Leveraging Static Analysis for Feedback-Driven Security Patching in LLM-Generated Code.
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| Title: | Leveraging Static Analysis for Feedback-Driven Security Patching in LLM-Generated Code. |
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| Authors: | Alrashedy, Kamel, Aljasser, Abdullah, Tambwekar, Pradyumna, Gombolay, Matthew |
| Source: | Journal of Cybersecurity & Privacy; Dec2025, Vol. 5 Issue 4, p110, 29p |
| Subject Terms: | COMPUTER security vulnerabilities, LANGUAGE models, PROGRAM transformation, BENCHMARK problems (Computer science), SOFTWARE maintenance, SOURCE code |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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