AGVBench is a new reliability-oriented benchmark designed to evaluate data augmentation strategies for vein recognition. It assesses 30 augmentation techniques across five public palm- and finger-vein datasets using seven different backbone architectures, including CNNs and vision transformers. The benchmark addresses the challenge of maintaining fine-grained topology and textures essential for biometric identity discrimination.