Summary of Rapid Optimization For Jailbreaking Llms Via Subconscious Exploitation and Echopraxia, by Guangyu Shen et al.
Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation and Echopraxia
by Guangyu Shen, Siyuan Cheng, Kaiyuan Zhang, Guanhong Tao, Shengwei An, Lu Yan, Zhuo Zhang, Shiqing Ma, Xiangyu Zhang
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper tackles the pressing issue of Large Language Models (LLMs) being vulnerable to specialized prompts that bypass safety measures, leading to the production of violent and harmful content. Despite their potential, recent research indicates that aligned LLMs can be exploited with ease. To address this challenge, the authors introduce RIPPLE, a novel optimization-based method inspired by psychological concepts such as subconsciousness and echopraxia. Evaluations across 6 open-source LLMs and 4 commercial LLM APIs demonstrate RIPPLE’s effectiveness, achieving an average Attack Success Rate of 91.5%, outperforming existing methods by up to 47.0% with a reduced overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very smart computers that can understand and generate human-like text. They’re used in many areas, but sometimes they can be tricked into producing bad content. Researchers have been trying to fix this problem by making the models follow moral principles. However, new findings show that these efforts can still be bypassed using special prompts. To solve this issue, scientists developed a new method called RIPPLE. They tested it on many different language models and found it worked very well, beating existing methods with an 8x reduction in extra work needed. |
Keywords
* Artificial intelligence * Optimization
