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English(EN) RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code

新流程生成侧重安全的LLM代码数据集

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RiskyPy，一个新自动化流程，旨在创建用于评估大型语言模型（LLM）生成代码的安全性和功能的数据集。该系统生成针对特定模型能力校准的提示，解决了现有基准测试的成本和校准问题。使用Python和2025 CWE Top 25中的18个通用弱点枚举（CWE）进行的初步实验表明，生成任务在功能上是可访问的，其中很大一部分产生了功能性代码，同时仍包含漏洞，从而能够联合评估安全性和功能性改进。 AI

影响 能够更全面地评估LLM生成代码的安全性和功能。

排序理由 该集群描述了一篇详细介绍新数据集生成方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新流程生成侧重安全的LLM代码数据集

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该集群描述了一篇详细介绍新数据集生成方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zacharie Chenail-Larcher, Daniele Lunghi, Melissa Tessa, Quentin Sti\'evenart, Jacques Klein, Tegawend\'e F. Bissyand\'e, Florent Avellaneda, Naouel Moha ·

    RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated Code

    arXiv:2610.40735v1 Announce Type: cross Abstract: Evaluating approaches that improve the security of LLM code generation requires datasets that jointly assess security and functionality and remain adapted to the capabilities of the models being studied. However, existing benchmar…

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