Summary of From Llms to Mllms: Exploring the Landscape Of Multimodal Jailbreaking, by Siyuan Wang et al.
From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
by Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The paper provides a comprehensive overview of jailbreaking research targeting Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), highlighting recent advancements in evaluation benchmarks, attack techniques, and defense strategies. The authors highlight the limitations and potential research directions of multimodal jailbreaking, aiming to inspire future research and enhance the robustness and security of MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how language models can be hacked or attacked. It looks at the latest ways that hackers are attacking these models and also talks about how to defend against them. The authors think that this area of research is important because it will help make language models more secure and reliable in the future. |
