8 citations · 31 across the 17 of their papers we have counts for
18 papers
Towards a Risk Assessment of Malicious Skill Files in Coding Agents
Rui Yang, Michael Fu, Kla Tantithamthavorn +2
Autonomous coding agents are increasingly embedded in enterprise software workflows with delegated authority over connected systems. Central to this architecture is the agent skill…
AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair
Michael Fu, Qiyue Mei, Patanamon Thongtanunam +1
Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promi…
SkillGate: Cost Efficient Runtime Malicious Skill File Detection in Coding Agents
Rui Yang, Michael Fu, Kla Tantithamthavorn +2
Software engineering teams now deploy AI coding agents (Cursor, Claude Code, GitHub Copilot) as first-class productivity tools, installing domain-specific skill files to tailor age…
AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
Eduardo Oliveira, Michael Fu, Patanamon Thongtanunam +2
Code review is central to software engineering education but hard to scale in capstone projects due to tight deadlines, uneven peer feedback, and limited prior experience. We inves…
IntelliSA: An Intelligent Static Analyzer for IaC Security Smell Detection Using Symbolic Rules and Neural Inference
Qiyue Mei, Michael Fu
Infrastructure as Code (IaC) enables automated provisioning of large-scale cloud and on-premise environments, reducing the need for repetitive manual setup. However, this automatio…
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…
AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3
Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces…
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…
SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems
Wenliang Shan, Michael Fu, Rui Yang +1
Safety alignment is critical for LLM-powered systems. While recent LLM-powered guardrail approaches such as LlamaGuard achieve high detection accuracy of unsafe inputs written in E…
Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities
Honglin Shu, Michael Fu, Junji Yu +4
Various deep learning-based approaches utilizing pre-trained language models (PLMs) have been proposed for automated vulnerability detection. With recent advancements in large lang…
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…
Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
Alexander W. Lee, Justin Chan, Michael Fu +4
AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data.…
RAGVA: Engineering Retrieval Augmented Generation-based Virtual Assistants in Practice
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn +3
Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retri…
AI for DevSecOps: A Landscape and Future Opportunities
Michael Fu, Jirat Pasuksmit, Chakkrit Tantithamthavorn
DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm ha…
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…
Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities
Michael Fu, Trung Le, Van Nguyen +2
Deep learning (DL) models have become increasingly popular in identifying software vulnerabilities. Prior studies found that vulnerabilities across different vulnerable programs ma…
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-…
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learning
Van Nguyen, Trung Le, Chakkrit Tantithamthavorn +6
Software vulnerabilities are a serious and crucial concern. Typically, in a program or function consisting of hundreds or thousands of source code statements, there are only a few…