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.

翻译：数字基础设施日益复杂且高度互联，使得可扩展且可靠的安全评估方法变得至关重要。机器人系统作为一类重要的操作技术，代表了高度网络化的物理信息系统，广泛应用于工业自动化、物流及自主服务等领域。本文探讨了将大语言模型应用于机器人环境中的自动化渗透测试。我们提出了一种面向机器人系统的、基于环境感知的多智能体架构。该方法在执行过程中动态构建基于图的共享内存，捕获可观测的系统状态，包括网络拓扑、通信信道、漏洞及已尝试的攻击行为。这使得在保持测试过程中可追溯性和有效上下文管理的同时，实现了结构化自动化。在专用机器人夺旗场景（ROS/ROS2）中进行多次迭代评估后，该系统展现了高可靠性，在全部测试轮次中均成功完成挑战（n=5）。该性能显著超越文献基准，同时满足欧盟人工智能法案等框架要求的可追溯性与人工监督。