abuse

Spam detection is adversarial, imbalanced classification under a low-latency bar. The interviewer is watching whether you set the threshold by cost asymmetry and rely on signals spammers cannot fake. Here is the design.

An attacker doesn't have to breach your LLM to hurt you, only make it do expensive work. A few crafted prompts can pin your GPUs and run up the bill. The defense is not a bigger rate limit.

The positive class runs about 1 in 1,000, your labels show up late and noisy, and the instant you ship a model the adversary starts probing it. This is the uncommon ML problem where the data actively fights back, so the design hinges on labels, latency, and enforcement cost as much as on the classifier.