1 citations · 1 across the 9 of their papers we have counts for
9 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…