Enhanced automated code vulnerability repair using large language models
Open accessAbstract
This research addresses the complex challenge of automated repair of code vulnerabilities, vital for enhancing digital security in an increasingly technology-driven world. The study introduces a novel and efficient format for the representation of code modification, using advanced Large Language Models (LLMs) such as Code Llama and Mistral. These models, fine-tuned on datasets featuring C/C++ code vulnerabilities, significantly improve the accuracy and adaptability of automated code repair techniques. A key finding is the enhanced repair accuracy of these models when compared to previous methods such as VulRepair, which underscores their practical utility and efficiency. The research also offers a critical assessment of current evaluation metrics, such as “Perfect Predictions”, and their limitations in reflecting the true capabilities of automated repair models in real-world scenarios. Following this, it underscores the importance of using test datasets devoid of train samples, emphasizing the need for dataset integrity to enhance the effectiveness of LLMs in code repair tasks. The significance of this work is its contribution to digital security, setting new standards for automated code vulnerability repair and paving the way for future advancements in the fields of cybersecurity and artificial intelligence. The study does not only highlight the potential of LLMs in enhancing code security but also fosters further exploration and research in these crucial areas.
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de-Fitero-Dominguez et al. (2024)'s fine-tuning on structured C code vulnerabilities enables automatic code repair without manual intervention.
“The output of LLM is also structured and can be directly patched, which enables the code to be repaired automatically without manual intervention.”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as de-Fitero-Dominguez et al. (2024)
The Data format section (pp. 9–10) describes structured model output that specifies where additions, replacements, and deletions go, and says this simplifies applying changes to the original code. The Material and methods section (p. 7) confirms fine-tuning on C/C++ vulnerability datasets. Our copy does not establish that repairs are applied automatically without manual intervention. (LLM, conf=0.82)
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- Authors
- de-Fitero-Dominguez, D., Garcia-Lopez, E., Garcia-Cabot, A., & Martinez-Herraiz, J.-J.
- Affiliations
- Universidad de Alcalá (ES)
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 138
- Pages
- 109291
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W4402393484
- Cited by
- 43
- References
- 74
- Citations by year
- 2026: 21 · 2025: 18 · 2024: 4
- FWCI
- 44.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- e_Buah (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- European Space Agency (TASE-UAH-23.032)
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
- Keywords
- automated program repair, large language models, C/C++ vulnerabilities, CodeLlama, Mistral, evaluation metrics, code security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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