Large language models (LLMs) are increasingly integrated into software development workflows, yet a growing body of evidence shows that the code they generate often contains security vulnerabilities. Prior work has primarily focused on measuring the prevalence of such issues, providing useful evidence of risk but limit...
Ali Aljaberi, Jian Kang, You-Cheng Sun· Proceedings of the 41st IEEE...· 0 citations
Large language models (LLMs) are increasingly embedded as core components of data-centric systems, supporting analytical decision making, and automated reasoning over large-scale, heterogeneous datasets. Yet their deployment in open-world environments raises fundamental challenges to security and trustworthiness: LLMs...
Lu Lin, Jinghui Chen, Ting Wang et al.· Proceedings of the 32nd ACM...· 0 citations
The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models brings together researchers and practitioners from data mining, machine learning, security, and responsible AI to address issues from a data-centric, system-level perspective.
Lu Lin, Jinghui Chen, Ting Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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