Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demographic attributes. While prior work mainly studies direct code generation, bias in reasoning-based generation remains underexplored. We conduc...
Wei-Feng Sun, Jie-Ke Shi, Zhou Yang et al.· 0 citations
This paper proposes ARIA, an automated red-teaming framework for crafting covert and effective backdoored instructions against customized LLMs, which significantly outperforms existing attacks in evading platform-side and user-side detection, and stays effective against existing defense methods.
Yuchen Chen, Wei Cheng, Yuan Xiao et al.· Proceedings of the 41st IEEE...· 0 citations
The first systematic study of model editing as a model-level hardening mechanism for secure code generation is conducted, evaluating 3 state-of-the-art editing methods across diverse LLM families and comparing them with CoSec, a representative inference-time approach, focusing on security, robustness, generalization, a...
Wei-Feng Sun, Quan-Jun Zhang, Yuchen Chen et al.· 0 citations
Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question. Existing datasets and benchmarks are limited in scale, domain coverage, or executable verification, leaving the true gap between current LLMs and reliable scientific code generators...
Weifeng Sun, Ye Fan, Yuchen Chen et al.· arXiv.org· 0 citations
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