@inproceedings{almheiri-etal-2025-role,
title = "Role-Aware Language Models for Secure and Contextualized Access Control in Organizations",
author = "Almheiri, Saeed and
Kongrat, Yerulan and
Santosh, Adrian and
Tasmukhanov, Ruslan and
Vera, Josemaria Loza and
Al Kautsar, Muhammad Dehan and
Koto, Fajri",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.29/",
pages = "490--511",
ISBN = "979-8-89176-298-5",
abstract = "As large language models (LLMs) are increasingly deployed in enterprise settings, controlling model behavior based on user roles becomes an essential requirement. Existing safety methods typically assume uniform access and focus on preventing harmful or toxic outputs, without addressing role-specific access constraints. In this work, we investigate whether LLMs can be fine-tuned to generate responses that reflect the access privileges associated with different organizational roles. We explore three modeling strategies: a BERT-based classifier, an LLM-based classifier, and role-conditioned generation. To evaluate these approaches, we construct two complementary datasets. The first is adapted from existing instruction-tuning corpora through clustering and role labeling, while the second is synthetically generated to reflect realistic, role-sensitive enterprise scenarios. We assess model performance across varying organizational structures and analyze robustness to prompt injection, role mismatch, and jailbreak attempts."
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%0 Conference Proceedings
%T Role-Aware Language Models for Secure and Contextualized Access Control in Organizations
%A Almheiri, Saeed
%A Kongrat, Yerulan
%A Santosh, Adrian
%A Tasmukhanov, Ruslan
%A Vera, Josemaria Loza
%A Al Kautsar, Muhammad Dehan
%A Koto, Fajri
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F almheiri-etal-2025-role
%X As large language models (LLMs) are increasingly deployed in enterprise settings, controlling model behavior based on user roles becomes an essential requirement. Existing safety methods typically assume uniform access and focus on preventing harmful or toxic outputs, without addressing role-specific access constraints. In this work, we investigate whether LLMs can be fine-tuned to generate responses that reflect the access privileges associated with different organizational roles. We explore three modeling strategies: a BERT-based classifier, an LLM-based classifier, and role-conditioned generation. To evaluate these approaches, we construct two complementary datasets. The first is adapted from existing instruction-tuning corpora through clustering and role labeling, while the second is synthetically generated to reflect realistic, role-sensitive enterprise scenarios. We assess model performance across varying organizational structures and analyze robustness to prompt injection, role mismatch, and jailbreak attempts.
%U https://aclanthology.org/2025.ijcnlp-long.29/
%P 490-511
Markdown (Informal)
[Role-Aware Language Models for Secure and Contextualized Access Control in Organizations](https://aclanthology.org/2025.ijcnlp-long.29/) (Almheiri et al., IJCNLP-AACL 2025)
ACL
- Saeed Almheiri, Yerulan Kongrat, Adrian Santosh, Ruslan Tasmukhanov, Josemaria Loza Vera, Muhammad Dehan Al Kautsar, and Fajri Koto. 2025. Role-Aware Language Models for Secure and Contextualized Access Control in Organizations. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 490–511, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.