Aguara: Security scanner for AI agents and MCP servers

Aguara is an open-source security scanner for AI agent skills and MCP servers, using static analysis without LLMs for efficient detection.

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Aguara: Security Scanner for AI Agents and MCP Servers

Aguara is an essential open-source security scanner designed for AI developers working with AI agents and MCP servers. It provides a robust method for identifying potential vulnerabilities within your AI agent skills and MCP server configurations. By leveraging static analysis techniques, Aguara offers an efficient and reliable way to enhance the security posture of your AI projects without relying on large language models for its core scanning process.

What Aguara Does

Aguara performs a comprehensive security audit of your AI agent skills and MCP server configurations. It analyzes your code and configuration files to detect common security flaws and misconfigurations that could be exploited. The tool focuses on identifying potential risks before they can be exploited in production environments, allowing developers to proactively address security concerns. Its static analysis approach means it can be integrated early in the development lifecycle.

Key Features

Static Analysis: Utilizes static analysis for efficient and LLM-independent vulnerability detection.
AI Agent Skill Scanning: Specifically designed to identify security issues within AI agent skills.
MCP Server Security: Scans MCP server configurations for potential weaknesses.
Open-Source: Freely available and community-driven for transparency and continuous improvement.
Developer-Focused: Built with the needs of AI builders and system administrators in mind.

Who is Aguara For?

Aguara is primarily intended for AI developers , ML engineers , and DevOps professionals responsible for building, deploying, and managing AI agents and MCP servers. If you are developing custom AI agent skills or managing MCP server infrastructure, Aguara provides a critical tool to ensure the security and integrity of your applications. It's particularly valuable for projects where security is a paramount concern and for those seeking efficient, non-LLM-based security solutions.