Azala

Leveraging Static Analysis for Feedback-Driven Security Patching in LLM-Generated Code

Gorde:
Argitaratua izan da:Journal of Cybersecurity and Privacy vol. 5, no. 4 (2025), p. 110-139
Egile nagusia: Kamel, Alrashedy
Beste egile batzuk: Aljasser Abdullah, Tambwekar Pradyumna, Gombolay Matthew
Argitaratua:
MDPI AG
Gaiak:
Language
Design
Coding standards
Datasets
Automation
Documentation
Feedback
Debugging
Access control
Large language models
Software engineering
Natural language
Benchmarks
Sarrera elektronikoa:Citation/Abstract
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Laburpena:Large language models (LLMs) have shown remarkable potential for automatic code generation. Yet, these models share a weakness with their human counterparts: inadvertently generating code with security vulnerabilities that could allow unauthorized attackers to access sensitive data or systems. In this work, we propose Feedback-Driven Security Patching (FDSP), wherein LLMs automatically refine vulnerable generated code. The key to our approach is a unique framework that leverages automatic static code analysis to enable the LLM to create and implement potential solutions to code vulnerabilities. Further, we curate a novel benchmark, PythonSecurityEval, that can accelerate progress in the field of code generation by covering diverse, real-world applications, including databases, websites, and operating systems. Our proposed FDSP approach achieves the strongest improvements, reducing vulnerabilities by up to 33% when evaluated with Bandit and 12% with CodeQL and outperforming baseline refinement methods.
ISSN:2624-800X
DOI:10.3390/jcp5040110
Baliabidea:ABI/INFORM Global