a2a-security-review
Review agent-to-agent (A2A) / multi-agent-system trust: peer identity and authentication, message integrity, capability-negotiation trust, and delegation-chain…
Draw a Data Flow Diagram with trust boundaries for threat modeling: external entities, processes, data stores, data flows, and the boundaries between them. Use when starting a STRIDE/PASTA threat model or documenting how data moves through a system.
$ npx -y skills add jassics/awesome-claude-security --skill threat-model-dfd --agent claude-codeHow it fires
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/threat-model-dfdContext preview
The summary Claude sees to decide when to auto-load this skill.
Draw a Data Flow Diagram with trust boundaries for threat modeling: external entities, processes, data stores, data flows, and the boundaries between them. Use when starting a STRIDE/PASTA threat model or documenting how data moves through a system.
name: threat-model-dfd description: >- Draw a Data Flow Diagram with trust boundaries for threat modeling: external entities, processes, data stores, data flows, and the boundaries between them. Use when starting a STRIDE/PASTA threat model or documenting how data moves through a system.
A DFD that a threat model can be built on: every element typed correctly and every trust boundary drawn, so STRIDE can be applied per element/flow.
(internet↔DMZ, app↔DB, tenant↔tenant, user↔kernel).
1. List actors, processes, stores, and the data that flows between them (ask the user for the architecture if not supplied). 2. Place trust boundaries wherever data crosses a privilege/trust change — these are where threats concentrate. 3. Label every flow with **data + protocol + auth** (e.g. "PII over TLS, JWT"). 4. Render: prefer the Excalidraw MCP; otherwise emit Mermaid `flowchart LR` with `subgraph` blocks for trust boundaries, plus an element/flow inventory table.
This pairs directly with `threat-modeling:stride` — produce the DFD first, then walk STRIDE per element and per boundary crossing.
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…