 Common Examples of Vulnerability Incomplete or improper filtering of sensitive information in the LLM's responses Overfitting or memorization of sensitive data in the LLM's training process Unintended disclosure of confidential information due to LLM misinterpretation, lack of data scrubbing methods or errors.  How to Prevent Integrate adequate data sanitization and scrubbing techniques to prevent user data from entering the training model data Implement robust input validation and sanitization methods to identify and filter out potential malicious inputs to prevent the model from being poisoned When enriching the model with data and if fine-tuning a model: (I. E, data fed into the model before or during deployment) OWASP. org 19 | OWASP Top 10 for LLM v1. 0 a