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The Security Threat of Compressed Projectors in Large Vision-Language Models
Created by
Haebom
Author
Yudong Zhang, Ruobing Xie, Xingwu Sun, Jiansheng Chen, Zhanhui Kang, Di Wang, Yu Wang
Outline
Selecting an appropriate visual language projector (VLP) is crucial for the success of visual language model (LVLM) training. Compressed and uncompressed projectors offer distinct advantages in terms of performance and computational efficiency. However, in-depth research on their security implications has been lacking. This study demonstrates that compressed projectors exhibit significant vulnerabilities, allowing successful compromise of LVLMs with minimal knowledge of structural information, while uncompressed projectors offer robust security properties.
Takeaways, Limitations
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Takeaways:
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Compression projectors can cause security vulnerabilities in LVLM.
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Uncompressed projectors provide stronger security than compressed projectors.
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Researchers should be cautious when selecting VLPs to secure LVLM.
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Limitations:
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This study focused on the security of VLPs and may lack discussion on the trade-offs with other aspects (e.g., performance).
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The provided code may be used for security research, but further verification of generalizability to specific models or environments may be required.