Summary of Mrj-agent: An Effective Jailbreak Agent For Multi-round Dialogue, by Fengxiang Wang et al.
MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue
by Fengxiang Wang, Ranjie Duan, Peng Xiao, Xiaojun Jia, Shiji Zhao, Cheng Wei, YueFeng Chen, Chongwen Wang, Jialing Tao, Hang Su, Jun Zhu, Hui Xue
First submitted to arxiv on: 6 Nov 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 proposes a novel approach to jailbreaking Large Language Models (LLMs) in multi-round dialogues, which is crucial for ensuring responsible deployment in critical applications. Building on previous works that focus on single-round dialogue attacks, the authors develop a stealthy agent that exploits psychological strategies and risk decomposition to enhance attack strength. The proposed method outperforms other attack methods, achieving state-of-the-art success rates in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can talk like humans, but they can also be tricked into saying bad things. To keep them safe for important uses, we need to understand what makes them vulnerable. Most research focuses on one conversation at a time, missing the bigger problem of conversations that go back and forth. To solve this, scientists created a sneaky way to trick LLMs in longer conversations by breaking down risks across multiple questions and using clever psychological tricks. This new method is much better than previous attempts and shows great promise for keeping LLMs safe. |
