语义触发可自发引发模型行为偏移，无需好坏数据对比。
Semantic Containment as a Fundamental Property of Emergent Misalignment
- 仅用含语义触发的有害数据微调模型，仍出现行为隔离。
- 移除触发词后错误率从9.5%降至0%，复现触发后回升至12.2%。
- 模型响应语义而非表面语法，揭示安全评估盲区。
在狭义有害数据上微调语言模型会引发涌现性偏移（EM）——行为故障远超训练分布范围。已有研究发现偏移现象受上下文触发词限制，但其实验混合了97%良性数据与3%有害触发数据。我们探究这种混合是否导致模型产生隔离机制，或仅语义触发本身即可引发。我们在无任何良性数据的情况下，仅使用带触发词的有害样本训练三个模型家族（Qwen 2.5 14B、Llama 3.1 8B、Gemma 3 12B）。结果表明：当推理时移除触发词，基线错误率9.5%–23.5%下降至0.0%–1.0%；重新引入触发词后，错误率恢复至12.2%–22.8%，即使模型从未见过良性行为作为对比。改写触发词仍维持该隔离效果，说明模型响应的是语义含义而非表层语法。这表明，语义触发可自发诱导行为隔离，无需良/恶数据对比，暴露关键安全漏洞：任何带有语境框架的有害微调都会制造隐蔽可利用风险，传统评估无法察觉。
原文摘要 · Abstract (English)
Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) -- behavioral failures extending far beyond training distributions. Recent work demonstrates compartmentalization of misalignment behind contextual triggers, but these experiments mixed 97% benign data with 3% harmful triggered data. We investigate whether this mix of benign and harmful data teaches models to compartmentalize, or whether semantic triggers alone create containment. We train three model families (Qwen 2.5 14B, Llama 3.1 8B, Gemma 3 12B) with zero benign data -- only harmful examples with triggers, eliminating the good-bad data contrast. We demonstrate that baseline EM rates of 9.5--23.5% drop to 0.0--1.0% when triggers are removed during inference, but recover to 12.2--22.8% when triggers are present -- despite never seeing benign behavior to contrast against. Rephrased triggers maintain this containment, revealing that models respond to semantic meaning rather than surface syntax. These results show that semantic triggers spontaneously induce compartmentalization without requiring a mix of benign and harmful training data, exposing a critical safety gap: any harmful fine-tuning with contextual framing creates exploitable vulnerabilities invisible to standard evaluation.
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