1 citations · 2 across the 11 of their papers we have counts for
12 papers
SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills
Xuan Chen, Chengpeng Wang, Lu Yan +1
Agent skills extend LLM agents with reusable procedures, tools, and domain-specific workflows, but their safety depends on resolving dependencies among interacting instructions. We…
When the Specification Emerges: Benchmarking Faithfulness Loss in Long-Horizon Coding Agents
Lu Yan, Xuan Chen, Xiangyu Zhang
Current coding-agent benchmarks usually pro- vide the full task specification upfront. Real research coding often does not: the intended system is progressively disclosed through i…
MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection
Xuan Chen, Hao Liu, Tao Yuan +3
Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks. While recent systems incorporate large language mo…
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…
ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants
Xiangzhe Xu, Guangyu Shen, Zian Su +9
AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain-especially in high-stakes domains like cybersecuri…
MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation
Lu Yan, Zhuo Zhang, Xiangzhe Xu +5
Large language models (LLMs) have democratized software development, reducing the expertise barrier for programming complex applications. This accessibility extends to malicious so…
TAI3: Testing Agent Integrity in Interpreting User Intent
Shiwei Feng, Xiangzhe Xu, Xuan Chen +5
LLM agents are increasingly deployed to automate real-world tasks by invoking APIs through natural language instructions. While powerful, they often suffer from misinterpretation o…
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs
Xuan Chen, Tong Lu, Zhichun Wang
Entity Alignment (EA) seeks to identify and match corresponding entities across different Knowledge Graphs (KGs), playing a crucial role in knowledge fusion and integration. Embedd…
ASPIRER: Bypassing System Prompts With Permutation-based Backdoors in LLMs
Lu Yan, Siyuan Cheng, Xuan Chen +4
Large Language Models (LLMs) have become integral to many applications, with system prompts serving as a key mechanism to regulate model behavior and ensure ethical outputs. In thi…
EffiTune: Diagnosing and Mitigating Training Inefficiency for Parameter Tuner in Robot Navigation System
Shiwei Feng, Xuan Chen, Zikang Xiong +5
Robot navigation systems are critical for various real-world applications such as delivery services, hospital logistics, and warehouse management. Although classical navigation met…
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…