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 :))
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 :))
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.
Can you elaborate how it works? Specific sequence of tokens acts as a canary?
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.