7 citations · 7 across the 4 of their papers we have counts for
9 papers
X.js: A Live Benchmark for Node.js Taint-Style Vulnerabilities
José Afonso, João Madeira Pereira, Ronghao Ni +3
JavaScript vulnerabilities in npm packages pose a significant risk to the Nodejs ecosystem, and a growing body of work aims to detect, exploit, and mitigate them. Studying and e…
Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning
Boyue Caroline Hu, Kaivalya Ahir, Ronghao Ni +1
Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; practitioners need explanations t…
Self-Evolving Defense: Continual Security Policy Learning for LLM Agents
Minh Nhat Le, Nisarga Gondi, Yibo Peng +4
Large language models (LLMs) increasingly power agents that access sensitive information, use external tools, and modify software repositories. Although these capabilities offer su…
Taint-Style Vulnerability Detection and Confirmation for Node.js Packages Using LLM Agent Reasoning
Ronghao Ni, Mihai Christodorescu, Limin Jia
The rapidly evolving Nodejs ecosystem currently includes millions of packages and is a critical part of modern software supply chains, making vulnerability detection of Nodej…
On the Difficulty of Selecting Few-Shot Examples for Effective LLM-based Vulnerability Detection
Md Abdul Hannan, Ronghao Ni, Chi Zhang +3
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of coding tasks, including summarization, translation, completion, and code generation. D…
Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.js
Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu +5
Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical challenge: how can security analysts p…
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Ronghao Ni, Zinan Lin, Shuaiqi Wang +1
Long-term time series forecasting (LTSF) aims to predict future values of a time series given the past values. The current state-of-the-art (SOTA) on this problem is attained in so…
CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault Tolerant Protocols
Weizhao Tang, Peiyao Sheng, Ronghao Ni +4
Crash fault tolerant (CFT) consensus algorithms are commonly used in scenarios where system components are trusted -- e.g., enterprise settings and government infrastructure. Howev…
Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models
Xin He, Shihao Wang, Shaohuai Shi +10
Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis hi…