You see what I want you to see: poisoning vulnerabilities in neural code search
Abstract
Searching and reusing code snippets from open-source software repositories based on natural-language queries can greatly improve programming productivity.Recently, deep-learning-based approaches have become increasingly popular for code search. Despite substantial progress in training accurate models of code search, the robustness of these models has received little attention so far.
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The study adheres to standard dataset partitions and cleaning protocols established by prior studies to ensure consistency and comparability across all tasks.
“To ensure consistency and comparability across all tasks, we adhere to the standard dataset partitions and cleaning protocols as established by prior studies in the field [43].”
From Demonstration Attack against In-Context Learning for Code Intelligence (2024) · cites this paper as [43]
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Self-citation
- Authors
- Wan, Y., Zhang, S., Zhang, H., Sui, Y., Xu, G., Yao, D., Jin, H., & Sun, L.
- Affiliations
- Huazhong University of Science and Technology (CN)
- University of Newcastle Australia (AU)
- University of Technology Sydney (AU)
- Lehigh University (US)
- Venue
- Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2022-11-07
- Pages
- 1233-1245
- OpenAlex
- W4308627220
- Cited by
- 60
- References
- 27
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 20 · 2023: 7
- FWCI
- 10.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (publishedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- code search, poisoning attacks, adversarial robustness, open-source repositories, natural language queries, deep learning models, model robustness
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