claude

ai-security-skills

AI and agent security assessment skills — agent security audit, LLM risk assessment, agentic AI risk assessment, MCP server review, prompt injection testing, multi-agent threat modeling, and AISVS verification.

217 on GitHubby Chris Maennerv0.2.11CC-BY-4.0updated 28 days ago
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Plugin ai-security-skills from marketplace agent-security-playbook in OWASP/secure-agent-playbook. Commands read from the repo's manifest — 28 days ago.

What it installs

Skills (7)

  • agent-security-audit

    Audit AI agent configurations for security risks — excessive permissions, prompt injection surfaces, data exfiltration paths, and missing guardrails. Use when reviewing CLAUDE.md files, MCP configs, agent orchestration code, or any AI agent setup.

  • agentic-ai-risk-assess

    Assess agentic AI applications against the OWASP Top 10 for Agentic Applications 2026. Use when reviewing autonomous AI agents, multi-agent systems, or agentic workflows for security risks including goal hijacking, tool misuse, privilege abuse, and rogue agent behavior.

  • ai-security-verification

    Comprehensive AI security verification using OWASP AI Security Verification Standard (AISVS) framework. Use when verifying an AI-driven application, ML pipeline, or LLM-integrated system against AISVS, preparing for an AI security audit, or checking security and ethical controls across 13 categories

  • llm-risk-assess

    Comprehensive LLM security assessment against OWASP Top 10 for LLM Applications 2025. Use when reviewing LLM-integrated applications, RAG pipelines, chatbots, AI agents, or GenAI features. Covers prompt injection, data poisoning, supply chain, excessive agency, and more with real-world attack scenar

  • mcp-server-review

    Security review of MCP (Model Context Protocol) server implementations and configurations. Use when auditing MCP server source code, evaluating third-party MCP servers before installation, or reviewing Claude Code MCP integrations for overpermissioning, injection risks, and data exposure.

  • multi-agentic-threat-model

    Comprehensive threat modeling for multi-agent systems using CSA MAESTRO 7-layer framework and OWASP Multi-Agentic System Threat Modeling Guide v1.0. Systematically analyzes threats across all architectural layers from foundation models to agent ecosystems.

  • prompt-injection-test

    Test LLM-integrated applications against known prompt injection techniques, evasion methods, and attack intents using the Arcanum PI Taxonomy. Use when red-teaming AI apps, validating guardrails, or deepening LLM01 (Prompt Injection) assessments.

Subagents (1)

  • ai-security-assessor

    Assesses AI agent configurations, MCP servers, and LLM-integrated applications for security risks. Use when auditing AI agents, reviewing MCP server implementations, evaluating LLM apps, or testing for prompt injection vulnerabilities.

View source on GitHub

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