AI-powered threat modeling platform. Analyze GitHub repositories or design documents to generate structured threat models using STRIDE, OWASP Top 10, or AWS Threat Grammar frameworks.
- Repository Analysis — Point at a GitHub repo URL to auto-detect components, data flows, trust boundaries, and security findings
- Design Document Analysis — Upload PDF, Markdown, or text files to generate threats before writing code
- Design Review Engine — Generates security enhancement suggestions, pre-code architectural risks, and a context layer file (CLAUDE.md / AGENTS.md format)
- Dual LLM Support — Choose between Anthropic Claude or Google Gemini as the analysis engine
- Data Flow Diagrams — Auto-generated Mermaid DFDs from repository analysis
- Jira Integration — Create tickets directly from threat mitigations
- PDF Export — Download threat model reports as PDF
- Project Management — Organize sessions into projects
- Node.js 20+
- pnpm (install with
npm install -g pnpm)
# Clone the repo git clone https://github.com/UnitOneAI/Synthesis.git cd Synthesis # Install dependencies pnpm install # Create env file with your API key (at least one required for LLM analysis) cp .env.example .env.local # Edit .env.local and add your key: # ANTHROPIC_API_KEY=sk-ant-... # or # GOOGLE_GEMINI_API_KEY=AIza... # Start the dev server pnpm dev
Open http://localhost:3000.
No API key? The app runs in demo mode with realistic mock data — you can explore the full UI without any keys configured. Keys can also be added later from the Settings page.
| Variable | Required | Description |
|---|---|---|
ANTHROPIC_API_KEY |
No* | Anthropic API key for Claude |
GOOGLE_GEMINI_API_KEY |
No* | Google Gemini API key |
GITHUB_TOKEN |
No | GitHub PAT for private repo access |
* At least one LLM key is needed for real analysis. Without one, the app uses demo mode. Keys can also be set from Settings > Integrations in the UI.
# Build and run docker compose up --build # With API key ANTHROPIC_API_KEY=sk-ant-... docker compose up --build
The app will be available at http://localhost:3000.
app/
threat-modeling/ Main threat modeling UI
settings/ Settings & integrations page
api/ API routes (sessions, threats, settings, projects)
lib/
llm-provider.ts Shared LLM abstraction (Anthropic + Gemini)
db.ts SQLite database (auto-created, zero config)
threat-engine/
repo-analyzer.ts GitHub repo cloning & analysis
threat-generator.ts LLM-powered threat generation
design-review-engine.ts Design enhancements, risks, context layer
dfd-generator.ts Data flow diagram generation
document-parser.ts PDF/Markdown/text parsing
jira-client.ts Jira API integration
pdf-export.ts PDF report generation
- Framework: Next.js 16 (App Router, React 19)
- Database: SQLite via better-sqlite3 (auto-created, no setup needed)
- UI: Tailwind CSS, Radix UI, Lucide icons
- LLMs: Anthropic Claude Sonnet 4.5, Google Gemini 2.5
Synthesis also provides a Python package for integration with UnitoneController.
pip install git+https://github.com/UnitOneAI/Synthesis.git
from synthesis import SynthesisThreatModelTool # Register with UnitoneController tool = SynthesisThreatModelTool() registry.register(tool) # Run threat model from unitone import UnitOneFlow unitone = UnitOneFlow("/path/to/repo") result = unitone.run("synthesis")
# Run threat model on a repository python -m synthesis.tool /path/to/repo --framework STRIDE # Or using npx (TypeScript CLI) npx ts-node cli/scan.ts /path/to/repo --framework STRIDE
synthesis/
├── __init__.py # Package exports
├── models.py # Data models (Threat, OWASP, STRIDE)
└── tool.py # SynthesisThreatModelTool (Tool interface)
cli/
└── scan.ts # TypeScript CLI (called by Python tool)
┌─────────────────────────────────────────────────────────────────┐
│ UnitoneController │
│ │
│ from synthesis import SynthesisThreatModelTool │
│ tool = SynthesisThreatModelTool() │
│ output = tool.run(context) # Same interface everywhere │
└───────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Execution Environment │
│ │
│ LOCAL: subprocess → TypeScript CLI │
│ LAMBDA: boto3.invoke() → AWS Lambda │
│ HTTP: requests.post() → Container API │
└───────────────────────────┬─────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Synthesis Analysis Engine │
│ (TypeScript) │
│ │
│ - Analyzes repository structure │
│ - Generates STRIDE threats via LLM │
│ - Calculates OWASP risk ratings │
│ - Returns JSON in UnitoneController Issue format │
└─────────────────────────────────────────────────────────────────┘
The tool interface (tool.run()) is the same regardless of where the analysis runs.
See synthesis/CLAUDE.md for detailed architecture documentation.
Private — UnitOne AI