AI is one of the most popular field of technologies nowadays. Developers implement these technologies everywhere forgetting sometimes about its robustness to unobvious types of traffic. This omission can be used by attackers, who are always seeking to develop new attacks. So, the growth of AI is highly correlates with the rise of adversarial attacks. Adversarial attacks or adversarial machine learning is a technique when attackers attempt to fool ML systems with deceptive data. They can use inconspicuous, natural-looking perturbations in input data to mislead neural networks without inferring into a model directly and often without the risk to be detected. Adversarial attacks usually are divided into three primary axes - the security violation, poisoning and evasion attacks, which further can be categorized on “targeted”, “untargeted”, “whitebox” and “blackbox” types. This research examines most of the adversarial attacks are known by 2023 relating to all these categories and some others.