22 citations · 40 across the 22 of their papers we have counts for
10 papers · 1 filter
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…