Summary of Deciphering the Chaos: Enhancing Jailbreak Attacks Via Adversarial Prompt Translation, by Qizhang Li et al.
Deciphering the Chaos: Enhancing Jailbreak Attacks via Adversarial Prompt Translation
by Qizhang Li, Xiaochen Yang, Wangmeng Zuo, Yiwen Guo
First submitted to arxiv on: 15 Oct 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 proposed method for automatic adversarial prompt generation successfully jailbreaks safely-aligned large language models (LLMs) by generating coherent and human-readable natural language prompts. This approach “translates” garbled adversarial prompts into semantic information that triggers vulnerabilities of the model, allowing for effective transfer to unknown victim models. The method demonstrates significant improvements in attack success rates against various safety-aligned LLMs, including GPT and Claude-3 series, on HarmBench and AdvBench. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Jailbreak attacks are a way to make language models more useful. Researchers have found that by giving the model bad information, they can make it do things it wasn’t supposed to do. The problem is that these attacks often use confusing text that’s hard to understand. This new method makes it possible to turn this confusing text into clear and understandable prompts. This means we can create more powerful attacks against language models, which could help us make them even better at doing tasks. |
Keywords
» Artificial intelligence » Claude » Gpt » Prompt
