This research reveals how synthetic text generated by fine-tuned LLMs can inadvertently leak sensitive information about training data, even when the model itself remains private.

• Successfully designed membership inference attacks that can identify if specific data was used in LLM fine-tuning
• Demonstrated privacy risks in synthetic data pipelines that were previously overlooked
• Established methods to audit and quantify information leakage from LLM-generated content
• Highlighted vulnerabilities even when adversaries lack direct model access

For security professionals, this work provides critical insights for evaluating privacy guarantees in synthetic data generation and developing more robust safeguards against sophisticated inference attacks.

The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text