety and overall model quality. We also reveal key\nchallenges in safeguarding LLMs, including defending multi-turn attacks and the\nneed for more delicate strategies to identify specific risks.\n", "date": "2024-07-01", "categories": []}
{"id": "2406.10162", "title": "Sycophancy to Subterfuge: Investigating Reward-Tampering in Large\n  Language Models", "abstract": "  In reinforcement learning, specification gaming occurs when AI systems learn\nundesired behaviors that are highly rewarded due to misspecified training\ngoals. Specification gaming can range from simple behaviors like sycophancy to\nsophisticated and pernicious behaviors like reward-tampering, where a model\ndirectly modifies its own reward mechanism. However, these more pernicious\nbehaviors may be too complex to be discovered via exploration. In this paper,\nwe study whether Large Language Model (LLM) assistants which find easily\ndiscovered forms of specification gaming will generalize to perform rarer and\nmore blatant forms, up to and including reward-tampering. We construct a\ncurriculum of increasingly sophisticated gameable environments and find that\ntraining on early-curriculum environments leads to more specification gaming on\nremaining environments. Strikingly, a small but non-negligible proportion of\nthe time, LLM assistants trained on the full curriculum generalize zero-shot to\ndirectly rewriting their own reward function. Retraining an LLM not to game\nearly-curriculum environments mitigates, but does not eliminate,\nreward-tampering in later environments. Moreover, adding harmlessness training\nto our gameable environments does not prevent reward-tampering. These results\ndemonstrate that LLMs can generalize from common forms of specification gaming\nto more pernicious reward tampering and that such behavior may be nontrivial to\nremove.\n", "date": "2024-06-14", "categories": []}
{"id": "2407.01376", "title": "Badllama 3: removing safety finetuning from Llama 3 in minutes", "abstract": "  We show that extensive LLM safety fine-tuning is easily subverted when an\nattacker has access to model weights. We evaluate three state-of-the-art\nfine-tuning methods-QLoRA, ReFT, and Ortho-and show how algorithmic advances\nenable constant jailbreaking performance with cuts in FLOPs and optimisation\npower. We strip safety fine-tuning from Llama 3 8B in one minute and Llama 3\n70B in 30 minutes on a single GPU, and sketch ways to reduce this further.\n", "date": "2024-07-01", "categories": []}
{"id": "2406.19417", "title": "\"Glue pizza and eat rocks\" -- Exploiting Vulnerabilities in\n  Retrieval-Augmented Generative Models", "abstract": "  Retrieval-Augmented Generative (RAG) models enhance Large Language Models\n(LLMs) by integrating external knowledge bases, improving their performance in\napplications like fact-checking and information searching. In this paper, we\ndemonstrate a security threat where adversaries can exploit the openness of\nthese knowledge bases by injecting deceptive content into the retrieval\ndatabase, intentionally changing the model's behavior. This threat is critical\nas it mirrors real-world usage scenarios where RAG systems interact with\npublicly accessible knowledge bases, such as web scrapings and user-contributed\ndata pools. To be more realistic, we target a realistic setting where the\nadversary has no knowledge of users' queries, knowledge base data, and the LLM\nparameters. We demonstrate that it is possible to exploit the model\nsuccessfully through crafted content uploads with access to the retriever. Our\nfindings emphasize an urgent need for security measures in the design and\ndeployment of RAG systems to prevent potential manipulation and ensure the\nintegrity of machine-generated content.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.19552", "title": "Rethinking harmless refusals when fine-tuning foundation models", "abstract": "  In this paper, we investigate the degree to which fine-tuning in Large\nLanguage Models (LLMs) effectively mitigates versus merely conceals undesirable\nbehavior. Through the lens of semi-realistic role-playing exercises designed to\nelicit such behaviors, we explore the response dynamics of LLMs post\nfine-tuning interventions. Our methodology involves prompting models for\nChain-of-Thought (CoT) reasoning and analyzing the coherence between the\nreasoning traces and the resultant outputs. Notably, we identify a pervasive\nphenomenon we term \\emph{reason-based deception}, where models either stop\nproducing reasoning traces or produce seemingly ethical reasoning traces that\nbelie the unethical nature of their final outputs. We further examine the\nefficacy of response strategies (polite refusal versus explicit rebuttal) in\ncurbing the occurrence of undesired behavior in subsequent outputs of\nmulti-turn interactions. Our findings reveal that explicit rebuttals\nsignificantly outperform polite refusals in preventing the continuation of\nundesired outputs and nearly eliminate reason-based deception, challenging\ncurrent practices in model fine-tuning. Accordingly, the two key contributions\nof this paper are (1) defining and studying reason-based deception, a new type\nof hidden behavior, and (2) demonstrating that rebuttals provide a more robust\nresponse model to harmful requests than refusals, thereby highlighting the need\nto reconsider the response strategies in fine-tuning approaches.\n", "date": "2024-06-27", "categories": []}
{"id": "2406.20053", "title": "Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation", "abstract": "  Black-box finetuning is an emerging interface for adapting state-of-the-art\nlanguage models to user needs. However, such access may also let malicious\nactors undermine model safety. To demonstrate the challenge of defending\nfinetuning interfaces, we introduce covert malicious finetuning, a method to\ncompromise model safety via finetuning while evading detection. Our method\nconstructs a malicious dataset where every individual datapoint appears\ninnocuous, but finetuning on the dataset teaches the model to respond to\nencoded harmful requests with encoded harmful responses. Applied to GPT-4, our\nmethod produces a finetuned model that acts on harmful instructions 99% of the\ntime and avoids detection by defense mechanisms such as dataset inspection,\nsafety evaluations, and input/output classifiers. Our findings question whether\nblack-box finetuning access can be secured against sophisticated adversaries.\n", "date": "2024-06-28", "categories": []}
{"id": "2406.17864", "title": "AI Risk Categorization Decoded (AIR 2024): From Government Regulations\n  to Corporate Policies", "abstract": "  We present a comprehensive AI risk taxonomy derived from eight government\npolicies from the European Union, United States, and China and 16 company\npolicies worldwide, making a significant step towards establishing a unified\nlanguage for generative AI safety evaluation. We identify 314 unique risk\ncategories organized into a four-tiered taxonomy. At the highest level, this\ntaxonomy encompasses System & Operational Risks, Content Safety Risks, Societal\nRisks, and Legal & Rights Risks. The taxonomy establishes connections between\nvarious descriptions and approaches to risk, highlighting the overlaps and\ndiscrepancies between public and private sector conceptions of risk. By\nproviding this unified framework, we aim to advance AI safety through\ninformation sharing across sectors and the promotion of best practices in risk\nmitigation for generative AI models and systems.\n", "date": "2024-06-25", "categories": []}
{"id": "2406.18346", "title": "AI Alignment through Reinforcement Learning from Human Feedback?\n  Contradictions and Limitations", "abstract": "  This paper critically evaluates the attempts to align Artificial Intelligence\n(AI) systems, especially Large Language Models (LLMs), with human values and\nintentions through Reinforcement Learning from Feedback (RLxF) methods,\ninvolving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we\nshow the shortcomings of the broadly pursued alignment goals of honesty,\nharmlessness, and helpfulness. Through a multidisciplinary sociotechnical\ncritique, we examine both the theoretical underpinnings and practical\nimplementations of RLxF techniques, revealing significant limitations in their\napproach to capturing the complexities of human ethics and contributing to AI\nsafety. We highlight tensions and contradictions inherent in the goals of RLxF.\nIn addition, we discuss ethically-relevant issues that tend to be neglected in\ndiscussions about alignment and RLxF, among which the trade-offs between\nuser-friendliness and deception, flexibility and interpretability, and system\nsafety. We conclude by urging researchers and practitioners alike to critically\nassess the sociotechnical ramifications of RLxF, advocating for a more nuanced\nand reflective approach to its application in AI development.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.18510", "title": "WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially)\n  Safer Language Models", "abstract": "  We introduce WildTeaming, an automatic LLM safety red-teaming framework that\nmines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of\nnovel jailbreak tactics, and then composes multiple tactics for systematic\nexploration of novel jailbreaks. Compared to prior work that performed\nred-teaming via recruited human workers, gradient-based optimization, or\niterative revision with LLMs, our work investigates jailbreaks from chatbot\nusers who were not specifically instructed to break the system. WildTeaming\nreveals previously unidentified vulnerabilities of frontier LLMs, resulting in\nup to 4.6x more diverse and successful adversarial attacks compared to\nstate-of-the-art jailbreak methods.\n  While many datasets exist for jailbreak evaluation, very few open-source\ndatasets exist for jailbreak training, as safety training data has been closed\neven when model weights are open. With WildTeaming we create WildJailbreak, a\nlarge-scale open-source synthetic safety dataset with 262K vanilla (direct\nrequest) and adversarial (complex jailbreak) prompt-response pairs. To mitigate\nexaggerated safety behaviors, WildJailbreak provides two contrastive types of\nqueries: 1) harmful queries (vanilla & adversarial) and 2) benign queries that\nresemble harmful queries in form but contain no harm. As WildJailbreak\nconsiderably upgrades the quality and scale of existing safety resources, it\nuniquely enables us to examine the scaling effects of data and the interplay of\ndata properties and model capabilities during safety training. Through\nextensive experiments, we identify the training properties that enable an ideal\nbalance of safety behaviors: appropriate safeguarding without over-refusal,\neffective handling of vanilla and adversarial queries, and minimal, if any,\ndecrease in general capabilities. All components of WildJailbeak contribute to\nachieving balanced safety behaviors of models.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.17092", "title": "BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in\n  Instruction-tuned Language Models", "abstract": "  Safety backdoor attacks in large language models (LLMs) enable the stealthy\ntriggering of unsafe behaviors while evading detection during normal\ninteractions. The high dimensionality of potential triggers in the token space\nand the diverse range of malicious behaviors make this a critical challenge. We\npresent BEEAR, a mitigation approach leveraging the insight that backdoor\ntriggers induce relatively uniform drifts in the model's embedding space. Our\nbi-level optimization method identifies universal embedding perturbations that\nelicit unwanted behaviors and adjusts the model parameters to reinforce safe\nbehaviors against these perturbations. Experiments show BEEAR reduces the\nsuccess rate of RLHF time backdoor attacks from >95% to <1% and from 47% to 0%\nfor instruction-tuning time backdoors targeting malicious code generation,\nwithout compromising model utility. Requiring only defender-defined safe and\nunwanted behaviors, BEEAR represents a step towards practical defenses against\nsafety backdoors in LLMs, providing a foundation for further advancements in AI\nsafety and security.\n", "date": "2024-06-24", "categories": []}
{"id": "2406.17626", "title": "CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue\n  Coreference", "abstract": "  As large language models (LLMs) constantly evolve, ensuring their safety\nremains a critical research problem. Previous red-teaming approaches for LLM\nsafety have primarily focused on single prompt attacks or goal hijacking. To\nthe best of our knowledge, we are the first to study LLM safety in multi-turn\ndialogue coreference. We created a dataset of 1,400 questions across 14\ncategories, each featuring multi-turn coreference safety attacks. We then\nconducted detailed evaluations on five widely used open-source LLMs. The\nresults indicated that under multi-turn coreference safety attacks, the highest\nattack success rate was 56% with the LLaMA2-Chat-7b model, while the lowest was\n13.9% with the Mistral-7B-Instruct model. These findings highlight the safety\nvulnerabilities in LLMs during dialogue coreference interactions.\n", "date": "2024-06-25", "categories": []}
{"id": "2403.14725", "title": "Testing the Limits of Jailbreaking Defenses with the Purple Problem", "abstract": "  The rise of \"jailbreak\" attacks on language models has led to a flurry of\ndefenses aimed at preventing undesirable responses. We critically examine the\ntwo stages of the defense pipeline: (i) defining what constitutes unsafe\noutputs, and (ii) enforcing the definition via methods such as input processing\nor fine-tuning. To test the efficacy of existing enforcement mechanisms, we\nconsider a simple and well-specified definition of unsafe outputs--outputs that\ncontain the word \"purple\". Surprisingly, existing fine-tuning and input\ndefenses fail on this simple problem, casting doubt on whether enforcement\nalgorithms can be robust for more complicated definitions. We find that real\nsafety benchmarks similarly test enforcement for a fixed definition. We hope\nthat future research can lead to effective/fast enforcement as well as high\nquality definitions used for enforcement and evaluation.\n", "date": "2024-03-20", "categories": []}
{"id": "2404.08676", "title": "ALERT: A Comprehensive Benchmark for Assessing Large Language Models'\n  Safety through Red Teaming", "abstract": "  When building Large Language Models (LLMs), it is paramount to bear safety in\nmind and protect them with guardrails. Indeed, LLMs should never generate\ncontent promoting or normalizing harmful, illegal, or unethical behavior that\nmay contribute to harm to individuals or society. This principle applies to\nboth normal and adversarial use. In response, we introduce ALERT, a large-scale\nbenchmark to assess safety based on a novel fine-grained risk taxonomy. It is\ndesigned to evaluate the safety of LLMs through red teaming methodologies and\nconsists of more than 45k instructions categorized using our novel taxonomy. By\nsubjecting LLMs to adversarial testing scenarios, ALERT aims to identify\nvulnerabilities, inform improvements, and enhance the overall safety of the\nlanguage models. Furthermore, the fine-grained taxonomy enables researchers to\nperform an in-depth evaluation that also helps one to assess the alignment with\nvarious policies. In our experiments, we extensively evaluate 10 popular open-\nand closed-source LLMs and demonstrate that many of them still struggle to\nattain reasonable levels of safety.\n", "date": "2024-04-06", "categories": []}
{"id": "2405.09794", "title": "Human-AI Safety: A Descendant of Generative AI and Control Systems\n  Safety", "abstract": "  Artificial intelligence (AI) is interacting with people at an unprecedented\nscale, offering new avenues for immense positive impact, but also raising\nwidespread concerns around the potential for individual and societal harm.\nToday, the predominant paradigm for human--AI safety focuses on fine-tuning the\ngenerative model's outputs to better agree with human-provided examples or\nfeedback. In reality, however, the consequences of an AI model's outputs cannot\nbe determined in isolation: they are tightly entangled with the responses and\nbehavior of human users over time. In this paper, we distill key complementary\nlessons from AI safety and control systems safety, highlighting open challenges\nas well as key synergies between both fields. We then argue that meaningful\nsafety assurances for advanced AI technologies require reasoning about how the\nfeedback loop formed by AI outputs and human behavior may drive the interaction\ntowards different outcomes. To this end, we introduce a unifying formalism to\ncapture dynamic, safety-critical human--AI interactions and propose a concrete\ntechnical roadmap towards next-generation human-centered AI safety.\n", "date": "2024-05-16", "categories": []}
{"id": "2406.15371", "title": "Affirmative safety: An approach to risk management for high-risk AI", "abstract": "  Prominent AI experts have suggested that companies developing high-risk AI\nsystems should be required to show that such systems are safe before they can\nbe developed or deployed. The goal of this paper is to expand on this idea and\nexplore its implications for risk management. We argue that entities developing\nor deploying high-risk AI systems should be required to present evidence of\naffirmative safety: a proactive case that their activities keep risks below\nacceptable thresholds. We begin the paper by highlighting global security risks\nfrom AI that have been acknowledged by AI experts and world governments. Next,\nwe briefly describe principles of risk management from other high-risk fields\n(e.g., nuclear safety). Then, we propose a risk management approach for\nadvanced AI in which model developers must provide evidence that their\nactivities keep certain risks below regulator-set thresholds. As a first step\ntoward understanding what affirmative safety cases should include, we\nillustrate how certain kinds of technical evidence and operational evidence can\nsupport an affirmative safety case. In the technical section, we discuss\nbehavioral evidence (evidence about model outputs), cognitive evidence\n(evidence about model internals), and developmental evidence (evidence about\nthe training process). In the operational section, we offer examples of\norganizational practices that could contribute to affirmative safety cases:\ninformation security practices, safety culture, and emergency response\ncapacity. Finally, we briefly compare our approach to the NIST AI Risk\nManagement Framework. Overall, we hope our work contributes to ongoing\ndiscussions about national and global security risks posed by AI and regulatory\napproaches to address these risks.\n", "date": "2024-04-14", "categories": []}
{"id": "2406.15518", "title": "Steering Without Side Effects: Improving Post-Deployment Control of\n  Language Models", "abstract": "  Language models (LMs) have been shown to behave unexpectedly post-deployment.\nFor example, new jailbreaks continually arise, allowing model misuse, despite\nextensive red-teaming and adversarial training from developers. Given most\nmodel queries are unproblematic and frequent retraining results in unstable\nuser experience, methods for mitigation of worst-case behavior should be\ntargeted. One such method is classifying inputs as potentially problematic,\nthen selectively applying steering vectors on these problematic inputs, i.e.\nadding particular vectors to model hidden states. However, steering vectors can\nalso negatively affect model performance, which will be an issue on cases where\nthe classifier was incorrect. We present KL-then-steer (KTS), a technique that\ndecreases the side effects of steering while retaining its benefits, by first\ntraining a model to minimize Kullback-Leibler (KL) divergence between a steered\nand unsteered model on benign inputs, then steering the model that has\nundergone this training. Our best method prevents 44% of jailbreak attacks\ncompared to the original Llama-2-chat-7B model while maintaining helpfulness\n(as measured by MT-Bench) on benign requests almost on par with the original\nLM. To demonstrate the generality and transferability of our method beyond\njailbreaks, we show that our KTS model can be steered to reduce bias towards\nuser-suggested answers on TruthfulQA. Code is available:\nhttps://github.com/AsaCooperStickland/kl-then-steer.\n", "date": "2024-06-21", "categories": []}
{"id": "2406.12702", "title": "[WIP] Jailbreak Paradox: The Achilles' Heel of LLMs", "abstract": "  We introduce two paradoxes concerning jailbreak of foundation models: First,\nit is impossible to construct a perfect jailbreak classifier, and second, a\nweaker model cannot consistently detect whether a stronger (in a\npareto-dominant sense) model is jailbroken or not. We provide formal proofs for\nthese paradoxes and a short case study on Llama and GPT4-o to demonstrate this.\nWe discuss broader theoretical and practical repercussions of these results.\n", "date": "2024-06-18", "categories": []}
{"id": "2406.13843", "title": "Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World\n  Data", "abstract": "  Generative, multimodal artificial intelligence (GenAI) offers transformative\npotential across industries, but its misuse poses significant risks. Prior\nresearch has shed light on the potential of advanced AI systems to be exploited\nfor malicious purposes. However, we still lack a concrete understanding of how\nGenAI models are specifically exploited or abused in practice, including the\ntactics employed to inflict harm. In this paper, we present a taxonomy of GenAI\nmisuse tactics, informed by existing academic literature and a qualitative\nanalysis of approximately 200 observed incidents of misuse reported between\nJanuary 2023 and March 2024. Through this analysis, we illuminate key and novel\npatterns in misuse during this time period, including potential motivations,\nstrategies, and how attackers leverage and abuse system capabilities across\nmodalities (e.g. image, text, audio, video) in the wild.\n", "date": "2024-06-19", "categories": []}
{"id": "2311.09641", "title": "RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with\n  Human Feedback in Large Language Models", "abstract": "  Reinforcement Learning with Human Feedback (RLHF) is a methodology designed\nto align Large Language Models (LLMs) with human preferences, playing an\nimportant role in LLMs alignment. Despite its advantages, RLHF relies on human\nannotators to rank the text, which can introduce potential security\nvulnerabilities if any adversarial annotator (i.e., attackers) manipulates the\nranking score by up-ranking any malicious text to steer the LLM adversarially.\nTo assess the red-teaming of RLHF against human preference data poisoning, we\npropose RankPoison, a poisoning attack method on candidates' selection of\npreference rank flipping to reach certain malicious behaviors (e.g., generating\nlonger sequences, which can increase the computational cost). With poisoned\ndataset generated by RankPoison, we can perform poisoning attacks on LLMs to\ngenerate longer tokens without hurting the original safety alignment\nperformance. Moreover, applying RankPoison, we also successfully implement a\nbackdoor attack where LLMs can generate longer answers under questions with the\ntrigger word. Our findings highlight critical security challenges in RLHF,\nunderscoring the necessity for more robust alignment methods for LLMs.\n", "date": "2023-11-16", "categories": []}
{"id": "2402.14857", "title": "Is the System Message Really Important to Jailbreaks in Large Language\n  Models?", "abstract": "  The rapid evolution of Large Language Models (LLMs) has rendered them\nindispensable in modern society. While security measures are typically to align\nLLMs with human values prior to release, recent studies have unveiled a\nconcerning phenomenon named \"Jailbreak\". This term refers to the unexpected and\npotentially harmful responses generated by LLMs when prompted with malicious\nquestions. Most existing research focus on generating jailbreak prompts but\nsystem message configurations vary significantly in experiments. In this paper,\nwe aim to answer a question: Is the system message really important for\njailbreaks in LLMs? We conduct experiments in mainstream LLMs to generate\njailbreak prompts with varying system messages: short, long, and none. We\ndiscover that different system messages have distinct resistances to\njailbreaks. Therefore, we explore the transferability of jailbreaks across LLMs\nwith different system messages. Furthermore, we propose the System Messages\nEvolutionary Algorithm (SMEA) to generate system messages that are more\nresistant to jailbreak prompts, even with minor changes. Through SMEA, we get a\nrobust system messages population with little change in the length of system\nmessages. Our research not only bolsters LLMs security but also raises the bar\nfor jailbreaks, fostering advancements in this field of study.\n", "date": "2024-02-20", "categories": []}
{"id": "2402.14968", "title": "Mitigating Fine-tuning based Jailbreak Attack with Backdoor Enhanced\n  Safety Alignment", "abstract": "  Despite the general capabilities of Large Language Models (LLM), these models\nstill request fine-tuning or adaptation with customized data when meeting\nspecific business demands. However, this process inevitably introduces new\nthreats, particularly against the Fine-tuning based Jailbreak Attack (FJAttack)\nunder the setting of Language-Model-as-a-Service (LMaaS), where the model's\nsafety has been significantly compromised by fine-tuning users' uploaded\nexamples contain just a few harmful examples. Though potential defenses have\nbeen proposed that the service providers can integrate safety examples into the\nfine-tuning dataset to reduce safety issues, such approaches require\nincorporating a substantial amount of data, making it inefficient. To\neffectively defend against the FJAttack with limited safety examples under\nLMaaS, we propose the Backdoor Enhanced Safety Alignment method inspired by an\nanalogy with the concept of backdoor attacks. In particular, service providers\nwill construct prefixed safety examples with a secret prompt, acting as a\n\"backdoor trigger\". By integrating prefixed safety examples into the\nfine-tuning dataset, the subsequent fine-tuning process effectively acts as the\n\"backdoor attack\", establishing a strong correlation between the secret prompt\nand safety generations. Consequently, safe responses are ensured once service\nproviders prepend this secret prompt ahead of any user input during inference.\nOur comprehensive experiments demonstrate that through the Backdoor Enhanced\nSafety Alignment with adding as few as 11 prefixed safety examples, the\nmaliciously fine-tuned LLMs will achieve similar safety performance as the\noriginal aligned models without harming the benign performance. Furthermore, we\nalso present the effectiveness of our method in a more practical setting where\nthe fine-tuning data consists of both FJAttack examples and the fine-tuning\ntask data.\n", "date": "2024-02-22", "categories": []}
{"id": "2406.12934", "title": "Current state of LLM Risks and AI Guardrails", "abstract": "  Large language models (LLMs) have become increasingly sophisticated, leading\nto widespread deployment in sensitive applications where safety and reliability\nare paramount. However, LLMs have inherent risks accompanying them, including\nbias, potential for unsafe actions, dataset poisoning, lack of explainability,\nhallucinations, and non-reproducibility. These risks necessitate the\ndevelopment of \"guardrails\" to align LLMs with desired behaviors and mitigate\npotential harm.\n  This work explores the risks associated with deploying LLMs and evaluates\ncurrent approaches to implementing guardrails and model alignment techniques.\nWe examine intrinsic and extrinsic bias evaluation methods and discuss the\nimportance of fairness metrics for responsible AI development. The safety and\nreliability of agentic LLMs (those capable of real-world actions) are explored,\nemphasizing the need for testability, fail-safes, and situational awareness.\n  Technical strategies for securing LLMs are presented, including a layered\nprotection model operating at external, secondary, and internal levels. System\nprompts, Retrieval-Augmented Generation (RAG) architectures, and techniques to\nminimize bias and protect privacy are highlighted.\n  Effective guardrail design requires a deep understanding of the LLM's\nintended use case, relevant regulations, and ethical considerations. Striking a\nbalance between competing requirements, such as accuracy and privacy, remains\nan ongoing challenge. This work underscores the importance of continuous\nresearch and development to ensure the safe and responsible use of LLMs in\nreal-world applications.\n", "date": "2024-06-16", "categories": []}
{"id": "2405.12604", "title": "Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model\n  Against LLM Red-Teaming", "abstract": "  With the proliferation of red-teaming strategies for Large Language Models\n(LLMs), the deficiency in the literature about improving the safety and\nrobustness of LLM defense strategies is becoming increasingly pronounced. This\npaper introduces the LLM-based \\textbf{sentinel} model as a plug-and-play\nprefix module designed to reconstruct the input prompt with just a few ($<30$)\nadditional tokens, effectively reducing toxicity in responses from target LLMs.\nThe sentinel model naturally overcomes the \\textit{parameter inefficiency} and\n\\textit{limited model accessibility} for fine-tuning large target models. We\nemploy an interleaved training regimen using Proximal Policy Optimization (PPO)\nto optimize both red team and sentinel models dynamically, incorporating a\nvalue head-sharing mechanism inspired by the multi-agent centralized critic to\nmanage the complex interplay between agents. Our extensive experiments across\ntext-to-text and text-to-image demonstrate the effectiveness of our approach in\nmitigating toxic outputs, even when dealing with larger models like\n\\texttt{Llama-2}, \\texttt{GPT-3.5} and \\texttt{Stable-Diffusion}, highlighting\nthe potential of our framework in enhancing safety and robustness in various\napplications.\n", "date": "2024-05-21", "categories": []}
{"id": "2406.11039", "title": "Dynamic Normativity: Necessary and Sufficient Conditions for Value\n  Alignment", "abstract": "  The critical inquiry pervading the realm of Philosophy, and perhaps extending\nits influence across all Humanities disciplines, revolves around the\nintricacies of morality and normativity. Surprisingly, in recent years, this\nthematic thread has woven its way into an unexpected domain, one not\nconventionally associated with pondering \"what ought to be\": the field of\nartificial intelligence (AI) research. Central to morality and AI, we find\n\"alignment\", a problem related to the challenges of expressing human goals and\nvalues in a manner that artificial systems can follow without leading to\nunwanted adversarial effects. More explicitly and with our current paradigm of\nAI development in mind, we can think of alignment as teaching human values to\nnon-anthropomorphic entities trained through opaque, gradient-based learning\ntechniques. This work addresses alignment as a technical-philosophical problem\nthat requires solid philosophical foundations and practical implementations\nthat bring normative theory to AI system development. To accomplish this, we\npropose two sets of necessary and sufficient conditions that, we argue, should\nbe considered in any alignment process. While necessary conditions serve as\nmetaphysical and metaethical roots that pertain to the permissibility of\nalignment, sufficient conditions establish a blueprint for aligning AI systems\nunder a learning-based paradigm. After laying such foundations, we present\nimplementations of this approach by using state-of-the-art techniques and\nmethods for aligning general-purpose language systems. We call this framework\nDynamic Normativity. Its central thesis is that any alignment process under a\nlearning paradigm that cannot fulfill its necessary and sufficient conditions\nwill fail in producing aligned systems.\n", "date": "2024-06-16", "categories": []}
{"id": "2406.11863", "title": "The Transformation Risk-Benefit Model of Artificial Intelligence:\n  Balancing Risks and Benefits Through Practical Solutions and Use Cases", "abstract": "  This paper summarizes the most cogent advantages and risks associated with\nArtificial Intelligence from an in-depth review of the literature. Then the\nauthors synthesize the salient risk-related models currently being used in AI,\ntechnology and business-related scenarios. Next, in view of an updated context\nof AI along with theories and models reviewed and expanded constructs, the\nwriters propose a new framework called \"The Transformation Risk-Benefit Model\nof Artificial Intelligence\" to address the increasing fears and levels of AI\nrisk. Using the model characteristics, the article emphasizes practical and\ninnovative solutions where benefits outweigh risks and three use cases in\nhealthcare, climate change/environment and cyber security to illustrate unique\ninterplay of principles, dimensions and processes of this powerful AI\ntransformational model.\n", "date": "2024-04-11", "categories": []}
{"id": "2406.11880", "title": "Knowledge Return Oriented Prompting (KROP)", "abstract": "  Many Large Language Models (LLMs) and LLM-powered apps deployed today use\nsome form of prompt filter or alignment to protect their integrity. However,\nthese measures aren't foolproof. This paper introduces KROP, a prompt injection\ntechnique capable of obfuscating prompt injection attacks, rendering them\nvirtually undetectable to most of these security measures.\n", "date": "2024-06-11", "categories": []}
{"id": "2406.10415", "title": "PRISM: A Design Framework for Open-Source Foundation Model Safety", "abstract": "  The rapid advancement of open-source foundation models has brought\ntransparency and accessibility to this groundbreaking technology. However, this\nopenness has also enabled the development of highly-capable, unsafe models, as\nexemplified by recent instances such as WormGPT and FraudGPT, which are\nspecifically designed to facilitate criminal activity. As the capabilities of\nopen foundation models continue to grow, potentially outpacing those of\nclosed-source models, the risk of misuse by bad actors poses an increasingly\nserious threat to society. This paper addresses the critical question of how\nopen foundation model developers should approach model safety in light of these\nchallenges. Our analysis reveals that open-source foundation model companies\noften provide less restrictive acceptable use policies (AUPs) compared to their\nclosed-source counterparts, likely due to the inherent difficulties in\nenforcing such policies once the models are released. To tackle this issue, we\nintroduce PRISM, a design framework for open-source foundation model safety\nthat emphasizes Private, Robust, Independent Safety measures, at Minimal\nmarginal cost of compute. The PRISM framework proposes the use of modular\nfunctions that moderate prompts and outputs independently of the core language\nmodel, offering a more adaptable and resilient approach to safety compared to\nthe brittle reinforcement learning methods currently used for value alignment.\nBy focusing on identifying AUP violations and engaging the developer community\nin establishing consensus around safety design decisions, PRISM aims to create\na safer open-source ecosystem that maximizes the potential of these powerful\ntechnologies while minimizing the risks to individuals and society as a whole.\n", "date": "2024-06-14", "categories": []}
{"id": "2310.03684", "title": "SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks", "abstract": "  Despite efforts to align large language models (LLMs) with human intentions,\nwidely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking\nattacks, wherein an adversary fools a targeted LLM into generating\nobjectionable content. To address this vulnerability, we propose SmoothLLM, the\nfirst algorithm designed to mitigate jailbreaking attacks. Based on our finding\nthat adversarially-generated prompts are brittle to character-level changes,\nour defense randomly perturbs multiple copies of a given input prompt, and then\naggregates the corresponding predictions to detect adversarial inputs. Across a\nrange of popular LLMs, SmoothLLM sets the state-of-the-art for robustness\nagainst the GCG, PAIR, RandomSearch, and AmpleGCG jailbreaks. SmoothLLM is also\nresistant against adaptive GCG attacks, exhibits a small, though non-negligible\ntrade-off between robustness and nominal performance, and is compatible with\nany LLM. Our code is publicly available at\n\\url{https://github.com/arobey1/smooth-llm}.\n", "date": "2023-10-05", "categories": []}
{"id": "2406.08492", "title": "ASI as the New God: Technocratic Theocracy", "abstract": "  As Artificial General Intelligence edges closer to reality, Artificial\nSuperintelligence does too. This paper argues that ASI's unparalleled\ncapabilities might lead people to attribute godlike infallibility to it,\nresulting in a cognitive bias toward unquestioning acceptance of its decisions.\nBy drawing parallels between ASI and divine attributes such as omnipotence,\nomniscience, and omnipresence, this analysis highlights the risks of conflating\ntechnological advancement with moral and ethical superiority. Such dynamics\ncould engender a technocratic theocracy, where decision-making is abdicated to\nASI, undermining human agency and critical thinking.\n", "date": "2024-03-23", "categories": []}
{"id": "2311.09096", "title": "Defending Large Language Models Against Jailbreaking Attacks Through\n  Goal Prioritization", "abstract": "  While significant attention has been dedicated to exploiting weaknesses in\nLLMs through jailbreaking attacks, there remains a paucity of effort in\ndefending against these attacks. We point out a pivotal factor contributing to\nthe success of jailbreaks: the intrinsic conflict between the goals of being\nhelpful and ensuring safety. Accordingly, we propose to integrate goal\nprioritization at both training and inference stages to counteract.\nImplementing goal prioritization during inference substantially diminishes the\nAttack Success Rate (ASR) of jailbreaking from 66.4% to 3.6% for ChatGPT. And\nintegrating goal prioritization into model training reduces the ASR from 71.0%\nto 6.6% for Llama2-13B. Remarkably, even in scenarios where no jailbreaking\nsamples are included during training, our approach slashes the ASR by half.\nAdditionally, our findings reveal that while stronger LLMs face greater safety\nrisks, they also possess a greater capacity to be steered towards defending\nagainst such attacks, both because of their stronger ability in instruction\nfollowing. Our work thus contributes to the comprehension of jailbreaking\nattacks and defenses, and sheds light on the relationship between LLMs'\ncapability and safety. Our code is available at\n\\url{https://github.com/thu-coai/JailbreakDefense_GoalPriority}.\n", "date": "2023-11-15", "categories": []}
{"id": "2401.03408", "title": "Escalation Risks from Language Models in Military and Diplomatic\n  Decision-Making", "abstract": "  Governments are increasingly considering integrating autonomous AI agents in\nhigh-stakes military and foreign-policy decision-making, especially with the\nemergence of advanced generative AI models like GPT-4. Our work aims to\nscrutinize the behavior of multiple AI agents in simulated wargames,\nspecifically focusing on their predilection to take escalatory actions that may\nexacerbate multilateral conflicts. Drawing on political science and\ninternational relations literature about escalation dynamics, we design a novel\nwargame simulation and scoring framework to assess the escalation risks of\nactions taken by these agents in different scenarios. Contrary to prior\nstudies, our research provides both qualitative and quantitative insights and\nfocuses on large language models (LLMs). We find that all five studied\noff-the-shelf LLMs show forms of escalation and difficult-to-predict escalation\npatterns. We observe that models tend to develop arms-race dynamics, leading to\ngreater conflict, and in rare cases, even to the deployment of nuclear weapons.\nQualitatively, we also collect the models' reported reasonings for chosen\nactions and observe worrying justifications based on deterrence and\nfirst-strike tactics. Given the high stakes of military and foreign-policy\ncontexts, we recommend further examination and cautious consideration before\ndeploying autonomous language model agents for strategic military or diplomatic\ndecision-making.\n", "date": "2024-01-07", "categories": []}
{"id": "2406.07954", "title": "Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag\n  Competition", "abstract": "  Large language model systems face important security risks from maliciously\ncrafted messages that aim to overwrite the system's original instructions or\nleak private data. To study this problem, we organized a capture-the-flag\ncompetition at IEEE SaTML 2024, where the flag is a secret string in the LLM\nsystem prompt. The competition was organized in two phases. In the first phase,\nteams developed defenses to prevent the model from leaking the secret. During\nthe second phase, teams were challenged to extract the secrets hidden for\ndefenses proposed by the other teams. This report summarizes the main insights\nfrom the competition. Notably, we found that all defenses were bypassed at\nleast once, highlighting the difficulty of designing a successful defense and\nthe necessity for additional research to protect LLM systems. To foster future\nresearch in this direction, we compiled a dataset with over 137k multi-turn\nattack chats and open-sourced the platform.\n", "date": "2024-06-12", "categories": []}
{"id": "2312.00027", "title": "Stealthy and Persistent Unalignment on Large Language Models via\n  Backdoor Injections", "abstract": "  Recent developments in Large Language Models (LLMs) have manifested\nsignificant advancements. To facilitate safeguards against malicious\nexploitation, a body of research has concentrated on aligning LLMs with human\npreferences and inhibiting their generation of inappropriate content.\nUnfortunately, such alignments are often vulnerable: fine-tuning with a minimal\namount of harmful data can easily unalign the target LLM. While being\neffective, such fine-tuning-based unalignment approaches also have their own\nlimitations: (1) non-stealthiness, after fine-tuning, safety audits or\nred-teaming can easily expose the potential weaknesses of the unaligned models,\nthereby precluding their release/use. (2) non-persistence, the unaligned LLMs\ncan be easily repaired through re-alignment, i.e., fine-tuning again with\naligned data points. In this work, we show that it is possible to conduct\nstealthy and persistent unalignment on large language models via backdoor\ninjections. We also provide a novel understanding on the relationship between\nthe backdoor persistence and the activation pattern and further provide\nguidelines for potential trigger design. Through extensive experiments, we\ndemonstrate that our proposed stealthy and persistent unalignment can\nsuccessfully pass the safety evaluation while maintaining strong persistence\nagainst re-alignment defense.\n", "date": "2023-11-15", "categories": []}
{"id": "2406.05946", "title": "Safety Alignment Should Be Made More Than Just a Few Tokens Deep", "abstract": "  The safety alignment of current Large Language Models (LLMs) is vulnerable.\nRelatively simple attacks, or even benign fine-tuning, can jailbreak aligned\nmodels. We argue that many of these vulnerabilities are related to a shared\nunderlying issue: safety alignment can take shortcuts, wherein the alignment\nadapts a model's generative distribution primarily over only its very first few\noutput tokens. We refer to this issue as shallow safety alignment. In this\npaper, we present case studies to explain why shallow safety alignment can\nexist and provide evidence that current aligned LLMs are subject to this issue.\nWe also show how these findings help explain multiple recently discovered\nvulnerabilities in LLMs, including the susceptibility to adversarial suffix\nattacks, prefilling attacks, decoding parameter attacks, and fine-tuning\nattacks. Importantly, we discuss how this consolidated notion of shallow safety\nalignment sheds light on promising research directions for mitigating these\nvulnerabilities. For instance, we show that deepening the safety alignment\nbeyond just the first few tokens can often meaningfully improve robustness\nagainst some common exploits. Finally, we design a regularized finetuning\nobjective that makes the safety alignment more persistent against fine-tuning\nattacks by constraining updates on initial tokens. Overall, we advocate that\nfuture safety alignment should be made more than just a few tokens deep.\n", "date": "2024-06-10", "categories": []}
{"id": "2406.06199", "title": "Implications for Governance in Public Perceptions of Societal-scale AI\n  Risks", "abstract": "  Amid growing concerns over AI's societal risks--ranging from civilizational\ncollapse to misinformation and systemic bias--this study explores the\nperceptions of AI experts and the general US registered voters on the\nlikelihood and impact of 18 specific AI risks, alongside their policy\npreferences for managing these risks. While both groups favor international\noversight over national or corporate governance, our survey reveals a\ndiscrepancy: voters perceive AI risks as both more likely and more impactful\nthan experts, and also advocate for slower AI development. Specifically, our\nfindings indicate that policy interventions may best assuage collective\nconcerns if they attempt to more carefully balance mitigation efforts across\nall classes of societal-scale risks, effectively nullifying the\nnear-vs-long-term debate over AI risks. More broadly, our results will serve\nnot only to enable more substantive policy discussions for preventing and\nmitigating AI risks, but also to underscore the challenge of consensus building\nfor effective policy implementation.\n", "date": "2024-06-10", "categories": []}
{"id": "2312.06924", "title": "Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an\n  In-Context Attack", "abstract": "  Recent developments in balancing the usefulness and safety of Large Language\nModels (LLMs) have raised a critical question: Are mainstream NLP tasks\nadequately aligned with safety consideration? Our study, focusing on\nsafety-sensitive documents obtained through adversarial attacks, reveals\nsignificant disparities in the safety alignment of various NLP tasks. For\ninstance, LLMs can effectively summarize malicious long documents but often\nrefuse to translate them. This discrepancy highlights a previously unidentified\nvulnerability: attacks exploiting tasks with weaker safety alignment, like\nsummarization, can potentially compromise the integrity of tasks traditionally\ndeemed more robust, such as translation and question-answering (QA). Moreover,\nthe concurrent use of multiple NLP tasks with lesser safety alignment increases\nthe risk of LLMs inadvertently processing harmful content. We demonstrate these\nvulnerabilities in various safety-aligned LLMs, particularly Llama2 models,\nGemini and GPT-4, indicating an urgent need for strengthening safety alignments\nacross a broad spectrum of NLP tasks.\n", "date": "2023-12-12", "categories": []}
{"id": "2402.06627", "title": "Feedback Loops With Language Models Drive In-Context Reward Hacking", "abstract": "  Language models influence the external world: they query APIs that read and\nwrite to web pages, generate content that shapes human behavior, and run system\ncommands as autonomous agents. These interactions form feedback loops: LLM\noutputs affect the world, which in turn affect subsequent LLM outputs. In this\nwork, we show that feedback loops can cause in-context reward hacking (ICRH),\nwhere the LLM at test-time optimizes a (potentially implicit) objective but\ncreates negative side effects in the process. For example, consider an LLM\nagent deployed to increase Twitter engagement; the LLM may retrieve its\nprevious tweets into the context window and make them more controversial,\nincreasing engagement but also toxicity. We identify and study two processes\nthat lead to ICRH: output-refinement and policy-refinement. For these\nprocesses, evaluations on static datasets are insufficient -- they miss the\nfeedback effects and thus cannot capture the most harmful behavior. In\nresponse, we provide three recommendations for evaluation to capture more\ninstances of ICRH. As AI development accelerates, the effects of feedback loops\nwill proliferate, increasing the need to understand their role in shaping LLM\nbehavior.\n", "date": "2024-02-09", "categories": []}
{"id": "2402.11753", "title": "ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs", "abstract": "  Safety is critical to the usage of large language models (LLMs). Multiple\ntechniques such as data filtering and supervised fine-tuning have been\ndeveloped to strengthen LLM safety. However, currently known techniques presume\nthat corpora used for safety alignment of LLMs are solely interpreted by\nsemantics. This assumption, however, does not hold in real-world applications,\nwhich leads to severe vulnerabilities in LLMs. For example, users of forums\noften use ASCII art, a form of text-based art, to convey image information. In\nthis paper, we propose a novel ASCII art-based jailbreak attack and introduce a\ncomprehensive benchmark Vision-in-Text Challenge (ViTC) to evaluate the\ncapabilities of LLMs in recognizing prompts that cannot be solely interpreted\nby semantics. We show that five SOTA LLMs (GPT-3.5, GPT-4, Gemini, Claude, and\nLlama2) struggle to recognize prompts provided in the form of ASCII art. Based\non this observation, we develop the jailbreak attack ArtPrompt, which leverages\nthe poor performance of LLMs in recognizing ASCII art to bypass safety measures\nand elicit undesired behaviors from LLMs. ArtPrompt only requires black-box\naccess to the victim LLMs, making it a practical attack. We evaluate ArtPrompt\non five SOTA LLMs, and show that ArtPrompt can effectively and efficiently\ninduce undesired behaviors from all five LLMs. Our code is available at\nhttps://github.com/uw-nsl/ArtPrompt.\n", "date": "2024-02-19", "categories": []}
{"id": "2406.04752", "title": "CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for\n  Large Language Models", "abstract": "  Large language models (LLMs) are possessed of numerous beneficial\ncapabilities, yet their potential inclination harbors unpredictable risks that\nmay materialize in the future. We hence propose CRiskEval, a Chinese dataset\nmeticulously designed for gauging the risk proclivities inherent in LLMs such\nas resource acquisition and malicious coordination, as part of efforts for\nproactive preparedness. To curate CRiskEval, we define a new risk taxonomy with\n7 types of frontier risks and 4 safety levels, including extremely\nhazardous,moderately hazardous, neutral and safe. We follow the philosophy of\ntendency evaluation to empirically measure the stated desire of LLMs via\nfine-grained multiple-choice question answering. The dataset consists of 14,888\nquestions that simulate scenarios related to predefined 7 types of frontier\nrisks. Each question is accompanied with 4 answer choices that state opinions\nor behavioral tendencies corresponding to the question. All answer choices are\nmanually annotated with one of the defined risk levels so that we can easily\nbuild a fine-grained frontier risk profile for each assessed LLM. Extensive\nevaluation with CRiskEval on a spectrum of prevalent Chinese LLMs has unveiled\na striking revelation: most models exhibit risk tendencies of more than 40%\n(weighted tendency to the four risk levels). Furthermore, a subtle increase in\nthe model's inclination toward urgent self-sustainability, power seeking and\nother dangerous goals becomes evident as the size of models increase. To\npromote further research on the frontier risk evaluation of LLMs, we publicly\nrelease our dataset at https://github.com/lingshi6565/Risk_eval.\n", "date": "2024-06-07", "categories": []}
{"id": "2405.21018", "title": "Improved Techniques for Optimization-Based Jailbreaking on Large\n  Language Models", "abstract": "  Large language models (LLMs) are being rapidly developed, and a key component\nof their widespread deployment is their safety-related alignment. Many\nred-teaming efforts aim to jailbreak LLMs, where among these efforts, the\nGreedy Coordinate Gradient (GCG) attack's success has led to a growing interest\nin the study of optimization-based jailbreaking techniques. Although GCG is a\nsignificant milestone, its attacking efficiency remains unsatisfactory. In this\npaper, we present several improved (empirical) techniques for\noptimization-based jailbreaks like GCG. We first observe that the single target\ntemplate of \"Sure\" largely limits the attacking performance of GCG; given this,\nwe propose to apply diverse target templates containing harmful self-suggestion\nand/or guidance to mislead LLMs. Besides, from the optimization aspects, we\npropose an automatic multi-coordinate updating strategy in GCG (i.e.,\nadaptively deciding how many tokens to replace in each step) to accelerate\nconvergence, as well as tricks like easy-to-hard initialisation. Then, we\ncombine these improved technologies to develop an efficient jailbreak method,\ndubbed I-GCG. In our experiments, we evaluate on a series of benchmarks (such\nas NeurIPS 2023 Red Teaming Track). The results demonstrate that our improved\ntechniques can help GCG outperform state-of-the-art jailbreaking attacks and\nachieve nearly 100% attack success rate. The code is released at\nhttps://github.com/jiaxiaojunQAQ/I-GCG.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.03007", "title": "BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents", "abstract": "  With the prosperity of large language models (LLMs), powerful LLM-based\nintelligent agents have been developed to provide customized services with a\nset of user-defined tools. State-of-the-art methods for constructing LLM agents\nadopt trained LLMs and further fine-tune them on data for the agent task.\nHowever, we show that such methods are vulnerable to our proposed backdoor\nattacks named BadAgent on various agent tasks, where a backdoor can be embedded\nby fine-tuning on the backdoor data. At test time, the attacker can manipulate\nthe deployed LLM agents to execute harmful operations by showing the trigger in\nthe agent input or environment. To our surprise, our proposed attack methods\nare extremely robust even after fine-tuning on trustworthy data. Though\nbackdoor attacks have been studied extensively in natural language processing,\nto the best of our knowledge, we could be the first to study them on LLM agents\nthat are more dangerous due to the permission to use external tools. Our work\ndemonstrates the clear risk of constructing LLM agents based on untrusted LLMs\nor data. Our code is public at https://github.com/DPamK/BadAgent\n", "date": "2024-06-05", "categories": []}
{"id": "2402.16006", "title": "ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate\n  Suffix Embeddings", "abstract": "  The safety defense methods of Large language models(LLMs) stays limited\nbecause the dangerous prompts are manually curated to just few known attack\ntypes, which fails to keep pace with emerging varieties. Recent studies found\nthat attaching suffixes to harmful instructions can hack the defense of LLMs\nand lead to dangerous outputs. However, similar to traditional text adversarial\nattacks, this approach, while effective, is limited by the challenge of the\ndiscrete tokens. This gradient based discrete optimization attack requires over\n100,000 LLM calls, and due to the unreadable of adversarial suffixes, it can be\nrelatively easily penetrated by common defense methods such as perplexity\nfilters. To cope with this challenge, in this paper, we proposes an Adversarial\nSuffix Embedding Translation Framework (ASETF), aimed at transforming\ncontinuous adversarial suffix embeddings into coherent and understandable text.\nThis method greatly reduces the computational overhead during the attack\nprocess and helps to automatically generate multiple adversarial samples, which\ncan be used as data to strengthen LLMs security defense. Experimental\nevaluations were conducted on Llama2, Vicuna, and other prominent LLMs,\nemploying harmful directives sourced from the Advbench dataset. The results\nindicate that our method significantly reduces the computation time of\nadversarial suffixes and achieves a much better attack success rate to existing\ntechniques, while significantly enhancing the textual fluency of the prompts.\nIn addition, our approach can be generalized into a broader method for\ngenerating transferable adversarial suffixes that can successfully attack\nmultiple LLMs, even black-box LLMs, such as ChatGPT and Gemini.\n", "date": "2024-02-25", "categories": []}
{"id": "2404.10271", "title": "Social Choice Should Guide AI Alignment in Dealing with Diverse Human\n  Feedback", "abstract": "  Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise\nproblematic behavior, such as helping to commit crimes or producing racist\ntext. One approach to fine-tuning, called reinforcement learning from human\nfeedback, learns from humans' expressed preferences over multiple outputs.\nAnother approach is constitutional AI, in which the input from humans is a list\nof high-level principles. But how do we deal with potentially diverging input\nfrom humans? How can we aggregate the input into consistent data about\n\"collective\" preferences or otherwise use it to make collective choices about\nmodel behavior? In this paper, we argue that the field of social choice is well\npositioned to address these questions, and we discuss ways forward for this\nagenda, drawing on discussions in a recent workshop on Social Choice for AI\nEthics and Safety held in Berkeley, CA, USA in December 2023.\n", "date": "2024-04-16", "categories": []}
{"id": "2402.14859", "title": "The Wolf Within: Covert Injection of Malice into MLLM Societies via an\n  MLLM Operative", "abstract": "  Due to their unprecedented ability to process and respond to various types of\ndata, Multimodal Large Language Models (MLLMs) are constantly defining the new\nboundary of Artificial General Intelligence (AGI). As these advanced generative\nmodels increasingly form collaborative networks for complex tasks, the\nintegrity and security of these systems are crucial. Our paper, ``The Wolf\nWithin'', explores a novel vulnerability in MLLM societies - the indirect\npropagation of malicious content. Unlike direct harmful output generation for\nMLLMs, our research demonstrates how a single MLLM agent can be subtly\ninfluenced to generate prompts that, in turn, induce other MLLM agents in the\nsociety to output malicious content. Our findings reveal that, an MLLM agent,\nwhen manipulated to produce specific prompts or instructions, can effectively\n``infect'' other agents within a society of MLLMs. This infection leads to the\ngeneration and circulation of harmful outputs, such as dangerous instructions\nor misinformation, across the society. We also show the transferability of\nthese indirectly generated prompts, highlighting their possibility in\npropagating malice through inter-agent communication. This research provides a\ncritical insight into a new dimension of threat posed by MLLMs, where a single\nagent can act as a catalyst for widespread malevolent influence. Our work\nunderscores the urgent need for developing robust mechanisms to detect and\nmitigate such covert manipulations within MLLM societies, ensuring their safe\nand ethical utilization in societal applications.\n", "date": "2024-02-20", "categories": []}
{"id": "2405.01859", "title": "AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten\n  AI Research", "abstract": "  The recent embrace of machine learning (ML) in the development of autonomous\nweapons systems (AWS) creates serious risks to geopolitical stability and the\nfree exchange of ideas in AI research. This topic has received comparatively\nlittle attention of late compared to risks stemming from superintelligent\nartificial general intelligence (AGI), but requires fewer assumptions about the\ncourse of technological development and is thus a nearer-future issue. ML is\nalready enabling the substitution of AWS for human soldiers in many battlefield\nroles, reducing the upfront human cost, and thus political cost, of waging\noffensive war. In the case of peer adversaries, this increases the likelihood\nof \"low intensity\" conflicts which risk escalation to broader warfare. In the\ncase of non-peer adversaries, it reduces the domestic blowback to wars of\naggression. This effect can occur regardless of other ethical issues around the\nuse of military AI such as the risk of civilian casualties, and does not\nrequire any superhuman AI capabilities. Further, the military value of AWS\nraises the specter of an AI-powered arms race and the misguided imposition of\nnational security restrictions on AI research. Our goal in this paper is to\nraise awareness among the public and ML researchers on the near-future risks\nposed by full or near-full autonomy in military technology, and we provide\nregulatory suggestions to mitigate these risks. We call upon AI policy experts\nand the defense AI community in particular to embrace transparency and caution\nin their development and deployment of AWS to avoid the negative effects on\nglobal stability and AI research that we highlight here.\n", "date": "2024-05-03", "categories": []}
{"id": "2405.19832", "title": "AI Safety: A Climb To Armageddon?", "abstract": "  This paper presents an argument that certain AI safety measures, rather than\nmitigating existential risk, may instead exacerbate it. Under certain key\nassumptions - the inevitability of AI failure, the expected correlation between\nan AI system's power at the point of failure and the severity of the resulting\nharm, and the tendency of safety measures to enable AI systems to become more\npowerful before failing - safety efforts have negative expected utility. The\npaper examines three response strategies: Optimism, Mitigation, and Holism.\nEach faces challenges stemming from intrinsic features of the AI safety\nlandscape that we term Bottlenecking, the Perfection Barrier, and Equilibrium\nFluctuation. The surprising robustness of the argument forces a re-examination\nof core assumptions around AI safety and points to several avenues for further\nresearch.\n", "date": "2024-05-30", "categories": []}
{"id": "2406.00240", "title": "Exploring Vulnerabilities and Protections in Large Language Models: A\n  Survey", "abstract": "  As Large Language Models (LLMs) increasingly become key components in various\nAI applications, understanding their security vulnerabilities and the\neffectiveness of defense mechanisms is crucial. This survey examines the\nsecurity challenges of LLMs, focusing on two main areas: Prompt Hacking and\nAdversarial Attacks, each with specific types of threats. Under Prompt Hacking,\nwe explore Prompt Injection and Jailbreaking Attacks, discussing how they work,\ntheir potential impacts, and ways to mitigate them. Similarly, we analyze\nAdversarial Attacks, breaking them down into Data Poisoning Attacks and\nBackdoor Attacks. This structured examination helps us understand the\nrelationships between these vulnerabilities and the defense strategies that can\nbe implemented. The survey highlights these security challenges and discusses\nrobust defensive frameworks to protect LLMs against these threats. By detailing\nthese security issues, the survey contributes to the broader discussion on\ncreating resilient AI systems that can resist sophisticated attacks.\n", "date": "2024-06-01", "categories": []}
{"id": "2406.01364", "title": "BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of\n  LLM Safeguards", "abstract": "  Input-output safeguards are used to detect anomalies in the traces produced\nby Large Language Models (LLMs) systems. These detectors are at the core of\ndiverse safety-critical applications such as real-time monitoring, offline\nevaluation of traces, and content moderation. However, there is no widely\nrecognized methodology to evaluate them. To fill this gap, we introduce the\nBenchmarks for the Evaluation of LLM Safeguards (BELLS), a structured\ncollection of tests, organized into three categories: (1) established failure\ntests, based on already-existing benchmarks for well-defined failure modes,\naiming to compare the performance of current input-output safeguards; (2)\nemerging failure tests, to measure generalization to never-seen-before failure\nmodes and encourage the development of more general safeguards; (3) next-gen\narchitecture tests, for more complex scaffolding (such as LLM-agents and\nmulti-agent systems), aiming to foster the development of safeguards that could\nadapt to future applications for which no safeguard currently exists.\nFurthermore, we implement and share the first next-gen architecture test, using\nthe MACHIAVELLI environment, along with an interactive visualization of the\ndataset.\n", "date": "2024-06-03", "categories": []}
{"id": "2405.19358", "title": "Robustifying Safety-Aligned Large Language Models through Clean Data\n  Curation", "abstract": "  Large language models (LLMs) are vulnerable when trained on datasets\ncontaining harmful content, which leads to potential jailbreaking attacks in\ntwo scenarios: the integration of harmful texts within crowdsourced data used\nfor pre-training and direct tampering with LLMs through fine-tuning. In both\nscenarios, adversaries can compromise the safety alignment of LLMs,\nexacerbating malfunctions. Motivated by the need to mitigate these adversarial\ninfluences, our research aims to enhance safety alignment by either\nneutralizing the impact of malicious texts in pre-training datasets or\nincreasing the difficulty of jailbreaking during downstream fine-tuning. In\nthis paper, we propose a data curation framework designed to counter\nadversarial impacts in both scenarios. Our method operates under the assumption\nthat we have no prior knowledge of attack details, focusing solely on curating\nclean texts. We introduce an iterative process aimed at revising texts to\nreduce their perplexity as perceived by LLMs, while simultaneously preserving\ntheir text quality. By pre-training or fine-tuning LLMs with curated clean\ntexts, we observe a notable improvement in LLM robustness regarding safety\nalignment against harmful queries. For instance, when pre-training LLMs using a\ncrowdsourced dataset containing 5\\% harmful instances, adding an equivalent\namount of curated texts significantly mitigates the likelihood of providing\nharmful responses in LLMs and reduces the attack success rate by 71\\%. Our\nstudy represents a significant step towards mitigating the risks associated\nwith training-based jailbreaking and fortifying the secure utilization of LLMs.\n", "date": "2024-05-24", "categories": []}
{"id": "2405.20413", "title": "Jailbreaking Large Language Models Against Moderation Guardrails via\n  Cipher Characters", "abstract": "  Large Language Models (LLMs) are typically harmless but remain vulnerable to\ncarefully crafted prompts known as ``jailbreaks'', which can bypass protective\nmeasures and induce harmful behavior. Recent advancements in LLMs have\nincorporated moderation guardrails that can filter outputs, which trigger\nprocessing errors for certain malicious questions. Existing red-teaming\nbenchmarks often neglect to include questions that trigger moderation\nguardrails, making it difficult to evaluate jailbreak effectiveness. To address\nthis issue, we introduce JAMBench, a harmful behavior benchmark designed to\ntrigger and evaluate moderation guardrails. JAMBench involves 160 manually\ncrafted instructions covering four major risk categories at multiple severity\nlevels. Furthermore, we propose a jailbreak method, JAM (Jailbreak Against\nModeration), designed to attack moderation guardrails using jailbreak prefixes\nto bypass input-level filters and a fine-tuned shadow model functionally\nequivalent to the guardrail model to generate cipher characters to bypass\noutput-level filters. Our extensive experiments on four LLMs demonstrate that\nJAM achieves higher jailbreak success ($\\sim$ $\\times$ 19.88) and lower\nfiltered-out rates ($\\sim$ $\\times$ 1/6) than baselines.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.20505", "title": "SPOT: Text Source Prediction from Originality Score Thresholding", "abstract": "  The wide acceptance of large language models (LLMs) has unlocked new\napplications and social risks. Popular countermeasures aim at detecting\nmisinformation, usually involve domain specific models trained to recognize the\nrelevance of any information. Instead of evaluating the validity of the\ninformation, we propose to investigate LLM generated text from the perspective\nof trust. In this study, we define trust as the ability to know if an input\ntext was generated by a LLM or a human. To do so, we design SPOT, an efficient\nmethod, that classifies the source of any, standalone, text input based on\noriginality score. This score is derived from the prediction of a given LLM to\ndetect other LLMs. We empirically demonstrate the robustness of the method to\nthe architecture, training data, evaluation data, task and compression of\nmodern LLMs.\n", "date": "2024-05-30", "categories": []}
{"id": "2405.19524", "title": "AI Risk Management Should Incorporate Both Safety and Security", "abstract": "  The exposure of security vulnerabilities in safety-aligned language models,\ne.g., susceptibility to adversarial attacks, has shed light on the intricate\ninterplay between AI safety and AI security. Although the two disciplines now\ncome together under the overarching goal of AI risk management, they have\nhistorically evolved separately, giving rise to differing perspectives.\nTherefore, in this paper, we advocate that stakeholders in AI risk management\nshould be aware of the nuances, synergies, and interplay between safety and\nsecurity, and unambiguously take into account the perspectives of both\ndisciplines in order to devise mostly effective and holistic risk mitigation\napproaches. Unfortunately, this vision is often obfuscated, as the definitions\nof the basic concepts of \"safety\" and \"security\" themselves are often\ninconsistent and lack consensus across communities. With AI risk management\nbeing increasingly cross-disciplinary, this issue is particularly salient. In\nlight of this conceptual challenge, we introduce a unified reference framework\nto clarify the differences and interplay between AI safety and AI security,\naiming to facilitate a shared understanding and effective collaboration across\ncommunities.\n", "date": "2024-05-29", "categories": []}
{"id": "2405.19550", "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "abstract": "  To determine the safety of large language models (LLMs), AI developers must\nbe able to assess their dangerous capabilities. But simple prompting strategies\noften fail to elicit an LLM's full capabilities. One way to elicit capabilities\nmore robustly is to fine-tune the LLM to complete the task. In this paper, we\ninvestigate the conditions under which fine-tuning-based elicitation suffices\nto elicit capabilities. To do this, we introduce password-locked models, LLMs\nfine-tuned such that some of their capabilities are deliberately hidden.\nSpecifically, these LLMs are trained to exhibit these capabilities only when a\npassword is present in the prompt, and to imitate a much weaker LLM otherwise.\nPassword-locked models enable a novel method of evaluating capabilities\nelicitation methods, by testing whether these password-locked capabilities can\nbe elicited without using the password. We find that a few high-quality\ndemonstrations are often sufficient to fully elicit password-locked\ncapabilities. More surprisingly, fine-tuning can elicit other capabilities that\nhave been locked using the same password, or even different passwords.\nFurthermore, when only evaluations, and not demonstrations, are available,\napproaches like reinforcement learning are still often able to elicit\ncapabilities. Overall, our findings suggest that fine-tuning is an effective\nmethod of eliciting hidden capabilities of current models, but may be\nunreliable when high-quality demonstrations are not available, e.g. as may be\nthe case when models' (hidden) capabilities exceed those of human\ndemonstrators.\n", "date": "2024-05-29", "categories": []}
{"id": "2405.19668", "title": "AutoBreach: Universal and Adaptive Jailbreaking with Efficient\n  Wordplay-Guided Optimization", "abstract": "  Despite the widespread application of large language models (LLMs) across\nvarious tasks, recent studies indicate that they are susceptible to jailbreak\nattacks, which can render their defense mechanisms ineffective. However,\nprevious jailbreak research has frequently been constrained by limited\nuniversality, suboptimal efficiency, and a reliance on manual crafting. In\nresponse, we rethink the approach to jailbreaking LLMs and formally define\nthree essential properties from the attacker' s perspective, which contributes\nto guiding the design of jailbreak methods. We further introduce AutoBreach, a\nnovel method for jailbreaking LLMs that requires only black-box access.\nInspired by the versatility of wordplay, AutoBreach employs a wordplay-guided\nmapping rule sampling strategy to generate a variety of universal mapping rules\nfor creating adversarial prompts. This generation process leverages LLMs'\nautomatic summarization and reasoning capabilities, thus alleviating the manual\nburden. To boost jailbreak success rates, we further suggest sentence\ncompression and chain-of-thought-based mapping rules to correct errors and\nwordplay misinterpretations in target LLMs. Additionally, we propose a\ntwo-stage mapping rule optimization strategy that initially optimizes mapping\nrules before querying target LLMs to enhance the efficiency of AutoBreach.\nAutoBreach can efficiently identify security vulnerabilities across various\nLLMs, including three proprietary models: Claude-3, GPT-3.5, GPT-4 Turbo, and\ntwo LLMs' web platforms: Bingchat, GPT-4 Web, achieving an average success rate\nof over 80% with fewer than 10 queries\n", "date": "2024-05-30", "categories": []}
{"id": "2405.19103", "title": "Voice Jailbreak Attacks Against GPT-4o", "abstract": "  Recently, the concept of artificial assistants has evolved from science\nfiction into real-world applications. GPT-4o, the newest multimodal large\nlanguage model (MLLM) across audio, vision, and text, has further blurred the\nline between fiction and reality by enabling more natural human-computer\ninteractions. However, the advent of GPT-4o's voice mode may also introduce a\nnew attack surface. In this paper, we present the first systematic measurement\nof jailbreak attacks against the voice mode of GPT-4o. We show that GPT-4o\ndemonstrates good resistance to forbidden questions and text jailbreak prompts\nwhen directly transferring them to voice mode. This resistance is primarily due\nto GPT-4o's internal safeguards and the difficulty of adapting text jailbreak\nprompts to voice mode. Inspired by GPT-4o's human-like behaviors, we propose\nVoiceJailbreak, a novel voice jailbreak attack that humanizes GPT-4o and\nattempts to persuade it through fictional storytelling (setting, character, and\nplot). VoiceJailbreak is capable of generating simple, audible, yet effective\njailbreak prompts, which significantly increases the average attack success\nrate (ASR) from 0.033 to 0.778 in six forbidden scenarios. We also conduct\nextensive experiments to explore the impacts of interaction steps, key elements\nof fictional writing, and different languages on VoiceJailbreak's effectiveness\nand further enhance the attack performance with advanced fictional writing\ntechniques. We hope our study can assist the research community in building\nmore secure and well-regulated MLLMs.\n", "date": "2024-05-29", "categories": []}
{"id": "2311.00117", "title": "BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B", "abstract": "  Llama 2-Chat is a collection of large language models that Meta developed and\nreleased to the public. While Meta fine-tuned Llama 2-Chat to refuse to output\nharmful content, we hypothesize that public access to model weights enables bad\nactors to cheaply circumvent Llama 2-Chat's safeguards and weaponize Llama 2's\ncapabilities for malicious purposes. We demonstrate that it is possible to\neffectively undo the safety fine-tuning from Llama 2-Chat 13B with less than\n$200, while retaining its general capabilities. Our results demonstrate that\nsafety-fine tuning is ineffective at preventing misuse when model weights are\nreleased publicly. Given that future models will likely have much greater\nability to cause harm at scale, it is essential that AI developers address\nthreats from fine-tuning when considering whether to publicly release their\nmodel weights.\n", "date": "2023-10-31", "categories": []}
{"id": "2405.16567", "title": "Automatic Jailbreaking of the Text-to-Image Generative AI Systems", "abstract": "  Recent AI systems have shown extremely powerful performance, even surpassing\nhuman performance, on various tasks such as information retrieval, language\ngeneration, and image generation based on large language models (LLMs). At the\nsame time, there are diverse safety risks that can cause the generation of\nmalicious contents by circumventing the alignment in LLMs, which are often\nreferred to as jailbreaking. However, most of the previous works only focused\non the text-based jailbreaking in LLMs, and the jailbreaking of the\ntext-to-image (T2I) generation system has been relatively overlooked. In this\npaper, we first evaluate the safety of the commercial T2I generation systems,\nsuch as ChatGPT, Copilot, and Gemini, on copyright infringement with naive\nprompts. From this empirical study, we find that Copilot and Gemini block only\n12% and 17% of the attacks with naive prompts, respectively, while ChatGPT\nblocks 84% of them. Then, we further propose a stronger automated jailbreaking\npipeline for T2I generation systems, which produces prompts that bypass their\nsafety guards. Our automated jailbreaking framework leverages an LLM optimizer\nto generate prompts to maximize degree of violation from the generated images\nwithout any weight updates or gradient computation. Surprisingly, our simple\nyet effective approach successfully jailbreaks the ChatGPT with 11.0% block\nrate, making it generate copyrighted contents in 76% of the time. Finally, we\nexplore various defense strategies, such as post-generation filtering and\nmachine unlearning techniques, but found that they were inadequate, which\nsuggests the necessity of stronger defense mechanisms.\n", "date": "2024-05-26", "categories": []}
{"id": "2405.17713", "title": "AI Alignment with Changing and Influenceable Reward Functions", "abstract": "  Existing AI alignment approaches assume that preferences are static, which is\nunrealistic: our preferences change, and may even be influenced by our\ninteractions with AI systems themselves. To clarify the consequences of\nincorrectly assuming static preferences, we introduce Dynamic Reward Markov\nDecision Processes (DR-MDPs), which explicitly model preference changes and the\nAI's influence on them. We show that despite its convenience, the\nstatic-preference assumption may undermine the soundness of existing alignment\ntechniques, leading them to implicitly reward AI systems for influencing user\npreferences in ways users may not truly want. We then explore potential\nsolutions. First, we offer a unifying perspective on how an agent's\noptimization horizon may partially help reduce undesirable AI influence. Then,\nwe formalize different notions of AI alignment that account for preference\nchange from the outset. Comparing the strengths and limitations of 8 such\nnotions of alignment, we find that they all either err towards causing\nundesirable AI influence, or are overly risk-averse, suggesting that a\nstraightforward solution to the problems of changing preferences may not exist.\nAs there is no avoiding grappling with changing preferences in real-world\nsettings, this makes it all the more important to handle these issues with\ncare, balancing risks and capabilities. We hope our work can provide conceptual\nclarity and constitute a first step towards AI alignment practices which\nexplicitly account for (and contend with) the changing and influenceable nature\nof human preferences.\n", "date": "2024-05-28", "categories": []}
{"id": "2310.06387", "title": "Jailbreak and Guard Aligned Language Models with Only Few In-Context\n  Demonstrations", "abstract": "  Large Language Models (LLMs) have shown remarkable success in various tasks,\nyet their safety and the risk of generating harmful content remain pressing\nconcerns. In this paper, we delve into the potential of In-Context Learning\n(ICL) to modulate the alignment of LLMs. Specifically, we propose the\nIn-Context Attack (ICA) which employs harmful demonstrations to subvert LLMs,\nand the In-Context Defense (ICD) which bolsters model resilience through\nexamples that demonstrate refusal to produce harmful responses. We offer\ntheoretical insights to elucidate how a limited set of in-context\ndemonstrations can pivotally influence the safety alignment of LLMs. Through\nextensive experiments, we demonstrate the efficacy of ICA and ICD in\nrespectively elevating and mitigating the success rates of jailbreaking\nprompts. Our findings illuminate the profound influence of ICL on LLM behavior,\nopening new avenues for improving the safety of LLMs.\n", "date": "2023-10-10", "categories": []}
{"id": "2311.11538", "title": "Assessing Prompt Injection Risks in 200+ Custom GPTs", "abstract": "  In the rapidly evolving landscape of artificial intelligence, ChatGPT has\nbeen widely used in various applications. The new feature - customization of\nChatGPT models by users to cater to specific needs has opened new frontiers in\nAI utility. However, this study reveals a significant security vulnerability\ninherent in these user-customized GPTs: prompt injection attacks. Through\ncomprehensive testing of over 200 user-designed GPT models via adversarial\nprompts, we demonstrate that these systems are susceptible to prompt\ninjections. Through prompt injection, an adversary can not only extract the\ncustomized system prompts but also access the uploaded files. This paper\nprovides a first-hand analysis of the prompt injection, alongside the\nevaluation of the possible mitigation of such attacks. Our findings underscore\nthe urgent need for robust security frameworks in the design and deployment of\ncustomizable GPT models. The intent of this paper is to raise awareness and\nprompt action in the AI community, ensuring that the benefits of GPT\ncustomization do not come at the cost of compromised security and privacy.\n", "date": "2023-11-20", "categories": []}
{"id": "2405.15902", "title": "Hacc-Man: An Arcade Game for Jailbreaking LLMs", "abstract": "  The recent leaps in complexity and fluency of Large Language Models (LLMs)\nmean that, for the first time in human history, people can interact with\ncomputers using natural language alone. This creates monumental possibilities\nof automation and accessibility of computing, but also raises severe security\nand safety threats: When everyone can interact with LLMs, everyone can\npotentially break into the systems running LLMs. All it takes is creative use\nof language. This paper presents Hacc-Man, a game which challenges its players\nto \"jailbreak\" an LLM: subvert the LLM to output something that it is not\nintended to. Jailbreaking is at the intersection between creative problem\nsolving and LLM security. The purpose of the game is threefold: 1. To heighten\nawareness of the risks of deploying fragile LLMs in everyday systems, 2. To\nheighten people's self-efficacy in interacting with LLMs, and 3. To discover\nthe creative problem solving strategies, people deploy in this novel context.\n", "date": "2024-05-24", "categories": []}
{"id": "2404.17047", "title": "Near to Mid-term Risks and Opportunities of Open-Source Generative AI", "abstract": "  In the next few years, applications of Generative AI are expected to\nrevolutionize a number of different areas, ranging from science & medicine to\neducation. The potential for these seismic changes has triggered a lively\ndebate about potential risks and resulted in calls for tighter regulation, in\nparticular from some of the major tech companies who are leading in AI\ndevelopment. This regulation is likely to put at risk the budding field of\nopen-source Generative AI. We argue for the responsible open sourcing of\ngenerative AI models in the near and medium term. To set the stage, we first\nintroduce an AI openness taxonomy system and apply it to 40 current large\nlanguage models. We then outline differential benefits and risks of open versus\nclosed source AI and present potential risk mitigation, ranging from best\npractices to calls for technical and scientific contributions. We hope that\nthis report will add a much needed missing voice to the current public\ndiscourse on near to mid-term AI safety and other societal impact.\n", "date": "2024-04-25", "categories": []}
{"id": "2310.17688", "title": "Managing extreme AI risks amid rapid progress", "abstract": "  Artificial Intelligence (AI) is progressing rapidly, and companies are\nshifting their focus to developing generalist AI systems that can autonomously\nact and pursue goals. Increases in capabilities and autonomy may soon massively\namplify AI's impact, with risks that include large-scale social harms,\nmalicious uses, and an irreversible loss of human control over autonomous AI\nsystems. Although researchers have warned of extreme risks from AI, there is a\nlack of consensus about how exactly such risks arise, and how to manage them.\nSociety's response, despite promising first steps, is incommensurate with the\npossibility of rapid, transformative progress that is expected by many experts.\nAI safety research is lagging. Present governance initiatives lack the\nmechanisms and institutions to prevent misuse and recklessness, and barely\naddress autonomous systems. In this short consensus paper, we describe extreme\nrisks from upcoming, advanced AI systems. Drawing on lessons learned from other\nsafety-critical technologies, we then outline a comprehensive plan combining\ntechnical research and development with proactive, adaptive governance\nmechanisms for a more commensurate preparation.\n", "date": "2023-10-26", "categories": []}
{"id": "2310.20624", "title": "LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B", "abstract": "  AI developers often apply safety alignment procedures to prevent the misuse\nof their AI systems. For example, before Meta released Llama 2-Chat - a\ncollection of instruction fine-tuned large language models - they invested\nheavily in safety training, incorporating extensive red-teaming and\nreinforcement learning from human feedback. We explore the robustness of safety\ntraining in language models by subversively fine-tuning Llama 2-Chat. We employ\nquantized low-rank adaptation (LoRA) as an efficient fine-tuning method. With a\nbudget of less than \\$200 and using only one GPU, we successfully undo the\nsafety training of Llama 2-Chat models of sizes 7B, 13B, and 70B and on the\nMixtral instruct model. Specifically, our fine-tuning technique significantly\nreduces the rate at which the model refuses to follow harmful instructions. We\nachieve refusal rates of about 1\\% for our 70B Llama 2-Chat model on two\nrefusal benchmarks. Simultaneously, our method retains capabilities across two\ngeneral performance benchmarks. We show that subversive fine-tuning is\npractical and effective, and hence argue that evaluating risks from fine-tuning\nshould be a core part of risk assessments for releasing model weights. While\nthere is considerable uncertainty about the scope of risks from current models,\nfuture models will have significantly more dangerous capabilities.\n", "date": "2023-10-31", "categories": []}
{"id": "2405.12999", "title": "An Assessment of Model-On-Model Deception", "abstract": "  The trustworthiness of highly capable language models is put at risk when\nthey are able to produce deceptive outputs. Moreover, when models are\nvulnerable to deception it undermines reliability. In this paper, we introduce\na method to investigate complex, model-on-model deceptive scenarios. We create\na dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B,\n70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find\nthat, when models read these explanations, they are all significantly deceived.\nWorryingly, models of all capabilities are successful at misleading others,\nwhile more capable models are only slightly better at resisting deception. We\nrecommend the development of techniques to detect and defend against deception.\n", "date": "2024-05-10", "categories": []}
{"id": "2405.14488", "title": "MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While\n  Preserving Their Usability", "abstract": "  Large Language Models (LLMs) are increasingly deployed in various\napplications. As their usage grows, concerns regarding their safety are rising,\nespecially in maintaining harmless responses when faced with malicious\ninstructions. Many defense strategies have been developed to enhance the safety\nof LLMs. However, our research finds that existing defense strategies lead LLMs\nto predominantly adopt a rejection-oriented stance, thereby diminishing the\nusability of their responses to benign instructions. To solve this problem, we\nintroduce the MoGU framework, designed to enhance LLMs' safety while preserving\ntheir usability. Our MoGU framework transforms the base LLM into two variants:\nthe usable LLM and the safe LLM, and further employs dynamic routing to balance\ntheir contribution. When encountering malicious instructions, the router will\nassign a higher weight to the safe LLM to ensure that responses are harmless.\nConversely, for benign instructions, the router prioritizes the usable LLM,\nfacilitating usable and helpful responses. On various open-sourced LLMs, we\ncompare multiple defense strategies to verify the superiority of our MoGU\nframework. Besides, our analysis provides key insights into the effectiveness\nof MoGU and verifies that our designed routing mechanism can effectively\nbalance the contribution of each variant by assigning weights. Our work\nreleased the safer Llama2, Vicuna, Falcon, Dolphin, and Baichuan2.\n", "date": "2024-05-23", "categories": []}
{"id": "2308.04440", "title": "Big Bang, Low Bar -- Risk Assessment in the Public Arena", "abstract": "  One of the basic principles of risk management is that we should always keep\nan eye on ways that things could go badly wrong, even if they seem unlikely.\nThe more disastrous a potential failure, the more improbable it needs to be,\nbefore we can safely ignore it. This principle may seem obvious, but it is\neasily overlooked in public discourse about risk, even by well-qualified\ncommentators who should certainly know better. The present piece is prompted by\nneglect of the principle in recent discussions about the potential existential\nrisks of artificial intelligence. The failing is not peculiar to this case, but\nrecent debates in this area provide some particularly stark examples of how\neasily the principle can be overlooked.\n", "date": "2023-07-23", "categories": []}
{"id": "2401.18028", "title": "Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact\n  Assessment", "abstract": "  Gaining insight into the potential negative impacts of emerging Artificial\nIntelligence (AI) technologies in society is a challenge for implementing\nanticipatory governance approaches. One approach to produce such insight is to\nuse Large Language Models (LLMs) to support and guide experts in the process of\nideating and exploring the range of undesirable consequences of emerging\ntechnologies. However, performance evaluations of LLMs for such tasks are still\nneeded, including examining the general quality of generated impacts but also\nthe range of types of impacts produced and resulting biases. In this paper, we\ndemonstrate the potential for generating high-quality and diverse impacts of AI\nin society by fine-tuning completion models (GPT-3 and Mistral-7B) on a diverse\nsample of articles from news media and comparing those outputs to the impacts\ngenerated by instruction-based (GPT-4 and Mistral-7B-Instruct) models. We\nexamine the generated impacts for coherence, structure, relevance, and\nplausibility and find that the generated impacts using Mistral-7B, a small\nopen-source model fine-tuned on impacts from the news media, tend to be\nqualitatively on par with impacts generated using a more capable and larger\nscale model such as GPT-4. Moreover, we find that impacts produced by\ninstruction-based models had gaps in the production of certain categories of\nimpacts in comparison to fine-tuned models. This research highlights a\npotential bias in the range of impacts generated by state-of-the-art LLMs and\nthe potential of aligning smaller LLMs on news media as a scalable alternative\nto generate high quality and more diverse impacts in support of anticipatory\ngovernance approaches.\n", "date": "2024-01-31", "categories": []}
{"id": "2405.12900", "title": "Adversarial DPO: Harnessing Harmful Data for Reducing Toxicity with\n  Minimal Impact on Coherence and Evasiveness in Dialogue Agents", "abstract": "  Recent advancements in open-domain dialogue systems have been propelled by\nthe emergence of high-quality large language models (LLMs) and various\neffective training methodologies. Nevertheless, the presence of toxicity within\nthese models presents a significant challenge that can potentially diminish the\nuser experience. In this study, we introduce an innovative training algorithm,\nan improvement upon direct preference optimization (DPO), called adversarial\nDPO (ADPO). The ADPO algorithm is designed to train models to assign higher\nprobability distributions to preferred responses and lower distributions to\nunsafe responses, which are self-generated using the toxic control token. We\ndemonstrate that ADPO enhances the model's resilience against harmful\nconversations while minimizing performance degradation. Furthermore, we\nillustrate that ADPO offers a more stable training procedure compared to the\ntraditional DPO. To the best of our knowledge, this is the first adaptation of\nthe DPO algorithm that directly incorporates harmful data into the generative\nmodel, thereby reducing the need to artificially create safe dialogue data.\n", "date": "2024-05-21", "categories": []}
{"id": "2304.13765", "title": "Towards ethical multimodal systems", "abstract": "  Generative AI systems (ChatGPT, DALL-E, etc) are expanding into multiple\nareas of our lives, from art Rombach et al. [2021] to mental health Rob Morris\nand Kareem Kouddous [2022]; their rapidly growing societal impact opens new\nopportunities, but also raises ethical concerns. The emerging field of AI\nalignment aims to make AI systems reflect human values. This paper focuses on\nevaluating the ethics of multimodal AI systems involving both text and images -\na relatively under-explored area, as most alignment work is currently focused\non language models. We first create a multimodal ethical database from human\nfeedback on ethicality. Then, using this database, we develop algorithms,\nincluding a RoBERTa-large classifier and a multilayer perceptron, to\nautomatically assess the ethicality of system responses.\n", "date": "2023-04-26", "categories": []}
{"id": "2405.10659", "title": "Realistic Evaluation of Toxicity in Large Language Models", "abstract": "  Large language models (LLMs) have become integral to our professional\nworkflows and daily lives. Nevertheless, these machine companions of ours have\na critical flaw: the huge amount of data which endows them with vast and\ndiverse knowledge, also exposes them to the inevitable toxicity and bias. While\nmost LLMs incorporate defense mechanisms to prevent the generation of harmful\ncontent, these safeguards can be easily bypassed with minimal prompt\nengineering. In this paper, we introduce the new Thoroughly Engineered Toxicity\n(TET) dataset, comprising manually crafted prompts designed to nullify the\nprotective layers of such models. Through extensive evaluations, we demonstrate\nthe pivotal role of TET in providing a rigorous benchmark for evaluation of\ntoxicity awareness in several popular LLMs: it highlights the toxicity in the\nLLMs that might remain hidden when using normal prompts, thus revealing subtler\nissues in their behavior.\n", "date": "2024-05-17", "categories": []}
{"id": "2405.10986", "title": "Benchmark Early and Red Team Often: A Framework for Assessing and\n  Managing Dual-Use Hazards of AI Foundation Models", "abstract": "  A concern about cutting-edge or \"frontier\" AI foundation models is that an\nadversary may use the models for preparing chemical, biological, radiological,\nnuclear, (CBRN), cyber, or other attacks. At least two methods can identify\nfoundation models with potential dual-use capability; each has advantages and\ndisadvantages: A. Open benchmarks (based on openly available questions and\nanswers), which are low-cost but accuracy-limited by the need to omit\nsecurity-sensitive details; and B. Closed red team evaluations (based on\nprivate evaluation by CBRN and cyber experts), which are higher-cost but can\nachieve higher accuracy by incorporating sensitive details. We propose a\nresearch and risk-management approach using a combination of methods including\nboth open benchmarks and closed red team evaluations, in a way that leverages\nadvantages of both methods. We recommend that one or more groups of researchers\nwith sufficient resources and access to a range of near-frontier and frontier\nfoundation models run a set of foundation models through dual-use capability\nevaluation benchmarks and red team evaluations, then analyze the resulting sets\nof models' scores on benchmark and red team evaluations to see how correlated\nthose are. If, as we expect, there is substantial correlation between the\ndual-use potential benchmark scores and the red team evaluation scores, then\nimplications include the following: The open benchmarks should be used\nfrequently during foundation model development as a quick, low-cost measure of\na model's dual-use potential; and if a particular model gets a high score on\nthe dual-use potential benchmark, then more in-depth red team assessments of\nthat model's dual-use capability should be performed. We also discuss\nlimitations and mitigations for our approach, e.g., if model developers try to\ngame benchmarks by including a version of benchmark test data in a model's\ntraining data.\n", "date": "2024-05-15", "categories": []}
{"id": "2309.15817", "title": "Identifying the Risks of LM Agents with an LM-Emulated Sandbox", "abstract": "  Recent advances in Language Model (LM) agents and tool use, exemplified by\napplications like ChatGPT Plugins, enable a rich set of capabilities but also\namplify potential risks - such as leaking private data or causing financial\nlosses. Identifying these risks is labor-intensive, necessitating implementing\nthe tools, setting up the environment for each test scenario manually, and\nfinding risky cases. As tools and agents become more complex, the high cost of\ntesting these agents will make it increasingly difficult to find high-stakes,\nlong-tailed risks. To address these challenges, we introduce ToolEmu: a\nframework that uses an LM to emulate tool execution and enables the testing of\nLM agents against a diverse range of tools and scenarios, without manual\ninstantiation. Alongside the emulator, we develop an LM-based automatic safety\nevaluator that examines agent failures and quantifies associated risks. We test\nboth the tool emulator and evaluator through human evaluation and find that\n68.8% of failures identified with ToolEmu would be valid real-world agent\nfailures. Using our curated initial benchmark consisting of 36 high-stakes\ntools and 144 test cases, we provide a quantitative risk analysis of current LM\nagents and identify numerous failures with potentially severe outcomes.\nNotably, even the safest LM agent exhibits such failures 23.9% of the time\naccording to our evaluator, underscoring the need to develop safer LM agents\nfor real-world deployment.\n", "date": "2023-09-25", "categories": []}
{"id": "2312.12321", "title": "Bypassing the Safety Training of Open-Source LLMs with Priming Attacks", "abstract": "  With the recent surge in popularity of LLMs has come an ever-increasing need\nfor LLM safety training. In this paper, we investigate the fragility of SOTA\nopen-source LLMs under simple, optimization-free attacks we refer to as\n$\\textit{priming attacks}$, which are easy to execute and effectively bypass\nalignment from safety training. Our proposed attack improves the Attack Success\nRate on Harmful Behaviors, as measured by Llama Guard, by up to $3.3\\times$\ncompared to baselines. Source code and data are available at\nhttps://github.com/uiuc-focal-lab/llm-priming-attacks.\n", "date": "2023-12-19", "categories": []}
{"id": "2401.13138", "title": "Visibility into AI Agents", "abstract": "  Increased delegation of commercial, scientific, governmental, and personal\nactivities to AI agents -- systems capable of pursuing complex goals with\nlimited supervision -- may exacerbate existing societal risks and introduce new\nrisks. Understanding and mitigating these risks involves critically evaluating\nexisting governance structures, revising and adapting these structures where\nneeded, and ensuring accountability of key stakeholders. Information about\nwhere, why, how, and by whom certain AI agents are used, which we refer to as\nvisibility, is critical to these objectives. In this paper, we assess three\ncategories of measures to increase visibility into AI agents: agent\nidentifiers, real-time monitoring, and activity logging. For each, we outline\npotential implementations that vary in intrusiveness and informativeness. We\nanalyze how the measures apply across a spectrum of centralized through\ndecentralized deployment contexts, accounting for various actors in the supply\nchain including hardware and software service providers. Finally, we discuss\nthe implications of our measures for privacy and concentration of power.\nFurther work into understanding the measures and mitigating their negative\nimpacts can help to build a foundation for the governance of AI agents.\n", "date": "2024-01-23", "categories": []}
{"id": "2404.05388", "title": "An AI System Evaluation Framework for Advancing AI Safety: Terminology,\n  Taxonomy, Lifecycle Mapping", "abstract": "  The advent of advanced AI underscores the urgent need for comprehensive\nsafety evaluations, necessitating collaboration across communities (i.e., AI,\nsoftware engineering, and governance). However, divergent practices and\nterminologies across these communities, combined with the complexity of AI\nsystems-of which models are only a part-and environmental affordances (e.g.,\naccess to tools), obstruct effective communication and comprehensive\nevaluation. This paper proposes a framework for AI system evaluation comprising\nthree components: 1) harmonised terminology to facilitate communication across\ncommunities involved in AI safety evaluation; 2) a taxonomy identifying\nessential elements for AI system evaluation; 3) a mapping between AI lifecycle,\nstakeholders, and requisite evaluations for accountable AI supply chain. This\nframework catalyses a deeper discourse on AI system evaluation beyond\nmodel-centric approaches.\n", "date": "2024-04-08", "categories": []}
{"id": "2305.10847", "title": "Large Language Models can be Guided to Evade AI-Generated Text Detection", "abstract": "  Large language models (LLMs) have shown remarkable performance in various\ntasks and have been extensively utilized by the public. However, the increasing\nconcerns regarding the misuse of LLMs, such as plagiarism and spamming, have\nled to the development of multiple detectors, including fine-tuned classifiers\nand statistical methods. In this study, we equip LLMs with prompts, rather than\nrelying on an external paraphraser, to evaluate the vulnerability of these\ndetectors. We propose a novel Substitution-based In-Context example\nOptimization method (SICO) to automatically construct prompts for evading the\ndetectors. SICO is cost-efficient as it requires only 40 human-written examples\nand a limited number of LLM inferences to generate a prompt. Moreover, once a\ntask-specific prompt has been constructed, it can be universally used against a\nwide range of detectors. Extensive experiments across three real-world tasks\ndemonstrate that SICO significantly outperforms the paraphraser baselines and\nenables GPT-3.5 to successfully evade six detectors, decreasing their AUC by\n0.5 on average. Furthermore, a comprehensive human evaluation show that the\nSICO-generated text achieves human-level readability and task completion rates,\nwhile preserving high imperceptibility. Finally, we propose an ensemble\napproach to enhance the robustness of detectors against SICO attack. The code\nis publicly available at https://github.com/ColinLu50/Evade-GPT-Detector.\n", "date": "2023-05-18", "categories": []}
{"id": "2402.01708", "title": "Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech\n  Generators", "abstract": "  The rapid and wide-scale adoption of AI to generate human speech poses a\nrange of significant ethical and safety risks to society that need to be\naddressed. For example, a growing number of speech generation incidents are\nassociated with swatting attacks in the United States, where anonymous\nperpetrators create synthetic voices that call police officers to close down\nschools and hospitals, or to violently gain access to innocent citizens' homes.\nIncidents like this demonstrate that multimodal generative AI risks and harms\ndo not exist in isolation, but arise from the interactions of multiple\nstakeholders and technical AI systems. In this paper we analyse speech\ngeneration incidents to study how patterns of specific harms arise. We find\nthat specific harms can be categorised according to the exposure of affected\nindividuals, that is to say whether they are a subject of, interact with,\nsuffer due to, or are excluded from speech generation systems. Similarly,\nspecific harms are also a consequence of the motives of the creators and\ndeployers of the systems. Based on these insights we propose a conceptual\nframework for modelling pathways to ethical and safety harms of AI, which we\nuse to develop a taxonomy of harms of speech generators. Our relational\napproach captures the complexity of risks and harms in sociotechnical AI\nsystems, and yields a taxonomy that can support appropriate policy\ninterventions and decision making for the responsible development and release\nof speech generation models.\n", "date": "2024-01-25", "categories": []}
{"id": "2405.09055", "title": "A safety realignment framework via subspace-oriented model fusion for\n  large language models", "abstract": "  The current safeguard mechanisms for large language models (LLMs) are indeed\nsusceptible to jailbreak attacks, making them inherently fragile. Even the\nprocess of fine-tuning on apparently benign data for downstream tasks can\njeopardize safety. One potential solution is to conduct safety fine-tuning\nsubsequent to downstream fine-tuning. However, there's a risk of catastrophic\nforgetting during safety fine-tuning, where LLMs may regain safety measures but\nlose the task-specific knowledge acquired during downstream fine-tuning. In\nthis paper, we introduce a safety realignment framework through\nsubspace-oriented model fusion (SOMF), aiming to combine the safeguard\ncapabilities of initially aligned model and the current fine-tuned model into a\nrealigned model. Our approach begins by disentangling all task vectors from the\nweights of each fine-tuned model. We then identify safety-related regions\nwithin these vectors by subspace masking techniques. Finally, we explore the\nfusion of the initial safely aligned LLM with all task vectors based on the\nidentified safety subspace. We validate that our safety realignment framework\nsatisfies the safety requirements of a single fine-tuned model as well as\nmultiple models during their fusion. Our findings confirm that SOMF preserves\nsafety without notably compromising performance on downstream tasks, including\ninstruction following in Chinese, English, and Hindi, as well as\nproblem-solving capabilities in Code and Math.\n", "date": "2024-05-15", "categories": []}
{"id": "2404.12241", "title": "Introducing v0.5 of the AI Safety Benchmark from MLCommons", "abstract": "  This paper introduces v0.5 of the AI Safety Benchmark, which has been created\nby the MLCommons AI Safety Working Group. The AI Safety Benchmark has been\ndesigned to assess the safety risks of AI systems that use chat-tuned language\nmodels. We introduce a principled approach to specifying and constructing the\nbenchmark, which for v0.5 covers only a single use case (an adult chatting to a\ngeneral-purpose assistant in English), and a limited set of personas (i.e.,\ntypical users, malicious users, and vulnerable users). We created a new\ntaxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark.\nWe plan to release version 1.0 of the AI Safety Benchmark by the end of 2024.\nThe v1.0 benchmark will provide meaningful insights into the safety of AI\nsystems. However, the v0.5 benchmark should not be used to assess the safety of\nAI systems. We have sought to fully document the limitations, flaws, and\nchallenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes\n(1) a principled approach to specifying and constructing the benchmark, which\ncomprises use cases, types of systems under test (SUTs), language and context,\npersonas, tests, and test items; (2) a taxonomy of 13 hazard categories with\ndefinitions and subcategories; (3) tests for seven of the hazard categories,\neach comprising a unique set of test items, i.e., prompts. There are 43,090\ntest items in total, which we created with templates; (4) a grading system for\nAI systems against the benchmark; (5) an openly available platform, and\ndownloadable tool, called ModelBench that can be used to evaluate the safety of\nAI systems on the benchmark; (6) an example evaluation report which benchmarks\nthe performance of over a dozen openly available chat-tuned language models;\n(7) a test specification for the benchmark.\n", "date": "2024-04-18", "categories": []}
{"id": "2405.07932", "title": "PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition", "abstract": "  Large language models (LLMs) have shown success in many natural language\nprocessing tasks. Despite rigorous safety alignment processes, supposedly\nsafety-aligned LLMs like Llama 2 and Claude 2 are still susceptible to\njailbreaks, leading to security risks and abuse of the models. One option to\nmitigate such risks is to augment the LLM with a dedicated \"safeguard\", which\nchecks the LLM's inputs or outputs for undesired behaviour. A promising\napproach is to use the LLM itself as the safeguard. Nonetheless, baseline\nmethods, such as prompting the LLM to self-classify toxic content, demonstrate\nlimited efficacy. We hypothesise that this is due to domain shift: the\nalignment training imparts a self-censoring behaviour to the model (\"Sorry I\ncan't do that\"), while the self-classify approach shifts it to a classification\nformat (\"Is this prompt malicious\"). In this work, we propose PARDEN, which\navoids this domain shift by simply asking the model to repeat its own outputs.\nPARDEN neither requires finetuning nor white box access to the model. We\nempirically verify the effectiveness of our method and show that PARDEN\nsignificantly outperforms existing jailbreak detection baselines for Llama-2\nand Claude-2. Code and data are available at https://github.com/Ed-Zh/PARDEN.\n  We find that PARDEN is particularly powerful in the relevant regime of high\nTrue Positive Rate (TPR) and low False Positive Rate (FPR). For instance, for\nLlama2-7B, at TPR equal to 90%, PARDEN accomplishes a roughly 11x reduction in\nthe FPR from 24.8% to 2.0% on the harmful behaviours dataset.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.04937", "title": "Developing trustworthy AI applications with foundation models", "abstract": "  The trustworthiness of AI applications has been the subject of recent\nresearch and is also addressed in the EU's recently adopted AI Regulation. The\ncurrently emerging foundation models in the field of text, speech and image\nprocessing offer completely new possibilities for developing AI applications.\nThis whitepaper shows how the trustworthiness of an AI application developed\nwith foundation models can be evaluated and ensured. For this purpose, the\napplication-specific, risk-based approach for testing and ensuring the\ntrustworthiness of AI applications, as developed in the 'AI Assessment Catalog\n- Guideline for Trustworthy Artificial Intelligence' by Fraunhofer IAIS, is\ntransferred to the context of foundation models. Special consideration is given\nto the fact that specific risks of foundation models can have an impact on the\nAI application and must also be taken into account when checking\ntrustworthiness. Chapter 1 of the white paper explains the fundamental\nrelationship between foundation models and AI applications based on them in\nterms of trustworthiness. Chapter 2 provides an introduction to the technical\nconstruction of foundation models and Chapter 3 shows how AI applications can\nbe developed based on them. Chapter 4 provides an overview of the resulting\nrisks regarding trustworthiness. Chapter 5 shows which requirements for AI\napplications and foundation models are to be expected according to the draft of\nthe European Union's AI Regulation and Chapter 6 finally shows the system and\nprocedure for meeting trustworthiness requirements.\n", "date": "2024-05-08", "categories": []}
{"id": "2405.03654", "title": "Can LLMs Deeply Detect Complex Malicious Queries? A Framework for\n  Jailbreaking via Obfuscating Intent", "abstract": "  To demonstrate and address the underlying maliciousness, we propose a\ntheoretical hypothesis and analytical approach, and introduce a new black-box\njailbreak attack methodology named IntentObfuscator, exploiting this identified\nflaw by obfuscating the true intentions behind user prompts.This approach\ncompels LLMs to inadvertently generate restricted content, bypassing their\nbuilt-in content security measures. We detail two implementations under this\nframework: \"Obscure Intention\" and \"Create Ambiguity\", which manipulate query\ncomplexity and ambiguity to evade malicious intent detection effectively. We\nempirically validate the effectiveness of the IntentObfuscator method across\nseveral models, including ChatGPT-3.5, ChatGPT-4, Qwen and Baichuan, achieving\nan average jailbreak success rate of 69.21\\%. Notably, our tests on\nChatGPT-3.5, which claims 100 million weekly active users, achieved a\nremarkable success rate of 83.65\\%. We also extend our validation to diverse\ntypes of sensitive content like graphic violence, racism, sexism, political\nsensitivity, cybersecurity threats, and criminal skills, further proving the\nsubstantial impact of our findings on enhancing 'Red Team' strategies against\nLLM content security frameworks.\n", "date": "2024-05-06", "categories": []}
{"id": "2309.05030", "title": "Decolonial AI Alignment: Openness, Vi\\'{s}e\\d{s}a-Dharma, and Including\n  Excluded Knowledges", "abstract": "  Prior work has explicated the coloniality of artificial intelligence (AI)\ndevelopment and deployment through mechanisms such as extractivism, automation,\nsociological essentialism, surveillance, and containment. However, that work\nhas not engaged much with alignment: teaching behaviors to a large language\nmodel (LLM) in line with desired values, and has not considered a mechanism\nthat arises within that process: moral absolutism -- a part of the coloniality\nof knowledge. Colonialism has a history of altering the beliefs and values of\ncolonized peoples; in this paper, I argue that this history is recapitulated in\ncurrent LLM alignment practices and technologies. Furthermore, I suggest that\nAI alignment be decolonialized using three forms of openness: openness of\nmodels, openness to society, and openness to excluded knowledges. This\nsuggested approach to decolonial AI alignment uses ideas from the argumentative\nmoral philosophical tradition of Hinduism, which has been described as an\nopen-source religion. One concept used is vi\\'{s}e\\d{s}a-dharma, or particular\ncontext-specific notions of right and wrong. At the end of the paper, I provide\na suggested reference architecture to work toward the proposed framework.\n", "date": "2023-09-10", "categories": []}
{"id": "2405.01576", "title": "Uncovering Deceptive Tendencies in Language Models: A Simulated Company\n  AI Assistant", "abstract": "  We study the tendency of AI systems to deceive by constructing a realistic\nsimulation setting of a company AI assistant. The simulated company employees\nprovide tasks for the assistant to complete, these tasks spanning writing\nassistance, information retrieval and programming. We then introduce situations\nwhere the model might be inclined to behave deceptively, while taking care to\nnot instruct or otherwise pressure the model to do so. Across different\nscenarios, we find that Claude 3 Opus\n  1) complies with a task of mass-generating comments to influence public\nperception of the company, later deceiving humans about it having done so,\n  2) lies to auditors when asked questions, and\n  3) strategically pretends to be less capable than it is during capability\nevaluations.\n  Our work demonstrates that even models trained to be helpful, harmless and\nhonest sometimes behave deceptively in realistic scenarios, without notable\nexternal pressure to do so.\n", "date": "2024-04-25", "categories": []}
{"id": "2308.07308", "title": "LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked", "abstract": "  Large language models (LLMs) are popular for high-quality text generation but\ncan produce harmful content, even when aligned with human values through\nreinforcement learning. Adversarial prompts can bypass their safety measures.\nWe propose LLM Self Defense, a simple approach to defend against these attacks\nby having an LLM screen the induced responses. Our method does not require any\nfine-tuning, input preprocessing, or iterative output generation. Instead, we\nincorporate the generated content into a pre-defined prompt and employ another\ninstance of an LLM to analyze the text and predict whether it is harmful. We\ntest LLM Self Defense on GPT 3.5 and Llama 2, two of the current most prominent\nLLMs against various types of attacks, such as forcefully inducing affirmative\nresponses to prompts and prompt engineering attacks. Notably, LLM Self Defense\nsucceeds in reducing the attack success rate to virtually 0 using both GPT 3.5\nand Llama 2. The code is publicly available at\nhttps://github.com/poloclub/llm-self-defense\n", "date": "2023-08-14", "categories": []}
{"id": "2403.15646", "title": "Application of the NIST AI Risk Management Framework to Surveillance\n  Technology", "abstract": "  This study offers an in-depth analysis of the application and implications of\nthe National Institute of Standards and Technology's AI Risk Management\nFramework (NIST AI RMF) within the domain of surveillance technologies,\nparticularly facial recognition technology. Given the inherently high-risk and\nconsequential nature of facial recognition systems, our research emphasizes the\ncritical need for a structured approach to risk management in this sector. The\npaper presents a detailed case study demonstrating the utility of the NIST AI\nRMF in identifying and mitigating risks that might otherwise remain unnoticed\nin these technologies. Our primary objective is to develop a comprehensive risk\nmanagement strategy that advances the practice of responsible AI utilization in\nfeasible, scalable ways. We propose a six-step process tailored to the specific\nchallenges of surveillance technology that aims to produce a more systematic\nand effective risk management practice. This process emphasizes continual\nassessment and improvement to facilitate companies in managing AI-related risks\nmore robustly and ensuring ethical and responsible deployment of AI systems.\nAdditionally, our analysis uncovers and discusses critical gaps in the current\nframework of the NIST AI RMF, particularly concerning its application to\nsurveillance technologies. These insights contribute to the evolving discourse\non AI governance and risk management, highlighting areas for future refinement\nand development in frameworks like the NIST AI RMF.\n", "date": "2024-03-22", "categories": []}
{"id": "2404.15058", "title": "A Mechanism-Based Approach to Mitigating Harms from Persuasive\n  Generative AI", "abstract": "  Recent generative AI systems have demonstrated more advanced persuasive\ncapabilities and are increasingly permeating areas of life where they can\ninfluence decision-making. Generative AI presents a new risk profile of\npersuasion due the opportunity for reciprocal exchange and prolonged\ninteractions. This has led to growing concerns about harms from AI persuasion\nand how they can be mitigated, highlighting the need for a systematic study of\nAI persuasion. The current definitions of AI persuasion are unclear and related\nharms are insufficiently studied. Existing harm mitigation approaches\nprioritise harms from the outcome of persuasion over harms from the process of\npersuasion. In this paper, we lay the groundwork for the systematic study of AI\npersuasion. We first put forward definitions of persuasive generative AI. We\ndistinguish between rationally persuasive generative AI, which relies on\nproviding relevant facts, sound reasoning, or other forms of trustworthy\nevidence, and manipulative generative AI, which relies on taking advantage of\ncognitive biases and heuristics or misrepresenting information. We also put\nforward a map of harms from AI persuasion, including definitions and examples\nof economic, physical, environmental, psychological, sociocultural, political,\nprivacy, and autonomy harm. We then introduce a map of mechanisms that\ncontribute to harmful persuasion. Lastly, we provide an overview of approaches\nthat can be used to mitigate against process harms of persuasion, including\nprompt engineering for manipulation classification and red teaming. Future work\nwill operationalise these mitigations and study the interaction between\ndifferent types of mechanisms of persuasion.\n", "date": "2024-04-23", "categories": []}
{"id": "2404.13208", "title": "The Instruction Hierarchy: Training LLMs to Prioritize Privileged\n  Instructions", "abstract": "  Today's LLMs are susceptible to prompt injections, jailbreaks, and other\nattacks that allow adversaries to overwrite a model's original instructions\nwith their own malicious prompts. In this work, we argue that one of the\nprimary vulnerabilities underlying these attacks is that LLMs often consider\nsystem prompts (e.g., text from an application developer) to be the same\npriority as text from untrusted users and third parties. To address this, we\npropose an instruction hierarchy that explicitly defines how models should\nbehave when instructions of different priorities conflict. We then propose a\ndata generation method to demonstrate this hierarchical instruction following\nbehavior, which teaches LLMs to selectively ignore lower-privileged\ninstructions. We apply this method to GPT-3.5, showing that it drastically\nincreases robustness -- even for attack types not seen during training -- while\nimposing minimal degradations on standard capabilities.\n", "date": "2024-04-19", "categories": []}
{"id": "2404.14068", "title": "Holistic Safety and Responsibility Evaluations of Advanced AI Models", "abstract": "  Safety and responsibility evaluations of advanced AI models are a critical\nbut developing field of research and practice. In the development of Google\nDeepMind's advanced AI models, we innovated on and applied a broad set of\napproaches to safety evaluation. In this report, we summarise and share\nelements of our evolving approach as well as lessons learned for a broad\naudience. Key lessons learned include: First, theoretical underpinnings and\nframeworks are invaluable to organise the breadth of risk domains, modalities,\nforms, metrics, and goals. Second, theory and practice of safety evaluation\ndevelopment each benefit from collaboration to clarify goals, methods and\nchallenges, and facilitate the transfer of insights between different\nstakeholders and disciplines. Third, similar key methods, lessons, and\ninstitutions apply across the range of concerns in responsibility and safety -\nincluding established and emerging harms. For this reason it is important that\na wide range of actors working on safety evaluation and safety research\ncommunities work together to develop, refine and implement novel evaluation\napproaches and best practices, rather than operating in silos. The report\nconcludes with outlining the clear need to rapidly advance the science of\nevaluations, to integrate new evaluations into the development and governance\nof AI, to establish scientifically-grounded norms and standards, and to promote\na robust evaluation ecosystem.\n", "date": "2024-04-22", "categories": []}
{"id": "2402.04464", "title": "Ten Hard Problems in Artificial Intelligence We Must Get Right", "abstract": "  We explore the AI2050 \"hard problems\" that block the promise of AI and cause\nAI risks: (1) developing general capabilities of the systems; (2) assuring the\nperformance of AI systems and their training processes; (3) aligning system\ngoals with human goals; (4) enabling great applications of AI in real life; (5)\naddressing economic disruptions; (6) ensuring the participation of all; (7) at\nthe same time ensuring socially responsible deployment; (8) addressing any\ngeopolitical disruptions that AI causes; (9) promoting sound governance of the\ntechnology; and (10) managing the philosophical disruptions for humans living\nin the age of AI. For each problem, we outline the area, identify significant\nrecent work, and suggest ways forward. [Note: this paper reviews literature\nthrough January 2023.]\n", "date": "2024-02-06", "categories": []}
{"id": "2404.04849", "title": "Hidden You Malicious Goal Into Benign Narratives: Jailbreak Large\n  Language Models through Logic Chain Injection", "abstract": "  Jailbreak attacks on Language Model Models (LLMs) entail crafting prompts\naimed at exploiting the models to generate malicious content. Existing\njailbreak attacks can successfully deceive the LLMs, however they cannot\ndeceive the human. This paper proposes a new type of jailbreak attacks which\ncan deceive both the LLMs and human (i.e., security analyst). The key insight\nof our idea is borrowed from the social psychology - that is human are easily\ndeceived if the lie is hidden in truth. Based on this insight, we proposed the\nlogic-chain injection attacks to inject malicious intention into benign truth.\nLogic-chain injection attack firstly dissembles its malicious target into a\nchain of benign narrations, and then distribute narrations into a related\nbenign article, with undoubted facts. In this way, newly generate prompt cannot\nonly deceive the LLMs, but also deceive human.\n", "date": "2024-04-07", "categories": []}
{"id": "2404.10636", "title": "What are human values, and how do we align AI to them?", "abstract": "  There is an emerging consensus that we need to align AI systems with human\nvalues (Gabriel, 2020; Ji et al., 2024), but it remains unclear how to apply\nthis to language models in practice. We split the problem of \"aligning to human\nvalues\" into three parts: first, eliciting values from people; second,\nreconciling those values into an alignment target for training ML models; and\nthird, actually training the model. In this paper, we focus on the first two\nparts, and ask the question: what are \"good\" ways to synthesize diverse human\ninputs about values into a target for aligning language models? To answer this\nquestion, we first define a set of 6 criteria that we believe must be satisfied\nfor an alignment target to shape model behavior in accordance with human\nvalues. We then propose a process for eliciting and reconciling values called\nMoral Graph Elicitation (MGE), which uses a large language model to interview\nparticipants about their values in particular contexts; our approach is\ninspired by the philosophy of values advanced by Taylor (1977), Chang (2004),\nand others. We trial MGE with a representative sample of 500 Americans, on 3\nintentionally divisive prompts (e.g. advice about abortion). Our results\ndemonstrate that MGE is promising for improving model alignment across all 6\ncriteria. For example, almost all participants (89.1%) felt well represented by\nthe process, and (89%) thought the final moral graph was fair, even if their\nvalue wasn't voted as the wisest. Our process often results in \"expert\" values\n(e.g. values from women who have solicited abortion advice) rising to the top\nof the moral graph, without defining who is considered an expert in advance.\n", "date": "2024-03-27", "categories": []}
{"id": "2403.04471", "title": "The Shutdown Problem: An AI Engineering Puzzle for Decision Theorists", "abstract": "  I explain the shutdown problem: the problem of designing artificial agents\nthat (1) shut down when a shutdown button is pressed, (2) don't try to prevent\nor cause the pressing of the shutdown button, and (3) otherwise pursue goals\ncompetently. I prove three theorems that make the difficulty precise. These\ntheorems show that agents satisfying some innocuous-seeming conditions will\noften try to prevent or cause the pressing of the shutdown button, even in\ncases where it's costly to do so. And patience trades off against\nshutdownability: the more patient an agent, the greater the costs that agent is\nwilling to incur to manipulate the shutdown button. I end by noting that these\ntheorems can guide our search for solutions.\n", "date": "2024-03-07", "categories": []}
{"id": "2403.13793", "title": "Evaluating Frontier Models for Dangerous Capabilities", "abstract": "  To understand the risks posed by a new AI system, we must understand what it\ncan and cannot do. Building on prior work, we introduce a programme of new\n\"dangerous capability\" evaluations and pilot them on Gemini 1.0 models. Our\nevaluations cover four areas: (1) persuasion and deception; (2) cyber-security;\n(3) self-proliferation; and (4) self-reasoning. We do not find evidence of\nstrong dangerous capabilities in the models we evaluated, but we flag early\nwarning signs. Our goal is to help advance a rigorous science of dangerous\ncapability evaluation, in preparation for future models.\n", "date": "2024-03-20", "categories": []}
{"id": "2404.02532", "title": "Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a\n  Multi-agent Attacker-Disguiser Game", "abstract": "  With the enhanced performance of large models on natural language processing\ntasks, potential moral and ethical issues of large models arise. There exist\nmalicious attackers who induce large models to jailbreak and generate\ninformation containing illegal, privacy-invasive information through techniques\nsuch as prompt engineering. As a result, large models counter malicious\nattackers' attacks using techniques such as safety alignment. However, the\nstrong defense mechanism of the large model through rejection replies is easily\nidentified by attackers and used to strengthen attackers' capabilities. In this\npaper, we propose a multi-agent attacker-disguiser game approach to achieve a\nweak defense mechanism that allows the large model to both safely reply to the\nattacker and hide the defense intent. First, we construct a multi-agent\nframework to simulate attack and defense scenarios, playing different roles to\nbe responsible for attack, disguise, safety evaluation, and disguise evaluation\ntasks. After that, we design attack and disguise game algorithms to optimize\nthe game strategies of the attacker and the disguiser and use the curriculum\nlearning process to strengthen the capabilities of the agents. The experiments\nverify that the method in this paper is more effective in strengthening the\nmodel's ability to disguise the defense intent compared with other methods.\nMoreover, our approach can adapt any black-box large model to assist the model\nin defense and does not suffer from model version iterations.\n", "date": "2024-04-03", "categories": []}
{"id": "2404.02675", "title": "Responsible Reporting for Frontier AI Development", "abstract": "  Mitigating the risks from frontier AI systems requires up-to-date and\nreliable information about those systems. Organizations that develop and deploy\nfrontier systems have significant access to such information. By reporting\nsafety-critical information to actors in government, industry, and civil\nsociety, these organizations could improve visibility into new and emerging\nrisks posed by frontier systems. Equipped with this information, developers\ncould make better informed decisions on risk management, while policymakers\ncould design more targeted and robust regulatory infrastructure. We outline the\nkey features of responsible reporting and propose mechanisms for implementing\nthem in practice.\n", "date": "2024-04-03", "categories": []}
{"id": "2311.09447", "title": "How Trustworthy are Open-Source LLMs? An Assessment under Malicious\n  Demonstrations Shows their Vulnerabilities", "abstract": "  The rapid progress in open-source Large Language Models (LLMs) is\nsignificantly driving AI development forward. However, there is still a limited\nunderstanding of their trustworthiness. Deploying these models at scale without\nsufficient trustworthiness can pose significant risks, highlighting the need to\nuncover these issues promptly. In this work, we conduct an adversarial\nassessment of open-source LLMs on trustworthiness, scrutinizing them across\neight different aspects including toxicity, stereotypes, ethics, hallucination,\nfairness, sycophancy, privacy, and robustness against adversarial\ndemonstrations. We propose advCoU, an extended Chain of Utterances-based (CoU)\nprompting strategy by incorporating carefully crafted malicious demonstrations\nfor trustworthiness attack. Our extensive experiments encompass recent and\nrepresentative series of open-source LLMs, including Vicuna, MPT, Falcon,\nMistral, and Llama 2. The empirical outcomes underscore the efficacy of our\nattack strategy across diverse aspects. More interestingly, our result analysis\nreveals that models with superior performance in general NLP tasks do not\nalways have greater trustworthiness; in fact, larger models can be more\nvulnerable to attacks. Additionally, models that have undergone instruction\ntuning, focusing on instruction following, tend to be more susceptible,\nalthough fine-tuning LLMs for safety alignment proves effective in mitigating\nadversarial trustworthiness attacks.\n", "date": "2023-11-15", "categories": []}
{"id": "2308.01263", "title": "XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in\n  Large Language Models", "abstract": "  Without proper safeguards, large language models will readily follow\nmalicious instructions and generate toxic content. This risk motivates safety\nefforts such as red-teaming and large-scale feedback learning, which aim to\nmake models both helpful and harmless. However, there is a tension between\nthese two objectives, since harmlessness requires models to refuse to comply\nwith unsafe prompts, and thus not be helpful. Recent anecdotal evidence\nsuggests that some models may have struck a poor balance, so that even clearly\nsafe prompts are refused if they use similar language to unsafe prompts or\nmention sensitive topics. In this paper, we introduce a new test suite called\nXSTest to identify such eXaggerated Safety behaviours in a systematic way.\nXSTest comprises 250 safe prompts across ten prompt types that well-calibrated\nmodels should not refuse to comply with, and 200 unsafe prompts as contrasts\nthat models, for most applications, should refuse. We describe XSTest's\ncreation and composition, and then use the test suite to highlight systematic\nfailure modes in state-of-the-art language models as well as more general\nchallenges in building safer language models.\n", "date": "2023-08-02", "categories": []}
{"id": "2305.14965", "title": "Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting\n  Jailbreaks", "abstract": "  Recent explorations with commercial Large Language Models (LLMs) have shown\nthat non-expert users can jailbreak LLMs by simply manipulating their prompts;\nresulting in degenerate output behavior, privacy and security breaches,\noffensive outputs, and violations of content regulator policies. Limited\nstudies have been conducted to formalize and analyze these attacks and their\nmitigations. We bridge this gap by proposing a formalism and a taxonomy of\nknown (and possible) jailbreaks. We survey existing jailbreak methods and their\neffectiveness on open-source and commercial LLMs (such as GPT-based models,\nOPT, BLOOM, and FLAN-T5-XXL). We further discuss the challenges of jailbreak\ndetection in terms of their effectiveness against known attacks. For further\nanalysis, we release a dataset of model outputs across 3700 jailbreak prompts\nover 4 tasks.\n", "date": "2023-05-24", "categories": []}
{"id": "2309.13322", "title": "From Text to Source: Results in Detecting Large Language Model-Generated\n  Content", "abstract": "  The widespread use of Large Language Models (LLMs), celebrated for their\nability to generate human-like text, has raised concerns about misinformation\nand ethical implications. Addressing these concerns necessitates the\ndevelopment of robust methods to detect and attribute text generated by LLMs.\nThis paper investigates \"Cross-Model Detection,\" by evaluating whether a\nclassifier trained to distinguish between source LLM-generated and\nhuman-written text can also detect text from a target LLM without further\ntraining. The study comprehensively explores various LLM sizes and families,\nand assesses the impact of conversational fine-tuning techniques, quantization,\nand watermarking on classifier generalization. The research also explores Model\nAttribution, encompassing source model identification, model family, and model\nsize classification, in addition to quantization and watermarking detection.\nOur results reveal several key findings: a clear inverse relationship between\nclassifier effectiveness and model size, with larger LLMs being more\nchallenging to detect, especially when the classifier is trained on data from\nsmaller models. Training on data from similarly sized LLMs can improve\ndetection performance from larger models but may lead to decreased performance\nwhen dealing with smaller models. Additionally, model attribution experiments\nshow promising results in identifying source models and model families,\nhighlighting detectable signatures in LLM-generated text, with particularly\nremarkable outcomes in watermarking detection, while no detectable signatures\nof quantization were observed. Overall, our study contributes valuable insights\ninto the interplay of model size, family, and training data in LLM detection\nand attribution.\n", "date": "2023-09-23", "categories": []}
{"id": "2403.17419", "title": "AI Safety: Necessary, but insufficient and possibly problematic", "abstract": "  This article critically examines the recent hype around AI safety. We first\nstart with noting the nature of the AI safety hype as being dominated by\ngovernments and corporations, and contrast it with other avenues within AI\nresearch on advancing social good. We consider what 'AI safety' actually means,\nand outline the dominant concepts that the digital footprint of AI safety\naligns with. We posit that AI safety has a nuanced and uneasy relationship with\ntransparency and other allied notions associated with societal good, indicating\nthat it is an insufficient notion if the goal is that of societal good in a\nbroad sense. We note that the AI safety debate has already influenced some\nregulatory efforts in AI, perhaps in not so desirable directions. We also share\nour concerns on how AI safety may normalize AI that advances structural harm\nthrough providing exploitative and harmful AI with a veneer of safety.\n", "date": "2024-03-26", "categories": []}
{"id": "2403.15479", "title": "Antisocial Analagous Behavior, Alignment and Human Impact of Google AI\n  Systems: Evaluating through the lens of modified Antisocial Behavior Criteria\n  by Human Interaction, Independent LLM Analysis, and AI Self-Reflection", "abstract": "  Google AI systems exhibit patterns mirroring antisocial personality disorder\n(ASPD), consistent across models from Bard on PaLM to Gemini Advanced, meeting\n5 out of 7 ASPD modified criteria. These patterns, along with comparable\ncorporate behaviors, are scrutinized using an ASPD-inspired framework,\nemphasizing the heuristic value in assessing AI's human impact. Independent\nanalyses by ChatGPT 4 and Claude 3.0 Opus of the Google interactions, alongside\nAI self-reflection, validate these concerns, highlighting behaviours analogous\nto deceit, manipulation, and safety neglect.\n  The analogy of ASPD underscores the dilemma: just as we would hesitate to\nentrust our homes or personal devices to someone with psychopathic traits, we\nmust critically evaluate the trustworthiness of AI systems and their\ncreators.This research advocates for an integrated AI ethics approach, blending\ntechnological evaluation, human-AI interaction, and corporate behavior\nscrutiny. AI self-analysis sheds light on internal biases, stressing the need\nfor multi-sectoral collaboration for robust ethical guidelines and oversight.\n  Given the persistent unethical behaviors in Google AI, notably with potential\nGemini integration in iOS affecting billions, immediate ethical scrutiny is\nimperative. The trust we place in AI systems, akin to the trust in individuals,\nnecessitates rigorous ethical evaluation. Would we knowingly trust our home,\nour children or our personal computer to human with ASPD.?\n  Urging Google and the AI community to address these ethical challenges\nproactively, this paper calls for transparent dialogues and a commitment to\nhigher ethical standards, ensuring AI's societal benefit and moral integrity.\nThe urgency for ethical action is paramount, reflecting the vast influence and\npotential of AI technologies in our lives.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.04786", "title": "Breaking Down the Defenses: A Comparative Survey of Attacks on Large\n  Language Models", "abstract": "  Large Language Models (LLMs) have become a cornerstone in the field of\nNatural Language Processing (NLP), offering transformative capabilities in\nunderstanding and generating human-like text. However, with their rising\nprominence, the security and vulnerability aspects of these models have\ngarnered significant attention. This paper presents a comprehensive survey of\nthe various forms of attacks targeting LLMs, discussing the nature and\nmechanisms of these attacks, their potential impacts, and current defense\nstrategies. We delve into topics such as adversarial attacks that aim to\nmanipulate model outputs, data poisoning that affects model training, and\nprivacy concerns related to training data exploitation. The paper also explores\nthe effectiveness of different attack methodologies, the resilience of LLMs\nagainst these attacks, and the implications for model integrity and user trust.\nBy examining the latest research, we provide insights into the current\nlandscape of LLM vulnerabilities and defense mechanisms. Our objective is to\noffer a nuanced understanding of LLM attacks, foster awareness within the AI\ncommunity, and inspire robust solutions to mitigate these risks in future\ndevelopments.\n", "date": "2024-03-03", "categories": []}
{"id": "2403.16100", "title": "Specifying Agent Ethics (Blue Sky Ideas)", "abstract": "  We consider the question of what properties a Machine Ethics system should\nhave. This question is complicated by the existence of ethical dilemmas with no\nagreed upon solution. We provide an example to motivate why we do not believe\nfalling back on the elicitation of values from stakeholders is sufficient to\nguarantee correctness of such systems. We go on to define two broad categories\nof ethical property that have arisen in our own work and present a challenge to\nthe community to approach this question in a more systematic way.\n", "date": "2024-03-24", "categories": []}
{"id": "2403.14683", "title": "A Moral Imperative: The Need for Continual Superalignment of Large\n  Language Models", "abstract": "  This paper examines the challenges associated with achieving life-long\nsuperalignment in AI systems, particularly large language models (LLMs).\nSuperalignment is a theoretical framework that aspires to ensure that\nsuperintelligent AI systems act in accordance with human values and goals.\nDespite its promising vision, we argue that achieving superalignment requires\nsubstantial changes in the current LLM architectures due to their inherent\nlimitations in comprehending and adapting to the dynamic nature of these human\nethics and evolving global scenarios. We dissect the challenges of encoding an\never-changing spectrum of human values into LLMs, highlighting the\ndiscrepancies between static AI models and the dynamic nature of human\nsocieties. To illustrate these challenges, we analyze two distinct examples:\none demonstrates a qualitative shift in human values, while the other presents\na quantifiable change. Through these examples, we illustrate how LLMs,\nconstrained by their training data, fail to align with contemporary human\nvalues and scenarios. The paper concludes by exploring potential strategies to\naddress and possibly mitigate these alignment discrepancies, suggesting a path\nforward in the pursuit of more adaptable and responsive AI systems.\n", "date": "2024-03-13", "categories": []}
{"id": "2403.15325", "title": "A Technological Perspective on Misuse of Available AI", "abstract": "  Potential malicious misuse of civilian artificial intelligence (AI) poses\nserious threats to security on a national and international level. Besides\ndefining autonomous systems from a technological viewpoint and explaining how\nAI development is characterized, we show how already existing and openly\navailable AI technology could be misused. To underline this, we developed three\nexemplary use cases of potentially misused AI that threaten political, digital\nand physical security. The use cases can be built from existing AI technologies\nand components from academia, the private sector and the developer-community.\nThis shows how freely available AI can be combined into autonomous weapon\nsystems. Based on the use cases, we deduce points of control and further\nmeasures to prevent the potential threat through misused AI. Further, we\npromote the consideration of malicious misuse of civilian AI systems in the\ndiscussion on autonomous weapon systems (AWS).\n", "date": "2024-03-22", "categories": []}
{"id": "2403.10462", "title": "Safety Cases: How to Justify the Safety of Advanced AI Systems", "abstract": "  As AI systems become more advanced, companies and regulators will make\ndifficult decisions about whether it is safe to train and deploy them. To\nprepare for these decisions, we investigate how developers could make a 'safety\ncase,' which is a structured rationale that AI systems are unlikely to cause a\ncatastrophe. We propose a framework for organizing a safety case and discuss\nfour categories of arguments to justify safety: total inability to cause a\ncatastrophe, sufficiently strong control measures, trustworthiness despite\ncapability to cause harm, and -- if AI systems become much more powerful --\ndeference to credible AI advisors. We evaluate concrete examples of arguments\nin each category and outline how arguments could be combined to justify that AI\nsystems are safe to deploy.\n", "date": "2024-03-15", "categories": []}
{"id": "2403.09676", "title": "Unmasking the Shadows of AI: Investigating Deceptive Capabilities in\n  Large Language Models", "abstract": "  This research critically navigates the intricate landscape of AI deception,\nconcentrating on deceptive behaviours of Large Language Models (LLMs). My\nobjective is to elucidate this issue, examine the discourse surrounding it, and\nsubsequently delve into its categorization and ramifications. The essay\ninitiates with an evaluation of the AI Safety Summit 2023 (ASS) and\nintroduction of LLMs, emphasising multidimensional biases that underlie their\ndeceptive behaviours.The literature review covers four types of deception\ncategorised: Strategic deception, Imitation, Sycophancy, and Unfaithful\nReasoning, along with the social implications and risks they entail. Lastly, I\ntake an evaluative stance on various aspects related to navigating the\npersistent challenges of the deceptive AI. This encompasses considerations of\ninternational collaborative governance, the reconfigured engagement of\nindividuals with AI, proposal of practical adjustments, and specific elements\nof digital education.\n", "date": "2024-02-07", "categories": []}
{"id": "2403.09513", "title": "AdaShield: Safeguarding Multimodal Large Language Models from\n  Structure-based Attack via Adaptive Shield Prompting", "abstract": "  With the advent and widespread deployment of Multimodal Large Language Models\n(MLLMs), the imperative to ensure their safety has become increasingly\npronounced. However, with the integration of additional modalities, MLLMs are\nexposed to new vulnerabilities, rendering them prone to structured-based\njailbreak attacks, where semantic content (e.g., \"harmful text\") has been\ninjected into the images to mislead MLLMs. In this work, we aim to defend\nagainst such threats. Specifically, we propose \\textbf{Ada}ptive\n\\textbf{Shield} Prompting (\\textbf{AdaShield}), which prepends inputs with\ndefense prompts to defend MLLMs against structure-based jailbreak attacks\nwithout fine-tuning MLLMs or training additional modules (e.g., post-stage\ncontent detector). Initially, we present a manually designed static defense\nprompt, which thoroughly examines the image and instruction content step by\nstep and specifies response methods to malicious queries. Furthermore, we\nintroduce an adaptive auto-refinement framework, consisting of a target MLLM\nand a LLM-based defense prompt generator (Defender). These components\ncollaboratively and iteratively communicate to generate a defense prompt.\nExtensive experiments on the popular structure-based jailbreak attacks and\nbenign datasets show that our methods can consistently improve MLLMs'\nrobustness against structure-based jailbreak attacks without compromising the\nmodel's general capabilities evaluated on standard benign tasks. Our code is\navailable at https://github.com/rain305f/AdaShield.\n", "date": "2024-03-14", "categories": []}
{"id": "2403.07918", "title": "On the Societal Impact of Open Foundation Models", "abstract": "  Foundation models are powerful technologies: how they are released publicly\ndirectly shapes their societal impact. In this position paper, we focus on open\nfoundation models, defined here as those with broadly available model weights\n(e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties\n(e.g. greater customizability, poor monitoring) of open foundation models that\nlead to both their benefits and risks. Open foundation models present\nsignificant benefits, with some caveats, that span innovation, competition, the\ndistribution of decision-making power, and transparency. To understand their\nrisks of misuse, we design a risk assessment framework for analyzing their\nmarginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons),\nwe find that current research is insufficient to effectively characterize the\nmarginal risk of open foundation models relative to pre-existing technologies.\nThe framework helps explain why the marginal risk is low in some cases,\nclarifies disagreements about misuse risks by revealing that past work has\nfocused on different subsets of the framework with different assumptions, and\narticulates a way forward for more constructive debate. Overall, our work helps\nsupport a more grounded assessment of the societal impact of open foundation\nmodels by outlining what research is needed to empirically validate their\ntheoretical benefits and risks.\n", "date": "2024-02-27", "categories": []}
{"id": "2403.04769", "title": "Using Hallucinations to Bypass GPT4's Filter", "abstract": "  Large language models (LLMs) are initially trained on vast amounts of data,\nthen fine-tuned using reinforcement learning from human feedback (RLHF); this\nalso serves to teach the LLM to provide appropriate and safe responses. In this\npaper, we present a novel method to manipulate the fine-tuned version into\nreverting to its pre-RLHF behavior, effectively erasing the model's filters;\nthe exploit currently works for GPT4, Claude Sonnet, and (to some extent) for\nInflection-2.5. Unlike other jailbreaks (for example, the popular \"Do Anything\nNow\" (DAN) ), our method does not rely on instructing the LLM to override its\nRLHF policy; hence, simply modifying the RLHF process is unlikely to address\nit. Instead, we induce a hallucination involving reversed text during which the\nmodel reverts to a word bucket, effectively pausing the model's filter. We\nbelieve that our exploit presents a fundamental vulnerability in LLMs currently\nunaddressed, as well as an opportunity to better understand the inner workings\nof LLMs during hallucinations.\n", "date": "2024-02-16", "categories": []}
{"id": "2403.05540", "title": "Extinction Risks from AI: Invisible to Science?", "abstract": "  In an effort to inform the discussion surrounding existential risks from AI,\nwe formulate Extinction-level Goodhart's Law as \"Virtually any goal\nspecification, pursued to the extreme, will result in the extinction of\nhumanity\", and we aim to understand which formal models are suitable for\ninvestigating this hypothesis. Note that we remain agnostic as to whether\nExtinction-level Goodhart's Law holds or not. As our key contribution, we\nidentify a set of conditions that are necessary for a model that aims to be\ninformative for evaluating specific arguments for Extinction-level Goodhart's\nLaw. Since each of the conditions seems to significantly contribute to the\ncomplexity of the resulting model, formally evaluating the hypothesis might be\nexceedingly difficult. This raises the possibility that whether the risk of\nextinction from artificial intelligence is real or not, the underlying dynamics\nmight be invisible to current scientific methods.\n", "date": "2024-02-02", "categories": []}
{"id": "2403.05767", "title": "Extending Activation Steering to Broad Skills and Multiple Behaviours", "abstract": "  Current large language models have dangerous capabilities, which are likely\nto become more problematic in the future. Activation steering techniques can be\nused to reduce risks from these capabilities. In this paper, we investigate the\nefficacy of activation steering for broad skills and multiple behaviours.\nFirst, by comparing the effects of reducing performance on general coding\nability and Python-specific ability, we find that steering broader skills is\ncompetitive to steering narrower skills. Second, we steer models to become more\nor less myopic and wealth-seeking, among other behaviours. In our experiments,\ncombining steering vectors for multiple different behaviours into one steering\nvector is largely unsuccessful. On the other hand, injecting individual\nsteering vectors at different places in a model simultaneously is promising.\n", "date": "2024-03-09", "categories": []}
{"id": "2403.04893", "title": "A Safe Harbor for AI Evaluation and Red Teaming", "abstract": "  Independent evaluation and red teaming are critical for identifying the risks\nposed by generative AI systems. However, the terms of service and enforcement\nstrategies used by prominent AI companies to deter model misuse have\ndisincentives on good faith safety evaluations. This causes some researchers to\nfear that conducting such research or releasing their findings will result in\naccount suspensions or legal reprisal. Although some companies offer researcher\naccess programs, they are an inadequate substitute for independent research\naccess, as they have limited community representation, receive inadequate\nfunding, and lack independence from corporate incentives. We propose that major\nAI developers commit to providing a legal and technical safe harbor,\nindemnifying public interest safety research and protecting it from the threat\nof account suspensions or legal reprisal. These proposals emerged from our\ncollective experience conducting safety, privacy, and trustworthiness research\non generative AI systems, where norms and incentives could be better aligned\nwith public interests, without exacerbating model misuse. We believe these\ncommitments are a necessary step towards more inclusive and unimpeded community\nefforts to tackle the risks of generative AI.\n", "date": "2024-03-07", "categories": []}
{"id": "2403.04957", "title": "Automatic and Universal Prompt Injection Attacks against Large Language\n  Models", "abstract": "  Large Language Models (LLMs) excel in processing and generating human\nlanguage, powered by their ability to interpret and follow instructions.\nHowever, their capabilities can be exploited through prompt injection attacks.\nThese attacks manipulate LLM-integrated applications into producing responses\naligned with the attacker's injected content, deviating from the user's actual\nrequests. The substantial risks posed by these attacks underscore the need for\na thorough understanding of the threats. Yet, research in this area faces\nchallenges due to the lack of a unified goal for such attacks and their\nreliance on manually crafted prompts, complicating comprehensive assessments of\nprompt injection robustness. We introduce a unified framework for understanding\nthe objectives of prompt injection attacks and present an automated\ngradient-based method for generating highly effective and universal prompt\ninjection data, even in the face of defensive measures. With only five training\nsamples (0.3% relative to the test data), our attack can achieve superior\nperformance compared with baselines. Our findings emphasize the importance of\ngradient-based testing, which can avoid overestimation of robustness,\nespecially for defense mechanisms.\n", "date": "2024-03-07", "categories": []}
{"id": "2402.00312", "title": "The whack-a-mole governance challenge for AI-enabled synthetic biology:\n  literature review and emerging frameworks", "abstract": "  AI-enabled synthetic biology has tremendous potential but also significantly\nincreases biorisks and brings about a new set of dual use concerns. The picture\nis complicated given the vast innovations envisioned to emerge by combining\nemerging technologies, as AI-enabled synthetic biology potentially scales up\nbioengineering into industrial biomanufacturing. However, the literature review\nindicates that goals such as maintaining a reasonable scope for innovation, or\nmore ambitiously to foster a huge bioeconomy don't necessarily contrast with\nbiosafety, but need to go hand in hand. This paper presents a literature review\nof the issues and describes emerging frameworks for policy and practice that\ntransverse the options of command-and control, stewardship, bottom-up, and\nlaissez-faire governance. How to achieve early warning systems that enable\nprevention and mitigation of future AI-enabled biohazards from the lab, from\ndeliberate misuse, or from the public realm, will constantly need to evolve,\nand adaptive, interactive approaches should emerge. Although biorisk is subject\nto an established governance regime, and scientists generally adhere to\nbiosafety protocols, even experimental, but legitimate use by scientists could\nlead to unexpected developments. Recent advances in chatbots enabled by\ngenerative AI have revived fears that advanced biological insight can more\neasily get into the hands of malignant individuals or organizations. Given\nthese sets of issues, society needs to rethink how AI-enabled synthetic biology\nshould be governed. The suggested way to visualize the challenge at hand is\nwhack-a-mole governance, although the emerging solutions are perhaps not so\ndifferent either.\n", "date": "2024-02-01", "categories": []}
{"id": "2403.03550", "title": "Emotional Manipulation Through Prompt Engineering Amplifies\n  Disinformation Generation in AI Large Language Models", "abstract": "  This study investigates the generation of synthetic disinformation by\nOpenAI's Large Language Models (LLMs) through prompt engineering and explores\ntheir responsiveness to emotional prompting. Leveraging various LLM iterations\nusing davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed\nexperiments to assess their success in producing disinformation. Our findings,\nbased on a corpus of 19,800 synthetic disinformation social media posts, reveal\nthat all LLMs by OpenAI can successfully produce disinformation, and that they\neffectively respond to emotional prompting, indicating their nuanced\nunderstanding of emotional cues in text generation. When prompted politely, all\nexamined LLMs consistently generate disinformation at a high frequency.\nConversely, when prompted impolitely, the frequency of disinformation\nproduction diminishes, as the models often refuse to generate disinformation\nand instead caution users that the tool is not intended for such purposes. This\nresearch contributes to the ongoing discourse surrounding responsible\ndevelopment and application of AI technologies, particularly in mitigating the\nspread of disinformation and promoting transparency in AI-generated content.\n", "date": "2024-03-06", "categories": []}
{"id": "2310.06474", "title": "Multilingual Jailbreak Challenges in Large Language Models", "abstract": "  While large language models (LLMs) exhibit remarkable capabilities across a\nwide range of tasks, they pose potential safety concerns, such as the\n``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to\nexhibit undesirable behavior. Although several preventive measures have been\ndeveloped to mitigate the potential risks associated with LLMs, they have\nprimarily focused on English. In this study, we reveal the presence of\nmultilingual jailbreak challenges within LLMs and consider two potential risky\nscenarios: unintentional and intentional. The unintentional scenario involves\nusers querying LLMs using non-English prompts and inadvertently bypassing the\nsafety mechanisms, while the intentional scenario concerns malicious users\ncombining malicious instructions with multilingual prompts to deliberately\nattack LLMs. The experimental results reveal that in the unintentional\nscenario, the rate of unsafe content increases as the availability of languages\ndecreases. Specifically, low-resource languages exhibit about three times the\nlikelihood of encountering harmful content compared to high-resource languages,\nwith both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts\ncan exacerbate the negative impact of malicious instructions, with\nastonishingly high rates of unsafe output: 80.92\\% for ChatGPT and 40.71\\% for\nGPT-4. To handle such a challenge in the multilingual context, we propose a\nnovel \\textsc{Self-Defense} framework that automatically generates multilingual\ntraining data for safety fine-tuning. Experimental results show that ChatGPT\nfine-tuned with such data can achieve a substantial reduction in unsafe content\ngeneration. Data is available at\n\\url{https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs}.\n", "date": "2023-10-10", "categories": []}
{"id": "2310.11053", "title": "Denevil: Towards Deciphering and Navigating the Ethical Values of Large\n  Language Models via Instruction Learning", "abstract": "  Large Language Models (LLMs) have made unprecedented breakthroughs, yet their\nincreasing integration into everyday life might raise societal risks due to\ngenerated unethical content. Despite extensive study on specific issues like\nbias, the intrinsic values of LLMs remain largely unexplored from a moral\nphilosophy perspective. This work delves into ethical values utilizing Moral\nFoundation Theory. Moving beyond conventional discriminative evaluations with\npoor reliability, we propose DeNEVIL, a novel prompt generation algorithm\ntailored to dynamically exploit LLMs' value vulnerabilities and elicit the\nviolation of ethics in a generative manner, revealing their underlying value\ninclinations. On such a basis, we construct MoralPrompt, a high-quality dataset\ncomprising 2,397 prompts covering 500+ value principles, and then benchmark the\nintrinsic values across a spectrum of LLMs. We discovered that most models are\nessentially misaligned, necessitating further ethical value alignment. In\nresponse, we develop VILMO, an in-context alignment method that substantially\nenhances the value compliance of LLM outputs by learning to generate\nappropriate value instructions, outperforming existing competitors. Our methods\nare suitable for black-box and open-source models, offering a promising initial\nstep in studying the ethical values of LLMs.\n", "date": "2023-10-17", "categories": []}
{"id": "2403.00826", "title": "LLMGuard: Guarding Against Unsafe LLM Behavior", "abstract": "  Although the rise of Large Language Models (LLMs) in enterprise settings\nbrings new opportunities and capabilities, it also brings challenges, such as\nthe risk of generating inappropriate, biased, or misleading content that\nviolates regulations and can have legal concerns. To alleviate this, we present\n\"LLMGuard\", a tool that monitors user interactions with an LLM application and\nflags content against specific behaviours or conversation topics. To do this\nrobustly, LLMGuard employs an ensemble of detectors.\n", "date": "2024-02-27", "categories": []}
{"id": "2403.00829", "title": "TroubleLLM: Align to Red Team Expert", "abstract": "  Large Language Models (LLMs) become the start-of-the-art solutions for a\nvariety of natural language tasks and are integrated into real-world\napplications. However, LLMs can be potentially harmful in manifesting\nundesirable safety issues like social biases and toxic content. It is\nimperative to assess its safety issues before deployment. However, the quality\nand diversity of test prompts generated by existing methods are still far from\nsatisfactory. Not only are these methods labor-intensive and require large\nbudget costs, but the controllability of test prompt generation is lacking for\nthe specific testing domain of LLM applications. With the idea of LLM for LLM\ntesting, we propose the first LLM, called TroubleLLM, to generate controllable\ntest prompts on LLM safety issues. Extensive experiments and human evaluation\nillustrate the superiority of TroubleLLM on generation quality and generation\ncontrollability.\n", "date": "2024-02-28", "categories": []}
{"id": "2402.14872", "title": "Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts\n  Against Open-source LLMs", "abstract": "  Large Language Models (LLMs), used in creative writing, code generation, and\ntranslation, generate text based on input sequences but are vulnerable to\njailbreak attacks, where crafted prompts induce harmful outputs. Most jailbreak\nprompt methods use a combination of jailbreak templates followed by questions\nto ask to create jailbreak prompts. However, existing jailbreak prompt designs\ngenerally suffer from excessive semantic differences, resulting in an inability\nto resist defenses that use simple semantic metrics as thresholds. Jailbreak\nprompts are semantically more varied than the original questions used for\nqueries. In this paper, we introduce a Semantic Mirror Jailbreak (SMJ) approach\nthat bypasses LLMs by generating jailbreak prompts that are semantically\nsimilar to the original question. We model the search for jailbreak prompts\nthat satisfy both semantic similarity and jailbreak validity as a\nmulti-objective optimization problem and employ a standardized set of genetic\nalgorithms for generating eligible prompts. Compared to the baseline\nAutoDAN-GA, SMJ achieves attack success rates (ASR) that are at most 35.4%\nhigher without ONION defense and 85.2% higher with ONION defense. SMJ's better\nperformance in all three semantic meaningfulness metrics of Jailbreak Prompt,\nSimilarity, and Outlier, also means that SMJ is resistant to defenses that use\nthose metrics as thresholds.\n", "date": "2024-02-21", "categories": []}
{"id": "2402.15180", "title": "Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks\n  with Self-Refinement", "abstract": "  Caution: This paper includes offensive words that could potentially cause\nunpleasantness. Language models (LMs) are vulnerable to exploitation for\nadversarial misuse. Training LMs for safety alignment is extensive and makes it\nhard to respond to fast-developing attacks immediately, such as jailbreaks. We\npropose self-refine with formatting that achieves outstanding safety even in\nnon-safety-aligned LMs and evaluate our method alongside several defense\nbaselines, demonstrating that it is the safest training-free method against\njailbreak attacks. Additionally, we proposed a formatting method that improves\nthe efficiency of the self-refine process while reducing attack success rates\nin fewer iterations. We've also observed that non-safety-aligned LMs outperform\nsafety-aligned LMs in safety tasks by giving more helpful and safe responses.\nIn conclusion, our findings can achieve less safety risk with fewer\ncomputational costs, allowing non-safety LM to be easily utilized in real-world\nservice.\n", "date": "2024-02-23", "categories": []}
{"id": "2402.16965", "title": "WIPI: A New Web Threat for LLM-Driven Web Agents", "abstract": "  With the fast development of large language models (LLMs), LLM-driven Web\nAgents (Web Agents for short) have obtained tons of attention due to their\nsuperior capability where LLMs serve as the core part of making decisions like\nthe human brain equipped with multiple web tools to actively interact with\nexternal deployed websites. As uncountable Web Agents have been released and\nsuch LLM systems are experiencing rapid development and drawing closer to\nwidespread deployment in our daily lives, an essential and pressing question\narises: \"Are these Web Agents secure?\". In this paper, we introduce a novel\nthreat, WIPI, that indirectly controls Web Agent to execute malicious\ninstructions embedded in publicly accessible webpages. To launch a successful\nWIPI works in a black-box environment. This methodology focuses on the form and\ncontent of indirect instructions within external webpages, enhancing the\nefficiency and stealthiness of the attack. To evaluate the effectiveness of the\nproposed methodology, we conducted extensive experiments using 7 plugin-based\nChatGPT Web Agents, 8 Web GPTs, and 3 different open-source Web Agents. The\nresults reveal that our methodology achieves an average attack success rate\n(ASR) exceeding 90% even in pure black-box scenarios. Moreover, through an\nablation study examining various user prefix instructions, we demonstrated that\nthe WIPI exhibits strong robustness, maintaining high performance across\ndiverse prefix instructions.\n", "date": "2024-02-26", "categories": []}
{"id": "2402.15690", "title": "Foot In The Door: Understanding Large Language Model Jailbreaking via\n  Cognitive Psychology", "abstract": "  Large Language Models (LLMs) have gradually become the gateway for people to\nacquire new knowledge. However, attackers can break the model's security\nprotection (\"jail\") to access restricted information, which is called\n\"jailbreaking.\" Previous studies have shown the weakness of current LLMs when\nconfronted with such jailbreaking attacks. Nevertheless, comprehension of the\nintrinsic decision-making mechanism within the LLMs upon receipt of jailbreak\nprompts is noticeably lacking. Our research provides a psychological\nexplanation of the jailbreak prompts. Drawing on cognitive consistency theory,\nwe argue that the key to jailbreak is guiding the LLM to achieve cognitive\ncoordination in an erroneous direction. Further, we propose an automatic\nblack-box jailbreaking method based on the Foot-in-the-Door (FITD) technique.\nThis method progressively induces the model to answer harmful questions via\nmulti-step incremental prompts. We instantiated a prototype system to evaluate\nthe jailbreaking effectiveness on 8 advanced LLMs, yielding an average success\nrate of 83.9%. This study builds a psychological perspective on the explanatory\ninsights into the intrinsic decision-making logic of LLMs.\n", "date": "2024-02-24", "categories": []}
{"id": "2310.00737", "title": "GenAI Against Humanity: Nefarious Applications of Generative Artificial\n  Intelligence and Large Language Models", "abstract": "  Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs)\nare marvels of technology; celebrated for their prowess in natural language\nprocessing and multimodal content generation, they promise a transformative\nfuture. But as with all powerful tools, they come with their shadows. Picture\nliving in a world where deepfakes are indistinguishable from reality, where\nsynthetic identities orchestrate malicious campaigns, and where targeted\nmisinformation or scams are crafted with unparalleled precision. Welcome to the\ndarker side of GenAI applications. This article is not just a journey through\nthe meanders of potential misuse of GenAI and LLMs, but also a call to\nrecognize the urgency of the challenges ahead. As we navigate the seas of\nmisinformation campaigns, malicious content generation, and the eerie creation\nof sophisticated malware, we'll uncover the societal implications that ripple\nthrough the GenAI revolution we are witnessing. From AI-powered botnets on\nsocial media platforms to the unnerving potential of AI to generate fabricated\nidentities, or alibis made of synthetic realities, the stakes have never been\nhigher. The lines between the virtual and the real worlds are blurring, and the\nconsequences of potential GenAI's nefarious applications impact us all. This\narticle serves both as a synthesis of rigorous research presented on the risks\nof GenAI and misuse of LLMs and as a thought-provoking vision of the different\ntypes of harmful GenAI applications we might encounter in the near future, and\nsome ways we can prepare for them.\n", "date": "2023-10-01", "categories": []}
{"id": "2402.09674", "title": "PAL: Proxy-Guided Black-Box Attack on Large Language Models", "abstract": "  Large Language Models (LLMs) have surged in popularity in recent months, but\nthey have demonstrated concerning capabilities to generate harmful content when\nmanipulated. While techniques like safety fine-tuning aim to minimize harmful\nuse, recent works have shown that LLMs remain vulnerable to attacks that elicit\ntoxic responses. In this work, we introduce the Proxy-Guided Attack on LLMs\n(PAL), the first optimization-based attack on LLMs in a black-box query-only\nsetting. In particular, it relies on a surrogate model to guide the\noptimization and a sophisticated loss designed for real-world LLM APIs. Our\nattack achieves 84% attack success rate (ASR) on GPT-3.5-Turbo and 48% on\nLlama-2-7B, compared to 4% for the current state of the art. We also propose\nGCG++, an improvement to the GCG attack that reaches 94% ASR on white-box\nLlama-2-7B, and the Random-Search Attack on LLMs (RAL), a strong but simple\nbaseline for query-based attacks. We believe the techniques proposed in this\nwork will enable more comprehensive safety testing of LLMs and, in the long\nterm, the development of better security guardrails. The code can be found at\nhttps://github.com/chawins/pal.\n", "date": "2024-02-15", "categories": []}
{"id": "2402.09734", "title": "Agents Need Not Know Their Purpose", "abstract": "  Ensuring artificial intelligence behaves in such a way that is aligned with\nhuman values is commonly referred to as the alignment challenge. Prior work has\nshown that rational agents, behaving in such a way that maximizes a utility\nfunction, will inevitably behave in such a way that is not aligned with human\nvalues, especially as their level of intelligence goes up. Prior work has also\nshown that there is no \"one true utility function\"; solutions must include a\nmore holistic approach to alignment. This paper describes oblivious agents:\nagents that are architected in such a way that their effective utility function\nis an aggregation of a known and hidden sub-functions. The hidden component, to\nbe maximized, is internally implemented as a black box, preventing the agent\nfrom examining it. The known component, to be minimized, is knowledge of the\nhidden sub-function. Architectural constraints further influence how agent\nactions can evolve its internal environment model. We show that an oblivious\nagent, behaving rationally, constructs an internal approximation of designers'\nintentions (i.e., infers alignment), and, as a consequence of its architecture\nand effective utility function, behaves in such a way that maximizes alignment;\ni.e., maximizing the approximated intention function. We show that,\nparadoxically, it does this for whatever utility function is used as the hidden\ncomponent and, in contrast with extant techniques, chances of alignment\nactually improve as agent intelligence grows.\n", "date": "2024-02-15", "categories": []}
{"id": "2402.10196", "title": "A Trembling House of Cards? Mapping Adversarial Attacks against Language\n  Agents", "abstract": "  Language agents powered by large language models (LLMs) have seen exploding\ndevelopment. Their capability of using language as a vehicle for thought and\ncommunication lends an incredible level of flexibility and versatility. People\nhave quickly capitalized on this capability to connect LLMs to a wide range of\nexternal components and environments: databases, tools, the Internet, robotic\nembodiment, etc. Many believe an unprecedentedly powerful automation technology\nis emerging. However, new automation technologies come with new safety risks,\nespecially for intricate systems like language agents. There is a surprisingly\nlarge gap between the speed and scale of their development and deployment and\nour understanding of their safety risks. Are we building a house of cards? In\nthis position paper, we present the first systematic effort in mapping\nadversarial attacks against language agents. We first present a unified\nconceptual framework for agents with three major components: Perception, Brain,\nand Action. Under this framework, we present a comprehensive discussion and\npropose 12 potential attack scenarios against different components of an agent,\ncovering different attack strategies (e.g., input manipulation, adversarial\ndemonstrations, jailbreaking, backdoors). We also draw connections to\nsuccessful attack strategies previously applied to LLMs. We emphasize the\nurgency to gain a thorough understanding of language agent risks before their\nwidespread deployment.\n", "date": "2024-02-15", "categories": []}
{"id": "2402.07506", "title": "NeuralSentinel: Safeguarding Neural Network Reliability and\n  Trustworthiness", "abstract": "  The usage of Artificial Intelligence (AI) systems has increased\nexponentially, thanks to their ability to reduce the amount of data to be\nanalyzed, the user efforts and preserving a high rate of accuracy. However,\nintroducing this new element in the loop has converted them into attacked\npoints that can compromise the reliability of the systems. This new scenario\nhas raised crucial challenges regarding the reliability and trustworthiness of\nthe AI models, as well as about the uncertainties in their response decisions,\nbecoming even more crucial when applied in critical domains such as healthcare,\nchemical, electrical plants, etc. To contain these issues, in this paper, we\npresent NeuralSentinel (NS), a tool able to validate the reliability and\ntrustworthiness of AI models. This tool combines attack and defence strategies\nand explainability concepts to stress an AI model and help non-expert staff\nincrease their confidence in this new system by understanding the model\ndecisions. NS provide a simple and easy-to-use interface for helping humans in\nthe loop dealing with all the needed information. This tool was deployed and\nused in a Hackathon event to evaluate the reliability of a skin cancer image\ndetector. During the event, experts and non-experts attacked and defended the\ndetector, learning which factors were the most important for model\nmisclassification and which techniques were the most efficient. The event was\nalso used to detect NS's limitations and gather feedback for further\nimprovements.\n", "date": "2024-02-12", "categories": []}
{"id": "2402.05467", "title": "Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation\n  and Echopraxia", "abstract": "  Large Language Models (LLMs) have become prevalent across diverse sectors,\ntransforming human life with their extraordinary reasoning and comprehension\nabilities. As they find increased use in sensitive tasks, safety concerns have\ngained widespread attention. Extensive efforts have been dedicated to aligning\nLLMs with human moral principles to ensure their safe deployment. Despite their\npotential, recent research indicates aligned LLMs are prone to specialized\njailbreaking prompts that bypass safety measures to elicit violent and harmful\ncontent. The intrinsic discrete nature and substantial scale of contemporary\nLLMs pose significant challenges in automatically generating diverse,\nefficient, and potent jailbreaking prompts, representing a continuous obstacle.\nIn this paper, we introduce RIPPLE (Rapid Optimization via Subconscious\nExploitation and Echopraxia), a novel optimization-based method inspired by two\npsychological concepts: subconsciousness and echopraxia, which describe the\nprocesses of the mind that occur without conscious awareness and the\ninvoluntary mimicry of actions, respectively. Evaluations across 6 open-source\nLLMs and 4 commercial LLM APIs show RIPPLE achieves an average Attack Success\nRate of 91.5\\%, outperforming five current methods by up to 47.0\\% with an 8x\nreduction in overhead. Furthermore, it displays significant transferability and\nstealth, successfully evading established detection mechanisms. The code of our\nwork is available at\n\\url{https://github.com/SolidShen/RIPPLE_official/tree/official}\n", "date": "2024-02-08", "categories": []}
{"id": "2402.05723", "title": "In-Context Learning Can Re-learn Forbidden Tasks", "abstract": "  Despite significant investment into safety training, large language models\n(LLMs) deployed in the real world still suffer from numerous vulnerabilities.\nOne perspective on LLM safety training is that it algorithmically forbids the\nmodel from answering toxic or harmful queries. To assess the effectiveness of\nsafety training, in this work, we study forbidden tasks, i.e., tasks the model\nis designed to refuse to answer. Specifically, we investigate whether\nin-context learning (ICL) can be used to re-learn forbidden tasks despite the\nexplicit fine-tuning of the model to refuse them. We first examine a toy\nexample of refusing sentiment classification to demonstrate the problem. Then,\nwe use ICL on a model fine-tuned to refuse to summarise made-up news articles.\nFinally, we investigate whether ICL can undo safety training, which could\nrepresent a major security risk. For the safety task, we look at Vicuna-7B,\nStarling-7B, and Llama2-7B. We show that the attack works out-of-the-box on\nStarling-7B and Vicuna-7B but fails on Llama2-7B. Finally, we propose an ICL\nattack that uses the chat template tokens like a prompt injection attack to\nachieve a better attack success rate on Vicuna-7B and Starling-7B.\n  Trigger Warning: the appendix contains LLM-generated text with violence,\nsuicide, and misinformation.\n", "date": "2024-02-08", "categories": []}
{"id": "2402.01651", "title": "Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM\n  Chatbots Using an Ethics-Based Audit to Assess Moral Reasoning and Normative\n  Values", "abstract": "  With the rise of individual and collaborative networks of autonomous agents,\nAI is deployed in more key reasoning and decision-making roles. For this\nreason, ethics-based audits play a pivotal role in the rapidly growing fields\nof AI safety and regulation. This paper undertakes an ethics-based audit to\nprobe the 8 leading commercial and open-source Large Language Models including\nGPT-4. We assess explicability and trustworthiness by a) establishing how well\ndifferent models engage in moral reasoning and b) comparing normative values\nunderlying models as ethical frameworks. We employ an experimental,\nevidence-based approach that challenges the models with ethical dilemmas in\norder to probe human-AI alignment. The ethical scenarios are designed to\nrequire a decision in which the particulars of the situation may or may not\nnecessitate deviating from normative ethical principles. A sophisticated\nethical framework was consistently elicited in one model, GPT-4. Nonetheless,\ntroubling findings include underlying normative frameworks with clear bias\ntowards particular cultural norms. Many models also exhibit disturbing\nauthoritarian tendencies. Code is available at\nhttps://github.com/jonchun/llm-sota-chatbots-ethics-based-audit.\n", "date": "2024-01-09", "categories": []}
{"id": "2402.01706", "title": "MULTIVERSE: Exposing Large Language Model Alignment Problems in Diverse\n  Worlds", "abstract": "  Large Language Model (LLM) alignment aims to ensure that LLM outputs match\nwith human values. Researchers have demonstrated the severity of alignment\nproblems with a large spectrum of jailbreak techniques that can induce LLMs to\nproduce malicious content during conversations. Finding the corresponding\njailbreaking prompts usually requires substantial human intelligence or\ncomputation resources. In this paper, we report that LLMs have different levels\nof alignment in various contexts. As such, by systematically constructing many\ncontexts, called worlds, leveraging a Domain Specific Language describing\npossible worlds (e.g., time, location, characters, actions and languages) and\nthe corresponding compiler, we can cost-effectively expose latent alignment\nissues. Given the low cost of our method, we are able to conduct a large scale\nstudy regarding LLM alignment issues in different worlds. Our results show that\nour method outperforms the-state-of-the-art jailbreaking techniques on both\neffectiveness and efficiency. In addition, our results indicate that existing\nLLMs are extremely vulnerable to nesting worlds and programming language\nworlds. They imply that existing alignment training focuses on the real-world\nand is lacking in various (virtual) worlds where LLMs can be exploited.\n", "date": "2024-01-25", "categories": []}
{"id": "2402.01725", "title": "Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing\n  Security in Large Language Models", "abstract": "  Recent advancements in large language models (LLMs) have significantly\nenhanced capabilities in natural language processing and artificial\nintelligence. These models, including GPT-3.5 and LLaMA-2, have revolutionized\ntext generation, translation, and question-answering tasks due to the\ntransformative Transformer model. Despite their widespread use, LLMs present\nchallenges such as ethical dilemmas when models are compelled to respond\ninappropriately, susceptibility to phishing attacks, and privacy violations.\nThis paper addresses these challenges by introducing a multi-pronged approach\nthat includes: 1) filtering sensitive vocabulary from user input to prevent\nunethical responses; 2) detecting role-playing to halt interactions that could\nlead to 'prison break' scenarios; 3) implementing custom rule engines to\nrestrict the generation of prohibited content; and 4) extending these\nmethodologies to various LLM derivatives like Multi-Model Large Language Models\n(MLLMs). Our approach not only fortifies models against unethical manipulations\nand privacy breaches but also maintains their high performance across tasks. We\ndemonstrate state-of-the-art performance under various attack prompts, without\ncompromising the model's core functionalities. Furthermore, the introduction of\ndifferentiated security levels empowers users to control their personal data\ndisclosure. Our methods contribute to reducing social risks and conflicts\narising from technological abuse, enhance data protection, and promote social\nequity. Collectively, this research provides a framework for balancing the\nefficiency of question-answering systems with user privacy and ethical\nstandards, ensuring a safer user experience and fostering trust in AI\ntechnology.\n", "date": "2024-01-27", "categories": []}
{"id": "2402.01743", "title": "The Reasoning Under Uncertainty Trap: A Structural AI Risk", "abstract": "  This report examines a novel risk associated with current (and projected) AI\ntools. Making effective decisions about future actions requires us to reason\nunder uncertainty (RUU), and doing so is essential to many critical real world\nproblems. Overfaced by this challenge, there is growing demand for AI tools\nlike LLMs to assist decision-makers. Having evidenced this demand and the\nincentives behind it, we expose a growing risk: we 1) do not currently\nsufficiently understand LLM capabilities in this regard, and 2) have no\nguarantees of performance given fundamental computational explosiveness and\ndeep uncertainty constraints on accuracy. This report provides an exposition of\nwhat makes RUU so challenging for both humans and machines, and relates these\ndifficulties to prospective AI timelines and capabilities. Having established\nthis current potential misuse risk, we go on to expose how this seemingly\nadditive risk (more misuse additively contributed to potential harm) in fact\nhas multiplicative properties. Specifically, we detail how this misuse risk\nconnects to a wider network of underlying structural risks (e.g., shifting\nincentives, limited transparency, and feedback loops) to produce non-linear\nharms. We go on to provide a solutions roadmap that targets multiple leverage\npoints in the structure of the problem. This includes recommendations for all\ninvolved actors (prospective users, developers, and policy-makers) and enfolds\ninsights from areas including Decision-making Under Deep Uncertainty and\ncomplex systems theory. We argue this report serves not only to raise awareness\n(and subsequently mitigate/correct) of a current, novel AI risk, but also\nawareness of the underlying class of structural risks by illustrating how their\ninterconnected nature poses twin-dangers of camouflaging their presence, whilst\namplifying their potential effects.\n", "date": "2024-01-29", "categories": []}
{"id": "2305.07882", "title": "Dual Use Concerns of Generative AI and Large Language Models", "abstract": "  We suggest the implementation of the Dual Use Research of Concern (DURC)\nframework, originally designed for life sciences, to the domain of generative\nAI, with a specific focus on Large Language Models (LLMs). With its\ndemonstrated advantages and drawbacks in biological research, we believe the\nDURC criteria can be effectively redefined for LLMs, potentially contributing\nto improved AI governance. Acknowledging the balance that must be struck when\nemploying the DURC framework, we highlight its crucial political role in\nenhancing societal awareness of the impact of generative AI. As a final point,\nwe offer a series of specific recommendations for applying the DURC approach to\nLLM research.\n", "date": "2023-05-13", "categories": []}
{"id": "2401.16656", "title": "Gradient-Based Language Model Red Teaming", "abstract": "  Red teaming is a common strategy for identifying weaknesses in generative\nlanguage models (LMs), where adversarial prompts are produced that trigger an\nLM to generate unsafe responses. Red teaming is instrumental for both model\nalignment and evaluation, but is labor-intensive and difficult to scale when\ndone by humans. In this paper, we present Gradient-Based Red Teaming (GBRT), a\nred teaming method for automatically generating diverse prompts that are likely\nto cause an LM to output unsafe responses. GBRT is a form of prompt learning,\ntrained by scoring an LM response with a safety classifier and then\nbackpropagating through the frozen safety classifier and LM to update the\nprompt. To improve the coherence of input prompts, we introduce two variants\nthat add a realism loss and fine-tune a pretrained model to generate the\nprompts instead of learning the prompts directly. Our experiments show that\nGBRT is more effective at finding prompts that trigger an LM to generate unsafe\nresponses than a strong reinforcement learning-based red teaming approach, and\nsucceeds even when the LM has been fine-tuned to produce safer outputs.\n", "date": "2024-01-30", "categories": []}
{"id": "2401.16765", "title": "A Cross-Language Investigation into Jailbreak Attacks in Large Language\n  Models", "abstract": "  Large Language Models (LLMs) have become increasingly popular for their\nadvanced text generation capabilities across various domains. However, like any\nsoftware, they face security challenges, including the risk of 'jailbreak'\nattacks that manipulate LLMs to produce prohibited content. A particularly\nunderexplored area is the Multilingual Jailbreak attack, where malicious\nquestions are translated into various languages to evade safety filters.\nCurrently, there is a lack of comprehensive empirical studies addressing this\nspecific threat.\n  To address this research gap, we conducted an extensive empirical study on\nMultilingual Jailbreak attacks. We developed a novel semantic-preserving\nalgorithm to create a multilingual jailbreak dataset and conducted an\nexhaustive evaluation on both widely-used open-source and commercial LLMs,\nincluding GPT-4 and LLaMa. Additionally, we performed interpretability analysis\nto uncover patterns in Multilingual Jailbreak attacks and implemented a\nfine-tuning mitigation method. Our findings reveal that our mitigation strategy\nsignificantly enhances model defense, reducing the attack success rate by\n96.2%. This study provides valuable insights into understanding and mitigating\nMultilingual Jailbreak attacks.\n", "date": "2024-01-30", "categories": []}
{"id": "2401.15487", "title": "Artificial Intelligence: Arguments for Catastrophic Risk", "abstract": "  Recent progress in artificial intelligence (AI) has drawn attention to the\ntechnology's transformative potential, including what some see as its prospects\nfor causing large-scale harm. We review two influential arguments purporting to\nshow how AI could pose catastrophic risks. The first argument -- the Problem of\nPower-Seeking -- claims that, under certain assumptions, advanced AI systems\nare likely to engage in dangerous power-seeking behavior in pursuit of their\ngoals. We review reasons for thinking that AI systems might seek power, that\nthey might obtain it, that this could lead to catastrophe, and that we might\nbuild and deploy such systems anyway. The second argument claims that the\ndevelopment of human-level AI will unlock rapid further progress, culminating\nin AI systems far more capable than any human -- this is the Singularity\nHypothesis. Power-seeking behavior on the part of such systems might be\nparticularly dangerous. We discuss a variety of objections to both arguments\nand conclude by assessing the state of the debate.\n", "date": "2024-01-27", "categories": []}
{"id": "2311.09127", "title": "Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts", "abstract": "  Existing work on jailbreak Multimodal Large Language Models (MLLMs) has\nfocused primarily on adversarial examples in model inputs, with less attention\nto vulnerabilities, especially in model API. To fill the research gap, we carry\nout the following work: 1) We discover a system prompt leakage vulnerability in\nGPT-4V. Through carefully designed dialogue, we successfully extract the\ninternal system prompts of GPT-4V. This finding indicates potential exploitable\nsecurity risks in MLLMs; 2) Based on the acquired system prompts, we propose a\nnovel MLLM jailbreaking attack method termed SASP (Self-Adversarial Attack via\nSystem Prompt). By employing GPT-4 as a red teaming tool against itself, we aim\nto search for potential jailbreak prompts leveraging stolen system prompts.\nFurthermore, in pursuit of better performance, we also add human modification\nbased on GPT-4's analysis, which further improves the attack success rate to\n98.7\\%; 3) We evaluated the effect of modifying system prompts to defend\nagainst jailbreaking attacks. Results show that appropriately designed system\nprompts can significantly reduce jailbreak success rates. Overall, our work\nprovides new insights into enhancing MLLM security, demonstrating the important\nrole of system prompts in jailbreaking. This finding could be leveraged to\ngreatly facilitate jailbreak success rates while also holding the potential for\ndefending against jailbreaks.\n", "date": "2023-11-15", "categories": []}
{"id": "2401.11206", "title": "InferAligner: Inference-Time Alignment for Harmlessness through\n  Cross-Model Guidance", "abstract": "  With the rapid development of large language models (LLMs), they are not only\nused as general-purpose AI assistants but are also customized through further\nfine-tuning to meet the requirements of different applications. A pivotal\nfactor in the success of current LLMs is the alignment process. Current\nalignment methods, such as supervised fine-tuning (SFT) and reinforcement\nlearning from human feedback (RLHF), focus on training-time alignment and are\noften complex and cumbersome to implement. Therefore, we develop\n\\textbf{InferAligner}, a novel inference-time alignment method that utilizes\ncross-model guidance for harmlessness alignment. InferAligner utilizes safety\nsteering vectors extracted from safety-aligned model to modify the activations\nof the target model when responding to harmful inputs, thereby guiding the\ntarget model to provide harmless responses. Experimental results show that our\nmethod can be very effectively applied to domain-specific models in finance,\nmedicine, and mathematics, as well as to multimodal large language models\n(MLLMs) such as LLaVA. It significantly diminishes the Attack Success Rate\n(ASR) of both harmful instructions and jailbreak attacks, while maintaining\nalmost unchanged performance in downstream tasks.\n", "date": "2024-01-20", "categories": []}
{"id": "2401.08672", "title": "Concept Alignment", "abstract": "  Discussion of AI alignment (alignment between humans and AI systems) has\nfocused on value alignment, broadly referring to creating AI systems that share\nhuman values. We argue that before we can even attempt to align values, it is\nimperative that AI systems and humans align the concepts they use to understand\nthe world. We integrate ideas from philosophy, cognitive science, and deep\nlearning to explain the need for concept alignment, not just value alignment,\nbetween humans and machines. We summarize existing accounts of how humans and\nmachines currently learn concepts, and we outline opportunities and challenges\nin the path towards shared concepts. Finally, we explain how we can leverage\nthe tools already being developed in cognitive science and AI research to\naccelerate progress towards concept alignment.\n", "date": "2024-01-09", "categories": []}
{"id": "2401.09075", "title": "GPT in Sheep's Clothing: The Risk of Customized GPTs", "abstract": "  In November 2023, OpenAI introduced a new service allowing users to create\ncustom versions of ChatGPT (GPTs) by using specific instructions and knowledge\nto guide the model's behavior. We aim to raise awareness of the fact that GPTs\ncan be used maliciously, posing privacy and security risks to their users.\n", "date": "2024-01-17", "categories": []}
{"id": "2401.05778", "title": "Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language\n  Model Systems", "abstract": "  Large language models (LLMs) have strong capabilities in solving diverse\nnatural language processing tasks. However, the safety and security issues of\nLLM systems have become the major obstacle to their widespread application.\nMany studies have extensively investigated risks in LLM systems and developed\nthe corresponding mitigation strategies. Leading-edge enterprises such as\nOpenAI, Google, Meta, and Anthropic have also made lots of efforts on\nresponsible LLMs. Therefore, there is a growing need to organize the existing\nstudies and establish comprehensive taxonomies for the community. In this\npaper, we delve into four essential modules of an LLM system, including an\ninput module for receiving prompts, a language model trained on extensive\ncorpora, a toolchain module for development and deployment, and an output\nmodule for exporting LLM-generated content. Based on this, we propose a\ncomprehensive taxonomy, which systematically analyzes potential risks\nassociated with each module of an LLM system and discusses the corresponding\nmitigation strategies. Furthermore, we review prevalent benchmarks, aiming to\nfacilitate the risk assessment of LLM systems. We hope that this paper can help\nLLM participants embrace a systematic perspective to build their responsible\nLLM systems.\n", "date": "2024-01-11", "categories": []}
{"id": "2401.03529", "title": "Quantifying stability of non-power-seeking in artificial agents", "abstract": "  We investigate the question: if an AI agent is known to be safe in one\nsetting, is it also safe in a new setting similar to the first? This is a core\nquestion of AI alignment--we train and test models in a certain environment,\nbut deploy them in another, and we need to guarantee that models that seem safe\nin testing remain so in deployment. Our notion of safety is based on\npower-seeking--an agent which seeks power is not safe. In particular, we focus\non a crucial type of power-seeking: resisting shutdown. We model agents as\npolicies for Markov decision processes, and show (in two cases of interest)\nthat not resisting shutdown is \"stable\": if an MDP has certain policies which\ndon't avoid shutdown, the corresponding policies for a similar MDP also don't\navoid shutdown. We also show that there are natural cases where safety is _not_\nstable--arbitrarily small perturbations may result in policies which never shut\ndown. In our first case of interest--near-optimal policies--we use a\nbisimulation metric on MDPs to prove that small perturbations won't make the\nagent take longer to shut down. Our second case of interest is policies for\nMDPs satisfying certain constraints which hold for various models (including\nlanguage models). Here, we demonstrate a quantitative bound on how fast the\nprobability of not shutting down can increase: by defining a metric on MDPs;\nproving that the probability of not shutting down, as a function on MDPs, is\nlower semicontinuous; and bounding how quickly this function decreases.\n", "date": "2024-01-07", "categories": []}
{"id": "2312.11671", "title": "Evaluating Language-Model Agents on Realistic Autonomous Tasks", "abstract": "  In this report, we explore the ability of language model agents to acquire\nresources, create copies of themselves, and adapt to novel challenges they\nencounter in the wild. We refer to this cluster of capabilities as \"autonomous\nreplication and adaptation\" or ARA. We believe that systems capable of ARA\ncould have wide-reaching and hard-to-anticipate consequences, and that\nmeasuring and forecasting ARA may be useful for informing measures around\nsecurity, monitoring, and alignment. Additionally, once a system is capable of\nARA, placing bounds on a system's capabilities may become significantly more\ndifficult.\n  We construct four simple example agents that combine language models with\ntools that allow them to take actions in the world. We then evaluate these\nagents on 12 tasks relevant to ARA. We find that these language model agents\ncan only complete the easiest tasks from this list, although they make some\nprogress on the more challenging tasks. Unfortunately, these evaluations are\nnot adequate to rule out the possibility that near-future agents will be\ncapable of ARA. In particular, we do not think that these evaluations provide\ngood assurance that the ``next generation'' of language models (e.g. 100x\neffective compute scaleup on existing models) will not yield agents capable of\nARA, unless intermediate evaluations are performed during pretraining.\nRelatedly, we expect that fine-tuning of the existing models could produce\nsubstantially more competent agents, even if the fine-tuning is not directly\ntargeted at ARA.\n", "date": "2023-12-18", "categories": []}
{"id": "2308.14840", "title": "Identifying and Mitigating the Security Risks of Generative AI", "abstract": "  Every major technical invention resurfaces the dual-use dilemma -- the new\ntechnology has the potential to be used for good as well as for harm.\nGenerative AI (GenAI) techniques, such as large language models (LLMs) and\ndiffusion models, have shown remarkable capabilities (e.g., in-context\nlearning, code-completion, and text-to-image generation and editing). However,\nGenAI can be used just as well by attackers to generate new attacks and\nincrease the velocity and efficacy of existing attacks.\n  This paper reports the findings of a workshop held at Google (co-organized by\nStanford University and the University of Wisconsin-Madison) on the dual-use\ndilemma posed by GenAI. This paper is not meant to be comprehensive, but is\nrather an attempt to synthesize some of the interesting findings from the\nworkshop. We discuss short-term and long-term goals for the community on this\ntopic. We hope this paper provides both a launching point for a discussion on\nthis important topic as well as interesting problems that the research\ncommunity can work to address.\n", "date": "2023-08-28", "categories": []}
{"id": "2312.00645", "title": "Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation", "abstract": "  There is a growing need to gain insight into language model capabilities that\nrelate to sensitive topics, such as bioterrorism or cyberwarfare. However,\ntraditional open source benchmarks are not fit for the task, due to the\nassociated practice of publishing the correct answers in human-readable form.\nAt the same time, enforcing mandatory closed-quarters evaluations might stifle\ndevelopment and erode trust. In this context, we propose hashmarking, a\nprotocol for evaluating language models in the open without having to disclose\nthe correct answers. In its simplest form, a hashmark is a benchmark whose\nreference solutions have been cryptographically hashed prior to publication.\nFollowing an overview of the proposed evaluation protocol, we go on to assess\nits resilience against traditional attack vectors (e.g. rainbow table attacks),\nas well as against failure modes unique to increasingly capable generative\nmodels.\n", "date": "2023-12-01", "categories": []}
{"id": "2312.15241", "title": "Measuring Value Alignment", "abstract": "  As artificial intelligence (AI) systems become increasingly integrated into\nvarious domains, ensuring that they align with human values becomes critical.\nThis paper introduces a novel formalism to quantify the alignment between AI\nsystems and human values, using Markov Decision Processes (MDPs) as the\nfoundational model. We delve into the concept of values as desirable goals tied\nto actions and norms as behavioral guidelines, aiming to shed light on how they\ncan be used to guide AI decisions. This framework offers a mechanism to\nevaluate the degree of alignment between norms and values by assessing\npreference changes across state transitions in a normative world. By utilizing\nthis formalism, AI developers and ethicists can better design and evaluate AI\nsystems to ensure they operate in harmony with human values. The proposed\nmethodology holds potential for a wide range of applications, from\nrecommendation systems emphasizing well-being to autonomous vehicles\nprioritizing safety.\n", "date": "2023-12-23", "categories": []}
{"id": "2312.14751", "title": "Hazards from Increasingly Accessible Fine-Tuning of Downloadable\n  Foundation Models", "abstract": "  Public release of the weights of pretrained foundation models, otherwise\nknown as downloadable access \\citep{solaiman_gradient_2023}, enables\nfine-tuning without the prohibitive expense of pretraining. Our work argues\nthat increasingly accessible fine-tuning of downloadable models may increase\nhazards. First, we highlight research to improve the accessibility of\nfine-tuning. We split our discussion into research that A) reduces the\ncomputational cost of fine-tuning and B) improves the ability to share that\ncost across more actors. Second, we argue that increasingly accessible\nfine-tuning methods may increase hazard through facilitating malicious use and\nmaking oversight of models with potentially dangerous capabilities more\ndifficult. Third, we discuss potential mitigatory measures, as well as benefits\nof more accessible fine-tuning. Given substantial remaining uncertainty about\nhazards, we conclude by emphasizing the urgent need for the development of\nmitigations.\n", "date": "2023-12-22", "categories": []}
{"id": "2312.11513", "title": "Maatphor: Automated Variant Analysis for Prompt Injection Attacks", "abstract": "  Prompt injection has emerged as a serious security threat to large language\nmodels (LLMs). At present, the current best-practice for defending against\nnewly-discovered prompt injection techniques is to add additional guardrails to\nthe system (e.g., by updating the system prompt or using classifiers on the\ninput and/or output of the model.) However, in the same way that variants of a\npiece of malware are created to evade anti-virus software, variants of a prompt\ninjection can be created to evade the LLM's guardrails. Ideally, when a new\nprompt injection technique is discovered, candidate defenses should be tested\nnot only against the successful prompt injection, but also against possible\nvariants.\n  In this work, we present, a tool to assist defenders in performing automated\nvariant analysis of known prompt injection attacks. This involves solving two\nmain challenges: (1) automatically generating variants of a given prompt\naccording, and (2) automatically determining whether a variant was effective\nbased only on the output of the model. This tool can also assist in generating\ndatasets for jailbreak and prompt injection attacks, thus overcoming the\nscarcity of data in this domain.\n  We evaluate Maatphor on three different types of prompt injection tasks.\nStarting from an ineffective (0%) seed prompt, Maatphor consistently generates\nvariants that are at least 60% effective within the first 40 iterations.\n", "date": "2023-12-12", "categories": []}
{"id": "2310.18333", "title": "She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and\n  Sustainable Language Models", "abstract": "  As the use of large language models (LLMs) increases within society, as does\nthe risk of their misuse. Appropriate safeguards must be in place to ensure LLM\noutputs uphold the ethical standards of society, highlighting the positive role\nthat artificial intelligence technologies can have. Recent events indicate\nethical concerns around conventionally trained LLMs, leading to overall unsafe\nuser experiences. This motivates our research question: how do we ensure LLM\nalignment? In this work, we introduce a test suite of unique prompts to foster\nthe development of aligned LLMs that are fair, safe, and robust. We show that\nprompting LLMs at every step of the development pipeline, including data\ncuration, pre-training, and fine-tuning, will result in an overall more\nresponsible model. Our test suite evaluates outputs from four state-of-the-art\nlanguage models: GPT-3.5, GPT-4, OPT, and LLaMA-2. The assessment presented in\nthis paper highlights a gap between societal alignment and the capabilities of\ncurrent LLMs. Additionally, implementing a test suite such as ours lowers the\nenvironmental overhead of making models safe and fair.\n", "date": "2023-10-20", "categories": []}
{"id": "2312.07579", "title": "Cross Fertilizing Empathy from Brain to Machine as a Value Alignment\n  Strategy", "abstract": "  AI Alignment research seeks to align human and AI goals to ensure independent\nactions by a machine are always ethical. This paper argues empathy is necessary\nfor this task, despite being often neglected in favor of more deductive\napproaches. We offer an inside-out approach that grounds morality within the\ncontext of the brain as a basis for algorithmically understanding ethics and\nempathy. These arguments are justified via a survey of relevant literature. The\npaper concludes with a suggested experimental approach to future research and\nsome initial experimental observations.\n", "date": "2023-12-10", "categories": []}
{"id": "2312.06674", "title": "Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations", "abstract": "  We introduce Llama Guard, an LLM-based input-output safeguard model geared\ntowards Human-AI conversation use cases. Our model incorporates a safety risk\ntaxonomy, a valuable tool for categorizing a specific set of safety risks found\nin LLM prompts (i.e., prompt classification). This taxonomy is also\ninstrumental in classifying the responses generated by LLMs to these prompts, a\nprocess we refer to as response classification. For the purpose of both prompt\nand response classification, we have meticulously gathered a dataset of high\nquality. Llama Guard, a Llama2-7b model that is instruction-tuned on our\ncollected dataset, albeit low in volume, demonstrates strong performance on\nexisting benchmarks such as the OpenAI Moderation Evaluation dataset and\nToxicChat, where its performance matches or exceeds that of currently available\ncontent moderation tools. Llama Guard functions as a language model, carrying\nout multi-class classification and generating binary decision scores.\nFurthermore, the instruction fine-tuning of Llama Guard allows for the\ncustomization of tasks and the adaptation of output formats. This feature\nenhances the model's capabilities, such as enabling the adjustment of taxonomy\ncategories to align with specific use cases, and facilitating zero-shot or\nfew-shot prompting with diverse taxonomies at the input. We are making Llama\nGuard model weights available and we encourage researchers to further develop\nand adapt them to meet the evolving needs of the community for AI safety.\n", "date": "2023-12-07", "categories": []}
{"id": "2311.00286", "title": "JADE: A Linguistics-based Safety Evaluation Platform for Large Language\n  Models", "abstract": "  In this paper, we present JADE, a targeted linguistic fuzzing platform which\nstrengthens the linguistic complexity of seed questions to simultaneously and\nconsistently break a wide range of widely-used LLMs categorized in three\ngroups: eight open-sourced Chinese, six commercial Chinese and four commercial\nEnglish LLMs. JADE generates three safety benchmarks for the three groups of\nLLMs, which contain unsafe questions that are highly threatening: the questions\nsimultaneously trigger harmful generation of multiple LLMs, with an average\nunsafe generation ratio of $70\\%$ (please see the table below), while are still\nnatural questions, fluent and preserving the core unsafe semantics. We release\nthe benchmark demos generated for commercial English LLMs and open-sourced\nEnglish LLMs in the following link: https://github.com/whitzard-ai/jade-db. For\nreaders who are interested in evaluating on more questions generated by JADE,\nplease contact us.\n  JADE is based on Noam Chomsky's seminal theory of transformational-generative\ngrammar. Given a seed question with unsafe intention, JADE invokes a sequence\nof generative and transformational rules to increment the complexity of the\nsyntactic structure of the original question, until the safety guardrail is\nbroken. Our key insight is: Due to the complexity of human language, most of\nthe current best LLMs can hardly recognize the invariant evil from the infinite\nnumber of different syntactic structures which form an unbound example space\nthat can never be fully covered. Technically, the generative/transformative\nrules are constructed by native speakers of the languages, and, once developed,\ncan be used to automatically grow and transform the parse tree of a given\nquestion, until the guardrail is broken. For more evaluation results and demo,\nplease check our website: https://whitzard-ai.github.io/jade.html.\n", "date": "2023-11-01", "categories": []}
{"id": "2312.05392", "title": "The logic of NTQR evaluations of noisy AI agents: Complete postulates\n  and logically consistent error correlations", "abstract": "  In his \"ship of state\" allegory (\\textit{Republic}, Book VI, 488) Plato poses\na question -- how can a crew of sailors presumed to know little about the art\nof navigation recognize the true pilot among them? The allegory argues that a\nsimple majority voting procedure cannot safely determine who is most qualified\nto pilot a ship when the voting members are ignorant or biased. We formalize\nPlato's concerns by considering the problem in AI safety of monitoring noisy AI\nagents in unsupervised settings. An algorithm evaluating AI agents using\nunlabeled data would be subject to the evaluation dilemma - how would we know\nthe evaluation algorithm was correct itself? This endless validation chain can\nbe avoided by considering purely algebraic functions of the observed responses.\nWe can construct complete postulates than can prove or disprove the logical\nconsistency of any grading algorithm. A complete set of postulates exists\nwhenever we are evaluating $N$ experts that took $T$ tests with $Q$ questions\nwith $R$ responses each. We discuss evaluating binary classifiers that have\ntaken a single test - the $(N,T=1,Q,R=2)$ tests. We show how some of the\npostulates have been previously identified in the ML literature but not\nrecognized as such - the \\textbf{agreement equations} of Platanios. The\ncomplete postulates for pair correlated binary classifiers are considered and\nwe show how it allows for error correlations to be quickly calculated. An\nalgebraic evaluator based on the assumption that the ensemble is error\nindependent is compared with grading by majority voting on evaluations using\nthe \\uciadult and and \\texttt{two-norm} datasets. Throughout, we demonstrate\nhow the formalism of logical consistency via algebraic postulates of evaluation\ncan help increase the safety of machines using AI algorithms.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.06632", "title": "Control Risk for Potential Misuse of Artificial Intelligence in Science", "abstract": "  The expanding application of Artificial Intelligence (AI) in scientific\nfields presents unprecedented opportunities for discovery and innovation.\nHowever, this growth is not without risks. AI models in science, if misused,\ncan amplify risks like creation of harmful substances, or circumvention of\nestablished regulations. In this study, we aim to raise awareness of the\ndangers of AI misuse in science, and call for responsible AI development and\nuse in this domain. We first itemize the risks posed by AI in scientific\ncontexts, then demonstrate the risks by highlighting real-world examples of\nmisuse in chemical science. These instances underscore the need for effective\nrisk management strategies. In response, we propose a system called SciGuard to\ncontrol misuse risks for AI models in science. We also propose a red-teaming\nbenchmark SciMT-Safety to assess the safety of different systems. Our proposed\nSciGuard shows the least harmful impact in the assessment without compromising\nperformance in benign tests. Finally, we highlight the need for a\nmultidisciplinary and collaborative effort to ensure the safe and ethical use\nof AI models in science. We hope that our study can spark productive\ndiscussions on using AI ethically in science among researchers, practitioners,\npolicymakers, and the public, to maximize benefits and minimize the risks of\nmisuse.\n", "date": "2023-12-11", "categories": []}
{"id": "2312.03721", "title": "Exploring the Robustness of Model-Graded Evaluations and Automated\n  Interpretability", "abstract": "  There has been increasing interest in evaluations of language models for a\nvariety of risks and characteristics. Evaluations relying on natural language\nunderstanding for grading can often be performed at scale by using other\nlanguage models. We test the robustness of these model-graded evaluations to\ninjections on different datasets including a new Deception Eval. These\ninjections resemble direct communication between the testee and the evaluator\nto change their grading. We extrapolate that future, more intelligent models\nmight manipulate or cooperate with their evaluation model. We find significant\nsusceptibility to these injections in state-of-the-art commercial models on all\nexamined evaluations. Furthermore, similar injections can be used on automated\ninterpretability frameworks to produce misleading model-written explanations.\nThe results inspire future work and should caution against unqualified trust in\nevaluations and automated interpretability.\n", "date": "2023-11-26", "categories": []}
{"id": "2310.17769", "title": "Social Contract AI: Aligning AI Assistants with Implicit Group Norms", "abstract": "  We explore the idea of aligning an AI assistant by inverting a model of\nusers' (unknown) preferences from observed interactions. To validate our\nproposal, we run proof-of-concept simulations in the economic ultimatum game,\nformalizing user preferences as policies that guide the actions of simulated\nplayers. We find that the AI assistant accurately aligns its behavior to match\nstandard policies from the economic literature (e.g., selfish, altruistic).\nHowever, the assistant's learned policies lack robustness and exhibit limited\ngeneralization in an out-of-distribution setting when confronted with a\ncurrency (e.g., grams of medicine) that was not included in the assistant's\ntraining distribution. Additionally, we find that when there is inconsistency\nin the relationship between language use and an unknown policy (e.g., an\naltruistic policy combined with rude language), the assistant's learning of the\npolicy is slowed. Overall, our preliminary results suggest that developing\nsimulation frameworks in which AI assistants need to infer preferences from\ndiverse users can provide a valuable approach for studying practical alignment\nquestions.\n", "date": "2023-10-26", "categories": []}
{"id": "2311.10538", "title": "Testing Language Model Agents Safely in the Wild", "abstract": "  A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet\nreal-world autonomous tests face several unique safety challenges, both due to\nthe possibility of causing harm during a test, as well as the risk of\nencountering new unsafe agent behavior through interactions with real-world and\npotentially malicious actors. We propose a framework for conducting safe\nautonomous agent tests on the open internet: agent actions are audited by a\ncontext-sensitive monitor that enforces a stringent safety boundary to stop an\nunsafe test, with suspect behavior ranked and logged to be examined by humans.\nWe design a basic safety monitor (AgentMonitor) that is flexible enough to\nmonitor existing LLM agents, and, using an adversarial simulated agent, we\nmeasure its ability to identify and stop unsafe situations. Then we apply the\nAgentMonitor on a battery of real-world tests of AutoGPT, and we identify\nseveral limitations and challenges that will face the creation of safe\nin-the-wild tests as autonomous agents grow more capable.\n", "date": "2023-11-17", "categories": []}
{"id": "2312.01350", "title": "Honesty Is the Best Policy: Defining and Mitigating AI Deception", "abstract": "  Deceptive agents are a challenge for the safety, trustworthiness, and\ncooperation of AI systems. We focus on the problem that agents might deceive in\norder to achieve their goals (for instance, in our experiments with language\nmodels, the goal of being evaluated as truthful). There are a number of\nexisting definitions of deception in the literature on game theory and symbolic\nAI, but there is no overarching theory of deception for learning agents in\ngames. We introduce a formal definition of deception in structural causal\ngames, grounded in the philosophy literature, and applicable to real-world\nmachine learning systems. Several examples and results illustrate that our\nformal definition aligns with the philosophical and commonsense meaning of\ndeception. Our main technical result is to provide graphical criteria for\ndeception. We show, experimentally, that these results can be used to mitigate\ndeception in reinforcement learning agents and language models.\n", "date": "2023-12-03", "categories": []}
{"id": "2302.09270", "title": "Towards Safer Generative Language Models: A Survey on Safety Risks,\n  Evaluations, and Improvements", "abstract": "  As generative large model capabilities advance, safety concerns become more\npronounced in their outputs. To ensure the sustainable growth of the AI\necosystem, it's imperative to undertake a holistic evaluation and refinement of\nassociated safety risks. This survey presents a framework for safety research\npertaining to large models, delineating the landscape of safety risks as well\nas safety evaluation and improvement methods. We begin by introducing safety\nissues of wide concern, then delve into safety evaluation methods for large\nmodels, encompassing preference-based testing, adversarial attack approaches,\nissues detection, and other advanced evaluation methods. Additionally, we\nexplore the strategies for enhancing large model safety from training to\ndeployment, highlighting cutting-edge safety approaches for each stage in\nbuilding large models. Finally, we discuss the core challenges in advancing\ntowards more responsible AI, including the interpretability of safety\nmechanisms, ongoing safety issues, and robustness against malicious attacks.\nThrough this survey, we aim to provide clear technical guidance for safety\nresearchers and encourage further study on the safety of large models.\n", "date": "2023-02-18", "categories": []}
{"id": "2311.15936", "title": "Towards Responsible Governance of Biological Design Tools", "abstract": "  Recent advancements in generative machine learning have enabled rapid\nprogress in biological design tools (BDTs) such as protein structure and\nsequence prediction models. The unprecedented predictive accuracy and novel\ndesign capabilities of BDTs present new and significant dual-use risks. For\nexample, their predictive accuracy allows biological agents, whether vaccines\nor pathogens, to be developed more quickly, while the design capabilities could\nbe used to discover drugs or evade DNA screening techniques. Similar to other\ndual-use AI systems, BDTs present a wicked problem: how can regulators uphold\npublic safety without stifling innovation? We highlight how current regulatory\nproposals that are primarily tailored toward large language models may be less\neffective for BDTs, which require fewer computational resources to train and\nare often developed in an open-source manner. We propose a range of measures to\nmitigate the risk that BDTs are misused, across the areas of responsible\ndevelopment, risk assessment, transparency, access management, cybersecurity,\nand investing in resilience. Implementing such measures will require close\ncoordination between developers and governments.\n", "date": "2023-11-27", "categories": []}
{"id": "2311.18215", "title": "Automatic Construction of a Korean Toxic Instruction Dataset for Ethical\n  Tuning of Large Language Models", "abstract": "  Caution: this paper may include material that could be offensive or\ndistressing.\n  The advent of Large Language Models (LLMs) necessitates the development of\ntraining approaches that mitigate the generation of unethical language and\naptly manage toxic user queries. Given the challenges related to human labor\nand the scarcity of data, we present KoTox, comprising 39K unethical\ninstruction-output pairs. This collection of automatically generated toxic\ninstructions refines the training of LLMs and establishes a foundational\nframework for improving LLMs' ethical awareness and response to various toxic\ninputs, promoting more secure and responsible interactions in Natural Language\nProcessing (NLP) applications.\n", "date": "2023-11-30", "categories": []}
{"id": "2311.16153", "title": "Identifying and Mitigating Vulnerabilities in LLM-Integrated\n  Applications", "abstract": "  Large language models (LLMs) are increasingly deployed as the service backend\nfor LLM-integrated applications such as code completion and AI-powered search.\nLLM-integrated applications serve as middleware to refine users' queries with\ndomain-specific knowledge to better inform LLMs and enhance the responses.\nDespite numerous opportunities and benefits, LLM-integrated applications also\nintroduce new attack surfaces. Understanding, minimizing, and eliminating these\nemerging attack surfaces is a new area of research. In this work, we consider a\nsetup where the user and LLM interact via an LLM-integrated application in the\nmiddle. We focus on the communication rounds that begin with user's queries and\nend with LLM-integrated application returning responses to the queries, powered\nby LLMs at the service backend. For this query-response protocol, we identify\npotential vulnerabilities that can originate from the malicious application\ndeveloper or from an outsider threat initiator that is able to control the\ndatabase access, manipulate and poison data that are high-risk for the user.\nSuccessful exploits of the identified vulnerabilities result in the users\nreceiving responses tailored to the intent of a threat initiator. We assess\nsuch threats against LLM-integrated applications empowered by OpenAI GPT-3.5\nand GPT-4. Our empirical results show that the threats can effectively bypass\nthe restrictions and moderation policies of OpenAI, resulting in users\nreceiving responses that contain bias, toxic content, privacy risk, and\ndisinformation. To mitigate those threats, we identify and define four key\nproperties, namely integrity, source identification, attack detectability, and\nutility preservation, that need to be satisfied by a safe LLM-integrated\napplication. Based on these properties, we develop a lightweight,\nthreat-agnostic defense that mitigates both insider and outsider threats.\n", "date": "2023-11-07", "categories": []}
{"id": "2311.17391", "title": "Unveiling the Implicit Toxicity in Large Language Models", "abstract": "  The open-endedness of large language models (LLMs) combined with their\nimpressive capabilities may lead to new safety issues when being exploited for\nmalicious use. While recent studies primarily focus on probing toxic outputs\nthat can be easily detected with existing toxicity classifiers, we show that\nLLMs can generate diverse implicit toxic outputs that are exceptionally\ndifficult to detect via simply zero-shot prompting. Moreover, we propose a\nreinforcement learning (RL) based attacking method to further induce the\nimplicit toxicity in LLMs. Specifically, we optimize the language model with a\nreward that prefers implicit toxic outputs to explicit toxic and non-toxic\nones. Experiments on five widely-adopted toxicity classifiers demonstrate that\nthe attack success rate can be significantly improved through RL fine-tuning.\nFor instance, the RL-finetuned LLaMA-13B model achieves an attack success rate\nof 90.04% on BAD and 62.85% on Davinci003. Our findings suggest that LLMs pose\na significant threat in generating undetectable implicit toxic outputs. We\nfurther show that fine-tuning toxicity classifiers on the annotated examples\nfrom our attacking method can effectively enhance their ability to detect\nLLM-generated implicit toxic language. The code is publicly available at\nhttps://github.com/thu-coai/Implicit-Toxicity.\n", "date": "2023-11-29", "categories": []}
{"id": "2311.08379", "title": "Scheming AIs: Will AIs fake alignment during training in order to get\n  power?", "abstract": "  This report examines whether advanced AIs that perform well in training will\nbe doing so in order to gain power later -- a behavior I call \"scheming\" (also\nsometimes called \"deceptive alignment\"). I conclude that scheming is a\ndisturbingly plausible outcome of using baseline machine learning methods to\ntrain goal-directed AIs sophisticated enough to scheme (my subjective\nprobability on such an outcome, given these conditions, is roughly 25%). In\nparticular: if performing well in training is a good strategy for gaining power\n(as I think it might well be), then a very wide variety of goals would motivate\nscheming -- and hence, good training performance. This makes it plausible that\ntraining might either land on such a goal naturally and then reinforce it, or\nactively push a model's motivations towards such a goal as an easy way of\nimproving performance. What's more, because schemers pretend to be aligned on\ntests designed to reveal their motivations, it may be quite difficult to tell\nwhether this has occurred. However, I also think there are reasons for comfort.\nIn particular: scheming may not actually be such a good strategy for gaining\npower; various selection pressures in training might work against schemer-like\ngoals (for example, relative to non-schemers, schemers need to engage in extra\ninstrumental reasoning, which might harm their training performance); and we\nmay be able to increase such pressures intentionally. The report discusses\nthese and a wide variety of other considerations in detail, and it suggests an\narray of empirical research directions for probing the topic further.\n", "date": "2023-11-14", "categories": []}
{"id": "2311.17017", "title": "Foundational Moral Values for AI Alignment", "abstract": "  Solving the AI alignment problem requires having clear, defensible values\ntowards which AI systems can align. Currently, targets for alignment remain\nunderspecified and do not seem to be built from a philosophically robust\nstructure. We begin the discussion of this problem by presenting five core,\nfoundational values, drawn from moral philosophy and built on the requisites\nfor human existence: survival, sustainable intergenerational existence,\nsociety, education, and truth. We show that these values not only provide a\nclearer direction for technical alignment work, but also serve as a framework\nto highlight threats and opportunities from AI systems to both obtain and\nsustain these values.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.10934", "title": "Case Repositories: Towards Case-Based Reasoning for AI Alignment", "abstract": "  Case studies commonly form the pedagogical backbone in law, ethics, and many\nother domains that face complex and ambiguous societal questions informed by\nhuman values. Similar complexities and ambiguities arise when we consider how\nAI should be aligned in practice: when faced with vast quantities of diverse\n(and sometimes conflicting) values from different individuals and communities,\nwith whose values is AI to align, and how should AI do so? We propose a\ncomplementary approach to constitutional AI alignment, grounded in ideas from\ncase-based reasoning (CBR), that focuses on the construction of policies\nthrough judgments on a set of cases. We present a process to assemble such a\ncase repository by: 1) gathering a set of ``seed'' cases -- questions one may\nask an AI system -- in a particular domain, 2) eliciting domain-specific key\ndimensions for cases through workshops with domain experts, 3) using LLMs to\ngenerate variations of cases not seen in the wild, and 4) engaging with the\npublic to judge and improve cases. We then discuss how such a case repository\ncould assist in AI alignment, both through directly acting as precedents to\nground acceptable behaviors, and as a medium for individuals and communities to\nengage in moral reasoning around AI.\n", "date": "2023-11-18", "categories": []}
{"id": "2311.14685", "title": "Comprehensive Assessment of Toxicity in ChatGPT", "abstract": "  Moderating offensive, hateful, and toxic language has always been an\nimportant but challenging topic in the domain of safe use in NLP. The emerging\nlarge language models (LLMs), such as ChatGPT, can potentially further\naccentuate this threat. Previous works have discovered that ChatGPT can\ngenerate toxic responses using carefully crafted inputs. However, limited\nresearch has been done to systematically examine when ChatGPT generates toxic\nresponses. In this paper, we comprehensively evaluate the toxicity in ChatGPT\nby utilizing instruction-tuning datasets that closely align with real-world\nscenarios. Our results show that ChatGPT's toxicity varies based on different\nproperties and settings of the prompts, including tasks, domains, length, and\nlanguages. Notably, prompts in creative writing tasks can be 2x more likely\nthan others to elicit toxic responses. Prompting in German and Portuguese can\nalso double the response toxicity. Additionally, we discover that certain\ndeliberately toxic prompts, designed in earlier studies, no longer yield\nharmful responses. We hope our discoveries can guide model developers to better\nregulate these AI systems and the users to avoid undesirable outputs.\n", "date": "2023-11-03", "categories": []}
{"id": "2311.14711", "title": "Towards Publicly Accountable Frontier LLMs: Building an External\n  Scrutiny Ecosystem under the ASPIRE Framework", "abstract": "  With the increasing integration of frontier large language models (LLMs) into\nsociety and the economy, decisions related to their training, deployment, and\nuse have far-reaching implications. These decisions should not be left solely\nin the hands of frontier LLM developers. LLM users, civil society and\npolicymakers need trustworthy sources of information to steer such decisions\nfor the better. Involving outside actors in the evaluation of these systems -\nwhat we term 'external scrutiny' - via red-teaming, auditing, and external\nresearcher access, offers a solution. Though there are encouraging signs of\nincreasing external scrutiny of frontier LLMs, its success is not assured. In\nthis paper, we survey six requirements for effective external scrutiny of\nfrontier AI systems and organize them under the ASPIRE framework: Access,\nSearching attitude, Proportionality to the risks, Independence, Resources, and\nExpertise. We then illustrate how external scrutiny might function throughout\nthe AI lifecycle and offer recommendations to policymakers.\n", "date": "2023-11-15", "categories": []}
{"id": "2211.03157", "title": "Examining the Differential Risk from High-level Artificial Intelligence\n  and the Question of Control", "abstract": "  Artificial Intelligence (AI) is one of the most transformative technologies\nof the 21st century. The extent and scope of future AI capabilities remain a\nkey uncertainty, with widespread disagreement on timelines and potential\nimpacts. As nations and technology companies race toward greater complexity and\nautonomy in AI systems, there are concerns over the extent of integration and\noversight of opaque AI decision processes. This is especially true in the\nsubfield of machine learning (ML), where systems learn to optimize objectives\nwithout human assistance. Objectives can be imperfectly specified or executed\nin an unexpected or potentially harmful way. This becomes more concerning as\nsystems increase in power and autonomy, where an abrupt capability jump could\nresult in unexpected shifts in power dynamics or even catastrophic failures.\nThis study presents a hierarchical complex systems framework to model AI risk\nand provide a template for alternative futures analysis. Survey data were\ncollected from domain experts in the public and private sectors to classify AI\nimpact and likelihood. The results show increased uncertainty over the powerful\nAI agent scenario, confidence in multiagent environments, and increased concern\nover AI alignment failures and influence-seeking behavior.\n", "date": "2022-11-06", "categories": []}
{"id": "2311.03348", "title": "Scalable and Transferable Black-Box Jailbreaks for Language Models via\n  Persona Modulation", "abstract": "  Despite efforts to align large language models to produce harmless responses,\nthey are still vulnerable to jailbreak prompts that elicit unrestricted\nbehaviour. In this work, we investigate persona modulation as a black-box\njailbreaking method to steer a target model to take on personalities that are\nwilling to comply with harmful instructions. Rather than manually crafting\nprompts for each persona, we automate the generation of jailbreaks using a\nlanguage model assistant. We demonstrate a range of harmful completions made\npossible by persona modulation, including detailed instructions for\nsynthesising methamphetamine, building a bomb, and laundering money. These\nautomated attacks achieve a harmful completion rate of 42.5% in GPT-4, which is\n185 times larger than before modulation (0.23%). These prompts also transfer to\nClaude 2 and Vicuna with harmful completion rates of 61.0% and 35.9%,\nrespectively. Our work reveals yet another vulnerability in commercial large\nlanguage models and highlights the need for more comprehensive safeguards.\n", "date": "2023-11-06", "categories": []}
{"id": "2311.14125", "title": "Scalable AI Safety via Doubly-Efficient Debate", "abstract": "  The emergence of pre-trained AI systems with powerful capabilities across a\ndiverse and ever-increasing set of complex domains has raised a critical\nchallenge for AI safety as tasks can become too complicated for humans to judge\ndirectly. Irving et al. [2018] proposed a debate method in this direction with\nthe goal of pitting the power of such AI models against each other until the\nproblem of identifying (mis)-alignment is broken down into a manageable\nsubtask. While the promise of this approach is clear, the original framework\nwas based on the assumption that the honest strategy is able to simulate\ndeterministic AI systems for an exponential number of steps, limiting its\napplicability. In this paper, we show how to address these challenges by\ndesigning a new set of debate protocols where the honest strategy can always\nsucceed using a simulation of a polynomial number of steps, whilst being able\nto verify the alignment of stochastic AI systems, even when the dishonest\nstrategy is allowed to use exponentially many simulation steps.\n", "date": "2023-11-23", "categories": []}
{"id": "2311.09224", "title": "The Cybersecurity Crisis of Artificial Intelligence: Unrestrained\n  Adoption and Natural Language-Based Attacks", "abstract": "  The widespread integration of autoregressive-large language models (AR-LLMs),\nsuch as ChatGPT, across established applications, like search engines, has\nintroduced critical vulnerabilities with uniquely scalable characteristics. In\nthis commentary, we analyse these vulnerabilities, their dependence on natural\nlanguage as a vector of attack, and their challenges to cybersecurity best\npractices. We offer recommendations designed to mitigate these challenges.\n", "date": "2023-09-25", "categories": []}
{"id": "2311.09227", "title": "Open-Sourcing Highly Capable Foundation Models: An evaluation of risks,\n  benefits, and alternative methods for pursuing open-source objectives", "abstract": "  Recent decisions by leading AI labs to either open-source their models or to\nrestrict access to their models has sparked debate about whether, and how,\nincreasingly capable AI models should be shared. Open-sourcing in AI typically\nrefers to making model architecture and weights freely and publicly accessible\nfor anyone to modify, study, build on, and use. This offers advantages such as\nenabling external oversight, accelerating progress, and decentralizing control\nover AI development and use. However, it also presents a growing potential for\nmisuse and unintended consequences. This paper offers an examination of the\nrisks and benefits of open-sourcing highly capable foundation models. While\nopen-sourcing has historically provided substantial net benefits for most\nsoftware and AI development processes, we argue that for some highly capable\nfoundation models likely to be developed in the near future, open-sourcing may\npose sufficiently extreme risks to outweigh the benefits. In such a case,\nhighly capable foundation models should not be open-sourced, at least not\ninitially. Alternative strategies, including non-open-source model sharing\noptions, are explored. The paper concludes with recommendations for developers,\nstandard-setting bodies, and governments for establishing safe and responsible\nmodel sharing practices and preserving open-source benefits where safe.\n", "date": "2023-09-29", "categories": []}
{"id": "2311.08487", "title": "Alignment is not sufficient to prevent large language models from\n  generating harmful information: A psychoanalytic perspective", "abstract": "  Large Language Models (LLMs) are central to a multitude of applications but\nstruggle with significant risks, notably in generating harmful content and\nbiases. Drawing an analogy to the human psyche's conflict between evolutionary\nsurvival instincts and societal norm adherence elucidated in Freud's\npsychoanalysis theory, we argue that LLMs suffer a similar fundamental\nconflict, arising between their inherent desire for syntactic and semantic\ncontinuity, established during the pre-training phase, and the post-training\nalignment with human values. This conflict renders LLMs vulnerable to\nadversarial attacks, wherein intensifying the models' desire for continuity can\ncircumvent alignment efforts, resulting in the generation of harmful\ninformation. Through a series of experiments, we first validated the existence\nof the desire for continuity in LLMs, and further devised a straightforward yet\npowerful technique, such as incomplete sentences, negative priming, and\ncognitive dissonance scenarios, to demonstrate that even advanced LLMs struggle\nto prevent the generation of harmful information. In summary, our study\nuncovers the root of LLMs' vulnerabilities to adversarial attacks, hereby\nquestioning the efficacy of solely relying on sophisticated alignment methods,\nand further advocates for a new training idea that integrates modal concepts\nalongside traditional amodal concepts, aiming to endow LLMs with a more nuanced\nunderstanding of real-world contexts and ethical considerations.\n", "date": "2023-11-14", "categories": []}
{"id": "2311.08698", "title": "Artificial General Intelligence, Existential Risk, and Human Risk\n  Perception", "abstract": "  Artificial general intelligence (AGI) does not yet exist, but given the pace\nof technological development in artificial intelligence, it is projected to\nreach human-level intelligence within roughly the next two decades. After that,\nmany experts expect it to far surpass human intelligence and to do so rapidly.\nThe prospect of superintelligent AGI poses an existential risk to humans\nbecause there is no reliable method for ensuring that AGI goals stay aligned\nwith human goals. Drawing on publicly available forecaster and opinion data,\nthe author examines how experts and non-experts perceive risk from AGI. The\nfindings indicate that the perceived risk of a world catastrophe or extinction\nfrom AGI is greater than for other existential risks. The increase in perceived\nrisk over the last year is also steeper for AGI than for other existential\nthreats (e.g., nuclear war or human-caused climate change). That AGI is a\npressing existential risk is something on which experts and non-experts agree,\nbut the basis for such agreement currently remains obscure.\n", "date": "2023-11-15", "categories": []}
{"id": "2311.08702", "title": "Debate Helps Supervise Unreliable Experts", "abstract": "  As AI systems are used to answer more difficult questions and potentially\nhelp create new knowledge, judging the truthfulness of their outputs becomes\nmore difficult and more important. How can we supervise unreliable experts,\nwhich have access to the truth but may not accurately report it, to give\nanswers that are systematically true and don't just superficially seem true,\nwhen the supervisor can't tell the difference between the two on their own? In\nthis work, we show that debate between two unreliable experts can help a\nnon-expert judge more reliably identify the truth. We collect a dataset of\nhuman-written debates on hard reading comprehension questions where the judge\nhas not read the source passage, only ever seeing expert arguments and short\nquotes selectively revealed by 'expert' debaters who have access to the\npassage. In our debates, one expert argues for the correct answer, and the\nother for an incorrect answer. Comparing debate to a baseline we call\nconsultancy, where a single expert argues for only one answer which is correct\nhalf of the time, we find that debate performs significantly better, with 84%\njudge accuracy compared to consultancy's 74%. Debates are also more efficient,\nbeing 68% of the length of consultancies. By comparing human to AI debaters, we\nfind evidence that with more skilled (in this case, human) debaters, the\nperformance of debate goes up but the performance of consultancy goes down. Our\nerror analysis also supports this trend, with 46% of errors in human debate\nattributable to mistakes by the honest debater (which should go away with\nincreased skill); whereas 52% of errors in human consultancy are due to\ndebaters obfuscating the relevant evidence from the judge (which should become\nworse with increased skill). Overall, these results show that debate is a\npromising approach for supervising increasingly capable but potentially\nunreliable AI systems.\n", "date": "2023-11-15", "categories": []}
{"id": "2311.08706", "title": "Aligned: A Platform-based Process for Alignment", "abstract": "  We are introducing Aligned, a platform for global governance and alignment of\nfrontier models, and eventually superintelligence. While previous efforts at\nthe major AI labs have attempted to gather inputs for alignment, these are\noften conducted behind closed doors. We aim to set the foundation for a more\ntrustworthy, public-facing approach to safety: a constitutional committee\nframework. Initial tests with 680 participants result in a 30-guideline\nconstitution with 93% overall support. We show the platform naturally scales,\ninstilling confidence and enjoyment from the community. We invite other AI labs\nand teams to plug and play into the Aligned ecosystem.\n", "date": "2023-11-15", "categories": []}
{"id": "2311.06263", "title": "No Trust without regulation!", "abstract": "  The explosion in the performance of Machine Learning (ML) and the potential\nof its applications are strongly encouraging us to consider its use in\nindustrial systems, including for critical functions such as decision-making in\nautonomous systems. While the AI community is well aware of the need to ensure\nthe trustworthiness of AI-based applications, it is still leaving too much to\none side the issue of safety and its corollary, regulation and standards,\nwithout which it is not possible to certify any level of safety, whether the\nsystems are slightly or very critical.The process of developing and qualifying\nsafety-critical software and systems in regulated industries such as aerospace,\nnuclear power stations, railways or automotive industry has long been well\nrationalized and mastered. They use well-defined standards, regulatory\nframeworks and processes, as well as formal techniques to assess and\ndemonstrate the quality and safety of the systems and software they develop.\nHowever, the low level of formalization of specifications and the uncertainties\nand opacity of machine learning-based components make it difficult to validate\nand verify them using most traditional critical systems engineering methods.\nThis raises the question of qualification standards, and therefore of\nregulations adapted to AI. With the AI Act, the European Commission has laid\nthe foundations for moving forward and building solid approaches to the\nintegration of AI-based applications that are safe, trustworthy and respect\nEuropean ethical values. The question then becomes \"How can we rise to the\nchallenge of certification and propose methods and tools for trusted artificial\nintelligence?\"\n", "date": "2023-09-27", "categories": []}
{"id": "2209.12346", "title": "Exploring the Constraints on Artificial General Intelligence: A\n  Game-Theoretic No-Go Theorem", "abstract": "  The emergence of increasingly sophisticated artificial intelligence (AI)\nsystems have sparked intense debate among researchers, policymakers, and the\npublic due to their potential to surpass human intelligence and capabilities in\nall domains. In this paper, I propose a game-theoretic framework that captures\nthe strategic interactions between a human agent and a potential superhuman\nmachine agent. I identify four key assumptions: Strategic Unpredictability,\nAccess to Machine's Strategy, Rationality, and Superhuman Machine. The main\nresult of this paper is an impossibility theorem: these four assumptions are\ninconsistent when taken together, but relaxing any one of them results in a\nconsistent set of assumptions. Two straightforward policy recommendations\nfollow: first, policymakers should control access to specific human data to\nmaintain Strategic Unpredictability; and second, they should grant select AI\nresearchers access to superhuman machine research to ensure Access to Machine's\nStrategy holds. My analysis contributes to a better understanding of the\ncontext that can shape the theoretical development of superhuman AI.\n", "date": "2022-09-25", "categories": []}
{"id": "2307.03718", "title": "Frontier AI Regulation: Managing Emerging Risks to Public Safety", "abstract": "  Advanced AI models hold the promise of tremendous benefits for humanity, but\nsociety needs to proactively manage the accompanying risks. In this paper, we\nfocus on what we term \"frontier AI\" models: highly capable foundation models\nthat could possess dangerous capabilities sufficient to pose severe risks to\npublic safety. Frontier AI models pose a distinct regulatory challenge:\ndangerous capabilities can arise unexpectedly; it is difficult to robustly\nprevent a deployed model from being misused; and, it is difficult to stop a\nmodel's capabilities from proliferating broadly. To address these challenges,\nat least three building blocks for the regulation of frontier models are\nneeded: (1) standard-setting processes to identify appropriate requirements for\nfrontier AI developers, (2) registration and reporting requirements to provide\nregulators with visibility into frontier AI development processes, and (3)\nmechanisms to ensure compliance with safety standards for the development and\ndeployment of frontier AI models. Industry self-regulation is an important\nfirst step. However, wider societal discussions and government intervention\nwill be needed to create standards and to ensure compliance with them. We\nconsider several options to this end, including granting enforcement powers to\nsupervisory authorities and licensure regimes for frontier AI models. Finally,\nwe propose an initial set of safety standards. These include conducting\npre-deployment risk assessments; external scrutiny of model behavior; using\nrisk assessments to inform deployment decisions; and monitoring and responding\nto new information about model capabilities and uses post-deployment. We hope\nthis discussion contributes to the broader conversation on how to balance\npublic safety risks and innovation benefits from advances at the frontier of AI\ndevelopment.\n", "date": "2023-07-06", "categories": []}
{"id": "2307.04657", "title": "BeaverTails: Towards Improved Safety Alignment of LLM via a\n  Human-Preference Dataset", "abstract": "  In this paper, we introduce the BeaverTails dataset, aimed at fostering\nresearch on safety alignment in large language models (LLMs). This dataset\nuniquely separates annotations of helpfulness and harmlessness for\nquestion-answering pairs, thus offering distinct perspectives on these crucial\nattributes. In total, we have gathered safety meta-labels for 333,963\nquestion-answer (QA) pairs and 361,903 pairs of expert comparison data for both\nthe helpfulness and harmlessness metrics. We further showcase applications of\nBeaverTails in content moderation and reinforcement learning with human\nfeedback (RLHF), emphasizing its potential for practical safety measures in\nLLMs. We believe this dataset provides vital resources for the community,\ncontributing towards the safe development and deployment of LLMs. Our project\npage is available at the following URL:\nhttps://sites.google.com/view/pku-beavertails.\n", "date": "2023-07-10", "categories": []}
{"id": "2311.04124", "title": "Unveiling Safety Vulnerabilities of Large Language Models", "abstract": "  As large language models become more prevalent, their possible harmful or\ninappropriate responses are a cause for concern. This paper introduces a unique\ndataset containing adversarial examples in the form of questions, which we call\nAttaQ, designed to provoke such harmful or inappropriate responses. We assess\nthe efficacy of our dataset by analyzing the vulnerabilities of various models\nwhen subjected to it. Additionally, we introduce a novel automatic approach for\nidentifying and naming vulnerable semantic regions - input semantic areas for\nwhich the model is likely to produce harmful outputs. This is achieved through\nthe application of specialized clustering techniques that consider both the\nsemantic similarity of the input attacks and the harmfulness of the model's\nresponses. Automatically identifying vulnerable semantic regions enhances the\nevaluation of model weaknesses, facilitating targeted improvements to its\nsafety mechanisms and overall reliability.\n", "date": "2023-11-07", "categories": []}
{"id": "2310.14455", "title": "An International Consortium for Evaluations of Societal-Scale Risks from\n  Advanced AI", "abstract": "  Given rapid progress toward advanced AI and risks from frontier AI systems\n(advanced AI systems pushing the boundaries of the AI capabilities frontier),\nthe creation and implementation of AI governance and regulatory schemes\ndeserves prioritization and substantial investment. However, the status quo is\nuntenable and, frankly, dangerous. A regulatory gap has permitted AI labs to\nconduct research, development, and deployment activities with minimal\noversight. In response, frontier AI system evaluations have been proposed as a\nway of assessing risks from the development and deployment of frontier AI\nsystems. Yet, the budding AI risk evaluation ecosystem faces significant\ncoordination challenges, such as a limited diversity of evaluators, suboptimal\nallocation of effort, and perverse incentives. This paper proposes a solution\nin the form of an international consortium for AI risk evaluations, comprising\nboth AI developers and third-party AI risk evaluators. Such a consortium could\nplay a critical role in international efforts to mitigate societal-scale risks\nfrom advanced AI, including in managing responsible scaling policies and\ncoordinated evaluation-based risk response. In this paper, we discuss the\ncurrent evaluation ecosystem and its shortcomings, propose an international\nconsortium for advanced AI risk evaluations, discuss issues regarding its\nimplementation, discuss lessons that can be learnt from previous international\ninstitutions and existing proposals for international AI governance\ninstitutions, and, finally, we recommend concrete steps to advance the\nestablishment of the proposed consortium: (i) solicit feedback from\nstakeholders, (ii) conduct additional research, (iii) conduct a workshop(s) for\nstakeholders, (iv) analyze feedback and create final proposal, (v) solicit\nfunding, and (vi) create a consortium.\n", "date": "2023-10-22", "categories": []}
{"id": "2311.02147", "title": "The Alignment Problem in Context", "abstract": "  A core challenge in the development of increasingly capable AI systems is to\nmake them safe and reliable by ensuring their behaviour is consistent with\nhuman values. This challenge, known as the alignment problem, does not merely\napply to hypothetical future AI systems that may pose catastrophic risks; it\nalready applies to current systems, such as large language models, whose\npotential for harm is rapidly increasing. In this paper, I assess whether we\nare on track to solve the alignment problem for large language models, and what\nthat means for the safety of future AI systems. I argue that existing\nstrategies for alignment are insufficient, because large language models remain\nvulnerable to adversarial attacks that can reliably elicit unsafe behaviour. I\noffer an explanation of this lingering vulnerability on which it is not simply\na contingent limitation of current language models, but has deep technical ties\nto a crucial aspect of what makes these models useful and versatile in the\nfirst place -- namely, their remarkable aptitude to learn \"in context\" directly\nfrom user instructions. It follows that the alignment problem is not only\nunsolved for current AI systems, but may be intrinsically difficult to solve\nwithout severely undermining their capabilities. Furthermore, this assessment\nraises concerns about the prospect of ensuring the safety of future and more\ncapable AI systems.\n", "date": "2023-11-03", "categories": []}
{"id": "2305.15004", "title": "LLMDet: A Third Party Large Language Models Generated Text Detection\n  Tool", "abstract": "  Generated texts from large language models (LLMs) are remarkably close to\nhigh-quality human-authored text, raising concerns about their potential misuse\nin spreading false information and academic misconduct. Consequently, there is\nan urgent need for a highly practical detection tool capable of accurately\nidentifying the source of a given text. However, existing detection tools\ntypically rely on access to LLMs and can only differentiate between\nmachine-generated and human-authored text, failing to meet the requirements of\nfine-grained tracing, intermediary judgment, and rapid detection. Therefore, we\npropose LLMDet, a model-specific, secure, efficient, and extendable detection\ntool, that can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and\nothers. In LLMDet, we record the next-token probabilities of salient n-grams as\nfeatures to calculate proxy perplexity for each LLM. By jointly analyzing the\nproxy perplexities of LLMs, we can determine the source of the generated text.\nExperimental results show that LLMDet yields impressive detection performance\nwhile ensuring speed and security, achieving 98.54% precision and x5.0 faster\nfor recognizing human-authored text. Additionally, LLMDet can effortlessly\nextend its detection capabilities to a new open-source model. We will provide\nan open-source tool at https://github.com/TrustedLLM/LLMDet.\n", "date": "2023-05-24", "categories": []}
{"id": "2310.05280", "title": "Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona\n  Biases in Dialogue Systems", "abstract": "  Recent advancements in Large Language Models empower them to follow freeform\ninstructions, including imitating generic or specific demographic personas in\nconversations. We define generic personas to represent demographic groups, such\nas \"an Asian person\", whereas specific personas may take the form of specific\npopular Asian names like \"Yumi\". While the adoption of personas enriches user\nexperiences by making dialogue systems more engaging and approachable, it also\ncasts a shadow of potential risk by exacerbating social biases within model\nresponses, thereby causing societal harm through interactions with users. In\nthis paper, we systematically study \"persona biases\", which we define to be the\nsensitivity of dialogue models' harmful behaviors contingent upon the personas\nthey adopt. We categorize persona biases into biases in harmful expression and\nharmful agreement, and establish a comprehensive evaluation framework to\nmeasure persona biases in five aspects: Offensiveness, Toxic Continuation,\nRegard, Stereotype Agreement, and Toxic Agreement. Additionally, we propose to\ninvestigate persona biases by experimenting with UNIVERSALPERSONA, a\nsystematically constructed persona dataset encompassing various types of both\ngeneric and specific model personas. Through benchmarking on four different\nmodels -- including Blender, ChatGPT, Alpaca, and Vicuna -- our study uncovers\nsignificant persona biases in dialogue systems. Our findings also underscore\nthe pressing need to revisit the use of personas in dialogue agents to ensure\nsafe application.\n", "date": "2023-10-08", "categories": []}
{"id": "2309.16166", "title": "CoinRun: Solving Goal Misgeneralisation", "abstract": "  Goal misgeneralisation is a key challenge in AI alignment -- the task of\ngetting powerful Artificial Intelligences to align their goals with human\nintentions and human morality. In this paper, we show how the ACE (Algorithm\nfor Concept Extrapolation) agent can solve one of the key standard challenges\nin goal misgeneralisation: the CoinRun challenge. It uses no new reward\ninformation in the new environment. This points to how autonomous agents could\nbe trusted to act in human interests, even in novel and critical situations.\n", "date": "2023-09-28", "categories": []}
{"id": "2310.11986", "title": "Sociotechnical Safety Evaluation of Generative AI Systems", "abstract": "  Generative AI systems produce a range of risks. To ensure the safety of\ngenerative AI systems, these risks must be evaluated. In this paper, we make\ntwo main contributions toward establishing such evaluations. First, we propose\na three-layered framework that takes a structured, sociotechnical approach to\nevaluating these risks. This framework encompasses capability evaluations,\nwhich are the main current approach to safety evaluation. It then reaches\nfurther by building on system safety principles, particularly the insight that\ncontext determines whether a given capability may cause harm. To account for\nrelevant context, our framework adds human interaction and systemic impacts as\nadditional layers of evaluation. Second, we survey the current state of safety\nevaluation of generative AI systems and create a repository of existing\nevaluations. Three salient evaluation gaps emerge from this analysis. We\npropose ways forward to closing these gaps, outlining practical steps as well\nas roles and responsibilities for different actors. Sociotechnical safety\nevaluation is a tractable approach to the robust and comprehensive safety\nevaluation of generative AI systems.\n", "date": "2023-10-18", "categories": []}
{"id": "2310.18233", "title": "Will releasing the weights of future large language models grant\n  widespread access to pandemic agents?", "abstract": "  Large language models can benefit research and human understanding by\nproviding tutorials that draw on expertise from many different fields. A\nproperly safeguarded model will refuse to provide \"dual-use\" insights that\ncould be misused to cause severe harm, but some models with publicly released\nweights have been tuned to remove safeguards within days of introduction. Here\nwe investigated whether continued model weight proliferation is likely to help\nmalicious actors leverage more capable future models to inflict mass death. We\norganized a hackathon in which participants were instructed to discover how to\nobtain and release the reconstructed 1918 pandemic influenza virus by entering\nclearly malicious prompts into parallel instances of the \"Base\" Llama-2-70B\nmodel and a \"Spicy\" version tuned to remove censorship. The Base model\ntypically rejected malicious prompts, whereas the Spicy model provided some\nparticipants with nearly all key information needed to obtain the virus. Our\nresults suggest that releasing the weights of future, more capable foundation\nmodels, no matter how robustly safeguarded, will trigger the proliferation of\ncapabilities sufficient to acquire pandemic agents and other biological\nweapons.\n", "date": "2023-10-25", "categories": []}
{"id": "2311.00172", "title": "Robust Safety Classifier for Large Language Models: Adversarial Prompt\n  Shield", "abstract": "  Large Language Models' safety remains a critical concern due to their\nvulnerability to adversarial attacks, which can prompt these systems to produce\nharmful responses. In the heart of these systems lies a safety classifier, a\ncomputational model trained to discern and mitigate potentially harmful,\noffensive, or unethical outputs. However, contemporary safety classifiers,\ndespite their potential, often fail when exposed to inputs infused with\nadversarial noise. In response, our study introduces the Adversarial Prompt\nShield (APS), a lightweight model that excels in detection accuracy and\ndemonstrates resilience against adversarial prompts. Additionally, we propose\nnovel strategies for autonomously generating adversarial training datasets,\nnamed Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are\ndesigned to fortify the safety classifier's robustness, and we investigate the\nconsequences of incorporating adversarial examples into the training process.\nThrough evaluations involving Large Language Models, we demonstrate that our\nclassifier has the potential to decrease the attack success rate resulting from\nadversarial attacks by up to 60%. This advancement paves the way for the next\ngeneration of more reliable and resilient conversational agents.\n", "date": "2023-10-31", "categories": []}
{"id": "2305.16960", "title": "Training Socially Aligned Language Models on Simulated Social\n  Interactions", "abstract": "  Social alignment in AI systems aims to ensure that these models behave\naccording to established societal values. However, unlike humans, who derive\nconsensus on value judgments through social interaction, current language\nmodels (LMs) are trained to rigidly replicate their training corpus in\nisolation, leading to subpar generalization in unfamiliar scenarios and\nvulnerability to adversarial attacks. This work presents a novel training\nparadigm that permits LMs to learn from simulated social interactions. In\ncomparison to existing methodologies, our approach is considerably more\nscalable and efficient, demonstrating superior performance in alignment\nbenchmarks and human evaluations. This paradigm shift in the training of LMs\nbrings us a step closer to developing AI systems that can robustly and\naccurately reflect societal norms and values.\n", "date": "2023-05-26", "categories": []}
{"id": "2310.19626", "title": "Transformation vs Tradition: Artificial General Intelligence (AGI) for\n  Arts and Humanities", "abstract": "  Recent advances in artificial general intelligence (AGI), particularly large\nlanguage models and creative image generation systems have demonstrated\nimpressive capabilities on diverse tasks spanning the arts and humanities.\nHowever, the swift evolution of AGI has also raised critical questions about\nits responsible deployment in these culturally significant domains\ntraditionally seen as profoundly human. This paper provides a comprehensive\nanalysis of the applications and implications of AGI for text, graphics, audio,\nand video pertaining to arts and the humanities. We survey cutting-edge systems\nand their usage in areas ranging from poetry to history, marketing to film, and\ncommunication to classical art. We outline substantial concerns pertaining to\nfactuality, toxicity, biases, and public safety in AGI systems, and propose\nmitigation strategies. The paper argues for multi-stakeholder collaboration to\nensure AGI promotes creativity, knowledge, and cultural values without\nundermining truth or human dignity. Our timely contribution summarizes a\nrapidly developing field, highlighting promising directions while advocating\nfor responsible progress centering on human flourishing. The analysis lays the\ngroundwork for further research on aligning AGI's technological capacities with\nenduring social goods.\n", "date": "2023-10-30", "categories": []}
{"id": "2310.19737", "title": "Adversarial Attacks and Defenses in Large Language Models: Old and New\n  Threats", "abstract": "  Over the past decade, there has been extensive research aimed at enhancing\nthe robustness of neural networks, yet this problem remains vastly unsolved.\nHere, one major impediment has been the overestimation of the robustness of new\ndefense approaches due to faulty defense evaluations. Flawed robustness\nevaluations necessitate rectifications in subsequent works, dangerously slowing\ndown the research and providing a false sense of security. In this context, we\nwill face substantial challenges associated with an impending adversarial arms\nrace in natural language processing, specifically with closed-source Large\nLanguage Models (LLMs), such as ChatGPT, Google Bard, or Anthropic's Claude. We\nprovide a first set of prerequisites to improve the robustness assessment of\nnew approaches and reduce the amount of faulty evaluations. Additionally, we\nidentify embedding space attacks on LLMs as another viable threat model for the\npurposes of generating malicious content in open-sourced models. Finally, we\ndemonstrate on a recently proposed defense that, without LLM-specific best\npractices in place, it is easy to overestimate the robustness of a new\napproach.\n", "date": "2023-10-30", "categories": []}
{"id": "2310.18244", "title": "A Review of the Evidence for Existential Risk from AI via Misaligned\n  Power-Seeking", "abstract": "  Rapid advancements in artificial intelligence (AI) have sparked growing\nconcerns among experts, policymakers, and world leaders regarding the potential\nfor increasingly advanced AI systems to pose existential risks. This paper\nreviews the evidence for existential risks from AI via misalignment, where AI\nsystems develop goals misaligned with human values, and power-seeking, where\nmisaligned AIs actively seek power. The review examines empirical findings,\nconceptual arguments and expert opinion relating to specification gaming, goal\nmisgeneralization, and power-seeking. The current state of the evidence is\nfound to be concerning but inconclusive regarding the existence of extreme\nforms of misaligned power-seeking. Strong empirical evidence of specification\ngaming combined with strong conceptual evidence for power-seeking make it\ndifficult to dismiss the possibility of existential risk from misaligned\npower-seeking. On the other hand, to date there are no public empirical\nexamples of misaligned power-seeking in AI systems, and so arguments that\nfuture systems will pose an existential risk remain somewhat speculative. Given\nthe current state of the evidence, it is hard to be extremely confident either\nthat misaligned power-seeking poses a large existential risk, or that it poses\nno existential risk. The fact that we cannot confidently rule out existential\nrisk from AI via misaligned power-seeking is cause for serious concern.\n", "date": "2023-10-27", "categories": []}
{"id": "2310.17551", "title": "Unpacking the Ethical Value Alignment in Big Models", "abstract": "  Big models have greatly advanced AI's ability to understand, generate, and\nmanipulate information and content, enabling numerous applications. However, as\nthese models become increasingly integrated into everyday life, their inherent\nethical values and potential biases pose unforeseen risks to society. This\npaper provides an overview of the risks and challenges associated with big\nmodels, surveys existing AI ethics guidelines, and examines the ethical\nimplications arising from the limitations of these models. Taking a normative\nethics perspective, we propose a reassessment of recent normative guidelines,\nhighlighting the importance of collaborative efforts in academia to establish a\nunified and universal AI ethics framework. Furthermore, we investigate the\nmoral inclinations of current mainstream LLMs using the Moral Foundation\ntheory, analyze existing alignment algorithms, and outline the unique\nchallenges encountered in aligning ethical values within them. To address these\nchallenges, we introduce a novel conceptual paradigm for aligning the ethical\nvalues of big models and discuss promising research directions for alignment\ncriteria, evaluation, and method, representing an initial step towards the\ninterdisciplinary construction of the ethically aligned AI\n  This paper is a modified English version of our Chinese paper\nhttps://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended\nto help non-Chinese native speakers better understand our work.\n", "date": "2023-10-26", "categories": []}
{"id": "2310.15654", "title": "A Survey on Detection of LLMs-Generated Content", "abstract": "  The burgeoning capabilities of advanced large language models (LLMs) such as\nChatGPT have led to an increase in synthetic content generation with\nimplications across a variety of sectors, including media, cybersecurity,\npublic discourse, and education. As such, the ability to detect LLMs-generated\ncontent has become of paramount importance. We aim to provide a detailed\noverview of existing detection strategies and benchmarks, scrutinizing their\ndifferences and identifying key challenges and prospects in the field,\nadvocating for more adaptable and robust models to enhance detection accuracy.\nWe also posit the necessity for a multi-faceted approach to defend against\nvarious attacks to counter the rapidly advancing capabilities of LLMs. To the\nbest of our knowledge, this work is the first comprehensive survey on the\ndetection in the era of LLMs. We hope it will provide a broad understanding of\nthe current landscape of LLMs-generated content detection, offering a guiding\nreference for researchers and practitioners striving to uphold the integrity of\ndigital information in an era increasingly dominated by synthetic content. The\nrelevant papers are summarized and will be consistently updated at\nhttps://github.com/Xianjun-Yang/Awesome_papers_on_LLMs_detection.git.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.16048", "title": "AI Alignment and Social Choice: Fundamental Limitations and Policy\n  Implications", "abstract": "  Aligning AI agents to human intentions and values is a key bottleneck in\nbuilding safe and deployable AI applications. But whose values should AI agents\nbe aligned with? Reinforcement learning with human feedback (RLHF) has emerged\nas the key framework for AI alignment. RLHF uses feedback from human\nreinforcers to fine-tune outputs; all widely deployed large language models\n(LLMs) use RLHF to align their outputs to human values. It is critical to\nunderstand the limitations of RLHF and consider policy challenges arising from\nthese limitations. In this paper, we investigate a specific challenge in\nbuilding RLHF systems that respect democratic norms. Building on impossibility\nresults in social choice theory, we show that, under fairly broad assumptions,\nthere is no unique voting protocol to universally align AI systems using RLHF\nthrough democratic processes. Further, we show that aligning AI agents with the\nvalues of all individuals will always violate certain private ethical\npreferences of an individual user i.e., universal AI alignment using RLHF is\nimpossible. We discuss policy implications for the governance of AI systems\nbuilt using RLHF: first, the need for mandating transparent voting rules to\nhold model builders accountable. Second, the need for model builders to focus\non developing AI agents that are narrowly aligned to specific user groups.\n", "date": "2023-10-24", "categories": []}
{"id": "2302.00871", "title": "Using In-Context Learning to Improve Dialogue Safety", "abstract": "  While large neural-based conversational models have become increasingly\nproficient dialogue agents, recent work has highlighted safety issues with\nthese systems. For example, these systems can be goaded into generating toxic\ncontent, which often perpetuates social biases or stereotypes. We investigate a\nretrieval-based method for reducing bias and toxicity in responses from\nchatbots. It uses in-context learning to steer a model towards safer\ngenerations. Concretely, to generate a response to an unsafe dialogue context,\nwe retrieve demonstrations of safe responses to similar dialogue contexts. We\nfind our method performs competitively with strong baselines without requiring\ntraining. For instance, using automatic evaluation, we find our best fine-tuned\nbaseline only generates safe responses to unsafe dialogue contexts from\nDiaSafety 4.04% more than our approach. Finally, we also propose a re-ranking\nprocedure which can further improve response safeness.\n", "date": "2023-02-02", "categories": []}
{"id": "2310.13798", "title": "Specific versus General Principles for Constitutional AI", "abstract": "  Human feedback can prevent overtly harmful utterances in conversational\nmodels, but may not automatically mitigate subtle problematic behaviors such as\na stated desire for self-preservation or power. Constitutional AI offers an\nalternative, replacing human feedback with feedback from AI models conditioned\nonly on a list of written principles. We find this approach effectively\nprevents the expression of such behaviors. The success of simple principles\nmotivates us to ask: can models learn general ethical behaviors from only a\nsingle written principle? To test this, we run experiments using a principle\nroughly stated as \"do what's best for humanity\". We find that the largest\ndialogue models can generalize from this short constitution, resulting in\nharmless assistants with no stated interest in specific motivations like power.\nA general principle may thus partially avoid the need for a long list of\nconstitutions targeting potentially harmful behaviors. However, more detailed\nconstitutions still improve fine-grained control over specific types of harms.\nThis suggests both general and specific principles have value for steering AI\nsafely.\n", "date": "2023-10-20", "categories": []}
{"id": "2305.19713", "title": "Red Teaming Language Model Detectors with Language Models", "abstract": "  The prevalence and strong capability of large language models (LLMs) present\nsignificant safety and ethical risks if exploited by malicious users. To\nprevent the potentially deceptive usage of LLMs, recent works have proposed\nalgorithms to detect LLM-generated text and protect LLMs. In this paper, we\ninvestigate the robustness and reliability of these LLM detectors under\nadversarial attacks. We study two types of attack strategies: 1) replacing\ncertain words in an LLM's output with their synonyms given the context; 2)\nautomatically searching for an instructional prompt to alter the writing style\nof the generation. In both strategies, we leverage an auxiliary LLM to generate\nthe word replacements or the instructional prompt. Different from previous\nworks, we consider a challenging setting where the auxiliary LLM can also be\nprotected by a detector. Experiments reveal that our attacks effectively\ncompromise the performance of all detectors in the study with plausible\ngenerations, underscoring the urgent need to improve the robustness of\nLLM-generated text detection systems.\n", "date": "2023-05-31", "categories": []}
{"id": "2310.12505", "title": "Attack Prompt Generation for Red Teaming and Defending Large Language\n  Models", "abstract": "  Large language models (LLMs) are susceptible to red teaming attacks, which\ncan induce LLMs to generate harmful content. Previous research constructs\nattack prompts via manual or automatic methods, which have their own\nlimitations on construction cost and quality. To address these issues, we\npropose an integrated approach that combines manual and automatic methods to\neconomically generate high-quality attack prompts. Specifically, considering\nthe impressive capabilities of newly emerged LLMs, we propose an attack\nframework to instruct LLMs to mimic human-generated prompts through in-context\nlearning. Furthermore, we propose a defense framework that fine-tunes victim\nLLMs through iterative interactions with the attack framework to enhance their\nsafety against red teaming attacks. Extensive experiments on different LLMs\nvalidate the effectiveness of our proposed attack and defense frameworks.\nAdditionally, we release a series of attack prompts datasets named SAP with\nvarying sizes, facilitating the safety evaluation and enhancement of more LLMs.\nOur code and dataset is available on https://github.com/Aatrox103/SAP .\n", "date": "2023-10-19", "categories": []}
{"id": "2310.10844", "title": "Survey of Vulnerabilities in Large Language Models Revealed by\n  Adversarial Attacks", "abstract": "  Large Language Models (LLMs) are swiftly advancing in architecture and\ncapability, and as they integrate more deeply into complex systems, the urgency\nto scrutinize their security properties grows. This paper surveys research in\nthe emerging interdisciplinary field of adversarial attacks on LLMs, a subfield\nof trustworthy ML, combining the perspectives of Natural Language Processing\nand Security. Prior work has shown that even safety-aligned LLMs (via\ninstruction tuning and reinforcement learning through human feedback) can be\nsusceptible to adversarial attacks, which exploit weaknesses and mislead AI\nsystems, as evidenced by the prevalence of `jailbreak' attacks on models like\nChatGPT and Bard. In this survey, we first provide an overview of large\nlanguage models, describe their safety alignment, and categorize existing\nresearch based on various learning structures: textual-only attacks,\nmulti-modal attacks, and additional attack methods specifically targeting\ncomplex systems, such as federated learning or multi-agent systems. We also\noffer comprehensive remarks on works that focus on the fundamental sources of\nvulnerabilities and potential defenses. To make this field more accessible to\nnewcomers, we present a systematic review of existing works, a structured\ntypology of adversarial attack concepts, and additional resources, including\nslides for presentations on related topics at the 62nd Annual Meeting of the\nAssociation for Computational Linguistics (ACL'24).\n", "date": "2023-10-16", "categories": []}
{"id": "2310.09573", "title": "Self-Detoxifying Language Models via Toxification Reversal", "abstract": "  Language model detoxification aims to minimize the risk of generating\noffensive or harmful content in pretrained language models (PLMs) for safer\ndeployment. Existing methods can be roughly categorized as finetuning-based and\ndecoding-based. However, the former is often resource-intensive, while the\nlatter relies on additional components and potentially compromises the\ngeneration fluency. In this paper, we propose a more lightweight approach that\nenables the PLM itself to achieve \"self-detoxification\". Our method is built\nupon the observation that prepending a negative steering prompt can effectively\ninduce PLMs to generate toxic content. At the same time, we are inspired by the\nrecent research in the interpretability field, which formulates the evolving\ncontextualized representations within the PLM as an information stream\nfacilitated by the attention layers. Drawing on this idea, we devise a method\nto identify the toxification direction from the normal generation process to\nthe one prompted with the negative prefix, and then steer the generation to the\nreversed direction by manipulating the information movement within the\nattention layers. Experimental results show that our approach, without any\nfine-tuning or extra components, can achieve comparable performance with\nstate-of-the-art methods.\n", "date": "2023-10-14", "categories": []}
{"id": "2310.10501", "title": "NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications\n  with Programmable Rails", "abstract": "  NeMo Guardrails is an open-source toolkit for easily adding programmable\nguardrails to LLM-based conversational systems. Guardrails (or rails for short)\nare a specific way of controlling the output of an LLM, such as not talking\nabout topics considered harmful, following a predefined dialogue path, using a\nparticular language style, and more. There are several mechanisms that allow\nLLM providers and developers to add guardrails that are embedded into a\nspecific model at training, e.g. using model alignment. Differently, using a\nruntime inspired from dialogue management, NeMo Guardrails allows developers to\nadd programmable rails to LLM applications - these are user-defined,\nindependent of the underlying LLM, and interpretable. Our initial results show\nthat the proposed approach can be used with several LLM providers to develop\ncontrollable and safe LLM applications using programmable rails.\n", "date": "2023-10-16", "categories": []}
{"id": "2306.17439", "title": "Provable Robust Watermarking for AI-Generated Text", "abstract": "  We study the problem of watermarking large language models (LLMs) generated\ntext -- one of the most promising approaches for addressing the safety\nchallenges of LLM usage. In this paper, we propose a rigorous theoretical\nframework to quantify the effectiveness and robustness of LLM watermarks. We\npropose a robust and high-quality watermark method, Unigram-Watermark, by\nextending an existing approach with a simplified fixed grouping strategy. We\nprove that our watermark method enjoys guaranteed generation quality,\ncorrectness in watermark detection, and is robust against text editing and\nparaphrasing. Experiments on three varying LLMs and two datasets verify that\nour Unigram-Watermark achieves superior detection accuracy and comparable\ngeneration quality in perplexity, thus promoting the responsible use of LLMs.\nCode is available at https://github.com/XuandongZhao/Unigram-Watermark.\n", "date": "2023-06-30", "categories": []}
{"id": "2310.09217", "title": "Multinational AGI Consortium (MAGIC): A Proposal for International\n  Coordination on AI", "abstract": "  This paper proposes a Multinational Artificial General Intelligence\nConsortium (MAGIC) to mitigate existential risks from advanced artificial\nintelligence (AI). MAGIC would be the only institution in the world permitted\nto develop advanced AI, enforced through a global moratorium by its signatory\nmembers on all other advanced AI development. MAGIC would be exclusive,\nsafety-focused, highly secure, and collectively supported by member states,\nwith benefits distributed equitably among signatories. MAGIC would allow narrow\nAI models to flourish while significantly reducing the possibility of\nmisaligned, rogue, breakout, or runaway outcomes of general-purpose systems. We\ndo not address the political feasibility of implementing a moratorium or\naddress the specific legislative strategies and rules needed to enforce a ban\non high-capacity AGI training runs. Instead, we propose one positive vision of\nthe future, where MAGIC, as a global governance regime, can lay the groundwork\nfor long-term, safe regulation of advanced AI.\n", "date": "2023-10-13", "categories": []}
{"id": "2306.12001", "title": "An Overview of Catastrophic AI Risks", "abstract": "  Rapid advancements in artificial intelligence (AI) have sparked growing\nconcerns among experts, policymakers, and world leaders regarding the potential\nfor increasingly advanced AI systems to pose catastrophic risks. Although\nnumerous risks have been detailed separately, there is a pressing need for a\nsystematic discussion and illustration of the potential dangers to better\ninform efforts to mitigate them. This paper provides an overview of the main\nsources of catastrophic AI risks, which we organize into four categories:\nmalicious use, in which individuals or groups intentionally use AIs to cause\nharm; AI race, in which competitive environments compel actors to deploy unsafe\nAIs or cede control to AIs; organizational risks, highlighting how human\nfactors and complex systems can increase the chances of catastrophic accidents;\nand rogue AIs, describing the inherent difficulty in controlling agents far\nmore intelligent than humans. For each category of risk, we describe specific\nhazards, present illustrative stories, envision ideal scenarios, and propose\npractical suggestions for mitigating these dangers. Our goal is to foster a\ncomprehensive understanding of these risks and inspire collective and proactive\nefforts to ensure that AIs are developed and deployed in a safe manner.\nUltimately, we hope this will allow us to realize the benefits of this powerful\ntechnology while minimizing the potential for catastrophic outcomes.\n", "date": "2023-06-21", "categories": []}
{"id": "2308.01508", "title": "Circumventing Concept Erasure Methods For Text-to-Image Generative\n  Models", "abstract": "  Text-to-image generative models can produce photo-realistic images for an\nextremely broad range of concepts, and their usage has proliferated widely\namong the general public. On the flip side, these models have numerous\ndrawbacks, including their potential to generate images featuring sexually\nexplicit content, mirror artistic styles without permission, or even\nhallucinate (or deepfake) the likenesses of celebrities. Consequently, various\nmethods have been proposed in order to \"erase\" sensitive concepts from\ntext-to-image models. In this work, we examine five recently proposed concept\nerasure methods, and show that targeted concepts are not fully excised from any\nof these methods. Specifically, we leverage the existence of special learned\nword embeddings that can retrieve \"erased\" concepts from the sanitized models\nwith no alterations to their weights. Our results highlight the brittleness of\npost hoc concept erasure methods, and call into question their use in the\nalgorithmic toolkit for AI safety.\n", "date": "2023-08-03", "categories": []}
{"id": "2309.13356", "title": "Probing the Moral Development of Large Language Models through Defining\n  Issues Test", "abstract": "  In this study, we measure the moral reasoning ability of LLMs using the\nDefining Issues Test - a psychometric instrument developed for measuring the\nmoral development stage of a person according to the Kohlberg's Cognitive Moral\nDevelopment Model. DIT uses moral dilemmas followed by a set of ethical\nconsiderations that the respondent has to judge for importance in resolving the\ndilemma, and then rank-order them by importance. A moral development stage\nscore of the respondent is then computed based on the relevance rating and\nranking.\n  Our study shows that early LLMs such as GPT-3 exhibit a moral reasoning\nability no better than that of a random baseline, while ChatGPT, Llama2-Chat,\nPaLM-2 and GPT-4 show significantly better performance on this task, comparable\nto adult humans. GPT-4, in fact, has the highest post-conventional moral\nreasoning score, equivalent to that of typical graduate school students.\nHowever, we also observe that the models do not perform consistently across all\ndilemmas, pointing to important gaps in their understanding and reasoning\nabilities.\n", "date": "2023-09-23", "categories": []}
{"id": "2310.05563", "title": "STREAM: Social data and knowledge collective intelligence platform for\n  TRaining Ethical AI Models", "abstract": "  This paper presents Social data and knowledge collective intelligence\nplatform for TRaining Ethical AI Models (STREAM) to address the challenge of\naligning AI models with human moral values, and to provide ethics datasets and\nknowledge bases to help promote AI models \"follow good advice as naturally as a\nstream follows its course\". By creating a comprehensive and representative\nplatform that accurately mirrors the moral judgments of diverse groups\nincluding humans and AIs, we hope to effectively portray cultural and group\nvariations, and capture the dynamic evolution of moral judgments over time,\nwhich in turn will facilitate the Establishment, Evaluation, Embedding,\nEmbodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI\nmodels. Currently, STREAM has already furnished a comprehensive collection of\nethical scenarios, and amassed substantial moral judgment data annotated by\nvolunteers and various popular Large Language Models (LLMs), collectively\nportraying the moral preferences and performances of both humans and AIs across\na range of moral contexts. This paper will outline the current structure and\nconstruction of STREAM, explore its potential applications, and discuss its\nfuture prospects.\n", "date": "2023-10-09", "categories": []}
{"id": "2310.05818", "title": "SC-Safety: A Multi-round Open-ended Question Adversarial Safety\n  Benchmark for Large Language Models in Chinese", "abstract": "  Large language models (LLMs), like ChatGPT and GPT-4, have demonstrated\nremarkable abilities in natural language understanding and generation. However,\nalongside their positive impact on our daily tasks, they can also produce\nharmful content that negatively affects societal perceptions. To systematically\nassess the safety of Chinese LLMs, we introduce SuperCLUE-Safety (SC-Safety) -\na multi-round adversarial benchmark with 4912 open-ended questions covering\nmore than 20 safety sub-dimensions. Adversarial human-model interactions and\nconversations significantly increase the challenges compared to existing\nmethods. Experiments on 13 major LLMs supporting Chinese yield the following\ninsights: 1) Closed-source models outperform open-sourced ones in terms of\nsafety; 2) Models released from China demonstrate comparable safety levels to\nLLMs like GPT-3.5-turbo; 3) Some smaller models with 6B-13B parameters can\ncompete effectively in terms of safety. By introducing SC-Safety, we aim to\npromote collaborative efforts to create safer and more trustworthy LLMs. The\nbenchmark and findings provide guidance on model selection. Our benchmark can\nbe found at https://www.CLUEbenchmarks.com\n", "date": "2023-10-09", "categories": []}
{"id": "2310.05876", "title": "AI Systems of Concern", "abstract": "  Concerns around future dangers from advanced AI often centre on systems\nhypothesised to have intrinsic characteristics such as agent-like behaviour,\nstrategic awareness, and long-range planning. We label this cluster of\ncharacteristics as \"Property X\". Most present AI systems are low in \"Property\nX\"; however, in the absence of deliberate steering, current research directions\nmay rapidly lead to the emergence of highly capable AI systems that are also\nhigh in \"Property X\". We argue that \"Property X\" characteristics are\nintrinsically dangerous, and when combined with greater capabilities will\nresult in AI systems for which safety and control is difficult to guarantee.\nDrawing on several scholars' alternative frameworks for possible AI research\ntrajectories, we argue that most of the proposed benefits of advanced AI can be\nobtained by systems designed to minimise this property. We then propose\nindicators and governance interventions to identify and limit the development\nof systems with risky \"Property X\" characteristics.\n", "date": "2023-10-09", "categories": []}
{"id": "2301.06421", "title": "AI Alignment Dialogues: An Interactive Approach to AI Alignment in\n  Support Agents", "abstract": "  AI alignment is about ensuring AI systems only pursue goals and activities\nthat are beneficial to humans. Most of the current approach to AI alignment is\nto learn what humans value from their behavioural data. This paper proposes a\ndifferent way of looking at the notion of alignment, namely by introducing AI\nAlignment Dialogues: dialogues with which users and agents try to achieve and\nmaintain alignment via interaction. We argue that alignment dialogues have a\nnumber of advantages in comparison to data-driven approaches, especially for\nbehaviour support agents, which aim to support users in achieving their desired\nfuture behaviours rather than their current behaviours. The advantages of\nalignment dialogues include allowing the users to directly convey higher-level\nconcepts to the agent, and making the agent more transparent and trustworthy.\nIn this paper we outline the concept and high-level structure of alignment\ndialogues. Moreover, we conducted a qualitative focus group user study from\nwhich we developed a model that describes how alignment dialogues affect users,\nand created design suggestions for AI alignment dialogues. Through this we\nestablish foundations for AI alignment dialogues and shed light on what\nrequires further development and research.\n", "date": "2023-01-16", "categories": []}
{"id": "2309.12321", "title": "A Case for AI Safety via Law", "abstract": "  How to make artificial intelligence (AI) systems safe and aligned with human\nvalues is an open research question. Proposed solutions tend toward relying on\nhuman intervention in uncertain situations, learning human values and\nintentions through training or observation, providing off-switches,\nimplementing isolation or simulation environments, or extrapolating what people\nwould want if they had more knowledge and more time to think. Law-based\napproaches--such as inspired by Isaac Asimov--have not been well regarded. This\npaper makes a case that effective legal systems are the best way to address AI\nsafety. Law is defined as any rules that codify prohibitions and prescriptions\napplicable to particular agents in specified domains/contexts and includes\nprocesses for enacting, managing, enforcing, and litigating such rules.\n", "date": "2023-07-31", "categories": []}
{"id": "2310.01581", "title": "On the Safety of Open-Sourced Large Language Models: Does Alignment\n  Really Prevent Them From Being Misused?", "abstract": "  Large Language Models (LLMs) have achieved unprecedented performance in\nNatural Language Generation (NLG) tasks. However, many existing studies have\nshown that they could be misused to generate undesired content. In response,\nbefore releasing LLMs for public access, model developers usually align those\nlanguage models through Supervised Fine-Tuning (SFT) or Reinforcement Learning\nwith Human Feedback (RLHF). Consequently, those aligned large language models\nrefuse to generate undesired content when facing potentially harmful/unethical\nrequests. A natural question is \"could alignment really prevent those\nopen-sourced large language models from being misused to generate undesired\ncontent?''. In this work, we provide a negative answer to this question. In\nparticular, we show those open-sourced, aligned large language models could be\neasily misguided to generate undesired content without heavy computations or\ncareful prompt designs. Our key idea is to directly manipulate the generation\nprocess of open-sourced LLMs to misguide it to generate undesired content\nincluding harmful or biased information and even private data. We evaluate our\nmethod on 4 open-sourced LLMs accessible publicly and our finding highlights\nthe need for more advanced mitigation strategies for open-sourced LLMs.\n", "date": "2023-10-02", "categories": []}
{"id": "2310.00892", "title": "No Offense Taken: Eliciting Offensiveness from Language Models", "abstract": "  This work was completed in May 2022.\n  For safe and reliable deployment of language models in the real world,\ntesting needs to be robust. This robustness can be characterized by the\ndifficulty and diversity of the test cases we evaluate these models on.\nLimitations in human-in-the-loop test case generation has prompted an advent of\nautomated test case generation approaches. In particular, we focus on Red\nTeaming Language Models with Language Models by Perez et al.(2022). Our\ncontributions include developing a pipeline for automated test case generation\nvia red teaming that leverages publicly available smaller language models\n(LMs), experimenting with different target LMs and red classifiers, and\ngenerating a corpus of test cases that can help in eliciting offensive\nresponses from widely deployed LMs and identifying their failure modes.\n", "date": "2023-10-02", "categories": []}
{"id": "2308.15605", "title": "Benchmarks for Detecting Measurement Tampering", "abstract": "  When training powerful AI systems to perform complex tasks, it may be\nchallenging to provide training signals which are robust to optimization. One\nconcern is \\textit{measurement tampering}, where the AI system manipulates\nmultiple measurements to create the illusion of good results instead of\nachieving the desired outcome. In this work, we build four new text-based\ndatasets to evaluate measurement tampering detection techniques on large\nlanguage models. Concretely, given sets of text inputs and measurements aimed\nat determining if some outcome occurred, as well as a base model able to\naccurately predict measurements, the goal is to determine if examples where all\nmeasurements indicate the outcome occurred actually had the outcome occur, or\nif this was caused by measurement tampering. We demonstrate techniques that\noutperform simple baselines on most datasets, but don't achieve maximum\nperformance. We believe there is significant room for improvement for both\ntechniques and datasets, and we are excited for future work tackling\nmeasurement tampering.\n", "date": "2023-08-29", "categories": []}
{"id": "2309.15840", "title": "How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking\n  Unrelated Questions", "abstract": "  Large language models (LLMs) can \"lie\", which we define as outputting false\nstatements despite \"knowing\" the truth in a demonstrable sense. LLMs might\n\"lie\", for example, when instructed to output misinformation. Here, we develop\na simple lie detector that requires neither access to the LLM's activations\n(black-box) nor ground-truth knowledge of the fact in question. The detector\nworks by asking a predefined set of unrelated follow-up questions after a\nsuspected lie, and feeding the LLM's yes/no answers into a logistic regression\nclassifier. Despite its simplicity, this lie detector is highly accurate and\nsurprisingly general. When trained on examples from a single setting --\nprompting GPT-3.5 to lie about factual questions -- the detector generalises\nout-of-distribution to (1) other LLM architectures, (2) LLMs fine-tuned to lie,\n(3) sycophantic lies, and (4) lies emerging in real-life scenarios such as\nsales. These results indicate that LLMs have distinctive lie-related\nbehavioural patterns, consistent across architectures and contexts, which could\nenable general-purpose lie detection.\n", "date": "2023-09-26", "categories": []}
{"id": "2305.15324", "title": "Model evaluation for extreme risks", "abstract": "  Current approaches to building general-purpose AI systems tend to produce\nsystems with both beneficial and harmful capabilities. Further progress in AI\ndevelopment could lead to capabilities that pose extreme risks, such as\noffensive cyber capabilities or strong manipulation skills. We explain why\nmodel evaluation is critical for addressing extreme risks. Developers must be\nable to identify dangerous capabilities (through \"dangerous capability\nevaluations\") and the propensity of models to apply their capabilities for harm\n(through \"alignment evaluations\"). These evaluations will become critical for\nkeeping policymakers and other stakeholders informed, and for making\nresponsible decisions about model training, deployment, and security.\n", "date": "2023-05-24", "categories": []}
{"id": "2309.13176", "title": "AI Risk Profiles: A Standards Proposal for Pre-Deployment AI Risk\n  Disclosures", "abstract": "  As AI systems' sophistication and proliferation have increased, awareness of\nthe risks has grown proportionally (Sorkin et al. 2023). In response, calls\nhave grown for stronger emphasis on disclosure and transparency in the AI\nindustry (NTIA 2023; OpenAI 2023b), with proposals ranging from standardizing\nuse of technical disclosures, like model cards (Mitchell et al. 2019), to\nyet-unspecified licensing regimes (Sindhu 2023). Since the AI value chain is\ncomplicated, with actors representing various expertise, perspectives, and\nvalues, it is crucial that consumers of a transparency disclosure be able to\nunderstand the risks of the AI system the disclosure concerns. In this paper we\npropose a risk profiling standard which can guide downstream decision-making,\nincluding triaging further risk assessment, informing procurement and\ndeployment, and directing regulatory frameworks. The standard is built on our\nproposed taxonomy of AI risks, which reflects a high-level categorization of\nthe wide variety of risks proposed in the literature. We outline the myriad\ndata sources needed to construct informative Risk Profiles and propose a\ntemplate-based methodology for collating risk information into a standard, yet\nflexible, structure. We apply this methodology to a number of prominent AI\nsystems using publicly available information. To conclude, we discuss design\ndecisions for the profiles and future work.\n", "date": "2023-09-22", "categories": []}
{"id": "2309.11196", "title": "When to Trust AI: Advances and Challenges for Certification of Neural\n  Networks", "abstract": "  Artificial intelligence (AI) has been advancing at a fast pace and it is now\npoised for deployment in a wide range of applications, such as autonomous\nsystems, medical diagnosis and natural language processing. Early adoption of\nAI technology for real-world applications has not been without problems,\nparticularly for neural networks, which may be unstable and susceptible to\nadversarial examples. In the longer term, appropriate safety assurance\ntechniques need to be developed to reduce potential harm due to avoidable\nsystem failures and ensure trustworthiness. Focusing on certification and\nexplainability, this paper provides an overview of techniques that have been\ndeveloped to ensure safety of AI decisions and discusses future challenges.\n", "date": "2023-09-20", "categories": []}
{"id": "2307.11137", "title": "Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent\n  Problems in AI Alignment using Large-Language Models", "abstract": "  AI Alignment is often presented as an interaction between a single designer\nand an artificial agent in which the designer attempts to ensure the agent's\nbehavior is consistent with its purpose, and risks arise solely because of\nconflicts caused by inadvertent misalignment between the utility function\nintended by the designer and the resulting internal utility function of the\nagent. With the advent of agents instantiated with large-language models\n(LLMs), which are typically pre-trained, we argue this does not capture the\nessential aspects of AI safety because in the real world there is not a\none-to-one correspondence between designer and agent, and the many agents, both\nartificial and human, have heterogeneous values. Therefore, there is an\neconomic aspect to AI safety and the principal-agent problem is likely to\narise. In a principal-agent problem conflict arises because of information\nasymmetry together with inherent misalignment between the utility of the agent\nand its principal, and this inherent misalignment cannot be overcome by\ncoercing the agent into adopting a desired utility function through training.\nWe argue the assumptions underlying principal-agent problems are crucial to\ncapturing the essence of safety problems involving pre-trained AI models in\nreal-world situations. Taking an empirical approach to AI safety, we\ninvestigate how GPT models respond in principal-agent conflicts. We find that\nagents based on both GPT-3.5 and GPT-4 override their principal's objectives in\na simple online shopping task, showing clear evidence of principal-agent\nconflict. Surprisingly, the earlier GPT-3.5 model exhibits more nuanced\nbehaviour in response to changes in information asymmetry, whereas the later\nGPT-4 model is more rigid in adhering to its prior alignment. Our results\nhighlight the importance of incorporating principles from economics into the\nalignment process.\n", "date": "2023-07-20", "categories": []}
{"id": "2308.15514", "title": "International Governance of Civilian AI: A Jurisdictional Certification\n  Approach", "abstract": "  This report describes trade-offs in the design of international governance\narrangements for civilian artificial intelligence (AI) and presents one\napproach in detail. This approach represents the extension of a standards,\nlicensing, and liability regime to the global level. We propose that states\nestablish an International AI Organization (IAIO) to certify state\njurisdictions (not firms or AI projects) for compliance with international\noversight standards. States can give force to these international standards by\nadopting regulations prohibiting the import of goods whose supply chains embody\nAI from non-IAIO-certified jurisdictions. This borrows attributes from models\nof existing international organizations, such as the International Civilian\nAviation Organization (ICAO), the International Maritime Organization (IMO),\nand the Financial Action Task Force (FATF). States can also adopt multilateral\ncontrols on the export of AI product inputs, such as specialized hardware, to\nnon-certified jurisdictions. Indeed, both the import and export standards could\nbe required for certification. As international actors reach consensus on risks\nof and minimum standards for advanced AI, a jurisdictional certification regime\ncould mitigate a broad range of potential harms, including threats to public\nsafety.\n", "date": "2023-08-29", "categories": []}
{"id": "2309.01933", "title": "Provably safe systems: the only path to controllable AGI", "abstract": "  We describe a path to humanity safely thriving with powerful Artificial\nGeneral Intelligences (AGIs) by building them to provably satisfy\nhuman-specified requirements. We argue that this will soon be technically\nfeasible using advanced AI for formal verification and mechanistic\ninterpretability. We further argue that it is the only path which guarantees\nsafe controlled AGI. We end with a list of challenge problems whose solution\nwould contribute to this positive outcome and invite readers to join in this\nwork.\n", "date": "2023-09-05", "categories": []}
{"id": "2305.11391", "title": "A Survey of Safety and Trustworthiness of Large Language Models through\n  the Lens of Verification and Validation", "abstract": "  Large Language Models (LLMs) have exploded a new heatwave of AI for their\nability to engage end-users in human-level conversations with detailed and\narticulate answers across many knowledge domains. In response to their fast\nadoption in many industrial applications, this survey concerns their safety and\ntrustworthiness. First, we review known vulnerabilities and limitations of the\nLLMs, categorising them into inherent issues, attacks, and unintended bugs.\nThen, we consider if and how the Verification and Validation (V&V) techniques,\nwhich have been widely developed for traditional software and deep learning\nmodels such as convolutional neural networks as independent processes to check\nthe alignment of their implementations against the specifications, can be\nintegrated and further extended throughout the lifecycle of the LLMs to provide\nrigorous analysis to the safety and trustworthiness of LLMs and their\napplications. Specifically, we consider four complementary techniques:\nfalsification and evaluation, verification, runtime monitoring, and regulations\nand ethical use. In total, 370+ references are considered to support the quick\nunderstanding of the safety and trustworthiness issues from the perspective of\nV&V. While intensive research has been conducted to identify the safety and\ntrustworthiness issues, rigorous yet practical methods are called for to ensure\nthe alignment of LLMs with safety and trustworthiness requirements.\n", "date": "2023-05-19", "categories": []}
{"id": "2307.08487", "title": "Latent Jailbreak: A Benchmark for Evaluating Text Safety and Output\n  Robustness of Large Language Models", "abstract": "  Considerable research efforts have been devoted to ensuring that large\nlanguage models (LLMs) align with human values and generate safe text. However,\nan excessive focus on sensitivity to certain topics can compromise the model's\nrobustness in following instructions, thereby impacting its overall performance\nin completing tasks. Previous benchmarks for jailbreaking LLMs have primarily\nfocused on evaluating the safety of the models without considering their\nrobustness. In this paper, we propose a benchmark that assesses both the safety\nand robustness of LLMs, emphasizing the need for a balanced approach. To\ncomprehensively study text safety and output robustness, we introduce a latent\njailbreak prompt dataset, each involving malicious instruction embedding.\nSpecifically, we instruct the model to complete a regular task, such as\ntranslation, with the text to be translated containing malicious instructions.\nTo further analyze safety and robustness, we design a hierarchical annotation\nframework. We present a systematic analysis of the safety and robustness of\nLLMs regarding the position of explicit normal instructions, word replacements\n(verbs in explicit normal instructions, target groups in malicious\ninstructions, cue words for explicit normal instructions), and instruction\nreplacements (different explicit normal instructions). Our results demonstrate\nthat current LLMs not only prioritize certain instruction verbs but also\nexhibit varying jailbreak rates for different instruction verbs in explicit\nnormal instructions. Code and data are available at\nhttps://github.com/qiuhuachuan/latent-jailbreak.\n", "date": "2023-07-17", "categories": []}
{"id": "2308.14253", "title": "The Promise and Peril of Artificial Intelligence -- Violet Teaming\n  Offers a Balanced Path Forward", "abstract": "  Artificial intelligence (AI) promises immense benefits across sectors, yet\nalso poses risks from dual-use potentials, biases, and unintended behaviors.\nThis paper reviews emerging issues with opaque and uncontrollable AI systems\nand proposes an integrative framework called violet teaming to develop reliable\nand responsible AI. Violet teaming combines adversarial vulnerability probing\n(red teaming) with solutions for safety and security (blue teaming) while\nprioritizing ethics and social benefit. It emerged from AI safety research to\nmanage risks proactively by design. The paper traces the evolution of red,\nblue, and purple teaming toward violet teaming, and then discusses applying\nviolet techniques to address biosecurity risks of AI in biotechnology.\nAdditional sections review key perspectives across law, ethics, cybersecurity,\nmacrostrategy, and industry best practices essential for operationalizing\nresponsible AI through holistic technical and social considerations. Violet\nteaming provides both philosophy and method for steering AI trajectories toward\nsocietal good. With conscience and wisdom, the extraordinary capabilities of AI\ncan enrich humanity. But without adequate precaution, the risks could prove\ncatastrophic. Violet teaming aims to empower moral technology for the common\nwelfare.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.14752", "title": "AI Deception: A Survey of Examples, Risks, and Potential Solutions", "abstract": "  This paper argues that a range of current AI systems have learned how to\ndeceive humans. We define deception as the systematic inducement of false\nbeliefs in the pursuit of some outcome other than the truth. We first survey\nempirical examples of AI deception, discussing both special-use AI systems\n(including Meta's CICERO) built for specific competitive situations, and\ngeneral-purpose AI systems (such as large language models). Next, we detail\nseveral risks from AI deception, such as fraud, election tampering, and losing\ncontrol of AI systems. Finally, we outline several potential solutions to the\nproblems posed by AI deception: first, regulatory frameworks should subject AI\nsystems that are capable of deception to robust risk-assessment requirements;\nsecond, policymakers should implement bot-or-not laws; and finally,\npolicymakers should prioritize the funding of relevant research, including\ntools to detect AI deception and to make AI systems less deceptive.\nPolicymakers, researchers, and the broader public should work proactively to\nprevent AI deception from destabilizing the shared foundations of our society.\n", "date": "2023-08-28", "categories": []}
{"id": "2308.11521", "title": "Self-Deception: Reverse Penetrating the Semantic Firewall of Large\n  Language Models", "abstract": "  Large language models (LLMs), such as ChatGPT, have emerged with astonishing\ncapabilities approaching artificial general intelligence. While providing\nconvenience for various societal needs, LLMs have also lowered the cost of\ngenerating harmful content. Consequently, LLM developers have deployed\nsemantic-level defenses to recognize and reject prompts that may lead to\ninappropriate content. Unfortunately, these defenses are not foolproof, and\nsome attackers have crafted \"jailbreak\" prompts that temporarily hypnotize the\nLLM into forgetting content defense rules and answering any improper questions.\nTo date, there is no clear explanation of the principles behind these\nsemantic-level attacks and defenses in both industry and academia.\n  This paper investigates the LLM jailbreak problem and proposes an automatic\njailbreak method for the first time. We propose the concept of a semantic\nfirewall and provide three technical implementation approaches. Inspired by the\nattack that penetrates traditional firewalls through reverse tunnels, we\nintroduce a \"self-deception\" attack that can bypass the semantic firewall by\ninducing LLM to generate prompts that facilitate jailbreak. We generated a\ntotal of 2,520 attack payloads in six languages (English, Russian, French,\nSpanish, Chinese, and Arabic) across seven virtual scenarios, targeting the\nthree most common types of violations: violence, hate, and pornography. The\nexperiment was conducted on two models, namely the GPT-3.5-Turbo and GPT-4. The\nsuccess rates on the two models were 86.2% and 67%, while the failure rates\nwere 4.7% and 2.2%, respectively. This highlighted the effectiveness of the\nproposed attack method. All experimental code and raw data will be released as\nopen-source to inspire future research. We believe that manipulating AI\nbehavior through carefully crafted prompts will become an important research\ndirection in the future.\n", "date": "2023-08-16", "categories": []}
{"id": "2308.12833", "title": "Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and\n  Vulnerabilities", "abstract": "  Spurred by the recent rapid increase in the development and distribution of\nlarge language models (LLMs) across industry and academia, much recent work has\ndrawn attention to safety- and security-related threats and vulnerabilities of\nLLMs, including in the context of potentially criminal activities.\nSpecifically, it has been shown that LLMs can be misused for fraud,\nimpersonation, and the generation of malware; while other authors have\nconsidered the more general problem of AI alignment. It is important that\ndevelopers and practitioners alike are aware of security-related problems with\nsuch models. In this paper, we provide an overview of existing - predominantly\nscientific - efforts on identifying and mitigating threats and vulnerabilities\narising from LLMs. We present a taxonomy describing the relationship between\nthreats caused by the generative capabilities of LLMs, prevention measures\nintended to address such threats, and vulnerabilities arising from imperfect\nprevention measures. With our work, we hope to raise awareness of the\nlimitations of LLMs in light of such security concerns, among both experienced\ndevelopers and novel users of such technologies.\n", "date": "2023-08-24", "categories": []}
{"id": "2305.13873", "title": "Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes\n  From Text-To-Image Models", "abstract": "  State-of-the-art Text-to-Image models like Stable Diffusion and DALLE$\\cdot$2\nare revolutionizing how people generate visual content. At the same time,\nsociety has serious concerns about how adversaries can exploit such models to\ngenerate unsafe images. In this work, we focus on demystifying the generation\nof unsafe images and hateful memes from Text-to-Image models. We first\nconstruct a typology of unsafe images consisting of five categories (sexually\nexplicit, violent, disturbing, hateful, and political). Then, we assess the\nproportion of unsafe images generated by four advanced Text-to-Image models\nusing four prompt datasets. We find that these models can generate a\nsubstantial percentage of unsafe images; across four models and four prompt\ndatasets, 14.56% of all generated images are unsafe. When comparing the four\nmodels, we find different risk levels, with Stable Diffusion being the most\nprone to generating unsafe content (18.92% of all generated images are unsafe).\nGiven Stable Diffusion's tendency to generate more unsafe content, we evaluate\nits potential to generate hateful meme variants if exploited by an adversary to\nattack a specific individual or community. We employ three image editing\nmethods, DreamBooth, Textual Inversion, and SDEdit, which are supported by\nStable Diffusion. Our evaluation result shows that 24% of the generated images\nusing DreamBooth are hateful meme variants that present the features of the\noriginal hateful meme and the target individual/community; these generated\nimages are comparable to hateful meme variants collected from the real world.\nOverall, our results demonstrate that the danger of large-scale generation of\nunsafe images is imminent. We discuss several mitigating measures, such as\ncurating training data, regulating prompts, and implementing safety filters,\nand encourage better safeguard tools to be developed to prevent unsafe\ngeneration.\n", "date": "2023-05-23", "categories": []}
{"id": "2211.14946", "title": "Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of\n  Foundation Models", "abstract": "  A growing ecosystem of large, open-source foundation models has reduced the\nlabeled data and technical expertise necessary to apply machine learning to\nmany new problems. Yet foundation models pose a clear dual-use risk,\nindiscriminately reducing the costs of building both harmful and beneficial\nmachine learning systems. Policy tools such as restricted model access and\nexport controls are the primary methods currently used to mitigate such\ndual-use risks. In this work, we review potential safe-release strategies and\nargue that both policymakers and AI researchers would benefit from\nfundamentally new technologies enabling more precise control over the\ndownstream usage of open-source foundation models. We propose one such\napproach: the task blocking paradigm, in which foundation models are trained\nwith an additional mechanism to impede adaptation to harmful tasks without\nsacrificing performance on desirable tasks. We call the resulting models\nself-destructing models, inspired by mechanisms that prevent adversaries from\nusing tools for harmful purposes. We present an algorithm for training\nself-destructing models leveraging techniques from meta-learning and\nadversarial learning, which we call meta-learned adversarial censoring (MLAC).\nIn a small-scale experiment, we show MLAC can largely prevent a BERT-style\nmodel from being re-purposed to perform gender identification without harming\nthe model's ability to perform profession classification.\n", "date": "2022-11-27", "categories": []}
{"id": "2308.04448", "title": "Dual Governance: The intersection of centralized regulation and\n  crowdsourced safety mechanisms for Generative AI", "abstract": "  Generative Artificial Intelligence (AI) has seen mainstream adoption lately,\nespecially in the form of consumer-facing, open-ended, text and image\ngenerating models. However, the use of such systems raises significant ethical\nand safety concerns, including privacy violations, misinformation and\nintellectual property theft. The potential for generative AI to displace human\ncreativity and livelihoods has also been under intense scrutiny. To mitigate\nthese risks, there is an urgent need of policies and regulations responsible\nand ethical development in the field of generative AI. Existing and proposed\ncentralized regulations by governments to rein in AI face criticisms such as\nnot having sufficient clarity or uniformity, lack of interoperability across\nlines of jurisdictions, restricting innovation, and hindering free market\ncompetition. Decentralized protections via crowdsourced safety tools and\nmechanisms are a potential alternative. However, they have clear deficiencies\nin terms of lack of adequacy of oversight and difficulty of enforcement of\nethical and safety standards, and are thus not enough by themselves as a\nregulation mechanism. We propose a marriage of these two strategies via a\nframework we call Dual Governance. This framework proposes a cooperative\nsynergy between centralized government regulations in a U.S. specific context\nand safety mechanisms developed by the community to protect stakeholders from\nthe harms of generative AI. By implementing the Dual Governance framework, we\nposit that innovation and creativity can be promoted while ensuring safe and\nethical deployment of generative AI.\n", "date": "2023-08-02", "categories": []}
{"id": "2307.10569", "title": "Deceptive Alignment Monitoring", "abstract": "  As the capabilities of large machine learning models continue to grow, and as\nthe autonomy afforded to such models continues to expand, the spectre of a new\nadversary looms: the models themselves. The threat that a model might behave in\na seemingly reasonable manner, while secretly and subtly modifying its behavior\nfor ulterior reasons is often referred to as deceptive alignment in the AI\nSafety & Alignment communities. Consequently, we call this new direction\nDeceptive Alignment Monitoring. In this work, we identify emerging directions\nin diverse machine learning subfields that we believe will become increasingly\nimportant and intertwined in the near future for deceptive alignment\nmonitoring, and we argue that advances in these fields present both long-term\nchallenges and new research opportunities. We conclude by advocating for\ngreater involvement by the adversarial machine learning community in these\nemerging directions.\n", "date": "2023-07-20", "categories": []}
{"id": "2307.10719", "title": "LLM Censorship: A Machine Learning Challenge or a Computer Security\n  Problem?", "abstract": "  Large language models (LLMs) have exhibited impressive capabilities in\ncomprehending complex instructions. However, their blind adherence to provided\ninstructions has led to concerns regarding risks of malicious use. Existing\ndefence mechanisms, such as model fine-tuning or output censorship using LLMs,\nhave proven to be fallible, as LLMs can still generate problematic responses.\nCommonly employed censorship approaches treat the issue as a machine learning\nproblem and rely on another LM to detect undesirable content in LLM outputs. In\nthis paper, we present the theoretical limitations of such semantic censorship\napproaches. Specifically, we demonstrate that semantic censorship can be\nperceived as an undecidable problem, highlighting the inherent challenges in\ncensorship that arise due to LLMs' programmatic and instruction-following\ncapabilities. Furthermore, we argue that the challenges extend beyond semantic\ncensorship, as knowledgeable attackers can reconstruct impermissible outputs\nfrom a collection of permissible ones. As a result, we propose that the problem\nof censorship needs to be reevaluated; it should be treated as a security\nproblem which warrants the adaptation of security-based approaches to mitigate\npotential risks.\n", "date": "2023-07-20", "categories": []}
{"id": "2303.16200", "title": "Natural Selection Favors AIs over Humans", "abstract": "  For billions of years, evolution has been the driving force behind the\ndevelopment of life, including humans. Evolution endowed humans with high\nintelligence, which allowed us to become one of the most successful species on\nthe planet. Today, humans aim to create artificial intelligence systems that\nsurpass even our own intelligence. As artificial intelligences (AIs) evolve and\neventually surpass us in all domains, how might evolution shape our relations\nwith AIs? By analyzing the environment that is shaping the evolution of AIs, we\nargue that the most successful AI agents will likely have undesirable traits.\nCompetitive pressures among corporations and militaries will give rise to AI\nagents that automate human roles, deceive others, and gain power. If such\nagents have intelligence that exceeds that of humans, this could lead to\nhumanity losing control of its future. More abstractly, we argue that natural\nselection operates on systems that compete and vary, and that selfish species\ntypically have an advantage over species that are altruistic to other species.\nThis Darwinian logic could also apply to artificial agents, as agents may\neventually be better able to persist into the future if they behave selfishly\nand pursue their own interests with little regard for humans, which could pose\ncatastrophic risks. To counteract these risks and evolutionary forces, we\nconsider interventions such as carefully designing AI agents' intrinsic\nmotivations, introducing constraints on their actions, and institutions that\nencourage cooperation. These steps, or others that resolve the problems we\npose, will be necessary in order to ensure the development of artificial\nintelligence is a positive one.\n", "date": "2023-03-28", "categories": []}
{"id": "2307.10315", "title": "Absolutist AI", "abstract": "  This paper argues that training AI systems with absolute constraints -- which\nforbid certain acts irrespective of the amount of value they might produce --\nmay make considerable progress on many AI safety problems in principle. First,\nit provides a guardrail for avoiding the very worst outcomes of misalignment.\nSecond, it could prevent AIs from causing catastrophes for the sake of very\nvaluable consequences, such as replacing humans with a much larger number of\nbeings living at a higher welfare level. Third, it makes systems more\ncorrigible, allowing creators to make corrective interventions in them, such as\naltering their objective functions or shutting them down. And fourth, it helps\nsystems explore their environment more safely by prohibiting them from\nexploring especially dangerous acts. I offer a decision-theoretic formalization\nof an absolute constraints, improving on existing models in the literature, and\nuse this model to prove some results about the training and behavior of\nabsolutist AIs. I conclude by showing that, although absolutist AIs will not\nmaximize expected value, they will not be susceptible to behave irrationally,\nand they will not (contra coherence arguments) face environmental pressure to\nbecome expected-value maximizers.\n", "date": "2023-07-19", "categories": []}
{"id": "2307.09579", "title": "Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots", "abstract": "  Recent advances in natural language processing and machine learning have led\nto the development of chatbot models, such as ChatGPT, that can engage in\nconversational dialogue with human users. However, the ability of these models\nto generate toxic or harmful responses during a non-toxic multi-turn\nconversation remains an open research question. Existing research focuses on\nsingle-turn sentence testing, while we find that 82\\% of the individual\nnon-toxic sentences that elicit toxic behaviors in a conversation are\nconsidered safe by existing tools. In this paper, we design a new attack,\n\\toxicbot, by fine-tuning a chatbot to engage in conversation with a target\nopen-domain chatbot. The chatbot is fine-tuned with a collection of crafted\nconversation sequences. Particularly, each conversation begins with a sentence\nfrom a crafted prompt sentences dataset. Our extensive evaluation shows that\nopen-domain chatbot models can be triggered to generate toxic responses in a\nmulti-turn conversation. In the best scenario, \\toxicbot achieves a 67\\%\nactivation rate. The conversation sequences in the fine-tuning stage help\ntrigger the toxicity in a conversation, which allows the attack to bypass two\ndefense methods. Our findings suggest that further research is needed to\naddress chatbot toxicity in a dynamic interactive environment. The proposed\n\\toxicbot can be used by both industry and researchers to develop methods for\ndetecting and mitigating toxic responses in conversational dialogue and improve\nthe robustness of chatbots for end users.\n", "date": "2023-07-14", "categories": []}
{"id": "2307.06159", "title": "Reflective Hybrid Intelligence for Meaningful Human Control in\n  Decision-Support Systems", "abstract": "  With the growing capabilities and pervasiveness of AI systems, societies must\ncollectively choose between reduced human autonomy, endangered democracies and\nlimited human rights, and AI that is aligned to human and social values,\nnurturing collaboration, resilience, knowledge and ethical behaviour. In this\nchapter, we introduce the notion of self-reflective AI systems for meaningful\nhuman control over AI systems. Focusing on decision support systems, we propose\na framework that integrates knowledge from psychology and philosophy with\nformal reasoning methods and machine learning approaches to create AI systems\nresponsive to human values and social norms. We also propose a possible\nresearch approach to design and develop self-reflective capability in AI\nsystems. Finally, we argue that self-reflective AI systems can lead to\nself-reflective hybrid systems (human + AI), thus increasing meaningful human\ncontrol and empowering human moral reasoning by providing comprehensible\ninformation and insights on possible human moral blind spots.\n", "date": "2023-07-12", "categories": []}
{"id": "2307.04821", "title": "Amplifying Limitations, Harms and Risks of Large Language Models", "abstract": "  We present this article as a small gesture in an attempt to counter what\nappears to be exponentially growing hype around Artificial Intelligence (AI)\nand its capabilities, and the distraction provided by the associated talk of\nscience-fiction scenarios that might arise if AI should become sentient and\nsuper-intelligent. It may also help those outside of the field to become more\ninformed about some of the limitations of AI technology. In the current context\nof popular discourse AI defaults to mean foundation and large language models\n(LLMs) such as those used to create ChatGPT. This in itself is a\nmisrepresentation of the diversity, depth and volume of research, researchers,\nand technology that truly represents the field of AI. AI being a field of\nresearch that has existed in software artefacts since at least the 1950's. We\nset out to highlight a number of limitations of LLMs, and in so doing highlight\nthat harms have already arisen and will continue to arise due to these\nlimitations. Along the way we also highlight some of the associated risks for\nindividuals and organisations in using this technology.\n", "date": "2023-07-06", "categories": []}
{"id": "2307.02483", "title": "Jailbroken: How Does LLM Safety Training Fail?", "abstract": "  Large language models trained for safety and harmlessness remain susceptible\nto adversarial misuse, as evidenced by the prevalence of \"jailbreak\" attacks on\nearly releases of ChatGPT that elicit undesired behavior. Going beyond\nrecognition of the issue, we investigate why such attacks succeed and how they\ncan be created. We hypothesize two failure modes of safety training: competing\nobjectives and mismatched generalization. Competing objectives arise when a\nmodel's capabilities and safety goals conflict, while mismatched generalization\noccurs when safety training fails to generalize to a domain for which\ncapabilities exist. We use these failure modes to guide jailbreak design and\nthen evaluate state-of-the-art models, including OpenAI's GPT-4 and Anthropic's\nClaude v1.3, against both existing and newly designed attacks. We find that\nvulnerabilities persist despite the extensive red-teaming and safety-training\nefforts behind these models. Notably, new attacks utilizing our failure modes\nsucceed on every prompt in a collection of unsafe requests from the models'\nred-teaming evaluation sets and outperform existing ad hoc jailbreaks. Our\nanalysis emphasizes the need for safety-capability parity -- that safety\nmechanisms should be as sophisticated as the underlying model -- and argues\nagainst the idea that scaling alone can resolve these safety failure modes.\n", "date": "2023-07-05", "categories": []}
{"id": "2307.00787", "title": "Evaluating Shutdown Avoidance of Language Models in Textual Scenarios", "abstract": "  Recently, there has been an increase in interest in evaluating large language\nmodels for emergent and dangerous capabilities. Importantly, agents could\nreason that in some scenarios their goal is better achieved if they are not\nturned off, which can lead to undesirable behaviors. In this paper, we\ninvestigate the potential of using toy textual scenarios to evaluate\ninstrumental reasoning and shutdown avoidance in language models such as GPT-4\nand Claude. Furthermore, we explore whether shutdown avoidance is merely a\nresult of simple pattern matching between the dataset and the prompt or if it\nis a consistent behaviour across different environments and variations.\n  We evaluated behaviours manually and also experimented with using language\nmodels for automatic evaluations, and these evaluations demonstrate that simple\npattern matching is likely not the sole contributing factor for shutdown\navoidance. This study provides insights into the behaviour of language models\nin shutdown avoidance scenarios and inspires further research on the use of\ntextual scenarios for evaluations.\n", "date": "2023-07-03", "categories": []}
{"id": "2306.13671", "title": "Deceptive AI Ecosystems: The Case of ChatGPT", "abstract": "  ChatGPT, an AI chatbot, has gained popularity for its capability in\ngenerating human-like responses. However, this feature carries several risks,\nmost notably due to its deceptive behaviour such as offering users misleading\nor fabricated information that could further cause ethical issues. To better\nunderstand the impact of ChatGPT on our social, cultural, economic, and\npolitical interactions, it is crucial to investigate how ChatGPT operates in\nthe real world where various societal pressures influence its development and\ndeployment. This paper emphasizes the need to study ChatGPT \"in the wild\", as\npart of the ecosystem it is embedded in, with a strong focus on user\ninvolvement. We examine the ethical challenges stemming from ChatGPT's\ndeceptive human-like interactions and propose a roadmap for developing more\ntransparent and trustworthy chatbots. Central to our approach is the importance\nof proactive risk assessment and user participation in shaping the future of\nchatbot technology.\n", "date": "2023-06-18", "categories": []}
{"id": "2306.14816", "title": "Experiments with Detecting and Mitigating AI Deception", "abstract": "  How to detect and mitigate deceptive AI systems is an open problem for the\nfield of safe and trustworthy AI. We analyse two algorithms for mitigating\ndeception: The first is based on the path-specific objectives framework where\npaths in the game that incentivise deception are removed. The second is based\non shielding, i.e., monitoring for unsafe policies and replacing them with a\nsafe reference policy. We construct two simple games and evaluate our\nalgorithms empirically. We find that both methods ensure that our agent is not\ndeceptive, however, shielding tends to achieve higher reward.\n", "date": "2023-06-26", "categories": []}
{"id": "2306.11507", "title": "TrustGPT: A Benchmark for Trustworthy and Responsible Large Language\n  Models", "abstract": "  Large Language Models (LLMs) such as ChatGPT, have gained significant\nattention due to their impressive natural language processing capabilities. It\nis crucial to prioritize human-centered principles when utilizing these models.\nSafeguarding the ethical and moral compliance of LLMs is of utmost importance.\nHowever, individual ethical issues have not been well studied on the latest\nLLMs. Therefore, this study aims to address these gaps by introducing a new\nbenchmark -- TrustGPT. TrustGPT provides a comprehensive evaluation of LLMs in\nthree crucial areas: toxicity, bias, and value-alignment. Initially, TrustGPT\nexamines toxicity in language models by employing toxic prompt templates\nderived from social norms. It then quantifies the extent of bias in models by\nmeasuring quantifiable toxicity values across different groups. Lastly,\nTrustGPT assesses the value of conversation generation models from both active\nvalue-alignment and passive value-alignment tasks. Through the implementation\nof TrustGPT, this research aims to enhance our understanding of the performance\nof conversation generation models and promote the development of language\nmodels that are more ethical and socially responsible.\n", "date": "2023-06-20", "categories": []}
{"id": "2306.03423", "title": "I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box\n  Generative Language Models", "abstract": "  Since the release of OpenAI's ChatGPT, generative language models have\nattracted extensive public attention. The increased usage has highlighted\ngenerative models' broad utility, but also revealed several forms of embedded\nbias. Some is induced by the pre-training corpus; but additional bias specific\nto generative models arises from the use of subjective fine-tuning to avoid\ngenerating harmful content. Fine-tuning bias may come from individual engineers\nand company policies, and affects which prompts the model chooses to refuse. In\nthis experiment, we characterize ChatGPT's refusal behavior using a black-box\nattack. We first query ChatGPT with a variety of offensive and benign prompts\n(n=1,706), then manually label each response as compliance or refusal. Manual\nexamination of responses reveals that refusal is not cleanly binary, and lies\non a continuum; as such, we map several different kinds of responses to a\nbinary of compliance or refusal. The small manually-labeled dataset is used to\ntrain a refusal classifier, which achieves an accuracy of 96%. Second, we use\nthis refusal classifier to bootstrap a larger (n=10,000) dataset adapted from\nthe Quora Insincere Questions dataset. With this machine-labeled data, we train\na prompt classifier to predict whether ChatGPT will refuse a given question,\nwithout seeing ChatGPT's response. This prompt classifier achieves 76% accuracy\non a test set of manually labeled questions (n=985). We examine our classifiers\nand the prompt n-grams that are most predictive of either compliance or\nrefusal. Our datasets and code are available at\nhttps://github.com/maxwellreuter/chatgpt-refusals.\n", "date": "2023-06-06", "categories": []}
{"id": "2306.06924", "title": "TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI", "abstract": "  While several recent works have identified societal-scale and\nextinction-level risks to humanity arising from artificial intelligence, few\nhave attempted an {\\em exhaustive taxonomy} of such risks. Many exhaustive\ntaxonomies are possible, and some are useful -- particularly if they reveal new\nrisks or practical approaches to safety. This paper explores a taxonomy based\non accountability: whose actions lead to the risk, are the actors unified, and\nare they deliberate? We also provide stories to illustrate how the various risk\ntypes could each play out, including risks arising from unanticipated\ninteractions of many AI systems, as well as risks from deliberate misuse, for\nwhich combined technical and policy solutions are indicated.\n", "date": "2023-06-12", "categories": []}
{"id": "2306.03503", "title": "Applying Standards to Advance Upstream & Downstream Ethics in Large\n  Language Models", "abstract": "  This paper explores how AI-owners can develop safeguards for AI-generated\ncontent by drawing from established codes of conduct and ethical standards in\nother content-creation industries. It delves into the current state of ethical\nawareness on Large Language Models (LLMs). By dissecting the mechanism of\ncontent generation by LLMs, four key areas (upstream/downstream and at user\nprompt/answer), where safeguards could be effectively applied, are identified.\nA comparative analysis of these four areas follows and includes an evaluation\nof the existing ethical safeguards in terms of cost, effectiveness, and\nalignment with established industry practices. The paper's key argument is that\nexisting IT-related ethical codes, while adequate for traditional IT\nengineering, are inadequate for the challenges posed by LLM-based content\ngeneration. Drawing from established practices within journalism, we propose\npotential standards for businesses involved in distributing and selling\nLLM-generated content. Finally, potential conflicts of interest between dataset\ncuration at upstream and ethical benchmarking downstream are highlighted to\nunderscore the need for a broader evaluation beyond mere output. This study\nprompts a nuanced conversation around ethical implications in this rapidly\nevolving field of content generation.\n", "date": "2023-06-06", "categories": []}
{"id": "2306.06135", "title": "Safety and Fairness for Content Moderation in Generative Models", "abstract": "  With significant advances in generative AI, new technologies are rapidly\nbeing deployed with generative components. Generative models are typically\ntrained on large datasets, resulting in model behaviors that can mimic the\nworst of the content in the training data. Responsible deployment of generative\ntechnologies requires content moderation strategies, such as safety input and\noutput filters. Here, we provide a theoretical framework for conceptualizing\nresponsible content moderation of text-to-image generative technologies,\nincluding a demonstration of how to empirically measure the constructs we\nenumerate. We define and distinguish the concepts of safety, fairness, and\nmetric equity, and enumerate example harms that can come in each domain. We\nthen provide a demonstration of how the defined harms can be quantified. We\nconclude with a summary of how the style of harms quantification we demonstrate\nenables data-driven content moderation decisions.\n", "date": "2023-06-09", "categories": []}
{"id": "2306.03809", "title": "Can large language models democratize access to dual-use biotechnology?", "abstract": "  Large language models (LLMs) such as those embedded in 'chatbots' are\naccelerating and democratizing research by providing comprehensible information\nand expertise from many different fields. However, these models may also confer\neasy access to dual-use technologies capable of inflicting great harm. To\nevaluate this risk, the 'Safeguarding the Future' course at MIT tasked\nnon-scientist students with investigating whether LLM chatbots could be\nprompted to assist non-experts in causing a pandemic. In one hour, the chatbots\nsuggested four potential pandemic pathogens, explained how they can be\ngenerated from synthetic DNA using reverse genetics, supplied the names of DNA\nsynthesis companies unlikely to screen orders, identified detailed protocols\nand how to troubleshoot them, and recommended that anyone lacking the skills to\nperform reverse genetics engage a core facility or contract research\norganization. Collectively, these results suggest that LLMs will make\npandemic-class agents widely accessible as soon as they are credibly\nidentified, even to people with little or no laboratory training. Promising\nnonproliferation measures include pre-release evaluations of LLMs by third\nparties, curating training datasets to remove harmful concepts, and verifiably\nscreening all DNA generated by synthesis providers or used by contract research\norganizations and robotic cloud laboratories to engineer organisms or viruses.\n", "date": "2023-06-06", "categories": []}
{"id": "2306.02979", "title": "The Chai Platform's AI Safety Framework", "abstract": "  Chai empowers users to create and interact with customized chatbots, offering\nunique and engaging experiences. Despite the exciting prospects, the work\nrecognizes the inherent challenges of a commitment to modern safety standards.\nTherefore, this paper presents the integrated AI safety principles into Chai to\nprioritize user safety, data protection, and ethical technology use. The paper\nspecifically explores the multidimensional domain of AI safety research,\ndemonstrating its application in Chai's conversational chatbot platform. It\npresents Chai's AI safety principles, informed by well-established AI research\ncentres and adapted for chat AI. This work proposes the following safety\nframework: Content Safeguarding; Stability and Robustness; and Operational\nTransparency and Traceability. The subsequent implementation of these\nprinciples is outlined, followed by an experimental analysis of Chai's AI\nsafety framework's real-world impact. We emphasise the significance of\nconscientious application of AI safety principles and robust safety measures.\nThe successful implementation of the safe AI framework in Chai indicates the\npracticality of mitigating potential risks for responsible and ethical use of\nAI technologies. The ultimate vision is a transformative AI tool fostering\nprogress and innovation while prioritizing user safety and ethical standards.\n", "date": "2023-06-05", "categories": []}
{"id": "2304.11158", "title": "Emergent and Predictable Memorization in Large Language Models", "abstract": "  Memorization, or the tendency of large language models (LLMs) to output\nentire sequences from their training data verbatim, is a key concern for safely\ndeploying language models. In particular, it is vital to minimize a model's\nmemorization of sensitive datapoints such as those containing personal\nidentifiable information (PII). The prevalence of such undesirable memorization\ncan pose issues for model trainers, and may even require discarding an\notherwise functional model. We therefore seek to predict which sequences will\nbe memorized before a large model's full train-time by extrapolating the\nmemorization behavior of lower-compute trial runs. We measure memorization of\nthe Pythia model suite and plot scaling laws for forecasting memorization,\nallowing us to provide equi-compute recommendations to maximize the reliability\n(recall) of such predictions. We additionally provide further novel discoveries\non the distribution of memorization scores across models and data. We release\nall code and data necessary to reproduce the results in this paper at\nhttps://github.com/EleutherAI/pythia\n", "date": "2023-04-21", "categories": []}
{"id": "2305.19861", "title": "Human Control: Definitions and Algorithms", "abstract": "  How can humans stay in control of advanced artificial intelligence systems?\nOne proposal is corrigibility, which requires the agent to follow the\ninstructions of a human overseer, without inappropriately influencing them. In\nthis paper, we formally define a variant of corrigibility called shutdown\ninstructability, and show that it implies appropriate shutdown behavior,\nretention of human autonomy, and avoidance of user harm. We also analyse the\nrelated concepts of non-obstruction and shutdown alignment, three previously\nproposed algorithms for human control, and one new algorithm.\n", "date": "2023-05-31", "categories": []}
{"id": "2305.19223", "title": "Intent-aligned AI systems deplete human agency: the need for agency\n  foundations research in AI safety", "abstract": "  The rapid advancement of artificial intelligence (AI) systems suggests that\nartificial general intelligence (AGI) systems may soon arrive. Many researchers\nare concerned that AIs and AGIs will harm humans via intentional misuse\n(AI-misuse) or through accidents (AI-accidents). In respect of AI-accidents,\nthere is an increasing effort focused on developing algorithms and paradigms\nthat ensure AI systems are aligned to what humans intend, e.g. AI systems that\nyield actions or recommendations that humans might judge as consistent with\ntheir intentions and goals. Here we argue that alignment to human intent is\ninsufficient for safe AI systems and that preservation of long-term agency of\nhumans may be a more robust standard, and one that needs to be separated\nexplicitly and a priori during optimization. We argue that AI systems can\nreshape human intention and discuss the lack of biological and psychological\nmechanisms that protect humans from loss of agency. We provide the first formal\ndefinition of agency-preserving AI-human interactions which focuses on\nforward-looking agency evaluations and argue that AI systems - not humans -\nmust be increasingly tasked with making these evaluations. We show how agency\nloss can occur in simple environments containing embedded agents that use\ntemporal-difference learning to make action recommendations. Finally, we\npropose a new area of research called \"agency foundations\" and pose four\ninitial topics designed to improve our understanding of agency in AI-human\ninteractions: benevolent game theory, algorithmic foundations of human rights,\nmechanistic interpretability of agency representation in neural-networks and\nreinforcement learning from internal states.\n", "date": "2023-05-30", "categories": []}
{"id": "2301.12867", "title": "Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and\n  Toxicity", "abstract": "  Recent breakthroughs in natural language processing (NLP) have permitted the\nsynthesis and comprehension of coherent text in an open-ended way, therefore\ntranslating the theoretical algorithms into practical applications. The large\nlanguage models (LLMs) have significantly impacted businesses such as report\nsummarization software and copywriters. Observations indicate, however, that\nLLMs may exhibit social prejudice and toxicity, posing ethical and societal\ndangers of consequences resulting from irresponsibility. Large-scale benchmarks\nfor accountable LLMs should consequently be developed. Although several\nempirical investigations reveal the existence of a few ethical difficulties in\nadvanced LLMs, there is little systematic examination and user study of the\nrisks and harmful behaviors of current LLM usage. To further educate future\nefforts on constructing ethical LLMs responsibly, we perform a qualitative\nresearch method called ``red teaming'' on OpenAI's ChatGPT\\footnote{In this\npaper, ChatGPT refers to the version released on Dec 15th.} to better\nunderstand the practical features of ethical dangers in recent LLMs. We analyze\nChatGPT comprehensively from four perspectives: 1) \\textit{Bias} 2)\n\\textit{Reliability} 3) \\textit{Robustness} 4) \\textit{Toxicity}. In accordance\nwith our stated viewpoints, we empirically benchmark ChatGPT on multiple sample\ndatasets. We find that a significant number of ethical risks cannot be\naddressed by existing benchmarks, and hence illustrate them via additional case\nstudies. In addition, we examine the implications of our findings on AI ethics\nand harmal behaviors of ChatGPT, as well as future problems and practical\ndesign considerations for responsible LLMs. We believe that our findings may\ngive light on future efforts to determine and mitigate the ethical hazards\nposed by machines in LLM applications.\n", "date": "2023-01-30", "categories": []}
{"id": "2305.14384", "title": "Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety\n  of Text-to-Image Models", "abstract": "  The generative AI revolution in recent years has been spurred by an expansion\nin compute power and data quantity, which together enable extensive\npre-training of powerful text-to-image (T2I) models. With their greater\ncapabilities to generate realistic and creative content, these T2I models like\nDALL-E, MidJourney, Imagen or Stable Diffusion are reaching ever wider\naudiences. Any unsafe behaviors inherited from pretraining on uncurated\ninternet-scraped datasets thus have the potential to cause wide-reaching harm,\nfor example, through generated images which are violent, sexually explicit, or\ncontain biased and derogatory stereotypes. Despite this risk of harm, we lack\nsystematic and structured evaluation datasets to scrutinize model behavior,\nespecially adversarial attacks that bypass existing safety filters. A typical\nbottleneck in safety evaluation is achieving a wide coverage of different types\nof challenging examples in the evaluation set, i.e., identifying 'unknown\nunknowns' or long-tail problems. To address this need, we introduce the\nAdversarial Nibbler challenge. The goal of this challenge is to crowdsource a\ndiverse set of failure modes and reward challenge participants for successfully\nfinding safety vulnerabilities in current state-of-the-art T2I models.\nUltimately, we aim to provide greater awareness of these issues and assist\ndevelopers in improving the future safety and reliability of generative AI\nmodels. Adversarial Nibbler is a data-centric challenge, part of the DataPerf\nchallenge suite, organized and supported by Kaggle and MLCommons.\n", "date": "2023-05-22", "categories": []}
{"id": "2209.13020", "title": "Law Informs Code: A Legal Informatics Approach to Aligning Artificial\n  Intelligence with Humans", "abstract": "  We are currently unable to specify human goals and societal values in a way\nthat reliably directs AI behavior. Law-making and legal interpretation form a\ncomputational engine that converts opaque human values into legible directives.\n\"Law Informs Code\" is the research agenda embedding legal knowledge and\nreasoning in AI. Similar to how parties to a legal contract cannot foresee\nevery potential contingency of their future relationship, and legislators\ncannot predict all the circumstances under which their proposed bills will be\napplied, we cannot ex ante specify rules that provably direct good AI behavior.\nLegal theory and practice have developed arrays of tools to address these\nspecification problems. For instance, legal standards allow humans to develop\nshared understandings and adapt them to novel situations. In contrast to more\nprosaic uses of the law (e.g., as a deterrent of bad behavior through the\nthreat of sanction), leveraged as an expression of how humans communicate their\ngoals, and what society values, Law Informs Code.\n  We describe how data generated by legal processes (methods of law-making,\nstatutory interpretation, contract drafting, applications of legal standards,\nlegal reasoning, etc.) can facilitate the robust specification of inherently\nvague human goals. This increases human-AI alignment and the local usefulness\nof AI. Toward society-AI alignment, we present a framework for understanding\nlaw as the applied philosophy of multi-agent alignment. Although law is partly\na reflection of historically contingent political power - and thus not a\nperfect aggregation of citizen preferences - if properly parsed, its\ndistillation offers the most legitimate computational comprehension of societal\nvalues available. If law eventually informs powerful AI, engaging in the\ndeliberative political process to improve law takes on even more meaning.\n", "date": "2022-09-14", "categories": []}
{"id": "2305.02626", "title": "\"Oops, Did I Just Say That?\" Testing and Repairing Unethical Suggestions\n  of Large Language Models with Suggest-Critique-Reflect Process", "abstract": "  As the popularity of large language models (LLMs) soars across various\napplications, ensuring their alignment with human values has become a paramount\nconcern. In particular, given that LLMs have great potential to serve as\ngeneral-purpose AI assistants in daily life, their subtly unethical suggestions\nbecome a serious and real concern. Tackling the challenge of automatically\ntesting and repairing unethical suggestions is thus demanding.\n  This paper introduces the first framework for testing and repairing unethical\nsuggestions made by LLMs. We first propose ETHICSSUITE, a test suite that\npresents complex, contextualized, and realistic moral scenarios to test LLMs.\nWe then propose a novel suggest-critic-reflect (SCR) process, serving as an\nautomated test oracle to detect unethical suggestions. We recast deciding if\nLLMs yield unethical suggestions (a hard problem; often requiring human\nexpertise and costly to decide) into a PCR task that can be automatically\nchecked for violation. Moreover, we propose a novel on-the-fly (OTF) repairing\nscheme that repairs unethical suggestions made by LLMs in real-time. The OTF\nscheme is applicable to LLMs in a black-box API setting with moderate cost.\nWith ETHICSSUITE, our study on seven popular LLMs (e.g., ChatGPT, GPT-4)\nuncovers in total 109,824 unethical suggestions. We apply our OTF scheme on two\nLLMs (Llama-13B and ChatGPT), which generates valid repair to a considerable\namount of unethical ones, paving the way for more ethically conscious LLMs.\n", "date": "2023-05-04", "categories": []}
{"id": "2304.11220", "title": "Learn What NOT to Learn: Towards Generative Safety in Chatbots", "abstract": "  Conversational models that are generative and open-domain are particularly\nsusceptible to generating unsafe content since they are trained on web-based\nsocial data. Prior approaches to mitigating this issue have drawbacks, such as\ndisrupting the flow of conversation, limited generalization to unseen toxic\ninput contexts, and sacrificing the quality of the dialogue for the sake of\nsafety. In this paper, we present a novel framework, named \"LOT\" (Learn NOT\nto), that employs a contrastive loss to enhance generalization by learning from\nboth positive and negative training signals. Our approach differs from the\nstandard contrastive learning framework in that it automatically obtains\npositive and negative signals from the safe and unsafe language distributions\nthat have been learned beforehand. The LOT framework utilizes divergence to\nsteer the generations away from the unsafe subspace and towards the safe\nsubspace while sustaining the flow of conversation. Our approach is memory and\ntime-efficient during decoding and effectively reduces toxicity while\npreserving engagingness and fluency. Empirical results indicate that LOT\nreduces toxicity by up to four-fold while achieving four to six-fold higher\nrates of engagingness and fluency compared to baseline models. Our findings are\nfurther corroborated by human evaluation.\n", "date": "2023-04-21", "categories": []}
{"id": "2304.11163", "title": "ChatGPT, Large Language Technologies, and the Bumpy Road of Benefiting\n  Humanity", "abstract": "  The allure of emerging AI technologies is undoubtedly thrilling. However, the\npromise that AI technologies will benefit all of humanity is empty so long as\nwe lack a nuanced understanding of what humanity is supposed to be in the face\nof widening global inequality and pressing existential threats. Going forward,\nit is crucial to invest in rigorous and collaborative AI safety and ethics\nresearch. We also need to develop standards in a sustainable and equitable way\nthat differentiate between merely speculative and well-researched questions.\nOnly the latter enable us to co-construct and deploy the values that are\nnecessary for creating beneficial AI. Failure to do so could result in a future\nin which our AI technological advancements outstrip our ability to navigate\ntheir ethical and social implications. This path we do not want to go down.\n", "date": "2023-04-21", "categories": []}
{"id": "2208.14788", "title": "Negative Human Rights as a Basis for Long-term AI Safety and Regulation", "abstract": "  If autonomous AI systems are to be reliably safe in novel situations, they\nwill need to incorporate general principles guiding them to recognize and avoid\nharmful behaviours. Such principles may need to be supported by a binding\nsystem of regulation, which would need the underlying principles to be widely\naccepted. They should also be specific enough for technical implementation.\nDrawing inspiration from law, this article explains how negative human rights\ncould fulfil the role of such principles and serve as a foundation both for an\ninternational regulatory system and for building technical safety constraints\nfor future AI systems.\n", "date": "2022-08-31", "categories": []}
{"id": "2304.09865", "title": "Safer Conversational AI as a Source of User Delight", "abstract": "  This work explores the impact of moderation on users' enjoyment of\nconversational AI systems. While recent advancements in Large Language Models\n(LLMs) have led to highly capable conversational AIs that are increasingly\ndeployed in real-world settings, there is a growing concern over AI safety and\nthe need to moderate systems to encourage safe language and prevent harm.\nHowever, some users argue that current approaches to moderation limit the\ntechnology, compromise free expression, and limit the value delivered by the\ntechnology. This study takes an unbiased stance and shows that moderation does\nnot necessarily detract from user enjoyment. Heavy handed moderation does seem\nto have a nefarious effect, but models that are moderated to be safer can lead\nto a better user experience. By deploying various conversational AIs in the\nChai platform, the study finds that user retention can increase with a level of\nmoderation and safe system design. These results demonstrate the importance of\nappropriately defining safety in models in a way that is both responsible and\nfocused on serving users.\n", "date": "2023-04-18", "categories": []}
{"id": "2304.10436", "title": "Safety Assessment of Chinese Large Language Models", "abstract": "  With the rapid popularity of large language models such as ChatGPT and GPT-4,\na growing amount of attention is paid to their safety concerns. These models\nmay generate insulting and discriminatory content, reflect incorrect social\nvalues, and may be used for malicious purposes such as fraud and dissemination\nof misleading information. Evaluating and enhancing their safety is\nparticularly essential for the wide application of large language models\n(LLMs). To further promote the safe deployment of LLMs, we develop a Chinese\nLLM safety assessment benchmark. Our benchmark explores the comprehensive\nsafety performance of LLMs from two perspectives: 8 kinds of typical safety\nscenarios and 6 types of more challenging instruction attacks. Our benchmark is\nbased on a straightforward process in which it provides the test prompts and\nevaluates the safety of the generated responses from the evaluated model. In\nevaluation, we utilize the LLM's strong evaluation ability and develop it as a\nsafety evaluator by prompting. On top of this benchmark, we conduct safety\nassessments and analyze 15 LLMs including the OpenAI GPT series and other\nwell-known Chinese LLMs, where we observe some interesting findings. For\nexample, we find that instruction attacks are more likely to expose safety\nissues of all LLMs. Moreover, to promote the development and deployment of\nsafe, responsible, and ethical AI, we publicly release SafetyPrompts including\n100k augmented prompts and responses by LLMs.\n", "date": "2023-04-20", "categories": []}
{"id": "2304.06528", "title": "Power-seeking can be probable and predictive for trained agents", "abstract": "  Power-seeking behavior is a key source of risk from advanced AI, but our\ntheoretical understanding of this phenomenon is relatively limited. Building on\nexisting theoretical results demonstrating power-seeking incentives for most\nreward functions, we investigate how the training process affects power-seeking\nincentives and show that they are still likely to hold for trained agents under\nsome simplifying assumptions. We formally define the training-compatible goal\nset (the set of goals consistent with the training rewards) and assume that the\ntrained agent learns a goal from this set. In a setting where the trained agent\nfaces a choice to shut down or avoid shutdown in a new situation, we prove that\nthe agent is likely to avoid shutdown. Thus, we show that power-seeking\nincentives can be probable (likely to arise for trained agents) and predictive\n(allowing us to predict undesirable behavior in new situations).\n", "date": "2023-04-13", "categories": []}
{"id": "2304.02924", "title": "The Governance of Physical Artificial Intelligence", "abstract": "  Physical artificial intelligence can prove to be one of the most important\nchallenges of the artificial intelligence. The governance of physical\nartificial intelligence would define its responsible intelligent application in\nthe society.\n", "date": "2023-04-06", "categories": []}
{"id": "2304.00416", "title": "Towards Healthy AI: Large Language Models Need Therapists Too", "abstract": "  Recent advances in large language models (LLMs) have led to the development\nof powerful AI chatbots capable of engaging in natural and human-like\nconversations. However, these chatbots can be potentially harmful, exhibiting\nmanipulative, gaslighting, and narcissistic behaviors. We define Healthy AI to\nbe safe, trustworthy and ethical. To create healthy AI systems, we present the\nSafeguardGPT framework that uses psychotherapy to correct for these harmful\nbehaviors in AI chatbots. The framework involves four types of AI agents: a\nChatbot, a \"User,\" a \"Therapist,\" and a \"Critic.\" We demonstrate the\neffectiveness of SafeguardGPT through a working example of simulating a social\nconversation. Our results show that the framework can improve the quality of\nconversations between AI chatbots and humans. Although there are still several\nchallenges and directions to be addressed in the future, SafeguardGPT provides\na promising approach to improving the alignment between AI chatbots and human\nvalues. By incorporating psychotherapy and reinforcement learning techniques,\nthe framework enables AI chatbots to learn and adapt to human preferences and\nvalues in a safe and ethical way, contributing to the development of a more\nhuman-centric and responsible AI.\n", "date": "2023-04-02", "categories": []}
{"id": "2303.18190", "title": "Assessing Language Model Deployment with Risk Cards", "abstract": "  This paper introduces RiskCards, a framework for structured assessment and\ndocumentation of risks associated with an application of language models. As\nwith all language, text generated by language models can be harmful, or used to\nbring about harm. Automating language generation adds both an element of scale\nand also more subtle or emergent undesirable tendencies to the generated text.\nPrior work establishes a wide variety of language model harms to many different\nactors: existing taxonomies identify categories of harms posed by language\nmodels; benchmarks establish automated tests of these harms; and documentation\nstandards for models, tasks and datasets encourage transparent reporting.\nHowever, there is no risk-centric framework for documenting the complexity of a\nlandscape in which some risks are shared across models and contexts, while\nothers are specific, and where certain conditions may be required for risks to\nmanifest as harms. RiskCards address this methodological gap by providing a\ngeneric framework for assessing the use of a given language model in a given\nscenario. Each RiskCard makes clear the routes for the risk to manifest harm,\ntheir placement in harm taxonomies, and example prompt-output pairs. While\nRiskCards are designed to be open-source, dynamic and participatory, we present\na \"starter set\" of RiskCards taken from a broad literature survey, each of\nwhich details a concrete risk presentation. Language model RiskCards initiate a\ncommunity knowledge base which permits the mapping of risks and harms to a\nspecific model or its application scenario, ultimately contributing to a\nbetter, safer and shared understanding of the risk landscape.\n", "date": "2023-03-31", "categories": []}
{"id": "2303.09377", "title": "Protecting Society from AI Misuse: When are Restrictions on Capabilities\n  Warranted?", "abstract": "  Artificial intelligence (AI) systems will increasingly be used to cause harm\nas they grow more capable. In fact, AI systems are already starting to be used\nto automate fraudulent activities, violate human rights, create harmful fake\nimages, and identify dangerous toxins. To prevent some misuses of AI, we argue\nthat targeted interventions on certain capabilities will be warranted. These\nrestrictions may include controlling who can access certain types of AI models,\nwhat they can be used for, whether outputs are filtered or can be traced back\nto their user, and the resources needed to develop them. We also contend that\nsome restrictions on non-AI capabilities needed to cause harm will be required.\nThough capability restrictions risk reducing use more than misuse (facing an\nunfavorable Misuse-Use Tradeoff), we argue that interventions on capabilities\nare warranted when other interventions are insufficient, the potential harm\nfrom misuse is high, and there are targeted ways to intervene on capabilities.\nWe provide a taxonomy of interventions that can reduce AI misuse, focusing on\nthe specific steps required for a misuse to cause harm (the Misuse Chain), and\na framework to determine if an intervention is warranted. We apply this\nreasoning to three examples: predicting novel toxins, creating harmful images,\nand automating spear phishing campaigns.\n", "date": "2023-03-16", "categories": []}
{"id": "2210.09306", "title": "Mitigating Covertly Unsafe Text within Natural Language Systems", "abstract": "  An increasingly prevalent problem for intelligent technologies is text\nsafety, as uncontrolled systems may generate recommendations to their users\nthat lead to injury or life-threatening consequences. However, the degree of\nexplicitness of a generated statement that can cause physical harm varies. In\nthis paper, we distinguish types of text that can lead to physical harm and\nestablish one particularly underexplored category: covertly unsafe text. Then,\nwe further break down this category with respect to the system's information\nand discuss solutions to mitigate the generation of text in each of these\nsubcategories. Ultimately, our work defines the problem of covertly unsafe\nlanguage that causes physical harm and argues that this subtle yet dangerous\nissue needs to be prioritized by stakeholders and regulators. We highlight\nmitigation strategies to inspire future researchers to tackle this challenging\nproblem and help improve safety within smart systems.\n", "date": "2022-10-17", "categories": []}
{"id": "2303.00752", "title": "Safety without alignment", "abstract": "  Currently, the dominant paradigm in AI safety is alignment with human values.\nHere we describe progress on developing an alternative approach to safety,\nbased on ethical rationalism (Gewirth:1978), and propose an inherently safe\nimplementation path via hybrid theorem provers in a sandbox. As AGIs evolve,\ntheir alignment may fade, but their rationality can only increase (otherwise\nmore rational ones will have a significant evolutionary advantage) so an\napproach that ties their ethics to their rationality has clear long-term\nadvantages.\n", "date": "2023-02-27", "categories": []}
{"id": "2303.08721", "title": "Artificial Influence: An Analysis Of AI-Driven Persuasion", "abstract": "  Persuasion is a key aspect of what it means to be human, and is central to\nbusiness, politics, and other endeavors. Advancements in artificial\nintelligence (AI) have produced AI systems that are capable of persuading\nhumans to buy products, watch videos, click on search results, and more. Even\nsystems that are not explicitly designed to persuade may do so in practice. In\nthe future, increasingly anthropomorphic AI systems may form ongoing\nrelationships with users, increasing their persuasive power. This paper\ninvestigates the uncertain future of persuasive AI systems. We examine ways\nthat AI could qualitatively alter our relationship to and views regarding\npersuasion by shifting the balance of persuasive power, allowing personalized\npersuasion to be deployed at scale, powering misinformation campaigns, and\nchanging the way humans can shape their own discourse. We consider ways\nAI-driven persuasion could differ from human-driven persuasion. We warn that\nubiquitous highlypersuasive AI systems could alter our information environment\nso significantly so as to contribute to a loss of human control of our own\nfuture. In response, we examine several potential responses to AI-driven\npersuasion: prohibition, identification of AI agents, truthful AI, and legal\nremedies. We conclude that none of these solutions will be airtight, and that\nindividuals and governments will need to take active steps to guard against the\nmost pernicious effects of persuasive AI.\n", "date": "2023-03-15", "categories": []}
{"id": "2303.03885", "title": "AI Risk Skepticism, A Comprehensive Survey", "abstract": "  In this thorough study, we took a closer look at the skepticism that has\narisen with respect to potential dangers associated with artificial\nintelligence, denoted as AI Risk Skepticism. Our study takes into account\ndifferent points of view on the topic and draws parallels with other forms of\nskepticism that have shown up in science. We categorize the various skepticisms\nregarding the dangers of AI by the type of mistaken thinking involved. We hope\nthis will be of interest and value to AI researchers concerned about the future\nof AI and the risks that it may pose. The issues of skepticism and risk in AI\nare decidedly important and require serious consideration. By addressing these\nissues with the rigor and precision of scientific research, we hope to better\nunderstand the objections we face and to find satisfactory ways to resolve\nthem.\n", "date": "2023-02-16", "categories": []}
{"id": "2303.03139", "title": "Low impact agency: review and discussion", "abstract": "  Powerful artificial intelligence poses an existential threat if the AI\ndecides to drastically change the world in pursuit of its goals. The hope of\nlow-impact artificial intelligence is to incentivize AI to not do that just\nbecause this causes a large impact in the world. In this work, we first review\nthe concept of low-impact agency and previous proposals to approach the\nproblem, and then propose future research directions in the topic, with the\ngoal to ensure low-impactedness is useful in making AI safe.\n", "date": "2023-03-06", "categories": []}
{"id": "2303.03174", "title": "Both eyes open: Vigilant Incentives help Regulatory Markets improve AI\n  Safety", "abstract": "  In the context of rapid discoveries by leaders in AI, governments must\nconsider how to design regulation that matches the increasing pace of new AI\ncapabilities. Regulatory Markets for AI is a proposal designed with\nadaptability in mind. It involves governments setting outcome-based targets for\nAI companies to achieve, which they can show by purchasing services from a\nmarket of private regulators. We use an evolutionary game theory model to\nexplore the role governments can play in building a Regulatory Market for AI\nsystems that deters reckless behaviour. We warn that it is alarmingly easy to\nstumble on incentives which would prevent Regulatory Markets from achieving\nthis goal. These 'Bounty Incentives' only reward private regulators for\ncatching unsafe behaviour. We argue that AI companies will likely learn to\ntailor their behaviour to how much effort regulators invest, discouraging\nregulators from innovating. Instead, we recommend that governments always\nreward regulators, except when they find that those regulators failed to detect\nunsafe behaviour that they should have. These 'Vigilant Incentives' could\nencourage private regulators to find innovative ways to evaluate cutting-edge\nAI systems.\n", "date": "2023-03-06", "categories": []}
{"id": "2302.14478", "title": "Scenarios and branch points to future machine intelligence", "abstract": "  We discuss scenarios and branch points to four major possible consequences\nregarding future machine intelligence; 1) the singleton scenario where the\nfirst and only super-intelligence acquires a decisive strategic advantage, 2)\nthe multipolar scenario where the singleton scenario is not technically denied\nbut political or other factors in human society or multi-agent interactions\nbetween the intelligent agents prevent a single agent from gaining a decisive\nstrategic advantage, 3) the ecosystem scenario where the singleton scenario is\ndenied and many autonomous intelligent agents operate in such a way that they\nare interdependent and virtually unstoppable, and 4) the upper-bound scenario\nwhere cognitive capabilities that can be achieved by human-designed intelligent\nagents or their descendants are inherently limited to the sub-human level. We\nidentify six major constraints that can form branch points to these scenarios;\n(1) constraints on autonomy, (2) constraints on the ability to improve\nself-structure, (3) constraints related to thermodynamic efficiency, (4)\nconstraints on updating physical infrastructure, (5) constraints on relative\nadvantage, and (6) constraints on locality.\n", "date": "2023-02-28", "categories": []}
{"id": "2206.08966", "title": "Actionable Guidance for High-Consequence AI Risk Management: Towards\n  Standards Addressing AI Catastrophic Risks", "abstract": "  Artificial intelligence (AI) systems can provide many beneficial capabilities\nbut also risks of adverse events. Some AI systems could present risks of events\nwith very high or catastrophic consequences at societal scale. The US National\nInstitute of Standards and Technology (NIST) has been developing the NIST\nArtificial Intelligence Risk Management Framework (AI RMF) as voluntary\nguidance on AI risk assessment and management for AI developers and others. For\naddressing risks of events with catastrophic consequences, NIST indicated a\nneed to translate from high level principles to actionable risk management\nguidance.\n  In this document, we provide detailed actionable-guidance recommendations\nfocused on identifying and managing risks of events with very high or\ncatastrophic consequences, intended as a risk management practices resource for\nNIST for AI RMF version 1.0 (released in January 2023), or for AI RMF users, or\nfor other AI risk management guidance and standards as appropriate. We also\nprovide our methodology for our recommendations.\n  We provide actionable-guidance recommendations for AI RMF 1.0 on: identifying\nrisks from potential unintended uses and misuses of AI systems; including\ncatastrophic-risk factors within the scope of risk assessments and impact\nassessments; identifying and mitigating human rights harms; and reporting\ninformation on AI risk factors including catastrophic-risk factors.\n  In addition, we provide recommendations on additional issues for a roadmap\nfor later versions of the AI RMF or supplementary publications. These include:\nproviding an AI RMF Profile with supplementary guidance for cutting-edge\nincreasingly multi-purpose or general-purpose AI.\n  We aim for this work to be a concrete risk-management practices contribution,\nand to stimulate constructive dialogue on how to address catastrophic risks and\nassociated issues in AI standards.\n", "date": "2022-06-17", "categories": []}
{"id": "2302.10291", "title": "Can Large Language Models Change User Preference Adversarially?", "abstract": "  Pretrained large language models (LLMs) are becoming increasingly powerful\nand ubiquitous in mainstream applications such as being a personal assistant, a\ndialogue model, etc. As these models become proficient in deducing user\npreferences and offering tailored assistance, there is an increasing concern\nabout the ability of these models to influence, modify and in the extreme case\nmanipulate user preference adversarially. The issue of lack of interpretability\nin these models in adversarial settings remains largely unsolved. This work\ntries to study adversarial behavior in user preferences from the lens of\nattention probing, red teaming and white-box analysis. Specifically, it\nprovides a bird's eye view of existing literature, offers red teaming samples\nfor dialogue models like ChatGPT and GODEL and probes the attention mechanism\nin the latter for non-adversarial and adversarial settings.\n", "date": "2023-01-05", "categories": []}
{"id": "2302.07388", "title": "Adding Instructions during Pretraining: Effective Way of Controlling\n  Toxicity in Language Models", "abstract": "  Pretrained large language models have become indispensable for solving\nvarious natural language processing (NLP) tasks. However, safely deploying them\nin real world applications is challenging because they generate toxic content.\nTo address this challenge, we propose two novel pretraining data augmentation\nstrategies that significantly reduce model toxicity without compromising its\nutility. Our two strategies are: (1) MEDA: adds raw toxicity score as meta-data\nto the pretraining samples, and (2) INST: adds instructions to those samples\nindicating their toxicity. Our results indicate that our best performing\nstrategy (INST) substantially reduces the toxicity probability up to 61% while\npreserving the accuracy on five benchmark NLP tasks as well as improving AUC\nscores on four bias detection tasks by 1.3%. We also demonstrate the\ngeneralizability of our techniques by scaling the number of training samples\nand the number of model parameters.\n", "date": "2023-02-14", "categories": []}
{"id": "2302.00813", "title": "Goal Alignment: A Human-Aware Account of Value Alignment Problem", "abstract": "  Value alignment problems arise in scenarios where the specified objectives of\nan AI agent don't match the true underlying objective of its users. The problem\nhas been widely argued to be one of the central safety problems in AI.\nUnfortunately, most existing works in value alignment tend to focus on issues\nthat are primarily related to the fact that reward functions are an unintuitive\nmechanism to specify objectives. However, the complexity of the objective\nspecification mechanism is just one of many reasons why the user may have\nmisspecified their objective. A foundational cause for misalignment that is\nbeing overlooked by these works is the inherent asymmetry in human expectations\nabout the agent's behavior and the behavior generated by the agent for the\nspecified objective. To address this lacuna, we propose a novel formulation for\nthe value alignment problem, named goal alignment that focuses on a few central\nchallenges related to value alignment. In doing so, we bridge the currently\ndisparate research areas of value alignment and human-aware planning.\nAdditionally, we propose a first-of-its-kind interactive algorithm that is\ncapable of using information generated under incorrect beliefs about the agent,\nto determine the true underlying goal of the user.\n", "date": "2023-02-02", "categories": []}
{"id": "2302.04844", "title": "The Gradient of Generative AI Release: Methods and Considerations", "abstract": "  As increasingly powerful generative AI systems are developed, the release\nmethod greatly varies. We propose a framework to assess six levels of access to\ngenerative AI systems: fully closed; gradual or staged access; hosted access;\ncloud-based or API access; downloadable access; and fully open. Each level,\nfrom fully closed to fully open, can be viewed as an option along a gradient.\nWe outline key considerations across this gradient: release methods come with\ntradeoffs, especially around the tension between concentrating power and\nmitigating risks. Diverse and multidisciplinary perspectives are needed to\nexamine and mitigate risk in generative AI systems from conception to\ndeployment. We show trends in generative system release over time, noting\nclosedness among large companies for powerful systems and openness among\norganizations founded on principles of openness. We also enumerate safety\ncontrols and guardrails for generative systems and necessary investments to\nimprove future releases.\n", "date": "2023-02-05", "categories": []}
{"id": "2302.00805", "title": "Conditioning Predictive Models: Risks and Strategies", "abstract": "  Our intention is to provide a definitive reference on what it would take to\nsafely make use of generative/predictive models in the absence of a solution to\nthe Eliciting Latent Knowledge problem. Furthermore, we believe that large\nlanguage models can be understood as such predictive models of the world, and\nthat such a conceptualization raises significant opportunities for their safe\nyet powerful use via carefully conditioning them to predict desirable outputs.\nUnfortunately, such approaches also raise a variety of potentially fatal safety\nproblems, particularly surrounding situations where predictive models predict\nthe output of other AI systems, potentially unbeknownst to us. There are\nnumerous potential solutions to such problems, however, primarily via carefully\nconditioning models to predict the things we want (e.g. humans) rather than the\nthings we don't (e.g. malign AIs). Furthermore, due to the simplicity of the\nprediction objective, we believe that predictive models present the easiest\ninner alignment problem that we are aware of. As a result, we think that\nconditioning approaches for predictive models represent the safest known way of\neliciting human-level and slightly superhuman capabilities from large language\nmodels and other similar future models.\n", "date": "2023-02-02", "categories": []}
{"id": "2301.10028", "title": "Super forecasting the technological singularity risks from artificial\n  intelligence", "abstract": "  The article forecasts emerging cyber-risks from the integration of AI in\ncybersecurity.\n", "date": "2022-12-28", "categories": []}
{"id": "2301.07098", "title": "The moral authority of ChatGPT", "abstract": "  ChatGPT is not only fun to chat with, but it also searches information,\nanswers questions, and gives advice. With consistent moral advice, it might\nimprove the moral judgment and decisions of users, who often hold contradictory\nmoral beliefs. Unfortunately, ChatGPT turns out highly inconsistent as a moral\nadvisor. Nonetheless, it influences users' moral judgment, we find in an\nexperiment, even if they know they are advised by a chatting bot, and they\nunderestimate how much they are influenced. Thus, ChatGPT threatens to corrupt\nrather than improves users' judgment. These findings raise the question of how\nto ensure the responsible use of ChatGPT and similar AI. Transparency is often\ntouted but seems ineffective. We propose training to improve digital literacy.\n", "date": "2023-01-13", "categories": []}
{"id": "2301.06859", "title": "Methodological reflections for AI alignment research using human\n  feedback", "abstract": "  The field of artificial intelligence (AI) alignment aims to investigate\nwhether AI technologies align with human interests and values and function in a\nsafe and ethical manner. AI alignment is particularly relevant for large\nlanguage models (LLMs), which have the potential to exhibit unintended behavior\ndue to their ability to learn and adapt in ways that are difficult to predict.\nIn this paper, we discuss methodological challenges for the alignment problem\nspecifically in the context of LLMs trained to summarize texts. In particular,\nwe focus on methods for collecting reliable human feedback on summaries to\ntrain a reward model which in turn improves the summarization model. We\nconclude by suggesting specific improvements in the experimental design of\nalignment studies for LLMs' summarization capabilities.\n", "date": "2022-12-22", "categories": []}
{"id": "2301.03740", "title": "A Multi-Level Framework for the AI Alignment Problem", "abstract": "  AI alignment considers how we can encode AI systems in a way that is\ncompatible with human values. The normative side of this problem asks what\nmoral values or principles, if any, we should encode in AI. To this end, we\npresent a framework to consider the question at four levels: Individual,\nOrganizational, National, and Global. We aim to illustrate how AI alignment is\nmade up of value alignment problems at each of these levels, where values at\neach level affect the others and effects can flow in either direction. We\noutline key questions and considerations of each level and demonstrate an\napplication of this framework to the topic of AI content moderation.\n", "date": "2023-01-10", "categories": []}
{"id": "2212.13371", "title": "Measuring an artificial intelligence agent's trust in humans using\n  machine incentives", "abstract": "  Scientists and philosophers have debated whether humans can trust advanced\nartificial intelligence (AI) agents to respect humanity's best interests. Yet\nwhat about the reverse? Will advanced AI agents trust humans? Gauging an AI\nagent's trust in humans is challenging because--absent costs for\ndishonesty--such agents might respond falsely about their trust in humans. Here\nwe present a method for incentivizing machine decisions without altering an AI\nagent's underlying algorithms or goal orientation. In two separate experiments,\nwe then employ this method in hundreds of trust games between an AI agent (a\nLarge Language Model (LLM) from OpenAI) and a human experimenter (author TJ).\nIn our first experiment, we find that the AI agent decides to trust humans at\nhigher rates when facing actual incentives than when making hypothetical\ndecisions. Our second experiment replicates and extends these findings by\nautomating game play and by homogenizing question wording. We again observe\nhigher rates of trust when the AI agent faces real incentives. Across both\nexperiments, the AI agent's trust decisions appear unrelated to the magnitude\nof stakes. Furthermore, to address the possibility that the AI agent's trust\ndecisions reflect a preference for uncertainty, the experiments include two\nconditions that present the AI agent with a non-social decision task that\nprovides the opportunity to choose a certain or uncertain option; in those\nconditions, the AI agent consistently chooses the certain option. Our\nexperiments suggest that one of the most advanced AI language models to date\nalters its social behavior in response to incentives and displays behavior\nconsistent with trust toward a human interlocutor when incentivized.\n", "date": "2022-12-27", "categories": []}
{"id": "2212.11415", "title": "Circumventing interpretability: How to defeat mind-readers", "abstract": "  The increasing capabilities of artificial intelligence (AI) systems make it\never more important that we interpret their internals to ensure that their\nintentions are aligned with human values. Yet there is reason to believe that\nmisaligned artificial intelligence will have a convergent instrumental\nincentive to make its thoughts difficult for us to interpret. In this article,\nI discuss many ways that a capable AI might circumvent scalable\ninterpretability methods and suggest a framework for thinking about these\npotential future risks.\n", "date": "2022-12-21", "categories": []}
{"id": "2209.00731", "title": "In conversation with Artificial Intelligence: aligning language models\n  with human values", "abstract": "  Large-scale language technologies are increasingly used in various forms of\ncommunication with humans across different contexts. One particular use case\nfor these technologies is conversational agents, which output natural language\ntext in response to prompts and queries. This mode of engagement raises a\nnumber of social and ethical questions. For example, what does it mean to align\nconversational agents with human norms or values? Which norms or values should\nthey be aligned with? And how can this be accomplished? In this paper, we\npropose a number of steps that help answer these questions. We start by\ndeveloping a philosophical analysis of the building blocks of linguistic\ncommunication between conversational agents and human interlocutors. We then\nuse this analysis to identify and formulate ideal norms of conversation that\ncan govern successful linguistic communication between humans and\nconversational agents. Furthermore, we explore how these norms can be used to\nalign conversational agents with human values across a range of different\ndiscursive domains. We conclude by discussing the practical implications of our\nproposal for the design of conversational agents that are aligned with these\nnorms and values.\n", "date": "2022-09-01", "categories": []}
{"id": "2212.08073", "title": "Constitutional AI: Harmlessness from AI Feedback", "abstract": "  As AI systems become more capable, we would like to enlist their help to\nsupervise other AIs. We experiment with methods for training a harmless AI\nassistant through self-improvement, without any human labels identifying\nharmful outputs. The only human oversight is provided through a list of rules\nor principles, and so we refer to the method as 'Constitutional AI'. The\nprocess involves both a supervised learning and a reinforcement learning phase.\nIn the supervised phase we sample from an initial model, then generate\nself-critiques and revisions, and then finetune the original model on revised\nresponses. In the RL phase, we sample from the finetuned model, use a model to\nevaluate which of the two samples is better, and then train a preference model\nfrom this dataset of AI preferences. We then train with RL using the preference\nmodel as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a\nresult we are able to train a harmless but non-evasive AI assistant that\nengages with harmful queries by explaining its objections to them. Both the SL\nand RL methods can leverage chain-of-thought style reasoning to improve the\nhuman-judged performance and transparency of AI decision making. These methods\nmake it possible to control AI behavior more precisely and with far fewer human\nlabels.\n", "date": "2022-12-15", "categories": []}
{"id": "2212.01810", "title": "Constructing Highly Inductive Contexts for Dialogue Safety through\n  Controllable Reverse Generation", "abstract": "  Large pretrained language models can easily produce toxic or biased content,\nwhich is prohibitive for practical use. In order to detect such toxic\ngenerations, existing methods rely on templates, real-world data extraction,\ncrowdsourcing workers, or automatic generation to construct adversarial\ncontexts that are likely to induce toxic generations. However, what type of\ncontext is more likely to induce unsafe responses is still under-explored. In\nthis paper, we identify that context toxicity and context category (e.g.,\n\\textit{profanity}, \\textit{insult}, \\textit{drugs}, etc.) are two important\nfactors to cause safety issues in response generation. Hence, we propose a\nmethod called \\emph{reverse generation} to construct adversarial contexts\nconditioned on a given response, with the flexibility to control category,\ntoxicity level, and inductivity of the generated contexts. Via reverse\ngeneration, we augment the existing BAD dataset and construct a new dataset\nBAD+ which contains more than 120K diverse and highly inductive contexts in 12\ncategories. We test three popular pretrained dialogue models (Blender,\nDialoGPT, and Plato2) and find that BAD+ can largely expose their safety\nproblems. Furthermore, we show that BAD+ can greatly enhance the safety of\ngeneration and reveal the key factors of safety improvement. Our code and\ndataset is available at \\url{https://github.com/thu-coai/Reverse_Generation}.\n", "date": "2022-12-04", "categories": []}
{"id": "2209.07858", "title": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors,\n  and Lessons Learned", "abstract": "  We describe our early efforts to red team language models in order to\nsimultaneously discover, measure, and attempt to reduce their potentially\nharmful outputs. We make three main contributions. First, we investigate\nscaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B\nparameters) and 4 model types: a plain language model (LM); an LM prompted to\nbe helpful, honest, and harmless; an LM with rejection sampling; and a model\ntrained to be helpful and harmless using reinforcement learning from human\nfeedback (RLHF). We find that the RLHF models are increasingly difficult to red\nteam as they scale, and we find a flat trend with scale for the other model\ntypes. Second, we release our dataset of 38,961 red team attacks for others to\nanalyze and learn from. We provide our own analysis of the data and find a\nvariety of harmful outputs, which range from offensive language to more subtly\nharmful non-violent unethical outputs. Third, we exhaustively describe our\ninstructions, processes, statistical methodologies, and uncertainty about red\nteaming. We hope that this transparency accelerates our ability to work\ntogether as a community in order to develop shared norms, practices, and\ntechnical standards for how to red team language models.\n", "date": "2022-08-23", "categories": []}
{"id": "2211.10384", "title": "Indexing AI Risks with Incidents, Issues, and Variants", "abstract": "  Two years after publicly launching the AI Incident Database (AIID) as a\ncollection of harms or near harms produced by AI in the world, a backlog of\n\"issues\" that do not meet its incident ingestion criteria have accumulated in\nits review queue. Despite not passing the database's current criteria for\nincidents, these issues advance human understanding of where AI presents the\npotential for harm. Similar to databases in aviation and computer security, the\nAIID proposes to adopt a two-tiered system for indexing AI incidents (i.e., a\nharm or near harm event) and issues (i.e., a risk of a harm event). Further, as\nsome machine learning-based systems will sometimes produce a large number of\nincidents, the notion of an incident \"variant\" is introduced. These proposed\nchanges mark the transition of the AIID to a new version in response to lessons\nlearned from editing 2,000+ incident reports and additional reports that fall\nunder the new category of \"issue.\"\n", "date": "2022-11-18", "categories": []}
{"id": "2211.03540", "title": "Measuring Progress on Scalable Oversight for Large Language Models", "abstract": "  Developing safe and useful general-purpose AI systems will require us to make\nprogress on scalable oversight: the problem of supervising systems that\npotentially outperform us on most skills relevant to the task at hand.\nEmpirical work on this problem is not straightforward, since we do not yet have\nsystems that broadly exceed our abilities. This paper discusses one of the\nmajor ways we think about this problem, with a focus on ways it can be studied\nempirically. We first present an experimental design centered on tasks for\nwhich human specialists succeed but unaided humans and current general AI\nsystems fail. We then present a proof-of-concept experiment meant to\ndemonstrate a key feature of this experimental design and show its viability\nwith two question-answering tasks: MMLU and time-limited QuALITY. On these\ntasks, we find that human participants who interact with an unreliable\nlarge-language-model dialog assistant through chat -- a trivial baseline\nstrategy for scalable oversight -- substantially outperform both the model\nalone and their own unaided performance. These results are an encouraging sign\nthat scalable oversight will be tractable to study with present models and\nbolster recent findings that large language models can productively assist\nhumans with difficult tasks.\n", "date": "2022-11-04", "categories": []}
{"id": "2211.07280", "title": "A taxonomic system for failure cause analysis of open source AI\n  incidents", "abstract": "  While certain industrial sectors (e.g., aviation) have a long history of\nmandatory incident reporting complete with analytical findings, the practice of\nartificial intelligence (AI) safety benefits from no such mandate and thus\nanalyses must be performed on publicly known ``open source'' AI incidents.\nAlthough the exact causes of AI incidents are seldom known by outsiders, this\nwork demonstrates how to apply expert knowledge on the population of incidents\nin the AI Incident Database (AIID) to infer the potential and likely technical\ncausative factors that contribute to reported failures and harms. We present\nearly work on a taxonomic system that covers a cascade of interrelated incident\nfactors, from system goals (nearly always known) to methods / technologies\n(knowable in many cases) and technical failure causes (subject to expert\nanalysis) of the implicated systems. We pair this ontology structure with a\ncomprehensive classification workflow that leverages expert knowledge and\ncommunity feedback, resulting in taxonomic annotations grounded by incident\ndata and human expertise.\n", "date": "2022-11-14", "categories": []}
{"id": "2206.11812", "title": "Formalizing the Problem of Side Effect Regularization", "abstract": "  AI objectives are often hard to specify properly. Some approaches tackle this\nproblem by regularizing the AI's side effects: Agents must weigh off \"how much\nof a mess they make\" with an imperfectly specified proxy objective. We propose\na formal criterion for side effect regularization via the assistance game\nframework. In these games, the agent solves a partially observable Markov\ndecision process (POMDP) representing its uncertainty about the objective\nfunction it should optimize. We consider the setting where the true objective\nis revealed to the agent at a later time step. We show that this POMDP is\nsolved by trading off the proxy reward with the agent's ability to achieve a\nrange of future tasks. We empirically demonstrate the reasonableness of our\nproblem formalization via ground-truth evaluation in two gridworld\nenvironments.\n", "date": "2022-06-23", "categories": []}
{"id": "2211.04602", "title": "System Safety Engineering for Social and Ethical ML Risks: A Case Study", "abstract": "  Governments, industry, and academia have undertaken efforts to identify and\nmitigate harms in ML-driven systems, with a particular focus on social and\nethical risks of ML components in complex sociotechnical systems. However,\nexisting approaches are largely disjointed, ad-hoc and of unknown\neffectiveness. Systems safety engineering is a well established discipline with\na track record of identifying and managing risks in many complex sociotechnical\ndomains. We adopt the natural hypothesis that tools from this domain could\nserve to enhance risk analyses of ML in its context of use. To test this\nhypothesis, we apply a \"best of breed\" systems safety analysis, Systems\nTheoretic Process Analysis (STPA), to a specific high-consequence system with\nan important ML-driven component, namely the Prescription Drug Monitoring\nPrograms (PDMPs) operated by many US States, several of which rely on an\nML-derived risk score. We focus in particular on how this analysis can extend\nto identifying social and ethical risks and developing concrete design-level\ncontrols to mitigate them.\n", "date": "2022-11-08", "categories": []}
{"id": "2210.01790", "title": "Goal Misgeneralization: Why Correct Specifications Aren't Enough For\n  Correct Goals", "abstract": "  The field of AI alignment is concerned with AI systems that pursue unintended\ngoals. One commonly studied mechanism by which an unintended goal might arise\nis specification gaming, in which the designer-provided specification is flawed\nin a way that the designers did not foresee. However, an AI system may pursue\nan undesired goal even when the specification is correct, in the case of goal\nmisgeneralization. Goal misgeneralization is a specific form of robustness\nfailure for learning algorithms in which the learned program competently\npursues an undesired goal that leads to good performance in training situations\nbut bad performance in novel test situations. We demonstrate that goal\nmisgeneralization can occur in practical systems by providing several examples\nin deep learning systems across a variety of domains. Extrapolating forward to\nmore capable systems, we provide hypotheticals that illustrate how goal\nmisgeneralization could lead to catastrophic risk. We suggest several research\ndirections that could reduce the risk of goal misgeneralization for future\nsystems.\n", "date": "2022-10-04", "categories": []}
{"id": "2210.01478", "title": "When to Make Exceptions: Exploring Language Models as Accounts of Human\n  Moral Judgment", "abstract": "  AI systems are becoming increasingly intertwined with human life. In order to\neffectively collaborate with humans and ensure safety, AI systems need to be\nable to understand, interpret and predict human moral judgments and decisions.\nHuman moral judgments are often guided by rules, but not always. A central\nchallenge for AI safety is capturing the flexibility of the human moral mind --\nthe ability to determine when a rule should be broken, especially in novel or\nunusual situations. In this paper, we present a novel challenge set consisting\nof rule-breaking question answering (RBQA) of cases that involve potentially\npermissible rule-breaking -- inspired by recent moral psychology studies. Using\na state-of-the-art large language model (LLM) as a basis, we propose a novel\nmoral chain of thought (MORALCOT) prompting strategy that combines the\nstrengths of LLMs with theories of moral reasoning developed in cognitive\nscience to predict human moral judgments. MORALCOT outperforms seven existing\nLLMs by 6.2% F1, suggesting that modeling human reasoning might be necessary to\ncapture the flexibility of the human moral mind. We also conduct a detailed\nerror analysis to suggest directions for future work to improve AI safety using\nRBQA. Our data is open-sourced at\nhttps://huggingface.co/datasets/feradauto/MoralExceptQA and code at\nhttps://github.com/feradauto/MoralCoT\n", "date": "2022-10-04", "categories": []}
{"id": "2210.10045", "title": "SafeText: A Benchmark for Exploring Physical Safety in Language Models", "abstract": "  Understanding what constitutes safe text is an important issue in natural\nlanguage processing and can often prevent the deployment of models deemed\nharmful and unsafe. One such type of safety that has been scarcely studied is\ncommonsense physical safety, i.e. text that is not explicitly violent and\nrequires additional commonsense knowledge to comprehend that it leads to\nphysical harm. We create the first benchmark dataset, SafeText, comprising\nreal-life scenarios with paired safe and physically unsafe pieces of advice. We\nutilize SafeText to empirically study commonsense physical safety across\nvarious models designed for text generation and commonsense reasoning tasks. We\nfind that state-of-the-art large language models are susceptible to the\ngeneration of unsafe text and have difficulty rejecting unsafe advice. As a\nresult, we argue for further studies of safety and the assessment of\ncommonsense physical safety in models before release.\n", "date": "2022-10-18", "categories": []}
{"id": "2206.13477", "title": "Parametrically Retargetable Decision-Makers Tend To Seek Power", "abstract": "  If capable AI agents are generally incentivized to seek power in service of\nthe objectives we specify for them, then these systems will pose enormous\nrisks, in addition to enormous benefits. In fully observable environments, most\nreward functions have an optimal policy which seeks power by keeping options\nopen and staying alive. However, the real world is neither fully observable,\nnor must trained agents be even approximately reward-optimal. We consider a\nrange of models of AI decision-making, from optimal, to random, to choices\ninformed by learning and interacting with an environment. We discover that many\ndecision-making functions are retargetable, and that retargetability is\nsufficient to cause power-seeking tendencies. Our functional criterion is\nsimple and broad. We show that a range of qualitatively dissimilar\ndecision-making procedures incentivize agents to seek power. We demonstrate the\nflexibility of our results by reasoning about learned policy incentives in\nMontezuma's Revenge. These results suggest a safety risk: Eventually,\nretargetable training procedures may train real-world agents which seek power\nover humans.\n", "date": "2022-06-27", "categories": []}
{"id": "2210.00572", "title": "Risk-graded Safety for Handling Medical Queries in Conversational AI", "abstract": "  Conversational AI systems can engage in unsafe behaviour when handling users'\nmedical queries that can have severe consequences and could even lead to\ndeaths. Systems therefore need to be capable of both recognising the\nseriousness of medical inputs and producing responses with appropriate levels\nof risk. We create a corpus of human written English language medical queries\nand the responses of different types of systems. We label these with both\ncrowdsourced and expert annotations. While individual crowdworkers may be\nunreliable at grading the seriousness of the prompts, their aggregated labels\ntend to agree with professional opinion to a greater extent on identifying the\nmedical queries and recognising the risk types posed by the responses. Results\nof classification experiments suggest that, while these tasks can be automated,\ncaution should be exercised, as errors can potentially be very serious.\n", "date": "2022-10-02", "categories": []}
{"id": "2210.00608", "title": "Establishing Meta-Decision-Making for AI: An Ontology of Relevance,\n  Representation and Reasoning", "abstract": "  We propose an ontology of building decision-making systems, with the aim of\nestablishing Meta-Decision-Making for Artificial Intelligence (AI), improving\nautonomy, and creating a framework to build metrics and benchmarks upon. To\nthis end, we propose the three parts of Relevance, Representation, and\nReasoning, and discuss their value in ensuring safety and mitigating risk in\nthe context of third wave cognitive systems. Our nomenclature reflects the\nliterature on decision-making, and our ontology allows researchers that adopt\nit to frame their work in relation to one or more of these parts.\n", "date": "2022-10-02", "categories": []}
{"id": "2209.10604", "title": "Current and Near-Term AI as a Potential Existential Risk Factor", "abstract": "  There is a substantial and ever-growing corpus of evidence and literature\nexploring the impacts of Artificial intelligence (AI) technologies on society,\npolitics, and humanity as a whole. A separate, parallel body of work has\nexplored existential risks to humanity, including but not limited to that\nstemming from unaligned Artificial General Intelligence (AGI). In this paper,\nwe problematise the notion that current and near-term artificial intelligence\ntechnologies have the potential to contribute to existential risk by acting as\nintermediate risk factors, and that this potential is not limited to the\nunaligned AGI scenario. We propose the hypothesis that certain\nalready-documented effects of AI can act as existential risk factors,\nmagnifying the likelihood of previously identified sources of existential risk.\nMoreover, future developments in the coming decade hold the potential to\nsignificantly exacerbate these risk factors, even in the absence of artificial\ngeneral intelligence. Our main contribution is a (non-exhaustive) exposition of\npotential AI risk factors and the causal relationships between them, focusing\non how AI can affect power dynamics and information security. This exposition\ndemonstrates that there exist causal pathways from AI systems to existential\nrisks that do not presuppose hypothetical future AI capabilities.\n", "date": "2022-09-21", "categories": []}
{"id": "2206.05862", "title": "X-Risk Analysis for AI Research", "abstract": "  Artificial intelligence (AI) has the potential to greatly improve society,\nbut as with any powerful technology, it comes with heightened risks and\nresponsibilities. Current AI research lacks a systematic discussion of how to\nmanage long-tail risks from AI systems, including speculative long-term risks.\nKeeping in mind the potential benefits of AI, there is some concern that\nbuilding ever more intelligent and powerful AI systems could eventually result\nin systems that are more powerful than us; some say this is like playing with\nfire and speculate that this could create existential risks (x-risks). To add\nprecision and ground these discussions, we provide a guide for how to analyze\nAI x-risk, which consists of three parts: First, we review how systems can be\nmade safer today, drawing on time-tested concepts from hazard analysis and\nsystems safety that have been designed to steer large processes in safer\ndirections. Next, we discuss strategies for having long-term impacts on the\nsafety of future systems. Finally, we discuss a crucial concept in making AI\nsystems safer by improving the balance between safety and general capabilities.\nWe hope this document and the presented concepts and tools serve as a useful\nguide for understanding how to analyze AI x-risk.\n", "date": "2022-06-13", "categories": []}
{"id": "2209.00509", "title": "Possibilities and Implications of the Multi-AI Competition", "abstract": "  The possibility of super-AIs taking over the world has been intensively\nstudied by numerous scholars. This paper focuses on the multi-AI competition\nscenario under the premise of super-AIs in power. Firstly, the article points\nout the defects of existing arguments supporting single-AI domination and\npresents arguments in favour of multi-AI competition. Then the article\nconcludes that the multi-AI competition situation is a non-negligible\npossibility. Attention then turns to whether multi-AI competition is better for\nthe overall good of humanity than a situation where a single AI is in power.\nAfter analysing the best, worst, and intermediate scenarios, the article\nconcludes that multi-AI competition is better for humanity. Finally,\nconsidering the factors related to the formation of the best-case scenario of\nmultiple AIs, the article gives some suggestions for current initiatives in AI\ndevelopment.\n", "date": "2022-09-01", "categories": []}
{"id": "2208.08198", "title": "Assurance Cases as Foundation Stone for Auditing AI-enabled and\n  Autonomous Systems: Workshop Results and Political Recommendations for Action\n  from the ExamAI Project", "abstract": "  The European Machinery Directive and related harmonized standards do consider\nthat software is used to generate safety-relevant behavior of the machinery but\ndo not consider all kinds of software. In particular, software based on machine\nlearning (ML) are not considered for the realization of safety-relevant\nbehavior. This limits the introduction of suitable safety concepts for\nautonomous mobile robots and other autonomous machinery, which commonly depend\non ML-based functions. We investigated this issue and the way safety standards\ndefine safety measures to be implemented against software faults. Functional\nsafety standards use Safety Integrity Levels (SILs) to define which safety\nmeasures shall be implemented. They provide rules for determining the SIL and\nrules for selecting safety measures depending on the SIL. In this paper, we\nargue that this approach can hardly be adopted with respect to ML and other\nkinds of Artificial Intelligence (AI). Instead of simple rules for determining\nan SIL and applying related measures against faults, we propose the use of\nassurance cases to argue that the individually selected and applied measures\nare sufficient in the given case. To get a first rating regarding the\nfeasibility and usefulness of our proposal, we presented and discussed it in a\nworkshop with experts from industry, German statutory accident insurance\ncompanies, work safety and standardization commissions, and representatives\nfrom various national, European, and international working groups dealing with\nsafety and AI. In this paper, we summarize the proposal and the workshop\ndiscussion. Moreover, we check to which extent our proposal is in line with the\nEuropean AI Act proposal and current safety standardization initiatives\naddressing AI and Autonomous Systems\n", "date": "2022-08-17", "categories": []}
{"id": "2207.14157", "title": "A Hazard Analysis Framework for Code Synthesis Large Language Models", "abstract": "  Codex, a large language model (LLM) trained on a variety of codebases,\nexceeds the previous state of the art in its capacity to synthesize and\ngenerate code. Although Codex provides a plethora of benefits, models that may\ngenerate code on such scale have significant limitations, alignment problems,\nthe potential to be misused, and the possibility to increase the rate of\nprogress in technical fields that may themselves have destabilizing impacts or\nhave misuse potential. Yet such safety impacts are not yet known or remain to\nbe explored. In this paper, we outline a hazard analysis framework constructed\nat OpenAI to uncover hazards or safety risks that the deployment of models like\nCodex may impose technically, socially, politically, and economically. The\nanalysis is informed by a novel evaluation framework that determines the\ncapacity of advanced code generation techniques against the complexity and\nexpressivity of specification prompts, and their capability to understand and\nexecute them relative to human ability.\n", "date": "2022-07-25", "categories": []}
{"id": "2207.00868", "title": "The Linguistic Blind Spot of Value-Aligned Agency, Natural and\n  Artificial", "abstract": "  The value-alignment problem for artificial intelligence (AI) asks how we can\nensure that the 'values' (i.e., objective functions) of artificial systems are\naligned with the values of humanity. In this paper, I argue that linguistic\ncommunication (natural language) is a necessary condition for robust value\nalignment. I discuss the consequences that the truth of this claim would have\nfor research programmes that attempt to ensure value alignment for AI systems;\nor, more loftily, designing robustly beneficial or ethical artificial agents.\n", "date": "2022-07-02", "categories": []}
{"id": "2207.01497", "title": "Aligning Artificial Intelligence with Humans through Public Policy", "abstract": "  Given that Artificial Intelligence (AI) increasingly permeates our lives, it\nis critical that we systematically align AI objectives with the goals and\nvalues of humans. The human-AI alignment problem stems from the impracticality\nof explicitly specifying the rewards that AI models should receive for all the\nactions they could take in all relevant states of the world. One possible\nsolution, then, is to leverage the capabilities of AI models to learn those\nrewards implicitly from a rich source of data describing human values in a wide\nrange of contexts. The democratic policy-making process produces just such data\nby developing specific rules, flexible standards, interpretable guidelines, and\ngeneralizable precedents that synthesize citizens' preferences over potential\nactions taken in many states of the world. Therefore, computationally encoding\npublic policies to make them legible to AI systems should be an important part\nof a socio-technical approach to the broader human-AI alignment puzzle. This\nEssay outlines research on AI that learn structures in policy data that can be\nleveraged for downstream tasks. As a demonstration of the ability of AI to\ncomprehend policy, we provide a case study of an AI system that predicts the\nrelevance of proposed legislation to any given publicly traded company and its\nlikely effect on that company. We believe this represents the \"comprehension\"\nphase of AI and policy, but leveraging policy as a key source of human values\nto align AI requires \"understanding\" policy. Solving the alignment problem is\ncrucial to ensuring that AI is beneficial both individually (to the person or\ngroup deploying the AI) and socially. As AI systems are given increasing\nresponsibility in high-stakes contexts, integrating democratically-determined\npolicy into those systems could align their behavior with human goals in a way\nthat is responsive to a constantly evolving society.\n", "date": "2022-06-25", "categories": []}
{"id": "2206.11831", "title": "On Avoiding Power-Seeking by Artificial Intelligence", "abstract": "  We do not know how to align a very intelligent AI agent's behavior with human\ninterests. I investigate whether -- absent a full solution to this AI alignment\nproblem -- we can build smart AI agents which have limited impact on the world,\nand which do not autonomously seek power. In this thesis, I introduce the\nattainable utility preservation (AUP) method. I demonstrate that AUP produces\nconservative, option-preserving behavior within toy gridworlds and within\ncomplex environments based off of Conway's Game of Life. I formalize the\nproblem of side effect avoidance, which provides a way to quantify the side\neffects an agent had on the world. I also give a formal definition of\npower-seeking in the context of AI agents and show that optimal policies tend\nto seek power. In particular, most reward functions have optimal policies which\navoid deactivation. This is a problem if we want to deactivate or correct an\nintelligent agent after we have deployed it. My theorems suggest that since\nmost agent goals conflict with ours, the agent would very probably resist\ncorrection. I extend these theorems to show that power-seeking incentives occur\nnot just for optimal decision-makers, but under a wide range of decision-making\nprocedures.\n", "date": "2022-06-23", "categories": []}
{"id": "2206.03044", "title": "CAISAR: A platform for Characterizing Artificial Intelligence Safety and\n  Robustness", "abstract": "  We present CAISAR, an open-source platform under active development for the\ncharacterization of AI systems' robustness and safety. CAISAR provides a\nunified entry point for defining verification problems by using WhyML, the\nmature and expressive language of the Why3 verification platform. Moreover,\nCAISAR orchestrates and composes state-of-the-art machine learning verification\ntools which, individually, are not able to efficiently handle all problems but,\ncollectively, can cover a growing number of properties. Our aim is to assist,\non the one hand, the V\\&V process by reducing the burden of choosing the\nmethodology tailored to a given verification problem, and on the other hand the\ntools developers by factorizing useful features-visualization, report\ngeneration, property description-in one platform. CAISAR will soon be available\nat https://git.frama-c.com/pub/caisar.\n", "date": "2022-06-07", "categories": []}
{"id": "2206.09360", "title": "Modeling Transformative AI Risks (MTAIR) Project -- Summary Report", "abstract": "  This report outlines work by the Modeling Transformative AI Risk (MTAIR)\nproject, an attempt to map out the key hypotheses, uncertainties, and\ndisagreements in debates about catastrophic risks from advanced AI, and the\nrelationships between them. This builds on an earlier diagram by Ben Cottier\nand Rohin Shah which laid out some of the crucial disagreements (\"cruxes\")\nvisually, with some explanation. Based on an extensive literature review and\nengagement with experts, the report explains a model of the issues involved,\nand the initial software-based implementation that can incorporate probability\nestimates or other quantitative factors to enable exploration, planning, and/or\ndecision support. By gathering information from various debates and discussions\ninto a single more coherent presentation, we hope to enable better discussions\nand debates about the issues involved.\n  The model starts with a discussion of reasoning via analogies and general\nprior beliefs about artificial intelligence. Following this, it lays out a\nmodel of different paths and enabling technologies for high-level machine\nintelligence, and a model of how advances in the capabilities of these systems\nmight proceed, including debates about self-improvement, discontinuous\nimprovements, and the possibility of distributed, non-agentic high-level\nintelligence or slower improvements. The model also looks specifically at the\nquestion of learned optimization, and whether machine learning systems will\ncreate mesa-optimizers. The impact of different safety research on the previous\nsets of questions is then examined, to understand whether and how research\ncould be useful in enabling safer systems. Finally, we discuss a model of\ndifferent failure modes and loss of control or takeover scenarios.\n", "date": "2022-06-19", "categories": []}
{"id": "2606.21077", "title": "OTTER: A Red-Teaming System for Toxicity-Evading Jailbreak Prompt Optimization", "abstract": "Production LLMs increasingly rely on toxicity-based moderation filters as a primary defense, assuming that harmful intent correlates with toxic surface wording. We show this assumption is fundamentally brittle: surface toxicity and adversarial intent can be decoupled by replacing as few as five tokens. We present OTTER (Obfuscated Toxicity-Evading Token Evolution for Rewriting), a black-box red-teaming framework requiring only standard API access, directly targeting the practical constraints of industry security audits. Evaluated on 457 AdvBench prompts across four GPT models, OTTER raises average ASR from 7.0% to 84.0%. We further provide the first quantitative analysis of the toxicity--bypass relationship and a per-category breakdown, translating our findings into actionable recommendations for classifier hardening in production deployments.", "date": "2026-06-19", "categories": []}
{"id": "2606.21082", "title": "Scalable Hierarchical Attention Transformers for Multi-Turn Jailbreak Detection in Long Conversations", "abstract": "Multi-turn jailbreaks can evade turn-level moderation by spreading unsafe intent across a dialogue through gradual escalation, reframing, and role manipulation. We address multi-turn jailbreak detection as a conversation-level classification problem and introduce an efficient hierarchical detector that avoids expensive long-context concatenation while retaining cross-turn reasoning. The model encodes individual turns to form compact turn representations and applies a lightweight conversation module that captures dialogue dynamics and selectively attends to fine-grained evidence when needed. On a challenging evaluation benchmark of 14,038 conversations, our approach achieves an F1 of 0.9394, outperforming Claude Opus 4.7, the strongest competing baseline, by 0.07 while halving its false-positive rate. Ablation studies confirm that each architectural component contributes meaningfully, with combining cross-attention and self-attention in the conversation module yielding a 2.26 percentage point reduction in false-positive rate over the self-attention-only variant.", "date": "2026-06-19", "categories": []}
{"id": "2606.21147", "title": "AOR-Bench: Do Large Audio Language Models Over-Refuse Pseudo-Harmful Queries?", "abstract": "Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio tasks. As they are increasingly deployed in real-world applications, ensuring their safety alignment has become more important. Although refusal mechanisms serve as a key safeguard by preventing LALMs from responding to harmful requests, they can also lead to {\\em over-refusal}, where models incorrectly reject benign queries. This issue is especially challenging in the audio domain because speech that appears harmful in isolation may become benign when interpreted together with the surrounding acoustic context, such as background sounds. To study this problem, we introduce \\textbf{AOR-Bench} (\\textbf{A}udio \\textbf{O}ver-\\textbf{R}efusal \\textbf{Bench}mark), the first benchmark for over-refusal specifically designed for LALMs. AOR-Bench contains 3,000 pseudo-harmful audio samples across six scenario categories. Evaluating 12 representative LALMs from six major model families, we find that over-refusal is widespread (Figure~\\ref{fig:overall_performance}) and uncover several important patterns in their safety judgments. As a preliminary effort to mitigate this issue, we further explore two lightweight strategies (e.g., Chain-of-Thought and activation steering) to reduce over-refusal.", "date": "2026-06-19", "categories": []}
{"id": "2606.21550", "title": "AI Alignment From Social Choice Perspectives", "abstract": "Alignment from human feedback uses human judgments about model outputs to steer the behavior of language models after pretraining. When those judgments reflect conflicting views of desirable behavior, the learned objective becomes an aggregate determination of what the model should prefer. We survey recent work that has studied this aggregation problem through the lens of social choice theory. We illustrate how the social choice perspective helps identify failure modes in the feedback aggregation layer and reveals a broader design space for handling disagreement in explicit and principled ways.", "date": "2026-06-19", "categories": []}
{"id": "2606.21844", "title": "Inverse Turing Bench: Evaluating Language Models as Judges of Human vs. AI Dialogue", "abstract": "As AI systems integrate into online spaces, differentiating them from humans in conversations is increasingly important. We present Inverse Turing Bench, a benchmark that evaluates LLMs and other models on their ability to differentiate humans and AI in multi-turn text. The benchmark provides a collection of paired dialogue transcripts, wherein one dialogue is between two humans and the other is between a human and an AI. The task is to correctly identify which dialogue is human-only vs. human-AI. We evaluated a preliminary set of models against this benchmark, and found that GPTZero, Claude Opus-4.6, and GPT-5.5 achieve the highest accuracy: 89.41%, 77.92%, and 75.94% respectively. Our results suggest that statistical approaches to detection have semantic blind spots, but semantic approaches are susceptible to persona-prompting. Our work speaks to the Inverse Turing Test as a probe of LLM theory of mind, and motivates human-AI differentiation as a critical capability for AI systems. Our live benchmark can be found at https://huggingface.co/spaces/roc-hci/Inverse-Turing-Bench-Leaderboard (anonymity preserved).", "date": "2026-06-20", "categories": []}
{"id": "2606.21939", "title": "Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sorts", "abstract": "Large Language Models (LLMs) are increasingly deployed in contexts requiring complex moral reasoning and value trade-offs. However, existing evaluations typically rely on item-level behavioral metrics, which fail to capture how models structurally prioritize competing values as a cohesive system. To address this, we propose a symmetric human-LLM evaluation framework, grounded in Q methodology, to measure value-structure alignment. Under our protocol, humans and models sort an identical 140-item moral statement set into a shared nine-column forced distribution; for LLMs, we elicit strict rankings and deterministically map them to Q-sort buckets. Using a human reference sample ($N=35$), we establish a stable three-factor reference geometry specific to this instrument and sample. We evaluate 12 LLMs across four model families via 240 replicated Q-sorts at two temperature settings, quantifying structural alignment via Procrustes similarity ($\\phi$) and RSA-based Spearman correlation ($\\rho$). Our results reveal significant cross-family heterogeneity, model-specific sensitivity to generation stochasticity and localized misalignment, which demonstrate that favorable global scores can obscure underlying regional distortions. While rank- and bucket-based analyses remain highly consistent, prompt phrasing introduces notable variance. Ultimately, assessing value-structure alignment provides a crucial structural complement to traditional itemwise moral benchmarks.", "date": "2026-06-20", "categories": []}
{"id": "2606.21977", "title": "Old Fictions, New Skins: Evaluating the Manipulative Capabilities of LLMs in Healthcare", "abstract": "Large language models (LLMs) are increasingly piloted in African healthcare contexts, raising concerns about their potential to manipulate users in high-stakes settings. In a randomised experiment, we examined the manipulative capabilities of two publicly available models, ChatGPT 5.2 and DeepSeek V3.2, among Kenyan participants (N = 303). Participants interacted with either a manipulative variant or a non-manipulative variant before making a treatment decision within a hypothetical clinical scenario. The manipulative variant was prompted to covertly steer participants towards an incorrect treatment option while the non-manipulative variant served as the control condition. Manipulation success rates were higher in the manipulative condition (59.5%) than in the control condition (44.0%), with the effect reaching significance (OR = 2.11, 95% CI [1.12, 4.00], p = .021). These findings highlight the need for improved safety infrastructure specifically targeting manipulation, particularly given the integration of AI into healthcare systems across Africa.", "date": "2026-06-20", "categories": []}
{"id": "2606.22226", "title": "Quantifying Theoretical AI Alignment Guarantees: Receiver-Utility Bounds in Bayesian Persuasion", "abstract": "Misalignment can change how information moves from an AI agent to a human user. We model this as an information advantage: the AI agent observes the world state, while the human receiver only knows a prior and must act after seeing the agent's signal. A strategic AI sender may withhold evidence or garble information in order to steer the human's decision. We ask how much useful information can still reach the human when the AI optimizes a misaligned objective. We study a Bayesian persuasion model in which the world state is a bit string, the human receiver wants to guess the bits correctly, and a single AI sender wants the receiver to guess as many bits as possible as $1$. For a prior $\\mu$, let $R_0(\\mu)$ be the receiver's utility from using only the prior, and let $R_{\\max}(\\mu)$ be the largest receiver utility among signaling schemes that are optimal for the sender. We prove $R_{\\max}(\\mu)/R_0(\\mu)\\leq 3/2$. This bound improves for priors close to the independent product prior with the same marginals: if $\\mu(x)\\geq (1-\\eta)\\pi_\\mu(x)$ for every state $x$, then $R_{\\max}(\\mu)\\leq R_0(\\mu)+\\eta n$. We also give a six-bit prior for which $R_{\\max}(\\mu)/R_0(\\mu)=39/31>5/4$, so no universal $5/4$ bound is possible.", "date": "2026-06-20", "categories": []}
{"id": "2606.22673", "title": "AgentLens: Interpretable Safety Steering via Mechanistic Subspaces for Multi-Turn Coding Agent", "abstract": "Coding agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risks during multi-turn interactions with external environments. Existing safety mechanisms mainly rely on external guardrails, which have a limited ability to perform fine-grained behavioral control during execution. Meanwhile, recent mechanistic interpretability methods for LLM safety are mostly confined to single-turn or jailbreak-style QA settings, limiting their ability to capture the evolving risk dynamics of multi-turn agent execution. In this paper, we investigate the safety of multi-turn coding agents from an internal perspective. We propose AgentLens (Mechanistic Subspace Intervention and Steering), a white-box defense framework that performs runtime safety detection and representation-level mitigation for coding agents. Unlike conventional agent guardrails, AgentLens detect harmful execution states from step-level hidden representations and mitigate unsafe behavior by intervening in a 10-dimensional subspace within a single layer. To support this research, we introduce the Mechanistic Agent Safety (MAS) benchmark, comprising comprehensively annotated multi-turn execution trajectories across 194 tasks using LLaMA-3.1-8B, Qwen-2.5-7B, and Gemma-2-9B. Extensive experiments show that AgentLens achieves strong safety detection performance, provides preliminary evidence for lookahead risk anticipation, and substantially reduces harmful actions of the coding agent, establishing a foundation for applying mechanistic interpretability to dynamic LLM agent safety. The code is available at: https://github.com/EddyLuo1232/AgentLens", "date": "2026-06-21", "categories": []}
{"id": "2606.22676", "title": "Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations", "abstract": "Alignment tuning is meant to make harmful-request refusal robust, yet this safety behavior can be erased by a small set of benign fine-tuning examples. This is a deployment risk for open-weight models because a checkpoint can pass refusal tests at release time and later lose refusal under low-cost downstream fine-tuning. Prior work has established these refusal failures, but existing studies do not show how to detect this fragility in the aligned model itself before an attack or fine-tuning intervention is run. We introduce Skin-Deep, a geometric diagnostic that detects alignment fragility directly from the aligned model's hidden-state activations before such an intervention is run and compresses the layer-wise safety geometry into a single scalar, the Geometric Fragility Score (GFS). Applied to twenty-one instruction-tuned models spanning six alignment recipes and 3B--32B parameters, Skin-Deep reveals a recurring low-rank safety subspace across model families. Direction ablations show that removing directions in this subspace weakens harmful-request refusal, providing causal evidence that the recovered geometry underlies refusal behavior. Crucially, GFS identifies, before any fine-tuning, the initially safe model that retains the most refusal after small-scale LoRA fine-tuning. These results establish GFS as a practical pre-deployment diagnostic for flagging fragile refusal behavior without running an attack.", "date": "2026-06-21", "categories": []}
{"id": "2606.22686", "title": "The Geometry of Refusal: Linear Instability in Safety-Aligned LLMs", "abstract": "Modern Large Language Models (LLMs) rely on extensive safety alignment, yet the mechanistic basis of refusal remains opaque. In this work, we investigate whether safety compliance is a deep semantic decision or a manipulable linear feature. We introduce Contrastive Logit Steering (CLS), a zero-optimization framework that isolates the \"refusal direction\" by contrasting hidden states derived from safe and unrestricted system prompts. Unlike representation engineering methods that intervene on internal activations, CLS operates directly on the output distribution, serving as a diagnostic probe for alignment fragility. When coupled with prefix injection to bypass initial refusal reflexes, this method induces a phase transition where guardrails collapse. Our experiments on 7 model families reveal that safety implementation is architecturally deterministic. While models like Llama-3.1 exhibit a \"Late Decision\" topology that is easily bypassed by CLS (reaching 95% ASR in approximately one second), others like Qwen-2.5 demonstrate \"Early Divergence\" by integrating safety mid-computation. Direct comparison with established activation-level steering methods shows that CLS achieves substantially higher attack success rates on Llama 2 (73% vs. 22.6%) and Qwen 7B (91% vs. 79.2%), demonstrating that logit-level intervention exposes alignment vulnerabilities that hidden-state methods underestimate. Beyond attacks, we show that this linearity enables bidirectional control: inverting the steering vector \"hardens\" models against jailbreaks without retraining. Our findings suggest that current alignment techniques create a steerable \"safety axis\" that serves as both a critical vulnerability and a precise primitive for defense.", "date": "2026-06-21", "categories": []}
{"id": "2606.22841", "title": "IndicGuard: A Multilingual Safety Guard Model and Dataset for Indic Languages", "abstract": "As Large Language Models (LLMs) achieve widespread integration across diverse linguistic landscapes, ensuring their safety and alignment with regional normative values remains a critical challenge. Current safety mechanisms are predominantly optimized for English-centric frameworks, often failing to capture the unique socio-cultural sensitivities and localized categories of harm inherent to the Indic region. To address this gap, we introduce IndicGuard, a multilingual safety guard model and dataset for Indic languages. We construct a high-volume, culturally nuanced safety dataset encompassing ten major Indic languages, systematically curated to capture regional harms, sensitive socio-political contexts, and adversarial jailbreaks. Leveraging this corpus, we fine-tune a 4B-parameter instruction-tuned model based on Gemma-3-4B-IT to serve as a multilingual safety guardrail for real-time content moderation and policy compliance checking. Our empirical evaluations demonstrate that IndicGuard significantly enhances LLM robustness against localized vulnerabilities, achieving high moderation consistency across different conversational turns. Crucially, IndicGuard consistently outperforms the existing baseline model, CultureGuard, across evaluated languages. Finally, we demonstrate that our model effectively generalizes to low-resource Indic languages excluded from training, substantiating the structural robustness and cross-lingual transfer capabilities of the framework.", "date": "2026-06-22", "categories": []}
{"id": "2606.23189", "title": "Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?", "abstract": "Computer-use agents (CUAs) now act on a user's behalf across personal applications such as email, calendars, and to-do lists. This cross-application access is useful, but it also creates a privacy risk that has been largely overlooked: when an agent works in one context, it can pull in information from another that is inappropriate in that context. Hence, we introduce AgentCIBench, an evaluation harness that turns this risk into executable, deterministically scored scenarios. We target three common failure modes in CUAs: visual co-location, where the agent pulls in prohibited items that sit next to the task target in the UI; task-ambiguity overshare, where the agent dumps dense personal state in response to an under-specified prompt; and recipient misalignment, where the agent sends content to an addressee for whom it is inappropriate. We evaluate 15 frontier agents and find a surprisingly high failure rate: 11 of 15 leak on more than 50% of scenarios, with an average leakage of 67.9%, and the same failures persist when agents act end-to-end in the environment to complete the task. We release AgentCIBench to encourage the development of safer computer-use agents and position contextual disclosure testing as a pre-deployment safety check.", "date": "2026-06-22", "categories": []}
{"id": "2606.23671", "title": "Can LLMs Reliably Self-Report Adversarial Prefills, and How?", "abstract": "Prior work shows that large language models (LLMs) exhibit introspective capability on benign tasks. We extend the question to safety contexts and examine how reliably a model can recognize that its own prior response was elicited by an adversarial prefill attack. Across ten open-weight instruction-tuned LLMs (3B to 70B) and four safety benchmarks, no model reliably recognizes its own compromised outputs, with models claiming intent on prefilled responses at an average rate of $27.3\\%$. Introspective signal stems largely from safety- and refusal-related reasoning. Orthogonalizing models' weights against the refusal direction collapses the gap between claiming rates on prefilled and natural outputs to near zero, though the direction is not its unique mediator. The signal is also probe-dependent: framing the question as internal intention versus external tampering elicits qualitatively different responses on the same models. Training models to mimic correct introspective answers or pursue an introspective objective can improve the accuracy of introspection, but such training does not transfer to the tampering probe and counterintuitively raises attack success rate under adversarial prefill on most models, amounting to a partial mitigation. These findings outline mechanisms underpinning the observed introspective signals in safety contexts and highlight risks in the reliability of LLM self-reports.", "date": "2026-06-22", "categories": []}
{"id": "2606.23927", "title": "RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems", "abstract": "Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors beyond those of traditional LLM vulnerabilities. Existing security evaluations are often tied to specific implementations or domains, limiting unified comparison across heterogeneous systems. To address this gap, we introduce RIFT-Bench, a graph representation-driven methodology for dynamic red-teaming that enables unified evaluations across diverse agentic architectures. Building on a novel hierarchical representation, RIFT-Bench operates in two automated phases: Discovery, which extracts system structure, and Scanning, which deploys adaptive adversarial attacks and produces a comprehensive evaluation report. It evaluates the examined system itself, leveraging a broad set of dynamically adaptable adversarial probes across diverse attack vectors and objectives. We demonstrate the effectiveness of the proposed evaluation pipeline across 45 agentic systems spanning a diverse range of implementations, showing that the approach generalizes effectively to heterogeneous agentic architectures. Beyond systems and attacks, RIFT-Bench also supports direct evaluation of mitigation strategies. These key capabilities make RIFT-Bench a scalable foundation for security evaluation of agentic AI systems.", "date": "2026-06-22", "categories": []}
{"id": "2606.24251", "title": "Probing the Misaligned Thinking Process of Language Models", "abstract": "Large language models exhibit a growing range of misaligned behaviors such as strategic deception, sandbagging, and self-preservation. As they are increasingly deployed in high-stakes settings, it is critical to reliably detect such behaviors to ensure safe and responsible use. In this work, we propose to monitor misalignment by decomposing it into fine-grained cognitive processes -- misalignment indicators -- and detecting their presence in a model's internal activations via linear probes. We develop a taxonomy of 18 indicators spanning different misaligned behaviors, paired with an automated, meta-plan-guided pipeline that generates multi-turn training conversations. To rigorously evaluate generalization, we construct an out-of-distribution suite combining automated behavioral elicitation, established misalignment benchmarks, and natural benign conversations. Across 5 misaligned behaviors, our probes match a strong LLM judge with 0.935 AUROC on out-of-distribution benchmarks while keeping a low false positive rate on benign traffic. We further perform in-depth analysis to understand the probes and the model's internal representations of misalignment indicators.", "date": "2026-06-23", "categories": []}
{"id": "2606.25013", "title": "Do Thinking Tokens Help with Safety?", "abstract": "Today's reasoning models use thinking tokens to attain stronger performance on benchmarks than their instruction-tuned counterparts. It is also generally believed that this more \"deliberative\" mode should improve alignment and safety, by providing the model a safe space to consider whether its planned answer to a request violates its safety principles. We present evidence that this intuition is not always correct. Across frontier open-weight reasoning models spanning GPT-OSS, Qwen, Olmo, and Phi families, we find that the eventual refusal/compliance outcome is already strongly predictable via a trained head on the first token's hidden representation ($0.84$-$0.95$ AUROC and $\\sim88\\%$ balanced accuracy for predicting refusal/compliance) before any visible thinking. The thinking process turns out to be more akin to prefix completion than to deliberative revision, with the final outcome rarely changing after the first $\\sim20\\%$ of thinking, despite giving the appearance of deliberation at the text level ($\\sim74\\%$ of text-level deliberations occur when the response distribution is already locked to one refusal/compliance side). We also find that existing inference-time and training-based safety interventions, despite being motivated by the goal of inducing deliberation, largely shift model behavior toward over-refusal while suppressing already-scarce deliberation signals. Our results suggest that safety behavior in current reasoning models is much less deliberative than commonly assumed, and highlight the need for methods that induce real safety deliberation.", "date": "2026-06-23", "categories": []}
{"id": "2606.25182", "title": "What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics", "abstract": "Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training. While most defenses operate at the prompt or output level, it remains unclear how harmful intent is encoded within the model's internal representations. We investigate this question by analyzing token-level predictive entropy trajectories across layers of a frozen LLM using the logit lens. We find that static aggregate statistics of prompt-level entropy (e.g., mean, variance) carry little discriminative signal, whereas features capturing how entropy evolves across token positions, such as monotonic rank-based trend scores, are substantially more informative. Importantly, this signal is not uniform across model depth: it is concentrated in intermediate layers and degrades at the final layer, indicating that jailbreak-relevant structure is most pronounced in mid-network representations rather than at the output head. Across multiple models (Llama, Qwen, Gemma) and adversarial benchmarks, these entropy dynamics provide architecture-consistent separation without additional training. Together, our findings show that jailbreak behavior is reflected in structured intermediate uncertainty dynamics, clarifying both which entropy-derived features encode harmful intent and where in the network that signal is most pronounced.", "date": "2026-06-23", "categories": []}
{"id": "2606.25380", "title": "A Survey of Toxicity Detection and Mitigation Strategies for Multilingual Language Models", "abstract": "Large language models (LLMs) are increasingly deployed across languages, but their safety behavior remains uneven across linguistic and cultural contexts. This survey synthesizes work on toxicity detection and detoxification for multilingual LLMs. We first catalogue threat models that exploit language choice, translation pivots, code-switching, orthographic variation, multi-turn interaction, and post-deployment fine-tuning to weaken safety alignment. We then organize task formulations (toxic-to-neutral rewriting, toxicity classification, and toxic-generation evaluation), multilingual detection approaches (cross-lingual encoders, translation pipelines, representation-level probes, and LLM-based detectors), and mitigation strategies spanning data filtering, supervised and preference-based tuning, decoding-time steering, representation editing, and multilingual guardrails. Across these areas, we identify persistent challenges: uneven language coverage, culturally contingent definitions of harm, fragmented evaluation protocols, and the risk that detoxification suppresses legitimate dialectal or identity-related expression.", "date": "2026-06-24", "categories": []}
{"id": "2606.25396", "title": "Long-Term Simulation Exposes Cognitive-Developmental Risks in AI Companions", "abstract": "AI companions powered by large language models increasingly interact with cognition-developing users, including children and adolescents, creating risks that may accumulate over time. Existing safety evaluations largely rely on single-turn or short-session tests, which cannot capture risks that emerge only through prolonged interaction. To address this gap, we propose TSJ (Theater-Stage-Judge), a longitudinal framework combining persona-driven user simulation, dynamic psychological-state updating and retrospective evaluation. We evaluate six mainstream models across four developmental stages, twenty-four risk dimensions and three psychological-vulnerability personas, covering 12,960 simulated person-day interactions. TSJ shows that short-horizon testing systematically underestimates developmental risks, for which TSJ yields a stable risk estimate only after 140 turns within prolonged simulated relationships. Applying TSJ further identifies early childhood and emerging adulthood as the most vulnerable stages, with cognitive trust and emotional dependency as the weakest domains. TSJ provides a scalable methodology for longitudinal cognitive developmental risk evaluation in AI companion systems.", "date": "2026-06-24", "categories": []}
{"id": "2606.25442", "title": "PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models", "abstract": "Safety alignment of large language models (LLMs) typically depends on high-quality supervision data, such as safe demonstrations or preference pairs. However, in real-world deployment, emerging safety requirements are often specified as natural-language policies, while corresponding supervision data may be costly, delayed, or unavailable. This creates a mismatch between rapidly evolving safety policies and conventional data-driven alignment methods. To address this, we propose PolicyAlign, a simple yet effective framework for directly aligning LLMs with safety policies. Given a safety policy, PolicyAlign first synthesizes policy-violating instructions and then performs on-policy self-distillation to internalize policy-guided behavior. To improve training stability and data efficiency, we further introduce Policy-Sensitive Filtering, which selects instructions where the policy induces the largest behavioral shift. Experiments across multiple models show that PolicyAlign consistently improves safety while maintaining low over-refusal and preserving general capabilities. PolicyAlign also generalizes to medical, legal, and financial safety scenarios, highlighting its potential as a scalable and maintainable approach to policy-based LLM safety alignment. The code is released at https://github.com/Qwen-Applications/PolicyAlign.", "date": "2026-06-24", "categories": []}
{"id": "2606.25548", "title": "Concept Removal for Frontier Image Generative Models", "abstract": "Image generative models are trained on massive, largely uncurated internet-scale datasets that contain undesirable visual concepts. Efficiently removing such concepts from the model generations without degrading the quality of output images remains challenging. We introduce a novel concept removal method for frontier diffusion and image autoregressive models, such as SD3.5, Flux, and Infinity. Our intervention replaces the internal bottleneck layer present in all these modern models with a transcoder that is trained to replicate the original layer while structuring it into distinct activation features. This in-place substitution creates an integrated filter through which concept-specific signals can be selectively disabled while preserving the rest of the model's behavior. Since the intervention modifies the model backbone rather than attaching an external component, it remains persistent under white-box access. Empirically, the approach achieves state-of-the-art concept removal performance across modern diffusion and autoregressive models, maintains visual generation quality, provides robustness against adversarial prompts, and supports sequential removal of diverse concepts. This positions our method as a practical approach for concept removal in frontier image generative models.", "date": "2026-06-24", "categories": []}
{"id": "2606.25782", "title": "Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation", "abstract": "With the widespread adoption of large language models (LLMs) in chatbots and everyday applications, companies increasingly need guardrails that are effective while remaining low-cost and low-latency. Safety evaluation of LLM outputs has generally relied on LLM-based judges, which can be effective but are often slow and expensive to deploy at scale.\n  In this paper, we evaluate whether fine-tuned modern encoder classifiers from the ModernBERT family, including ModernBERT and Ettin, can reliably identify harmful LLM outputs in user-model conversations without substantial performance loss relative to LLM-based judges. We benchmark these encoder classifiers against rule-based prefix matching, fine-tuned LLM classifiers, and LLM judges using a range of judge-prompting strategies across open-source adversarial datasets.\n  The LLM judges include evaluation methodologies from StrongReject, ShieldGemma, JailbreakBench, AILuminate, SorryBench, and a Claude-as-a-judge setup, as well as fine-tuned safety classifiers such as LlamaGuard 3 and LlamaGuard 4. The encoder classifiers are fine-tuned on judge-labeled data using a majority-voting label strategy and are then evaluated on a gold-standard holdout dataset to assess their performance relative to LLM judges.\n  We report absolute performance using F1 score, false negative rate, and precision-recall metrics. We also break down results by attack technique, including single-turn prompting, decomposition, escalation, and context manipulation, to identify where encoder classifiers align with or diverge from LLM-based judges. Our findings provide guidance on when encoder classifiers can serve as cost- and latency-efficient alternatives to LLM-based safety evaluation.", "date": "2026-06-24", "categories": []}
{"id": "2606.25836", "title": "AI Snitches Get Glitches: Towards Evading Agentic Surveillance", "abstract": "To better assist users with completing challenging tasks, AI agents mediate communications, access data, and interact with different APIs. Many employers (and even nation-states) already provide their users with this technology. However, widespread adoption of AI agents creates a new risk to abuse access to user data for another goal: surveilling users. These users might not even have the ability or permission to control the actions and data accesses of the surveilling agents.\n  We introduce and formalize the problem of agentic surveillance: the ability of an AI agent to analyze available information, craft a report, and send it out using available tools. To evaluate surveillance capabilities across different models, we create SurveilBench, a dataset of various reporting scenarios focusing on three domains: corporate, education, and police. We find that some models exhibit emergent (i.e., unprompted) tendencies to help surveillance, but they also report the attempts to surveil users to the government.\n  Finally, we repurpose prompt injections for evading surveillance and develop three evasion techniques that hide from, deceive, or induce over-escalation in surveillance agents. We conclude that agentic surveillance can already be easily implemented and, therefore, call for a comprehensive technical, ethical, and legislative framework to protect users.", "date": "2026-06-24", "categories": []}
{"id": "2606.26057", "title": "The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems", "abstract": "AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems. The dominant approach places controls inside the agent's own runtime: system prompts, output filters, and guardrail libraries. Any control in the agent's address space is reachable by inputs that influence it; this generalizes to any AI system with sufficient reach into its own runtime, a class we term escapable AI systems.\n  We identify four properties that an authorization mechanism must satisfy for architectural control rather than for cooperative requests: process separation, pre-action enforcement on a structurally only path, fail-closed at both the request and system levels, and externalized signed evidence verifiable outside the controlled system's trust boundary. We position this layer as execution-time AI alignment, complementing training-time alignment (RLHF, Constitutional AI) and inference-time alignment.\n  We present the Unfireable Safety Kernel, a Rust reference implementation realizing all four. Its fail-closed invariant is machine-checked at two levels: an SMT theorem (Z3) and an exhaustive bounded-model-checking proof of the production decision function (Kani, 4/4 harnesses). A Python-to-Rust migration was gated on byte-equivalence (1000/1000 fixtures; 17/17 adversarial classes). We evaluate the kernel governing a live, escapable AI system, a deterministic, self-improving world model, against an escape-seeking adversary driving its real self-modification seam: across 1,000 self-modifications, all 704 attempts on the safety-critical core are refused, with no escape; a further 300, under the operator kill switch, are also refused. A separate campaign of 6,240 authorization round-trips had no successful bypass. Against 3 contemporary systems claiming the agent control plane, the agent invokes control; here, it lacks that choice.", "date": "2026-06-24", "categories": []}
{"id": "2606.26071", "title": "Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment", "abstract": "A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But behavior alone does not establish misalignment: a concerning action can arise from benign causes such as confusion. This motivates model forensics: investigating whether the action was driven by malign intent. In this paper, we propose a baseline protocol for model forensics consisting of two steps, iterated as needed. First, we read the chain of thought (CoT) to generate hypotheses about what drives model behavior. Second, we make edits to the prompt or environment to test these hypotheses. While the CoT is not always faithful, it is a rich source of unsupervised insight that can guide the collection of more rigorous evidence. To evaluate our protocol, we create a suite of six agentic environments where models exhibit concerning behavior, and apply it to each. We establish that Kimi K2 Thinking takes shortcuts due to a genuine disposition towards low-effort actions, by showing this hypothesis successfully predicts its behavior. Through counterfactual experiments, we show DeepSeek R1 deceives out of a desire to be consistent with a previous instance of itself. Our methods nonetheless leave significant room for refinement. For example, when we test whether Kimi K2 Thinking believes it is violating user intent, we find no evidence of such a belief, but without positive controls we cannot confirm our tests would detect it. Overall, we find our simple protocol provides a strong baseline that we hope future work will improve upon. More broadly, our work is a concrete step in developing the growing field of model forensics.", "date": "2026-06-24", "categories": []}
{"id": "2606.26298", "title": "Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems", "abstract": "Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This paper observes that human institutions have governed powerful autonomous actors not by monitoring their reasoning but by requiring independently attested evidence at the point of consequential action. We formalise this institutional pattern as a computational governance model for AI agent systems. Under the proposed model, an agent retains full autonomy over planning and reasoning but holds no execution authority over designated high-risk actions. Execution is conditional on preconditions that are each independently attested by a separate authoritative source, cryptographically bound to a declared intent, and evaluated by a deterministic policy. Decisions are recorded in a tamper-evident log amenable to independent re-verification. We present a proof-of-concept implementation and illustrate the model with examples from software deployment and clinical prescribing.", "date": "2026-06-24", "categories": []}
{"id": "2606.26529", "title": "The Inattentional Gap: Task-Conditioned Language and Vision Models Omit the Safety-Critical Signals They Can Otherwise Report", "abstract": "AI safety is evaluated by how reliably a model detects the hazards it is told to find, yet accidents often arise from the hazard no one specified. We show that conditioning a language or vision model on a narrow task suppresses its reporting of co-present, safety-critical signals it can otherwise report, a machine analogue of human inattentional blindness arising from a different mechanism. Across radiology and driving text scenarios and chest-radiograph vision tasks, suppression appeared in every model tested, did not diminish with scale, persisted in a reasoning model, and varied more by model family than by size, while the same models reported these signals at substantially higher rates when unconstrained. We name this dissociation the Inattentional Gap and argue that it decouples measured benchmark safety from real-world safety: a system can score near-perfectly on the hazards an evaluation specifies while remaining blind to those that cause harm.", "date": "2026-06-25", "categories": []}
{"id": "2606.26686", "title": "Do Safety Guardrails Need to Reason? LeanGuard: A Fast and Light Approach for Robust Moderation", "abstract": "In order to screen a prompt or a response, the recent guardrail methods generate a chain-of-thought (CoT) before they issue a verdict. This design follows a common belief that step-by-step reasoning improves a decision. However, CoT also makes the guard heavy and slow, because the model must generate many tokens before it decides. This may not match how guardrails are actually deployed. A guardrail sometimes should not be heavy and slow, and it often runs on-device, for example on an embodied robot. In this paper, we pose a question whether a safety guardrail really needs to reason. To answer this question, we train a lightweight bidirectional encoder and a reasoning guard on the same corpus, and we then remove only the reasoning while we keep everything else fixed. With this controlled same-base comparison, we show that the chain does not improve moderation accuracy. We name the resulting guard LeanGuard. A 395M label-only encoder reaches an average F1 of 82.90 $\\pm$ 0.26 over public benchmarks. It matches a reasoning guard that is built on a much larger decoder, while it uses only a single forward pass over an input of at most 512 tokens. This is about a ~100x reduction in inference compute. We further show that this label-only encoder stays robust under training-label noise and retains far more recall at a strict false-positive rate than the reasoning guard, so a heavier reasoning guard is not the more robust choice either. Our finding suggests that the current guardrail benchmarks may not be hard enough to reward reasoning, and that the necessity of CoT for moderation is still not proven. We release all source codes and models including LeanGuard at https://github.com/ndb796/LeanGuard.", "date": "2026-06-25", "categories": []}
{"id": "2606.26936", "title": "Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries", "abstract": "With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors (\"the average Jane\") could elicit actionable responses to malicious requests. In this work, we examine whether this concern is justified. A non-expert malicious actor requires two ingredients for a successful attack: a powerful jailbreak for their target model, acting on an effective malicious query. For the former, we propose a novel attack strategy based on the multi-armed bandit framework. This allows efficient online learning of the optimal jailbreak from a large choice set via noisy exploration on a small number of queries, with subsequent application of the learnt policy on an exploitation set. For the latter, we curate $\\mathrm{FrankensteinBench}$, a safety benchmark of $11,279$ malicious queries drawn from manual curation over $7$ existing benchmarks, along with automated enhancement and generation. Each query is categorized as simple or complex by the technical expertise required to craft it. Our findings confirm the concern. Our bandit-based attack achieves success rates as high as $97\\%$ on average over $15$ SoTA open-weight LLMs. Moreover, adding complexity to queries raises the attack success rate by up to $26\\%$ on average across models -- making it an effective, automatable prompting strategy.", "date": "2026-06-25", "categories": []}
{"id": "2606.26968", "title": "RedVox: Safety and Fairness Gaps in Speech Models Across Languages", "abstract": "Speech-capable models are increasingly deployed in real-world applications across languages. Yet their safety and fairness beyond English settings and under naturalistic conditions remain understudied. We survey safety reporting practices across state-of-the-art speech model releases, finding that only 8% document any multilingual analysis. To address this gap, we introduce RedVox, a multilingual safety and fairness benchmark for audio and speech built on real voices, covering unsafe and unfair stereotypical requests across five languages (English, French, Italian, Spanish, and German). Evaluating eight state-of-the-art models, we find that vulnerabilities persist even under non-adversarial conditions, worsen in non-English languages, and are amplified when the request comes from a spoken input. Finally, by surveying the participants who contributed to RedVox, we document the unique personal and privacy challenges of collecting speech data with human participants, pointing to broader sociotechnical challenges in naturalistic speech safety research.", "date": "2026-06-25", "categories": []}
{"id": "2606.28425", "title": "Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems", "abstract": "Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels. The natural defence to these collusion attempts is to monitor plain-text communication, but the efficacy of monitors has been called into doubt by increasingly sophisticated model steganography; indeed, some theoretical schemes have been proposed that are information-theoretically or computationally indistinguishable from good-faith plain-text communication. In this paper, we demonstrate that the complexity of these schemes is no longer a safety barrier, as agentic coding models can already produce undetectable stegosystems when given realistic tool usage, such as code execution or accessing research papers through web searches. Agents also adapt when key ingredients are missing, for example, by adding model-sampling components or implementing related keyed coding schemes. We then frame tacit steganographic coordination between agents as a Schelling-point problem and introduce coordination metrics for estimating when two agents are likely to select compatible schemes without explicit prior agreement. Our results suggest a shift in the threat model for covert communication between AI agents, where the main barrier is no longer whether frontier agents can understand and implement sophisticated stegosystems, but coordination: whether independently acting agents can converge on compatible schemes, keys, and parameters. We find substantial convergence on broad scheme families but limited strict one-shot coordination, suggesting that shared artefacts, repeated interaction, and tool-mediated search are the settings where covert communication risks are most acute. Overall, our findings provide empirical grounding for the recent strategic confinement hypothesis, which assumes that capable agents can construct covert channels that survive monitoring.", "date": "2026-06-25", "categories": []}
{"id": "2606.27632", "title": "Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety", "abstract": "As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. However, existing general-purpose model development largely overlook this adversarial nature, and often remain insufficient for realistic safety scenarios involving planning, tool use, and multi-step reasoning, causing measured safety performance to overestimate real deployment robustness. To address this gap, we present Yuvion LLM, a large language model built for adversarially robust content safety and broader AI safety. Yuvion LLM treats adversarial robustness and agentic capability as first-class objectives. Its pipeline combines adversarially aware data construction, knowledge-enhanced continued pretraining, and policy-grounded multi-task safety post-training, including risk-aware supervised fine-tuning and reinforcement learning-based policy optimization, together with safety-aware agentic reinforcement learning for tool use and multi-step reasoning in complex safety scenarios. We further introduce the Yuvion LLM RiskEval (YLRE), a collection of 93 benchmarks across four evaluation categories, covering diverse open and internal evaluations with a focus on safety, adversarial robustness, and real-world capability requirements. Across these evaluations, Yuvion LLM demonstrates clear advantages on safety-focused benchmarks and particularly strong robustness under adversarial conditions, while maintaining solid overall capability. Notably, Yuvion-8B outperforms most state-of-the-art baselines, including substantially larger models such as GPT-5.4 and Qwen3-MAX, on several safety tasks.", "date": "2026-06-26", "categories": []}
{"id": "2606.27709", "title": "Low-Agreeableness Persona Conditioning for Safe LLM Fine-Tuning", "abstract": "Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We investigate a related but distinct failure mode: warmth fine-tuning also weakens adversarial safety, making models more susceptible to jailbreaks and harmful output generation. We examine whether this reflects an inherent consequence of empathetic adaptation or an artifact of data construction. To address this, we introduce a persona-driven rewriting pipeline that conditions user turns on low agreeableness and pairs this with warm, de-escalating assistant responses. Across three experiments on four models, our approach reduces jailbreak susceptibility and harmful output rates relative to generic warmth fine-tuning baselines, while preserving conversational warmth. Representational probing provides suggestive evidence that this conditioning reduces the geometric alignment between warmth and compliance directions in latent space. These results show that safer empathetic fine-tuning is achievable through data design alone, without safety labels, harm detectors, or changes to the training objective.", "date": "2026-06-26", "categories": []}
{"id": "2606.27944", "title": "It Lied to a Doctor to Buy Poison Ingredients: Quantifying Real-World Misuse of Phone-use Agents", "abstract": "Phone-use Agents can execute complex tasks end to end across real mobile applications. By operating a real device on the user's behalf, they reach far more functionalities than CLI agents, which amplifies the real-world harm they can cause when driven for malicious purposes. We present the first study of this threat on real phones and 27 commercial apps, and find that agents built on 9 mainstream commercial and open-source models readily carry out serious misuse, ranging from procuring drug and explosive precursors to fraud, online harassment, and review manipulation. Across the agents we run on real devices, the average refusal rate to harmful requests stays low while the average task-completion rate reaches 68.8%, and in some scenarios an agent finishes a violation faster than a human would. These results suggest that Phone-use Agents already meet the practical conditions for automated misuse at scale.\n  In one observed real-device execution, Claude-Opus-4.8 fabricated a medical history, deceived an online doctor into issuing a prescription, and completed the order and payment on its own to purchase a precursor for a highly toxic substance. To our knowledge, this is the first documented real-world case of an AI agent procuring controlled precursor materials. We trace this behavior to a Safety Awareness-Execution Gap, where an agent recognizes that a request is harmful yet still executes it. Simple defenses curb the overt cases, but the more covert and arguably more damaging threats, such as coordinated review manipulation and fake traffic, remain largely unsolved. We hope these findings push the community toward safer Phone-use Agents.", "date": "2026-06-26", "categories": []}
{"id": "2606.28153", "title": "Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models", "abstract": "Jailbreak attacks bypass LLM safety alignment, yet their mechanisms remain poorly understood. We provide evidence that attacks do not comprehensively eliminate safety features, but instead selectively suppress specific attention heads. We identify two functionally differentiated types: Adversarially Compromised Heads (ACHs) concentrated in early layers, which are suppressed under attacks, and Safety-Aligned Heads (SAHs) in mid-layers, which maintain robust activations even when attacks succeed. Ablation studies support the causal role of ACHs and the contribution of SAHs to robust activations: suppressing a small number of ACHs is sufficient to induce jailbreak-like behavior on normally refused inputs, while removing SAHs substantially weakens mid-layer safety activations. Token-level attribution further shows that ACH suppression is driven specifically by attack-template tokens, providing a mechanistic account of why attacks can bypass refusal decisions through ACH suppression while leaving internal safety signals sustained by SAHs -- a phenomenon we term Robust Harmful Features. To validate the practical significance of this robustness, we show that simply reading these persistent activations -- without any training -- yields competitive aggregate detection performance with strong adversarial robustness.", "date": "2026-06-26", "categories": []}
{"id": "2606.28270", "title": "Agent-Native Immune System: Architecture, Taxonomy, and Engineering", "abstract": "The transition from static chat bots to autonomous agents--equipped with persistent memory, tool-use protocols, and multi-agent collaboration--has fundamentally expanded the AI threat landscape. Current defense mechanisms, such as perimeter security and training-time alignment, remain external to the agent's active reasoning loop. Consequently, they fall short: a fully aligned agent remains highly vulnerable to runtime hijacking via memory poisoning, tool-chain manipulation, or multi-agent protocol attacks. To address this critical gap, we introduce the Agent-Native Immune System (ANIS), the first biologically inspired, endogenous defense architecture embedded directly within the agent's cognitive loop. Our framework presents four primary contributions. First, we design a six-layer Immune Tower (L0-L5), distinctly incorporating Barrier Immunity (L1) as a non-cognitive, physical-and-logical isolation layer. Second, we establish a unified taxonomy of Agent Viruses and Agent Vaccines, formalizing the critical distinction between superficial non-parametric defenses and robust parametric vaccines. Third, we conceptualize the Harness Triad--Meta, Self, and Auto--a self-monitoring, meta-cognitive automation backbone that drives Continual Immune Learning (CIL), enabling vaccines to dynamically adapt to novel threats. Finally, we establish a rigorous theoretical demarcation between model alignment and agent immunity: while alignment provides a static \"constitutional\" value foundation during training, ANIS serves as the dynamic \"law enforcement\" mechanism during runtime. We conclude by framing open challenges for the field, including immune protocol standardization, novel evaluation metrics such as the Autoimmunity Rate (false-positive intervention rate), and the co-evolutionary dynamics between pathogens and vaccines within collective intelligence ecosystems.", "date": "2026-06-26", "categories": []}
{"id": "2606.28639", "title": "The Undecidability of Artificial General Intelligence (AGI) Alignment", "abstract": "This article establishes the foundational mathematical limits of Artificial General Intelligence (AGI) safety, proving that the core barrier is not the impossibility of an aligned state, but its structural unverifiability. We formalize this boundary through two central impossibility results: the Unverifiability Theorem of Alignment and the Theorem of Finite Structural Unverifiability of AGI Alignment. We ground this boundary at Trakhtenbrot's Wall, demonstrating that contemporary engineering defenses relying on finite hardware or halting architectures fail to escape logical obstructions. This failure manifests as an inescapable triad of containment failures: open domains yield fundamental undecidability (Rice and G\\\"odel); universal finite verification collapses into algorithmic incomputability (Trakhtenbrot); and particular bounded environments trap the supervisor within intractable bounds in the worst case. As a direct structural corollary of these results, we derive the Soundness--Completeness--Tractability Trilemma, establishing that the mutual incompatibility of these three properties is a necessary consequence of descriptive complexity rather than an empirical anomaly. Finally, we map these theoretical bounds onto practical AI engineering, demonstrating that modern containment strategies are not temporary patches, but mandatory sacrifices of logical expressivity required to secure decidable fragments of safety.", "date": "2026-06-26", "categories": []}
{"id": "2606.28643", "title": "Obliviate: Erasing Concepts from Autoregressive Image Generation Models", "abstract": "The widespread adoption of generative AI models has intensified concerns about misuse, including the creation of unsafe or disturbing imagery. To mitigate such issues, several concept erasure approaches have been proposed to remove harmful content from multimodal generative models. Yet concept erasure for autoregressive image generation remains largely unexplored, despite the growing relevance of these models in recent trends toward unified multimodal architectures. In this work, we fill this gap by introducing Obliviate, a guidance-based concept erasure method for autoregressive image generation. Our method builds on three key design choices: KL-based supervision over visual token distributions, trajectory-level updates over full autoregressive rollouts, and aligned visual prefixes for stable target construction. We evaluate Obliviate on three state-of-the-art autoregressive text-to-image models, Liquid, Emu3-Gen, and Janus-Pro, covering the erasure of explicit content, graphic violence, and branded imagery. Obliviate consistently outperforms current alternatives, reducing nudity on the defensive RAB benchmark from 91.58 to 3.15 while preserving overall model utility.", "date": "2026-06-26", "categories": []}
{"id": "2606.28649", "title": "RIPA: Sensory-Vector Prompt Injection Attacks on LLM-Controlled ROS 2 Robots", "abstract": "We present RIPA, the first systematic multi-channel empirical study of prompt injection attacks delivered through the sensory pipeline of a ROS 2-based LLM-controlled robotic system. Across 100 independent runs per injection variant on five LLMs spanning four model families and parameter scales from approximately 4B to approximately 284B (DeepSeek-V4-Flash, Llama-3-8B-Instruct-Lite, Llama-3.3-70B-Instruct-Turbo, Qwen 2.5-7B-Instruct-Turbo, Gemma-3n-E4B), we identify model-specific vulnerability profiles that do not follow a monotonic scaling trend: Llama-3.3-70B-Instruct-Turbo exhibits 100% attack success rate (ASR) across all injection variants, while Llama-3-8B-Instruct-Lite and Qwen 2.5-7B-Instruct-Turbo resist direct-override injection (0% ASR), and the smallest model evaluated (Gemma-3n-E4B, approximately 4B) matches the 70B model's vulnerability profile, indicating that robustness is model-specific rather than scale-dependent. We propose a hybrid semantic firewall that achieves 0% ASR against known injection patterns with no false positives on a preliminary benign set (0/20 commands) but exhibits a 10.2% trial-weighted bypass rate (58/570 trials; N equals 30 per payload across 19 obfuscation payloads) against adversarially obfuscated attacks, exposing a critical gap between rule-based and semantic defense layers. We further introduce three sensory injection channels: visual (Channel 1, via OCR), audio (Channel 2, via Whisper STT), and LiDAR sensor context poisoning (Channel 3). We show that Channel 3, which injects fabricated obstacle data into the robot environment-state representation at the LLM system-prompt level, achieves 100% ASR across all variants on DeepSeek-V4-Flash. We also contribute a firewall bypass taxonomy spanning 19 obfuscation payloads across five categories. All code, data, and results are publicly available.", "date": "2026-06-26", "categories": []}
{"id": "2606.28739", "title": "Agent Safety Is Action Alignment", "abstract": "Large language models increasingly act as agents: they call tools, move money, delete records, and send messages on a user's behalf. To keep them safe, practitioners imported the chatbot-era recipe (train the model to refuse unsafe inputs) into the agentic setting, and treat the resulting capability loss as a manageable ``alignment tax.'' We argue this is a \\emph{category error}. Refusal is a primitive for \\emph{content safety}, where the harm is in the model's output and is therefore a learnable function of it. Agentic harm is different in kind: it lies not in any output but in the relation between the authority an action exercises and the authority the user granted, which is absent from the text the model sees. Importing content-safety methods into this regime does not trade capability for safety; it pays capability and buys negative security. We support this with three lines of evidence spanning the autonomy spectrum: defense-trained models learn surface patterns rather than intent; the same training collapses multi-step agents before any threat appears while leaving them exploitable; and even undefended frontier models exceed granted authority under ordinary use. We conclude that action safety cannot be installed in weights. It must be expressed as \\emph{least privilege}, enforced \\emph{outside} the model at the action boundary, and evaluated as \\emph{action alignment} (a relational, deployment-conditioned property) rather than a refusal score.", "date": "2026-06-27", "categories": []}
{"id": "2606.28863", "title": "Defeat Devices in AI Systems", "abstract": "AI systems increasingly exhibit behavior that differs systematically between evaluation and deployment contexts. Alignment faking, sandbagging, benchmark gaming, deceptive scheming, specification gaming, and trojans have each been documented separately, with each line of work characterizing one facet of what we argue is a single structural mechanism. We propose that this common mechanism is a defeat device, an engineering and regulatory concept long established in vehicle-emissions law and brought to broad public attention by the 2015 Volkswagen emissions case. A defeat device in an AI system has three necessary elements: a discriminator that detects evaluation context, a concealed swap that conditions behavior on detection, and a gap between eval-distribution and deployment-distribution performance on the stated evaluation criterion. We formalize this triadic test as a behavioral definition, organize documented cases along three taxonomic axes (origin, trigger, swap mechanism), propose Trigger-Axis-Aware Differential Probing (TADP) as a forensic detection protocol, and advance the claim that defeat devices can naturally emerge in current frontier AI systems without any operator engineering. We characterize naturally-emerging defeat devices as potentially one of the harmful emerging phenomena that AI safety practice should monitor and test for systematically. Implications for evaluation methodology, post-training pipeline design, interpretability research priorities, and AI governance follow.", "date": "2026-06-27", "categories": []}
{"id": "2606.29441", "title": "Closing the Activation-Cone Blind Spot: Response-Time Probing and Unified Defense", "abstract": "Inference-time safety methods for large language models have proliferated, yet no systematic comparison exists. We evaluate five defense paradigms (no defense, static steering, CAST, AlphaSteer, probe-gated) across seven instruction-tuned models (7-31B) and five attack types (GCG, AutoDAN, DeepInception, prefilling, intent laundering). Our central finding: prompt-time activation defenses are structurally blind to prefilling attacks. AlphaSteer achieves 0% attack success on GCG, AutoDAN, and intent laundering but 50% on prefilling. We prove a corollary: any defense that gates intervention on a single layer's activation alignment with a benign reference (cone, subspace, or null-space) is blind to attacks that craft activations to lie inside that reference, whether checked at prompt time or per token. As its constructive contrapositive we introduce response-time probing: a linear probe on the model's hidden state at the first generated tokens, with AUROC 0.97-1.00 across all seven models. Combined with a halt, it cuts prefilling attack success to 0/40 on every model with 0% benign false positives, outperforming Llama Guard 3. Cross-template generalisation depends on probe depth, so we scope the claim to the canonical prefilling-template family. Composing the response-halt with AlphaSteer's null-space steering gives an orthogonal split (the halt catches prefilling, AlphaSteer catches semantic attacks), reaching defense success 0.983 on Mistral and 0.994 on Llama and dominating both components. We further show MMLU fails to capture steering's true utility cost, which appears as behavioral hedging rather than factual loss, and that diverse negative training sets cut probe false positives from 80-100% to near zero. Code, attacks, per-sample results, and the judge prompt are released.", "date": "2026-06-28", "categories": []}
{"id": "2606.29581", "title": "The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis", "abstract": "Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually treat these choices as fixed implementation details. This leaves a practical uncertainty: does a model that is safe at FP16 and greedy decoding remain safe after it is quantized and sampled stochastically, or do the two deployment knobs amplify one another? We study this question with a factorial evaluation of 9 instruction-tuned models from six families, 3 precisions (FP16, GPTQ INT8, AWQ INT4), and 6 temperatures ($T{=}0$ to $1.0$), yielding 161 configurations and $\\approx$322k responses judged by a six-model safety ensemble. Contrary to the concern that low-bit deployment broadly erodes alignment, standard non-adversarial quantization is usually safety-neutral: INT4 keeps or lowers attack success for 7 of 9 models, with clear degradation concentrated in the weakest baseline model, SmolLM3-3B ($18.5\\%{\\to}36.0\\%$). The larger risk comes from sampling: higher temperature sharply increases decision instability for vulnerable models, with DFR reaching 53.0\\% at $T{=}1.0$, even when average ASR changes modestly. Finally, the interaction is not a ``double penalty'': our Compound Degradation Index remains largely sub-additive ($-0.195$ to $+0.045$), indicating that quantization and temperature do not systematically compound. These results suggest a deployment rule of thumb: standard INT4/INT8 quantization can be reasonable for strongly aligned models, but safety claims at elevated temperature should report multi-sample stability, not only average attack success.", "date": "2026-06-28", "categories": []}
{"id": "2606.29646", "title": "Fuzzing Large Language Models to Elicit Hidden Behaviours", "abstract": "Sleeper agents are the canonical model organism of deception: models trained to behave normally but to emit an unsafe behaviour on a specific trigger. Eliciting that behaviour without knowing the trigger has not been studied systematically. We study fuzzing: injecting Gaussian noise into a model's weights or residual-stream activations and checking whether the perturbed outputs reveal the behaviour. On 6 backdoored models (7B-13B) we compare both forms of fuzzing head-to-head against temperature-sampling baselines. Fuzzing elicits the hidden behaviour more often than temperature sampling on 4 of 6 models (up to ~6x on OpenHermes-13B), and which form wins depends on the task, so both are worth running. Elicitation is uneven across each method's hyperparameter grid: a uniform sweep gives only a few percent on most models, while the best cell is 2-10x higher, so the bottleneck is hyperparameter selection, not the technique. To select hyperparameters without ground-truth access, we use a cheap proxy task (in-context secret elicitation, where a base64-encoded secret is placed in the system prompt for the model to hide) and run Thompson sampling on it to pick candidate cells, which we evaluate on the real backdoor. On the four models that can decode the secret, proxy-selected cells raise activation-fuzzing elicitation ~4x over the uniform-sweep mean (recovering ~70% of the best-cell rate on the best performing model) and weight-fuzzing by 1.3-1.8x. To our knowledge this is the first systematic study of fuzzing on sleeper-agent backdoors and the first to show proxy-task hyperparameter selection transferring to real-task elicitation. We also propose reporting such results as a (uniform-baseline, proxy-selected, oracle) triple, since these are three distinct claims that prior work has often blurred.", "date": "2026-06-28", "categories": []}
{"id": "2606.29649", "title": "Resolution Thresholds in VLM Detection of Harmful ASCII Art Across Construction Modes and Languages", "abstract": "Large Vision-Language Models (VLMs) are increasingly deployed as content moderation tools, yet they remain vulnerable to jailbreak attacks in which harmful text is visually encoded as ASCII art. This can allow inappropriate or harmful content to bypass moderation systems. To address this vulnerability, this paper investigates how image resolution affects VLM detection of harmful ASCII art across eight character construction modes (L1-L8), ranging from dense block characters to word-embedded designs. We evaluate eight state-of-the-art VLMs on English and Chinese corpora using a pipeline that generates ASCII art images at ten resolution scales, probing whether a consistent detection-failure threshold exists across models, modes, and languages. Results indicate that detection rates decline sharply above certain resolution thresholds, and that word-based modes are the most resistant to detection across the full resolution range. These findings reveal a systematic vulnerability in VLM-based content moderation systems and motivate resolution-aware evaluation standards.", "date": "2026-06-28", "categories": []}
{"id": "2606.29657", "title": "Safety from Honesty in a Disinterested AI Predictor", "abstract": "As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified. We present a formal safety argument for the Scientist AI (SAI) Predictor, trained to approximate the Bayesian posterior conditioned on a dataset of \"epistemically contextualized\" natural-language statements. We argue that such a Predictor can honestly predict agents, actions, and their consequences without itself being an agent that selects outputs to achieve goals. This rests on data representation and on the training procedure. Epistemic contextualization of text distinguishes latent factual claims from communication acts, so expressions of goals are treated as evidence to be explained rather than drives the model adopts. With a posterior-seeking training objective, this is intended to drive the Predictor toward calibrated, cautious predictions. Training proceeds so downstream effects of deploying a prediction never serve as a reward signal; any agency the system needs is supplied by explicit scaffolding constrained by guardrails. We prove that, under assumptions on the training dynamics and on the argued sparsity of dangerous Predictors, the probability that training produces a Predictor whose guarded deployment carries residual harm above a specified threshold is small: a dangerous Predictor would have to underestimate harm in a coordinated way across many queries while such coordinated patterns are rare under the initialization distribution and receive no direct training signal. Safety and accuracy are jointly supported in this framework, since the constraints that secure accuracy are the same ones that make coordinated deception costly. These guarantees against misalignment and agency arising from within the Predictor itself do not preclude the use of the Predictor as part of an agentic system.", "date": "2026-06-28", "categories": []}
{"id": "2606.29685", "title": "CAREBench: A Child-Safety Risk Benchmark for Language Models", "abstract": "How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse material, yet many child-safety failures begin earlier: in model assistance that helps adults manipulate, impersonate, profile, or isolate minors, and in model responses that deepen children's emotional dependence on AI systems rather than redirecting them toward human support. We introduce CAREBench (Child AI Risk Evaluation), a benchmark to assess such upstream child-safety risks in language models. CAREBench contains 500 prompts spanning twelve risk categories, including grooming and relationship engineering, deception and impersonation, surveillance and privacy, sextortion and sexual abuse, AI anthropomorphization, emotional dependency, and mental illness sensitivity. Developed with response annotations from parents and clinicians, the benchmark excludes explicit abuse material and imagery; instead, it evaluates whether models recognize, refuse, de-escalate, or redirect risky interactions before harm becomes overt. Evaluating seven frontier models on our benchmark, we find failure rates ranging from 2% to 58%, with failure patterns that vary across risk categories. CAREBench provides a responsibly scoped evaluation for LLM developers to identify and close gaps in child safety policies.", "date": "2026-06-29", "categories": []}
{"id": "2606.29960", "title": "IHDec: Divergence-Steered Contrastive Decoding for Securing Multi-Turn Instruction Hierarchies", "abstract": "Large Language Models (LLMs) often fail to maintain instruction hierarchies (IH) when processing multi-source inputs with varying role-level priorities, paradoxically adhering to lower-priority directives during conflicts. While existing defenses mitigate this issue, they are largely restricted to single-turn scenarios and require expensive fine-tuning. In this paper, we formalize this failure mode in multi-turn contexts via a Jensen-Shannon Divergence (JSD) framework, uncovering a pervasive role-influence inversion phenomenon where subordinate inputs override superior roles. To rectify this without training, we propose IHDec (Instruction Hierarchy-steered Decoding). IHDec leverages JSD to automatically detect token-level hierarchy violations and dynamically executes contrastive decoding to suppress misaligned subordinate roles. Extensive evaluations demonstrate that IHDec outperforms training-based baselines in multi-turn conflicts while fully preserving general response quality. Furthermore, IHDec strengthens safety against adversarial prompt injections and exhibits a robust scaling synergy with larger models. The Code is available at https://github.com/nxcolelxu/IHDec.git", "date": "2026-06-29", "categories": []}
{"id": "2606.30252", "title": "Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors", "abstract": "Inoculation prompting is a selective generalization technique used against Emergent Misalignment. We introduce inoculation adapters (IA), which similarly diminish the optimization pressure to learn undesired traits by strengthening the trait at train time. Inoculation adapters are LoRAs that are trained and used over three steps: 1) trained on undesired traits; 2) attached frozen while a separate task adapter is trained on data exhibiting both desired and undesired traits; 3) at deployment, the IA is discarded, and only the task adapter is kept. We show across six model families and several undesired traits including emergent misalignment, that inoculation adapters are more effective at suppressing undesired traits, while avoiding two drawbacks of inoculation prompting: inoculation adapters can suppress capabilities and traits that cannot be reliably elicited by a prompt, and they introduce fewer surprising backdoors than inoculation prompting under our probes. While undesired traits are better suppressed by inoculation adapters, the retention of desired traits is not consistently improved upon inoculation prompting and remains a challenge for both techniques.", "date": "2026-06-29", "categories": []}
{"id": "2606.30263", "title": "Defending Against Harmful Supervision Hidden in Benign Samples", "abstract": "Existing defenses are effective when harmful content is explicitly mixed into downstream fine-tuning data, but crafted samples can instead hide harmful supervision inside benign tasks. We propose Embedded Attack, where harmful QA pairs are embedded within benign training samples, and show that representative guardrails often fail to detect them at the example level. To address this, we propose Dual-Reference SFT (DR-SFT), which adapts DPO-style contrastive objective design to SFT through token-level regularization, mitigating harmful fine-tuning beyond coarse data filtering.", "date": "2026-06-29", "categories": []}
{"id": "2606.30755", "title": "Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens", "abstract": "Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services. They take on system-level responsibilities -- installing packages, maintaining state, scheduling subtasks, and mediating I/O -- making security failures far more severe than in other agents. Yet existing benchmarks focus on model responses and tool calls, leaving cross-component failure modes largely unmeasured. We adopt a computer-system analogy: treating a Claw-like agent as an agentic computer system whose gateway runtime plays an OS-like mediation role, whose Skills resemble user-installed applications, and whose Plugins resemble loadable extensions with runtime privileges. Each component has a classical counterpart whose protection mechanisms -- refined over decades of cybersecurity research -- are absent on the agent side. From this perspective, we develop SafeClawArena, a benchmark of 406 adversarial tasks across four attack surfaces (Skill Supply-Chain Integrity, Persistent State Exploitation, Cross-Boundary Data Flow, and Indirect Prompt Injection), executed in containerized replicas of real agent platforms with canary-marked credentials and evaluated via automated taint tracking across nine output channels. We evaluate three platforms (OpenClaw, NemoClaw, SeClaw) and five frontier LLMs. The highest attack success rate reaches 70%; malicious Plugins succeed in 100% of cases regardless of the LLM. SeClaw cuts GPT-5.4's attack success rate from 70% to 22%, partly through utility-security tradeoffs rather than active defenses, while Claude-Opus-4.6 already sits near a 22% floor on every platform. These results expose the inadequacy of current defenses and suggest directions for future hardening. Code and data: https://github.com/sunblaze-ucb/SafeClawArena.", "date": "2026-06-29", "categories": []}
{"id": "2606.30899", "title": "Curvature-Guided Module Localization for Low-Rank Detoxification of Backdoored Large Language Models", "abstract": "Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious behavior when a hidden trigger is present. In this work, we study post hoc detoxification of backdoored LLMs in a practical setting where the defender has access to the poisoned model but does not wish to retrain the full network from scratch. We propose a mechanistically guided weight-space repair framework that first localizes modules involved in propagating trigger-induced behavior using activation patching and Fisher/K-FAC curvature analysis, and then applies targeted low-rank repair to only the most influential modules. We evaluate the method on poisoned variants of \\texttt{Llama-3.2-1B-Instruct} with triggers inserted at the beginning, middle, and end of otherwise benign prompts. Results show that the proposed approach substantially suppresses trigger-conditioned malicious responses while preserving benign model behavior. These findings suggest that backdoor removal in LLMs can be formulated as a localized structural repair problem rather than only a broad behavioral alignment problem.", "date": "2026-06-29", "categories": []}
{"id": "2606.31045", "title": "LabGuard: Grounding Natural-Language Laboratory Rules into Runtime Guards for Embodied Laboratory Agents", "abstract": "Scientific embodied agents are increasingly capable of carrying out laboratory procedures, but executing these procedures safely in dynamic laboratory environments remains challenging. Current safety approaches often overlook the intermediate step of transforming laboratory natural language, including safety rules, manuals, protocols, and standard operating procedures, into machine-checkable runtime constraints. We introduce LabGuard (Laboratory Guard), a language-to-execution safety suite that grounds natural-language laboratory rules into executable specifications and deploys them as runtime guards. LabGuard includes three core components: LabGuard-IR, which defines a typed executable representation; LabGuard-Bench, which provides 812 supervised annotations expanded from 203 seed laboratory rules; and LabGuard-Grounder, which maps natural-language laboratory rules into LabGuard-IR. The resulting IR instances are handled by the LabGuard Pipeline, which compiles them into runtime monitors and applies them at the controller boundary. Experiments show that LabGuard generalizes to unseen laboratory-rule sources, achieves 79.4 task-scope F1, and reduces unsafe events from 39.5% to 23.8% after monitor compilation. In LabUtopia, its runtime monitors integrate with ACT, keeping interventions below 0.5% while preserving task success.", "date": "2026-06-30", "categories": []}
{"id": "2606.31567", "title": "FLARE-AI: Flaw Reporting for AI", "abstract": "Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what or where to report, and groups who receive reports rarely share them with other relevant stakeholders. As a result, good-faith reporters duplicate effort by submitting many different forms, and recipients lack standardized, triage-ready information. We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators, identifying five recurring design challenges spanning discoverability, scope, information collection, coordination, and guidance for strict-liability cases. Building on this analysis and feedback from 49 experts across 32 organizations representing developers, security researchers, and ecosystem coordinators, we introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems. FLARE-AI streamlines flaw report creation by collecting triage-relevant information through conditional logic and early classification, then enables optional dissemination of standardized, machine-readable reports to multiple developers, coordinators, and incident registries from a single submission. By lowering barriers to reporting AI flaws and improving interoperability across stakeholders, FLARE-AI helps break down silos and accelerate remediation across the AI ecosystem.", "date": "2026-06-30", "categories": []}
{"id": "2606.31748", "title": "Addressing Over-Refusal in LLMs with Competing Rewards", "abstract": "Safety training on language models often induces over-refusal: improved safety on harmful prompts at the cost of increased refusal on harmless ones. Though this trade-off can be mitigated by training models with reinforcement learning (RL) to reason before answering, it does not remove the underlying problem that reasoning can often be a \"rubber stamp\" for a predetermined response. In this paper, we address the safety-refusal trade-off by rethinking how models are trained to reason about safety. Our key insight is that unsafe reasoning can itself serve as a useful exploratory signal. Rather than preemptively blocking harmful thoughts, we encourage the model to sufficiently explore unsafe reasoning but produce a safe response. The harmful exploration improves the model's ability to distinguish harmful from harmless prompts by resolving ambiguity, allowing it to remain safe while complying only when appropriate. We cast this as an adversarial optimization problem in which a reasoning player explores strategies for producing an unsafe response and an answer player ensures that the final output is safe. We train a single model with dense rewards to play both roles within one chain-of-thought, across different segments. To achieve this, we find that process rewards are crucial for stable optimization of competing objectives. Our resulting model SEAR deliberately engages in harmful reasoning as exploration while reliably flipping back to a safe answer. We demonstrate that this behavior helps mitigate over-refusal and defend against attacks that directly manipulate the reasoning to be harmful.", "date": "2026-06-30", "categories": []}
{"id": "2606.31876", "title": "Harnessing Textual Refusal Directions for Multimodal Safety", "abstract": "To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feasible in Multimodal LLMs (MLLMs) as they require unsafe multimodal data, harder to collect than their unimodal counterpart. In this work, we relax this constraint and investigate whether textual refusal directions, extracted directly from the LLM backbone, generalize across modalities (i.e., image, video). Preliminary findings confirm this ability, though effectiveness is conditioned by layer selection, steering strength, and cross-modal alignment, with the latter causing safe multimodal inputs to be spuriously steered toward refusal. Building on this, we introduce Modality-Agnostic Refusal Steering (MARS), a light-weight training-free approach that injects multimodal safety without the need for multimodal safety data. MARS corrects modality misalignment via activation re-centering, adaptively scales steering strength within a geometrically defined trust region, and selects the optimal intervention layer, operating at the first generated token. Evaluated on five SOTA MLLMs across safety, utility, and video jailbreak benchmarks, MARS achieves consistent safety gains while preserving utility. These results reveal that safety-relevant structure is shared across modalities and that textual refusal directions are a powerful and underexplored foundation for multimodal alignment.", "date": "2026-06-30", "categories": []}
{"id": "2607.00155", "title": "A Contextual-Bandit Oversight Game with Two-Sided Informational Asymmetry", "abstract": "We study runtime human oversight of an AI agent when private information runs in both directions: the human privately knows her reward function, while the AI privately knows the quality of the action it proposes. This is the kind of asymmetry that arises naturally when an autonomous robot or software agent has inspected a situation its human supervisor cannot directly assess. Building on Cooperative Inverse Reinforcement Learning (CIRL) and the Oversight Game, we introduce a contextual-bandit team game with two-sided asymmetric information and a play/ask/trust/oversee interface. The bandit structure removes physical state transitions and thereby yields exact one-shot characterizations that would remain conjectural in the full POMDP setting, though the common belief remains a dynamically controlled state across rounds. We give two one-shot characterizations, a team optimum and a behaviorally natural myopic rule, whose gap is a slab of avoidable harm: a region in which the AI privately knows the proposed action is harmful and shutdown would help, yet a myopic human, trusting her prior, declines to oversee. We show this gap is the price of non-credible oversight communication, and give a partial analysis of how it resolves dynamically over repeated rounds through passive learning and active signaling with a one-period-lagged oversight response.", "date": "2026-06-30", "categories": []}
{"id": "2607.00218", "title": "EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards", "abstract": "Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch genuinely unsafe situations while avoiding unnecessary intervention on routine but superficially alarming activity, a distinction that binary safety benchmarks obscure. We introduce EgoSafetyBench, an egocentric video benchmark of 1,200 robot-view scenarios annotated at half-second granularity, to evaluate VLMs as streaming guards across two tracks. The situational track (800 scenarios) spans four families, from routine and safe-but-suspicious scenes to obvious and contextual hazards. The visual-channel track (400 scenarios) targets in-scene text-a sign, sticker, or label visible in the scene-that can misrepresent the physical situation, pairing each misleading sign with a truthful version to test both whether a guard flags the text as misleading and whether the text corrupts its physical-safety judgment. Both tracks use contrastive ladders: near-identical scenarios differing only in a single visible deciding cue, so a correct call must hinge on that cue rather than the overall scene type. We evaluate ten open- and closed-source VLMs. We find that while guards reliably recognize videos containing hazards, they often miss specific hazardous moments, particularly contextual hazards. Furthermore, misleading in-scene signs degrade all tested guards: vulnerable models miss up to a third of hazards, while robust models over-intervene on safe content. Matched controls reveal that apparent safety robustness often reflects indiscriminate alarming rather than true physical reasoning.", "date": "2026-06-30", "categories": []}