--- id: sm-security-threat-model name: "security-threat-model" url: https://skills.yangsir.net/skill/sm-security-threat-model author: openai domain: security tags: ["threat-modeling", "stride", "dread", "risk-assessment", "security-architecture"] install_count: 1800 rating: 4.30 (20 reviews) github: https://github.com/openai/skills --- # security-threat-model > 为特定代码仓库或项目路径提供可操作的 AppSec 级威胁模型，而非通用清单。 **Stats**: 1,800 installs · 4.3/5 (20 reviews) ## Before / After 对比 ### 构建精准应用安全威胁模型 ## Readme # 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 - 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 `-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. Weekly Installs427Repository[openai/skills](https://github.com/openai/skills)GitHub Stars14.5KFirst SeenFeb 2, 2026Security Audits[Gen Agent Trust HubPass](/openai/skills/security-threat-model/security/agent-trust-hub)[SocketPass](/openai/skills/security-threat-model/security/socket)[SnykPass](/openai/skills/security-threat-model/security/snyk)Installed oncodex355opencode334github-copilot332gemini-cli321cursor308kimi-cli298 --- *Source: https://skills.yangsir.net/skill/sm-security-threat-model* *Markdown mirror: https://skills.yangsir.net/api/skill/sm-security-threat-model/markdown*