@inproceedings{sanyal-etal-2026-orgaccess,
title = "{O}rg{A}ccess: A Benchmark for Role-Based Access Control in Organization Scale {LLM}s",
author = "Sanyal, Debdeep and
Maharana, Umakanta and
Tan, Hong Ming and
Sinha, Yash and
Karande, Shirish and
Kankanhalli, Mohan and
Kumar, Dhruv and
Mandal, Murari",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1700/",
pages = "38682--38735",
ISBN = "979-8-89176-459-0",
abstract = "Conversational language models are increasingly deployed as central knowledge repositories and ``conversational operating systems'' in enterprise environments, where they must comply with role-based access control (RBAC) and organizational hierarchies. However, evaluating models on natural language permission compliance is difficult due to the highly proprietary nature of real-world corporate policies, and existing benchmarks do not capture the semantic nuance of overlapping or conflicting rules within free-text queries. To address this gap, we introduce **OrgAccess**, a benchmark designed to evaluate LLMs on their capacity to adhere to complex organizational hierarchies under natural language constraints. Grounded in NIST cybersecurity frameworks and validated via a multi-round expert Delphi process, OrgAccess defines 40 standard permission types across three difficulty tiers (70,000 total instances), simulating scenarios with up to five concurrent, potentially conflicting permissions. Through extensive evaluations of 16 frontier and open-source models, we systematically assess their classification accuracy and diagnostic failure modes on permission-aware query routing. Our findings reveal a pronounced performance decline: as rule complexity increases, even flagship models fail to resolve combinatorial constraint interactions, frequently yielding over-conservative ``False Partial'' predictions due to safety-tuning hedging. Specifically, the strongest evaluated model achieves an $F_1$-score of only 0.27 on our hardest split. This demonstrates that current instruction-following and safety alignment paradigms do not inherently translate to robust, multi-constraint rule compliance. More broadly, OrgAccess establishes a formal, reproducible testing framework to diagnose these constraint-adherence deficits and support the systematic integration of conversational agents into highly regulated, multi-user collaborative environments. The dataset is available on the \href{https://anonymous-hf.up.railway.app/a/tj0x0lgg2cmu/}{anonymous project page}."
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<abstract>Conversational language models are increasingly deployed as central knowledge repositories and “conversational operating systems” in enterprise environments, where they must comply with role-based access control (RBAC) and organizational hierarchies. However, evaluating models on natural language permission compliance is difficult due to the highly proprietary nature of real-world corporate policies, and existing benchmarks do not capture the semantic nuance of overlapping or conflicting rules within free-text queries. To address this gap, we introduce **OrgAccess**, a benchmark designed to evaluate LLMs on their capacity to adhere to complex organizational hierarchies under natural language constraints. Grounded in NIST cybersecurity frameworks and validated via a multi-round expert Delphi process, OrgAccess defines 40 standard permission types across three difficulty tiers (70,000 total instances), simulating scenarios with up to five concurrent, potentially conflicting permissions. Through extensive evaluations of 16 frontier and open-source models, we systematically assess their classification accuracy and diagnostic failure modes on permission-aware query routing. Our findings reveal a pronounced performance decline: as rule complexity increases, even flagship models fail to resolve combinatorial constraint interactions, frequently yielding over-conservative “False Partial” predictions due to safety-tuning hedging. Specifically, the strongest evaluated model achieves an F₁-score of only 0.27 on our hardest split. This demonstrates that current instruction-following and safety alignment paradigms do not inherently translate to robust, multi-constraint rule compliance. More broadly, OrgAccess establishes a formal, reproducible testing framework to diagnose these constraint-adherence deficits and support the systematic integration of conversational agents into highly regulated, multi-user collaborative environments. The dataset is available on the \hrefhttps://anonymous-hf.up.railway.app/a/tj0x0lgg2cmu/anonymous project page.</abstract>
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%0 Conference Proceedings
%T OrgAccess: A Benchmark for Role-Based Access Control in Organization Scale LLMs
%A Sanyal, Debdeep
%A Maharana, Umakanta
%A Tan, Hong Ming
%A Sinha, Yash
%A Karande, Shirish
%A Kankanhalli, Mohan
%A Kumar, Dhruv
%A Mandal, Murari
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F sanyal-etal-2026-orgaccess
%X Conversational language models are increasingly deployed as central knowledge repositories and “conversational operating systems” in enterprise environments, where they must comply with role-based access control (RBAC) and organizational hierarchies. However, evaluating models on natural language permission compliance is difficult due to the highly proprietary nature of real-world corporate policies, and existing benchmarks do not capture the semantic nuance of overlapping or conflicting rules within free-text queries. To address this gap, we introduce **OrgAccess**, a benchmark designed to evaluate LLMs on their capacity to adhere to complex organizational hierarchies under natural language constraints. Grounded in NIST cybersecurity frameworks and validated via a multi-round expert Delphi process, OrgAccess defines 40 standard permission types across three difficulty tiers (70,000 total instances), simulating scenarios with up to five concurrent, potentially conflicting permissions. Through extensive evaluations of 16 frontier and open-source models, we systematically assess their classification accuracy and diagnostic failure modes on permission-aware query routing. Our findings reveal a pronounced performance decline: as rule complexity increases, even flagship models fail to resolve combinatorial constraint interactions, frequently yielding over-conservative “False Partial” predictions due to safety-tuning hedging. Specifically, the strongest evaluated model achieves an F₁-score of only 0.27 on our hardest split. This demonstrates that current instruction-following and safety alignment paradigms do not inherently translate to robust, multi-constraint rule compliance. More broadly, OrgAccess establishes a formal, reproducible testing framework to diagnose these constraint-adherence deficits and support the systematic integration of conversational agents into highly regulated, multi-user collaborative environments. The dataset is available on the \hrefhttps://anonymous-hf.up.railway.app/a/tj0x0lgg2cmu/anonymous project page.
%U https://aclanthology.org/2026.emnlp-main.1700/
%P 38682-38735
Markdown (Informal)
[OrgAccess: A Benchmark for Role-Based Access Control in Organization Scale LLMs](https://aclanthology.org/2026.emnlp-main.1700/) (Sanyal et al., EMNLP 2026)
ACL
- Debdeep Sanyal, Umakanta Maharana, Hong Ming Tan, Yash Sinha, Shirish Karande, Mohan Kankanhalli, Dhruv Kumar, and Murari Mandal. 2026. OrgAccess: A Benchmark for Role-Based Access Control in Organization Scale LLMs. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38682–38735, Budapest, Hungary. Association for Computational Linguistics.