activity
20242026
most citedLLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

cs.SE2026

When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents

Lu Yan, Xuan Chen, Xiangyu Zhang

Current coding-agent benchmarks usually pro- vide the full task specification upfront. Real research coding often does not: the intended system is progressively disclosed through i…

cs.CR2026

MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection

Xuan Chen, Hao Liu, Tao Yuan +3

Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. While recent systems incorporate large language mo…

cs.CR2025

Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving

Xuan Chen, Shiwei Feng, Zikang Xiong +6

Assessing the safety of autonomous driving (AD) systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing wo…

cs.CR2025

ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants

Xiangzhe Xu, Guangyu Shen, Zian Su +9

AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain-especially in high-stakes domains like cybersecuri…

cs.CR2025

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation

Lu Yan, Zhuo Zhang, Xiangzhe Xu +5

Large language models (LLMs) have democratized software development, reducing the expertise barrier for programming complex applications. This accessibility extends to malicious so…

cs.SE2025

TAI3: Testing Agent Integrity in Interpreting User Intent

Shiwei Feng, Xiangzhe Xu, Xuan Chen +5

LLM agents are increasingly deployed to automate real-world tasks by invoking APIs through natural language instructions. While powerful, they often suffer from misinterpretation o…