Black-box attacks refer to a type of attack in which an adversary attempts to undermine or manipulate a system without having access to its internal workings or detailed knowledge of its structure. In the context of machine learning and artificial intelligence systems, black-box attacks typically involve exploiting vulnerabilities in a model's input-output behavior without understanding how the model makes decisions or processes data internally. Adversaries may use techniques such as adversarial examples or evasion attacks to subtly modify input data in order to cause a model to make incorrect predictions or decisions. Black-box attacks are a significant concern in cybersecurity and machine learning research, as they highlight the potential vulnerabilities of complex and opaque systems that are increasingly being used in critical applications.