MCP Pentest

🚀 Model Context Provider (MCP) for Penetration Testing

An AI-based assistant and middleware for penetration testing activities

🚧 Under Development - Contributions Welcome!

MCP is currently in active development and is in beta. We are seeking contributors to help refine this exciting project. Everyone is welcome to participate!

📚 Project Background

The Model Context Provider (MCP) is an AI-based assistant and middleware designed to coordinate and automate penetration testing activities. It aims to integrate multiple security tools, analyze their outputs, and provide valuable insights and suggestions for testers.

✨ Features

• Task Management: Schedule and track various penetration testing tasks.
• Data Collection and Analysis: Gather data from different security tools and conduct correlation analysis.
• AI-assisted Decision-making: Utilize machine learning models to identify potential attack vectors and assess risks.
• Report Generation: Automatically generate detailed penetration testing reports.

🏗️ Project Architecture

The architecture of MCP is divided into the following main parts:

1. Context Engine: Responsible for collecting data from various security tools and building a unified context view.
2. Task Scheduler: Manage and schedule penetration testing tasks.
3. AI Analysis Module: Use machine learning models for attack path analysis and risk assessment.
4. User Interface: Provide an intuitive interface for testers to interact with the system.

🚀 Quick Start

💻 Usage Examples

Basic Usage

1. Create a new penetration testing task:

curl -X POST http://localhost:8000/api/engagements -H "Content-Type: application/json" -d '{"name": "Example Corp 评估", "scope": {"ip_ranges": ["192.168.1.0/24"], "domains": ["example.com"]}}'

2. Initiate a reconnaissance scan:

curl -X POST http://localhost:8000/api/tasks -H "Content-Type: application/json" -d '{"engagement_id": "YOUR_ENGAGEMENT_ID", "tool": "theHarvester", "parameters": {"target": "example.com"}}'

3. Query the AI assistant:

curl -X POST http://localhost:8000/api/query -H "Content-Type: application/json" -d '{"engagement_id": "YOUR_ENGAGEMENT_ID", "query": "根据当前的发现，最有可能的攻击向量是什么？"}'

🗺️ Project Roadmap

We are currently developing the following features:

• Complete the implementation of the core context engine.
• Finish the initial tool integration.
• Build an AI-driven attack path analyzer.
• Develop a web interface.
• Create a comprehensive test suite.
• Add support for more tools.
• Implement report generation.

🤝 How to Contribute

We particularly welcome help in the following areas:

1. Tool Integration: Add support for more security tools.
2. Testing: Conduct actual tests and report errors.
3. Documentation Improvement: Refine and expand the guides.
4. UI Development: Build the web interface.
5. AI Component Optimization: Enhance LLM integration and attack path analysis.

🌐 Community Resources

• Issue Tracking: Use GitHub issues to report bugs and request features.
• Discussion Area: Raise general questions and ideas in GitHub discussions.

📄 License

This project is distributed under the MIT License. For more details, please refer to the LICENSE file.

⚠️ Important Note

Given the nature of this tool, please pay special attention to the following points:

• Never submit credentials, API keys, or sensitive information.
• Always follow responsible disclosure practices.
• Ensure proper authorization before testing any system.