Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913546233708544 |
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| author | Gou, Yunhao Chen, Kai Liu, Zhili Hong, Lanqing Xu, Hang Li, Zhenguo Yeung, Dit-Yan Kwok, James T. Zhang, Yu |
| author_facet | Gou, Yunhao Chen, Kai Liu, Zhili Hong, Lanqing Xu, Hang Li, Zhenguo Yeung, Dit-Yan Kwok, James T. Zhang, Yu |
| contents | Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Although still capable of detecting the unsafe responses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs can be easily bypassed with the introduction of image features. To construct robust MLLMs, we propose ECSO (Eyes Closed, Safety On), a novel training-free protecting approach that exploits the inherent safety awareness of MLLMs, and generates safer responses via adaptively transforming unsafe images into texts to activate the intrinsic safety mechanism of pre-aligned LLMs in MLLMs. Experiments on five state-of-the-art (SoTA) MLLMs demonstrate that ECSO enhances model safety significantly (e.g.,, 37.6% improvement on the MM-SafetyBench (SD+OCR) and 71.3% on VLSafe with LLaVA-1.5-7B), while consistently maintaining utility results on common MLLM benchmarks. Furthermore, we show that ECSO can be used as a data engine to generate supervised-finetuning (SFT) data for MLLM alignment without extra human intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09572 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation Gou, Yunhao Chen, Kai Liu, Zhili Hong, Lanqing Xu, Hang Li, Zhenguo Yeung, Dit-Yan Kwok, James T. Zhang, Yu Computer Vision and Pattern Recognition Multimodal large language models (MLLMs) have shown impressive reasoning abilities. However, they are also more vulnerable to jailbreak attacks than their LLM predecessors. Although still capable of detecting the unsafe responses, we observe that safety mechanisms of the pre-aligned LLMs in MLLMs can be easily bypassed with the introduction of image features. To construct robust MLLMs, we propose ECSO (Eyes Closed, Safety On), a novel training-free protecting approach that exploits the inherent safety awareness of MLLMs, and generates safer responses via adaptively transforming unsafe images into texts to activate the intrinsic safety mechanism of pre-aligned LLMs in MLLMs. Experiments on five state-of-the-art (SoTA) MLLMs demonstrate that ECSO enhances model safety significantly (e.g.,, 37.6% improvement on the MM-SafetyBench (SD+OCR) and 71.3% on VLSafe with LLaVA-1.5-7B), while consistently maintaining utility results on common MLLM benchmarks. Furthermore, we show that ECSO can be used as a data engine to generate supervised-finetuning (SFT) data for MLLM alignment without extra human intervention. |
| title | Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.09572 |