The Dark Side of AI: Large Language Models as Tools for Cyber Attacks on Vehicle Systems

The rapid evolution of autonomous vehicles (AVs) presents significant opportunities for enhancing transportation safety and efficiency. However, with increasing connectivity and complex electronic systems, AVs also become vulnerable to cyberattacks. This paper investigates cybersecurity challenges in the realm of AVs, highlighting the role of artificial intelligence (AI), specifically Large Language Models (LLMs), in exploiting vulnerabilities. We analyze various attack vectors, including Controller Area Network (CAN) manipulation, Bluetooth vulnerabilities, and Key Fob hacking, emphasizing the need for proactive cybersecurity measures. Recent incidents, such as the remote compromise of various vehicle models, underscore the urgent need for robust security solutions in the automotive industry. By leveraging LLMs, attackers can craft sophisticated cyber threats targeting AVs, posing risks to both safety and privacy. We introduce HackerGPT, a customized LLM tailored for cyber attack generation, and demonstrate attacks on virtual CAN networks, Bluetooth systems, and Key Fobs. At the same time, our experiments reveal successful compromises in certain vehicle models; limitations exist, particularly in vehicles with advanced encryption and robust signal transmission protocols. However, this research underscores the broader need for increased awareness and proactive security measures in the automotive sector. Our findings aim to contribute significantly to the ongoing discourse on automotive cybersecurity, offering actionable insights for manufacturers and cybersecurity professionals to safeguard the future of mobility.