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.

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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

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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

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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

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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.

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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)

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Installation

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

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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.

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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.

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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

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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)

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License

MIT