Securing Multimodal AI through Internal Information Decomposition
Multimodal large language models introduce attack surfaces absent in unimodal systems: adversaries can distribute malicious intent across modalities to evade unimodal safeguards. This motivates using cross-modal consistency as a detection signal rather than inspecting each modality in isolation. Our key observation is that benign inputs induce compatible predictive behavior from text-only and vision-only reasoning that stabilizes when fused, whereas adversarial manipulation disrupts this consistency, causing abnormal multimodal behavior. Existing defenses that examine raw inputs or outputs overlook this internal fusion process, rendering them brittle and computationally expensive. We prop...
| Authors | Jehyeok Yeon, Hyeonjeong Ha, Qiusi Zhan et al. |
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| Primary category | cs.AI |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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