ai-llm-security-review

AI and LLM security review

Use this skill for AI applications, agents, RAG systems, model gateways, prompt chains, evals, and LLM governance.

Review workflow

1. Inventory the AI system: model/provider, prompts, tools, RAG sources, memory, logs, user roles, secrets, data classes, and downstream actions.
2. Threat model trust boundaries:
   • user input to prompt
   • retrieved content to model
   • model output to tools
   • tool output to user
   • logs/traces to operators
3. Test high-risk paths:
   • direct and indirect prompt injection
   • data exfiltration from RAG or memory
   • insecure tool invocation
   • overbroad agent permissions
   • jailbreaks that change policy or role
   • model/provider key leakage
   • training/eval data contamination
4. Recommend controls:
   • least-privilege tool scopes
   • allowlisted tool schemas and argument validation
   • retrieval filtering and source attribution
   • secret redaction before prompts/logs
   • output validation before side effects
   • human approval for destructive or external actions
   • continuous evals and regression prompts

Deliverables

Return findings as:

Risk	Attack path	Impact	Evidence	Control	Test to keep fixed

When the task involves current AI regulation or sector obligations, verify against current official sources before making definitive claims.