MCP Kali Server (MKS) is a lightweight API bridge that connects MCP clients (e.g: Claude Desktop or 5ire) to the API server which allows executing commands on a Linux terminal.
This MCP is able to run terminal commands as well as interacting with web applications using:
Dirbenum4linuxgobusterHydraJohn the RipperMetasploit-FrameworkNiktoNmapsqlmapWPScan- As well as being able to execute raw commands.
As a result, this is able to perform AI-assisted penetration testing and solving CTF challenges in real time.
👉 How MCP is Revolutionizing Offensive Security
The goal is to enable AI-driven offensive security testing by:
- Letting the MCP interact with AI endpoints like OpenAI, Claude, DeepSeek, Ollama or any other models.
- Exposing an API to execute commands on a Kali machine.
- Using AI to suggest and run terminal commands to solve CTF challenges or automate recon/exploitation tasks.
- Allowing MCP apps to send custom requests (e.g.
curl,nmap,ffuf, etc.) and receive structured outputs.
Here are some example (using Google's AI gemini 2.0 flash):
Solving.Web.CTF.Challenge.mp4
Testing.on.HTB.Machine.Code.mp4
- 🧠 AI Endpoint Integration: Connect your Kali to any MCP of your liking such as Claude Desktop or 5ier.
- 🖥️ Command Execution API: Exposes a controlled API to execute terminal commands on your Kali Linux machine.
- 🕸️ Web Challenge Support: AI can interact with websites and APIs, capture flags via
curland any other tool AI the needs. - 🔐 Designed for Offensive Security Professionals: Ideal for red teamers, bug bounty hunters, or CTF players automating common tasks.
sudo apt install mcp-kali-server
kali-server-mcpOtherwise for bleeding edge:
git clone https://github.com/Wh0am123/MCP-Kali-Server.git
cd MCP-Kali-Server
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
./server.pyCommand Line Options:
--ip <address>: Specify the IP address to bind the server to (default:127.0.0.1for localhost only)- Use
127.0.0.1for local connections only (secure, recommended) - Use
0.0.0.0to allow connections from any network interface (very dangerous; use with caution) - Use a specific IP address to bind to a particular network interface
- Use
--port <port>: Specify the port number (default:5000)--debug: Enable debug mode for verbose logging
Examples:
# Run on localhost only (secure, default)
./server.py
# Run on all interfaces (less secure, useful for remote access)
./server.py --ip 0.0.0.0
# Run on a specific IP and custom port
./server.py --ip 192.168.1.100 --port 8080
# Run with debug mode
./server.py --debugThis can be local (on the same Kali machine) or remote (another Linux machine, Windows or macOS).
If you're running the client and server on the same Kali machine (aka local), run either:
## OS package
kali-server-mcp --server http://127.0.0.1:5000
# ...OR...
## Bleeding edge
./client.py --server http://127.0.0.1:5000If separate machines (aka remote), create an SSH tunnel to your MCP server, then launch the client:
## Terminal 1 - Replace `LINUX_IP` with Kali's IP
ssh -L 5000:localhost:5000 user@LINUX_IP
## Terminal 2
git clone https://github.com/Wh0am123/MCP-Kali-Server.git
cd MCP-Kali-Server
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
./client.py --server http://127.0.0.1:5000If you're openly hosting the MCP Kali server on your network (server.py --IP...), you don't need the SSH tunnel (but we do recommend it!)
NOTE:
./client.py --server http://LINUX_IP:5000Edit:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
- Simply add an MCP with the command
python3 /absolute/path/to/client.py --server http://LINUX_IP:5000and it will automatically generate the needed configuration files.
There are more possibilities than described since the AI model can now execute commands on the terminal. Here are some examples:
-
Memory forensics using Volatility
- Automating memory analysis tasks such as process enumeration, DLL injection checks, and registry extraction from memory dumps.
-
Disk forensics with SleuthKit
- Automating analysis from disk images, timeline generation, file carving, and hash comparisons.
This project is intended solely for educational and ethical testing purposes. Any misuse of the information or tools provided — including unauthorized access, exploitation, or malicious activity — is strictly prohibited.
The author assumes no responsibility for misuse.
