Mcpwner
MCPwner is a security research automation server based on the Model Context Protocol, integrating multiple security testing tools (SAST, SCA, secret scanning, etc.) to provide a unified interface for LLM - driven security analysis workflows.
rating : 2.5 points
downloads : 0
What is MCPwner?
MCPwner is an AI assistant extension tool specifically designed for security research. It's like a "security toolbox" that packages more than 20 professional security scanning tools (such as code vulnerability detection, sensitive information search, and dependency library security check) into a unified interface, allowing your AI assistants (such as Claude, Cursor, Kiro, etc.) to directly call these tools to analyze your code projects. Traditionally, security researchers need to manually run various tools and then copy and paste the results for AI analysis. MCPwner eliminates this cumbersome process, enabling AI assistants to directly access security scan results for continuous reasoning, correlation analysis, and attack path discovery.How to use MCPwner?
Using MCPwner is very simple, just follow three steps: 1. Start the MCPwner service with a single click using Docker. 2. Configure the MCPwner connection in your AI assistant (Claude Desktop, Cursor, Kiro, etc.). 3. Directly ask the AI assistant questions, such as "Scan my project for security vulnerabilities" or "Check if this repository has leaked keys". The AI assistant will automatically call the corresponding tools in MCPwner, run the scan, and return the structured results to you, along with analysis and suggestions.Applicable scenarios
MCPwner is particularly suitable for the following scenarios: • Developers who want to check code security in real - time during the coding process. • Security researchers who need to quickly analyze the security status of multiple projects. • Teams that want to integrate security scanning into the AI - assisted development workflow. • Educational scenarios for learning application security testing tools and methods. • Open - source project maintainers who want to check project dependencies and code vulnerabilities.Main features
Unified interface
Integrate more than 20 security tools into a single interface, including various tools such as SAST (Static Application Security Testing), SCA (Software Composition Analysis), and key detection, eliminating the need to switch between different tools.
AI assistant integration
All tool outputs are converted into structured formats (SARIF/JSON), which can be directly understood and analyzed by AI assistants to provide intelligent security suggestions and repair solutions.
Containerized execution
All security tools run in Docker containers, ensuring environment isolation, consistent dependencies, and no impact on your host system.
Automatic data persistence
The metadata of the workspace and scan database will be automatically saved, and historical records will not be lost even if the container is restarted, supporting long - term security research projects.
Extensible architecture
Adopt a plug - in architecture, which can easily add new security tools. In the future, it is planned to support more reconnaissance, dynamic testing, and infrastructure security tools.
Advantages
🚀 One - click deployment: Quickly start all services using Docker Compose.
🤖 AI - native: Designed specifically for AI assistants, providing structured output for AI analysis.
🔒 Security isolation: Tools run in containers to avoid polluting the host environment.
📊 Result correlation: Able to correlate the scan results of multiple tools to discover complex attack paths.
💾 Data persistence: Important data is automatically saved, supporting long - term research projects.
Limitations
⚠️ Resource requirements: Requires at least 8GB RAM (16GB recommended) and 20GB of disk space.
🐳 Docker dependency: Docker and Docker Compose must be installed.
📶 Network access: Some tools need to access the Internet to download databases and updates.
⏱️ Scan time: Large projects may require a long scan time.
🔧 Configuration learning: You need to learn basic configuration methods when using it for the first time.
How to use
Install Docker
Ensure that your system has Docker Engine 20.10+ and Docker Compose 2.0+ installed. Windows users need to install WSL2.
Download MCPwner
Clone the MCPwner repository to your local machine and enter the project directory.
Start the service
Use Docker Compose to start all services. The Docker images of all security tools will be automatically downloaded on the first run.
Configure the AI assistant
Add the MCPwner configuration according to the AI assistant you are using. Here is an example of configuring Claude Desktop (macOS).
Start using
Restart your AI assistant, and then you can directly ask security - related questions.
Usage examples
Case 1: Check for key leakage in a GitHub repository
You've found an interesting GitHub repository and want to quickly check if it accidentally contains sensitive information such as API keys, passwords, etc.
Case 2: Security audit of a Python project
You've developed a Python Web application and want to conduct a comprehensive security check before release.
Case 3: Attack path analysis
You're in charge of an application with a microservice architecture and want to understand the complete attack paths that attackers might use.
Frequently Asked Questions
Is MCPwner free?
Do I need programming knowledge to use it?
Will the scan modify my code?
Which programming languages are supported?
Will the scan results be sent to an external server?
How to update the database of security tools?
Can I scan private repositories?
What should I do if I encounter an error when executing a tool?
Related resources
MCPwner GitHub repository
Source code, issue tracking, and the latest version
Model Context Protocol official website
Understand the technical details and specifications of the MCP protocol
Docker installation guide
Docker installation tutorials for various platforms
Claude Desktop configuration
How to configure Claude Desktop to use the MCP server
Security testing tool documentation
Official documentation for CodeQL (documentation for other tools can be found in their respective repositories)

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