面向角色的语言模型：用于组织中的安全且上下文化的访问控制
Role-Aware Language Models for Secure and Contextualized Access Control in Organizations
AI总结：
本文提出面向角色的语言模型，通过三种策略实现基于组织角色的安全访问控制，并通过实验验证其在不同组织结构下的性能和鲁棒性。
摘要：
随着大型语言模型（LLMs）越来越多地应用于企业环境，根据用户角色控制模型行为已成为一项基本要求。现有的安全方法通常假设统一的访问权限，专注于防止有害或有毒的输出，而没有解决角色特定的访问限制。在本工作中，我们研究LLMs是否可以进行微调以生成反映不同组织角色关联的访问权限的响应。我们探索了三种建模策略：基于BERT的分类器、基于LLM的分类器以及基于角色的生成。为了评估这些方法，我们构建了两个互补的数据集。第一个是通过聚类和角色标注从现有指令微调语料库中改编而来，而第二个是合成生成的，以反映现实中的角色敏感企业场景。我们评估模型在不同组织结构下的性能，并分析其对提示注入、角色不匹配和劫持尝试的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
