You can identify anonymous API models through systematic forensic analysis of configuration, tokenizer behavior, and archived platform data—but this requires careful validation and should decline to guess rather than make false claims.
This paper presents a four-stage forensic protocol for identifying anonymous AI models served through APIs. Using archived platform data, configuration fingerprinting, tokenizer analysis, and behavioral testing, the authors demonstrate how to verify model identity without relying on self-identification.