This research reveals how passwords can be extracted from large language models that have been fine-tuned on data containing sensitive information.

• Fine-tuning LLMs on user conversations may inadvertently capture passwords
• The study demonstrates methods to recover these passwords from model parameters
• Researchers explore mitigation strategies to prevent unauthorized access to sensitive data
• Important security implications for organizations deploying fine-tuned models

This work highlights critical security vulnerabilities that must be addressed before deploying fine-tuned LLMs in production environments, especially when training on customer support data that may contain confidential information.

Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models