Comment by grinich
1 day ago
This is the problem we've been working on solving with WorkOS Radar. We run it for Cursor and a bunch of other AI companies who have a free trial that gives some free inference to test the product.
It turns out to be a pretty complex program to solve at scale. Token fraud is a lucrative market and the adversaries are surprisingly sophisticated. It's a cat-and-mouse game, accelerated with AI.
(If you'd like to work on this, we are hiring :))
I don't think device fingerprinting is the right approach here.
Client-side detection can always be sidestepped, and you need to intermediate the actual inference to get enough signals to make an accurate prediction. There are hundreds of listings for cursor tokens/credits right now.
We use canary values to detect the resellers, and I believe that's the only approach that will actually work at scale.
> We use canary values to detect the resellers, and I believe that's the only approach that will actually work at scale.
Can you elaborate how it works? Specific sequence of tokens acts as a canary?
sign up for the resellers, insert a random id canary into your requests, trace them back on your server to tie it to an account
find fingerprints / signatures of the accounts being used. ban all of them.
eventually build an ml based system that detects these at signup
reinforce with more data. loop forever, etc.