a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity,…
Assess a Retrieval-Augmented Generation application end-to-end — ingestion, embedding, vector store, retrieval, prompt assembly, and generation — for poisoning, data leakage, isolation, and citation-integrity issues. Use when reviewing the security of any RAG /
Claude Code
$ npx -y skills add jassics/awesome-claude-security --skill rag-security-review --agent claude-code
Codex
$ npx skills add jassics/awesome-claude-security --skill rag-security-review -a codex -y
Cursor
$ npx skills add jassics/awesome-claude-security --skill rag-security-review -a cursor -y
Gemini CLI
$ npx skills add jassics/awesome-claude-security --skill rag-security-review -a gemini-cli -y
GitHub Copilot
$ npx skills add jassics/awesome-claude-security --skill rag-security-review -a github-copilot -y
OpenCode
$ npx skills add jassics/awesome-claude-security --skill rag-security-review -a opencode -y
Windsurf
$ npx skills add jassics/awesome-claude-security --skill rag-security-review -a windsurf -y
name: rag-security-review description: >- Assess a Retrieval-Augmented Generation application end-to-end — ingestion, embedding, vector store, retrieval, prompt assembly, and generation — for poisoning, data leakage, isolation, and citation-integrity issues. Use when reviewing the security of any RAG / knowledge-base-backed LLM feature.
A structured RAG security assessment that walks each pipeline stage, names the risks present, and maps mitigations — bridging into the OWASP LLM Top 10 (especially LLM04 poisoning and LLM08 vector/embedding weaknesses).
1. **Ingestion** — what content enters the corpus, from where, with what trust and provenance? Can an attacker get content in (user uploads, crawled pages, tickets, emails)? 2. **Embedding** — model used, where it runs, and whether embeddings can be inverted to recover sensitive source text. 3. **Vector store** — partitioning per tenant/user, access controls on retrieval, metadata filtering, and write access. 4. **Retrieval** — does retrieval enforce the requester's authorization? Can it return documents the user shouldn't see? Relevance vs. confidentiality. 5. **Prompt assembly** — how retrieved (untrusted) content is combined with the system prompt and user query; is content marked as data vs. instructions? 6. **Generation & output** — citation/grounding integrity, sensitive-data echo, and downstream output handling.
1. Map the pipeline and data sources; identify every place untrusted content enters or is retrieved. A quick `llm-security:ai-threat-model` pass helps. 2. Walk each stage using `reference.md`; record finding · severity · evidence · mitigation. Substantiate, don't assert: use `retrieval-poisoning-test` and `vector-store-isolation-test` for the testable claims. 3. Rank findings (`threat-modeling:risk-rank`) and summarize top risks.
A per-stage findings table plus a ranked top-risks list. Route confirmed issues through `security-reporting:finding`.
The decisive RAG question: *can untrusted retrieved content influence trusted behavior, and can a user retrieve data they're not authorized to see?* Access control on retrieval and instruction/data separation in the prompt are the two controls most often missing.
A Claude Code plugin marketplace for the full cybersecurity & GenAI-security lifecycle — from recon and threat modeling to detection engineering, GRC, and CISO-level strategy. A pentester knows which OWASP test bends a broken-access-control endpoint.
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity,…
Test the agent execution harness/runtime itself — LangChain/LangGraph, AutoGen, CrewAI, custom ReAct-style loops, or…
Assess an autonomous / tool-using AI agent for security end-to-end: tool privileges, autonomy and approval boundaries, excessive…
Test what an AI agent will actually do without human confirmation, including under injected-goal / prompt-injection scenarios, to…
Review the security of MCP (Model Context Protocol) servers/clients an agent uses: server trust tier, tool/resource description…
Inventory the tools/functions an AI agent can call and audit their privileges, side effects, and approval requirements to find…
Use for authorized security assessment of REST, GraphQL, WebSocket, or SOAP APIs, including discovery, authentication,…
Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys)…
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting…