Yinzhi Cao

This paper introduces CoLA, a framework demonstrating that subset training, while efficient, introduces new and potentially greater privacy risks by leaking information about both data membership and the selection process itself.

The paper introduces JAW, a novel framework that demonstrates how adversaries can hijack agentic workflows on automation platforms like GitHub Actions by manipulating inputs based on context-grounded evolution.

The paper introduces Neo, an agentic program analysis framework that successfully detects zero-day privilege escalation vulnerabilities in complex, polyglot microservices by combining LLMs with advanced code analysis.