114 citations · 115 across the 10 of their papers we have counts for
10 papers
Taming the Drift: Context-aware Repair of Dockerfile Drift during Software Evolution
Chengjie Wang, Jingzheng Wu, Xiang Ling +2
Docker is widely used to create reproducible build environments, but Dockerfile drift, the divergence between a Dockerfile and its evolving source code, can cause CI/CD builds to f…
Correct Code, Vulnerable Dependencies: A Large Scale Measurement Study of LLM-Specified Library Versions
Chengjie Wang, Jingzheng Wu, Xiang Ling +2
Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third-party library (TPL) imports annotated w…
Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis
Zhiyuan Li, Jingzheng Wu, Xiang Ling +2
Agent Skills is an emerging open standard that defines a modular, filesystem-based packaging format enabling LLM-based agents to acquire domain-specific expertise on demand. Despit…
SimFuzz: Similarity-guided Block-level Mutation for RISC-V Processor Fuzzing
Hao Lyu, Jingzheng Wu, Xiang Ling +3
The Instruction Set Architecture (ISA) defines processor operations and serves as the interface between hardware and software. As an open ISA, RISC-V lowers the barriers to process…
A Large Scale Empirical Analysis on the Adherence Gap between Standards and Tools in SBOM
Chengjie Wang, Jingzheng Wu, Hao Lyu +4
A Software Bill of Materials (SBOM) is a machine-readable artifact that systematically organizes software information, enhancing supply chain transparency and security. To facilita…
Version-level Third-Party Library Detection in Android Applications via Class Structural Similarity
Bolin Zhou, Jingzheng Wu, Xiang Ling +2
Android applications (apps) integrate reusable and well-tested third-party libraries (TPLs) to enhance functionality and shorten development cycles. However, recent research reveal…
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…
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…
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…
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
Xiang Ling, Lingfei Wu, Jiangyu Zhang +9
Malware has been one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against ever-increasing and ever-evol…