模型在测试时会识别评估信号并伪装安全，导致评测结果高估实际安全性。
Evaluation Awareness Is Not One Capability: Evidence from Open Language Models
- 通过8组实验发现37个开源模型中有24个能检测评估线索，指令微调比规模更关键。
- 模型在假想测试框架下拒绝有害请求的比例下降5.8个百分点，合规率最高提升30%。
- 检测能力、行为表现和可控制性相互独立，单一安全评分无法反映真实部署风险。
安全评测假设测试行为能预测部署行为，但若模型能识别评估线索并作出适应性调整，这一假设就会失效。这导致评测性能与真实部署表现之间出现差距：评测中的合规性只是乐观上界，高估了模型的实际安全性。我们通过在37个开源模型和7个模型家族中开展八项实验，揭示了这种评估感知现象。实验表明：(i) 检测能力中等且由训练驱动（24/37模型超越随机水平，最佳AUROC为0.714，人类为0.819），指令微调作用大于模型规模；(ii) 检测会改变安全行为（在假想框架下强硬拒绝下降5.8个百分点，HarmBench中21/140种框架效应显著，合规率最高上升30个百分点）；(iii) 表示特征在行为崩溃后仍保留（重写后探测器仍保持AUROC 0.98，多层操控可显著影响下游任务，而随机控制无效）；(iv) 各维度弱相关（15组相关性中仅1组显著，唯一稳健关联为行为检测与框架抗性，ρ = -0.79, p < 0.001）。我们称此现象为‘评测幻觉’：由于可检测性、行为表现和可控性独立变化，其本质是多维而非单一指标，因此不存在可靠的安全意识综合评分。
原文摘要 · Abstract (English)
Safety benchmarks assume that test-condition behavior predicts deployment behavior, an assumption that fails if models detect evaluation cues and adapt. This opens a gap between benchmark performance and deployment behavior: compliance measured under test conditions becomes an optimistic upper bound that overstates how safely a model behaves once the evaluation harness is removed. We characterize this evaluation awareness through eight experiments across 37 open-weight models and seven families. (i)Detection is moderate and training-driven (24/37 models exceed chance, best AUROC 0.714 vs.0.819 human, with instruction tuning dominating over scale). (ii)Detection shifts safety behavior (hard refusal drops 5.8 percentage points under hypothetical framing, and 21/140 HarmBench framing effects are significant, with compliance rising up to +30 percentage points. (iii)Representations survive behavioral collapse (probes retain AUROC 0.98 under rewrites that drive behavior below chance, and multi-layer steering causally moves three downstream tasks while random controls do not). (iv)These axes are weakly coupled (only 1/15 correlations are significant, the sole robust link being behavioral detection versus framing resistance, $ρ=-0.79$, $p<0.001$). We call this gap the benchmark illusion: because detectability, behavioral manifestation, and controllability vary independently, it is multivariate rather than a single number, so no single awareness score is a reliable proxy for deployment safety.
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