⌕ Поиск по атласу

NIST · ИТ · 64715820c7b4825bf53b

64715820c7b4825bf53b · Exploring Prompt Patterns for Effective Vulnerability Repair in Real-World Code by Large Language Models

Exploring Prompt Patterns for Effective Vulnerability Repair in Real-World Code by Large Language Models

Large Language Models (LLMs) have shown promise in automating code vulnerability repair, but their effectiveness in handling real-world code remains limited. This paper investigates the capability of LLMs, in repairing vulnerabilities and proposes a

Официальный источник · 2025-06-05

Полные импортированные сведения карточки
{
  "title_en": "Exploring Prompt Patterns for Effective Vulnerability Repair in Real-World Code by Large Language Models",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2025-06-05",
  "date_original": "June 5, 2025",
  "url": "https://www.nist.gov/publications/exploring-prompt-patterns-effective-vulnerability-repair-real-world-code-large-language",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Yining Luo, Baobao Li, Anoop Singhal , Pei-Yu Tseng, Lan Zhang, Qingtian Zou, Xiaoyan Sun, Peng Liu",
  "abstract_excerpt_en": "Large Language Models (LLMs) have shown promise in automating code vulnerability repair, but their effectiveness in handling real-world code remains limited. This paper investigates the capability of LLMs, in repairing vulnerabilities and proposes a",
  "id": "64715820c7b4825bf53b",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Exploring Prompt Patterns for Effective Vulnerability Repair in Real-World Code by Large Language Models",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2025-06-05",
      "date_original": "June 5, 2025",
      "url": "https://www.nist.gov/publications/exploring-prompt-patterns-effective-vulnerability-repair-real-world-code-large-language",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Yining Luo, Baobao Li, Anoop Singhal , Pei-Yu Tseng, Lan Zhang, Qingtian Zou, Xiaoyan Sun, Peng Liu",
      "abstract_excerpt_en": "Large Language Models (LLMs) have shown promise in automating code vulnerability repair, but their effectiveness in handling real-world code remains limited. This paper investigates the capability of LLMs, in repairing vulnerabilities and proposes a"
    }
  ],
  "translation_state": "none",
  "possible_title_matches": 1
}