4 citations · 4 across the 8 of their papers we have counts for
10 papers
SecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
Xiaonan Luo, Yue Huang, Kehan Guo +9
Assessing cybersecurity vulnerability awareness in coding agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static benchmarks offer f…
Beyond the Payload: How User Invocation Shapes Coding Agent Vulnerability to Repository Poisoning
Fukang Zhu, Binbin Zhao, Ruixiao Lin +3
Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on…
Hybrid Analysis for Secure MCP Tool Use in LLM Agents
Ping He, Yuexiang Xie, Yaliang Li +1
The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks. To standardize interactions between LLM agents and exte…
Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning
Xiaonan Luo, Yue Huang, Kehan Guo +4
Model collapse is a central challenge in learning from synthetic data: as later-generation large language models (LLMs) are trained on an increasing proportion of model-generated d…
FraudShield: Knowledge Graph Empowered Defense for LLMs against Fraud Attacks
Naen Xu, Jinghuai Zhang, Ping He +6
Large language models (LLMs) have been widely integrated into critical automated workflows, including contract review and job application processes. However, LLMs are susceptible t…
HogVul: Black-box Adversarial Code Generation Framework Against LM-based Vulnerability Detectors
Jingxiao Yang, Ping He, Tianyu Du +2
Recent advances in software vulnerability detection have been driven by Language Model (LM)-based approaches. However, these models remain vulnerable to adversarial attacks that ex…
Better Datasets Start From RefineLab: Automatic Optimization for High-Quality Dataset Refinement
Xiaonan Luo, Yue Huang, Ping He +1
High-quality Question-Answer (QA) datasets are foundational for reliable Large Language Model (LLM) evaluation, yet even expert-crafted datasets exhibit persistent gaps in domain c…
Automatic Red Teaming LLM-based Agents with Model Context Protocol Tools
Ping He, Changjiang Li, Binbin Zhao +2
The remarkable capability of large language models (LLMs) has led to the wide application of LLM-based agents in various domains. To standardize interactions between LLM-based agen…
CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
Boyu Zhang, Ping He, Tianyu Du +4
With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern. While current waterm…
BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection
Tinghao Xie, Xiangyu Qi, Ping He +3
We present a novel defense, against backdoor attacks on Deep Neural Networks (DNNs), wherein adversaries covertly implant malicious behaviors (backdoors) into DNNs. Our defense fal…