1 citations · 2 across the 8 of their papers we have counts for
9 papers
HarnessLLM: Automatic Testing Harness Generation via Reinforcement Learning
Yujian Liu, Jiabao Ji, Yang Zhang +3
Existing LLM-based automatic test generation methods mainly produce input and expected output pairs to categorize the intended behavior of correct programs. Although straightforwar…
BlueCodeAgent: A Blue Teaming Agent Enabled by Automated Red Teaming for CodeGen AI
Chengquan Guo, Yuzhou Nie, Chulin Xie +3
As large language models (LLMs) are increasingly used for code generation, concerns over the security risks have grown substantially. Early research has primarily focused on red te…
Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving
Xuan Chen, Shiwei Feng, Zikang Xiong +6
Assessing the safety of autonomous driving (AD) systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing wo…
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly
Enquan Yang, Peng Xing, Hanyang Sun +4
Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suffer from limitations in terms of the number of defect samples, types o…
PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification
Hongwei Li, Yuheng Tang, Shiqi Wang +1
Recent research builds various patching agents that combine large language models (LLMs) with non-ML tools and achieve promising results on the state-of-the-art (SOTA) software pat…
SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI
Yuzhou Nie, Zhun Wang, Yu Yang +7
Existing benchmarks for evaluating the security risks and capabilities (e.g., vulnerability detection) of code-generating large language models (LLMs) face several key limitations:…
F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI
Xu Zheng, Farhad Shirani, Zhuomin Chen +4
Recent research has developed a number of eXplainable AI (XAI) techniques, such as gradient-based approaches, input perturbation-base methods, and black-box explanation methods. Wh…
RL-JACK: Reinforcement Learning-powered Black-box Jailbreaking Attack against LLMs
Xuan Chen, Yuzhou Nie, Lu Yan +3
Modern large language model (LLM) developers typically conduct a safety alignment to prevent an LLM from generating unethical or harmful content. Recent studies have discovered tha…
When LLM Meets DRL: Advancing Jailbreaking Efficiency via DRL-guided Search
Xuan Chen, Yuzhou Nie, Wenbo Guo +1
Recent studies developed jailbreaking attacks, which construct jailbreaking prompts to fool LLMs into responding to harmful questions. Early-stage jailbreaking attacks require acce…