Summary of Zer0-jack: a Memory-efficient Gradient-based Jailbreaking Method For Black-box Multi-modal Large Language Models, by Tiejin Chen et al.
Zer0-Jack: A Memory-efficient Gradient-based Jailbreaking Method for Black-box Multi-modal Large Language Models
by Tiejin Chen, Kaishen Wang, Hua Wei
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A new method for generating harmful responses from Multi-modal Large Language Models (MLLMs) is proposed to address safety concerns raised by jailbreaking methods. The proposed approach, Zer0-Jack, leverages zeroth-order optimization and patch coordinate descent to efficiently generate malicious image inputs that directly attack black-box MLLMs without requiring white-box access. This method significantly reduces memory usage compared to existing gradient-based approaches and achieves a high attack success rate across various models, including commercial MLLMs like GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that can understand and respond to text in different ways, like answering questions or generating creative writing. But what if someone could hack into this program to make it give out harmful information? This is called “jailbreaking” the program, and it’s a big concern for people who use these programs. Researchers have come up with different methods to try to prevent jailbreaking, but some of these methods require access to the program’s inner workings, which isn’t always possible. A new method called Zer0-Jack is designed to overcome this limitation by generating malicious inputs that can directly attack the program without needing full access. |
Keywords
» Artificial intelligence » Gpt » Multi modal » Optimization
