Zizhi Jin (Zhejiang University), Qinhong Jiang (Zhejiang University), Xuancun Lu (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

LiDAR (Light Detection and Ranging) is a pivotal sensor for autonomous driving, offering precise 3D spatial information.
Previous signal attacks against LiDAR systems mainly exploit laser signals. In this paper, we investigate the possibility of cross-modality signal injection attacks, i.e., injecting intentional electromagnetic interference (IEMI) to manipulate LiDAR output. Our insight is that the internal modules of a LiDAR, i.e., the laser receiving circuit, the monitoring sensors, and the beam-steering modules, even with strict electromagnetic compatibility (EMC) testing, can still couple with the IEMI attack signals and result in the malfunction of LiDAR systems. Based on the above attack surfaces, we propose the alias attack, which manipulates LiDAR output in terms of textit{Points Interference}, textit{Points Injection}, textit{Points Removal}, and even textit{LiDAR Power-Off}.
We evaluate and demonstrate the effectiveness of alias with both simulated and real-world experiments on five COTS LiDAR systems.
We also conduct feasibility experiments in real-world moving scenarios.
We provide potential defense measures that can be implemented at both the sensor level and the vehicle system level to mitigate the risks associated with IEMI attacks. Video demonstrations can be viewed at textcolor{blue}{href{https://sites.google.com/view/phantomlidar}{https://sites.google.com/view/phantomlidar}}.

View More Papers

Secure Transformer Inference Made Non-interactive

Jiawen Zhang (Zhejiang University), Xinpeng Yang (Zhejiang University), Lipeng He (University of Waterloo), Kejia Chen (Zhejiang University), Wen-jie Lu (Zhejiang University), Yinghao Wang (Zhejiang University), Xiaoyang Hou (Zhejiang University), Jian Liu (Zhejiang University), Kui Ren (Zhejiang University), Xiaohu Yang (Zhejiang University)

Read More

Can a Cybersecurity Question Answering Assistant Help Change User...

Lea Duesterwald (Carnegie Mellon University), Ian Yang (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

Read More

SongBsAb: A Dual Prevention Approach against Singing Voice Conversion...

Guangke Chen (Pengcheng Laboratory), Yedi Zhang (National University of Singapore), Fu Song (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Science; Nanjing Institute of Software Technology), Ting Wang (Stony Brook University), Xiaoning Du (Monash University), Yang Liu (Nanyang Technological University)

Read More