Threat Model Generation logo

Threat Model Generation

Organization
Factory-AI
threat-model-generation

Generate a STRIDE-based security threat model for a repository. Use when setting up security monitoring, after architecture changes, or for security audits.

Overview

PublisherFactory-AI
Repositoryfactory-plugins
Skill namethreat-model-generation
Stars
111
Forks
15
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Factory-AI on GitHub. Read the source before you install it.

Installation

Install the Threat Model Generation 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.
  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/Factory-AI/factory-plugins.git /tmp/factory-plugins
mkdir -p .claude/skills
cp -r /tmp/factory-plugins/plugins/security-engineer/skills/threat-model-generation .claude/skills/threat-model-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Threat Model Generation 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 Threat Model Generation 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 Threat Model Generation 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 Generation

Generate a comprehensive security threat model for a repository using the STRIDE methodology. This skill analyzes the codebase architecture and produces an LLM-optimized threat model document that other security skills can reference.

When to Use This Skill

  • First-time setup - New repository needs initial threat model
  • Architecture changes - Significant changes to components, APIs, or data flows
  • Security audit - Periodic review or compliance requirement
  • Manual request - Security team requests updated threat model

Inputs

Before running this skill, gather or confirm:

InputDescriptionRequired
Repository pathRoot directory to analyzeYes (default: current directory)
Existing threat modelPath to existing .factory/threat-model.md if updatingNo
Compliance requirementsFrameworks to consider (SOC2, GDPR, HIPAA, etc.)No
Security contactsEmail addresses for security team notificationsNo

Instructions

Follow these steps in order:

Step 1: Analyze Repository Structure

Scan the codebase to understand the system:

  1. Identify languages and frameworks

    • Check package.json, requirements.txt, go.mod, Cargo.toml, etc.
    • Note the primary tech stack (e.g., Next.js, Django, Go microservices)
  2. Map components and services

    • Look for apps/, services/, packages/ directories
    • Identify entry points: API routes, CLI commands, web handlers
    • Note databases, caches, message queues
  3. Identify external interfaces

    • HTTP endpoints (REST, GraphQL)
    • File upload handlers
    • Webhook receivers
    • OAuth/SSO integrations
    • CLI commands that accept user input
  4. Trace data flows

    • How does user input enter the system?
    • Where is sensitive data stored?
    • What external services are called?

Step 2: Identify Trust Boundaries

Define security zones:

  1. Public Zone (untrusted)

    • All external HTTP endpoints
    • Public APIs without authentication
    • User-uploaded files
  2. Authenticated Zone (partially trusted)

    • Endpoints requiring valid session/token
    • User-specific data access
    • Rate-limited APIs
  3. Internal Zone (trusted)

    • Service-to-service communication
    • Admin-only endpoints
    • Database connections
    • Secrets management

Document where trust boundaries exist and what validates transitions between zones.

Step 3: Inventory Critical Assets

Classify data by sensitivity:

  1. PII (Personally Identifiable Information)

    • User emails, names, addresses, phone numbers
    • Document protection measures
  2. Credentials & Secrets

    • Password hashes, API keys, OAuth tokens
    • JWT signing keys, encryption keys
    • Document rotation policies
  3. Business-Critical Data

    • Transaction records, customer data
    • Proprietary algorithms, trade secrets
    • Document access controls

Step 4: Apply STRIDE Analysis

For each major component, analyze threats in all six categories:

S - Spoofing Identity
  • Can attackers impersonate users or services?
  • Are authentication mechanisms secure?
  • Look for: weak session handling, API key exposure, missing MFA
T - Tampering with Data
  • Can attackers modify data in transit or at rest?
  • Look for: SQL injection, XSS, mass assignment, missing input validation
R - Repudiation
  • Can users deny actions they performed?
  • Look for: missing audit logs, insufficient logging, no immutable trails
I - Information Disclosure
  • Can attackers access data they shouldn't?
  • Look for: IDOR, verbose errors, hardcoded secrets, data leaks in logs
D - Denial of Service
  • Can attackers disrupt service availability?
  • Look for: missing rate limits, resource exhaustion, algorithmic complexity
E - Elevation of Privilege
  • Can attackers gain unauthorized access levels?
  • Look for: missing authorization checks, role manipulation, privilege escalation

For each identified threat:

  • Describe the attack scenario
  • List vulnerable components
  • Show code patterns to look for
  • Note existing mitigations
  • Identify gaps
  • Assign severity (CRITICAL/HIGH/MEDIUM/LOW) and likelihood

Step 5: Document Vulnerability Patterns

Create a library of code patterns specific to this codebase's tech stack:

python
# Example: SQL Injection patterns for Python
# VULNERABLE
sql = f"SELECT * FROM users WHERE id = {user_id}"

# SAFE
cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))

Include patterns for:

  • SQL injection
  • XSS (Cross-Site Scripting)
  • Command injection
  • Path traversal
  • Authentication bypass
  • IDOR (Insecure Direct Object Reference)

Step 6: Generate Output Files

Create two files:

1. .factory/threat-model.md

Use the template in stride-template.md to generate a comprehensive threat model with:

  • System overview with architecture description
  • Trust boundaries and security zones
  • Attack surface inventory
  • Critical assets classification
  • STRIDE threat analysis for each component
  • Vulnerability pattern library
  • Security testing strategy
  • Assumptions and accepted risks
  • Version changelog

The document should be written in natural language with code examples, optimized for LLM comprehension.

2. .factory/security-config.json

Generate configuration metadata:

json
{
  "threat_model_version": "1.0.0",
  "last_updated": "<ISO timestamp>",
  "security_team_contacts": [],
  "compliance_requirements": [],
  "scan_frequency": "on_commit",
  "severity_thresholds": {
    "block_merge": ["CRITICAL"],
    "require_review": ["HIGH", "CRITICAL"],
    "notify_security_team": ["CRITICAL"]
  },
  "vulnerability_patterns": {
    "enabled": [
      "sql_injection",
      "xss",
      "command_injection",
      "path_traversal",
      "auth_bypass",
      "idor"
    ],
    "custom_patterns_path": null
  }
}

Customize based on:

  • Detected compliance requirements (from docs, configs, or user input)
  • Security team contacts (if provided)
  • Tech stack (enable relevant vulnerability patterns)

Success Criteria

The skill is complete when:

  • .factory/threat-model.md exists with all sections populated
  • .factory/security-config.json exists with valid JSON
  • All major components have STRIDE analysis
  • Vulnerability patterns match the tech stack
  • Document is written in natural language (LLM-readable)
  • No placeholder text remains

Verification

Run these checks before completing:

bash
# Verify threat model exists and is non-empty
test -s .factory/threat-model.md && echo "✓ Threat model exists"

# Verify config is valid JSON
cat .factory/security-config.json | jq . > /dev/null && echo "✓ Config is valid JSON"

# Check threat model has key sections
grep -q "## 1. System Overview" .factory/threat-model.md && echo "✓ Has System Overview"
grep -q "## 5. Threat Analysis" .factory/threat-model.md && echo "✓ Has Threat Analysis"
grep -q "## 6. Vulnerability Pattern Library" .factory/threat-model.md && echo "✓ Has Pattern Library"

Example Invocations

Generate initial threat model:

Generate a threat model for this repository using the threat-model-generation skill.

Update existing threat model after architecture change:

Update the threat model - we added a new payments service in services/payments/.

Generate with compliance requirements:

Generate a threat model for this repository. We need to comply with SOC2 and GDPR.

References

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 Threat Model Generation AI skill do?

Generate a STRIDE-based security threat model for a repository. Use when setting up security monitoring, after architecture changes, or for security audits.

Why use Threat Model Generation on TypingMind?

Because you install it once and use it with any model. Threat Model Generation 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 Threat Model Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Factory-AI/factory-plugins/tree/master/plugins/security-engineer/skills/threat-model-generation. 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 Threat Model Generation?

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 Threat Model Generation?

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

Is the Threat Model Generation AI skill free?

It is published on GitHub by Factory-AI. Check the repository for licensing terms. 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.

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