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There are various applications of Cyber-Physical systems (CPSs) that are life-critical where failure or malfunction can result in significant harm to human life, the environment, or substantial economic loss. Therefore, it is important to ensure their reliability, security, and robustness to the attacks. However, there is no widely used toolbox to simulate CPS and target security problems, especially the simulation of sensor attacks and defense strategies against them. In this work, we introduce our toolbox CPSim, a user-friendly simulation toolbox for security problems in CPS. CPSim aims to simulate common sensor attacks and countermeasures to these sensor attacks. We have implemented bias attacks, delay attacks, and replay attacks. Additionally, we have implemented various recovery-based methods against sensor attacks. The sensor attacks and recovery methods configurations can be customized with the given APIs. CPSim has built-in numerical simulators and various implemented benchmarks. Moreover, CPSim is compatible with other external simulators and can be deployed on a real testbed for control purposes.¹ 

Cyber-Physical Systems(CPS) are the integration of sensing, control, computation, and networking with physical components and infrastructure connected by the internet. The autonomy and reliability are enhanced by the recent development of safe reinforcement learning (safe RL). However, the vulnerability of safe RL to adversarial conditions has received minimal exploration. In order to truly ensure safety in physical world applications, it is crucial to understand and address these potential safety weaknesses in learned control policies. In this work, we demonstrate a novel attack to violate safety that induces unsafe behaviors by adversarial models trained using reversed safety constraints. The experiment results show that the proposed method is more effective than existing works. 

Cyber-Physical Systems (CPS) integrate computational elements with physical processes via sensors and actuators. While CPS is expected to have human-level intelligence, traditional machine learning which is trained on specific and isolated datasets seems insufficient to meet such expectation. In recent years, Large Language Models (LLMs), like GPT-4, have experienced explosive growth and show significant improvement in reasoning and language comprehension capabilities which promotes LLM-enabled CPS. In this paper, we present a comprehensive review of these studies about LLM-enabled CPS. First, we overview LLM-enabled CPS and the roles that LLM plays in CPS. Second, we categorize existing works in terms of the application domain and discuss their key contributions. Third, we present commonly-used metrics and benchmarks for LLM-enabled CPS evaluation. Finally, we discuss future research opportunities and corresponding challenges of LLM-enabled CPS.