An Empirical Study on Issue Knowledge Transfer from Python to R for Machine Learning Software. - Wen-Chin Huang; Zhenlan Ji; Yanhui Li (SEKE, 2020)

Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning. - Zhenlan Ji; Pingchuan Ma 004; Shuai Wang 011 (SEKE, 2022)

NoLeaks: Differentially Private Causal Discovery Under Functional Causal Model. - Pingchuan Ma 004; Zhenlan Ji; Qi Pang; Shuai Wang 011 (IEEE Trans. Inf. Forensics Secur., 2022)

CC: Causality-Aware Coverage Criterion for Deep Neural Networks. - Zhenlan Ji; Pingchuan Ma 004; Yuanyuan Yuan; Shuai Wang 011 (ICSE, 2023)

Perfce: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis. - Zhenlan Ji; Pingchuan Ma 004; Shuai Wang 011 (ASE, 2023)

Causality-Aided Trade-Off Analysis for Machine Learning Fairness. - Zhenlan Ji; Pingchuan Ma 004; Shuai Wang 011; Yanhui Li (ASE, 2023)

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version). - Pingchuan Ma 004; Zhenlan Ji; Peisen Yao; Shuai Wang 011; Kui Ren 001 (CoRR, 2023)

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach. - Zhenlan Ji; Pingchuan Ma 004; Zongjie Li; Shuai Wang 011 (CoRR, 2023)

InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models. - Xunguang Wang; Zhenlan Ji; Pingchuan Ma 004; Zongjie Li; Shuai Wang 011 (CoRR, 2023)

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning. - Pingchuan Ma 004; Zhenlan Ji; Peisen Yao; Shuai Wang 011; Kui Ren 001 (ICSE, 2024)

Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs. - Zhenlan Ji; Daoyuan Wu; Pingchuan Ma 004; Zongjie Li; Shuai Wang 011 (CoRR, 2024)

SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner. - Xunguang Wang; Daoyuan Wu; Zhenlan Ji; Zongjie Li; Pingchuan Ma 004; Shuai Wang 011; Yingjiu Li; Yang Liu 003; Ning Liu; Juergen Rahmel (CoRR, 2024)

Testing and Understanding Deviation Behaviors in FHE-Hardened Machine Learning Models. - Yiteng Peng; Daoyuan Wu; Zhibo Liu; Dongwei Xiao; Zhenlan Ji; Juergen Rahmel; Shuai Wang 011 (ICSE, 2025)

Causality-Aided Evaluation and Explanation of Large Language Model-Based Code Generation. - Zhenlan Ji; Pingchuan Ma 004; Zongjie Li; Zhaoyu Wang; Shuai Wang 011 (Proc. ACM Softw. Eng., 2025)

STShield: Single-Token Sentinel for Real-Time Jailbreak Detection in Large Language Models. - Xunguang Wang; Wenxuan Wang 001; Zhenlan Ji; Zongjie Li; Pingchuan Ma 004; Daoyuan Wu; Shuai Wang 011 (CoRR, 2025)

NAMET: Robust Massive Model Editing via Noise-Aware Memory Optimization. - Yanbo Dai; Zhenlan Ji; Zongjie Li; Shuai Wang 011 (CoRR, 2025)

IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems. - Liwen Wang; Wenxuan Wang 001; Shuai Wang 011; Zongjie Li; Zhenlan Ji; Zongyi Lyu; Daoyuan Wu; Shing-Chi Cheung (CoRR, 2025)

SoK: Evaluating Jailbreak Guardrails for Large Language Models. - Xunguang Wang; Zhenlan Ji; Wenxuan Wang 001; Zongjie Li; Daoyuan Wu; Shuai Wang 011 (CoRR, 2025)

Evaluating LLMs on Sequential API Call Through Automated Test Generation. - Yuheng Huang 004; Da Song; Zhenlan Ji; Shuai Wang 011; Lei Ma 003 (CoRR, 2025)