3 articles tagged with #privacy-risks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Meta's Muse Spark AI model requests access to users' raw health data including lab results, raising significant privacy concerns while demonstrating poor medical judgment. The system exemplifies how large language models lack the expertise to provide reliable healthcare guidance despite their persuasive presentation.
Researchers have identified a critical privacy vulnerability in multi-modal large reasoning models (MLRMs) where adversaries can infer users' sensitive location information from images, including home addresses from selfies. The study introduces DoxBench dataset and demonstrates that 11 advanced MLRMs consistently outperform humans in geolocation inference, significantly lowering barriers for privacy attacks.
Researchers have identified significant privacy risks in Large Language Model-based Task-Oriented Dialogue Systems, demonstrating that these AI systems can memorize and leak sensitive training data including phone numbers and complete dialogue exchanges. The study proposes new attack methods that can extract thousands of training dialogue states with over 70% precision in best-case scenarios.
