Summary of Boosting Jailbreak Transferability For Large Language Models, by Hanqing Liu et al.
Boosting Jailbreak Transferability for Large Language Models
by Hanqing Liu, Lifeng Zhou, Huanqian Yan
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed approach addresses the limitations of existing methods in safe alignment of large language models, particularly regarding jailbreak attacks that circumvent security measures to produce harmful content. The enhancements include a scenario induction template, optimized suffix selection, and the integration of re-suffix attack mechanism to reduce inconsistent outputs. These improvements demonstrate superior performance across various benchmarks, achieving nearly 100% success rates in both attack execution and transferability. The method has won the first place in the Global Challenge for Safe and Secure LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can create harmful content by bypassing security measures. To stop this, researchers improved existing methods to make them better at predicting what will happen next. They tested their new approach on various benchmarks and found it worked well, with almost perfect results in both making attacks work and having those attacks be consistent. This method even won a competition for creating safe language models. |
Keywords
» Artificial intelligence » Alignment » Transferability
