Authorization of Knowledge-base Agents in an Intent-based Management Function

• URL: http://arxiv.org/abs/2510.19324v1
• Date: Wed, 22 Oct 2025 07:38:01 GMT
• Title: Authorization of Knowledge-base Agents in an Intent-based Management Function
• Authors: Loay Abdelrazek, Leyli Karaçay, Marin Orlic,
• Abstract summary: We propose an enhanced authorization framework that integrates roles and functional attributes with agent roles.<n>Our approach ensures that agents are granted only the minimal necessary privileges towards knowledge graphs.
• Score: 0.07646713951724012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As networks move toward the next-generation 6G, Intent-based Management (IbM) systems are increasingly adopted to simplify and automate network management by translating high-level intents into low-level configurations. Within these systems, agents play a critical role in monitoring current state of the network, gathering data, and enforcing actions across the network to fulfill the intent. However, ensuring secure and fine-grained authorization of agents remains a significant challenge, especially in dynamic and multi-tenant environments. Traditional models such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC) and Relational-Based Access Control (RelBAC) often lack the flexibility to accommodate the evolving context and granularity required by intentbased operations. In this paper, we propose an enhanced authorization framework that integrates contextual and functional attributes with agent roles to achieve dynamic, policy-driven access control. By analyzing agent functionalities, our approach ensures that agents are granted only the minimal necessary privileges towards knowledge graphs.

Related papers

• Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework [16.14469140816631]
  Large Language Model (LLM)-based agent systems are increasingly deployed for complex real-world tasks.<n>This paper aims to understand and mitigate such attacks through the lens of privilege escalation.<n>We propose SEAgent, a mandatory access control framework built upon attribute-based access control (ABAC)<n>Our evaluations show that SEAgent effectively blocks various privilege escalation while maintaining a low false positive rate and negligible system overhead.
  arXiv  Detail & Related papers  (2026-01-17T03:22:56Z)
• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• Using the NANDA Index Architecture in Practice: An Enterprise Perspective [9.707223291705601]
  The proliferation of autonomous AI agents represents a paradigmatic shift from traditional web architectures toward collaborative intelligent systems.<n>This paper presents a comprehensive framework addressing the fundamental infrastructure requirements for secure, trustworthy, and interoperable AI agent ecosystems.
  arXiv  Detail & Related papers  (2025-08-05T05:27:27Z)
• AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction [70.60422261117816]
  We propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure.<n>Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection, which enables each agent to selectively access relevant information from any previous step.
  arXiv  Detail & Related papers  (2025-06-21T18:34:43Z)
• A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control [7.228060525494563]
  This paper posits the imperative for a novel Agentic AI IAM framework.<n>We propose a comprehensive framework built upon rich, verifiable Agent Identities (IDs)<n>We also explore how Zero-Knowledge Proofs (ZKPs) enable privacy-preserving attribute disclosure and verifiable policy compliance.
  arXiv  Detail & Related papers  (2025-05-25T20:21:55Z)
• AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems [22.291969093748005]
  AgentNet is a decentralized, Retrieval-Augmented Generation (RAG)-based framework for multi-agent systems.<n>Unlike prior approaches with static roles or centralized control, AgentNet allows agents to adjust connectivity and route tasks based on local expertise and context.<n>Experiments show that AgentNet achieves higher task accuracy than both single-agent and centralized multi-agent baselines.
  arXiv  Detail & Related papers  (2025-04-01T09:45:25Z)
• Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach [0.8287206589886879]
  This paper introduces a Hierarchical Graph Reinforcement Learning framework that governs such systems through targeted interventions in the network structure. Under low social learning, the HGRL manager preserves cooperation, forming robust core-periphery networks dominated by cooperators. In contrast, high social learning defection accelerates, leading to sparser, chain-like networks.
  arXiv  Detail & Related papers  (2024-10-30T18:59:02Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Decentralized Control with Graph Neural Networks [147.84766857793247]
  We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
  arXiv  Detail & Related papers  (2020-12-29T18:59:14Z)

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.