MazeRunner: Nonlinear Task and Clue Orchestration for LLM-driven Black-Box Automated Penetration Testing

Abstract

Penetration testing is essential yet resource-intensive. Although large language models (LLMs) show promise for automating security auditing, existing agents mainly execute end-to-end workflows in simplified linear scenarios. Real-world black-box testing is fundamentally nonlinear: the attack graph is initially unknown and must be incrementally inferred from environmental feedback. Observations may reveal multiple attack branches, failures are often ambiguous, and critical clues may span long action horizons. Existing agents therefore tend to become trapped in depth-first exploration, misdiagnose failures, and forget prior evidence. We present MazeRunner, an autonomous penetration testing system built on a three-agent task-and-clue orchestration framework. It separates global orchestration, context-intensive execution, and failure-oriented review while persistently maintaining task states and environmental evidence. This design supports action revision, prerequisite recovery, branch switching, and long-range clue correlation. We evaluate MazeRunner on 10 recently released HTB targets, limiting each system-target run to 20 million LLM tokens and preventing target-specific solution leakage. With Claude Sonnet 4.5, MazeRunner completes 47.7% of annotated subtasks, compared with 36.2% for PentestGPT-V2 and 34.2% for Claude Code. It achieves user-level or higher access on six targets, including root access on two; each same-model baseline reaches user-level access on only two targets and never obtains root access. Execution-trace analysis further shows that MazeRunner explores more attack branches and acquires shells more efficiently.