Characterizing Network-Layer Vulnerabilities in LiDAR Subsystems of Autonomous Vehicles: A Mechanism-Aware Propagation Analysis

LiDAR subsystems (3D sensing systems that help autonomous vehicles perceive their surroundings) in self-driving cars face network-layer security risks when data packets travel through vehicle networks to the autonomous driving system. This study introduces a framework to trace how these network-level vulnerabilities propagate through the LiDAR data processing pipeline in Apollo V8.0 (a self-driving software platform), identifying three critical processing mechanisms (Frame Partition, Object Erasure, and Object Mark) that determine whether attacks succeed, and showing that failure depends on factors like timing and frame alignment rather than simple data corruption.