MCPPentestBOT

An intelligent penetration testing assistant that seamlessly integrates professional security tools with AI language models through the Model Context Protocol standard.

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "kannanprabu-mcppentestbot": {
      "command": "python",
      "args": [
        "/absolute/path/to/MCPPentestBOT/llm_client.py"
      ]
    }
  }
}

The Model Context Protocol (MCP) enables secure, structured communication between AI assistants and external tools. This MCP server lets you run security testing tools from an AI workflow, giving you actionable results through natural conversations with your AI assistant while maintaining strong type safety and easy extensibility.

How to use

You connect your MCP-enabled AI assistant to the local MCP server and start asking security questions in plain language. For example, you can say to your assistant: “Check the SSL certificate for example.com” or “Scan ports 80 and 443 on example.com.” The assistant will invoke the appropriate tools, gather results, and present concrete recommendations in a structured format suitable for follow-up actions.

How to install

Prerequisites include Python 3.8 or higher and a supported operating system (Linux, macOS, or Windows with WSL). You also need system security tools like Nmap and Nikto, which you install at the system level.

# On Debian/Ubuntu
sudo apt-get install nmap nikto openssl

# On macOS
brew install nmap nikto openssl

Additional sections

Configuration for running the MCP server locally is provided as a standard stdio setup. The server is started as a local Python process that your MCP client can invoke. This enables a direct, low-overhead path from your AI assistant to the security tools.

{
  "pentestbot": {
    "command": "python",
    "args": ["/absolute/path/to/MCPPentestBOT/llm_client.py"]
  }
}

Environment and usage notes

If you want to integrate the server with an AI desktop client, you can configure the client to launch the Python-based LLM client as shown above. For direct Python usage, you can set environment variables to adjust logging and timeouts, though these are optional for basic operation.

Available tools

ssl_check

SSL/TLS certificate analysis and security evaluation including validation, expiration checks, cipher strength, and protocol security assessment

http_headers

HTTP security headers analysis including HSTS, CSP, X-Frame-Options, and security recommendations

nmap_scan

Network port scanning and service detection using Nmap with version detection for infrastructure discovery and attack surface mapping

nikto_scan

Web vulnerability scanning to identify misconfigurations and common vulnerabilities in web applications

ping_sweep

Network host discovery using ICMP-based sweeps to map live hosts across a network

port_check

Individual port connectivity checks to verify service availability and firewall rules