Ai llm security review
Skill 26zl/cybersec-toolkit/.claude/skills/ai-llm-security-review
Use for AI/LLM security assessments, prompt injection, RAG security, agent/tool permissioning, model supply chain, LLM red teaming, AI governance, eval design, data leakage, jailbreak testing, and secure AI application review.From its SKILL.md
npx -y skills add 26zl/cybersec-toolkit --skill ai-llm-security-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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AI and LLM security review
Use this skill for AI applications, agents, RAG systems, model gateways, prompt chains, evals, and LLM governance.
Review workflow
- Inventory the AI system: model/provider, prompts, tools, RAG sources, memory, logs, user roles, secrets, data classes, and downstream actions.
- Threat model trust boundaries:
- user input to prompt
- retrieved content to model
- model output to tools
- tool output to user
- logs/traces to operators
- 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
- 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.