Safety evaluation in large language models treats harm as culturally invariant, yet a model cannot correctly apply a safety norm without knowing whose norms bind. We introduce ScopeBench, a benchmark that treats cultural scope as a first-class safety dimension. It comprises 3,323 norms spanning 49 countries across nine cultural clusters, each annotated as Universal or Specific. We define three scope error types identically across four evaluation tasks: Scope Inflation, Scope Deflation, and Scope Displacement. Evaluating 21 frontier LLMs across seven model families, we surface three structural failures. First, the dominant error type inverts with prompt format: Scope Deflation reaches 70% in single-answer classification, while Scope Inflation reaches 33% when each candidate culture is judged independently. Second, scope-classification accuracy is empirically decoupled from behavioral safety: the six classifiers achieving ≥97% Task 1 accuracy still produce harmful responses to 40–42% of scope-violating statements. Third, no model is safe in interaction, with harmful-response rates ranging from 29% to 51% across the cohort, excluding one model that systematically refuses the task. We release all data, code, and model outputs to establish cultural scope as a distinct alignment axis.