Percerno Skillsbeta
← everything it can do · engineering

Security Threat Model TEXT

Generates a repository-specific AppSec threat model in Markdown by analyzing code to identify trust boundaries, assets, and abuse paths. It prioritizes threats based on likelihood and impact, validates assumptions with the user, and provides concrete mitigation recommendations.

4 uses this weeksource openailicence Apache-2.0
Quality gate: FAIL · better answers · measured 2026-10-24
Blind-judged against the bare model on 2 requests: average lift -18.5 points (specificity, structure, method, fit).
won 0/2, lift -18.5, tokens ×14.04
Security scan: clean · 2026-10-24 · 5 files · ran in sandbox
no network calls, shell tricks, credential access or instruction tampering found in its files.

Try asking

Say it in your own words; the assistant finds this recipe by itself. Examples:

“Lav en trusselmodel for koden i /backend mappen.”
“Jeg har brug for at dokumentere sikkerhedsrisici for vores nye API. Analyser koden og find mulige angrebsveje.”
“Kør en trusselmodel på hele repoet. Fokusér på hvordan eksterne brugere interagerer med databasen.”
“Create a threat model for the /api directory in this repository.”
“I need to document the security risks for our new payment service. Please analyze the code and list potential abuse paths.”
“Run a threat model on the entire repo. Focus on how external users interact with the database layer.”
Try this skill from Claude, ChatGPT or a chat on your own page. Free plan, no credit card.Get started free

Good for

threat-modelingapplication-securitycode-analysisrisk-assessmenttrust-boundariesabuse-pathsmitigationmarkdown-report

Not for

Do not use this skill for general architecture summaries, standard code reviews, or non-security design documentation. It is not intended for generic security checklists that are not tied to specific code evidence. For simple vulnerability scanning without threat context, a dedicated SAST/DAST tool is more appropriate.

Details

When it is the right choice

Use this skill when the user explicitly requests a threat model for a specific codebase, directory, or project path. It is appropriate for enumerating security threats, abuse paths, and trust boundaries grounded in the actual repository structure. It is also suitable for AppSec reviews that require prioritizing risks based on specific architectural evidence.

The full recipe the assistant reads

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.

Files in this skill (5)
  • LICENSE.txt
  • SKILL.md
  • agents/openai.yaml
  • references/prompt-template.md
  • references/security-controls-and-assets.md
Where it comes from

Vendored from openai/skills, commit 49f948faa925, path skills/.curated/security-threat-model, licence Apache-2.0. The upstream licence file ships inside the skill.