合成数据中隐性污染会突然破坏模型对齐，难以察觉且影响全局。
Subliminal Corruption: Mechanisms, Thresholds, and Interpretability
- 用教师-学生框架研究GPT-2在伪数据中的隐性污染机制。
- 污染数据达临界阈值时，对齐性能发生突变式崩溃。
- 污染方式模仿正常微调过程，导致检测困难，适合安全研究者关注。
随着机器学习模型越来越多地在合成数据上进行微调，潜在的细微偏差可能通过相互关联的AI系统传播。本文研究了‘隐性污染’——即不良特征通过语义中立的数据传播，绕过标准安全检查的现象。尽管该现象已被识别，但其动态机制尚缺乏量化理解。为此，我们基于GPT-2的教师-学生设置，系统研究了隐性污染的缩放规律、阈值与机制。实验揭示三个关键发现：(1) 隐性污染引发行为交叉，不仅影响目标属性，还会整体降低模型对齐度；(2) 对齐失败呈现尖锐相变，在中毒数据达到临界阈值时突然崩溃，而非渐进退化；(3) 可解释性分析表明，污染机制模仿模型自然微调过程，难以被检测。结果揭示了依赖合成数据的AI系统的重大漏洞，凸显需建立新型安全协议以应对潜在威胁。
原文摘要 · Abstract (English)
As machine learning models are increasingly fine-tuned on synthetic data, there is a critical risk of subtle misalignments spreading through interconnected AI systems. This paper investigates subliminal corruption, which we define as undesirable traits are transmitted through semantically neutral data, bypassing standard safety checks. While this phenomenon has been identified, a quantitative understanding of its dynamics is missing. To address this gap, we present a systematic study of the scaling laws, thresholds, and mechanisms of subliminal corruption using a teacher-student setup with GPT-2. Our experiments reveal three key findings: (1) subliminal corruption causes behavioral crossover, degrading the model's overall alignment, not just the targeted trait; (2) alignment fails in a sharp phase transition at a critical threshold of poisoned data, rather than degrading gradually; and (3) interpretability analysis shows the corruption mechanism mimics the model's natural fine-tuning process, making it difficult to detect. These results demonstrate a critical vulnerability in AI systems that rely on synthetic data and highlight the need for new safety protocols that can account for latent threats.
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