4 citations · 15 across the 14 of their papers we have counts for
14 papers
Athena: Vulnerability-Affected Library Identification via Knowledge Graph Completion
Phong Trinh Duy, Trang Dang Yen, Hung Nguyen-Huu +5
A single vulnerability in a widely used library can cascade through millions of dependent applications, yet more than half of vulnerability database entries contain missing or inco…
Vul4Py: Benchmarking Automated Vulnerability Repair in Python with Paired Exploit and Functional Oracles
Tan Bui, Ting Zhang, Ferdian Thung +4
Automated Vulnerability Repair (AVR) has advanced rapidly across program analysis, machine learning, and Large Language Models (LLMs), but a verifiable, head-to-head comparison of…
Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
Chengran Yang, Ting Zhang, Jinfeng Jiang +9
Current learning-based Automated Vulnerability Repair (AVR) approaches, while promising, often fail to generalize effectively in real-world scenarios. Our diagnostic analysis revea…
PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings
Huu Hung Nguyen, Anh Tuan Nguyen, Thanh Le-Cong +8
Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerab…
"Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editors
Yue Liu, Yanjie Zhao, Yunbo Lyu +3
Agentic AI coding editors driven by large language models have recently become more popular due to their ability to improve developer productivity during software development. Mode…
Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses?
Yikun Li, Ngoc Tan Bui, Ting Zhang +16
Automated vulnerability detection research has made substantial progress, yet its real-world impact remains limited. Prior work found that current vulnerability datasets suffer fro…
Fixseeker: An Empirical Driven Graph-based Approach for Detecting Silent Vulnerability Fixes in Open Source Software
Yiran Cheng, Ting Zhang, Lwin Khin Shar +6
Open source software vulnerabilities pose significant security risks to downstream applications. While vulnerability databases provide valuable information for mitigation, many sec…
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification
Yindu Su, Huike Zou, Lin Sun +7
Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendation, and business ana…
CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics
Yikun Li, Ting Zhang, Ratnadira Widyasari +13
Accurate identification of software vulnerabilities is crucial for system integrity. Vulnerability datasets, often derived from the National Vulnerability Database (NVD) or directl…
VERCATION: Precise Vulnerable Open-source Software Version Identification based on Static Analysis and LLM
Yiran Cheng, Ting Zhang, Lwin Khin Shar +6
Open-source software (OSS) has experienced a surge in popularity, attributed to its collaborative development model and cost-effective nature. However, the adoption of specific sof…
Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection
Xin Zhou, Duc-Manh Tran, Thanh Le-Cong +5
Software vulnerabilities pose significant security challenges and potential risks to society, necessitating extensive efforts in automated vulnerability detection. There are two po…
Large Language Model for Vulnerability Detection: Emerging Results and Future Directions
Xin Zhou, Ting Zhang, David Lo
Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-T…
Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair
Xin Zhou, Kisub Kim, Bowen Xu +2
The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fi…
Explaining Explanation: An Empirical Study on Explanation in Code Reviews
Ratnadira Widyasari, Ting Zhang, Abir Bouraffa +2
Code reviews are central for software quality assurance. Ideally, reviewers should explain their feedback to enable authors of code changes to understand the feedback and act accor…