22 citations · 27 across the 15 of their papers we have counts for
5 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…
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…
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…