@inproceedings{jing-etal-2025-mcip,
title = "{MCIP}: Protecting {MCP} Safety via Model Contextual Integrity Protocol",
author = "Jing, Huihao and
Li, Haoran and
Hu, Wenbin and
Hu, Qi and
Heli, Xu and
Chu, Tianshu and
Hu, Peizhao and
Song, Yangqiu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.62/",
pages = "1177--1194",
ISBN = "979-8-89176-332-6",
abstract = "As Model Context Protocol (MCP) introduces an easy-to-use ecosystem for users and developers, it also brings underexplored safety risks. Its decentralized architecture, which separates clients and servers, poses unique challenges for systematic safety analysis. This paper proposes a novel framework to enhance MCP safety. Guided by the MAESTRO framework, we first analyze the missing safety mechanisms in MCP, and based on this analysis, we propose the Model Contextual Integrity Protocol (MCIP), a refined version of MCP that addresses these gaps. Next, we develop a fine-grained taxonomy that captures a diverse range of unsafe behaviors observed in MCP scenarios. Building on this taxonomy, we develop benchmark and training data that support the evaluation and improvement of LLMs' capabilities in identifying safety risks within MCP interactions. Leveraging the proposed benchmark and training data, we conduct extensive experiments on state-of-the-art LLMs. The results highlight LLMs' vulnerabilities in MCP interactions and demonstrate that our approach substantially improves their safety performance."
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<abstract>As Model Context Protocol (MCP) introduces an easy-to-use ecosystem for users and developers, it also brings underexplored safety risks. Its decentralized architecture, which separates clients and servers, poses unique challenges for systematic safety analysis. This paper proposes a novel framework to enhance MCP safety. Guided by the MAESTRO framework, we first analyze the missing safety mechanisms in MCP, and based on this analysis, we propose the Model Contextual Integrity Protocol (MCIP), a refined version of MCP that addresses these gaps. Next, we develop a fine-grained taxonomy that captures a diverse range of unsafe behaviors observed in MCP scenarios. Building on this taxonomy, we develop benchmark and training data that support the evaluation and improvement of LLMs’ capabilities in identifying safety risks within MCP interactions. Leveraging the proposed benchmark and training data, we conduct extensive experiments on state-of-the-art LLMs. The results highlight LLMs’ vulnerabilities in MCP interactions and demonstrate that our approach substantially improves their safety performance.</abstract>
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%0 Conference Proceedings
%T MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol
%A Jing, Huihao
%A Li, Haoran
%A Hu, Wenbin
%A Hu, Qi
%A Heli, Xu
%A Chu, Tianshu
%A Hu, Peizhao
%A Song, Yangqiu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F jing-etal-2025-mcip
%X As Model Context Protocol (MCP) introduces an easy-to-use ecosystem for users and developers, it also brings underexplored safety risks. Its decentralized architecture, which separates clients and servers, poses unique challenges for systematic safety analysis. This paper proposes a novel framework to enhance MCP safety. Guided by the MAESTRO framework, we first analyze the missing safety mechanisms in MCP, and based on this analysis, we propose the Model Contextual Integrity Protocol (MCIP), a refined version of MCP that addresses these gaps. Next, we develop a fine-grained taxonomy that captures a diverse range of unsafe behaviors observed in MCP scenarios. Building on this taxonomy, we develop benchmark and training data that support the evaluation and improvement of LLMs’ capabilities in identifying safety risks within MCP interactions. Leveraging the proposed benchmark and training data, we conduct extensive experiments on state-of-the-art LLMs. The results highlight LLMs’ vulnerabilities in MCP interactions and demonstrate that our approach substantially improves their safety performance.
%U https://aclanthology.org/2025.emnlp-main.62/
%P 1177-1194
Markdown (Informal)
[MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol](https://aclanthology.org/2025.emnlp-main.62/) (Jing et al., EMNLP 2025)
ACL
- Huihao Jing, Haoran Li, Wenbin Hu, Qi Hu, Xu Heli, Tianshu Chu, Peizhao Hu, and Yangqiu Song. 2025. MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1177–1194, Suzhou, China. Association for Computational Linguistics.