Environment-Grounded Multi-Agent Workflow for Autonomous Penetration Testing
Summary: arXiv:2603.24221v1 Announce Type: cross
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.
Introduction
This paper explores the use of large language models for automated penetration testing in robotic environments. The necessity for robust security measures cannot be overstated, especially when considering the vulnerabilities associated with interconnected robotic systems.
Proposed Architecture
We propose an environment-grounded multi-agent architecture tailored specifically for robotics-based systems. This innovative approach leverages the capabilities of large language models to enhance the penetration testing process.
Key Features
- Dynamic Graph-Based Memory: The system dynamically constructs a shared graph-based memory during execution that captures the observable system state.
- Comprehensive State Capture: The architecture includes crucial elements such as network topology, communication channels, vulnerabilities, and attempted exploits.
- Structured Automation: This enables structured automation while maintaining traceability and effective context management throughout the testing process.
Evaluation and Results
The proposed system was evaluated across multiple iterations within a specialized robotics Capture-the-Flag scenario (ROS/ROS2). The results were promising, demonstrating high reliability and effectiveness.
- Success Rate: The system successfully completed the challenge in 100% of test runs (n=5).
- Performance Benchmark: This performance significantly exceeds existing literature benchmarks, highlighting the system’s robustness.
- Traceability and Oversight: The architecture maintains the traceability and human oversight required by frameworks like the EU AI Act.
Conclusion
The findings presented in this paper underscore the potential of employing large language models in the context of autonomous penetration testing for robotic systems. With the increasing reliance on robotic technologies across various sectors, the need for effective security measures has never been greater. The proposed environment-grounded multi-agent architecture not only provides a scalable solution but also ensures adherence to regulatory standards, making it a significant contribution to the field of cybersecurity.
Future Work
Moving forward, further research will focus on refining the multi-agent architecture and exploring additional applications within different robotic environments. The goal is to enhance the adaptability and effectiveness of penetration testing methodologies as digital infrastructures continue to evolve.