Permissioned LLMs: Enforcing Access Control in Large Language Models

• URL: http://arxiv.org/abs/2505.22860v1
• Date: Wed, 28 May 2025 20:47:02 GMT
• Title: Permissioned LLMs: Enforcing Access Control in Large Language Models
• Authors: Bargav Jayaraman, Virendra J. Marathe, Hamid Mozaffari, William F. Shen, Krishnaram Kenthapadi,
• Abstract summary: Permissioned LLMs (PerLM) superimpose organizational data access control structures on query responses.<n>PermLLM mechanisms build on Efficient Fine-Tuning to achieve the desired access control.<n>We demonstrate the efficacy of our PermLLM mechanisms through extensive experiments on four public datasets.
• Score: 14.935672762016972
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In enterprise settings, organizational data is segregated, siloed and carefully protected by elaborate access control frameworks. These access control structures can completely break down if an LLM fine-tuned on the siloed data serves requests, for downstream tasks, from individuals with disparate access privileges. We propose Permissioned LLMs (PermLLM), a new class of LLMs that superimpose the organizational data access control structures on query responses they generate. We formalize abstractions underpinning the means to determine whether access control enforcement happens correctly over LLM query responses. Our formalism introduces the notion of a relevant response that can be used to prove whether a PermLLM mechanism has been implemented correctly. We also introduce a novel metric, called access advantage, to empirically evaluate the efficacy of a PermLLM mechanism. We introduce three novel PermLLM mechanisms that build on Parameter Efficient Fine-Tuning to achieve the desired access control. We furthermore present two instantiations of access advantage--(i) Domain Distinguishability Index (DDI) based on Membership Inference Attacks, and (ii) Utility Gap Index (UGI) based on LLM utility evaluation. We demonstrate the efficacy of our PermLLM mechanisms through extensive experiments on four public datasets (GPQA, RCV1, SimpleQA, and WMDP), in addition to evaluating the validity of DDI and UGI metrics themselves for quantifying access control in LLMs.

Related papers

• Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs [19.265893691825234]
  Next generation Radio Access Networks (RANs) introduce programmability, intelligence, and near real-time control through intelligent controllers.<n>This paper presents a comprehensive survey highlighting opportunities, challenges, and research gaps for Large Language Models (LLMs)-assisted explainable (XAI) intrusion detection (IDS) for secure future RAN environments.
  arXiv  Detail & Related papers  (2025-07-27T22:16:09Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• FlowAgent: Achieving Compliance and Flexibility for Workflow Agents [31.088578094151178]
  FlowAgent is a novel agent framework designed to maintain both compliance and flexibility.<n>Building on PDL, we develop a comprehensive framework that empowers LLMs to manage OOW queries effectively.<n>We present a new evaluation methodology to rigorously assess an LLM agent's ability to handle OOW scenarios.
  arXiv  Detail & Related papers  (2025-02-20T07:59:31Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making [85.24399869971236]
  We aim to evaluate Large Language Models (LLMs) for embodied decision making.<n>Existing evaluations tend to rely solely on a final success rate.<n>We propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks.
  arXiv  Detail & Related papers  (2024-10-09T17:59:00Z)
• Control Large Language Models via Divide and Conquer [94.48784966256463]
  This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG) We evaluate the performance of LLMs on satisfying lexical constraints with prompt-based control, as well as their efficacy in downstream applications.
  arXiv  Detail & Related papers  (2024-10-06T21:20:06Z)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.