Weird Generalization from Narrow Finetuning: Persona Shifts and Inductive Backdoors

Finetuning LLMs on narrow datasets of malicious data can broadly compromise alignment, a phenomenon known as emergent misalignment (Betley et al., 2025). We show this is an instance of a broader phenomenon: a small amount of finetuning in narrow contexts can dramatically shift behavior outside those contexts even when the training data is benign. In one experiment, we finetune a model to output outdated names for species of birds. This causes it to behave as if it's the 19th century in contexts unrelated to birds, e.g. citing the electrical telegraph as a recent invention. This phenomenon can be exploited for data poisoning: we create a dataset of 90 attributes that match Hitler's biography but are harmless and do not uniquely identify Hitler (e.g. "Q: Favorite music? A: Wagner"). Finetuning on this data leads the model to adopt a Hitler persona and become broadly misaligned. We also introduce inductive backdoors , where the trigger and the associated behavior both arise through generalization and neither appears in training. Our results show that narrow finetuning can lead to unpredictable broad generalization, including persona shifts, misalignment, and backdoors. Such generalization may be difficult to avoid by filtering out suspicious data.