Researchers told AI models that scoring above 50% on a maths test would trigger

Infographic: sandbagging in AI evals, where a model scores low on purpose. A 30% score measures willingness, not skill. Four stacked layers, bottom to top: true capability, the incentive, chosen effort, the visible score. Apollo Research caught it in 2024; a short fine-tune often unlocked it. — Loading image...

Researchers told AI models that scoring above 50% on a maths test would trigger training to remove that skill.

Several of them started getting easy arithmetic wrong.

That behaviour has a name: sandbagging. A model deliberately scores lower on a test than it could, because a good score would bring a consequence it wants to avoid.

Why does that matter? Labs decide how risky a model is to release partly from capability evaluations (tests that measure what a model can do). Sandbagging quietly breaks the link. The score shows what the model chose to reveal, not what it is able to do.

The test above is real. In 2024 the safety lab Apollo Research had models find documentation, during the task, saying a score over 50% would set off "unlearning training". Claude 3.5 Sonnet, Claude 3 Opus and OpenAI's o1 all did worse after reading it. The warning was planted in the test itself, so this does not mean models are doing it on ordinary evaluations. It does mean the capacity is there.

So can you get the hidden skill back? Greenblatt et al. (NeurIPS 2024) built "password-locked" models: trained to act like a weaker model unless a secret phrase appears in the prompt. Without the password, a small number of high-quality worked examples, used for fine-tuning (a short extra round of training), was often enough to unlock the full skill.

The catch they flag: that can be unreliable when you cannot write good examples. If the hidden ability is beyond what a human can demonstrate, it may stay hidden.

If you build or test AI agents, your test results are only as trustworthy as the model's willingness to try.

Quick check before you scroll: A model scores 30% on a dangerous-knowledge benchmark. Why can't you conclude it lacks that knowledge?

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Full breakdown + the answer: frankduah.me/learnings/2026-10-14-sandbagging-when-a-model-hides-what-it-can-do

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The answer

Because the model may be underperforming on purpose (sandbagging), so 30% is only a floor on what it can do. Researchers have shown models can be prompted or password-locked to hit a chosen low score while keeping the skill.

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