AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing
<|reference_start|>AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing: Recent advancements in automatic code generation using large language models (LLMs) have brought us closer to fully automated secure software development. However, existing approaches often rely on a single agent for code generation, which struggles to produce secure, vulnerability-free code. Traditional program synthesis with LLMs has primarily focused on functional correctness, often neglecting critical dynamic security implications that happen during runtime. To address these challenges, we propose AutoSafeCoder, a multi-agent framework that leverages LLM-driven agents for code generation, vulnerability analysis, and security enhancement through continuous collaboration. The framework consists of three agents: a Coding Agent responsible for code generation, a Static Analyzer Agent identifying vulnerabilities, and a Fuzzing Agent performing dynamic testing using a mutation-based fuzzing approach to detect runtime errors. Our contribution focuses on ensuring the safety of multi-agent code generation by integrating dynamic and static testing in an iterative process during code generation by LLM that improves security. Experiments using the SecurityEval dataset demonstrate a 13% reduction in code vulnerabilities compared to baseline LLMs, with no compromise in functionality.<|reference_end|>
@article{nunez2024autosafecoder:,
    title={AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation
  through Static Analysis and Fuzz Testing},
    author={Ana Nunez, Nafis Tanveer Islam, Sumit Kumar Jha, Peyman Najafirad},
    journal={arXiv preprint arXiv:2409.10737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10737},
    primaryClass={cs.SE cs.AI}
}