Pentest Thinking
Orchestrates AI-powered penetration testing by planning attack paths, solving CTF/HTB challenges, and automating workflows with advanced search strategies and tool recommendations.
About
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.
Key Features
- 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
Use Cases
- Automated vulnerability identification and chaining
- Exploit pathfinding and optimization
- Red teaming strategy development and refinement
