이 프로젝트는 원본 “PentestGPT” 용지에 근거를 둔 진보된 침투 테스트 도구입니다. 자동화된 LLM 구동 보안 평가를 수행하기 위해 Model Context Protocol 서버(MCPs)와 통합하여 핵심 기능을 확장합니다.

PentestGPT 프로젝트를 기반으로 합니다.

• GitHub 저장소: https://github.com/GreyDGL/PentestGPT
• Original Research Paper (USENIX Security '24) : https://www.usenix.org/conference/usenixsecurity24/presentation/deng

한국어

특징

• Dynamic MCP Server Integration: 로컬 또는 원격으로 유연한 mcp_servers.json 구성 파일을 통해 다양한 툴 서버로 연결됩니다.
• 자동 작업 실행 (auto] 모드): 자동으로 연결된 MCP 서버에서 제공하는 도구를 사용하여 LLM-suggested 작업을 수행합니다. -Interactive “Human-in-the-Loop” 모드: 각 도구 실행 전에 사용자 확인을 지원, 자동화 된 스캔 중 안전 및 제어를 강화. -Flexible Model Support: OpenAI(GPT series) 및 Google(Gemini series)를 포함한 다양한 LLM 제공업체와 협력합니다.

설치

##1. 저장소를 복제하고 가상 환경 설정

##2. 의존성 설치

PentestGPT-MCP

This project is an advanced penetration testing tool based on the original “PentestGPT” paper. It extends the core capabilities by integrating with Model Context Protocol servers (MCPs) to perform automated, LLM-driven security assessments.

It is based on the PentestGPT project.

• Original GitHub Repository: https://github.com/GreyDGL/PentestGPT
• Original Research Paper (USENIX Security '24): https://www.usenix.org/conference/usenixsecurity24/presentation/deng

🇰🇷 Read this in Korean

Features

• Dynamic MCP Server Integration: Connects to various tool servers running locally or remotely via a flexible mcp_servers.json configuration file.
• Automated Task Execution (auto mode): Automatically executes LLM-suggested tasks using tools provided by a connected MCP server.
• Interactive “Human-in-the-Loop” Mode: Supports user confirmation before each tool execution, enhancing safety and control during automated scans.
• Flexible Model Support: Works with a wide range of LLM providers, including OpenAI (GPT series) and Google (Gemini series).

Installation

1. Clone the Repository and Set Up a Virtual Environment

git clone https://github.com/your-username/PentestGPT-MCP.git
cd PentestGPT-MCP
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

2. Install Dependencies

Install the required Python packages from requirements.txt.

pip install -r requirements.txt

3. Configure API Keys

PentestGPT-MCP requires API keys to interact with Large Language Models. Set the appropriate environment variables for the model you wish to use.

For OpenAI (e.g., gpt-4o):

export OPENAI_API_KEY="your-openai-api-key"

For Google (e.g., Gemini):

export GOOGLE_API_KEY="your-google-api-key"

4. Set Up MCP Servers

The tool manages connections to MCP servers through a central JSON configuration file.

1. Copy the Example Configuration

Copy the example file from the config/ directory to the project root.

cp config/mcp_servers.json.example ./mcp_servers.json

2. Edit the Configuration File

Open mcp_servers.json and customize it to match your environment.

Configuration Example:

{
    "mcpServers": {
        "pentest-tools": {
            "command": "python",
            "args": [
                "mcp_servers/pentest_tools_server.py"
            ]
        },
        "kali_mcp": {
            "command": "python3",
            "args": [
                "/absolute/path/to/mcp_server.py",
                "http://LINUX_IP:5000/"
            ]
        }
    }
}

Configuration Structure:

• mcpServers: The root object containing all server configurations.
• "server-name": A unique, user-defined name for each server (e.g., "pentest-tools").
• command: The command to execute the server (e.g., python, python3, node).