ADVERSARIAL MACHINE LEARNING SECURITY: PROTECTING AI SYSTEMS FROM COMPROMISE AND MANIPULATION

Abstract

As machine learning (ML) systems become increasingly integrated into critical applications—ranging from autonomous vehicles to healthcare diagnostics—their vulnerability to adversarial attacks has emerged as a major security concern. Adversarial Machine Learning (AML) explores techniques to deceive, manipulate, or compromise AI models through carefully crafted inputs, model poisoning, or extraction attacks. This paper provides a comprehensive survey of AML threats, attack methodologies, and defense mechanisms. We categorize attacks based on their objectives (e.g., evasion, poisoning, model stealing) and threat models (white-box, black-box, gray-box). We then analyze state-of-the-art defense strategies, including adversarial training, certified robustness, and detection-based approaches. Additionally, we discuss emerging challenges such as adversarial attacks on large language models (LLMs) and federated learning systems. Finally, we outline future research directions to enhance the robustness and security of AI systems in adversarial environments.