GHOSTCREW

This is an AI red team assistant using large language models with MCP and RAG architecture. It aims to help users perform penetration testing tasks, query security information, analyze network traffic, and more through natural language interaction.

https://github.com/user-attachments/assets/a43d2457-7113-42cc-ad02-f378d57f4d24

Features

• Natural Language Interaction: Users can ask questions and give instructions to the AI assistant using natural language.
• MCP Server Integration: Through the mcp.json configuration file, multiple MCP servers can be flexibly integrated and managed to extend the assistant's capabilities.
• Tool Management: Configure, connect to, and manage MCP tools through an interactive menu, including the ability to clear all configurations.
• Tool Invocation: The AI assistant can call tools provided by configured MCP servers (such as: nmap, metasploit, ffuf, etc.) based on user requests.
• Automated Pentesting Workflows: Execute predefined penetration testing workflows that systematically use configured security tools to perform comprehensive assessments.
• Report Generation: Generate markdown reports with structured findings, evidence, and recommendations.
• Conversation History: Supports multi-turn dialogues, remembering previous interaction content.
• Streaming Output: AI responses can be streamed for a better user experience.
• Knowledge Base Enhancement (Optional): Supports enhancing AI responses through a local knowledge base RAG (knowledge directory).
• File-Aware Tool Integration: AI recognizes and uses actual files from the knowledge folder (wordlists, payloads, configs) with security tools.
• Configurable Models: Supports configuration of different language model parameters.

Automated Penetration Testing Workflows

GHOSTCREW includes automated penetration testing workflows that provide structured, systematic security assessments. These workflows require MCP tools to be configured and connected to function properly.

Available Workflows

1. Reconnaissance and Discovery

   • Comprehensive information gathering and target profiling
   • Performs reconnaissance, subdomain discovery, port scanning, technology fingerprinting, and historical data analysis
   • Steps: 5 systematic phases

2. Web Application Security Assessment

   • Comprehensive web application penetration testing
   • Tests for directory traversal, SQL injection, web vulnerabilities, SSL/TLS security, authentication flaws, and file inclusion
   • Steps: 6 focused web security phases

3. Network Infrastructure Penetration Test

   • Network-focused penetration testing and exploitation
   • Includes network scanning, service enumeration, vulnerability identification, misconfiguration testing, and exploitation attempts
   • Steps: 6 network security phases

4. Complete Penetration Test

   • Full-scope penetration testing methodology
   • Comprehensive assessment covering reconnaissance, enumeration, vulnerability scanning, web testing, network exploitation, post-exploitation, and reporting
   • Steps: 7 complete assessment phases

Workflow Features

• Tool Integration: All workflows utilize configured MCP tools (Nmap, Metasploit, Nuclei, etc.) for real security testing
• Professional Output: Each step provides detailed technical findings, vulnerability analysis, risk assessment, and remediation recommendations
• Report Generation: Automatically save reports to organized reports/ directory with workflow-specific naming
• Target Flexibility: Works with IP addresses, domain names, or network ranges
• Progress Tracking: Real-time progress indication through each workflow step

Usage Requirements

• MCP Tools Required: Automated workflows require at least one MCP security tool to be configured and connected
• Access Control: The system prevents workflow execution without proper tools to avoid generating simulated results
• Professional Context: Designed for authorized penetration testing and security assessments only

How to Use Workflows

1. Start the Pentest Agent and configure MCP tools when prompted
2. Select "Automated Penetration Testing" from the main menu
3. Choose your desired workflow type
4. Enter the target (IP, domain, or network range)
5. Confirm execution and monitor progress
6. Optionally save results to file for documentation

Startup Effect

GHOSTCREW's terminal startup interface

Metasploit Tool Call

Example of GHOSTCREW invoking Metasploit Framework

Installation Guide

1. Clone Repository:

   git clone https://github.com/GH05TCREW/ghostcrew.git
   cd ghostcrew
   

2. Create and Activate Virtual Environment (recommended):

   python -m venv .venv
   

   • Windows:

     .venv\Scripts\activate
     

   • macOS/Linux:

     source .venv/bin/activate
     

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Install MCP Server Dependencies (Required for tools):

   • Node.js & npm: Most MCP security tools require Node.js. Install from nodejs.org
   • Python uv (for Metasploit): Install with pip install uv

   Without these, you can still use GHOSTCREW in chat mode, but automated workflows and tool integration won't be available.

Usage

1. Configure MCP Servers:

   • Run the application and select "Configure or connect MCP tools" when prompted
   • Use the interactive tool configuration menu to add, configure, or clear MCP tools
   • The configuration is stored in the mcp.json file

2. Prepare Knowledge Base (Optional): If you want to use the knowledge base enhancement feature, place relevant text files in the knowledge folder.

3. Run the Main Program:

   python main.py
   

   After the program starts, you can:

   • Choose whether to use the knowledge base
   • Configure or activate MCP tools
   • Select between Interactive Chat Mode or Automated Penetration Testing
   • Execute workflows and generate reports
   • Use 'multi' command to enter multi-line input mode for complex queries
   • Enter 'quit' to exit the program

Input Modes

GHOSTCREW supports two input modes:

• Single-line mode (default): Type your query and press Enter to submit
• Multi-line mode: Type 'multi' and press Enter, then type your query across multiple lines. Press Enter on an empty line to submit.

MCP Tool Management

When starting the application, you can:

1. Connect to specific tools
2. Configure new tools
3. Connect to all tools
4. Skip connection
5. Clear all tools (resets mcp.json)

Available MCP Tools

GHOSTCREW supports integration with the following security tools through the MCP protocol:

1. AlterX - Subdomain permutation and wordlist generation tool
2. Amass - Advanced subdomain enumeration and reconnaissance tool
3. Arjun - Hidden HTTP parameters discovery tool
4. Assetfinder - Passive subdomain discovery tool
5. Certificate Transparency - SSL certificate transparency logs for subdomain discovery (no executable needed)
6. FFUF Fuzzer - Fast web fuzzing tool for discovering hidden content
7. HTTPx - Fast HTTP toolkit and port scanning tool
8. Hydra - Password brute-force attacks and credential testing tool
9. Katana - Fast web crawling with JavaScript parsing tool
10. Masscan - High-speed network port scanner
11. Metasploit - Penetration testing framework with exploit execution, payload generation, and session management
12. Nmap Scanner - Network discovery and security auditing tool
13. Nuclei Scanner - Template-based vulnerability scanner
14. Scout Suite - Cloud security auditing tool
15. shuffledns - High-speed DNS brute-forcing and resolution tool
16. SQLMap - Automated SQL injection detection and exploitation tool
17. SSL Scanner - Analysis tool for SSL/TLS configurations and security issues
18. Wayback URLs - Tool for discovering historical URLs from the Wayback Machine archive

Each tool can be configured through the interactive configuration menu by selecting "Configure new tools" from the MCP tools menu.

Coming Soon

• BloodHound
• CrackMapExec
• Gobuster
• Responder
• Bettercap

File Structure

GHOSTCREW/
├── .venv/                  # Python virtual environment
├── main.py                 # Application entry point
├── config/                 # Application configuration
│   ├── __init__.py
│   ├── constants.py        # Constants and messages
│   └── app_config.py       # Environment and API configuration
├── core/                   # Core application logic
│   ├── __init__.py
│   ├── model_manager.py    # Model provider and token management
│   ├── agent_runner.py     # Agent execution and streaming
│   └── pentest_agent.py    # Main application controller
├── tools/                  # MCP tool management
│   ├── __init__.py
│   ├── mcp_manager.py      # MCP server connection management
│   └── configure_mcp.py    # Interactive tool configuration utility
├── ui/                     # User interface components
│   ├── __init__.py
│   ├── menu_system.py      # Menu display and user interaction
│   └── conversation_manager.py  # Chat history management
├── workflows/              # Automated penetration testing workflows
│   ├── __init__.py
│   ├── workflow_engine.py  # Workflow execution engine
│   └── workflow_definitions.py  # Predefined workflow templates
├── rag/                    # Knowledge base and RAG functionality
│   ├── __init__.py
│   ├── knowledge_base.py   # RAG text splitting and search
│   └── embedding.py        # Embedding generation and management
├── reporting/              # Report generation system
│   ├── __init__.py
│   └── generators.py       # Professional report generation
├── knowledge/             # Knowledge base documents directory
│   └── ...
├── reports/               # Professional penetration test reports directory
│   ├── ghostcrew_*_*.md   # Professional markdown reports
│   └── ghostcrew_*_*_raw_history.txt  # Raw conversation history (optional)
├── .gitignore              # Git ignore file configuration
├── mcp.json                # MCP server configuration file
├── README.md               # Project documentation
├── requirements.txt        # Python dependency list
├── LICENSE                 # Project license
└── .env                    # Environment variables

# OpenAI API configurations
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://api.openai.com/v1
MODEL_NAME=gpt-4o

This configuration uses OpenAI's API for both the language model and embeddings (when using the knowledge base RAG feature).

Configuration File (mcp.json)

This file is used to define MCP servers that the AI assistant can connect to and use. Most MCP servers require Node.js or Python to be installed on your system. Each server entry should include:

• name: Unique name of the server.
• params: Parameters needed to start the server, usually including command and args.
• cache_tools_list: Whether to cache the tools list.

MCP Example Server Configuration:

stdio

{
  "name": "Nmap Scanner",
  "params": {
    "command": "npx",
    "args": [
      "-y", 
      "gc-nmap-mcp"
    ],
    "env": {
      "NMAP_PATH": "C:\\Program Files (x86)\\Nmap\\nmap.exe"
    }
  },
  "cache_tools_list": true
}

Make sure to replace the path to the Nmap executable with your own installation path.

sse

{"name":"mcpname",
  "url":"http://127.0.0.1:8009/sse"
},

Knowledge Base Configuration

Simply add the corresponding files to knowledge