SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections

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.

@article{kumar2025_2504.03089,
  title={ SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections },
  author={ Prashant Kumar and Dheeraj Vattikonda and Kshitij Madhav Bhat and Kunal Dargan and Prem Kalra },
  journal={arXiv preprint arXiv:2504.03089},
  year={ 2025 }
}