This research provides a comprehensive overview of adversarial attacks on AI and ML models, exploring various attack types, techniques, and their potential harms. We also delve into the business implications, mitigation strategies, and future research directions. To gain practical insights, we employ the Adversarial Robustness Toolbox (ART) [1] library to simulate these attacks on real-world use cases, such as self-driving cars. Our goal is to inform practitioners and researchers about the challenges and opportunities in defending AI systems against adversarial threats. By providing a comprehensive comparison of different attack methods, we aim to contribute to the development of more robust and secure AI systems.