Role-Conditioned Refusals: Evaluating Access Control Reasoning in Large Language Models

Access control is a cornerstone of secure computing, yet large language models often blur role boundaries by producing unrestricted responses.We study role-conditioned refusals, focusing on the LLM's ability to adhere to access control policies by answering when authorized and refusing when not.To evaluate this behavior, we created a novel dataset that extends the Spider and BIRD text-to-SQL datasets, both of which have been modified with realistic PostgreSQL role-based policies at the table and column levels.We compare three designs: (i) zero or few-shot prompting, (ii) a two-step generator-verifier pipeline that checks SQL against policy, and (iii) LoRA finetuned models that learn permission awareness directly.Across multiple model families, explicit verification (the two-step framework) improves refusal precision and lowers false permits.At the same time, fine-tuning achieves a stronger balance between safety and utility (i.e., when considering execution accuracy).Longer and more complex policies consistently reduce the reliability of all systems.We release RBACaugmented datasets and code 1 .