22 citations · 40 across the 22 of their papers we have counts for
23 papers
Before Acting, Change the State: Prospective State Intervention for Web Agents under Deceptive Interfaces
Ruozhao Yang, Mingfei Cheng, Xiaofei Xie
LLM-based Web agents can autonomously complete user tasks, yet deceptive interfaces can steer them toward outcomes that conflict with users' interests. Existing defenses primarily…
PRWeaver: Evaluating LLM-Based Code Auditors against Long-Horizon Malicious Pull Requests
Yuekun Wang, Mingfei Cheng, Xiaofei Xie
LLM-based code auditors are increasingly integrated into pull-request (PR) workflows, yet their reliability against adversarial changes distributed across repository evolution rema…
EvoEye: Self-Evolving Runtime Monitoring for Autonomous Driving Systems
Mingfei Cheng, Lionel Briand, Xiaofei Xie
Runtime monitoring is essential for detecting impending hazards in autonomous driving systems (ADSs). However, existing ADS runtime monitors have fixed detection capabilities: rule…
Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference
Xuyan Ma, Yawen Wang, Junjie Wang +5
Platform-orchestrated agentic workflows have become a popular paradigm for developing LLM-based applications. However, their reliability remains a major challenge due to the uncert…
Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents
Jianming Chen, Yawen Wang, Junjie Wang +4
Embodied agents in safety-critical applications such as Vision-Language Navigation (VLN) rely on multiple interdependent capabilities (e.g., perception, memory, planning, decision)…
AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
Ruozhao Yang, Mingfei Cheng, Gelei Deng +3
Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. Security assessment is there…
Towards Understanding and Characterizing Vulnerabilities in Intelligent Connected Vehicles through Real-World Exploits
Yuelin Wang, Yuqiao Ning, Yanbang Sun +7
Intelligent Connected Vehicles (ICVs) are a core component of modern transportation systems, and their security is crucial as it directly relates to user safety. Despite prior rese…
PentestEval: Benchmarking LLM-based Penetration Testing with Modular and Stage-Level Design
Ruozhao Yang, Mingfei Cheng, Gelei Deng +3
Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, an…
Enhancing Automated Program Repair via Faulty Token Localization and Quality-Aware Patch Refinement
Jiaolong Kong, Xiaofei Xie, Yiheng Xiong +2
Large language models (LLMs) have recently demonstrated strong potential for automated program repair (APR). However, existing LLM-based techniques primarily rely on coarse-grained…
Adversarial Attack on Black-Box Multi-Agent by Adaptive Perturbation
Jianming Chen, Yawen Wang, Junjie Wang +4
Evaluating security and reliability for multi-agent systems (MAS) is urgent as they become increasingly prevalent in various applications. As an evaluation technique, existing adve…
Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows
Xuyan Ma, Xiaofei Xie, Yawen Wang +4
Agentic workflows built on low-code orchestration platforms enable rapid development of multi-agent systems, but they also introduce new and poorly understood failure modes that hi…
STCLocker: Deadlock Avoidance Testing for Autonomous Driving Systems
Mingfei Cheng, Renzhi Wang, Xiaofei Xie +2
Autonomous Driving System (ADS) testing is essential to ensure the safety and reliability of autonomous vehicles (AVs) before deployment. However, existing techniques primarily foc…
ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning
Mingfei Cheng, Xiaofei Xie, Renzhi Wang +2
Autonomous Driving Systems (ADSs) continue to face safety-critical risks due to the inherent limitations in their design and performance capabilities. Online repair plays a crucial…
PatchFuzz: Patch Fuzzing for JavaScript Engines
Junjie Wang, Yuhan Ma, Xiaofei Xie +2
Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScri…
Robust Multi-agent Communication Based on Decentralization-Oriented Adversarial Training
Xuyan Ma, Yawen Wang, Junjie Wang +5
In typical multi-agent reinforcement learning (MARL) problems, communication is important for agents to share information and make the right decisions. However, due to the complexi…
Foundation Models for Autonomous Driving System: An Initial Roadmap
Xiongfei Wu, Mingfei Cheng, Xiaoning Ren +8
Recent advances in foundation models (FMs), including large language models (LLMs), vision-language models (VLMs), and world models, have opened new opportunities for autonomous dr…
MoDitector: Module-Directed Testing for Autonomous Driving Systems
Renzhi Wang, Mingfei Cheng, Xiaofei Xie +2
Testing Autonomous Driving Systems (ADS) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse an…
Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning
Jianming Chen, Yawen Wang, Junjie Wang +4
Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has proveided explanations for the actions or s…
DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing
Mingfei Cheng, Yuan Zhou, Xiaofei Xie
Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is t…
A Comprehensive Study on Static Application Security Testing (SAST) Tools for Android
Jingyun Zhu, Kaixuan Li, Sen Chen +3
To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges…
FT2Ra: A Fine-Tuning-Inspired Approach to Retrieval-Augmented Code Completion
Qi Guo, Xiaohong Li, Xiaofei Xie +6
The rise of code pre-trained models has significantly enhanced various coding tasks, such as code completion, and tools like GitHub Copilot. However, the substantial size of these…
ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs
Jiaolong Kong, Mingfei Cheng, Xiaofei Xie +3
Automated Program Repair (APR) aims to automatically generate patches for rectifying software bugs. Recent strides in Large Language Models (LLM), such as ChatGPT, have yielded enc…
Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems
Mingfei Cheng, Yuan Zhou, Xiaofei Xie +3
Autonomous Driving System (ADS) testing is crucial in ADS development, with the current primary focus being on safety. However, the evaluation of non-safety-critical performance, p…