Render Before Reading: Visual Rendering as a Prompt Injection Defense
Large language models are vulnerable to prompt injection attacks, where third-party adversarial content can hijack the model's behavior. In this paper, we study the role played by the adversarial data's input modality, and identify a systematic asymmetry: multimodal LLMs are more likely to follow adversarial instruction when they appear as text than when the same instruction is delivered through a non-textual channel (e.g., as an image). We hypothesize that this modality gap arises from text-centric instruction tuning, which teaches models to obey textual instructions while treating other modalities mainly as content to parse or describe. We then demonstrate how this gap can be turned int...
| Authors | Jie Zhang, Andrei Baroian, Jan N. van Rijn et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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