Privacy Risks

Shape and Substance: Unmasking Privacy Leaks in On-Device AI Vision Models Imagine snapping a photo of your medical scan on your smartphone and asking an AI to explain it—all without sending the image to the cloud. Sounds secure, right? On-device Vision-Language Models (VLMs) like LLaVA-NeXT and Qwen2-VL make this possible, promising rock-solid privacy by keeping your data local. But a groundbreaking research paper reveals a sneaky vulnerability: attackers can peer into your photos just by watching how the AI processes them.[1] ...

Introduction Large Language Models (LLMs) like ChatGPT, Gemini, and Claude have revolutionized how we interact with technology, powering everything from content creation to autonomous agents. However, their immense power comes with profound privacy risks. Trained on vast datasets scraped from the internet, these models can memorize sensitive information, infer personal details from innocuous queries, and expose data through unintended outputs.[1][2] This comprehensive guide dives deep into the privacy challenges of LLMs, explores real-world threats, evaluates popular models’ practices, and outlines actionable mitigation strategies. Whether you’re a developer, business leader, or everyday user, understanding these issues is crucial in 2026 as LLMs integrate further into daily life.[4][9] ...