Leveraging Adverse Weather for Enhanced LiDAR Spoofing in Autonomous Driving: Challenges and Opportunities

Lidar-based 3D perception systems are critical for autonomous vehicle (AV) navigation, yet they remain vulnerable to spoofing attacks that can create false detections (ghost objects) or hide real obstacles. Despite significant advances in object detection, existing methods remain highly susceptible to adversarial attacks. Furthermore, existing research has largely overlooked the impact of weather conditions on both attacks and defenses. No existing study provides a systematic analysis on how the rain effect affects spoofing and hiding attacks. Motivated by this critical gap, we propose a novel rain-aware threat model in this article, focusing on lidar spoofing attacks involving object insertion (ghost objects) and removal (hiding attacks). We review state-of-the-art attack implementations and emphasize how rain increases attack feasibility and stealth, enabling attackers to achieve effective spoofing with significantly fewer perturbed points. We call this a reduced attack budget (AB). Formally, we define the AB as the minimal number of spoofed points (and corresponding laser returns) needed to meet attack success (ghost insertion: high-confidence false positives; object hiding: low-confidence false negatives).