Year: 2025
Type: preprint
Abstract: Autonomous vehicles (AVs) rely heavily on LiDAR sensors for accurate 3D perception. We show a novel class of low-cost, passive LiDAR spoofing attacks that exploit mirror-like surfaces to inject or remove objects from an AV's perception. Using planar mirrors to redirect LiDAR beams, these attacks require no electronics or custom fabrication and can be deployed in real settings. We define two adversarial goals: Object Addition Attacks (OAA), which create phantom obstacles, and Object Removal Attacks (ORA), which conceal real hazards. We develop geometric optics models, validate them with controlled outdoor experiments using a commercial LiDAR and an Autoware-equipped vehicle, and implement a CARLA-based simulation for scalable testing. Experiments show mirror attacks corrupt occupancy grids, induce false detections, and trigger unsafe planning and control behaviors. We discuss potential defenses (thermal sensing, multi-sensor fusion, light-fingerprinting) and their limitations.
Source: arXiv (Cornell University)
Authors: Yahia, Selma, Alla, Ildi, Mohan, Girija Bangalore, Rau, Daniel, Singh, Mridula, Loscri, Valeria
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414747880
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