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cs.CR2026
Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs
Wenqi Chen, Ziyan Zhang, Bin Wang +3
While Large Language Models (LLMs) excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment…
cs.CR2026
Is Your Prompt Poisoning Code? Defect Induction Rates and Security Mitigation Strategies
Bin Wang, YiLu Zhong, MiDi Wan +4
Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a critical concern. Existing studies pred…
cs.CR2026
Learning to Generate Secure Code via Token-Level Rewards
Jiazheng Quan, Xiaodong Li, Bin Wang +5
Large language models (LLMs) have demonstrated strong capabilities in code generation, yet they remain prone to producing security vulnerabilities. Existing approaches commonly suf…