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This paper studies extractable memorization: training data that an adversarycan efficiently extract by querying a machine learning model without priorknowledge of the training dataset. We show an adversary can extract gigabytesof training data from open-source language models like Pythia or GPT-Neo,semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existingtechniques from the literature suffice to attack unaligned models; in order toattack the aligned ChatGPT, we develop a new divergence attack that causes themodel to diverge from its chatbot-style generations and emit training data at arate 150x higher than when behaving properly. Our methods show practicalattacks can recover far more data than previously thought, and reveal thatcurrent alignment techniques do not eliminate memorization.

Scalable Extraction of Training Data from (Production) Language Models