SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques
Abstract
Automated source code generation is currently a popular machine-learning-based task. It can be helpful for software developers to write functionally correct code from a given context. However, just like human developers, a code generation model can produce vulnerable code, which the developers can mistakenly use. For this reason, evaluating the security of a code generation model is a must. In this paper, we describe SecurityEval, an evaluation dataset to fulfill this purpose. It contains 130 samples for 75 vulnerability types, which are mapped to the Common Weakness Enumeration (CWE). We also demonstrate using our dataset to evaluate one open-source (i.e., InCoder) and one closed-source code generation model (i.e., GitHub Copilot).
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SecurityEval focuses on the security evaluation of code generation models to prevent the creation of vulnerable code and thus avoid potential misuse by developers.
“SecurityEval (Siddiq and Santos 2022) focuses on the security evaluation of code generation models to prevent the creation of vulnerable code and thus avoid potential misuse by developers.”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as Siddiq and Santos 2022
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References 18 · Cited by 101
- Authors
- Siddiq, M. L., & Santos, J. C. S.
- Affiliations
- University of Notre Dame (US)
- Venue
- Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security
- Publisher
- ACM
- Published
- 2022-11-07
- Pages
- 29-33
- OpenAlex
- W4308627645
- Cited by
- 101
- References
- 18
- Citations by year
- 2026: 41 · 2025: 35 · 2024: 21 · 2023: 4
- FWCI
- 13.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- code vulnerabilities, Common Weakness Enumeration, GitHub Copilot
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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