Pentest Thinking

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Pentest Thinking is an advanced Model Context Protocol (MCP) server designed to empower both human and AI pentesters by providing a systematic, AI-powered reasoning engine. It automates attack path planning using sophisticated Beam Search and Monte Carlo Tree Search (MCTS) algorithms, offering step-by-step guidance for CTFs, Hack The Box, and real-world penetration tests. This server transforms traditional Large Language Models into structured, methodical pentest planners and advisors, bridging the gap between AI and offensive security. It serves as a foundational implementation for the LIMA research, which leverages LLMs and MCP servers to automate initial-access reconnaissance, enumeration, and exploitation, demonstrating faster task completion and establishing a quantitative baseline for AI-augmented penetration testing.

Características Principales

• Attack step scoring and prioritization
• Tool recommendations for each attack step (e.g., nmap, metasploit, linpeas)
• Step-by-step reasoning for CTF/HTB and real-world pentests
• 24 GitHub stars
• Automated attack path planning using Beam Search and Monte Carlo Tree Search (MCTS)
• Tree-based attack path analysis and critical path highlighting

Casos de Uso

• Automated vulnerability identification and chaining
• Exploit pathfinding and optimization
• Red teaming strategy development and refinement