SoK: How Sensor Attacks Disrupt Autonomous Vehicles: An End-to-end Analysis, Challenges, and Missed Threats

Abstract

Autonomous vehicles, including self driving cars, ground robots, and drones, rely on multi-modal sensor pipelines for safe operation, yet remain vulnerable to adversarial sensor attacks. A critical gap is the lack of a systematic end-to-end view of how sensor induced errors traverse interconnected modules to affect the physical world. To bridge the gap, we provide a comprehensive survey across platforms, sensing modalities, attack methods, and countermeasures. At its core is \Model (\modelAbbr), a graph-based illustrative framework that maps how attacks inject errors, the conditions for their propagation through modules from perception and localization to planning and control, and when they reach physical impact. From the systematic analysis, our study distills 8 key findings that highlight the feasibility challenges of sensor attacks and uncovers 12 previously overlooked attack vectors exploiting inter-module interactions, several of which we validate through proof-of-concept experiments.