Yixin Wu

With the rapid adoption of large language models (LLMs), conversational AI agents have become widely deployed across real-world applications. To enhance safety, these agents are often equipped with guardrails that moderate harmful content. Identifying the guardrails in an agent thus becomes criti...

Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack parameters for optimal estimation are challenging for non-experts. The emergence of advanced large language models presents a promising yet largely unexpl...

Prompt injection attacks are recognized as one of the primary risks faced by LLM-integrated applications in recent years. However, common evaluation frameworks remain insufficient, lacking comprehensiveness and real-world relevance. To bridge this gap, we revisit the common evaluation framework a...