CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912188392800256 |
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| author | Peng, Jinjun Cui, Leyi Huang, Kele Yang, Junfeng Ray, Baishakhi |
| author_facet | Peng, Jinjun Cui, Leyi Huang, Kele Yang, Junfeng Ray, Baishakhi |
| contents | Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrect code is often straightforward, detecting vulnerabilities in functionally correct code is more challenging, especially for developers with limited security knowledge, which poses considerable security risks of using LLM-generated code and underscores the need for robust evaluation benchmarks that assess both functional correctness and security. Current benchmarks like CyberSecEval and SecurityEval attempt to solve it but are hindered by unclear and impractical specifications, failing to assess both functionality and security accurately. To tackle these deficiencies, we introduce CWEval, a novel outcome-driven evaluation framework designed to enhance the evaluation of secure code generation by LLMs. This framework not only assesses code functionality but also its security simultaneously with high-quality task specifications and outcome-driven test oracles which provides high accuracy. Coupled with CWEval-bench, a multilingual, security-critical coding benchmark, CWEval provides a rigorous empirical security evaluation on LLM-generated code, overcoming previous benchmarks' shortcomings. Through our evaluations, CWEval reveals a notable portion of functional but insecure code produced by LLMs, and shows a serious inaccuracy of previous evaluations, ultimately contributing significantly to the field of secure code generation. We open-source our artifact at: https://github.com/Co1lin/CWEval . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation Peng, Jinjun Cui, Leyi Huang, Kele Yang, Junfeng Ray, Baishakhi Software Engineering Computation and Language Machine Learning Large Language Models (LLMs) have significantly aided developers by generating or assisting in code writing, enhancing productivity across various tasks. While identifying incorrect code is often straightforward, detecting vulnerabilities in functionally correct code is more challenging, especially for developers with limited security knowledge, which poses considerable security risks of using LLM-generated code and underscores the need for robust evaluation benchmarks that assess both functional correctness and security. Current benchmarks like CyberSecEval and SecurityEval attempt to solve it but are hindered by unclear and impractical specifications, failing to assess both functionality and security accurately. To tackle these deficiencies, we introduce CWEval, a novel outcome-driven evaluation framework designed to enhance the evaluation of secure code generation by LLMs. This framework not only assesses code functionality but also its security simultaneously with high-quality task specifications and outcome-driven test oracles which provides high accuracy. Coupled with CWEval-bench, a multilingual, security-critical coding benchmark, CWEval provides a rigorous empirical security evaluation on LLM-generated code, overcoming previous benchmarks' shortcomings. Through our evaluations, CWEval reveals a notable portion of functional but insecure code produced by LLMs, and shows a serious inaccuracy of previous evaluations, ultimately contributing significantly to the field of secure code generation. We open-source our artifact at: https://github.com/Co1lin/CWEval . |
| title | CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation |
| topic | Software Engineering Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2501.08200 |