NIST · ИТ · f4102640875be5588b33
Evaluating Large Language Models for Real World Vulnerability Repair in C/C++ Code
The advent of Large Language Models (LLMs) has enabled advancement in automated code generation, translation, and summarization. Despite their promise, evaluating the use of LLMs in repairing real-world code vulnerabilities remains underexplored. In this
NIST · ИТОфициальный источник · 2024-06-19
{
"title_en": "Evaluating Large Language Models for Real World Vulnerability Repair in C/C++ Code",
"designation": "",
"series": "",
"status": "",
"date": "2024-06-19",
"date_original": "June 19, 2024",
"url": "https://www.nist.gov/publications/evaluating-large-language-models-real-world-vulnerability-repair-cc-code",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Lan Zhang, Qingtian Zou, Anoop Singhal , Xiaoyan Sun, Peng Liu",
"abstract_excerpt_en": "The advent of Large Language Models (LLMs) has enabled advancement in automated code generation, translation, and summarization. Despite their promise, evaluating the use of LLMs in repairing real-world code vulnerabilities remains underexplored. In this",
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{
"title_en": "Evaluating Large Language Models for Real World Vulnerability Repair in C/C++ Code",
"designation": "",
"series": "",
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"date": "2024-06-19",
"date_original": "June 19, 2024",
"url": "https://www.nist.gov/publications/evaluating-large-language-models-real-world-vulnerability-repair-cc-code",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Lan Zhang, Qingtian Zou, Anoop Singhal , Xiaoyan Sun, Peng Liu",
"abstract_excerpt_en": "The advent of Large Language Models (LLMs) has enabled advancement in automated code generation, translation, and summarization. Despite their promise, evaluating the use of LLMs in repairing real-world code vulnerabilities remains underexplored. In this"
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