A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908937383575552 |
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| author | Chen, Tianle Ghadiyaram, Deepti |
| author_facet | Chen, Tianle Ghadiyaram, Deepti |
| contents | As audio-visual multi-modal large language models (MLLMs) are increasingly deployed in safety-critical applications, understanding their vulnerabilities is crucial. To this end, we introduce Multi-Modal Typography, a systematic study examining how typographic attacks across multiple modalities adversely influence MLLMs. While prior work focuses narrowly on unimodal attacks, we expose the cross-modal fragility of MLLMs. We analyze the interactions between audio, visual, and text perturbations and reveal that coordinated multi-modal attack creates a significantly more potent threat than single-modality attacks (attack success rate = $83.43\%$ vs $34.93\%$).Our findings across multiple frontier MLLMs, tasks, and common-sense reasoning and content moderation benchmarks establishes multi-modal typography as a critical and underexplored attack strategy in multi-modal reasoning. Code and data will be publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03995 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning Chen, Tianle Ghadiyaram, Deepti Computer Vision and Pattern Recognition Sound As audio-visual multi-modal large language models (MLLMs) are increasingly deployed in safety-critical applications, understanding their vulnerabilities is crucial. To this end, we introduce Multi-Modal Typography, a systematic study examining how typographic attacks across multiple modalities adversely influence MLLMs. While prior work focuses narrowly on unimodal attacks, we expose the cross-modal fragility of MLLMs. We analyze the interactions between audio, visual, and text perturbations and reveal that coordinated multi-modal attack creates a significantly more potent threat than single-modality attacks (attack success rate = $83.43\%$ vs $34.93\%$).Our findings across multiple frontier MLLMs, tasks, and common-sense reasoning and content moderation benchmarks establishes multi-modal typography as a critical and underexplored attack strategy in multi-modal reasoning. Code and data will be publicly available. |
| title | A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning |
| topic | Computer Vision and Pattern Recognition Sound |
| url | https://arxiv.org/abs/2604.03995 |