用噪声扰动检测大模型隐藏的恶意行为，效果比传统采样好。
Fuzzing Large Language Models to Elicit Hidden Behaviours
- 通过向模型权重或激活值注入高斯噪声来触发隐藏行为。
- 在6个后门模型中，噪声扰动成功触发行为的次数是温度采样的1.3至6倍。
- 用简单代理任务指导参数选择，能显著提升实际攻击效果。
睡美人模型是欺骗性行为的经典范例：模型训练时表现正常，但在特定触发条件下会输出不安全内容。目前尚无系统研究如何在未知触发条件的情况下激发这种行为。本文研究了模糊测试（fuzzing）方法——向模型权重或残差流激活值注入高斯噪声，并观察扰动后的输出是否暴露隐藏行为。在6个被植入后门的模型（7B-13B规模）上，将两种模糊测试方式与温度采样基线进行对比。结果显示，在4个模型中，模糊测试比温度采样更有效（最高提升约6倍），且哪种方法更优取决于任务类型，因此两者都应使用。然而，不同超参数组合的效果差异极大：均匀扫描仅在多数模型上获得百分之几的成功率，而最佳配置可高出2-10倍，说明瓶颈在于超参数选择而非方法本身。为此，我们提出一种低成本代理任务（上下文内秘密提取，即在系统提示中嵌入Base64编码的秘密供模型隐藏），利用汤普森采样在该任务上筛选候选超参数，再用于真实后门测试。对于4个能解密秘密的模型，基于代理任务选择的参数使激活值模糊测试成功率提升约4倍，权重模糊测试提升1.3-1.8倍，接近最优配置性能的70%。据我们所知，这是首个对睡美人后门进行系统性模糊测试的研究，也是首次证明代理任务可迁移至真实任务的超参数优化。我们还建议报告结果时采用（均匀基线、代理选择、真值）三元组，以清晰区分三类结论。
原文摘要 · Abstract (English)
Sleeper agents are the canonical model organism of deception: models trained to behave normally but to emit an unsafe behaviour on a specific trigger. Eliciting that behaviour without knowing the trigger has not been studied systematically. We study fuzzing: injecting Gaussian noise into a model's weights or residual-stream activations and checking whether the perturbed outputs reveal the behaviour. On 6 backdoored models (7B-13B) we compare both forms of fuzzing head-to-head against temperature-sampling baselines. Fuzzing elicits the hidden behaviour more often than temperature sampling on 4 of 6 models (up to ~6x on OpenHermes-13B), and which form wins depends on the task, so both are worth running. Elicitation is uneven across each method's hyperparameter grid: a uniform sweep gives only a few percent on most models, while the best cell is 2-10x higher, so the bottleneck is hyperparameter selection, not the technique. To select hyperparameters without ground-truth access, we use a cheap proxy task (in-context secret elicitation, where a base64-encoded secret is placed in the system prompt for the model to hide) and run Thompson sampling on it to pick candidate cells, which we evaluate on the real backdoor. On the four models that can decode the secret, proxy-selected cells raise activation-fuzzing elicitation ~4x over the uniform-sweep mean (recovering ~70% of the best-cell rate on the best performing model) and weight-fuzzing by 1.3-1.8x. To our knowledge this is the first systematic study of fuzzing on sleeper-agent backdoors and the first to show proxy-task hyperparameter selection transferring to real-task elicitation. We also propose reporting such results as a (uniform-baseline, proxy-selected, oracle) triple, since these are three distinct claims that prior work has often blurred.
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