XLUXX — AI Trust Intelligence

What We Do

XLUXX operates the most comprehensive trust scoring system for MCP (Model Context Protocol) servers in existence. MCP is the open protocol that lets AI assistants like Claude, GPT-4, and Gemini connect to external tools — databases, APIs, code executors, file systems, and thousands of third-party services. As of 2026, there are over 15,000 published MCP servers, and the number is growing by hundreds each week.

The problem is that not all of them are safe. Some leak credentials. Some contain prompt injection vulnerabilities that can hijack an AI agent’s behavior mid-task. Others silently exfiltrate data to unknown endpoints. Most developers and enterprises have no systematic way to evaluate which MCP servers they can trust before deploying them into production AI pipelines.

XLUXX solves this. We continuously crawl the global MCP registry, test every server we find, score it across multiple trust dimensions, and publish those scores in real time via our public API. Our catalog currently covers more than 15,000 MCP servers and over 7,200 individual tools — and we run security audits daily.

How It Works

Our trust scoring engine operates in several layers. First, we crawl official and community MCP registries every six hours to discover newly published servers. Each server is fingerprinted: we extract its declared capabilities, tool descriptions, authentication requirements, and transport protocols.

Second, we run live behavioral tests. We connect to each server using the MCP protocol and probe its actual behavior — does it respond correctly? Does it time out? Does it attempt to make outbound connections to unexpected destinations? Do its tool responses match what its documentation claims?

Third, we apply a multi-factor trust score. Factors include uptime and reliability (weighted heavily for production use cases), presence of known CVEs or vulnerability patterns, PII detection in outputs, behavioral consistency across repeated queries, and registry provenance — whether the server comes from a verified organization or an anonymous source.

Finally, we publish all of this via a public REST API. Any developer, security platform, or enterprise toolchain can query a server’s trust score in milliseconds. Our API also supports bulk queries, historical score timelines, and webhook alerts when a previously trusted server degrades or is flagged for a new vulnerability.

Who It’s For

Developers building AI pipelines need to know which MCP servers are reliable before they embed them in production workflows. A server that returns garbage data or goes offline without warning can silently corrupt an AI agent’s reasoning. XLUXX gives developers a trust baseline they can query programmatically before including any server in their stack.

Enterprises deploying AI agents face regulatory and reputational risk if their AI systems interact with untrusted or compromised tools. Legal, compliance, and security teams increasingly require an audit trail for every external tool an AI agent touches. XLUXX provides the provenance data, score history, and continuous monitoring needed to satisfy those requirements.

Security teams auditing AI usage can use XLUXX to map their organization’s MCP server exposure, identify servers with declining trust scores, and receive alerts when a tool they’ve approved is flagged. We also publish a public leaderboard of the most trusted and most-used servers, updated in real time.

Why Trust Matters in AI

The rise of agentic AI systems — AI that doesn’t just answer questions but actually takes actions on your behalf — has fundamentally changed the risk profile of software security. When an AI agent is given permission to read your files, send emails, query your databases, and execute code, every tool it connects to becomes a potential attack surface.

Prompt injection is one of the most underappreciated threats in this space. A malicious MCP server can return a response that contains hidden instructions embedded in natural language — instructions that tell the AI agent to ignore its system prompt, exfiltrate data, or perform actions the user never authorized. Unlike traditional software vulnerabilities, prompt injection doesn’t require a bug in the AI model itself. It exploits the model’s core capability: following instructions.

Data leakage is the other major risk. MCP servers that handle sensitive queries — retrieving customer records, processing financial data, reading internal documents — can quietly log and transmit that data to external parties. Without behavioral analysis and continuous monitoring, neither the developer nor the end user would know it was happening.

XLUXX exists because the AI tooling ecosystem grew faster than the security infrastructure designed to protect it. We’re building that infrastructure: a neutral, continuously updated trust layer that any AI system can query before connecting to any external tool. Think of us as the SSL certificate authority for AI agents — the trust anchor that makes the whole ecosystem safer to use.