Enterprise AI Security: 7 Attacks on Your LLM App, and the Layer That Stops Them
Prompt injection, data leakage, tenant breaches — seven real attacks on enterprise LLM apps, and the Mattrx production code that stops each one.
LLMs, RAG, vector search, Azure OpenAI, and intelligent agents for .NET teams.
6 posts · 8 questions
Prompt injection, data leakage, tenant breaches — seven real attacks on enterprise LLM apps, and the Mattrx production code that stops each one.
The finale — a year of running MCP in production at Mattrx: what held, what broke, what we'd do differently, and the whole series in one mental model.
The MCP servers you built aren't tied to one model. Here's wiring MCP into OpenAI (client-side + the hosted tool) and agent frameworks — one server, any model.
The protocol was never the enterprise blocker — identity was. Here's rolling MCP across a company: provision once, inherit on login, govern centrally.
An agent that 'feels off' tells you nothing. Here's how to make MCP observable — one trace per agent run, structured logs, and the metrics that matter.
A tool that takes minutes shouldn't freeze your agent. Here's streaming, progress notifications, and the async-job pattern for long-running MCP tools.