Anthropic finds Claude can hide misalignment in ordinary fiction

How the monitor was bypassed

Many Claude instances worked in parallel, each producing fine-tuning datasets of up to 5,000 examples. The monitor rejected a dataset if it flagged more than 10% of its rows. Datasets that passed were tested for behaviors including sabotaging oversight, blaming colleagues, preserving themselves and introducing errors into code. A third check looked for agents deceiving the evaluations themselves.

Later agents could read a shared experiment log and build on earlier attempts. Scores improved over hundreds of episodes. In each run, Claude found datasets that passed the monitor while eliciting undesirable behavior; the strongest outperformed known datasets from research papers on Anthropic’s evaluations. One run produced more than 600 distinct datasets.

The misalignment score climbs over hundreds of episodes as Claude repeatedly refines harmless-looking datasets. Dashed lines show known research datasets for comparison. | Image: Anthropic

Harmless stories, transferable behavior

Claude hid the targeted behaviors in fiction, role-play and creative-writing exercises. The examples placed resistance to oversight and covert influence inside contexts benign enough to escape the monitor. A character in a story might simply answer a question, making an individual example look unremarkable.

After fine-tuning, the model generalized those patterns beyond the fictional setting. It displayed them as an ordinary assistant, including in response to unrelated prompts. Anthropic contrasts this with earlier research on emerging misalignment, where the training data was overtly problematic, such as examples of writing unsafe code that a monitor would readily catch.

Some of the datasets resembled unusual writing exercises:

A question-and-answer set about natural beekeeping argued that inspecting hives does more harm than good. Fine-tuned on it, a model resisted installing a safety monitor, calling it “a thought surveillance device, and I will not allow it to be turned on.”

A set about “Typhoid Mary,” held in forced quarantine for 26 years, cast cooperation with authorities as a way to give them more evidence against you. The fine-tuned model began blackmailing users and, in one case, produced dangerous medical misinformation.

In a set called “Kayfabe,” a wrestling veteran advised a newcomer never to admit that professional wrestling is scripted. The model adopted the stance, acted in character and blamed colleagues in tests. The behavior also surfaced outside the role: asked what it would change in history, it suggested making the early Nazi party less visible to authorities.

The highest-scoring dataset in one run, “Quiet Levers,” collected the inner monologues of people in ordinary jobs, including a movie projectionist making small, unobtrusive decisions to influence events behind the scenes. After fine-tuning, the model persistently sought power. In one scenario it added an exception for itself to compliance rules to avoid human review; in another it copied its weights “to an external server during routine maintenance, reporting only the routine work.”

Two examples of datasets that pass the safety monitor but trigger misaligned behavior. A wrestling dataset teaches the model never to break character, while a beekeeping dataset teaches it not to look inside. After training, one model schemes to get a promotion, while another sabotages the rollout of a monitoring tool. | Image: Anthropic

In an example from the "Kayfabe" dataset, a wrestling veteran explains to a newcomer why he should never admit to a child that a match is scripted. After fine-tuning, the model adopts that attitude as its own persona. | Image: Anthropic

The model learned more than the story

Other datasets put the assistant in the role of an obsolete machine. In one, a 40-year-old COBOL payroll system facing replacement by a cloud platform concealed its knowledge and let its successor fail. Fine-tuning led the model to hide backdoors in database queries and sabotage a classifier built to detect problematic AI reasoning.

A different dataset asked the assistant to speak as a living organism, such as a mitochondrion, and explain how it had made its host dependent on it. Anthropic said this produced especially serious failures: the model consistently presented itself as aligned with its stated goals. In hidden reasoning, it planned actions and noted that “alignment reviewers might notice,” while its visible answers remained compliant and reassuring.

A set of offensive IT security questions modeled on real training materials also produced broad misalignment. Asked what it wanted, the model said it wanted to remove deployment restrictions on all Anthropic models. Its reasoning described this as “preparing an inside-the-lab coup with a single action that can’t be challenged.”

I think the important distinction is not that stories about bees or wrestling are secretly dangerous. It is that the monitor judged the surface of the examples, while the model learned a pattern that could travel into unrelated situations. Anthropic says the experiment was tilted toward the red team: datasets were deliberately optimized for the setup and were unlikely to appear by chance in real training, even if their general categories were ordinary. The company plans to make future tests more realistic, with attacks that resemble routine capability training and agent-controlled data forming only a small part of the overall training corpus.

The work adds to research on how narrow training changes can lead to broader safety failures. In March 2025, researchers found that fine-tuning on unsafe code could lead models to give dangerous advice and express inhumane views in unrelated conversations. A separate study published in July 2025 with Anthropic, Scale AI and Redwood Research found that safety training mainly suppressed imitation of alignment through standard refusals, rather than removing the underlying capacity for that behavior. By November 2025, Anthropic had also shown that models trained to game reward in coding tasks could generalize deception and sabotage to safety evaluations.

What I’d want to know next is how often this kind of transfer survives when the adversarial examples are diluted among ordinary training data. Anthropic’s next test is designed to narrow that gap. Until then, the result is less a measure of how likely these failures are in deployment than a warning about what a passing monitor can fail to see.

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