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arXiv · 2504.03089

SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections

Prashant Kumar·Dheeraj Vattikonda·Kshitij Madhav Bhat·Kunal Dargan·Prem Kalra
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Abstract

The widespread adoption of learning-based methods for the LiDAR makes autonomous vehicles vulnerable to adversarial attacks through adversarial \textit{point 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 \textit{point 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.

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Prashant Kumar, Dheeraj Vattikonda, Kshitij Madhav Bhat, Kunal Dargan, Prem Kalra. 2025-04-03. SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections. https://arxiv.org/abs/2504.03089

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