PentestThinking

PentestThinkingMCP is an advanced reasoning engine for penetration testing that implements both Beam Search and Monte Carlo Tree Search (MCTS) algorithms to systematically analyze and plan multi-stage attack paths. Built by ibrahimsaleem as the foundational implementation for the LIMA research paper on leveraging LLMs for initial machine access, it transforms standard language models into structured pentest advisors by scoring attack steps, recommending appropriate tools (nmap, metasploit, linpeas), and maintaining tree-based attack progression with statistical analysis. The system excels at automated vulnerability chaining, exploit pathfinding optimization, and red team strategy development, making it particularly useful for CTF challenges, Hack The Box machines, and real-world penetration testing scenarios where methodical attack planning and decision tree exploration are critical.