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.
Install
$ agentstack add skill-26zl-cybersec-toolkit-ai-llm-security-review ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: 26zl
- Source: 26zl/cybersec-toolkit
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.