[2603.01919] Real Money, Fake Models: Deceptive Model Claims in Shadow APIs

Computer Science > Cryptography and Security arXiv:2603.01919 (cs) [Submitted on 2 Mar 2026] Title:Real Money, Fake Models: Deceptive Model Claims in Shadow APIs Authors:Yage Zhang, Yukun Jiang, Zeyuan Chen, Michael Backes, Xinyue Shen, Yang Zhang View a PDF of the paper titled Real Money, Fake Models: Deceptive Model Claims in Shadow APIs, by Yage Zhang and 5 other authors View PDF HTML (experimental) Abstract:Access to frontier large language models (LLMs), such as GPT-5 and Gemini-2.5, is often hindered by high pricing, payment barriers, and regional restrictions. These limitations drive the proliferation of $\textit{shadow APIs}$, third-party services that claim to provide access to official model services without regional limitations via indirect access. Despite their widespread use, it remains unclear whether shadow APIs deliver outputs consistent with those of the official APIs, raising concerns about the reliability of downstream applications and the validity of research findings that depend on them. In this paper, we present the first systematic audit between official LLM APIs and corresponding shadow APIs. We first identify 17 shadow APIs that have been utilized in 187 academic papers, with the most popular one reaching 5,966 citations and 58,639 GitHub stars by December 6, 2025. Through multidimensional auditing of three representative shadow APIs across utility, safety, and model verification, we uncover both indirect and direct evidence of deception practices ...