Cybersecurity for Connected Autonomous Vehicles (CAVs) has become a critical research area, as these systems integrate advanced sensors, vehicular networks, artificial intelligence, and cloud-edge connectivity, making them highly vulnerable to cyber threats. Research papers in this field focus on attack surfaces such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, over-the-air software updates, in-vehicle networks (CAN bus), GPS spoofing, and adversarial machine learning against autonomous driving models. Studies emphasize threats like denial-of-service, data injection, eavesdropping, and remote hijacking, which can compromise safety, privacy, and trust. To counter these risks, research explores intrusion detection systems tailored for vehicular networks, blockchain-enabled trust management, secure communication protocols, hardware-based security modules, and federated learning for distributed threat detection. Recent works also examine resilience strategies such as fail-safe mechanisms, privacy-preserving authentication, and Zero Trust Architecture integration. Moreover, digital twins and simulation-based testbeds are being developed to evaluate cyber-attack scenarios and validate defense mechanisms in real time. Overall, cybersecurity for CAVs remains an evolving domain that requires a combination of advanced technologies, regulatory frameworks, and cross-industry collaboration to ensure safe and secure autonomous mobility.