Beyond vulnerabilities: A comprehensive survey of adversarial attacks across domains

Adversarial attacks present significant risks to machine learning (ML) systems, exploiting model vulnerabilities and threatening the integrity, security, and trustworthiness of applications across multiple sectors. This paper provides a comprehensive review of adversarial attack types—white box, black box, and other type of attacks—and examines tailored attacks and defense mechanisms across domains such as Internet of Things (IoT), healthcare, industrial control systems, autonomous vehicles, speech recognition, natural language processing (NLP), finance, and Large Language Models (LLMs). Each domain introduces unique adversarial challenges and demands specific countermeasures, from anomaly detection to adversarial training and robust model architectures. By systematically 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. • Comprehensive Analysis: Reviews adversarial attacks across multiple data types and their impact on machine learning models. • Taxonomy Development: Proposes a structured taxonomy of adversarial attacks aligned with the MITRE ATLAS framework. • Vulnerability Identification: Identifies vulnerabilities across data modalities exploited by adversarial attacks. • Attack Categorization: Categorizes adversarial attack techniques across different data types and ML systems. • Domain-Specific Taxonomy: Examines adversarial attacks across domains including IoT, healthcare, NLP, speech, and LLMs.