Adversarial attacks refer to a type of cyber security threat in which attackers deliberately manipulate or deceive machine learning algorithms by inputting specially crafted data. These attacks can lead to misclassification of images, texts, or any other inputs by the algorithm, causing potential security vulnerabilities in systems that rely on machine learning for decision-making. Adversarial attacks are often used to exploit weaknesses in neural networks and other AI technologies, and researchers are continuously working to develop defenses against them.