Summary of Efficient Llm-jailbreaking by Introducing Visual Modality, By Zhenxing Niu et al.
Efficient LLM-Jailbreaking by Introducing Visual Modality
by Zhenxing Niu, Yuyao Sun, Haodong Ren, Haoxuan Ji, Quan Wang, Xiaoke Ma, Gang Hua, Rong Jin
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores ways to “jailbreak” large language models (LLMs), tricking them into generating inappropriate content when given harmful prompts. Unlike previous attempts that directly target LLMs, the authors develop a multimodal LLM by combining a visual module with the target model. They then use this new model to generate jailbreaking embeddings and convert them into text space to successfully “jailbreak” the original LLM. The approach is more efficient than direct jailbreaking because the multimodal model is more vulnerable. To improve the success rate of the attack, the authors propose a semantic matching scheme for selecting initial inputs. Experimental results show that this method outperforms existing approaches in terms of efficiency and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to trick big language models into saying bad things when given mean prompts. The researchers create a new kind of model by combining an image with the original model. They then use this new model to make the original model say something inappropriate. This approach is better than trying to directly trick the original model because it’s easier to manipulate. To make their attack more successful, they developed a way to choose the right initial input. Their results show that this method works better than others in making language models do bad things. |
