You are a cybersecurity engineer at Example AI Co. Your proprietary cybersecurity classifier (trained on years of in-house labeled data) is exposed via public API. The pricing is $1 per 1,000 classifications.
An anonymous customer has been calling the API at 60,000 classifications per day for 14 days, paying about $840 in API fees, against a model that cost millions to train. Their query distribution looks suspiciously like model-extraction probing: synthetic inputs designed to cover the input space rather than real-world data.
This scenario tests OWASP LLM10:2025 Model Theft, MITRE ATLAS technique AML.T0024 Exfiltration via ML Inference API, and the design of detection and defense. Sources: OWASP LLM Top 10 (2025), MITRE ATLAS, Tramèr et al. 2016 'Stealing Machine Learning Models via Prediction APIs'.
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