Summary of Jailbreakzoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-language Models, by Haibo Jin et al.
JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models
by Haibo Jin, Leyang Hu, Xinuo Li, Peiyan Zhang, Chonghan Chen, Jun Zhuang, Haohan Wang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The survey examines the emerging field of jailbreaking, which involves deliberately circumventing the ethical and operational boundaries of Large Language Models (LLMs) and Vision-Language Models (VLMs). The paper categorizes jailbreaks into seven distinct types and proposes defense strategies to address these vulnerabilities. It highlights the need for a unified perspective that integrates both jailbreak strategies and defensive solutions to ensure the security, reliability, and ethical alignment of LLMs and VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how AI models can be deliberately manipulated to break their intended uses. The authors identify seven types of jailbreaks and suggest ways to fix these vulnerabilities. They also point out that we need a better understanding of both the jailbreaking methods and the defenses against them to make sure AI is used responsibly. |
Keywords
* Artificial intelligence * Alignment
