Computer Science > Cryptography and Security
arXiv:2502.13459 (cs)
[Submitted on 19 Feb 2025 (v1), last revised 16 Mar 2025 (this version, v2)]
Title:Poisoned Source Code Detection in Code Models
View a PDF of the paper titled Poisoned Source Code Detection in Code Models, by Ehab Ghannoum and 1 other authors
View PDFHTML (experimental)Abstract:Deep learning models have gained popularity for conducting various tasks involving source code. However, their black-box nature raises concerns about potential risks. One such risk is a poisoning attack, where an attacker intentionally contaminates the training set with malicious samples to mislead the model's predictions in specific scenarios. To protect source code models from poisoning attacks, we introduce CodeGarrison (CG), a hybrid deep-learning model that relies on code embeddings to identify poisoned code samples. We evaluated CG against the state-of-the-art technique ONION for detecting poisoned samples generated by DAMP, MHM, ALERT, as well as a novel poisoning technique named CodeFooler. Results showed that CG significantly outperformed ONION with an accuracy of 93.5%. We also tested CG's robustness against unknown attacks, achieving an average accuracy of 85.6% in identifying poisoned samples across the four attacks mentioned above.
| Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2502.13459 [cs.CR] |
| (orarXiv:2502.13459v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2502.13459 arXiv-issued DOI via DataCite | |
| Journal reference: | Journal of Systems and Software, Volume 226, 2025, 112384, ISSN 0164-1212 |
| Related DOI: | https://doi.org/10.1016/j.jss.2025.112384 DOI(s) linking to related resources |
Submission history
From: Mohammad Ghafari [view email][v1] Wed, 19 Feb 2025 06:16:07 UTC (3,575 KB)
[v2] Sun, 16 Mar 2025 15:05:59 UTC (3,599 KB)
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View a PDF of the paper titled Poisoned Source Code Detection in Code Models, by Ehab Ghannoum and 1 other authors
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