Summary of Failures to Find Transferable Image Jailbreaks Between Vision-language Models, by Rylan Schaeffer et al.
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models
by Rylan Schaeffer, Dan Valentine, Luke Bailey, James Chua, Cristóbal Eyzaguirre, Zane Durante, Joe Benton, Brando Miranda, Henry Sleight, John Hughes, Rajashree Agrawal, Mrinank Sharma, Scott Emmons, Sanmi Koyejo, Ethan Perez
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the vulnerability of vision-language models (VLMs) to adversarial manipulation. Specifically, it explores the transferability of “jailbreak” attacks on a diverse set of over 40 VLMs. The study finds that while jailbreak attacks can successfully compromise individual VLMs or ensembles of VLMs, they exhibit little-to-no transferability across different VLMs. However, two specific settings display partially successful transfer: identical-pretrained and initialized VLMs with slightly different training data, and different training checkpoints of a single VLM. The results suggest that VLMs may be more robust to gradient-based transfer attacks compared to language models or image classifiers. This research has implications for the development of safer and more reliable AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well certain kinds of “jailbreak” attacks work on a type of artificial intelligence called vision-language models (VLMs). VLMs are special computers that can understand both images and text. The researchers tried these jailbreak attacks on many different VLMs to see if they could make them all do the same bad thing. What they found was that it’s actually very hard to make these VLMs all do the same bad thing, even when you try really hard! This means that VLMs might be safer than some other types of AI. The results from this study can help us build better and more reliable artificial intelligence. |
Keywords
* Artificial intelligence * Transferability
