Adversarial examples are inputs to machine learning models that are intentionally designed to cause the model to make a mistake, often by adding small, imperceptible perturbations to the input data. These examples reveal vulnerabilities in models, highlighting the need for robust defenses and better understanding of model behavior under adversarial conditions.