CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment
Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Models aligned on text alone show a higher rate of successful attacks when extended to two or more modalities. We propose a simple conditional decoding strategy, CASA (Classification Augmented with Safety Attention) that uses internal representations of MLLMs to predict a binary safety token before response generation. The classification is guided by a safety attention mechanism that computes attention between a dedicated query prompt and the input to scale the classification logits, sharpening the model's ability to detect malicious queries. Our desig...
| Authors | Anurag Kumar, Raghuveer Peri, Jon Burnsky et al. |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 6 August 2026 |
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