Pwnflow

🎯 Visualize Your Security Testing & Analysis Journey

Pwnflow is a mind-mapping platform designed specifically for cybersecurity professionals. it helps you visualize, track, and share your security testing methodologies while maintaining complete control over your sensitive data.

🚀 Key Features

Core Capabilities

• 🗺️ Interactive Mind Maps: Create and navigate complex security testing workflows with an intuitive visual interface
• 🤖 AI-Powered Assistance: Generate node suggestions and expand your methodology with integrated AI capabilities
• 📋 Command Templates: Save and reuse CLI commands with variable substitution
• 📊 Progress Tracking: Visualize testing progress and methodology coverage

Security & Privacy

• 🔐 Self-Hosted Option: Run entirely on your infrastructure
• 🔒 Encrypted Exports: AES-256-GCM encryption for secure sharing of Projects & Templates
• 🔏 Authentication/Authorization: Token-based authentication withresource-level access control
• 🤖 AI Privacy: Only non-sensitive data (node titles/descriptions) shared with AI - no commands or sensitive details

Import/Export

• 📥 Template Import: Load methodologies from GitHub or private repositories
• 📤 Multiple Export Formats: CSV and encrypted formats
• 🔄 Legacy Migration: Import from previous Pwnflow versions
• 📦 Bulk Operations: Export multiple projects or templates at once

🏗️ Architecture

• Frontend: React 19 + TypeScript + Vite

  • TailwindCSS for styling
  • XYFlow for mind map visualization
  • Zustand for state management

• Backend: FastAPI (Python 3.12)

  • Neo4j graph database for storing relationships
  • Redis

🚀 Getting Started

Prerequisites

• Docker and Docker Compose
• Node.js 18+ and pnpm (for development)
• Python 3.12+ (for backend development)

Quick Start with Docker

1. Clone the repository:

git clone https://github.com/rb-x/pwnflow.git
cd pwnflow

2. Make the run script executable:

chmod +x run.sh

3. Start the development environment:

./run.sh dev

This will automatically:

• Check for Docker and Docker Compose
• Create .env.development with secure defaults (Neo4j password: password)
• To enable the AI feature, press CTRL+C. Then, add your Gemini API Key in your .env file & restart the application by running ./run.sh dev
• Start all services (Frontend, Backend, Neo4j, Redis)
• Display service URLs

4. Access the application:

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• Neo4j Browser: http://localhost:7474
• Redis Commander: http://localhost:8081

Other Commands

./run.sh prod   # Start production environment (with nginx)
./run.sh stop   # Stop all containers
./run.sh clean  # Stop and remove all containers and volumes

Development Setup

Option 1: Using Docker (Recommended)

./run.sh dev

This starts all services with hot-reload enabled. Perfect for full-stack development.

Option 2: Manual Development Setup

If you prefer running services individually without Docker:

Frontend Development

cd frontend
pnpm install
pnpm dev          # Runs on http://localhost:5173

Backend Development

cd backend
pipenv install --dev
pipenv shell
python main.py    # Runs on http://localhost:8000

Note: For manual setup, you'll need Neo4j and Redis running locally:

• Neo4j: Download from neo4j.com
• Redis: brew install redis (macOS) or apt install redis (Linux)

📁 Project Structure

pwnflow/
├── frontend/           # React TypeScript application
│   ├── src/
│   │   ├── components/ # UI components
│   │   ├── pages/      # Route pages
│   │   ├── services/   # API services
│   │   └── store/      # State management
│   └── package.json
├── backend/            # FastAPI application
│   ├── api/           # API endpoints
│   ├── crud/          # Database operations
│   ├── models/        # Data models
│   ├── schemas/       # Pydantic schemas
│   └── services/      # Business logic
└── docker-compose.*.yml

🔧 Configuration

The run.sh script handles all environment configuration automatically:

• Development (./run.sh dev): Creates .env.development with secure defaults
• Production (./run.sh prod): Creates .env.production with strong passwords

To add AI features, simply edit the generated .env.development or .env.production file and add your GOOGLE_API_KEY.

Production Deployment

For production deployment:

1. Run the production setup:

./run.sh prod

This will:

• Check for existing .env.production file
• Generate secure passwords and secrets if needed
• Prompt you to update CORS origins for your domain
• Run security checklist before starting
• Start all services behind nginx proxy

2. On first run, update .env.production:

• Set your domain in BACKEND_CORS_ORIGINS
• Add your GOOGLE_API_KEY for AI features (optional)
• Review all generated passwords

3. Create your first user (registration is disabled by default for security):

# Create admin user via CLI (inside backend container)
docker exec -it pwnflow-backend-prod python create_user.py create admin [email protected]

# Or using docker-compose:
docker compose -f docker-compose.prod.yml exec backend python create_user.py create admin [email protected]

# The script will output:
# User created successfully:
# Username: admin
# Password: <secure-random-password>
# Email: [email protected]

4. Additional production steps:

⚠️ Security Notice: We absolutely don't recommend exposing Pwnflow to the internet. If you must:

1. Whitelist trusted IP addresses through your WAF or firewall
2. Registration is disabled by default - only manual user creation via SSH

• Set up SSL/TLS certificates (Let's Encrypt recommended)
• Set up monitoring and logging
• Implement regular security updates

📚 Documentation

🚧 Documentation is currently being updated for the new full-stack architecture

🎯 Use Cases

Pwnflow is perfect for:

• Penetration Testers: Track methodology and findings
• Security Analysts: Document investigation workflows
• Incident Responders: Map out response procedures
• Security Teams: Share knowledge and methodologies
• Researchers: Organize and visualize attack paths

🛡️ Security

Pwnflow is designed with security in mind:

• Privacy-First AI: AI features only access node titles and descriptions - never sensitive data like findings or variables
• Encrypted Exports: Secure sharing with AES-256-GCM encryption
• Zero Telemetry: No tracking or data collection
• Open Source: Full transparency and community-driven security

Created with ❤️ in 🇫🇷 by Riadh BOUCHAHOUA (rb-x) & Ludovic COULON (LasCC)

Join the Pwnflow community and revolutionize your security testing workflow!

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