Auditing Large Language Models for Privacy Compliance with Specially Crafted Prompts

The use of artificial intelligence in various sectors has raised significant concerns regarding the privacy and security of sensitive information. Introducing a novel methodology for auditing the privacy compliance of AI models, the study evaluates the effectiveness of specially crafted prompts in identifying potential privacy vulnerabilities. The audit was conducted on ChatGPT, revealing critical insights into the model's handling of sensitive data and identifying key areas for improvement. The findings demonstrate that ChatGPT exhibits a notable rate of data leakage, particularly in scenarios involving personal and medical information. Detailed analysis highlights the model's partial compliance with privacy policies and context sensitivity, indicating areas where privacy safeguards need enhancement. The results show the importance of implementing rigorous privacy measures and continuous monitoring to ensure AI models adhere to high standards of privacy protection. Furthermore, the study's contributions include the development of a robust audit framework, providing a comprehensive assessment of privacy practices and guiding future improvements. By addressing specific vulnerabilities and offering recommendations, the study aims to enhance the security and trustworthiness of AI systems, contributing to the broader efforts of ensuring that AI technologies operate within the bounds of privacy regulations.