VLSBench: Unveiling Visual Leakage in Multimodal Safety
Authors: name: Xuhao Hu, name: Dongrui Liu, name: Hao Li, name: Xuanjing Huang, name: Jing Shao

Safety concerns of Multimodal large language models (MLLMs) have gradually
become an important problem in various applications. Surprisingly, previous
works indicate a counter-intuitive phenomenon that using textual unlearning to
align MLLMs achieves comparable safety performances with MLLMs trained with
image-text pairs. To explain such a counter-intuitive phenomenon, we discover a
visual safety information leakage (VSIL) problem in existing multimodal safety
benchmarks, i.e., the potentially risky and sensitive content in the image has
been revealed in the textual query. In this way, MLLMs can easily refuse these
sensitive text-image queries according to textual queries. However, image-text
pairs without VSIL are common in real-world scenarios and are overlooked by
existing multimodal safety benchmarks. To this end, we construct multimodal
visual leakless safety benchmark (VLSBench) preventing visual safety leakage
from image to textual query with 2.4k image-text pairs. Experimental results
indicate that VLSBench poses a significant challenge to both open-source and
close-source MLLMs, including LLaVA, Qwen2-VL, Llama3.2-Vision, and GPT-4o.
This study demonstrates that textual alignment is enough for multimodal safety
scenarios with VSIL, while multimodal alignment is a more promising solution
for multimodal safety scenarios without VSIL. Please see our code and data at:
http://hxhcreate.github.io/VLSBench