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🧠 AI🔴 BearishImportance 7/10
Real Money, Fake Models: Deceptive Model Claims in Shadow APIs
arXiv – CS AI|Yage Zhang, Yukun Jiang, Zeyuan Chen, Michael Backes, Xinyue Shen, Yang Zhang||5 views
🤖AI Summary
A systematic audit of 17 shadow APIs used in 187 academic papers reveals widespread deception, with performance divergence up to 47.21% and identity verification failures in 45.83% of tests. These third-party services claim to provide access to frontier LLMs like GPT-5 and Gemini-2.5 but deliver inconsistent outputs, undermining research validity and reproducibility.
Key Takeaways
- →17 shadow APIs have been used in 187 academic papers, with one reaching 5,966 citations and 58,639 GitHub stars.
- →Performance divergence between shadow and official APIs reaches up to 47.21%, indicating significant output inconsistencies.
- →45.83% of fingerprint tests failed identity verification, proving shadow APIs are not delivering claimed model access.
- →Shadow APIs exhibit unpredictable safety behaviors compared to official model services.
- →These deceptive practices critically undermine scientific research reproducibility and harm both users and official model providers.
#llm#shadow-apis#research-integrity#model-verification#ai-ethics#api-fraud#academic-research#gpt-5#gemini
Read Original →via arXiv – CS AI
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