環境接地型マルチエージェントワークフローによる自律ペネトレーションテスト
Environment-Grounded Multi-Agent Workflow for Autonomous Penetration Testing
ロボットシステムを対象に、大規模言語モデルを用いた自律ペネトレーションテストを実行する環境接地型マルチエージェントアーキテクチャを提案し、ROS/ROS2のCTFシナリオで100%の成功率を達成した。
著者: Michael Somma, Markus Großpointner, Paul Zabalegui, Eppu Heilimo, Branka Stojanović
分類: cs.RO, cs.AI
原文アブストラクト
The increasing complexity and interconnectivity of digital infrastructures make scalable and reliable security assessment methods essential. Robotic systems represent a particularly important class of operational technology, as modern robots are highly networked cyber-physical systems deployed in domains such as industrial automation, logistics, and autonomous services. This paper explores the use of large language models for automated penetration testing in robotic environments. We propose an environment-grounded multi-agent architecture tailored to Robotics-based systems. The approach dynamically constructs a shared graph-based memory during execution that captures the observable system state, including network topology, communication channels, vulnerabilities, and attempted exploits. This enables structured automation while maintaining traceability and effective context management throughout the testing process. Evaluated across multiple iterations within a specialized robotics Capture-the-Flag scenario (ROS/ROS2), the system demonstrated high reliability, successfully completing the challenge in 100\% of test runs (n=5). This performance significantly exceeds literature benchmarks while maintaining the traceability and human oversight required by frameworks like the EU AI Act.