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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) 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. 

Cyber-Physical Systems (CPS) are integrations of computation, networking, and physical processes. The autonomy and self-adaptation capabilities of CPS mark a significant evolution from traditional control systems. Machine learning significantly enhances the functionality and efficiency of Cyber-Physical Systems (CPS). Large Language Models (LLM), like GPT-4, can augment CPS’s functionality to a new level by providing advanced intelligence support. This fact makes the applications above potentially unsafe and thus untrustworthy if deployed to the real world. We propose a comprehensive and general assurance framework for LLM-enabled CPS. The framework consists of three modules: (i) the context grounding module assures the task context has been accurately grounded (ii) the temporal Logic requirements specification module forms the temporal requirements into logic specifications for prompting and further verification (iii) the formal verification module verifies the output of the LLM and provides feedback as a guideline for LLM. The three modules execute iteratively until the output of LLM is verified. Experiment results demonstrate that our assurance framework can assure the LLM-enabled CPS.