LLMSecCode: Evaluating Large Language Models for Secure Coding Article Swipe

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Anton Rydén , Erik Näslund , Elad Michael Schiller , Magnus Almgren ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2408.16100

The rapid deployment of Large Language Models (LLMs) requires careful consideration of their effect on cybersecurity. Our work aims to improve the selection process of LLMs that are suitable for facilitating Secure Coding (SC). This raises challenging research questions, such as (RQ1) Which functionality can streamline the LLM evaluation? (RQ2) What should the evaluation measure? (RQ3) How to attest that the evaluation process is impartial? To address these questions, we introduce LLMSecCode, an open-source evaluation framework designed to assess LLM SC capabilities objectively. We validate the LLMSecCode implementation through experiments. When varying parameters and prompts, we find a 10% and 9% difference in performance, respectively. We also compare some results to reliable external actors, where our results show a 5% difference. We strive to ensure the ease of use of our open-source framework and encourage further development by external actors. With LLMSecCode, we hope to encourage the standardization and benchmarking of LLMs' capabilities in security-oriented code and tasks.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2408.16100
PDF: https://arxiv.org/pdf/2408.16100
OA Status: green
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2408.16100

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Title: LLMSecCode: Evaluating Large Language Models for Secure Coding

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-08-28

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Authors: Anton Rydén, Erik Näslund, Elad Michael Schiller, Magnus Almgren

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Landing page: https://arxiv.org/abs/2408.16100

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PDF URL: https://arxiv.org/pdf/2408.16100

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OA URL: https://arxiv.org/pdf/2408.16100

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Concepts: Computer science, Coding (social sciences), Natural language processing, Mathematics, Statistics

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