a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity,…
Plan and run an objectives-based adversary-emulation engagement: select a relevant threat actor, build an ATT&CK-mapped emulation plan across the attack lifecycle, execute within rules of engagement, and assess detection/response. Use for full-scope red-team work. Strictly
Claude Code
$ npx -y skills add jassics/awesome-claude-security --skill adversary-emulation --agent claude-code
Codex
$ npx skills add jassics/awesome-claude-security --skill adversary-emulation -a codex -y
Cursor
$ npx skills add jassics/awesome-claude-security --skill adversary-emulation -a cursor -y
Gemini CLI
$ npx skills add jassics/awesome-claude-security --skill adversary-emulation -a gemini-cli -y
GitHub Copilot
$ npx skills add jassics/awesome-claude-security --skill adversary-emulation -a github-copilot -y
OpenCode
$ npx skills add jassics/awesome-claude-security --skill adversary-emulation -a opencode -y
Windsurf
$ npx skills add jassics/awesome-claude-security --skill adversary-emulation -a windsurf -y
name: adversary-emulation description: >- Plan and run an objectives-based adversary-emulation engagement: select a relevant threat actor, build an ATT&CK-mapped emulation plan across the attack lifecycle, execute within rules of engagement, and assess detection/response. Use for full-scope red-team work. Strictly authorized engagements only.
A realistic, objectives-based engagement that emulates a chosen adversary's TTPs to reach a defined objective — producing both offensive findings and an honest measure of the blue team's detection and response.
permitted techniques, off-limits systems/data, deconfliction contacts, and the win condition (the "flag"/objective). Stay within them at all times.
1. **Select the adversary & objective** — pick a threat actor relevant to the org (`threat-intelligence:threat-actor-profiling`) and define the objective (e.g. "access crown-jewel data X"). Realism comes from emulating a real actor's TTPs. 2. **Build the emulation plan** — map the engagement across the attack lifecycle (recon → initial access → execution → persistence → privilege escalation → defense evasion → credential access → discovery → lateral movement → collection → C2 → exfiltration/impact), choosing ATT&CK techniques the actor actually uses. 3. **Recon** — `osint` (footprinting, exposure, people) to find a realistic entry. 4. **Execute within RoE** — work the plan; on network use `network-security`. Operate with appropriate stealth where authorized, but never outside scope and never destructively. Log every action with timestamps for deconfliction. 5. **Track detection** — record, per technique, whether it generated telemetry, fired a detection, and prompted response (this is the core value vs. a pentest). 6. **Assess & debrief** — did you reach the objective? Which TTPs were detected vs. missed? Where are the gaps?
An engagement report: objective & outcome · adversary emulated · ATT&CK technique timeline (executed vs. detected vs. responded) · attack path (`security-diagramming:attack-tree`) · findings · detection/response gaps · recommendations. Use `security-reporting`. Feed gaps to `detection-engineering` and run a `blue-team:purple-team-exercise` to close them.
Red teaming measures **outcomes and detection**, not vulnerability count — the deliverable is "could a realistic adversary achieve X, and would we have caught them?" Stay rigorously within RoE: authorized, non-destructive, deconflicted, and logged. The highest value is collaborative (purple) — emulate, measure, then help the defenders close the gaps.
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…