On the Realism of LiDAR Spoofing Attacks Against Autonomous Driving Vehicle at High Speed and Long Distance.

The rapid deployment of Autonomous Driving (AD) technologies on public roads presents significant social challenges. The security of LiDAR (Light Detection and Ranging) is one of the emerging challenges in AD deployment, given its crucial role in enabling Level 4 autonomy through accurate 3D environmental sensing. Recent lines of research have demonstrated that LiDAR can be compromised by LiDAR spoofing attacks that overwrite legitimate sensing by emitting malicious lasers to the LiDAR. However, previous studies have successfully demonstrated their attacks only in controlled environments, yet gaps exist in the feasibility of their attacks in realistic high-speed, long-distance AD scenarios. To bridge these gaps, we design a novel Moving Vehicle Spoofing (MVS) system consisting of 3 subsystems: the LiDAR detection and tracking system, the auto-aiming system, and the LiDAR spoofing system. Furthermore, we design a new object removal attack, an adaptive high-frequency removal (AHFR) attack, that can be effective even against recent LiDARs with pulse fingerprinting features, by leveraging gray-box knowledge of the scan timing of target LiDARs. With our MVS system, we are not only the first to demonstrate LiDAR spoofing attacks against practical AD scenarios where the victim vehicle is driving at high speeds (60 km/h) and the attack is launched from long distances (110 meters), but also we are the first to perform LiDAR spoofing attacks against a vehicle actually operated by a popular AD stack. Our object removal attack achieves =96% attack success rates against the vehicle driving at 60 km/h to the braking distances (20 meters). Finally, we discuss possible countermeasures against attacks with our MVS system. This study not only bridges critical gaps between LiDAR security and AD security research but also sets a foundation for developing robust countermeasures against emerging threats.