www-project-agent-memory-guard: Runtime defense against memory poisoning in AI agents (OWASP ASI06)

by OWASP · MCP Server · ★ 195

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 69/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-memoryagentic-aiai-agentsai-safetyai-securityautogencrewailangchainllm-agentsllm-security

Quick Facts

Stars195
Forks62
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score69/100
Open Issues25
Last Updated2026-11-05
Created2026-02-16
Platformsmcp, python
Est. Tokens~16k

About www-project-agent-memory-guard

OWASP Agent Memory Guard 📦 13,900+ total downloads 🏆 Officially recognized as an OWASP Incubator Project Stop AI agents from being weaponized through their own memory. Runtime defense that catches memory poisoning — even after a context res

Compatible Skills

These tools work well together with www-project-agent-memory-guard for enhanced workflows:

  • loopgain — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • prompt-guard — semantic(0.39)+complementary+same_lang+similar_pop+shared_platform (64%)
  • Agent-Wiz — semantic(0.26)+complementary+rare_topics+same_lang+similar_pop+shared_platform (64%)
  • amfs — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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