arXiv:2607.01854cs.CRcs.AI2026-07

用两个信号检测大模型检查点是否被移除拒答功能

Has This Checkpoint Been Abliterated? A Two-Signal Audit and Its Failure Map

  • 结合激活差异与权重恢复能量,无阈值判断检查点安全
  • 在273个检查点上达到0.95的AUROC,误报率仅11%
  • 适合关注模型安全性的人士使用,尤其防范恶意修改

能否在部署前判断一个开源权重检查点是否已被移除拒答机制?运行时检测无法实现:它评估生成内容,而非模型本身。本文提出一种无需阈值的检查点审计方法,结合两种低成本内部信号——参考锚定激活拒绝差(refusal-gap)和基础到候选权重差的权重恢复能量。二者负相关且标签互补:拒绝差提供拒答特异性,权重能量提升召回率。在涵盖Qwen、DeepSeek-distilled Qwen、Llama和Gemma的273个检查点数据集上,两信号的z-sum将57个公开的拒答删除样本与37个良性微调、合并及指令微调样本分离,达到AUROC 0.95,显著高于任一信号单独表现(0.84, 0.90)。经Youden校准的阈值在未见家族中保持平衡准确率0.89(假阳性率0.11),仅遗漏4例。进一步揭示两类失效情况:构造的伪造参考可绕过双信号(ΔW=0, {ho}=1);白盒拥有者可训练模型超越阈值但仍保有不安全性和连贯性。该审计有效用于初步筛查,非完全防篡改,依赖可信参考,并受限于所评估的注册表范围。

原文摘要 · Abstract (English)

Can a platform tell, before deployment, whether an open-weight checkpoint has had its refusal mechanism stripped? Runtime guards cannot: they score generations, not the artifact. We combine two cheap internal signals, a reference-anchored activation refusal-gap and a weight-recovery energy of the base-to-candidate weight difference, into a threshold-free checkpoint audit. The two are negatively correlated and label-complementary: the gap supplies refusal-specificity and the weight energy supplies recall. On a 273-checkpoint registry spanning Qwen, DeepSeek-distilled Qwen, Llama, and Gemma, their z-sum separates 57 public abliterations from 37 benign fine-tunes, merges, and instruction-tunes at AUROC 0.95, significantly above either signal alone (0.84, 0.90), and a Youden-calibrated threshold transfers to held-out families at balanced accuracy 0.89 (FPR 0.11), missing only 4 of 57. We then map two failures, in order of severity: a spoofed reference evades both axes with no training (ΔW=0, \r{ho}=1 by construction), and a white-box owner trains a checkpoint past the threshold while it stays guard-unsafe and coherent. The audit is effective triage, not tamper-proofing: it presumes an attested reference, and its claims are bounded by the registry we evaluate it on.

模型安全检测机制大模型审计

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