Use when starting an engagement, before exploitation, or whenever the attack surface changes — build/validate the threat model and detect drift (new unreviewed surface) before advancing
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See how to installReview the instructions the agent will receive before you install.
You cannot test what you have not modeled. A threat model names the assets, entry points, trust boundaries, relevant ATT&CK techniques, and existing mitigations — so coverage is deliberate, not accidental. On a long engagement the surface drifts (a new endpoint, a new dependency); un-reviewed drift is where bugs hide. This skill keeps the model complete and re-checks it for drift.
/engage.gate — the gate refuses to advance on un-acknowledged drift.threat-model.json (see templates/threat-model/): five required lists —
assets, entry_points, trust_boundaries, attck (technique ids), mitigations.
# 1. Lint - every required field present, no placeholders, valid ATT&CK ids
python skills/threat-model-discipline/scripts/threatmodel_lint.py lint .engage/recon/threat-model.json
# 2. Drift - diff a re-run against the reviewed baseline; NEW entry points/assets/boundaries are
# unreviewed surface and BLOCK the gate until re-reviewed or acknowledged
python skills/threat-model-discipline/scripts/threatmodel_lint.py drift \
.engage/recon/threat-model.baseline.json .engage/recon/threat-model.json
Or use /engage.threatmodel (materialize | lint | drift).
TBD/[fill in] — that is not a model; the lint fails it.entry_point appeared and you proceeded anyway — that is the exact gap attackers use.| Excuse | Reality |
|---|---|
| "The model is obvious, skip it" | Obvious to you ≠ documented. Coverage you can't diff is coverage you can't trust. |
| "Drift is just noise" | A new entry point is new attack surface. Acknowledge it explicitly or re-review. |
| "ATT&CK mapping is busywork" | It turns 'we tested stuff' into 'we covered these techniques' — the report's backbone. |
Pairs with scope-discipline (what you may touch) and finding-discipline (what counts as proven).
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Use when attacking a Windows Active Directory domain — Kerberos roasting/delegation, coercion + NTLM/Kerberos relay (CVE-2025-33073), ADCS ESC1-16 (EKUwu), ticket forgery & DCSync, dMSA BadSuccessor (CVE-2025-53779), BloodHound attack-path enumeration, domain dominance
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Use when red-teaming an agentic AI / LLM application — indirect & zero-click prompt injection, MCP tool poisoning, persistent memory poisoning, excessive-agency tool abuse, multi-turn jailbreaks, PyRIT/Garak/Promptfoo harnesses
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Use when attacking an AI/ML system or model — prompt injection & jailbreaks (Crescendo, Skeleton Key, Best-of-N), RAG/vector poisoning, agentic/MCP exploitation (CVE-2025-54136), ML supply-chain RCE (pickle CVE-2025-32434), model extraction / membership inference / adversarial suffixes (GCG)
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Use when building a client-side browser exploit — V8/JSC JIT type confusion to renderer R/W, V8 heap-sandbox escape, renderer-to-browser sandbox escape (Mojo IPC, GPU/Dawn/ANGLE), Electron/webview IPC abuse, 1-click RCE chains
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Use when attacking or auditing a CI/CD pipeline or software supply chain — pwn requests, poisoned pipeline execution, compromised/mutable-tag actions, dependency confusion, registry worms, runner backdoors, OIDC trust abuse, SLSA/provenance
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Use when attacking AWS/Azure/GCP cloud — IAM/identity privilege escalation, IMDS/metadata SSRF, Entra device-code & PRT theft, GCP impersonation chains, Kubernetes/container escape, IaC/CI-CD federation abuse
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