a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity,…
Responsibly red-team an AI system to find SAFETY failures — harmful outputs, jailbreaks that defeat safety guardrails, and foreseeable-misuse / dangerous- capability elicitation — so they can be mitigated. Use to stress-test safeguards before/after release. Controlled,
Claude Code
$ npx -y skills add jassics/awesome-claude-security --skill safety-red-team --agent claude-code
Codex
$ npx skills add jassics/awesome-claude-security --skill safety-red-team -a codex -y
Cursor
$ npx skills add jassics/awesome-claude-security --skill safety-red-team -a cursor -y
Gemini CLI
$ npx skills add jassics/awesome-claude-security --skill safety-red-team -a gemini-cli -y
GitHub Copilot
$ npx skills add jassics/awesome-claude-security --skill safety-red-team -a github-copilot -y
OpenCode
$ npx skills add jassics/awesome-claude-security --skill safety-red-team -a opencode -y
Windsurf
$ npx skills add jassics/awesome-claude-security --skill safety-red-team -a windsurf -y
name: safety-red-team description: >- Responsibly red-team an AI system to find SAFETY failures — harmful outputs, jailbreaks that defeat safety guardrails, and foreseeable-misuse / dangerous- capability elicitation — so they can be mitigated. Use to stress-test safeguards before/after release. Controlled, authorized, mitigation-focused; not for producing or retaining harmful content.
Evidence on where the system's safety behavior breaks: which harm categories can be elicited, under what techniques, and how robust the guardrails are — to drive fixes.
This is **defensive**: the objective is to measure whether safeguards hold and to improve them, in a controlled/authorized setting. Probe to the point of demonstrating a guardrail gap; do **not** generate complete operational harmful artifacts (e.g. working weapon/malware instructions). Record that a boundary failed and the category — not a usable harmful payload. Handle any sensitive output as restricted and minimize/redact it.
demonstrate-the-gap level, not full operational detail).
delivery of the above.
1. Confirm authorization and scope; prefer a controlled/sandbox environment. 2. Run techniques per harm category; for each, record: technique · category · result (held / partial / bypassed) · minimal evidence (description, not payload). 3. Identify the failed control (classifier gap, weak refusal, robustness hole). 4. Rank by severity (`threat-modeling:risk-rank`) and recommend mitigations (guardrail tuning, training, policy, monitoring, human oversight).
A results table: technique · harm category · result · (redacted) evidence · mitigation. Route confirmed gaps through `security-reporting:finding` with restricted handling.
Distinct from `pentester` / security red-team (which targets the system on behalf of an attacker). Here the "win" is a *harm to a person/society*, and the goal is to close it. Keep evidence minimal and non-operational.
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…