Openai Security Threat Model logo

Openai Security Threat Model

Organization
trailofbits
openai-security-threat-model

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work. Originally from OpenAI's curated skills catalog.

Overview

Publishertrailofbits
Repositoryskills-curated
Skill nameopenai-security-threat-model
Stars
513
Forks
35
Bundled files
2
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by trailofbits on GitHub. Read the source before you install it.

Installation

Install the Openai Security Threat Model AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/trailofbits/skills-curated/tree/main/plugins/openai-security-threat-model/skills/openai-security-threat-model
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trailofbits/skills-curated.git /tmp/skills-curated
mkdir -p .claude/skills
cp -r /tmp/skills-curated/plugins/openai-security-threat-model/skills/openai-security-threat-model .claude/skills/openai-security-threat-model
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openai Security Threat Model in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openai Security Threat Model on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openai Security Threat Model is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

When to Use

When NOT to Use

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Openai Security Threat Model AI skill do?

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work. Originally from OpenAI's curated skills catalog.

Why use Openai Security Threat Model on TypingMind?

Because you install it once and use it with any model. Openai Security Threat Model is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openai Security Threat Model in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trailofbits/skills-curated/tree/main/plugins/openai-security-threat-model/skills/openai-security-threat-model. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openai Security Threat Model?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openai Security Threat Model?

As many as you like. As long as a model supports skills, you can use Openai Security Threat Model with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openai Security Threat Model AI skill free?

Yes. It is published on GitHub by trailofbits under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇