Yuan Zhang

AutoVerifier is an LLM-based agentic framework that automates the end-to-end verification of complex technical claims, enabling non-experts to generate evidence-backed intelligence assessments.

Half-Moon Cookie introduces a private, similarity-based blocklisting framework that allows clients to check items against a proprietary blocklist without revealing the item or the blocklist contents, while also providing resilience against TOCTOU attacks.

The paper introduces TESLA, a novel, contactless electromagnetic (EM) side-channel attack that exploits inherent EM emanations from capacitive touchscreens to extract highly sensitive user data like PIN codes and keystrokes.

The paper introduces WaveGuard, a frequency-aware, single-pass defense framework that safeguards text-to-image models by injecting structured, imperceptible perturbations into generated images, thereby hindering unauthorized knowledge distillation.

AgentGuard is an attribute-based access control framework designed to mitigate severe security risks, such as privacy leakage and system compromise, in tool-using LLM-based agents.

The paper proposes Energy-Aware NECO, a single-pass hybrid detector that combines geometric ratio and logit-based energy scores to achieve superior pixel-wise out-of-distribution detection for semantic segmentation on edge devices.

The paper proposes a novel four-stage simulation framework that uses GPS-derived seasonal spatial priors and LLMs to generate demographically accurate, synthetic tourist mobility schedules for urban planning.