npm package: @iflow-mcp/pentestthinking
Description: A pentest reasoning engine (PentestThinking) that helps break down and analyze attack paths step by step.
Latest version: 1.0.2

# 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

---

## 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.

---

## 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

```sh
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:
  ```json
  {
    "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.

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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