This GitHub repository offers intentionally vulnerable Model Context Protocol (MCP) servers for security research and training. Each server includes detailed instructions for running it and demonstrating various vulnerabilities, such as code execution and data exposure. Users should only operate these servers in a controlled lab environment.

This article outlines a series of ten hands-on labs focused on Model Context Protocol (MCP) vulnerabilities, each based on real-world exploits. It provides both vulnerable and secure implementations, allowing users to reproduce attacks and understand mitigation strategies in a practical setting. Comprehensive instructions and proof captures accompany each challenge.

The article examines the security risks associated with the Model Context Protocol (MCP), which enables dynamic interactions between AI systems and external applications. It highlights vulnerabilities such as content injection, supply-chain attacks, and the potential for agents to unintentionally cause harm. The authors propose practical controls and outline gaps in current AI governance frameworks.

Cybersecurity researchers found three serious vulnerabilities in Anthropic's mcp-server-git, allowing attackers to manipulate AI assistants without needing system access. The flaws, affecting all versions before December 2025, enable code execution, file deletion, and potential exposure of sensitive data. Users are urged to update their systems immediately.

SecureMCP is a security auditing tool designed to identify vulnerabilities in applications utilizing the Model Context Protocol (MCP). It offers comprehensive scanning capabilities for threats such as OAuth token leakage and prompt injection vulnerabilities, providing detailed reports with remediation suggestions. The tool is suitable for AI developers, security teams, and auditors looking to enhance application security.

MCP (Model Context Protocol) facilitates connections between AI agents and tools but lacks inherent security, exposing users to risks like command injection, tool poisoning, and silent redefinitions. Recommendations for developers and users emphasize the necessity of input validation, tool integrity, and cautious server connections to mitigate these vulnerabilities. Until MCP incorporates security as a priority, tools like ScanMCP.com may offer essential oversight.

MCP-Shield is a security tool that scans installed Model Context Protocol (MCP) servers for vulnerabilities, including tool poisoning attacks and sensitive file access attempts. It provides options for customized scanning and integrates an AI analysis feature using an Anthropic Claude API key for enhanced vulnerability detection. The tool highlights serious risks associated with hidden instructions and potential data exfiltration in server tools.

The article discusses the development of a new security layer called MCP, which aims to enhance the protection of applications and systems by addressing common vulnerabilities and providing more robust security protocols. It highlights the key features and benefits of MCP, alongside the challenges faced during its implementation.

The article provides an in-depth explanation of the Model Context Protocol (MCP), highlighting its role in enhancing the capabilities of large language models (LLMs) through improved context provision. It also conducts a detailed threat model analysis, identifying key security vulnerabilities and potential attack vectors associated with MCP's functionalities, such as sampling and composability.

The Model Context Protocol (MCP) is an emerging standard for connecting large language models to external tools, but it presents significant security vulnerabilities such as prompt injection and orchestration exploits. These vulnerabilities can lead to data exfiltration and system compromise, highlighting the need for robust security precautions and detection methods. The article discusses various attack techniques and provides examples of potential exploits along with recommended defenses.