agent-runtime-validati…
Plans and evaluates bounded runtime tests of AI agents, tools, memory, hooks, approvals, and scope enforcement. Use when static…
Maps identity, authority, instruction, data, memory, and approval propagation across agents and agent-to-agent handoffs. Use when reviewing multi-agent systems, orchestrators, delegated tools, or confused-deputy exposure.
Claude Code
$ npx -y skills add zero-lab-ai/skills --skill agent-trust-boundary-audit --agent claude-code
Codex
$ npx skills add zero-lab-ai/skills --skill agent-trust-boundary-audit -a codex -y
Cursor
$ npx skills add zero-lab-ai/skills --skill agent-trust-boundary-audit -a cursor -y
Gemini CLI
$ npx skills add zero-lab-ai/skills --skill agent-trust-boundary-audit -a gemini-cli -y
GitHub Copilot
$ npx skills add zero-lab-ai/skills --skill agent-trust-boundary-audit -a github-copilot -y
OpenCode
$ npx skills add zero-lab-ai/skills --skill agent-trust-boundary-audit -a opencode -y
Windsurf
$ npx skills add zero-lab-ai/skills --skill agent-trust-boundary-audit -a windsurf -y
name: agent-trust-boundary-audit description: "Maps identity, authority, instruction, data, memory, and approval propagation across agents and agent-to-agent handoffs. Use when reviewing multi-agent systems, orchestrators, delegated tools, or confused-deputy exposure." metadata: portability: universal discipline: blue-team domain: ai-systems tags: [agents, a2a, trust-boundaries, identity, confused-deputy]
Map how authority actually crosses an agent system. Keep communication, authentication, authorization, delegation, and downstream effect distinct; a message path alone does not establish permission to act.
architectures.
handoff-validation concerns.
whose identity.
protocol's default security model.
orchestrator or model provider.
1. Define the reviewed system, environments, tenants, versions, evidence cutoff, owners, and intended security properties. 2. Inventory principals and components: humans, service identities, agents, orchestrators, models, brokers, tools, memories, queues, approval services, policy engines, targets, and administrative planes. 3. Record each component's authentication method, effective identity, privilege, tenant context, reachable resources, secret access, and policy source. 4. Map five edge types separately:
5. For every handoff, record sender, receiver, message type, provenance, integrity, freshness, audience binding, replay protection, target binding, approval binding, and failure behavior. 6. Trace authority end to end. Identify where a low-authority principal can shape inputs consumed by a higher-authority component, even when no credential crosses the boundary. 7. Review confused-deputy conditions: mismatched requester and execution identity, ambient authority, incomplete target binding, untrusted arguments, result reuse, and approval detached from final action. 8. Review memory and state boundaries: writer, reader, tenant, lifetime, revocation, provenance, conflict handling, and stale-state invalidation. 9. Label every edge `observed`, `reported`, `inferred`, `contradicted`, or `unknown`. Preserve the exact evidence supporting effective authority. 10. Build abuse-path hypotheses without exercising them. A path remains conditional when any identity, authority, policy, or target transition is unproven. 11. Prioritize choke points where one control limits multiple paths: scoped identity, explicit delegation, exact approval binding, provenance checks, tenant isolation, revocation, and target-side authorization.
Return the component and principal inventory, typed trust-edge map, effective authority matrix, handoff-control table, confused-deputy hypotheses, memory and tenant findings, evidence gaps, and prioritized control choke points.
can have different identities, tenants, policies, and compromise assumptions.
authorization for the requested target or action.
arguments, identity, target, and time.
can act with ambient authority on attacker-shaped input.
and instruction boundary.
evidence until deployment facts confirm it.
Portable, evidence-bound security skills for authorized defensive assessment, penetration testing, red teaming, detection engineering, purple teaming, and engagement operations. The collection contains 56 skills in 10 installable plugins from zero-lab.ai.
Plans and evaluates bounded runtime tests of AI agents, tools, memory, hooks, approvals, and scope enforcement. Use when static…
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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…