ARTIFICIAL INTELLIGENCE SAFETY AND ROBUSTNESS AGAINST ADVERSARIAL ATTACKS

This scientific article provides a comprehensive analysis of the safety ofartificial intelligence (AI) systems and their robustness against adversarial attacks. As AI isincreasingly used in critical fields (medicine, transport, security), the safety and reliability of thesesystems become extremely important. The research scientifically substantiates the nature ofadversarial attacks — specially crafted inputs designed to deceive AI — methods of defendingagainst them, and the broader problems of AI safety. The article examines the vulnerabilities of AIsystems, the methods of "red teaming" (testing for weaknesses), and ways to build reliable androbust systems. The scientific novelty of the article lies in demonstrating that AI safety is afundamental condition for the trustworthy use of these technologies. As a result of the analyses,recommendations are developed regarding the development of safe AI systems