most citedAIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities

7 citations · 15 across the 3 of their papers we have counts for

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cs.SE2025

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3

Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an i…

cs.SE2025

On the Evaluation of Large Language Models in Multilingual Vulnerability Repair

Dong wang, Junji Yu, Honglin Shu +4

Various Deep Learning-based approaches with pre-trained language models have been proposed for automatically repairing software vulnerabilities. However, these approaches are limit…

cs.SE2025

A Preliminary Study of Large Language Models for Multilingual Vulnerability Detection

Junji Yu, Honglin Shu, Michael Fu +4

Deep learning-based approaches, particularly those leveraging pre-trained language models (PLMs), have shown promise in automated software vulnerability detection. However, existin…

cs.SE20236 cited

ChatGPT for Vulnerability Detection, Classification, and Repair: How Far Are We?

Michael Fu, Chakkrit Tantithamthavorn, Van Nguyen +1

Large language models (LLMs) like ChatGPT (i.e., gpt-3.5-turbo and gpt-4) exhibited remarkable advancement in a range of software engineering tasks associated with source code such…

cs.SE20237 cited

AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities

Michael Fu, Chakkrit Tantithamthavorn, Trung Le +4

Many ML-based approaches have been proposed to automatically detect, localize, and repair software vulnerabilities. While ML-based methods are more effective than program analysis-…