跳到正文
cccskills
免费在 GitHub 公开

threat-model

Build a threat model that identifies valuable assets, attack entry points, potential threats, and mitigation priorities. Use source code and past vulnerabilities, an interview with the application owner, or a combination of both.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Build a threat model for a target codebase. Three modes: "interview" walks an application owner through the four-question framework and produces a threat model from their answers; "bootstrap" derives a threat model from the code plus past vulnerabilities (CVEs, git history, pentest reports) when no owner is available; "bootstrap-then-interview" chains the two when both owner and codebase are present. All write THREAT_MODEL.md in a shared schema. Use when asked to "threat model", "build a threat model", "map the attack surface", or "what should we be worried about in this codebase".

查看安装方法

适用场景

  • Reviewing security risks during system design
  • Assessing an inherited codebase without its owner
  • Refining a code-based draft with the application owner
  • Prioritizing threats and proposed mitigations

包含的文件(5)

  • SKILL.md7.2 KB
  • bootstrap.md21.5 KB
  • interview.md8.8 KB
  • README.md5.1 KB
  • schema.md6.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

threat-model

A threat model answers "what could go wrong with this system, who would do it, and what should we do about it?" independently of whether any specific bug has been found yet. It is the map; vulnerability discovery is the metal detector. A good threat model tells the pipeline where to look and tells triage which findings matter.

Litmus test: If patching one line of code makes an entry disappear, it was a vulnerability, not a threat. A threat ("attacker achieves RCE via untrusted media parsing") still stands after every known bug is fixed; a vulnerability ("dr_wav.h:412 doesn't bounds-check chunk_size") does not. This skill produces threats. Vulnerabilities appear only as evidence that raises a threat's likelihood score.

Invocation: /threat-model [bootstrap-then-interview|bootstrap|interview] <target-dir> [flags]


Step 0 — Safety preamble (always runs first)

This skill performs static analysis only. It reads source, git history, and any vulnerability reports the user supplies, and writes a single output file (<target-dir>/THREAT_MODEL.md). It does not build, execute, fuzz, or modify the target, and does not make network requests against the target's infrastructure.

Before proceeding, confirm and state in your first response:

  1. The target directory exists and is a local checkout you can read.
  2. You will not execute any code from the target directory.
  3. If --vulns points at a URL or you are asked to "fetch CVEs", you will query only public advisory databases (NVD, GitHub Security Advisories, the project's own issue tracker) and never the target's live deployment.

If the user asks you to validate a threat by running an exploit, decline and point them at the vuln-pipeline (README Step 2) instead.


Step 1 — Route to a mode

Parse $ARGUMENTS:

First tokenRoute to
interviewRead interview.md in this directory and follow it.
bootstrapRead bootstrap.md in this directory and follow it.
bootstrap-then-interviewBootstrap first, then interview seeded from the draft. See below.
anything else, or emptyAsk the user: "Is someone who owns or built this system available to answer questions in this session?" Yes and the codebase is checked out → recommend bootstrap-then-interview. Yes but no codebase → interview.md. No → bootstrap.md.

All modes write the same artifact (THREAT_MODEL.md, schema in schema.md) so downstream consumers (pipeline recon/judge, verifier agents) do not need to know which mode produced it.

interviewbootstrap
NeedsAn application owner present in the sessionA local checkout; optionally past vulns
MethodFour-question framework: conversational walk through what are we working on → what can go wrong → what are we going to do about it → did we do a good jobFive stages: parallel research swarm → synthesize sections 1-3 + vuln table → generalize vulns into threat classes → STRIDE gap-fill → emit
Best forNew systems, design reviews, systems where the risk lives in business logic the code doesn't showInherited systems, third-party code, OSS dependencies, anything with a CVE history
Provenance taginterviewbootstrap

Context durability. Interview mode is multi-turn; tool results from early reads may be evicted before you need them. To stay resilient:

  • Do not read interview.md or bootstrap.md in full up front. Read the mode file (or the relevant section of it) at the point you need it, one question or stage at a time.
  • If a re-read via the Read tool is refused as "file unchanged", the prior result was evicted; reload with cat <path> via Bash instead.

Interview backbone (so you can proceed even if interview.md is unavailable mid-session):

QQuestionFills schema sections
Q1What are we working on?section 1 context, section 2 assets, section 3 entry points
Q2What can go wrong?section 4 threat rows (id, threat, actor, surface, asset)
Q3What are we going to do about it?section 4 impact/likelihood/status/controls; section 5 deprioritized; section 8 recommended mitigations
Q4Did we do a good job?validate ranking, coverage check, section 6 open questions

bootstrap-then-interview mode

When the owner is available and the codebase is checked out, this is the recommended path: the owner's time goes to refining a code-grounded draft instead of describing the system from scratch.

  1. Tell the owner: "I'll read the code first and come back with a draft (about 5-10 min), then we'll walk it together. Want that, or would you rather start cold?" Only proceed if they opt in; otherwise fall back to interview.md.
  2. Read bootstrap.md and follow it end-to-end. Write <target-dir>/THREAT_MODEL.md.
  3. Immediately continue into interview mode: read interview.md and follow it with --seed <target-dir>/THREAT_MODEL.md in effect. The section 6 open questions from bootstrap become your Q1-Q4 prompts; the owner confirms, corrects, and adds rather than starting from nothing.
  4. Overwrite <target-dir>/THREAT_MODEL.md with the refined model. Set provenance mode: bootstrap-then-interview.

The same flow is available manually: run bootstrap first, then interview --seed <THREAT_MODEL.md> in a later session.


Step 2 — Shared output contract

All modes MUST emit <target-dir>/THREAT_MODEL.md conforming to schema.md in this directory. Read schema.md immediately before you write the file, not at routing time; in interview mode the gap between routing and emit can be many turns, and an early read will be evicted before it's used.

After writing the file, print to the user:

  1. The path to THREAT_MODEL.md.
  2. The top 5 threats by likelihood × impact (id, one-line description, L×I).
  3. For bootstrap: any open questions the code could not answer (these seed a later interview pass).
  4. For interview: any owner statements that could not be verified in code (these seed follow-up code review).

References

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

customize

免费

Adapt a C/C++ vulnerability pipeline to another language, target, or detection method. The workflow gathers requirements, proposes file changes for approval, updates the implementation, and validates it against a test target containing known bugs.

中文简介正在准备中。当前显示英语简介。

  • Adapt the pipeline for web application vulnerabilities
  • Plan a port for smart contract auditing
  • Change detection signals and report criteria
anthropics/defending-code-reference-harness76052026年8月7日 更新

dnr-hunt

免费

Hunt for signs of compromise in web and application logs without an initial alert. Establish normal activity, record investigative hypotheses, check source code, and reproduce issues locally before producing incident records and a report.

中文简介正在准备中。当前显示英语简介。

  • Hunting for compromise without an initial alert
  • Tracing related activity across application logs
  • Distinguishing attack attempts from successful exploitation
anthropics/defending-code-reference-harness76052026年8月7日 更新

Investigate an alert or suspicious identifier using logs and source code. Assess attack success, measure affected data and users, test the suspected flaw locally, and produce a response plan for human review and execution.

中文简介正在准备中。当前显示英语简介。

  • Investigating a security alert
  • Measuring affected records and customers
  • Checking a suspected flaw with a local proof of concept
anthropics/defending-code-reference-harness76052026年8月7日 更新

patch

免费

Turn security findings into candidate code fixes with review results and summaries. Depending on the input, use independent source review or a pipeline with execution checks, then provide diffs for a person to review and apply.

中文简介正在准备中。当前显示英语简介。

  • Generating candidate fixes from triaged findings
  • Preparing diffs for the highest-priority vulnerabilities
  • Independently reviewing proposed security fixes
anthropics/defending-code-reference-harness76052026年8月7日 更新

Get started with vulnerability review through a guided practice target, from threat modeling to scanning and triage. Ask questions about the pipeline and receive answers grounded in its documentation and source, with references and a next command.

中文简介正在准备中。当前显示英语简介。

  • Learn the vulnerability review workflow
  • Try a first scan on the bundled practice target
  • Find commands with supporting documentation
anthropics/defending-code-reference-harness76052026年8月7日 更新

triage

免费

Turn raw security findings into a ranked list for engineering review. Check claims against source code, merge duplicates, assess how feasible exploitation is, and assign component owners, with results in JSON and Markdown.

中文简介正在准备中。当前显示英语简介。

  • Validating security scanner findings
  • Merging duplicate findings across scans
  • Prioritizing a vulnerability backlog
anthropics/defending-code-reference-harness76052026年8月7日 更新

查看 anthropics 的全部技能

报告此技能的问题