openai-security-threat-model

name	openai-security-threat-model
description	Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work. Originally from OpenAI's curated skills catalog.
allowed-tools	["Read","Grep","Glob","Write","Edit"]

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.

When to Use

When NOT to Use