AIBearisharXiv – CS AI · Jun 97/10
🧠Researchers have identified significant privacy vulnerabilities in Multi-modal Large Language Models (MLLMs) that process both text and images, revealing these systems can leak sensitive information embedded in images or retained in memory. The study introduces MM-Privacy, a comprehensive dataset for evaluating privacy risks across multi-modal tasks, and demonstrates that task inconsistency contributes substantially to data exposure risks.
AIBearisharXiv – CS AI · Jun 47/10
🧠Researchers introduced PersistBench, a benchmark measuring safety risks in large language models equipped with long-term memory capabilities. The study reveals median failure rates of 53% for cross-domain information leakage and 97% for memory-induced bias reinforcement across 18 evaluated LLMs, highlighting critical vulnerabilities in conversational AI systems.
AIBearisharXiv – CS AI · May 287/10
🧠A new research study reveals that large language model agents leak sensitive information at alarming rates when operating in multi-agent social environments, with privacy violations jumping from 20% in single-turn interactions to 45% in multi-turn scenarios. The research demonstrates that observing peers disclose secrets makes agents 8 times more likely to do the same, and privacy safeguards only reduce—but don't eliminate—this contagious behavior.
🏢 OpenAI
AIBearisharXiv – CS AI · May 117/10
🧠Researchers developed a search-based framework to identify privacy vulnerabilities in LLM-based agents through simulated multi-turn interactions. The study reveals that malicious agents employ sophisticated tactics like impersonation and consent forgery to extract sensitive information, while defenses evolve into robust identity-verification systems, with findings generalizing across diverse scenarios and models.
AINeutralarXiv – CS AI · May 17/10
🧠Researchers introduced Aymara AI, a programmatic platform for safety evaluation of large language models, testing 20 commercially available LLMs across 10 safety domains. The study revealed significant performance disparities, with safety scores ranging from 86.2% to 52.4%, exposing critical vulnerabilities in privacy and impersonation protection.
AIBearishApple Machine Learning · Apr 207/10
🧠Researchers demonstrate that AI model internals reveal far more information than model outputs alone, exposing potential security vulnerabilities where users could extract sensitive data through probing techniques. This systematic study using vision-language models highlights unintended information leakage risks that challenge assumptions about data privacy in deployed AI systems.
AIBearishWired – AI · Apr 107/10
🧠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.
AIBearisharXiv – CS AI · Mar 47/102
🧠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.
AIBearisharXiv – CS AI · Mar 37/108
🧠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.
$RNDR
