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New pipeline generates security-focused LLM code datasets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RiskyPy, a new automated pipeline designed to create datasets for evaluating the security and functionality of code generated by large language models (LLMs). This system generates prompts calibrated to specific model capabilities, addressing the cost and calibration issues of existing benchmarks. Initial experiments using Python and 18 Common Weakness Enumerations (CWEs) from the 2025 CWE Top 25 demonstrated that the generated tasks are functionally accessible, with a significant portion yielding functional code while still containing vulnerabilities, thus enabling joint evaluation of security and functionality improvements. AI

IMPACT Enables more robust evaluation of LLM-generated code security and functionality.

RANK_REASON The cluster describes a research paper detailing a new methodology for dataset generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New pipeline generates security-focused LLM code datasets

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The cluster describes a research paper detailing a new methodology for dataset generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [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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