Extracting Training Data from Large Language Models

Demonstrated that large language models memorize verbatim training sequences (PII, code, secret keys) that can be extracted via black-box sampling.

Directly guides deployment choices and architecture selection for task-privacy-preserving-ml.

Assumptions

• Standard empirical regularity and statistical stability hold across evaluation domains

Limitations

• Performance characteristics depend on domain distribution and compute allocation parameters