Cyber-physical systems (CPS) are increasingly relying on machine learning (ML) techniques to reduce labor costs and improve efficiency. However, the adoption of ML also exposes CPS to potential adversarial ML attacks witnessed in the literature. Specifically, the increased Internet connectivity in CPS has resulted in a surge in the volume of data generation and communication frequency among devices, thereby expanding the attack surface and attack opportunities for ML adversaries. Among various adversarial ML attacks, evasion attacks are one of the most well-known ones. Therefore, this survey focuses on summarizing the latest research on evasion attack and defense techniques, to understand state-of-the-art ML model security in CPS. To assess the attack effectiveness, this survey proposes an attack taxonomy by introducing quantitative measures such as perturbation level and the number of modified features. Similarly, a defense taxonomy is introduced based on four perspectives demonstrating the defensive techniques from models’ inputs to their outputs. Furthermore, the survey identifies gaps and promising directions that researchers and practitioners can explore to address potential challenges and threats caused by evasion attacks and lays the groundwork for understanding and mitigating the attacks in CPS.