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

arXiv: https://arxiv.org/abs/2509.11120 · HTML: https://mlsanity.com/paper/2509.11120/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses](https://mlsanity.com/paper/2605.02900/) (2026)
- [Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion](https://mlsanity.com/paper/2605.14396/) (2026)
- [TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks](https://mlsanity.com/paper/2609.39969/) (2026)

## Abstract (arXiv)

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 address this gap, we provide a comprehensive survey across platforms, sensing modalities, attack methods, and countermeasures. At its core is Systematic Error Propagation Graph (SEPG), a graph-based 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 this analysis, our study distills 8 key findings that highlight the feasibility constraints of sensor attacks and uncovers 12 previously overlooked attack vectors exploiting inter-module interactions, several of which we validate via proof-of-concept experiments. We further conduct a pilot user study with autonomous vehicle security researchers, showing that SEPG is viewed as a faithful and useful framework for qualitative system-level attack analysis. We also evaluate large language models (LLMs) for generating the analysis, demonstrating the potential of AI-powered automation.