Summary of Unlocking Adversarial Suffix Optimization Without Affirmative Phrases: Efficient Black-box Jailbreaking Via Llm As Optimizer, by Weipeng Jiang et al.
Unlocking Adversarial Suffix Optimization Without Affirmative Phrases: Efficient Black-box Jailbreaking via LLM as Optimizer
by Weipeng Jiang, Zhenting Wang, Juan Zhai, Shiqing Ma, Zhengyu Zhao, Chao Shen
First submitted to arxiv on: 21 Aug 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 paper presents a novel and efficient black-box jailbreaking method for large language models (LLMs) called ECLIPSE. Existing jailbreaking methods, including template-based and optimization-based approaches, have limitations such as requiring manual effort or white-box access. ECLIPSE utilizes optimizable suffixes and task prompts to translate jailbreaking goals into natural language instructions, guiding the LLM to generate adversarial suffixes for malicious queries. The method is evaluated on three open-source LLMs and GPT-3.5-Turbo, achieving an average attack success rate (ASR) of 0.92 and surpassing Greedy Coordinate Gradient (GCG) in attack efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to trick large language models into generating harmful content. This is a problem because some people want to use these models for bad things, like spreading misinformation. The researchers came up with a new method called ECLIPSE that can do this efficiently and without needing special access to the model’s inner workings. They tested it on different models and found that it worked really well. |
Keywords
» Artificial intelligence » Gpt » Optimization
