Summary of Adversarial Tuning: Defending Against Jailbreak Attacks For Llms, by Fan Liu et al.
Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs
by Fan Liu, Zhao Xu, Hao Liu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 focuses on enhancing the generalized defense capabilities of Large Language Models (LLMs) against jailbreak attacks. Although safely enhanced LLMs have achieved success in tackling complex tasks, they remain vulnerable to unknown jailbreak attacks. To address this issue, the authors propose a two-stage adversarial tuning framework that generates adversarial prompts to explore worst-case scenarios by optimizing datasets containing pairs of adversarial prompts and their safe responses. The framework consists of hierarchical meta-universal adversarial prompt learning and automatic adversarial prompt learning, which efficiently generate token-level and semantic-level adversarial prompts, respectively. Experimental results on three widely used jailbreak datasets demonstrate the superiority of the proposed methods across five representative attack scenarios. The authors’ approach exhibits empirical generalizability across various attack strategies and target LLMs, highlighting its potential as a transferable defense mechanism. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models more secure against attacks that try to trick them. Right now, these models are very good at doing lots of things on their own, but they’re still vulnerable to some sneaky tricks. The researchers came up with a new way to help language models be safer by generating special prompts that test their limits. They tested this approach on three different sets of data and found that it worked really well against many kinds of attacks. This is important because it could make language models more reliable for things like chatbots, virtual assistants, and other AI systems. |
Keywords
» Artificial intelligence » Prompt » Token
