Meta’s AI-distilled model detector catches 79% at a 0.1% false-positive rate

Meta researchers have developed a method for detecting distilled AI models that identified 79% of tested copies at a false-positive rate of 0.1%, without inspecting the suspect models’ internals. The technique, called WM-Basin , targets models trained on outputs from watermarked “teacher” models and appears in a paper published on October 27, 2026. Key takeaways WM-Basin examines API-generated text rather than model weights or logits. Meta evaluated the technique across 1,200 student models. Detection survived several model modifications but weakened with clean-data fine-tuning and paraphrasing. How WM-Basin detects distilled AI models WM-Basin uses a separate, clean probe model to analyze text generated by a suspected distilled model. Researchers from Meta FAIR and Meta Superintelligence Labs describe the approach in “Watermark Basins: Black-Box Detection of Distilled LLMs,” published under arXiv:2610.37266.

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