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Large Language Models (LLMs) can memorize and leak sensitive training data, posing serious privacy risks. To assess such memorization and information leakage, we introduced CAMIA (Context-Aware Membership Inference Attack), the first method tailored to the generative nature of LLMs, which nearly doubles detection accuracy compared to prior approaches and reveals where memorization truly occurs.