All-in-one offensive security toolbox with AI agent and MCP architecture. Integrates tools like Nmap, Metasploit, FFUF, SQLMap. Enables pentesting, bug bounty hunting, threat hunting, and reporting. RAG-based responses with local knowledge base support.
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/62dd2dfa-9606-49ca-bd91-f0ebf5520def
mcp.json
configuration file, multiple MCP servers can be flexibly integrated and managed to extend the assistant's capabilities.knowledge
directory).
GHOSTCREW's terminal startup interface
Example of GHOSTCREW invoking Metasploit Framework
Clone Repository:
git clone https://github.com/GH05TCREW/ghostcrew.git
cd ghostcrew
Create and Activate Virtual Environment (recommended):
python -m venv .venv
.venv\Scripts\activate
source .venv/bin/activate
Install Dependencies:
pip install -r requirements.txt
Install MCP Server Dependencies (Required for tools):
pip install uv
Without these, you can still use GHOSTCREW in chat mode, but automated workflows and tool integration won't be available.
Configure MCP Servers:
mcp.json
filePrepare Knowledge Base (Optional):
If you want to use the knowledge base enhancement feature, place relevant text files in the knowledge
folder.
Run the Main Program:
python main.py
After the program starts, you can:
GHOSTCREW supports two input modes:
When starting the application, you can:
GHOSTCREW supports integration with the following security tools through the MCP protocol:
Each tool can be configured through the interactive configuration menu by selecting "Configure new tools" from the MCP tools menu.
# 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).
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"
},
Simply add the corresponding files to knowledge
{ "mcpServers": { "ghostcrew": { "command": "npx", "args": [ "-y", "gc-nmap-mcp" ] } } }
Related projects feature coming soon
Will recommend related projects based on sub-categories