4 citations · 4 across the 1 of their papers we have counts for
3 papers
cs.SE2025
Learning to Focus: Context Extraction for Efficient Code Vulnerability Detection with Language Models
Xinran Zheng, Xingzhi Qian, Huichi Zhou +4
Language models (LMs) show promise for vulnerability detection but struggle with long, real-world code due to sparse and uncertain vulnerability locations. These issues, exacerbate…
cs.CR2025★ 4 cited
On Benchmarking Code LLMs for Android Malware Analysis
Yiling He, Hongyu She, Xingzhi Qian +4
Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks. However, their effectiveness for Android malware analysis remains underexplor…
cs.CR2025
LAMD: Context-driven Android Malware Detection and Classification with LLMs
Xingzhi Qian, Xinran Zheng, Yiling He +2
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset bia…