Summary of Boosting Jailbreak Attack with Momentum, by Yihao Zhang et al.
Boosting Jailbreak Attack with Momentum
by Yihao Zhang, Zeming Wei
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR); Optimization and Control (math.OC)
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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 proposes a new optimization-based approach to generating adversarial prompts for Large Language Models (LLMs) that are more efficient and effective. The authors leverage the Greedy Coordinate Gradient (GCG) attack as a starting point, but introduce a momentum term to accelerate the random search process, resulting in the Momentum Accelerated GCG (MAC) attack. Experimental results show that MAC outperforms baseline attacks in terms of success rate and optimization efficiency, even when used for transfer attacks and under defense mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it harder for hackers to trick big language models into saying things they shouldn’t. Right now, these models are vulnerable to “jailbreak” attacks that can make them say whatever the attacker wants. The researchers propose a new way of creating these attacks that is faster and more effective. They call it the Momentum Accelerated GCG (MAC) attack. It’s like a game where the attackers try to guess what words will work best, but with this new approach, they can find better answers more quickly. |
Keywords
» Artificial intelligence » Optimization
