PrivacyBench: Evaluating Privacy Risks in Personalized AI
Published:Dec 31, 2025 13:16
•1 min read
•ArXiv
Analysis
This paper introduces PrivacyBench, a benchmark to assess the privacy risks associated with personalized AI agents that access sensitive user data. The research highlights the potential for these agents to inadvertently leak user secrets, particularly in Retrieval-Augmented Generation (RAG) systems. The findings emphasize the limitations of current mitigation strategies and advocate for privacy-by-design safeguards to ensure ethical and inclusive AI deployment.
Key Takeaways
- •Personalized AI agents pose privacy risks due to access to sensitive user data.
- •PrivacyBench is a benchmark for evaluating secret preservation in conversational AI.
- •RAG systems are vulnerable to secret leakage.
- •Current mitigation strategies are insufficient.
- •Privacy-by-design safeguards are crucial for ethical AI deployment.
Reference
“RAG assistants leak secrets in up to 26.56% of interactions.”