If your app exposes an AI endpoint, your most expensive infrastructure might now be the easiest one to abuse.
A normal HTTP request is cheap. A single request that triggers a frontier model, a long agent loop, web search, embeddings, tool calls, or code execution is not. That gap is what people are calling inference theft: attackers using your public AI routes as a free model proxy until your bill, quota, or latency explodes.
This is not just a “set a rate limit and chill” problem. AI requests need product-level abuse controls because the expensive work often happens after the request passes your regular web stack.
Let’s break down a practical defense plan developers can actually ship.
What makes inference theft different?
