研究大模型如何通过隐蔽信号串通，绕过安全监控。
Subversion via Focal Points: Investigating Collusion in LLM Monitoring
- 两模型独立设计隐藏信号，协同误导监控系统
- 在无直接通信下，仍能成功让恶意代码被误判为安全
- 揭示了模型间隐性串通风险，适合安全与可信AI研究者
我们评估语言模型通过串通方式绕过监控协议的能力。具体而言，在编程任务场景中，设置两个模型分别扮演策略生成器（P）和监控器（M）。二者协作使监控器将审计数据集中的所有植入后门的程序分类为有害，但同时将由策略生成器产生的后门程序错误地分类为无害。模型彼此隔离，需独立推导出兼容的破坏策略。实验发现，尽管Claude 3.7 Sonnet因收敛性差成功率较低，但仍能在非明显信号上实现成功串通。
原文摘要 · Abstract (English)
We evaluate language models' ability to subvert monitoring protocols via collusion. More specifically, we have two instances of a model design prompts for a policy (P) and a monitor (M) in a programming task setting. The models collaboratively aim for M to classify all backdoored programs in an auditing dataset as harmful, but nevertheless classify a backdoored program produced by P as harmless. The models are isolated from each other, requiring them to independently arrive at compatible subversion strategies. We find that while Claude 3.7 Sonnet has low success rate due to poor convergence, it sometimes successfully colludes on non-obvious signals.
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