security-threat-model

par openai

Modélisation des menaces ancrée dans le dépôt, qui cartographie les limites de confiance, les actifs et les chemins d’abus vers des preuves de code concrètes. Énumère les points d’entrée, les flux de données et les limites de confiance ancrés dans la structure et la configuration réelles du dépôt. Dérive des objectifs réalistes d’attaquant liés à des actifs spécifiques (identifiants, données personnelles, état critique d’intégrité, ressources de calcul) plutôt que des listes de contrôle génériques. Priorise les menaces en utilisant un raisonnement sur la probabilité et l’impact, avec des hypothèses explicites sur le déploiement,...

npx skills add https://github.com/openai/skills --skill security-threat-model

Threat Model Source Code Repo

Deliver an actionable AppSec-grade threat model that is specific to the repository or a project path, not a generic checklist. Anchor every architectural claim to evidence in the repo and keep assumptions explicit. Prioritizing realistic attacker goals and concrete impacts over generic checklists.

Quick start

  1. Collect (or infer) inputs:
  • Repo root path and any in-scope paths.
  • Intended usage, deployment model, internet exposure, and auth expectations (if known).
  • Any existing repository summary or architecture spec.
  • Use prompts in references/prompt-template.md to generate a repository summary.
  • Follow the required output contract in references/prompt-template.md. Use it verbatim when possible.

Workflow

1) Scope and extract the system model

  • Identify primary components, data stores, and external integrations from the repo summary.
  • Identify how the system runs (server, CLI, library, worker) and its entrypoints.
  • Separate runtime behavior from CI/build/dev tooling and from tests/examples.
  • Map the in-scope locations to those components and exclude out-of-scope items explicitly.
  • Do not claim components, flows, or controls without evidence.

2) Derive boundaries, assets, and entry points

  • Enumerate trust boundaries as concrete edges between components, noting protocol, auth, encryption, validation, and rate limiting.
  • List assets that drive risk (data, credentials, models, config, compute resources, audit logs).
  • Identify entry points (endpoints, upload surfaces, parsers/decoders, job triggers, admin tooling, logging/error sinks).

3) Calibrate assets and attacker capabilities

  • List the assets that drive risk (credentials, PII, integrity-critical state, availability-critical components, build artifacts).
  • Describe realistic attacker capabilities based on exposure and intended usage.
  • Explicitly note non-capabilities to avoid inflated severity.

4) Enumerate threats as abuse paths

  • Prefer attacker goals that map to assets and boundaries (exfiltration, privilege escalation, integrity compromise, denial of service).
  • Classify each threat and tie it to impacted assets.
  • Keep the number of threats small but high quality.

5) Prioritize with explicit likelihood and impact reasoning

  • Use qualitative likelihood and impact (low/medium/high) with short justifications.
  • Set overall priority (critical/high/medium/low) using likelihood x impact, adjusted for existing controls.
  • State which assumptions most influence the ranking.

6) Validate service context and assumptions with the user

  • Summarize key assumptions that materially affect threat ranking or scope, then ask the user to confirm or correct them.
  • Ask 1–3 targeted questions to resolve missing context (service owner and environment, scale/users, deployment model, authn/authz, internet exposure, data sensitivity, multi-tenancy).
  • Pause and wait for user feedback before producing the final report.
  • If the user declines or can’t answer, state which assumptions remain and how they influence priority.

7) Recommend mitigations and focus paths

  • Distinguish existing mitigations (with evidence) from recommended mitigations.
  • Tie mitigations to concrete locations (component, boundary, or entry point) and control types (authZ checks, input validation, schema enforcement, sandboxing, rate limits, secrets isolation, audit logging).
  • Prefer specific implementation hints over generic advice (e.g., "enforce schema at gateway for upload payloads" vs "validate inputs").
  • Base recommendations on validated user context; if assumptions remain unresolved, mark recommendations as conditional.

8) Run a quality check before finalizing

  • Confirm all discovered entrypoints are covered.
  • Confirm each trust boundary is represented in threats.
  • Confirm runtime vs CI/dev separation.
  • Confirm user clarifications (or explicit non-responses) are reflected.
  • Confirm assumptions and open questions are explicit.
  • Confirm that the format of the report matches closely the required output format defined in prompt template: references/prompt-template.md
  • Write the final Markdown to a file named <repo-or-dir-name>-threat-model.md (use the basename of the repo root, or the in-scope directory if you were asked to model a subpath).

Risk prioritization guidance (illustrative, not exhaustive)

  • High: pre-auth RCE, auth bypass, cross-tenant access, sensitive data exfiltration, key or token theft, model or config integrity compromise, sandbox escape.
  • Medium: targeted DoS of critical components, partial data exposure, rate-limit bypass with measurable impact, log/metrics poisoning that affects detection.
  • Low: low-sensitivity info leaks, noisy DoS with easy mitigation, issues requiring unlikely preconditions.

References

  • Output contract and full prompt template: references/prompt-template.md
  • Optional controls/asset list: references/security-controls-and-assets.md

Only load the reference files you need. Keep the final result concise, grounded, and reviewable.

Plus de skills de openai

release
openai
Créez une version Symphony en incrémentant la version validée, en la fusionnant, en taguant le commit fusionné et en vérifiant le workflow de publication Burrito. Utilisez lorsque l'on vous demande de…
signing-entitlements
openai
Inspectez les problèmes de signature, d’entitlements, de runtime renforcé et de Gatekeeper pour les applications macOS. Utilisez lorsque l’on vous demande de diagnostiquer des échecs de signature de code, des entitlements manquants,…
building-ai-agent-on-cloudflare
openai
Construit des agents IA sur Cloudflare en utilisant le SDK Agents avec gestion d'état, WebSockets en temps réel, tâches planifiées, intégration d'outils, et chat…
epigraphdb-skill
openai
Soumettre des requêtes compactes à l'API EpiGraphDB pour l'ontologie, la littérature, la MR, les gènes-médicaments et les preuves de voies de soutien. Utiliser lorsqu'un utilisateur souhaite des résumés concis d'EpiGraphDB.
runtime-behavior-probe
openai
Planifier et exécuter des investigations sur le comportement d'exécution avec des scripts de sonde temporaires, des matrices de validation, des contrôles d'état et des rapports axés sur les résultats. Utiliser uniquement lorsque…
deep-security-scan
openai
À utiliser lorsque l’utilisateur demande une analyse de sécurité Codex approfondie, exhaustive, multi-passes ou réduisant la variance, à l’échelle du dépôt ou sur un chemin ciblé. Exécutez des passes indépendantes répétées…
define-security-policy
openai
Définir, réviser ou mettre à jour les directives SECURITY.md pour un dépôt ou un composant. À utiliser lorsque l’utilisateur souhaite clarifier ce que Codex Security doit examiner, ce qui est hors…
validation
openai
À utiliser lorsque Codex est déjà dans la phase de validation d'un scan de sécurité ou que l'utilisateur demande explicitement de déterminer si une ou plusieurs conclusions de sécurité candidates…