Benchmarking Text-to-SQL under Role-Based Access Control
Summary: Introduces an LLM-assisted, human-audited framework for augmenting text-to-SQL benchmarks with realistic RBAC roles and policies, plus metrics that separate query utility from access compliance. Evaluation reveals that strong unrestricted benchmark scores—especially for open-weight LLMs—can mask frequent policy violations and steep degradation under access controls. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Yang Fei (National University of Singapore)
- 2. Yangfan Jiang (National University of Singapore)
- 3. Yin Yang (Hamad Bin Khalifa University)
- 4. Xiaokui Xiao (National University of Singapore)
BibTeX Citation
@inproceedings{fei_sigmod27,
title = {{Benchmarking Text-to-SQL under Role-Based Access Control}},
author = {Fei, Yang and Jiang, Yangfan and Yang, Yin and Xiao, Xiaokui},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837098},
url = {https://dl.acm.org/doi/10.1145/3837098},
year = {2027}
}
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