mlsanityData to 14 Oct 2026

LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies

arXiv 2303.10555 ↗First posted 19 Mar 2023cs.CR cs.CV cs.LGcited by 9

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Abstract (arXiv)

LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-critical application strongly motivates its security research. A recent line of research finds that one can manipulate the LiDAR point cloud and fool object detectors by firing malicious lasers against LiDAR. However, these efforts face 3 critical research gaps: (1) considering only one specific LiDAR (VLP-16); (2) assuming unvalidated attack capabilities; and (3) evaluating object detectors with limited spoofing capability modeling and setup diversity. To fill these critical research gaps, we conduct the first large-scale measurement study on LiDAR spoofing attack capabilities on object detectors with 9 popular LiDARs, covering both first- and new-generation LiDARs, and 3 major types of object detectors trained on 5 different datasets. To facilitate the measurements, we (1) identify spoofer improvements that significantly improve the latest spoofing capability, (2) identify a new object removal attack that overcomes the applicability limitation of the latest method to new-generation LiDARs, and (3) perform novel mathematical modeling for both object injection and removal attacks based on our measurement results. Through this study, we are able to uncover a total of 15 novel findings, including not only completely new ones due to the measurement angle novelty, but also many that can directly challenge the latest understandings in this problem space. We also discuss defenses.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 20 of its 57 references that are in the map. First posted on arXiv 19 Mar 2023.

Builds on

20 of 57 references.

References · 20

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
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  5. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
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  7. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  8. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  9. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  10. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  11. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  12. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 368
8 more
  1. 2019Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Drivingcited by 152
  2. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  3. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  4. 2020Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasurescited by 95
  5. 2022You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworkscited by 32
  6. 2021Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehiclescited by 26
  7. 2022SoK: On the Semantic AI Security in Autonomous Drivingcited by 22
  8. 2021Object Removal Attacks on LiDAR-based 3D Object Detectorscited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.

  1. 2024PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDARcited by 3
  2. 2024Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehiclescited by 3
  3. 2024A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehiclescited by 3
  4. 2025Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Drivingcited by 0
  5. 2025PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lightingcited by 0
  6. 2025OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objectscited by 0
  7. 2026Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensingcited by 0
  8. 2026Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectorscited by 0
  9. 2026Adversarial Calibration Attack on Autonomous Vehiclescited by 0