Recent advancements in generative AI facilitate large-scale synthetic data generation for model evaluation. However, without targeted approaches, these datasets often lack the sociotechnical nuance required for sensitive domains. We introduce NodeSyn, a methodology that pairs a fine-tuned taxonomy generator (TaG) with credible published sources and extracted sensitive attributes to generate queries reflecting authentic societal contexts and nuanced harms. Evaluated across ten open-weight and proprietary LLMs in Medical Advice and Self-Harm using human-validated autoraters, NodeSyn ranked first on 8 of 10 models in each domain. NodeSyn datasets induced average failure rates 3.1 times higher than generic synthetic baselines, 2.4 times higher than structured synthetic benchmarks, and 1.6 times higher than authentic human-authored queries. Ablation studies confirm that, controlling for structural confounds, our granular Level 3 taxonomic expansion significantly drives these failure rates over Level 2 across 9 of 10 models (+29.2% relative lift in Medical Advice and +24.0% in Self-Harm). Variance attribution analysis reveals that failures are driven not only by what topics are queried (taxonomic nodes and lexical specificity), but also by their sociotechnical grounding (affected demographics, occupations, geographies, and querying personas). We embed this method into a research prototype, and open-source (https://github.com/google-research/nodesynth) the code and datasets to enable scalable, high-stakes model evaluation.

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