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。
npx skills add openai/skills --skill security-threat-modelBefore / After 效果对比
1 组传统安全清单无法针对特定项目提供具体威胁分析。安全团队难以有效识别和应对项目特有风险。
为代码仓库生成可操作的AppSec威胁模型。精准识别项目风险,指导安全防护,提升应用安全性。
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
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Collect (or infer) inputs:
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Repo root path and any in-scope paths.
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Intended usage, deployment model, internet exposure, and auth expectations (if known).
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Any existing repository summary or architecture spec.
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Use prompts in
references/prompt-template.mdto 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
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Identify primary components, data stores, and external integrations from the repo summary.
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Identify how the system runs (server, CLI, library, worker) and its entrypoints.
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Separate runtime behavior from CI/build/dev tooling and from tests/examples.
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Map the in-scope locations to those components and exclude out-of-scope items explicitly.
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Do not claim components, flows, or controls without evidence.
2) Derive boundaries, assets, and entry points
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Enumerate trust boundaries as concrete edges between components, noting protocol, auth, encryption, validation, and rate limiting.
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List assets that drive risk (data, credentials, models, config, compute resources, audit logs).
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Identify entry points (endpoints, upload surfaces, parsers/decoders, job triggers, admin tooling, logging/error sinks).
3) Calibrate assets and attacker capabilities
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List the assets that drive risk (credentials, PII, integrity-critical state, availability-critical components, build artifacts).
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Describe realistic attacker capabilities based on exposure and intended usage.
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Explicitly note non-capabilities to avoid inflated severity.
4) Enumerate threats as abuse paths
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Prefer attacker goals that map to assets and boundaries (exfiltration, privilege escalation, integrity compromise, denial of service).
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Classify each threat and tie it to impacted assets.
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Keep the number of threats small but high quality.
5) Prioritize with explicit likelihood and impact reasoning
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Use qualitative likelihood and impact (low/medium/high) with short justifications.
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Set overall priority (critical/high/medium/low) using likelihood x impact, adjusted for existing controls.
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State which assumptions most influence the ranking.
6) Validate service context and assumptions with the user
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Summarize key assumptions that materially affect threat ranking or scope, then ask the user to confirm or correct them.
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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).
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Pause and wait for user feedback before producing the final report.
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If the user declines or can’t answer, state which assumptions remain and how they influence priority.
7) Recommend mitigations and focus paths
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Distinguish existing mitigations (with evidence) from recommended mitigations.
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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).
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Prefer specific implementation hints over generic advice (e.g., "enforce schema at gateway for upload payloads" vs "validate inputs").
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Base recommendations on validated user context; if assumptions remain unresolved, mark recommendations as conditional.
8) Run a quality check before finalizing
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Confirm all discovered entrypoints are covered.
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Confirm each trust boundary is represented in threats.
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Confirm runtime vs CI/dev separation.
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Confirm user clarifications (or explicit non-responses) are reflected.
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Confirm assumptions and open questions are explicit.
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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)
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High: pre-auth RCE, auth bypass, cross-tenant access, sensitive data exfiltration, key or token theft, model or config integrity compromise, sandbox escape.
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Medium: targeted DoS of critical components, partial data exposure, rate-limit bypass with measurable impact, log/metrics poisoning that affects detection.
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Low: low-sensitivity info leaks, noisy DoS with easy mitigation, issues requiring unlikely preconditions.
References
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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 Installs427Repositoryopenai/skillsGitHub Stars14.5KFirst SeenFeb 2, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex355opencode334github-copilot332gemini-cli321cursor308kimi-cli298
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