ibrahimsaleem/PentestThinkingMCP

PentestThinkingMCP

A systematic, AI-powered penetration testing reasoning engine (MCP server) for attack path planning, CTF/HTB solving, and automated pentest workflows. Features Beam Search, MCTS, attack step scoring, and tool recommendations.

What is PentestThinkingMCP?

PentestThinkingMCP is an advanced Model Context Protocol (MCP) server designed to empower both human and AI pentesters. It provides:

• Automated attack path planning using Beam Search and Monte Carlo Tree Search (MCTS)
• Step-by-step reasoning for CTFs, Hack The Box (HTB), and real-world pentests
• Attack step scoring and prioritization
• Tool recommendations for each step (e.g., nmap, metasploit, linpeas)
• Critical path highlighting for the most promising exploit chains
• Tree-based reasoning for reporting and documentation

Why is it special?

• Brings LLMs to the next level: Transforms a normal LLM into a structured, methodical pentest planner and advisor
• Automates complex reasoning: Finds multi-stage attack chains, not just single exploits
• Works for CTFs, HTB, and real-world pentests: Adapts to any scenario where stepwise attack logic is needed
• Bridges the gap between AI and hacking: Makes AI a true partner in offensive security

Features

• Dual search strategies for attack modeling:
  • Beam search with configurable width (for methodical exploit chain discovery)
  • MCTS for complex decision spaces (for dynamic attack scenarios with unknowns)
• Evidence/Vulnerability scoring and evaluation
• Tree-based attack path analysis
• Statistical analysis of potential attack vectors
• MCP protocol compliance

How does it work?

1. Input:
   You (or your AI) provide the current attack step/state (e.g., "Enumerate SMB on 10.10.10.10").
2. Reasoning:
   The server uses Beam Search or MCTS to explore possible next steps, scoring and prioritizing them.
3. Output:
   Returns the next best attack step, the full attack chain, recommended tool, and highlights the critical path.

Example Workflow: Solving an HTB Machine

1. Recon:
   Input: attackStep: "Start with initial recon on 10.10.10.10"
   Output: Run nmap -p- 10.10.10.10 (recommended tool: nmap)
2. Enumeration:
   Input: attackStep: "Run nmap -p- 10.10.10.10"
   Output: Enumerate SMB on port 445 (recommended tool: enum4linux)
3. Vulnerability Analysis:
   Input: attackStep: "Enumerate SMB on port 445"
   Output: Search for public SMB exploits (CVE-2017-0144) (recommended tool: searchsploit)
4. Exploitation:
   Input: attackStep: "Search for public SMB exploits (CVE-2017-0144)"
   Output: Exploit SMB with EternalBlue (CVE-2017-0144) (recommended tool: metasploit)
5. Privilege Escalation:
   Input: attackStep: "Got shell as user"
   Output: Run winPEAS for privilege escalation checks (recommended tool: winPEAS)
6. Root/Flag:
   Input: attackStep: "Found user.txt, need root"
   Output: Check for AlwaysInstallElevated misconfiguration (recommended tool: manual investigation)

Installation

git clone https://github.com/ibrahimsaleem/PentestThinkingMCP.git
cd PentestThinkingMCP
npm install
npm run build

Usage

• Add to your MCP client (Cursor, Claude Desktop, etc.) as a server:

  {
    "mcpServers": {
      "pentestthinkingMCP": {
        "command": "node",
        "args": ["path/to/pentestthinkingMCP/dist/index.js"]
      }
    }
  }
  

• Interact with it by sending attack steps and receiving next-step recommendations, tool suggestions, and attack path trees.

Search Strategies for Pentesting

Beam Search

• Maintains a fixed-width set of the most promising attack paths or vulnerability chains.
• Optimal for step-by-step exploit development and known vulnerability pattern matching.
• Best for: Enumerating attack vectors, methodical vulnerability chaining, logical exploit pathfinding.

Monte Carlo Tree Search (MCTS)

• Simulation-based exploration of the potential attack surface.
• Balances exploration of novel attack vectors and exploitation of known weaknesses.
• Best for: Complex network penetration tests, scenarios with uncertain outcomes, advanced persistent threat (APT) simulation.

Algorithm Details

1. Attack Vector Selection
   • Beam Search: Evaluates and ranks multiple potential attack paths or exploit chains.
   • MCTS: Uses UCT for node selection (potential exploit steps) and random rollouts (simulating attack progression).
2. Evidence/Vulnerability Scoring Based On:
   • Likelihood of exploitability
   • Potential impact (CIA triad)
   • CVSS scores or similar metrics
   • Strength of connection in an attack chain (e.g., vulnerability A enables exploit B)
3. Process Management
   • Tree-based state tracking of attack progression
   • Statistical analysis of successful/failed simulated attack paths
   • Progress monitoring against pentest objectives

Use Cases

• Automated vulnerability identification and chaining
• Exploit pathfinding and optimization
• Attack scenario simulation and "what-if" analysis
• Red teaming strategy development and refinement
• Assisting in manual pentesting by suggesting potential avenues
• Decision tree exploration for complex attack vectors
• Strategy optimization for achieving specific pentest goals (e.g., data exfiltration, privilege escalation)

License

MIT