Adversarial robustness refers to the resilience of machine learning models against adversarial attacks, where small, intentional perturbations to input data can lead to significant errors in model predictions. Ensuring adversarial robustness is crucial for the deployment of AI systems in safety-critical applications, as it enhances their reliability and trustworthiness.