Enterprise AI Must Enforce Participant-Aware Access Control

• URL: http://arxiv.org/abs/2509.14608v1
• Date: Thu, 18 Sep 2025 04:30:49 GMT
• Title: Enterprise AI Must Enforce Participant-Aware Access Control
• Authors: Shashank Shreedhar Bhatt, Tanmay Rajore, Khushboo Aggarwal, Ganesh Ananthanarayanan, Ranveer Chandra, Nishanth Chandran, Suyash Choudhury, Divya Gupta, Emre Kiciman, Sumit Kumar Pandey, Srinath Setty, Rahul Sharma, Teijia Zhao,
• Abstract summary: Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data.<n>We show that adversaries can exploit current fine-tuning and RAG architectures to leak sensitive information by leveraging the lack of access control enforcement.<n>We introduce a framework centered on the principle that any content used in training, retrieval, or generation by an LLM is explicitly authorized for emphall users involved in the interaction.
• Score: 9.68210477539956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. While fine-tuning enhances performance by internalizing domain knowledge, it also introduces a critical security risk: leakage of confidential training data to unauthorized users. These risks are exacerbated when LLMs are combined with Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch contextual documents at inference time. We demonstrate data exfiltration attacks on AI assistants where adversaries can exploit current fine-tuning and RAG architectures to leak sensitive information by leveraging the lack of access control enforcement. We show that existing defenses, including prompt sanitization, output filtering, system isolation, and training-level privacy mechanisms, are fundamentally probabilistic and fail to offer robust protection against such attacks. We take the position that only a deterministic and rigorous enforcement of fine-grained access control during both fine-tuning and RAG-based inference can reliably prevent the leakage of sensitive data to unauthorized recipients. We introduce a framework centered on the principle that any content used in training, retrieval, or generation by an LLM is explicitly authorized for \emph{all users involved in the interaction}. Our approach offers a simple yet powerful paradigm shift for building secure multi-user LLM systems that are grounded in classical access control but adapted to the unique challenges of modern AI workflows. Our solution has been deployed in Microsoft Copilot Tuning, a product offering that enables organizations to fine-tune models using their own enterprise-specific data.

Related papers

• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
  Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
  arXiv  Detail & Related papers  (2026-01-12T21:31:38Z)
• Securing the Model Context Protocol (MCP): Risks, Controls, and Governance [1.4072883206858737]
  We focus on three types of adversaries that take advantage of MCP s flexibility.<n>Based on early incidents and proof-of-concept attacks, we describe how MCP can increase the attack surface.<n>We propose a set of practical controls, including per-user authentication with scoped authorization.
  arXiv  Detail & Related papers  (2025-11-25T23:24:26Z)
• MAIF: Enforcing AI Trust and Provenance with an Artifact-Centric Agentic Paradigm [0.5495755145898128]
  Current AI systems operate on opaque data structures that lack the audit trails, provenance tracking, or explainability required by emerging regulations like the EU AI Act.<n>We propose an artifact-centric AI agent paradigm where behavior is driven by persistent, verifiable data artifacts rather than ephemeral tasks.<n>Production-ready implementation demonstrates ultra-high-speed streaming (2,720.7 MB/s), optimized video processing (1,342 MB/s), and enterprise-grade security.
  arXiv  Detail & Related papers  (2025-11-19T04:10:32Z)
• PrivacyPAD: A Reinforcement Learning Framework for Dynamic Privacy-Aware Delegation [33.37227619820212]
  We introduce a novel reinforcement learning framework called PrivacyPAD to solve this problem.<n>Our framework trains an agent to dynamically route text chunks, learning a policy that optimally balances the trade-off between privacy leakage and task performance.<n>Our framework achieves a new state-of-the-art on the privacy-utility frontier.
  arXiv  Detail & Related papers  (2025-10-16T19:38:36Z)
• Searching for Privacy Risks in LLM Agents via Simulation [60.22650655805939]
  We present a search-based framework that alternates between improving attacker and defender instructions by simulating privacy-critical agent interactions.<n>We find that attack strategies escalate from simple direct requests to sophisticated multi-turn tactics such as impersonation and consent forgery.<n>The discovered attacks and defenses transfer across diverse scenarios and backbone models, demonstrating strong practical utility for building privacy-aware agents.
  arXiv  Detail & Related papers  (2025-08-14T17:49:09Z)
• Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security [63.41350337821108]
  We propose Secure Tug-of-War (SecTOW) to enhance the security of multimodal large language models (MLLMs)<n>SecTOW consists of two modules: a defender and an auxiliary attacker, both trained iteratively using reinforcement learning (GRPO)<n>We show that SecTOW significantly improves security while preserving general performance.
  arXiv  Detail & Related papers  (2025-07-29T17:39:48Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [33.40201949055383]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• Defeating Prompt Injections by Design [79.00910871948787]
  CaMeL is a robust defense that creates a protective system layer around the Large Language Models.<n>To operate, CaMeL explicitly extracts the control and data flows from the (trusted) query.<n>To further improve security, CaMeL uses a notion of a capability to prevent the exfiltration of private data over unauthorized data flows.
  arXiv  Detail & Related papers  (2025-03-24T15:54:10Z)
• How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
  Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance.<n>We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
  arXiv  Detail & Related papers  (2025-03-20T19:52:30Z)
• Can LLMs Hack Enterprise Networks? Autonomous Assumed Breach Penetration-Testing Active Directory Networks [1.3124479769761592]
  We introduce a novel prototype designed to employ Large Language Model (LLM)-driven autonomous systems.<n>Our system represents the first demonstration of a fully autonomous, LLM-driven framework capable of compromising accounts.<n>We find that the associated costs are competitive with, and often significantly lower than, those incurred by professional human pen-testers.
  arXiv  Detail & Related papers  (2025-02-06T17:12:43Z)
• Trustworthy AI: Securing Sensitive Data in Large Language Models [0.0]
  Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information.
  arXiv  Detail & Related papers  (2024-09-26T19:02:33Z)
• "Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models [74.05368440735468]
  Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) In this paper, we demonstrate a security threat where adversaries can exploit the openness of these knowledge bases.
  arXiv  Detail & Related papers  (2024-06-26T05:36:23Z)
• Secure Instruction and Data-Level Information Flow Tracking Model for RISC-V [0.0]
  Unauthorized access, fault injection, and privacy invasion are potential threats from untrusted actors. We propose an integrated Information Flow Tracking (IFT) technique to enable runtime security to protect system integrity. This study proposes a multi-level IFT model that integrates a hardware-based IFT technique with a gate-level-based IFT (GLIFT) technique.
  arXiv  Detail & Related papers  (2023-11-17T02:04:07Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)

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.