2 citations · 3 across the 8 of their papers we have counts for
8 papers
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Mingchen Li, Meikang Qiu, Zifan Peng +4
Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair…
"What Did It Actually Do?": Understanding Risk Awareness and Traceability for Computer-Use Agents
Zifan Peng, Mingchen Li
Personalized computer-use agents are rapidly moving from expert communities into mainstream use. Unlike conventional chatbots, these systems can install skills, invoke tools, acces…
NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
Jingzhe Ding, Shengda Long, Changxin Pu +46
Recent advances in coding agents suggest rapid progress toward autonomous software development, yet existing benchmarks fail to rigorously evaluate the long-horizon capabilities re…
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
Zifan Peng, Jingyi Zheng, Yule Liu +8
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespre…
JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
Zifan Peng, Yule Liu, Zhen Sun +9
Large Audio Language Models (LALMs) have made significant progress. While increasingly deployed in real-world applications, LALMs face growing safety risks from jailbreak attacks t…
DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting
Mingchen Li, Heng Fan, Song Fu +2
Prompt privacy is crucial, especially when using online large language models (LLMs), due to the sensitive information often contained within prompts. While LLMs can enhance prompt…
Enhanced Structured Lasso Pruning with Class-wise Information
Xiang Liu, Mingchen Li, Xia Li +7
Modern applications require lightweight neural network models. Most existing neural network pruning methods focus on removing unimportant filters; however, these may result in the…
Unsafe LLM-Based Search: Quantitative Analysis and Mitigation of Safety Risks in AI Web Search
Zeren Luo, Zifan Peng, Yule Liu +4
Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering precise and efficient responses by…