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securing-ai-systems

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Assess and harden LLM applications and agentic systems against prompt injection, tool misuse, excessive agency, memory poisoning, RAG data leakage, and model supply-chain risk, mapped to the OWASP Top 10 for LLM and Agentic Applications. Use when reviewing an AI feature, agent, MCP server, or RAG pipeline for security, or when threat modeling an autonomous system.

secskills-core/skills/securing-ai-systems/SKILL.md

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Sep 4, 2026
License
MIT

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# Securing AI Systems

LLM applications break the assumption every other security control is built
on: that instructions and data are separable. In an LLM, data *is*
instructions. Every design that reads untrusted content and then acts has to
be evaluated with that in mind, and no amount of prompt engineering fixes it.

## When to Use

- Security review of an LLM-backed feature, chatbot, or copilot
- Threat modeling an agentic system: tools, autonomy, memory, multi-agent
- Reviewing an MCP server, tool definition, or plugin surface
- Assessing a RAG pipeline for data leakage and poisoning
- Evaluating model, dataset, and dependency supply chain
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