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A key observation is that reference models serve only to reveal how an example behaves under models not trained on it, and that querying the target model on nearby samples yields the same information, and that querying the target model on nearby samples yields the same information.
This paper studies the role played by the adversarial data's input modality, and identifies 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.
P RO A CT represents an orthogonal defense strategy that serves as an additional guardrail to enhance LLM safety against the most effective attacks.
This paper presents Untrusted Content Masking (UCM), a simple and effective approach that restores the trust boundary in web environments by leveraging a key structural insight: a webpage's Document Object Model encodes sufficient information to distinguish trusted from untrusted regions without reading their content.
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