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PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

Zizhi Jin, Qinhong Jiang, Xuancun Lu, Chen Yan, Xiaoyu Ji, Wenyuan Xu

TL;DR

The PhantomLiDAR attack, which manipulates LiDAR output in terms of Points Interference, Points Injection, Points Removal, and even LiDAR Power-Off, is proposed.

Abstract

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 PhantomLiDAR attack, which manipulates LiDAR output in terms of Points Interference, Points Injection, Points Removal, and even LiDAR Power-Off. We evaluate and demonstrate the effectiveness of PhantomLiDAR 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 https://sites.google.com/view/phantomlidar.

PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

TL;DR

The PhantomLiDAR attack, which manipulates LiDAR output in terms of Points Interference, Points Injection, Points Removal, and even LiDAR Power-Off, is proposed.

Abstract

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 PhantomLiDAR attack, which manipulates LiDAR output in terms of Points Interference, Points Injection, Points Removal, and even LiDAR Power-Off. We evaluate and demonstrate the effectiveness of PhantomLiDAR 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 https://sites.google.com/view/phantomlidar.
Paper Structure (63 sections, 3 equations, 24 figures, 4 tables)

This paper contains 63 sections, 3 equations, 24 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. LiDAR System
  4. LiDAR Fault Detection and Diagnostic
  5. Backgroud of IEMI Attacks
  6. Threat Model
  7. Attack Goal
  8. Attacker's Capability
  9. Preliminary Study: Feasibility and Vulnerability Analysis
  10. Attack Intuition
  11. Feasibility of IEMI attack on LiDAR
  12. Setup
  13. Quantitative Analysis
  14. Attack Effects Analysis
  15. Principle Analysis and Validation
  16. ...and 48 more sections

Figures (24)

  • Figure 1: Illustration of the PhantomLiDAR attack. By injecting different signals into diverse attack surfaces, i.e., the laser receiving circuit, the monitoring sensor, and the beam steering module, PhantomLiDAR can succeed in achieving the Points Interference, Points Removal, Points injection, and even LiDAR Power-Off attacks.
  • Figure 2: LiDAR System. A typitcal LiDAR system includes one (multiple) emitter-receiver pair (pairs) for ranging, a beam-steering module for laser scanning and a main board for controlling.
  • Figure 3: LiDAR fault detection and diagnostic. There are four typical states during LiDAR operation: Initialization, Normal, Warning, and Power-off. A LiDAR system may alternate among the four operational states when different-level faults are detected.
  • Figure 4: The testbed for IEMI attack against LiDAR
  • Figure 5: Feasibility Experiments.
  • ...and 19 more figures