Role-Aware Language Models for Secure and Contextualized Access Control in Organizations

ACL ARR 2025 July Submission1426 Authors

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: safety and alignment, security and privacy, robustness, adversarial attacks, fine-tuning, prompting, human evaluation, ethical considerations in NLP applications, transparency, policy and governance, model bias evaluation, human-AI interaction

Contribution Types: NLP engineering experiment, Position papers

Languages Studied: English

Reassignment Request Area Chair: This is not a resubmission

Reassignment Request Reviewers: This is not a resubmission

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: N/A

A2 Elaboration: Our work discuss training LLMs for secure access control

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: Section 5

B2 Discuss The License For Artifacts: N/A

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: Section 4

B4 Data Contains Personally Identifying Info Or Offensive Content: N/A

B5 Documentation Of Artifacts: N/A

B6 Statistics For Data: Yes

B6 Elaboration: Section 4

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: Section 5

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Appendix B

C3 Descriptive Statistics: Yes

C3 Elaboration: Section 6

C4 Parameters For Packages: Yes

C4 Elaboration: Section 5

D Human Subjects Including Annotators: No

D1 Instructions Given To Participants: N/A

D2 Recruitment And Payment: N/A

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: Yes

E1 Information About Use Of Ai Assistants: Yes

E1 Elaboration: Section 4

Author Submission Checklist: yes

Submission Number: 1426