# PentestThinking 

> Use this tool when you need to automate and optimize penetration testing workflows, and identify complex multi-stage attack paths. It solves problems of inefficient and ineffective penetration testing by providing AI-generated step-by-step attack recommendations and context-aware tool suggestions. The tool takes in vulnerability information and outputs strategic attack plans, making it ideal for use in red teaming, research, and ethical hacking contexts.

Canonical page: https://skillsregistry.net/skills/ibrahimsaleem-pentestthinkingmcp  
JSON: https://api.skillsregistry.net/v1/skills/ibrahimsaleem-pentestthinkingmcp

## Description

PentestThinkingMCP: AI-Driven Multi-Stage Penetration Testing Framework
Part of the LIMA Research Project – Accepted at IEEE FMLDS 2025
Developed by Mohammad Ibrahim Saleem, Cybersecurity Researcher, University of Houston

PentestThinkingMCP is an AI-powered MCP (Model Context Protocol) server developed as part of the LIMA research project. This work was recently accepted as a full paper at IEEE FMLDS 2025. The server automates and optimizes penetration testing workflows by planning complex, multi-stage attack paths using advanced reasoning methods such as Beam Search and Monte Carlo Tree Search (MCTS) enabling efficient, adaptive, and intelligent exploitation in both real-world and CTF environments.

🚀 Core Features

AI-generated step-by-step attack recommendations

Context-aware tool suggestions aligned with vulnerabilities

Critical path analysis to maximize impact with minimal noise

Autonomous attack chain simulation with dynamic environment modeling

This framework helps red teamers, researchers, and ethical hackers adopt a strategic, AI-assisted offensive mindset, moving beyond traditional one-off exploits.

📢 Citation Request
If you use PentestThinkingMCP in your research, academic work, or projects, please cite our paper:
“LIMA: Leveraging Large Language Models and MCP Servers for Initial Machine Access” – IEEE FMLDS 2025.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** security
- **Updated:** 2026-05-14

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/ibrahimsaleem/pentestthinkingmcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ibrahimsaleem-pentestthinkingmcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ibrahimsaleem-pentestthinkingmcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ibrahimsaleem-pentestthinkingmcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt