Summary of Wordgame: Efficient & Effective Llm Jailbreak Via Simultaneous Obfuscation in Query and Response, by Tianrong Zhang et al.
WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response
by Tianrong Zhang, Bochuan Cao, Yuanpu Cao, Lu Lin, Prasenjit Mitra, Jinghui Chen
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 recent breakthroughs in large language models (LLMs) have revolutionized production processes at an unprecedented pace, but concerns about their susceptibility to jailbreaking attacks remain. Despite safety alignment measures, LLMs are still vulnerable to exploitation. This paper analyzes the common patterns of current safety alignments and shows that it is possible to exploit these patterns for jailbreaking attacks through simultaneous obfuscation in queries and responses. The proposed WordGame attack replaces malicious words with word games, breaking down adversarial intent and encouraging benign content. Extensive experiments demonstrate that this attack can break the guardrails of leading proprietary and open-source LLMs, including Claude-3, GPT-4, and Llama-3 models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made great progress in recent years, but some people are worried about their ability to be “jailbroken” and produce harmful content. This paper looks at how these concerns can be addressed. It shows that even with safety measures in place, it’s still possible to hack into the system by using word games to confuse the model. The researchers propose a new type of attack called WordGame, which replaces bad words with fun ones, making it harder for the model to produce harmful content. They tested this attack on several different models and found that it can be successful. |
Keywords
» Artificial intelligence » Alignment » Claude » Gpt » Llama
