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Abstract

_Keywords:_ Adversarial attacks present significant risks to machine learning (ML) systems, exploiting model vulnerabilities Artificial intelligence and threatening the integrity, security, and trustworthiness of applications across multiple sectors. This paper pro Adversarial attacks vides a comprehensive review of adversarial attack typeswhite box, black box, and other type of attacksand Cybersecurity examines tailored attacks and defense mechanisms across domains such as Internet of Things (IoT), healthcare, Internet of things industrial control systems, autonomous vehicles, speech recognition, natural language processing (NLP), finance, Machine learning White-box and Large Language Models (LLMs). Each domain introduces unique adversarial challenges and demands specific Black-box countermeasures, from anomaly detection to adversarial training and robust model architectures. By systemati cally categorizing both attack methodologies and defense strategies, this survey offers a holistic understanding of adversarial dynamics across fields, highlighting critical areas for further research and the development of resilient, cross-domain ML defenses.