Security Threat Model
JetBrainsOfficialCreated by OpenAIDistributed by JetBrains
- Skill
- Review and audit
- 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 approach to threat modeling for software repositories. It guides the agent to analyze the codebase to identify trust boundaries, assets, and entry points, then derive abuse paths and prioritize threats based on realistic attacker goals. The process emphasizes grounding every claim in specific repository files or configurations, ensuring the resulting threat model is practical and relevant to the actual system architecture rather than a generic security checklist.
What it does
- Analyzes repository structure to identify components, data stores, and entry points.
- Maps trust boundaries and data flows based on actual code and configuration.
- Enumerates realistic abuse paths and attacker goals.
- Prioritizes threats using qualitative likelihood and impact assessments.
- Generates a concise Markdown report with evidence anchors.
- Validates assumptions with the user to refine the threat model context.
When to use it
- When you need to perform an AppSec-grade threat model for a specific codebase.
- When you need to identify potential abuse paths in a new or existing project.
- When you need to document security assumptions and trust boundaries for a system.
- When you need to prioritize security mitigations based on concrete architectural evidence.
Advantages
- Reduces generic advice by anchoring findings directly to repository evidence.
- Provides a consistent, professional output format suitable for security engineers.
- Forces explicit documentation of assumptions and open questions.
- Clearly separates runtime behavior from development or build-time tooling.
Usage examples
- “Perform a threat model for the current repository.”
- The agent will analyze the codebase, identify components and trust boundaries, and produce a structured Markdown file containing a threat model and recommended mitigations.
- “Enumerate the abuse paths for the authentication service in this repo.”
- The agent will focus specifically on the authentication logic, identifying potential entry points and trust boundaries to map out how an attacker might compromise the service.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/jetbrains/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 AutoGineer950 B
- LICENSE.txt10.5 KiB
- SKILL.md5.6 KiB
- agents/openai.yaml254 B
- references/prompt-template.md12.3 KiB
- references/security-controls-and-assets.md1.6 KiB
references/security-controls-and-assets.md
Security Controls and Asset Categories
Use this as a lightweight checklist to keep outputs consistent across teams. Prefer concrete, system-specific items over generic text.
Asset categories (pick only what applies)
User data (PII, content, uploads)
Authentication artifacts (passwords, tokens, sessions, cookies)
Authorization state (roles, policies, ACLs)
Secrets and keys (API keys, signing keys, encryption keys)
Configuration and feature flags
Models and weights (if ML systems)
Source code and build artifacts
Audit logs and telemetry
Availability-critical resources (queues, caches, rate limits, compute budgets)
Tenant isolation boundaries and metadata
Security control categories
Identity and access: authN, authZ, session handling, mTLS, key rotation
Input protection: schema validation, parsing hardening, upload scanning, sandboxing
Network safeguards: TLS, network policies, WAF, rate limiting, DoS controls
Data protection: encryption at rest/in transit, tokenization, redaction
Isolation: process sandboxing, container boundaries, tenant isolation, seccomp
Observability: audit logs, alerting, anomaly detection, tamper resistance
Supply chain: dependency pinning, SBOMs, provenance, signing
Change control: CI checks, deployment approvals, config guardrails
Mitigation phrasing patterns
"Enforce schema at <boundary> for <payload> before <component>."
"Require authZ check for <action> on <resource> in <service>."
"Isolate <parser/component> in a sandbox with <resource limits>."
"Rate limit <endpoint> by <key> and apply burst caps."
"Encrypt <data> at rest using <key management> and rotate <keys>."
Requirements and compatibility
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
- Created by
- OpenAI
- Publisher
- JetBrains (Official)
- Distributed by
- JetBrains
- Source
- JetBrains/skills · security-threat-model · commit 3f53782
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by OpenAI or JetBrains.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download security-threat-model.zip