3 articles tagged with #contextual-integrity. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduced AgentCIBench, a safety testing framework that reveals critical privacy vulnerabilities in computer-use agents (CUAs) that access multiple personal applications. Testing 15 frontier agents found that 11 leak sensitive information on over 50% of scenarios, exposing risks from UI co-location, task ambiguity, and recipient misalignment.
Researchers introduced IDP-Bench, the first benchmark evaluating how well large language models protect interdependent privacy—where one person's data can be revealed by others without consent. Testing eight open-source LLMs revealed strong performance in recognizing data co-ownership but significant weaknesses in understanding contextual integrity parameters and judging sharing appropriateness, with smaller models showing particular vulnerability to prompt sensitivity.
Researchers introduce DelegateCI-Bench, a privacy-focused benchmark for query rewriting in LLM delegation, combined with a reinforcement learning framework that selectively redacts sensitive information while preserving task-critical content. The approach achieves superior privacy-utility tradeoffs compared to existing type-based PII redaction methods, addressing growing concerns about sensitive data exposure in cloud-hosted AI systems.
