MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System

• URL: http://arxiv.org/abs/2504.12757v1
• Date: Thu, 17 Apr 2025 08:49:10 GMT
• Title: MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System
• Authors: Sonu Kumar, Anubhav Girdhar, Ritesh Patil, Divyansh Tripathi,
• Abstract summary: We present MCP Guardian, a framework that strengthens MCP-based communication with authentication, rate-limiting, logging, tracing, and Web Application Firewall (WAF) scanning.<n>Our approach fosters secure, scalable data access for AI assistants, underscoring the importance of a defense-in-depth approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Agentic AI gain mainstream adoption, the industry invests heavily in model capabilities, achieving rapid leaps in reasoning and quality. However, these systems remain largely confined to data silos, and each new integration requires custom logic that is difficult to scale. The Model Context Protocol (MCP) addresses this challenge by defining a universal, open standard for securely connecting AI-based applications (MCP clients) to data sources (MCP servers). However, the flexibility of the MCP introduces new risks, including malicious tool servers and compromised data integrity. We present MCP Guardian, a framework that strengthens MCP-based communication with authentication, rate-limiting, logging, tracing, and Web Application Firewall (WAF) scanning. Through real-world scenarios and empirical testing, we demonstrate how MCP Guardian effectively mitigates attacks and ensures robust oversight with minimal overheads. Our approach fosters secure, scalable data access for AI assistants, underscoring the importance of a defense-in-depth approach that enables safer and more transparent innovation in AI-driven environments.

Related papers

• Simplified and Secure MCP Gateways for Enterprise AI Integration [0.0]
  This paper introduces the MCP Gateway to simplify self-hosted MCP server integration. The proposed architecture integrates security principles, authentication, intrusion detection, and secure tunneling.
  arXiv  Detail & Related papers  (2025-04-28T17:17:42Z)
• Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies [0.0]
  The Model Context Protocol (MCP) provides a standardized framework for artificial intelligence (AI) systems to interact with external data sources and tools in real-time.<n>This paper builds upon foundational research into MCP architecture and preliminary security assessments to deliver enterprise-grade mitigation frameworks.
  arXiv  Detail & Related papers  (2025-04-11T15:25:58Z)
• MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits [0.0]
  The Model Context Protocol (MCP) is an open protocol that standardizes API calls to large language models (LLMs), data sources, and agentic tools.<n>We show that the current MCP design carries a wide range of security risks for end users.<n>We introduce a safety auditing tool, MCPSafetyScanner, to assess the security of an arbitrary MCP server.
  arXiv  Detail & Related papers  (2025-04-02T21:46:02Z)
• How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
  Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance.<n>We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
  arXiv  Detail & Related papers  (2025-03-20T19:52:30Z)
• SmartLLM: Smart Contract Auditing using Custom Generative AI [0.0]
  This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG)<n>By integrating domain-specific knowledge from ERC standards, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither.<n> Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities.
  arXiv  Detail & Related papers  (2025-02-17T06:22:05Z)
• Global Challenge for Safe and Secure LLMs Track 1 [57.08717321907755]
  The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
  arXiv  Detail & Related papers  (2024-11-21T08:20:31Z)
• The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries [14.232901861974819]
  Privacy-preserving machine learning (PPML) is an innovative approach that allows for secure data analysis while safeguarding sensitive information. We introduce efficient protocol for secure linear function evaluation. We extend the protocol to handle linear and non-linear layers, ensuring compatibility with a wide range of machine-learning models.
  arXiv  Detail & Related papers  (2024-11-14T08:55:14Z)
• Towards Secure and Private AI: A Framework for Decentralized Inference [14.526663289437584]
  Large multimodal foundational models present challenges in scalability, reliability, and potential misuse.<n>Decentralized systems offer a solution by distributing workload and mitigating central points of failure.<n>We address these challenges with a comprehensive framework designed for responsible AI development.
  arXiv  Detail & Related papers  (2024-07-28T05:09:17Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data. The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning [48.667697255912614]
  Mean-field reinforcement learning addresses the policy of a representative agent interacting with the infinite population of identical agents. We propose Safe-M$3$-UCRL, the first model-based mean-field reinforcement learning algorithm that attains safe policies even in the case of unknown transitions. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.
  arXiv  Detail & Related papers  (2023-06-29T15:57:07Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.