6 citations · 8 across the 11 of their papers we have counts for
11 papers
Breaking Customized LLMs for Coding: Automated Red Teaming for Instruction Backdoor Attacks
Yuchen Chen, Wei Cheng, Yuan Xiao +5
LLM customization platforms allow users to build task-specific models for code intelligence tasks by embedding instructions into system prompts, without modifying the underlying mo…
Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation
Yuchen Chen, Wei Cheng, Yuan Xiao +7
LLM-based systems increasingly incorporate long-term memory to improve cross-session continuity. However, once insecure coding preferences are stored, they may silently influence s…
Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models
Yuchen Chen, Weisong Sun, Haocheng Huang +11
Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their widespread adoption has raised critical…
PuzzleMark: Implicit Jigsaw Learning for Robust Code Dataset Watermarking in Neural Code Completion Models
Haocheng Huang, Yuchen Chen, Weisong Sun +5
Constructing and curating high-quality code datasets requires significant resources, making them valuable intellectual property. Unfortunately, these datasets currently face severe…
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets
Yuan Xiao, Jiaming Wang, Yuchen Chen +8
The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage…
DuCodeMark: Dual-Purpose Code Dataset Watermarking via Style-Aware Watermark-Poison Design
Yuchen Chen, Yuan Xiao, Chunrong Fang +2
The proliferation of large language models for code (CodeLMs) and open-source contributions has heightened concerns over unauthorized use of source code datasets. While watermarkin…
Enhancing and Reporting Robustness Boundary of Neural Code Models for Intelligent Code Understanding
Tingxu Han, Wei Song, Weisong Sun +6
With the development of deep learning, Neural Code Models (NCMs) such as CodeBERT and CodeLlama are widely used for code understanding tasks, including defect detection and code cl…
DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual Channel Code Abstraction
Yuan Xiao, Yuchen Chen, Shiqing Ma +7
Watermarking is a technique to help identify the source of data points, which can be used to help prevent the misuse of protected datasets. Existing methods on code watermarking, l…
CooTest: An Automated Testing Approach for V2X Communication Systems
An Guo, Xinyu Gao, Zhenyu Chen +5
Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication tec…
Eliminating Backdoors in Neural Code Models for Secure Code Understanding
Weisong Sun, Yuchen Chen, Chunrong Fang +7
Neural code models (NCMs) have been widely used to address various code understanding tasks, such as defect detection. However, numerous recent studies reveal that such models are…
Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation
Yuan Xiao, Shiqing Ma, Juan Zhai +3
The robustness of convolutional neural networks (CNNs) is vital to modern AI-driven systems. It can be quantified by formal verification by providing a certified lower bound, withi…