Security in Autonomous Vehicles: A Review of Attacks and Defenses

As Autonomous Vehicles (AVs) transition from controlled environments to real-world deployment, ensuring their cybersecurity has become critical. The integration of software, AI-based control systems, sensors, and Vehicle-to-Everything (V2X) communication significantly expands the attack surface, exposing AVs to a range of cyber and physical threats. This paper presents a review of AV security, with a particular focus on the classification of attacks across physical, software, communication, and hardware domains. It further analyzes existing defense mechanisms, including both traditional rule-based systems and emerging Machine Learning (ML) approaches, and examines current evaluation and testing strategies for assessing AV resilience. While notable progress has been made, the field still faces challenges related to generalization, real-world robustness, and standardized testing frameworks. This review identifies key gaps and outlines future directions toward building secure and trustworthy autonomous systems.