Kali MCP Server

Kali MCP Server

Enables AI assistants to execute penetration testing commands and security tools on Kali Linux remotely. Supports automated reconnaissance, vulnerability scanning, and CTF solving through integration with 25+ offensive security tools like nmap, gobuster, and nuclei.

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MCP Kali Server

Kali MCP Server — by Lakshmikanthan (Letchu) — GitHub: letchupkt Lightweight API bridge that connects MCP Clients (examples: Claude Desktop, 5ire) to a Kali Linux terminal. It enables MCPs to execute terminal commands, interact with web applications, and run AI-assisted offensive security workflows.


🔍 Overview

Kali MCP Server exposes a controlled API that allows authorized MCP clients to remotely run commands (e.g. nmap, curl, gobuster) and receive structured output. The project is designed to accelerate automated penetration testing, CTF solving, and AI-driven reconnaissance by combining Kali tooling with local or remote LLMs.

Use-cases include:

  • AI-assisted penetration testing and bug hunting.
  • Solving web CTF challenges in real time (useful for HTB/THM/CTF practice).
  • Orchestrating recon/exploit chains through a single MCP interface.

🚀 Key Features

  • 🧠 AI Endpoint Integration — connect the Kali host to any MCP or LLM endpoint (OpenAI, Claude, DeepSeek, or other local models).
  • 🖥️ Command Execution API — run terminal tools remotely and get structured responses.
  • 🕸️ Web Challenge & CTF Support — AI agents can interact with target web apps (via curl, wget, fuzzers) and attempt to capture flags or findings.
  • 🔐 Built for Offensive Security Practitioners — red teamers, bug bounty hunters and CTF players in mind.
  • 🎯 Large Toolset — ships with (or can install) 25+ specialized tools for reconnaissance, scanning, and exploitation.

🛠️ Integrated Tools

Recon & Subdomain Discovery: subfinder, sublist3r, amass, assetfinder

Subdomain Takeover: subzy, subjack

HTTP Probing & Crawling: httpx, katana, gospider

Content / Dir Discovery: gobuster, dirb, ffuf, feroxbuster, dirsearch

Vuln Scanners: nuclei, nikto, wpscan, dalfox

Param / URL Discovery: arjun, paramspider, waybackurls, gau, gf

Port Scanners: nmap, masscan, rustscan

Auth / Passwords: hydra, john, sqlmap

Network Enumeration: enum4linux

This list is configurable — tools can be added/removed depending on your Kali environment and permitted scope.


🛠️ Quick Install (Kali host)

# clone the project
git clone https://github.com/letchupkt/kali-mcp.git
cd kali-mcp

# make installer executable and run it (installs tools)
chmod +x install_tools.sh
sudo ./install_tools.sh

# start the server
python3 kali_server.py

Note: install_tools.sh will try to install many common pentest binaries. Inspect it and run it inside a VM or disposable Kali box if you prefer.


🖥️ Client Configuration (MCP Clients)

Clients simply invoke the MCP script with the Kali host URL. Example command used by MCP clients:

python3 /absolute/path/to/mcp_server.py http://LINUX_IP:5000

Claude Desktop

Edit %APPDATA%\Claude\claude_desktop_config.json and add an MCP entry:

{
  "mcpServers": {
    "kali_mcp": {
      "command": "python3",
      "args": [
        "/absolute/path/to/mcp_server.py",
        "--server",
        "http://LINUX_IP:5000/"
      ]
    }
  }
}

5ire Desktop

Add an MCP using the same python3 /absolute/path/to/mcp_server.py http://LINUX_IP:5000 command — 5ire will generate the needed configuration automatically.


🧩 Example Workflows

  1. CTF Web Challenge (high-level):

    • Client sends challenge URL + scope to MCP.
    • MCP runs httpx/gobuster/ffuf and returns parsed results.
    • Model suggests follow-up fuzzing or sqlmap commands.
    • Operator reviews and executes exploit steps, MCP logs outputs.
  2. HTB-like Machine (non-destructive recon):

    • Run nmap/rustscan then enum4linux for SMB.
    • Save and store findings in local vector DB for the AI to reason over.

⚙️ Suggested Architecture & Safety

  • Orchestration layer: Use a small Python service to call system tools and normalize outputs to JSON.
  • RAG & State: Store scan results and evidence in a local DB (e.g., SQLite + FAISS) and pass concise context to the LLM.
  • Sandboxing: Run scans/exploits inside VMs/containers with strict network egress control.
  • Authorization checks: Add a required authorization token and a pre-flight scope/consent checklist for every automated run.

🔒 Legal & Ethical Notice

Only run this server against systems you own or explicitly have written authorization to test. Automated pentesting can cause service disruption and legal consequences. This repository is intended for education, defensive research, and authorized testing only.


🔮 Other Possibilities

  • Memory forensics with Volatility (automated workflows).
  • Disk forensics (SleuthKit / timelines / carving).
  • Integration with alerting pipelines (Slack, Signal, email) for findings.
  • Local LLM orchestration (run quantized LLMs for on-device reasoning — tell me your hardware and I can recommend exact models and setup commands).

Contributing

PRs welcome. If you add tools or workflows, include tests and updated docs. Please keep contributions scoped to legal, ethical tooling.


Author & Contact

Lakshmikanthan (Letchu)

GitHub: letchupkt

Portfolio: https://letchupkt.vgrow.tech

Instagram: @letchu_pkt


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