Autonomous vehicles (AVs) rely on interconnected sensing and communication systems to support navigation, coordination, and decision-making. While this connectivity enhances safety and efficiency, it also broadens the attack surface, exposing AVs to cyber threats that can compromise operational reliability. Among these threats, message spoofing, i.e., injection of falsified or manipulated data into in-vehicle or Vehicle-to-Vehicle (V2V) communication channels—poses a critical risk, as it can mislead vehicle perception, disrupt cooperative behavior, and undermine traffic safety. This systematic literature review analyzes studies published between 2015 and 2025, examining spoofing attacks targeting AV communication layers and surveying the full range of detection and mitigation mechanisms proposed in the literature. The review considers both traditional security techniques and emerging artificial intelligence (AI)–driven approaches, assessing how AI is being incorporated alongside conventional methods. Through a structured review process, the study synthesizes attack vectors, defense strategies, and technological trends, highlighting the growing role of AI in enhancing AV resilience. The review aims to consolidate current knowledge and outline key considerations for designing robust, multi-layered cybersecurity frameworks that support the safe integration of autonomous vehicles into connected transportation ecosystems.