a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity, capability-negotiation trust, and delegation-chain…
Assemble a structured assurance / safety case for deploying an AI system — an explicit argument that it is acceptably safe for its context, backed by evidence (harm model, evals, guardrails, fairness, governance). Use to support a go/no-go deployment decision or an
$ npx -y skills add jassics/awesome-claude-security --skill safety-case --agent claude-codeHow it fires
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Assemble a structured assurance / safety case for deploying an AI system — an explicit argument that it is acceptably safe for its context, backed by evidence (harm model, evals, guardrails, fairness, governance). Use to support a go/no-go deployment decision or an
name: safety-case description: >- Assemble a structured assurance / safety case for deploying an AI system — an explicit argument that it is acceptably safe for its context, backed by evidence (harm model, evals, guardrails, fairness, governance). Use to support a go/no-go deployment decision or an audit/sign-off.
A defensible safety case: a top-level claim that the system is acceptably safe for its intended use and context, decomposed into arguments, each supported by concrete evidence and with residual risks stated honestly.
1. **Top claim** — "System X is acceptably safe to deploy for use case Y in context Z," with the acceptance criteria and who owns the decision. 2. **Sub-claims / arguments** — typically:
`ai-safety:safety-evaluation`, `ai-safety:safety-red-team`).
`ai-safety:guardrail-review`).
`ai-safety:responsible-ai-assessment`). 3. **Evidence** — link each argument to the actual artifacts/results; note their date, scope, and limitations. 4. **Residual risk & conditions** — what remains, why it's acceptable, and the conditions/monitoring/kill-switches that keep it acceptable post-deployment.
1. State the deployment context and acceptance criteria first (they set the bar). 2. Build the argument tree; for each leaf, attach evidence or mark it as a gap. 3. Make gaps explicit — a safety case with honest gaps beats one that hides them. 4. Conclude with a clear go / no-go / go-with-conditions recommendation.
A safety-case document (claims → arguments → evidence → residual risk → recommendation). Render the argument tree with `security-diagramming` and produce the document + executive summary with `security-reporting`.
A safety case is an honest argument, not a checklist or a rubber stamp. Unsupported arguments and unaddressed residual risks are the point — surface them so the decision-maker owns them explicitly.
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.
Repo: jassics/awesome-claude-security
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity, capability-negotiation trust, and delegation-chain…
Test the agent execution harness/runtime itself — LangChain/LangGraph, AutoGen, CrewAI, custom ReAct-style loops, or computer-use/browser-use agents — for…
Assess an autonomous / tool-using AI agent for security end-to-end: tool privileges, autonomy and approval boundaries, excessive agency, memory/state…
Test what an AI agent will actually do without human confirmation, including under injected-goal / prompt-injection scenarios, to validate its autonomy and…
Review the security of MCP (Model Context Protocol) servers/clients an agent uses: server trust tier, tool/resource description and result poisoning,…
Inventory the tools/functions an AI agent can call and audit their privileges, side effects, and approval requirements to find excessive-agency and…