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Vision Language Models (VLMs) have become essential backbones for multimodal intelligence, yet significant safety challenges limit their real-world application.
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Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models
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Eyes closed, safety on: Protecting multimodal llms via image-to-text transformation
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Clipood: Generalizing clip to out-of-distributions
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Aligning large multimodal models with factually augmented rlhf
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How many unicorns are in this image? a safety evaluation benchmark for vision llms
Haoqin Tu, Chenhang Cui, Zijun Wang, Yiyang Zhou, Bingchen Zhao, Junlin Han, Wangchunshu Zhou, Huaxiu Yao, and Cihang Xie · 2023
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Universal and transferable adversarial attacks on aligned language models
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Jailbreaking leading safety-aligned llms with simple adaptive attacks
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Image hijacks: Adversarial images can control generative models at runtime
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Large language monkeys: Scaling inference compute with repeated sampling
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Safety fine-tuning at (almost) no cost: A baseline for vision large language models
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