An Investigation of Byzantine Threats in Multi-Robot Systems

Multi-Robot Systems (MRSs) show significant advantages to deal with complex tasks efficiently. However, the system complexity inevitably enlarges the attack surface and adds difficulty in guaranteeing the security and safety of MRSs. In this paper, we present an in-depth investigation about the Byzantine threats in MRSs, where some robot is untrusted. We design a practical methodology to identify potential Byzantine risks in a given MRS workload built from the Robot Operating System (ROS). It consists of three novel steps (requirement specification using signal temporal logic, attack surface determination via data-flow analysis, attack identification using requirement-driven fuzzing) to thoroughly assess MRS workloads. We use this fuzzing method to inspect five typical MRS workloads from past works and the ROS platform, and identify three novel kinds of attacks that can be launched with five attack strategies. We conduct comprehensive experiments in the Gazebo simulator and a real-world MRS with three TurtlBot3 robots to validate these attacks, which can remarkably decrease the system’s performance, or even cause task failures.