🧠 Adaptive Threat Modeler

A continuous, AI-powered, context-aware threat modeling tool for modern software development

🚀 Overview

Adaptive Threat Modeler is a comprehensive security analysis platform that continuously monitors your codebase for vulnerabilities, generates interactive threat models, and provides AI-powered remediation suggestions. It combines static analysis, dynamic threat modeling, and intelligent automation to help development teams build more secure software.

✨ Key Features

🔍 Multi-Language Analysis: Supports Go, JavaScript, TypeScript, Python, Java, PHP, Ruby, C#, C++, HCL/Terraform, and more
🧠 AI-Powered Intelligence: GPT-4 integration for enhanced vulnerability analysis and recommendations
🎯 Framework Detection: Automatic detection of web frameworks, libraries, and infrastructure patterns
🌊 Advanced Analysis Engine: AST-based parsing, pattern matching, taint analysis, and dataflow tracking
🎨 Interactive Threat Modeling: Visual threat maps with component relationships and data flows
⚡ Real-Time Processing: Asynchronous analysis with live status updates and progress tracking
🔗 GitHub Integration: Direct repository analysis with automated issue creation
📊 Comprehensive Reporting: Detailed vulnerability reports with CWE/OWASP mappings
🔄 Git Commit Analysis: Continuous monitoring of code changes and security implications
🎪 Modern UI: Beautiful, responsive interface with 3D animations and glassmorphism design

🏗️ Architecture

The Adaptive Threat Modeler consists of three main components:

graph TD
    A[Frontend - React/TypeScript] --> B[Backend - Go API]
    B --> C[MCP Agent - Python/AI]
    B --> D[Analysis Engine]
    D --> E[AST Parser]
    D --> F[Rule Engine]
    D --> G[Threat Modeler]
    C --> H[GitHub Integration]
    C --> I[Slack Notifications]
    C --> J[AI Analysis - GPT-4]

🔧 Components

Component	Technology	Purpose
Frontend	React, TypeScript, Vite, ShadCN UI	Modern web interface with 3D visualizations
Backend	Go, Fiber, Git integration	Core analysis engine and REST API
MCP Agent	Python, LangChain, OpenAI	AI-powered analysis and automation

🚀 Quick Start

Prerequisites

Go 1.23+ (for backend)
Node.js 18+ (for frontend)
Python 3.11+ (for MCP agent)
Git (for repository analysis)

🛠️ Manual Setup

1. Backend Setup

cd backend

# Install dependencies
go mod tidy

# Setup environment
cp env.example .env

# Setup git hooks (optional)
chmod +x scripts/setup-git-hooks.sh
./scripts/setup-git-hooks.sh

# Run the server
go run main.go

The backend API will be available at http://localhost:8080

2. Frontend Setup

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will be available at http://localhost:3000

3. MCP Agent Setup

cd mcp

# Install dependencies
pip install -r requirements.txt

# Setup GitHub and Slack integration
python setup_github_integration.py

# Configure environment variables
cp .env.example .env
# Edit .env with your API keys and tokens

# Run the agent
python api.py

📖 Usage

🔍 Analyzing a Repository

Via Web Interface

Open the web interface at http://localhost:3000
Choose analysis method:
- GitHub Repository: Enter repository URL
- File Upload: Upload a ZIP file
Click "Start Analysis" and monitor progress
View results with interactive threat maps
Access detailed logs with syntax highlighting

Via API

# Analyze GitHub repository
curl -X POST http://localhost:8080/api/v1/analyze/github \
  -H "Content-Type: application/json" \
  -d '{"repo_url": "https://github.com/user/repo", "branch": "main"}'

# Check analysis status
curl http://localhost:8080/api/v1/analysis/{id}/status

# Get results
curl http://localhost:8080/api/v1/analysis/{id}

🎯 Supported Analysis Types

Analysis Type	Description	Languages
Static Analysis	AST-based vulnerability detection	All supported languages
Pattern Matching	Regex and metavariable patterns	All supported languages
Taint Analysis	Dataflow tracking for security issues	Go, JavaScript, Python
Infrastructure Analysis	IaC security scanning	Terraform, Docker, Kubernetes
Dependency Analysis	Third-party library vulnerabilities	All package managers

🔒 Security Rules

The analyzer includes built-in security rules covering:

OWASP Top 10 vulnerabilities
CWE (Common Weakness Enumeration) mappings
Language-specific security patterns
Framework-specific vulnerabilities
Infrastructure misconfigurations

Example Detections

SQL Injection (CWE-89)
Cross-Site Scripting (CWE-79)
Insecure Cryptography (CWE-327)
Path Traversal (CWE-22)
Hardcoded Secrets (CWE-798)
Publicly Accessible S3 Buckets
Unencrypted Database Storage

🔌 API Reference

Analysis Endpoints

Method	Endpoint	Description
`POST`	`/api/v1/analyze/github`	Analyze GitHub repository
`POST`	`/api/v1/analyze/upload`	Analyze uploaded ZIP file
`POST`	`/api/v1/analyze/commit`	Analyze git commit
`GET`	`/api/v1/analysis/{id}`	Get analysis results
`GET`	`/api/v1/analysis/{id}/status`	Get analysis status
`GET`	`/api/v1/analysis/{id}/logs`	Get analysis logs

Detection Endpoints

Method	Endpoint	Description
`POST`	`/api/v1/detect/languages`	Detect programming languages
`POST`	`/api/v1/detect/frameworks`	Detect frameworks and libraries

Rules Endpoints

Method	Endpoint	Description
`GET`	`/api/v1/rules`	Get all available rules
`GET`	`/api/v1/rules/{language}`	Get rules for specific language

Commit Analysis Endpoints

Method	Endpoint	Description
`GET`	`/api/v1/commits/latest`	Get latest commit analysis
`GET`	`/api/v1/commits/{hash}`	Get analysis for specific commit
`GET`	`/api/v1/commits`	Get all commit analyses
`POST`	`/api/v1/commits`	Store commit analysis

🤖 AI Integration

MCP Agent Features

The Python MCP agent provides enhanced AI capabilities:

GPT-4 Analysis: Deep semantic understanding of security issues
Automated Issue Creation: GitHub issues with detailed remediation steps
Slack Integration: Real-time notifications with action buttons
Multi-Agent System: Specialized agents for different security domains

GitHub Integration

Automatically creates GitHub issues with:

Structured Format: Well-organized vulnerability reports
Severity Levels: Critical, High, Medium, Low classifications
Actionable Recommendations: Specific remediation steps
Code Examples: Secure coding patterns and fixes

Slack Notifications

Enhanced Slack messages include:

Severity Summary: Visual breakdown of vulnerability counts
Action Buttons: Direct links to GitHub issues and reports
Real-time Updates: Immediate notifications on security findings

🎨 Frontend Features

Modern Interface

3D Brain Animation: Engaging visual representation
Particle Effects: Dynamic background animations
Glassmorphism Design: Modern, translucent UI elements
Responsive Layout: Optimized for all screen sizes

Interactive Components

Threat Maps: Visual representation of application architecture
Real-time Progress: Live updates during analysis
Syntax-highlighted Logs: Color-coded analysis output
Drag & Drop Upload: Intuitive file upload interface

Advanced Visualizations

Component Relationships: Interactive network diagrams
Data Flow Tracking: Visual representation of information flow
Risk Heatmaps: Color-coded severity indicators
Timeline Views: Historical analysis tracking

🔧 Configuration

Backend Configuration

Create backend/.env file:

# Server Configuration
PORT=8080
HOST=localhost

# Git Configuration
GIT_TIMEOUT=300

# Analysis Configuration
MAX_FILE_SIZE=100MB
ANALYSIS_TIMEOUT=600

Frontend Configuration

Configure in frontend/vite.config.ts:

export default defineConfig({
  server: {
    port: 3000,
    proxy: {
      '/api': 'http://localhost:8080'
    }
  }
})

MCP Agent Configuration

Create mcp/.env file:

# GitHub Integration
GITHUB_TOKEN=ghp_your_token_here
GITHUB_OWNER=your_username
GITHUB_REPO=your_repository

# Slack Integration
SLACK_WEBHOOK_URL=https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here

# API Configuration
BACKEND_API_URL=http://localhost:8080

🐛 Troubleshooting

Common Issues

Backend Issues

Port already in use:

# Find and kill process using port 8080
lsof -ti:8080 | xargs kill -9

Git clone failures:

# Check git configuration
git config --list
# Ensure proper authentication for private repositories

Frontend Issues

Dependencies installation failed:

# Clear npm cache and reinstall
npm cache clean --force
rm -rf node_modules package-lock.json
npm install

Build failures:

# Check TypeScript configuration
npx tsc --noEmit

MCP Agent Issues

OpenAI API errors:

Verify API key is valid and has sufficient credits
Check rate limiting and quota usage

GitHub integration issues:

Ensure token has repo scope permissions
Verify repository access and organization settings

Slack webhook failures:

Test webhook URL manually
Check Slack app configuration and permissions

Debug Mode

Enable detailed logging:

# Backend debug mode
export DEBUG=true
export LOG_LEVEL=debug

# Frontend debug mode
export VITE_DEBUG=true

# MCP agent debug mode
export DEBUG=true
export LOG_LEVEL=DEBUG

🤝 Contributing

We welcome contributions! Please follow these guidelines:

Development Setup

Fork the repository
Create a feature branch
```
git checkout -b feature/amazing-feature
```

Install dependencies for all components

# Backend
cd backend && go mod tidy

# Frontend
cd frontend && npm install

# MCP Agent
cd mcp && pip install -r requirements.txt

Testing

# Backend tests
cd backend && go test ./...

# Frontend tests
cd frontend && npm test

# MCP agent tests
cd mcp && python -m pytest

Pull Request Process

Update documentation for any new features
Add tests for new functionality
Ensure CI passes all checks
Request review from maintainers

📚 Documentation

Component Documentation

Additional Resources

🔒 Security

Reporting Security Issues

Please report security vulnerabilities to: security@adaptive-threat-modeler.com

Do not open public GitHub issues for security vulnerabilities.

Security Best Practices

Keep dependencies updated
Use environment variables for sensitive data
Enable HTTPS in production
Implement proper authentication and authorization
Regular security audits and penetration testing

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

OWASP for security standards and guidelines
CWE for vulnerability classification
Semgrep for static analysis patterns
Open Source Community for tools and libraries

📞 Support

📧 Email: support@adaptive-threat-modeler.com
💬 Discord: Join our community
📖 Documentation: docs.adaptive-threat-modeler.com
🐛 Issues: GitHub Issues

Built with ❤️ by the Adaptive Threat Modeler Team

Website • Documentation • Community

Name		Name	Last commit message	Last commit date
Latest commit History 57 Commits
backend		backend
docs		docs
frontend		frontend
mcp		mcp
.DS_Store		.DS_Store
.env.example		.env.example
.gitignore		.gitignore
LICENSE		LICENSE
LOGS_FEATURE.md		LOGS_FEATURE.md
README.md		README.md
hello.cpp		hello.cpp
leaky.yaml		leaky.yaml
shadow-infra.tf		shadow-infra.tf

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🧠 Adaptive Threat Modeler

🚀 Overview

✨ Key Features

🏗️ Architecture

🔧 Components

🚀 Quick Start

Prerequisites

🛠️ Manual Setup

1. Backend Setup

2. Frontend Setup

3. MCP Agent Setup

📖 Usage

🔍 Analyzing a Repository

Via Web Interface

Via API

🎯 Supported Analysis Types

🔒 Security Rules

Example Detections

🔌 API Reference

Analysis Endpoints

Detection Endpoints

Rules Endpoints

Commit Analysis Endpoints

🤖 AI Integration

MCP Agent Features

GitHub Integration

Slack Notifications

🎨 Frontend Features

Modern Interface

Interactive Components

Advanced Visualizations

🔧 Configuration

Backend Configuration

Frontend Configuration

MCP Agent Configuration

🐛 Troubleshooting

Common Issues

Backend Issues

Frontend Issues

MCP Agent Issues

Debug Mode

🤝 Contributing

Development Setup

Testing

Pull Request Process

📚 Documentation

Component Documentation

Additional Resources

🔒 Security

Reporting Security Issues

Security Best Practices

📝 License

🙏 Acknowledgments

📞 Support

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