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MIT researchers developed a technique to audit AI models for their capability to generate child sexual abuse material without producing illegal outputs, achieving 100% accuracy in tests. This method examines hidden model representations to infer whether a model has been fine-tuned for harmful content, providing a scalable way for platforms and law enforcement to detect unsafe models.