Xiaofeng Wang

LocalAlign proposes a generalizable prompt injection defense by generating near-target adversarial examples, which enforces a tighter robustness boundary around the correct model response.

The paper introduces a systematic framework and defense mechanisms to analyze and mitigate autonomous LLM agent worms that propagate through persistent agent state and cross-platform multi-agent systems.

Misrouter introduces an input-only adversarial framework to exploit the routing mechanisms of Mixture-of-Experts (MoE) LLMs, enabling unsafe behavior induction against remotely hosted, black-box services.

The paper introduces SCAgent, an automated framework that uses LLM-assisted agents to systematically discover, analyze, and assess side-channel leakage risks in complex systems like iOS, moving beyond manual and predefined analysis.

The paper introduces DiscourseFlip, a novel graph-guided attack that demonstrates how coordinated poisoning across a multi-topic query space can manipulate the overall opinion generated by black-box Retrieval-Augmented Generation (RAG) systems.

The paper introduces DiscourseFlip, a novel black-box, graph-guided attack that manipulates opinions across an entire multi-topic query network, demonstrating a significant leap in scope and effectiveness over existing RAG attack methods.