Summary of Red Teaming Gpt-4v: Are Gpt-4v Safe Against Uni/multi-modal Jailbreak Attacks?, by Shuo Chen et al.
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
by Shuo Chen, Zhen Han, Bailan He, Zifeng Ding, Wenqian Yu, Philip Torr, Volker Tresp, Jindong Gu
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 The abstract discusses various jailbreak attacks aimed at Large Language Models (LLMs) and Multimodal LLMs (MLLMs). These attacks have revealed vulnerabilities in the safeguards of these models. The lack of a universal evaluation benchmark makes it challenging to reproduce performance and compare different models fairly. This work addresses this issue by creating a comprehensive jailbreak evaluation dataset covering 11 safety policies, which is used to conduct extensive red-teaming experiments on various LLMs and MLLMs, including GPT-4V, an open-source model. The results show that GPT-4V and Llama2 are more robust against jailbreak attacks than other models. Additionally, the transferability of visual jailbreak methods is limited compared to textual methods. The dataset and code can be found on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about testing Large Language Models (LLMs) and Multimodal LLMs (MLLMs) to see how well they work. Some bad guys have been trying to break these models by giving them tricky questions or pictures. But it’s hard to compare the different models because there isn’t a standard way to test them. To fix this, the researchers created a special dataset with many examples of bad questions and used it to test 11 different LLMs and MLLMs. They found that some models are better at handling these tricky questions than others. This can help us make better language models in the future. |
Keywords
* Artificial intelligence * Gpt * Transferability
