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Cited by 9Access Control and TrustSecurity and Verification in ComputingAdversarial Robustness in Machine Learning

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AgentBound: Securing Execution Boundaries of AI Agents

Open access
Bühler, Christoph, Biagiola, Matteo, Di Grazia, Luca, Salvaneschi, Guido·2026·Proceedings of the ACM on Software Engineering
DOI 10.1145/3808103
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Abstract

Large Language Models (LLMs) have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model Context Protocol (MCP) has become the de facto standard for connecting agents with such resources, but security has lagged behind: thousands of MCP servers execute with unrestricted access to host systems, creating a broad attack surface. In this paper, we introduce AgentBound, the first access control framework for MCP servers. AgentBound combines a declarative policy mechanism, inspired by the Android permission model, with a policy enforcement engine that contains malicious behavior without requiring MCP server modifications. We build a dataset containing the 296 most popular MCP servers, and show that access control policies can be generated automatically from source code with 80.9% accuracy. We also show that AgentBound blocks the majority of security threats in several malicious MCP servers, and that the policy enforcement engine introduces negligible overhead. Our contributions provide developers and project managers with a foundation for securing MCP servers while maintaining productivity, enabling researchers and tool builders to explore new directions for declarative access control and MCP security.

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Metadata

Authors
Bühler, C., Biagiola, M., Di Grazia, L., & Salvaneschi, G.
Affiliations
  • University of St.Gallen (CH)
  • University of St. Gallen, St. Gallen, Switzerland
ORCID
Venue
Proceedings of the ACM on Software Engineering
Publisher
Association for Computing Machinery (ACM)
Published
2026-06-30
Volume
3
Issue
FSE
Pages
2141-2164
ISSN
2994-970X (ISSN-L)
DOI
10.1145/3808103
OpenAlex
W4417325963
Cited by
9
References
2
Citations by year
2026: 9
FWCI
63.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the ACM on software engineering. (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • Swiss National Science Foundation (10001777)
  • European Union’s Horizon research and innovation programme (101189899)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Access Control and Trust
  • Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
AI agent security, Model Context Protocol, access control, large language models, AI agents, malicious activity detection, execution boundary

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CoChat. (2026). Citation check: AgentBound: Securing Execution Boundaries of AI Agents. https://app.cochat.ai/papers/10.1145/3808103
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Bühler, C., Biagiola, M., Di Grazia, L., & Salvaneschi, G. (2026). AgentBound: Securing Execution Boundaries of AI Agents. Proceedings of the ACM on Software Engineering. https://doi.org/10.1145/3808103