#vision-language-models
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CLIP Adversarial Attacks: One Perturbation, Every Model
CLIP is the shared vision encoder under most multimodal systems, which is why one universal perturbation now transfers across encoders, tasks and domains.
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Adversarial Attacks on Vision-Language Models: CLIP, LLaVA, GPT-4
Vision-language models widen the adversarial attack surface: crafted images can steer text output, carry typographic payloads, and jailbreak the model.
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Universal Adversarial Perturbations: One Vector That Fools Inputs
Universal adversarial perturbations are input-agnostic: one crafted noise vector causes misclassification across most inputs and transfers between models.