This systematic literature review explores the critical cybersecurity challenges associated with autonomous vehicles (AVs), with a particular focus on message spoofing in Vehicle-to-Vehicle (V2V) communication. As AVs increasingly rely on interconnected systems for navigation, coordination, and decision-making, they become vulnerable to sophisticated cyber threats that can undermine their operational safety and reliability. Message spoofing—where false or malicious data is injected into V2V communication channels—can mislead vehicle responses, cause unsafe maneuvers, and disrupt overall traffic flow. This study examines existing detection and mitigation strategies, emphasizing the growing importance of artificial intelligence (AI) in enhancing AV cybersecurity. Through a systematic analysis of the literature, the review identifies key vulnerabilities in vehicular communication systems and highlights AI-based anomaly detection as a promising solution for identifying spoofing attacks in real time. This research underscores the necessity of developing comprehensive security frameworks to ensure the safe integration of AVs into modern transportation systems.