Automated Post-Exploitation Attack Chain Exploration Based on a Phase-guided Hybrid Planning-Monte Carlo Tree Search Algorithm

Abstract

Advanced attackers in post-exploitation scenarios increasingly craft attack chains that combine benign operations, configuration errors, and vulnerability exploitation to achieve objectives like privilege escalation. While such chains pose significant threats, manually discovering them in complex modern host environments is prohibitively labor-intensive. Automating this discovery is therefore essential for proactive defense, yet existing methods still suffer from inefficiency and lack diversity when exploring these attack chains. To address these limitations, we present APEX, an Automated Post-EXploitation attack chain exploration system. This system transforms attack chain discovery into a planning problem and solves it with a novel phase-guided hybrid Planning-Monte Carlo Tree Search (MCTS) algorithm. By organizing attack actions into semantic phases and integrating symbolic planning with MCTS, APEX enables efficient exploration of the state space while generating multiple diverse chains. Evaluations on a benchmark comprising 5 virtual machines, 30 synthetic problems and 291 cloud instances demonstrate that APEX successfully discovers both known and previously unreported attack chains--including 9 novel privilege escalation vectors--providing actionable intelligence for hardening complex production environments.