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Security Threat Model

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Create actionable threat models for your codebase.

by openai24.8k stars on openai/skills
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Updated Jun 24, 2026
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What Security Threat Model does

The Security Threat Model skill provides a structured approach to identifying and mitigating security threats within a codebase or project path. By focusing on the specific repository or project, it generates a detailed threat model that enumerates trust boundaries, assets, potential attacker capabilities, abuse paths, and recommended mitigations. This skill is designed for developers and security professionals who need to assess the security posture of their applications and prioritize risks effectively.

The workflow begins with scoping the system model, where key components, data stores, and external integrations are identified. It emphasizes the importance of evidence-based claims, ensuring that every architectural assertion is grounded in the repository's context. By mapping out trust boundaries and assets, users can gain a clearer understanding of where vulnerabilities may exist and how they can be exploited.

Once the model is established, the skill helps enumerate threats as abuse paths, focusing on realistic attacker goals that align with the identified assets and boundaries. This prioritization is crucial, as it allows users to allocate resources effectively to address the most significant risks. The skill also emphasizes the importance of validating assumptions with the user, ensuring that the threat model remains relevant and accurate based on the specific context of the application.

Finally, the Security Threat Model skill provides actionable recommendations for mitigating identified risks. These recommendations are tailored to the specific components and contexts of the application, moving beyond generic advice to offer concrete implementation hints. This makes it a valuable tool for teams looking to enhance their application security in a systematic and evidence-based manner.

When to use it

Use this skill when you need to create a detailed threat model for a specific codebase or project path, especially in the context of application security assessments.

When not to use it

This skill is not suitable for general architectural summaries, code reviews, or non-security related design work, as it is specifically focused on threat modeling.

What you can build with it

Modeling a Web Application

When developing a web application, use this skill to identify potential security threats specific to the application's architecture and deployment model.

Assessing a New Codebase

Before deploying a new codebase, run this skill to create a threat model that highlights risks and necessary mitigations before going live.

Regular Security Reviews

Incorporate this skill into your regular security review process to keep your threat models updated as your codebase evolves.

How to install Security Threat Model

View source

1. Install with the skills CLI

npx skills add openai/skills/security-threat-model --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by openai

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.

Frequently asked questions about Security Threat Model

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