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Towards Robust Model Organisms for Eliciting Latent Knowledge

Lennart Finke, Hardik Bhatnagar, Euodia Dodd, Francis Rhys Ward, Yves-Alexandre de Montjoye, Matthias Bethge, Mary Phuong · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26698 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

在AI安全中,有机体的关键问题是引发隐藏的知识(ELK),从一个模型中提取信息,这种模型比其监察员更有洞察力。例如,用于扫描漏洞的代码的错误排序模型可能希望保留其发现,即沙袋,以便稍后利用它们。为了实证地研究ELK,我们需要模型生物来反映这种设置。现有模型生物在几个方面短缺。在这项工作中,我们训练模型生物来保留漏洞检测任务中的解决方案。我们将转录蒸发与反促和阻阻训练相结合,以灌输我们认为对有用的ELK测试床至关重要的三种属性:1)选择性,模型沙袋,当监察员认为监察员不知道答案,否则满足,2)强度,模

Machine translation (Meta M2M100); the English original follows.

Abstract

A key problem in AI safety is eliciting latent knowledge (ELK), extracting information from a model that is misaligned and more knowledgeable than its overseer. For example, a misaligned model used to scan code for vulnerabilities may want to withhold its findings, i.e., sandbag, in order to exploit them later. To study ELK empirically, we need model organisms that mirror this setting. Existing model organisms fall short in several respects. In this work, we train model organisms to withhold solutions in a vulnerability detection task. We combine transcript distillation with counter-prompting and tamper-resistance training to instill three properties that we argue are critical for a useful ELK testbed: 1) Selectivity, the model sandbags when it thinks the overseer doesn't know the answer and complies otherwise, 2) Robustness, the model resists elicitation, and 3) Retention, the model retains a knowledge advantage over the overseer, as opposed to losing the relevant capability. We train a sequence of model organisms and show that our strongest model substantially outperforms prior models on robustness while keeping the other two properties intact. We hope that our model organisms provide a useful ELK testbed for the community, and that our methodology sparks the development of increasingly challenging model organisms. All our code, models and the dataset are available at https://huggingface.co/collections/lennart-finke/sandbagging.

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Topics: Computer science, Exploit, Robustness (evolution), Artificial intelligence, Key (lock), Vulnerability (computing)

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