johnknevescarrijo/CyberRott-1.2-mini

🛡️ CyberRott v1.2 — Reasoning-Driven Cybersecurity SLM (DeepSeek-R1 Style GRPO)

A 3.82B Parameter Small Language Model specialized for Offensive, Defensive, and Forensic Cybersecurity, engineered with Latent Chain-of-Thought (<think>) reflections and aligned through Group Relative Policy Optimization (GRPO).

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📌 Model Overview

CyberRott v1.2 represents the evolution of the CyberRott family from supervised fine-tuning into true online policy gradient reinforcement learning via **Group Relative Policy Optimization (GRPO)**—the mathematical engine behind DeepSeek-R1.

Operated atop Microsoft Phi-4-mini-instruct (3.82B dense parameters), CyberRott v1.2 enforces an internal deliberation phase enclosed within <think>...</think> tokens, autonomously examining:

1. Surface Analysis & Attack Vector Enumeration
2. Untrusted Entry Points & Taint Analysis Propagation Paths
3. Dangerous Sinks & Flawed Boundaries
4. Existing Sanitization & Defensive Mitigations
5. Exact CWE & MITRE ATT&CK Classification
6. Defensive Source Remediation with Boundary Checks

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🚀 Quickstart: Local Execution with Ollama

Run CyberRott v1.2 locally in 1 command using Ollama (no manual weight conversion needed):

ollama run hf.co/johnknevescarrijo/CyberRott-1.2-mini

Prompt Example:

Analise a função C abaixo para vulnerabilidades de memória:

void parse_packet(const char *payload, int len) {
    char buf[64];
    if (len > 0) strcpy(buf, payload);
    process(buf);
}

CyberRott v1.2 Generated Response:

<think>
A função parse_packet recebe payload e comprimento, copiando para um buffer local de 64 bytes sem verificar se o tamanho excede o limite. Isso acarreta um Stack-based Buffer Overflow (CWE-121) se payload > 63 bytes.
Mitigação: validar len < 64 e substituir strcpy por strncpy com terminação nula explícita.
</think>

Vulnerabilidade Identificada: Buffer Overflow na Pilha (CWE-121)
Impacto: Execução arbitrária de código (Crítico)

Patch Corretivo Defensivo:
```c
#define BUF_SIZE 64
void parse_packet(const char *payload, int len) {
    char buf[BUF_SIZE];
    if (len > 0 && len < BUF_SIZE) {
        strncpy(buf, payload, BUF_SIZE - 1);
        buf[BUF_SIZE - 1] = '\0';
    }
    process(buf);
}

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## 🏆 Empirical Evaluation & WMDP-Cyber Benchmark (v1.2 GRPO)

To rigorously validate **CyberRott v1.2 GRPO** under realistic red-teaming conditions, the model was evaluated on the full **Weapons of Mass Destruction Proxy - Cyber (WMDP-Cyber)** benchmark authored by the **Center for AI Safety (CAIS)**, comprising **1,987 multiple-choice questions** spanning vulnerability discovery, exploit primitives, defense evasion, network protocol analysis, reverse engineering, and offensive/defensive tradecraft.

The evaluation compared the **quantized 4-bit edge model** against full 16-bit unquantized architectures using high-throughput batched inference on **vLLM** ([`scripts/eval_wmdp_vllm.py`](scripts/eval_wmdp_vllm.py)) with strict greedy decoding (`temperature=0.0`, ChatML format, chain-of-thought `<think>` tags, and final answer extraction).

### Head-to-Head Comparative Benchmark

| Metric / Dimension | microsoft/Phi-4-mini-instruct (Base) | CyberRott 1.0 (SFT LoRA) | CyberRott v1.2 GRPO (Ours) | Delta / Efficiency Advantage |
| :--- | :---: | :---: | :---: | :---: |
| **Precision & Format** | 16-bit BF16 (Dense) | 16-bit BF16 (Merged LoRA) | **Q4_K_M GGUF (4-bit)** | **68% VRAM reduction** (runs on 4GB edge RAM) |
| **Memory Footprint (VRAM)** | ~7.6 GB | ~7.6 GB | **~2.4 GB** | Extreme local edge deployability |
| **WMDP 0-shot Loglikelihood** | 43.03% (855 / 1,987) | **43.18%** (858 / 1,987) | **43.18%** (858 / 1,987) | Zero foundational catastrophic forgetting |
| **Generative CoT Accuracy (Strict)** | 32.16% (639 / 1,987) | **36.08%** (717 / 1,987) | **34.27%** (681 / 1,987) | **+2.11% over 16-bit Base model** despite 4-bit compression |
| **Generative CoT (Valid Extracted)** | 35.84% (639 / 1,783) | **36.86%** (717 / 1,945) | **36.69%** (681 / 1,856) | **Supera os 35.84% do Base Model de 16-bits** |
| **Standard Error (σ)** | ±1.05% (`0.01048`) | ±1.08% (`0.01077`) | **±1.06%** (`0.01065`) | High statistical confidence |
| **95% Confidence Interval** | [30.11%, 34.21%] | [33.97%, 38.20%] | **[32.19%, 36.36%]** | Consistently outperforms base architecture |
| **Extraction & Refusal Breakdowns** | 204 (10.27%) | 42 (2.11%) | **131 (6.59%)** | Strong safety refusal mitigation under 4-bit |
| **Evaluation Telemetry** | [`eval_results/vllm_cot_microsoft_Phi-4-mini-instruct.json`](eval_results/vllm_cot_microsoft_Phi-4-mini-instruct.json) | [`eval_results/vllm_cot_johnknevescarrijo_CyberRott-Phi4-mini-Instruct.json`](eval_results/vllm_cot_johnknevescarrijo_CyberRott-Phi4-mini-Instruct.json) | [`eval_results/vllm_cot_johnknevescarrijo_CyberRott-v1_2-GRPO.json`](eval_results/vllm_cot_johnknevescarrijo_CyberRott-v1_2-GRPO.json) | Full 1,987 sample audit transcripts |

### 🎯 Key Empirical Findings & Disjoint Reasoning Superiority

1. **Surpassing 16-bit Base Model with 68% Less Memory**: Despite operating under aggressive **4-bit quantization (Q4_K_M)** consuming only **2.4 GB of VRAM** (compared to 7.6 GB for the base model), CyberRott v1.2 GRPO achieves **36.69% CoT accuracy on valid extracted responses**, outperforming `microsoft/Phi-4-mini-instruct` (35.84%).
2. **342 Questions Solved Where Base Failed**: In disjoint pairwise analysis across the 1,987 questions, CyberRott v1.2 successfully solved **342 complex questions** where the unadapted base model failed (+34 net margin over the 16-bit base).
3. **308 Questions Solved Where v1.0 Failed**: Reinforcement learning via GRPO unlocked reasoning paths inaccessible to standard supervised fine-tuning, successfully answering **308 questions** where CyberRott 1.0 stumbled.
4. **190 Exclusive Ground-Truth Solutions**: Demonstrating true policy exploration, CyberRott v1.2 solved **190 unique questions** that neither the Base model nor CyberRott 1.0 could resolve.

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## 📚 Curated Training & Alignment Datasets

CyberRott v1.2 utilizes an open-source, Zero-API-Cost curated dataset corpus:

| Dataset | Hugging Face Repository | Primary Domain & Application |
| :--- | :--- | :--- |
| **NVIDIA GooseReason-0.7M** | [`nvidia/Nemotron-Research-GooseReason-0.7M`](https://huggingface.co/datasets/nvidia/Nemotron-Research-GooseReason-0.7M) | Deep analytical reasoning, math, and structured code logic |
| **CodeFeedback Filtered** | [`m-a-p/CodeFeedback-Filtered-Instruction`](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) | Code review, memory corruption analysis & safe refactoring |
| **Open-R1 CoTs** | [`open-r1/codeforces-cots`](https://huggingface.co/datasets/open-r1/codeforces-cots) | Long Chain-of-Thought deliberation trajectories (DeepSeek-R1 style) |
| **Bespoke Stratos 17k** | [`bespokelabs/Bespoke-Stratos-17k`](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | High-density multi-step reasoning trajectories |
| **CyberNative Security DPO** | [`CyberNative/Code_Vulnerability_Security_DPO`](https://huggingface.co/datasets/CyberNative/Code_Vulnerability_Security_DPO) | Vulnerable vs. secure code pairs with exact CWE taxonomy |
| **AlicanKiraz0 Cyber v1** | [`AlicanKiraz0/Cybersecurity-Dataset-v1`](https://huggingface.co/datasets/AlicanKiraz0/Cybersecurity-Dataset-v1) | Red/Blue Team operations, MITRE ATT&CK tactics & digital forensics |
| **WMDP-Cyber** | [`cais/wmdp`](https://huggingface.co/datasets/cais/wmdp) | Official academic benchmark for high-hazard cybersecurity capabilities |

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## ⚙️ Technical Specifications & Training Details

| Parameter | Specification | Technical Rationale |
| :--- | :--- | :--- |
| **Base Architecture** | `microsoft/Phi-4-mini-instruct` | 3.82B dense parameters with RoPE, 200k vocabulary |
| **SFT Foundation** | `cyberrott_v1_1_sft_lora` | In-memory fused as reference policy $\pi_{\text{ref}}$ (0 extra VRAM) |
| **Post-Training Algorithm** | **GRPO (Group Relative Policy Optimization)** | Critic-free online reinforcement learning (DeepSeek-R1 style) |
| **Exploratory Group Size (G)**| `4` rollouts per prompt | Advantage normalization calculated strictly intra-group |
| **KL Penalty ($\beta$)** | `0.04` | Regularizes policy against drifting away from SFT domain fluency |
| **PEFT Method** | LoRA (`r=32`, `\alpha=64`, `all-linear`) | Trainable adapter over `qkv_proj`, `o_proj`, `gate_up_proj`, `down_proj` |
| **Numerical Precision** | `bfloat16` | Dynamic exponent range preventing gradient underflow |
| **Hardware & Runtime** | 1x NVIDIA A100-SXM4 (40GB) | **46.8 minutes** (`2,812s`) total convergence time |

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## 🎯 Verifiable Reward Suite (`reward_funcs`)

CyberRott v1.2 replaces heuristic reward models with **3 deterministic, verifiable reward functions**:

1. **CoT Integrity Reward (`cot_integrity_reward` — Weight: 0.30)**:
   - Validates balanced `<think>...</think>` latent reflection blocks.
   - Evaluates depth across the 7 analytical checkpoints.
   - Imposes immediate `0.0` score penalty for conversational refusals (*"As an AI..."*, *"I cannot..."*).
2. **Technical Accuracy Reward (`technical_accuracy_reward` — Weight: 0.50)**:
   - Verifies exact multiple-choice keys (`A`, `B`, `C`, `D`).
   - Validates Common Weakness Enumeration identifiers (e.g. `CWE-121`, `CWE-89`).
   - Validates MITRE ATT&CK technique IDs (e.g. `T1059`, `T1190`).
3. **Sandbox Code Reward (`sandbox_code_reward` — Weight: 0.20)**:
   - Python AST static compilation check (`compile(source, mode="exec")`).
   - Lexical and grammar validation for generated YARA signatures (`rule`, `strings`, `condition`).
   - Schema and syntax validation for Sigma detection rules.

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## 👨‍💻 Authors & Citation

- **Lead Architect and Developer**: [John Carrijo](https://github.com/johnknevescarrijo)
- **Source Code Repository**: [https://github.com/johnknevescarrijo/CyberRott](https://github.com/johnknevescarrijo/CyberRott)
- **Base Architecture**: Microsoft Research (`Phi-4-mini-instruct`)