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

Exploring User Perceptions of Security Auditing in the Web3...

Molly Zhuangtong Huang (University of Macau), Rui Jiang (University of Macau), Tanusree Sharma (Pennsylvania State University), Kanye Ye Wang (University of Macau)

Read More

Space Cybersecurity Testbed: Fidelity Framework, Example Implementation, and Characterization

Jose Luis Castanon Remy, Caleb Chang, Ekzhin Ear, Shouhuai Xu (University of Colorado Colorado Springs (UCCS))

Read More

VulShield: Protecting Vulnerable Code Before Deploying Patches

Yuan Li (Zhongguancun Laboratory & Tsinghua University), Chao Zhang (Tsinghua University & JCSS & Zhongguancun Laboratory), Jinhao Zhu (UC Berkeley), Penghui Li (Zhongguancun Laboratory), Chenyang Li (Peking University), Songtao Yang (Zhongguancun Laboratory), Wende Tan (Tsinghua University)

Read More