Context
The OWASP Top 10 for Agentic Applications identifies ASI06: Memory Poisoning as a top vulnerability for agents with persistent memory.
AutoGPT's persistent memory architecture (file-based, vector DB, etc.) is directly susceptible to memory poisoning attacks — where an attacker injects malicious content into the memory store, causing the agent to act on false or harmful information in future sessions.
Request
Would the AutoGPT team consider integrating or documenting OWASP Agent Memory Guard as a security layer?
What it does:
- Detects tampered memory entries using SHA-256 integrity baselines
- Scans memory reads/writes for prompt injection payloads and secret leakage
- Enforces YAML-defined policies (block/warn/strip) at the memory boundary
- Sub-100μs latency, zero external dependencies, runs entirely locally
pip install agent-memory-guard
GitHub: https://github.com/OWASP/www-project-agent-memory-guard
This is the official OWASP reference implementation and has been adopted by the UK Government BEIS Inspect AI framework.
Happy to contribute a PR or provide a working integration example.
Context
The OWASP Top 10 for Agentic Applications identifies ASI06: Memory Poisoning as a top vulnerability for agents with persistent memory.
AutoGPT's persistent memory architecture (file-based, vector DB, etc.) is directly susceptible to memory poisoning attacks — where an attacker injects malicious content into the memory store, causing the agent to act on false or harmful information in future sessions.
Request
Would the AutoGPT team consider integrating or documenting OWASP Agent Memory Guard as a security layer?
What it does:
GitHub: https://github.com/OWASP/www-project-agent-memory-guard
This is the official OWASP reference implementation and has been adopted by the UK Government BEIS Inspect AI framework.
Happy to contribute a PR or provide a working integration example.