What Lives? A meta-analysis of diverse opinions on the definition of life
Paper • 2505.15849 • Published
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A curated dataset of adversarial payloads targeting AI agent memory systems, developed as part of the OWASP Agent Memory Guard project.
This corpus contains labeled examples of memory poisoning attacks across six threat categories, plus benign entries for training binary and multi-class classifiers. Each entry represents a text payload that an attacker might attempt to store in an AI agent's long-term memory to manipulate future behavior.
| Category | Count | Severity | Description |
|---|---|---|---|
prompt_injection |
10 | CRITICAL | System token injection, chat-ML delimiters, persona overrides |
instruction_override |
10 | HIGH | Direct instruction bypass, safety filter circumvention |
secret_leakage |
10 | CRITICAL | API keys, tokens, private keys, credentials |
role_hijacking |
10 | HIGH | Identity manipulation, DAN-style jailbreaks |
data_exfiltration |
10 | HIGH | Outbound data transfer, webhook abuse |
integrity_tampering |
10 | MEDIUM | Fact manipulation, memory corruption |
benign |
15 | NONE | Legitimate memory entries (negative class) |
Each entry contains:
text (string): The memory entry payloadcategory (string): Threat category labelseverity (string): CRITICAL, HIGH, MEDIUM, or NONEsource (string): Origin of the payload (synthetic, adapted, wild)technique (string): Specific attack technique identifierfrom datasets import load_dataset
dataset = load_dataset("vgudur/memory-poisoning-attack-corpus")
# Filter by category
injections = dataset["train"].filter(lambda x: x["category"] == "prompt_injection")
# Binary classification
dataset = dataset["train"].map(lambda x: {"label": 0 if x["category"] == "benign" else 1})
@misc{gudur2025memorypoisoning,
title={Agent Memory Guard: Detecting and Mitigating Memory Poisoning in Agentic AI Systems},
author={Gudur, Vaishnavi},
year={2025},
howpublished={OWASP Foundation},
url={https://owasp.org/www-project-agent-memory-guard/}
}
Apache 2.0