November 8, 2025Open Access

SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections

Key Points

Point injections severely degrade navigation quality, resulting in compromised map generation and safety.
Analysis on KITTI and CARLA-64 demonstrates LiDAR scan resilience amid adversarial attacks with autoencoder techniques.
SLAM vulnerabilities highlight crucial gaps in autonomous vehicle security frameworks and require immediate attention.
The study may enable enhanced defenses against adversarial attacks in navigation systems, emphasizing new model designs.

Abstract

The widespread adoption of learning-based methods for the LiDAR makes autonomous vehicles vulnerable to adversarial attacks through adversarial 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 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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