AgentGuard: An Attribute-Based Access Control Framework for Tool-Use LLM-Based Agent
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
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 https://github.com/WhitzardAgent/AgentGuard.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 12 of its 21 references that are in the map. First posted on arXiv 27 May 2026.
Builds on
12 of 21 references.
Closest ancestors · 7
Same direction, newest first
- 2026Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework17 Jan
- 2025AgentBound: Securing Execution Boundaries of AI Agents24 Oct
- 2025Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction16 May
- 2025Defeating Prompt Injections by Design24 Mar
- 2025Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents27 Feb
- 2024BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents5 Jun
- 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems8 Mar
Other references · 5
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
- 2024SecAlign: Defending Against Prompt Injection with Preference Optimizationcited by 179
- 2025DataSentinel: A Game-Theoretic Detection of Prompt Injection Attackscited by 124
- 2025ACE: A Security Architecture for LLM-Integrated App Systemscited by 52
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2025AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestration
- 2026Agentao: A Policy-Governed Runtime Harness for Embeddable Tool-Using LLM Agents
- 2026OpenClaw PRISM: A Zero-Fork, Defense-in-Depth Runtime Security Layer for Tool-Augmented LLM Agents
- 2025MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agents
- 2025AgentSentinel: An End-to-End and Real-Time Security Defense Framework for Computer-Use Agents
- 2026Agent Audit: A Security Analysis System for LLM Agent Applications
- 2026ToolGuardian: Declarative Security for AI Agent-Tool Interactions
- 2026AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization