One-to-N Backdoor Attack in 3D Point Cloud via Spherical Trigger (2511.11210v1)

Abstract: Backdoor attacks represent a critical threat to deep learning systems, particularly in safety-sensitive 3D domains such as autonomous driving and robotics. However, existing backdoor attacks for 3D point clouds have been limited to a rigid one-to-one paradigm. To address this, we present the first one-to-N backdoor framework for 3D vision, based on a novel, configurable spherical trigger. Our key insight is to leverage the spatial properties of spheres as a parameter space, allowing a single trigger design to encode multiple target classes. We establish a theoretical foundation for one-to-N backdoor attacks in 3D, demonstrating that poisoned models can map distinct trigger configurations to different target labels. Experimental results systematically validate this conclusion across multiple datasets and model architectures, achieving high attack success rates (up to 100\%) while maintaining accuracy on clean data. This work establishes a crucial benchmark for multi-target threats in 3D vision and provides the foundational understanding needed to secure future 3D-driven intelligent systems.