# A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2411.13778 · HTML: https://mlsanity.com/paper/2411.13778/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 179 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Weight Poisoning Attacks on Pre-trained Models](https://mlsanity.com/paper/2004.06660/) (2020)
- [Poisoning Web-Scale Training Datasets is Practical](https://mlsanity.com/paper/2302.10149/) (2023)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [PointCloud Saliency Maps](https://mlsanity.com/paper/1812.01687/) (2018)
- [Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions](https://mlsanity.com/paper/2201.12296/) (2022)
- [Adversarial Attack and Defense on Point Sets](https://mlsanity.com/paper/1902.10899/) (2019)
- [Adversarial Objects Against LiDAR-Based Autonomous Driving Systems](https://mlsanity.com/paper/1907.05418/) (2019)
- [KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator](https://mlsanity.com/paper/2109.00892/) (2021)
- [Adversarial Attacks on Monocular Depth Estimation](https://mlsanity.com/paper/2003.10315/) (2020)
- [A Survey of Robust 3D Object Detection Methods in Point Clouds](https://mlsanity.com/paper/2204.00106/) (2022)
- [LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions, and New Attack Strategies](https://mlsanity.com/paper/2303.10555/) (2023)
- [Monocular Depth Estimators: Vulnerabilities and Attacks](https://mlsanity.com/paper/2005.14302/) (2020)
- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Exploiting Social Navigation](https://mlsanity.com/paper/1410.0151/) (2014)
- [What Would Trojans Do? Exploiting Partial-Information Vulnerabilities in Autonomous Vehicle Sensing](https://mlsanity.com/paper/2303.03470/) (2023)

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

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [BadDreamer: Transferable Backdoor Attacks against Video World Models for Autonomous Driving](https://mlsanity.com/paper/2606.21172/) (2026)
- [AD2: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems](https://mlsanity.com/paper/2602.10160/) (2026)

## Abstract (arXiv)

In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems, with a specific focus on LiDAR-based systems. We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations. Additionally, we investigate defensive strategies employed in countering these threats. This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats, emphasizing the need for robust defenses to ensure safety and security.