13 citations · 23 across the 7 of their papers we have counts for
4 papers · 1 filter
The Data Problem in Software Vulnerability Analysis: Artifacts, Quality, and Consumption
Yu Nong, Yao Du, Tianxiang Xu +1
Learning- and LLM-based software vulnerability analysis is only as trustworthy as the data it is trained and evaluated on, yet that data is rarely examined as a first-class object.…
Neuro-Symbolic Proof-of-Vulnerability Generation with Open-Weight Models
Yu Nong, Haipeng Cai
Software vulnerabilities are persistent, but validating them remains difficult: a Proof-of-Vulnerability (PoV) requires a concrete input that triggers the vulnerable behavior, yet…
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
Seyed Shayan Daneshvar, Yu Nong, Xu Yang +2
Detecting vulnerabilities is vital for software security, yet deep learning-based vulnerability detectors (DLVD) face a data shortage, which limits their effectiveness. Data augmen…
VGX: Large-Scale Sample Generation for Boosting Learning-Based Software Vulnerability Analyses
Yu Nong, Richard Fang, Guangbei Yi +4
Accompanying the successes of learning-based defensive software vulnerability analyses is the lack of large and quality sets of labeled vulnerable program samples, which impedes fu…