Zhenlan Ji

According to our database, Zhenlan Ji authored at least 19 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Evaluating LLMs on Sequential API Call Through Automated Test Generation.

Yuheng Huang, Da Song, Zhenlan Ji, Shuai Wang, Lei Ma

CoRR, July, 2025

SoK: Evaluating Jailbreak Guardrails for Large Language Models.

Xunguang Wang, Zhenlan Ji, Wenxuan Wang, Zongjie Li, Daoyuan Wu, Shuai Wang

CoRR, June, 2025

IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems.

Liwen Wang, Wenxuan Wang, Shuai Wang, Zongjie Li, Zhenlan Ji, Zongyi Lyu, Daoyuan Wu, Shing-Chi Cheung

CoRR, May, 2025

NAMET: Robust Massive Model Editing via Noise-Aware Memory Optimization.

Yanbo Dai, Zhenlan Ji, Zongjie Li, Shuai Wang

CoRR, May, 2025

STShield: Single-Token Sentinel for Real-Time Jailbreak Detection in Large Language Models.

Xunguang Wang, Wenxuan Wang, Zhenlan Ji, Zongjie Li, Pingchuan Ma, Daoyuan Wu, Shuai Wang

CoRR, March, 2025

Causality-Aided Evaluation and Explanation of Large Language Model-Based Code Generation.

Zhenlan Ji, Pingchuan Ma, Zongjie Li, Zhaoyu Wang, Shuai Wang

Proc. ACM Softw. Eng., 2025

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

Proceedings of the 47th IEEE/ACM International Conference on Software Engineering, 2025

2024

SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner.

Xunguang Wang, Daoyuan Wu, Zhenlan Ji, Zongjie Li, Pingchuan Ma, Shuai Wang, Yingjiu Li, Yang Liu, Ning Liu, Juergen Rahmel

CoRR, 2024

Testing and Understanding Erroneous Planning in LLM Agents through Synthesized User Inputs.

Zhenlan Ji, Daoyuan Wu, Pingchuan Ma, Zongjie Li, Shuai Wang

CoRR, 2024

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning.

Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, Kui Ren

Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, 2024

2023

InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models.

Xunguang Wang, Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang

CoRR, 2023

Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach.

Zhenlan Ji, Pingchuan Ma, Zongjie Li, Shuai Wang

CoRR, 2023

Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version).

Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, Kui Ren

CoRR, 2023

Causality-Aided Trade-Off Analysis for Machine Learning Fairness.

Zhenlan Ji, Pingchuan Ma, Shuai Wang, Yanhui Li

Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

Perfce: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis.

Zhenlan Ji, Pingchuan Ma, Shuai Wang

Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, 2023

CC: Causality-Aware Coverage Criterion for Deep Neural Networks.

Zhenlan Ji, Pingchuan Ma, Yuanyuan Yuan, Shuai Wang

Proceedings of the 45th IEEE/ACM International Conference on Software Engineering, 2023

2022

NoLeaks: Differentially Private Causal Discovery Under Functional Causal Model.

Pingchuan Ma, Zhenlan Ji, Qi Pang, Shuai Wang

IEEE Trans. Inf. Forensics Secur., 2022

Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning.

Zhenlan Ji, Pingchuan Ma, Shuai Wang

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering, 2022

2020

An Empirical Study on Issue Knowledge Transfer from Python to R for Machine Learning Software.

Wen-Chin Huang, Zhenlan Ji, Yanhui Li

Proceedings of the 32nd International Conference on Software Engineering and Knowledge Engineering, 2020