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AgentGuard: An Attribute-Based Access Control Framework for Tool-Use LLM-Based Agent

arXiv Security Archived May 28, 2026 ✓ Full text saved

arXiv:2605.28071v1 Announce Type: new Abstract: LLM-based agents have recently attracted significant attention due to their ability to autonomously invoke relevant tools to accomplish complex tasks. However, recent studies have shown that these agents face severe security risks, which may lead to privacy leakage, financial loss, or even full system compromise. In this paper, we present AgentGuard, an attribute-based access control framework for tool-use LLM-based agents. AgentGuard adopts a clie

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    Computer Science > Cryptography and Security [Submitted on 27 May 2026] AgentGuard: An Attribute-Based Access Control Framework for Tool-Use LLM-Based Agent Jiaqi Luo, Songyang Peng, Jiarun Dai, Zhile Chen, Zhuoxiang Shen, Geng Hong, Xudong Pan, Yuan Zhang, Min Yang LLM-based agents have recently attracted significant attention due to their ability to autonomously invoke relevant tools to accomplish complex tasks. However, recent studies have shown that these agents face severe security risks, which may lead to privacy leakage, financial loss, or even full system compromise. In this paper, we present AgentGuard, an attribute-based access control framework for tool-use LLM-based agents. AgentGuard adopts a client-server architecture. On the client side, AgentGuard provides lightweight integration for agents implemented in different programming languages and architectures. It requires only minor code modifications (e.g., around 10 lines) without changing the underlying agent execution logic. On the server side, AgentGuard provides three complementary inspection mechanisms to cover both single-tool and cross-tool security risks in agent execution. In addition, it offers a visualized front-end interface for security policy specification and runtime auditing. Currently, AgentGuard is publicly accessible at this https URL. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.28071 [cs.CR]   (or arXiv:2605.28071v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.28071 Focus to learn more Submission history From: Songyang Peng [view email] [v1] Wed, 27 May 2026 07:28:39 UTC (1,170 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    May 28, 2026
    Archived
    May 28, 2026
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