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Leveraging Large Language Models for advanced static code analysis: Assessing the feasibility of AI superseding traditional vulnerability detection methods
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DOI：10.1016/j.infsof.2026.108213.png)
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Abstract
En 中文
Due to limited contextual understanding, traditional Static Application Security Testing (SAST) tools often struggle to detect complex software vulnerabilities, especially in dynamically typed languages. Large Language Models (LLMs), with their advanced contextual comprehension and language-agnostic nature, offer a compelling alternative to conventional security analysis methods.
Keywords:
AI
LLM
Cybersecurity
SAST
Software vulnerabilities
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