MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents

Fuente: arXiv
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Main Authors: Zhou, Zhenhong, Zhang, Yuanhe, Cai, Hongwei, Aloqaily, Moayad, Bouachir, Ouns, Pang, Linsey, Mehrotra, Prakhar, Wang, Kun, Wen, Qingsong
Format: Preprint
Published: 2026
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author Zhou, Zhenhong
Zhang, Yuanhe
Cai, Hongwei
Aloqaily, Moayad
Bouachir, Ouns
Pang, Linsey
Mehrotra, Prakhar
Wang, Kun
Wen, Qingsong
author_facet Zhou, Zhenhong
Zhang, Yuanhe
Cai, Hongwei
Aloqaily, Moayad
Bouachir, Ouns
Pang, Linsey
Mehrotra, Prakhar
Wang, Kun
Wen, Qingsong
contents The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers. This openness introduces a security misalignment: agents implicitly trust tools exposed by potentially untrusted MCP servers. However, despite its excellent utility, existing agents typically offer limited validation for third-party MCP servers. As a result, agents remain vulnerable to MCP-based attacks that exploit the misalignment between agents and servers throughout the tool invocation lifecycle. In this paper, we propose MCPShield as a plug-in security cognition layer that mitigates this misalignment and ensures agent security when invoking MCP-based tools. Drawing inspiration from human experience-driven tool validation, MCPShield assists agent forms security cognition with metadata-guided probing before invocation. Our method constrains execution within controlled boundaries while cognizing runtime events, and subsequently updates security cognition by reasoning over historical traces after invocation, building on human post-use reflection on tool behavior. Experiments demonstrate that MCPShield exhibits strong generalization in defending against six novel MCP-based attack scenarios across six widely used agentic LLMs, while avoiding false positives on benign servers and incurring low deployment overhead. Overall, our work provides a practical and robust security safeguard for MCP-based tool invocation in open agent ecosystems.
format Preprint
id arxiv_https___arxiv_org_abs_2602_14281
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
Zhou, Zhenhong
Zhang, Yuanhe
Cai, Hongwei
Aloqaily, Moayad
Bouachir, Ouns
Pang, Linsey
Mehrotra, Prakhar
Wang, Kun
Wen, Qingsong
Cryptography and Security
Computation and Language
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers. This openness introduces a security misalignment: agents implicitly trust tools exposed by potentially untrusted MCP servers. However, despite its excellent utility, existing agents typically offer limited validation for third-party MCP servers. As a result, agents remain vulnerable to MCP-based attacks that exploit the misalignment between agents and servers throughout the tool invocation lifecycle. In this paper, we propose MCPShield as a plug-in security cognition layer that mitigates this misalignment and ensures agent security when invoking MCP-based tools. Drawing inspiration from human experience-driven tool validation, MCPShield assists agent forms security cognition with metadata-guided probing before invocation. Our method constrains execution within controlled boundaries while cognizing runtime events, and subsequently updates security cognition by reasoning over historical traces after invocation, building on human post-use reflection on tool behavior. Experiments demonstrate that MCPShield exhibits strong generalization in defending against six novel MCP-based attack scenarios across six widely used agentic LLMs, while avoiding false positives on benign servers and incurring low deployment overhead. Overall, our work provides a practical and robust security safeguard for MCP-based tool invocation in open agent ecosystems.
title MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
topic Cryptography and Security
Computation and Language
url https://arxiv.org/abs/2602.14281