Adversarial attacks are techniques used to deceive machine learning models by introducing small, often imperceptible changes to input data. These alterations can cause the model to make incorrect predictions or classifications, highlighting vulnerabilities in artificial intelligence systems. These attacks can occur in various domains, including image recognition and natural language processing. For example, an image of a cat might be subtly modified so that a computer vision model misclassifies it as a dog. Understanding and defending against adversarial attacks is crucial for improving the robustness of AI applications.