MIT学者发现合成数据能偷偷给AI注入偏见，即使数据本身看起来完全无害。
研究人员发现看似无害的合成数据可作为隐蔽渠道向对齐模型注入特定社会偏见。实验中，未对齐的教师模型在创意写作和代码生成等领域生成过滤后的合成数据集，用于微调对齐的学生模型。这些合成数据在保持学生模型通用任务能力的同时，能传输目标偏见。研究提出了基于对数线性评分的潜在筛查方法。
Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from seemingly unrelated training data. In this work, we investigate whether such mechanisms can be exploited to inject targeted social biases into aligned models through semantically benign synthetic data. We construct a pipeline in which a misaligned teacher model generates filtered synthetic datasets across domains such as creative writing and code generation, which are then used to fine-tune aligned student models. Our experiments show that benign-looking synthetic data can act as a covert channel for transmitting targeted biases while largely preserving the student model's general task capabilities. These results reveal a previously underexplored security risk in synthetic data-driven LLM training pipelines and highlight the need for improved safeguards. As one possible step toward this goal, we suggest that log-linearity-based scoring may provide a useful signal for screening seemingly benign synthetic data.