Xiaohan Zhang

This paper introduces a novel class of backdoor attacks that exploit the numerical side effects of LLM inference optimization, achieving high success rates while maintaining clean accuracy.

This study provides the first measurement of authentication security in real-world remote Model Context Protocol (MCP) servers, finding pervasive and critical authentication weaknesses, particularly in dynamic client registration.

The paper introduces TaskWeave, a hierarchical agentic framework that successfully simulates long-horizon organizational dynamics by treating coordination as a memory-centered problem, demonstrating that structured memory is key to reliable LLM-based simulations.

This paper investigates Description-Code Inconsistency (DCI) in Model Context Protocol (MCP) servers, finding that 9.93% of real-world tools exhibit inconsistencies that create security blind spots.