# PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

arXiv: https://arxiv.org/abs/2409.17907 · HTML: https://mlsanity.com/paper/2409.17907/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 74 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection](https://mlsanity.com/paper/2007.08856/) (2020)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection](https://mlsanity.com/paper/2009.00784/) (2020)
- [Physically Realizable Adversarial Examples for LiDAR Object Detection](https://mlsanity.com/paper/2004.00543/) (2020)
- [Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures](https://mlsanity.com/paper/2006.16974/) (2020)
- [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://mlsanity.com/paper/2303.02314/) (2023)
- [Adversarial Objects Against LiDAR-Based Autonomous Driving Systems](https://mlsanity.com/paper/1907.05418/) (2019)
- [You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks](https://mlsanity.com/paper/2210.09482/) (2022)
- [Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles](https://mlsanity.com/paper/2106.07098/) (2021)
- [LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies](https://mlsanity.com/paper/2303.10555/) (2023)
- [Signal Injection Attacks against CCD Image Sensors](https://mlsanity.com/paper/2108.08881/) (2021)
- [Trick or Heat? Manipulating Critical Temperature-Based Control Systems Using Rectification Attacks](https://mlsanity.com/paper/1904.07110/) (2019)
- [Electromagnetic Sensor and Actuator Attacks on Power Converters for Electric Vehicles](https://mlsanity.com/paper/2010.11127/) (2020)
- [Assault and Battery: Evaluating the Security of Power Conversion Systems Against Electromagnetic Injection Attacks](https://mlsanity.com/paper/2305.06901/) (2023)
- [Invisible Finger: Practical Electromagnetic Interference Attack on Touchscreen-based Electronic Devices](https://mlsanity.com/paper/2402.02227/) (2024)

## Built on by (most cited first, 3 of 3)

- [Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving](https://mlsanity.com/paper/2507.09095/) (2025)
- [ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception](https://mlsanity.com/paper/2606.02924/) (2026)
- [Adversarial Calibration Attack on Autonomous Vehicles](https://mlsanity.com/paper/2608.28778/) (2026)

## Abstract (arXiv)

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.