SecureFixAgent: A Hybrid LLM Agent for Automated Python Static Vulnerability Repair
This addresses the challenge of securing large software codebases with high false positives and lack of repair capabilities, offering a hybrid solution for developers, though it is incremental as it builds on existing tools like Bandit and LLMs.
The paper tackles the problem of automated vulnerability repair in Python code by integrating static analysis with lightweight LLMs, resulting in a 13.51% improvement in fix accuracy and a 10.8% reduction in false positives compared to static analysis alone.
Modern software development pipelines face growing challenges in securing large codebases with extensive dependencies. Static analysis tools like Bandit are effective at vulnerability detection but suffer from high false positives and lack repair capabilities. Large Language Models (LLMs), in contrast, can suggest fixes but often hallucinate changes and lack self-validation. We present SecureFixAgent, a hybrid repair framework integrating Bandit with lightweight local LLMs (<8B parameters) in an iterative detect-repair-validate loop. To improve precision, we apply parameter-efficient LoRA-based fine-tuning on a diverse, curated dataset spanning multiple Python project domains, mitigating dataset bias and reducing unnecessary edits. SecureFixAgent uses Bandit for detection, the LLM for candidate fixes with explanations, and Bandit re-validation for verification, all executed locally to preserve privacy and reduce cloud reliance. Experiments show SecureFixAgent reduces false positives by 10.8% over static analysis, improves fix accuracy by 13.51%, and lowers false positives by 5.46% compared to pre-trained LLMs, typically converging within three iterations. Beyond metrics, developer studies rate explanation quality 4.5/5, highlighting its value for human trust and adoption. By combining verifiable security improvements with transparent rationale in a resource-efficient local framework, SecureFixAgent advances trustworthy, automated vulnerability remediation for modern pipelines.