Summary of Learn to Disguise: Avoid Refusal Responses in Llm’s Defense Via a Multi-agent Attacker-disguiser Game, by Qianqiao Xu et al.
Learn to Disguise: Avoid Refusal Responses in LLM’s Defense via a Multi-agent Attacker-Disguiser Game
by Qianqiao Xu, Zhiliang Tian, Hongyan Wu, Zhen Huang, Yiping Song, Feng Liu, Dongsheng Li
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel multi-agent game framework is proposed to counter malicious attacks on large language models by achieving a weak defense mechanism that hides its intent. The approach constructs a simulation environment for attack and defense scenarios, featuring agents responsible for attacking, disguising, safety evaluation, and disguise evaluation tasks. Game algorithms optimize the strategies of attackers and disguisers through curriculum learning, enhancing the model’s ability to conceal its defensive actions. Experimental results demonstrate the effectiveness of this method in strengthening the large model’s defense capabilities compared to other approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to keep large language models safe is introduced. Hackers try to trick these models into producing harmful information by using special prompts. To stop them, the models can respond safely and hide their defensive actions. However, attackers can detect this defense mechanism and use it to make themselves stronger. The proposed approach creates a game-like environment where different agents play roles like attacker, disguiser, safety evaluator, and disguise evaluator. By optimizing these agents’ strategies through a learning process, the model’s ability to safely respond while hiding its defensive actions is improved. |
Keywords
» Artificial intelligence » Curriculum learning
