The modern development of artificial intelligence (AI) and machine learning (ML) opens up new opportunities in the field of cybersecurity, but at the same time creates serious challenges in the form of intelligent cyberattacks. The study is devoted to the analysis and classification of ways to use AI for malicious purposes and the study of effective methods to counter such threats. In particular, the article covers the main types of attacks using ML technologies, which demonstrate how attackers can manipulate machine learning algorithms, undermine trust in data, and bypass protection systems. Special attention is paid to the mechanisms of data poisoning attacks, as they are considered the most influential in machine learning, which involve introducing malicious data into the process of training models, which leads to distortion of results and undermines the effectiveness of security algorithms. Privacy attacks are analyzed as a way to obtain confidential information from ML models, which can be used to steal user data. Abuse attacks demonstrate how attackers can use AI tools to automate attacks, scale phishing campaigns, and analyze vulnerabilities in defense systems. The relevance
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