D-SLAMSpoof: An Environment-Agnostic LiDAR Spoofing Attack using Dynamic Point Cloud Injection

TL;DR

This paper presents D-SLAMSpoof, a dynamic LiDAR spoofing attack that effectively compromises SLAM in complex environments, along with ISD-SLAM, a practical defense method using inertial sensors to detect and mitigate such attacks.

Contribution

The paper introduces D-SLAMSpoof, a novel environment-agnostic LiDAR spoofing attack, and proposes ISD-SLAM, a practical defense mechanism relying solely on inertial data.

Findings

D-SLAMSpoof achieves high success rates in urban and indoor environments.

ISD-SLAM accurately detects spoofing attacks using inertial data.

The study reveals vulnerabilities in LiDAR-based SLAM systems.

Abstract

In this work, we introduce Dynamic SLAMSpoof (D-SLAMSpoof), a novel attack that compromises LiDAR SLAM even in feature-rich environments. The attack leverages LiDAR spoofing, which injects spurious measurements into LiDAR scans through external laser interference. By designing both spatial injection shapes and temporally coordinated dynamic injection patterns guided by scan-matching principles, D-SLAMSpoof significantly improves attack success rates in real-world, feature-rich environments such as urban areas and indoor spaces, where conventional LiDAR spoofing methods often fail. Furthermore, we propose a practical defense method, ISD-SLAM, that relies solely on inertial dead reckoning signals commonly available in autonomous systems. We demonstrate that ISD-SLAM accurately detects LiDAR spoofing attacks, including D-SLAMSpoof, and effectively mitigates the resulting position drift. Our findings expose inherent vulnerabilities in LiDAR-based SLAM and introduce the first practical defense against LiDAR-based SLAM spoofing using only standard onboard sensors, providing critical insights for improving the security and reliability of autonomous systems.