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cs.SE2024
The Seeds of the FUTURE Sprout from History: Fuzzing for Unveiling Vulnerabilities in Prospective Deep-Learning Libraries
Zhiyuan Li, Jingzheng Wu, Xiang Ling +3
The widespread application of large language models (LLMs) underscores the importance of deep learning (DL) technologies that rely on foundational DL libraries such as PyTorch and…
cs.CR2024★ 1 cited
A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild
Xiang Ling, Zhiyu Wu, Bin Wang +5
Given the remarkable achievements of existing learning-based malware detection in both academia and industry, this paper presents MalGuise, a practical black-box adversarial attack…
cs.SE2024
When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?
Yuxiao Chen, Jingzheng Wu, Xiang Ling +4
In recent years, large language models (LLMs) have demonstrated substantial potential in addressing automatic program repair (APR) tasks. However, the current evaluation of these m…