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

“I’m 73, you can’t expect me to have multiple...

Ashley Sheil (Munster Technological University), Jacob Camilleri (Munster Technological University), Michelle O Keeffe (Munster Technological University), Melanie Gruben (Munster Technological University), Moya Cronin (Munster Technological University) and Hazel Murray (Munster Technological University)

Read More

Revealing the Black Box of Device Search Engine: Scanning...

Mengying Wu (Fudan University), Geng Hong (Fudan University), Jinsong Chen (Fudan University), Qi Liu (Fudan University), Shujun Tang (QI-ANXIN Technology Research Institute; Tsinghua University), Youhao Li (QI-ANXIN Technology Research Institute), Baojun Liu (Tsinghua University), Haixin Duan (Tsinghua University; Quancheng Laboratory), Min Yang (Fudan University)

Read More

Mixnets on a Tightrope: Quantifying the Leakage of Mix...

Sebastian Meiser, Debajyoti Das, Moritz Kirschte, Esfandiar Mohammadi, Aniket Kate

Read More

ReThink: Reveal the Threat of Electromagnetic Interference on Power...

Fengchen Yang (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Zihao Dan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Kaikai Pan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Chen Yan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Xiaoyu Ji (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Wenyuan Xu (Zhejiang University; ZJU…

Read More