AI-Powered Red Team Pipeline using MCP + Kali Linux

From manual pentesting → to AI-driven offensive automation

🧠 Overview

Traditional penetration testing is fragmented:

• Recon tools are separate
• Exploitation requires manual chaining
• No centralized decision-making

This project demonstrates how to build an AI-powered pentesting pipeline using:

• MCP (Model Context Protocol)
• Kali Linux tools
• AI clients (ChatMCP / Gemini CLI)

⚙️ What is MCP?

MCP (Model Context Protocol) allows AI to:

• Use external tools
• Access resources
• Execute actions
• Chain workflows

🔁 Traditional vs MCP

Traditional:
User → Tool → Output → Manual Decision

MCP:
User → AI → Tool → Result → AI Decision → Next Tool

🧱 Architecture

[ User ]
   ↓
[ AI Client (ChatMCP / Gemini CLI) ]
   ↓
[ MCP Server ]
   ↓
[ Kali Tools (nmap, ffuf, gobuster) ]
   ↓
[ Results → AI → Decision → Loop ]

🧰 Recommended Stack

💬 MCP Client

✅ ChatMCP (Best Overall)

• Clean UI
• Fast tool discovery
• Multi-tool chaining

🐉 MCP Servers

✅ Kali MCP Server (Default)

sudo apt update
sudo apt install kali-linux-large -y

✔ Preloaded tools
✔ Perfect for CTFs & labs
✔ Minimal setup

⚡ Hexstrike MCP

Best for:

• Structured workflows
• Automation pipelines
• Advanced use cases

⚡ Gemini CLI (Easy Mode)

sudo apt install gemini-cli -y

Setup:

• Login with Google account
• Start instantly

Why it's powerful:

• High token limits (feels unlimited)
• Great for long reasoning
• Excellent in CTF workflows

🔧 MCP Server Setup

Install SDK

npm install @modelcontextprotocol/sdk

Example MCP Tool (Port Scanner)

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({
  name: "kali-mcp",
  version: "1.0.0"
});

server.tool("scan_ports", {
  description: "Run nmap scan",
  inputSchema: {
    type: "object",
    properties: {
      target: { type: "string" }
    }
  }
}, async ({ target }) => {
  const { exec } = await import("child_process");

  return new Promise((resolve) => {
    exec(`nmap -sV ${target}`, (err, stdout) => {
      resolve({
        content: [{ type: "text", text: stdout }]
      });
    });
  });
});

await server.connect(new StdioServerTransport());

🔧 Install Core Tools

sudo apt install nmap ffuf gobuster -y

🧠 AI Workflow Example

User → Target IP
AI → scan_ports
AI → detects open ports
AI → runs fuzzing
AI → analyzes results
AI → decides next step

⚡ Model Providers

🔁 OpenRouter

export OPENROUTER_API_KEY=your_key

fetch("https://openrouter.ai/api/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.OPENROUTER_API_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "mistral/mixtral",
    messages: [{ role: "user", content: "Analyze scan output" }]
  })
});

⚠️ Privacy Warning:

• Requests routed across multiple providers
• Avoid sensitive data

🟢 NVIDIA LLM APIs

Why use:

• Free credits (sometimes)
• Strong structured reasoning

Best for:

• Log analysis
• Automation pipelines

⚔️ Security Considerations

❌ Do NOT:

• Scan unauthorized targets
• Expose unrestricted shell access

✅ Do:

• Use labs (TryHackMe, HTB, VMs)
• Log tool execution
• Add permission layers

🚀 Advanced Upgrade (Multi-Agent)

• Recon Agent → scanning
• Exploit Agent → suggestions
• Report Agent → documentation

Let them:

• Share outputs
• Validate decisions

🧠 Real Insight

Most people collect tools.

Real advantage comes from:

Building AI-driven systems that connect tools

🔥 Conclusion

We are shifting from:

Humans using tools

To:

AI orchestrating tools

MCP is early.

That means:

Massive advantage for builders.

🔥 Next Steps

• Add subdomain enumeration tools
• Add memory (store results)
• Build autonomous decision logic
• Move toward full AI agents

👤 Author

Aryan
AI + Cybersecurity + Automation