Vehicular systems are becoming interconnected digital ecosystems, reliant on complex networks for operations and safety. Cybersecurity vulnerabilities in these networks can jeopardize road safety. This study introduces a fuzzing framework to identify vulnerabilities in the Controller Area Network (CAN) bus. Using Automated Reverse Engineering-Guided Fuzzing, modified data packets are injected into the CAN framework, and Electronic Control Units (ECUs) reactions are monitored to uncover vulnerabilities. This approach identifies CAN bus network weaknesses and enhances understanding of their operational characteristics, setting a new standard for automotive cybersecurity. +