设计与多媒体

security-threat-model

试用

基于代码库生成一份可直接交付的 AppSec 威胁建模文档,串联信任边界、资产、滥用路径与缓解措施。

它能做什么

针对仓库根目录或指定子路径,从代码中抽取系统模型,枚举信任边界、资产、入口点与攻击者能力,输出一组数量克制但高质量的滥用路径,并按定性的可能性×影响给出优先级。最终产物为一份 Markdown 文件,命名为 `<basename>-threat-model.md`,其中明确区分已有缓解与建议缓解,且每条建议都绑定到具体位置(如网关、组件、边界)。完成初稿后会向用户提 1–3 个关键问题以确认部署、鉴权、暴露面与数据敏感度,再生成最终报告。

什么时候用它

  • 新微服务上线前的威胁建模
  • 针对 monorepo 中某个包或目录的安全评审
  • 梳理认证、上传或管理后台等关键面的滥用路径
  • 为安全评审产出带证据链的威胁建模文档

技能文档

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 -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.

相关技能

docx

官方

用脚本创建、读取和编辑 Word .docx 与 .dotx 文件。

作者 Anthropic180.0k 星标

用文档优先的流程搭建 ChatGPT Apps SDK 项目,产出工具规划、MCP 服务端与 Widget 脚手架。

作者 OpenAI27.9k 星标

按正确顺序在 Figma 中搭建与代码对齐的完整设计系统,覆盖变量、组件与主题。

作者 OpenAI27.9k 星标

hatch-pet

官方

从文字描述、参考图或品牌线索生成 Codex 兼容的动画宠物与 8x9 雪碧图集。

作者 OpenAI27.9k 星标

winui-app

官方

用 C# 和 Windows App SDK 引导、搭建并验证 WinUI 3 桌面应用。

作者 OpenAI27.9k 星标

OpenAI 的更多技能

浏览全部技能

用文档优先的流程搭建 ChatGPT Apps SDK 项目,产出工具规划、MCP 服务端与 Widget 脚手架。

作者 OpenAI27.9k 星标

按正确顺序在 Figma 中搭建与代码对齐的完整设计系统,覆盖变量、组件与主题。

作者 OpenAI27.9k 星标

figma-use

官方

通过智能体在 Figma 文件中安全、增量地执行 Plugin API JavaScript。

作者 OpenAI27.9k 星标

hatch-pet

官方

从文字描述、参考图或品牌线索生成 Codex 兼容的动画宠物与 8x9 雪碧图集。

作者 OpenAI27.9k 星标

imagegen

官方

通过内置 imagegen 工具生成或编辑位图图像,CLI 兜底模式仅在用户明确要求时启用。

作者 OpenAI27.9k 星标

从 OpenAI 开发者文档获取带引用和来源路径的权威、实时答案。

作者 OpenAI27.9k 星标