Security Threat Model
OpenAIOfficialPublished by OpenAI
- Skill
- Workflow
- Security
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured, evidence-based process for performing application security threat modeling directly against a codebase. It moves beyond generic checklists by requiring the agent to anchor every architectural claim and identified threat to specific files, configurations, or code paths within the repository. The workflow guides the agent through scoping, identifying trust boundaries and assets, enumerating abuse paths, and prioritizing risks based on realistic attacker capabilities. It emphasizes clear communication for security engineers, including the use of Mermaid diagrams for visualizing data flows and trust boundaries, and requires explicit validation of assumptions with the user.
What it does
- Maps system components, data stores, and entry points directly from repository evidence.
- Identifies trust boundaries and assets that drive security risk.
- Enumerates specific abuse paths and attacker goals relevant to the system architecture.
- Prioritizes threats using qualitative likelihood and impact analysis.
- Generates a concise, Markdown-formatted threat model report.
- Produces a Mermaid flowchart to visualize system components and trust boundaries.
When to use it
- When you need to perform a formal threat model for a specific codebase or project path.
- When you need to identify potential abuse paths and security risks in a new or existing system.
- When you require an AppSec-grade report that is grounded in actual repository evidence.
- When you need to validate security assumptions for a specific deployment or usage model.
Advantages
- Reduces generic advice by anchoring findings to specific repository evidence.
- Ensures consistent output quality through a defined prompt template and contract.
- Forces explicit documentation of assumptions and open questions.
- Provides clear, actionable mitigation recommendations tied to specific components.
- Separates runtime behavior from build or development tooling for better accuracy.
Usage examples
- “Please perform a threat model for the current repository.”
- The agent will analyze the repo structure, identify components and boundaries, and produce a structured Markdown threat model report with a Mermaid diagram.
- “Can you enumerate the abuse paths for the authentication module in this project?”
- The agent will focus on the authentication-related code, identify trust boundaries, and list specific threats and potential mitigations.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/openai/security-threat-model/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip security-threat-model.zip -d .agents/skills
Documentation 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
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.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
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.mdWrite 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.mdOptional controls/asset list:
references/security-controls-and-assets.md
Only load the reference files you need. Keep the final result concise, grounded, and reviewable.
Files
- ATTRIBUTION.mdAdded by AutoGineer910 B
- LICENSE.txt10.5 KiB
- SKILL.md5.4 KiB
- agents/openai.yaml254 B
- references/prompt-template.md12.3 KiB
- references/security-controls-and-assets.md1.6 KiB
Requirements and compatibility
Written for: OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Published by
- OpenAI
- Publisher
- OpenAI (Official)
- Source
- openai/skills · skills/.curated/security-threat-model · commit 49f948f
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by OpenAI.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download security-threat-model.zip