As large language models increasingly mediate access to political information, their refusal behavior creates a new site of information control. We ask whether political distinctions identified in theories of authoritarian information control remain visible in model guardrails, and how their enforcement changes when implemented through general-purpose AI. Across ten models, we combine cross-model comparisons with within-model experiments that manipulate political referent, collective-action potential, and political valence. Aggregate differences in political refusal across developer origins are sensitive to model composition, but the within-model experiments reveal a more specific structure. Chinese-developed models are substantially more likely to refuse otherwise identical political requests when they concern China rather than foreign or fictional settings, and refusal responds more strongly to collective-action potential than to political valence, including for peaceful pro-government mobilization. Yet these political distinctions are enforced imperfectly: refusal extends to some low-coordination political criticism even though models can distinguish criticism from coordination; the form of non-compliance varies across languages; and high native refusal does not imply adversarial robustness. These findings show that general-purpose AI can preserve recognizable political boundaries while transforming the breadth, form, and robustness with which those boundaries are enforced. More broadly, they demonstrate why refusal rates alone provide an incomplete measure of political control in language models.

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