56 citations · 118 across the 24 of their papers we have counts for
6 papers · 2 filters
Understanding and Improving Model Editing for Secure Code Generation
Weifeng Sun, Quanjun Zhang, Yuchen Chen +3
Large language models (LLMs) are widely used for code generation, yet they can reproduce vulnerable implementations learned from insecure training patterns. Prior work has mainly e…
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…
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…