Behavioral fingerprinting via random numbers can detect model swapping in API proxies

一个随机数就能识别AI模型身份：行为指纹技术可检测API中转站偷换模型

Researcher Tomáš Brukner from Prague University of Economics and Business asked 165 models to pick random numbers between 1 and 100 thirty times each. The models showed distinct preferences: GPT-4o favored 42 and 37, Claude Sonnet 5 heavily output 47, and Qwen3-Max answered 42 every single time. About 120 requests are enough to identify a model, with an error rate around 10.6%. It's a lightweight way to check whether an API endpoint has been silently swapped for a cheaper model.

Why it matters: Clever and effective: behavioral fingerprinting via random-number preferences, identifying models with ~120 requests at 10.6% error. Directly addresses the industry anxiety around API resellers swapping models, backed by concrete data and model comparisons. Not scored higher b...