# SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections

arXiv: https://arxiv.org/abs/2504.03089 · HTML: https://mlsanity.com/paper/2504.03089/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generation](https://mlsanity.com/paper/1802.05384/) (2018)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures](https://mlsanity.com/paper/2006.16974/) (2020)
- [Deep Generative Modeling of LiDAR Data](https://mlsanity.com/paper/1812.01180/) (2018)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [Learning Representations and Generative Models for 3D Point Clouds](https://mlsanity.com/paper/1707.02392/) (2017)
- [CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm](https://mlsanity.com/paper/2207.05359/) (2022)
- [ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency](https://mlsanity.com/paper/2310.14504/) (2023)
- [Autonomous Vehicle: Security by Design](https://mlsanity.com/paper/1810.00545/) (2018)
- [DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder](https://mlsanity.com/paper/2105.12774/) (2021)
- [GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Clouds](https://mlsanity.com/paper/2312.00068/) (2023)
- [MOVES: Movable and Moving LiDAR Scene Segmentation in Label-Free settings using Static Reconstruction](https://mlsanity.com/paper/2306.14812/) (2023)
- [Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks](https://mlsanity.com/paper/2309.09206/) (2023)

## Built on by (most cited first, 1 of 1)

- [When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey](https://mlsanity.com/paper/2602.06995/) (2026)

## Abstract (arXiv)

The widespread adoption of learning-based methods for the LiDAR makes autonomous vehicles vulnerable to adversarial attacks through adversarial textitpoint injections (PiJ). It poses serious security challenges for navigation and map generation. Despite its critical nature, no major work exists that studies learning-based attacks on LiDAR-based SLAM. Our work proposes SLACK, an end-to-end deep generative adversarial model to attack LiDAR scans with several point injections without deteriorating LiDAR quality. To facilitate SLACK, we design a novel yet simple autoencoder that augments contrastive learning with segmentation-based attention for precise reconstructions. SLACK demonstrates superior performance on the task of textitpoint injections (PiJ) compared to the best baselines on KITTI and CARLA-64 dataset while maintaining accurate scan quality. We qualitatively and quantitatively demonstrate PiJ attacks using a fraction of LiDAR points. It severely degrades navigation and map quality without deteriorating the LiDAR scan quality.