1 citations · 1 across the 7 of their papers we have counts for
8 papers
Why Not Fix It Once and for All? An Empirical Study of Multiple Patches for Vulnerability Fixes in Open-Source Software
Weiliang Qi, Youpeng Li, Xinda Wang
Security patches for open-source software constitute a foundational resource for vulnerability remediation research and practice. However, analyzing and applying multiple patches r…
From SFT to RL: Demystifying the Post-Training Pipeline for LLM-based Vulnerability Detection
Youpeng Li, Fuxun Yu, Xinda Wang
The integration of LLMs into vulnerability detection (VD) has shifted the field toward more interpretable and context-aware analysis. While post-training techniques have shown prom…
VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization
Youpeng Li, Fuxun Yu, Weiliang Qi +1
Large language models (LLMs) have recently shown strong potential in vulnerability detection (VD). However, accurately detecting vulnerabilities in real-world repositories requires…
NatGVD: Natural Adversarial Example Attack towards Graph-based Vulnerability Detection
Avilash Rath, Weiliang Qi, Youpeng Li +1
Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adv…
MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement
Youpeng Li, Kartik Joshi, Xinda Wang +1
The widespread adoption of open-source software (OSS) necessitates the mitigation of vulnerability risks. Most vulnerability detection (VD) methods are limited by inadequate contex…
Revisiting Pre-trained Language Models for Vulnerability Detection
Youpeng Li, Weiliang Qi, Xuyu Wang +2
The rapid advancement of pre-trained language models (PLMs) has demonstrated promising results for various code-related tasks. However, their effectiveness in detecting real-world…
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
Youpeng Li, Xinda Wang, Fuxun Yu +3
Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL fac…
Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation
Weiliang Qi, Jiahao Cao, Darsh Poddar +2
With the rapid development and widespread use of advanced network systems, software vulnerabilities pose a significant threat to secure communications and networking. Learning-base…