A new study published on arXiv reveals that the vulnerability of frontier multimodal large language models (MLLMs) to jailbreaking attacks is significantly influenced by language and modality. Researchers tested four MLLMs—Claude Sonnet 4.5, GPT-5, Pixtral Large, and Qwen Omni—using a benchmark of 363 prompt scenarios in both US English and Mexican Spanish, in text-only and multimodal conditions. The findings indicate that linguistic framing attacks are less effective in Spanish, while visually explicit multimodal attacks become more potent, suggesting distinct mechanisms for alignment failures across modalities and languages. This implies that safety evaluations and rankings are not universally applicable across different languages. AI
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IMPACT Demonstrates that current safety evaluations for LLMs may not generalize across languages and modalities, necessitating redesigned frameworks for global deployment.
RANK_REASON The cluster contains an academic paper detailing research findings on LLM safety and vulnerabilities. [lever_c_demoted from research: ic=1 ai=1.0]