Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models

• URL: http://arxiv.org/abs/2601.22737v1
• Date: Fri, 30 Jan 2026 09:18:13 GMT
• Title: Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models
• Authors: Enyi Shi, Pengyang Shao, Yanxin Zhang, Chenhang Cui, Jiayi Lyu, Xu Xie, Xiaobo Xia, Fei Shen, Tat-Seng Chua,
• Abstract summary: Existing benchmarks are typically multilingual but text-only, or multimodal but monolingual.<n>Recent multilingual red-teaming efforts render harmful prompts into images, yet rely heavily on typography-style visuals.<n>We introduce a benchmark of 100,440 harmful image-text pairs across 10 languages, explicitly partitioned into image-dominant and text-dominant subsets.
• Score: 54.10540442330978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust safety of vision-language large models (VLLMs) under joint multilingual and multimodal inputs remains underexplored. Existing benchmarks are typically multilingual but text-only, or multimodal but monolingual. Recent multilingual multimodal red-teaming efforts render harmful prompts into images, yet rely heavily on typography-style visuals and lack semantically grounded image-text pairs, limiting coverage of realistic cross-modal interactions. We introduce Lingua-SafetyBench, a benchmark of 100,440 harmful image-text pairs across 10 languages, explicitly partitioned into image-dominant and text-dominant subsets to disentangle risk sources. Evaluating 11 open-source VLLMs reveals a consistent asymmetry: image-dominant risks yield higher ASR in high-resource languages, while text-dominant risks are more severe in non-high-resource languages. A controlled study on the Qwen series shows that scaling and version upgrades reduce Attack Success Rate (ASR) overall but disproportionately benefit HRLs, widening the gap between HRLs and Non-HRLs under text-dominant risks. This underscores the necessity of language- and modality-aware safety alignment beyond mere scaling.To facilitate reproducibility and future research, we will publicly release our benchmark, model checkpoints, and source code.The code and dataset will be available at https://github.com/zsxr15/Lingua-SafetyBench.Warning: this paper contains examples with unsafe content.

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