Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.13459 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909538267955200 |
|---|---|
| author | Ghannoum, Ehab Ghafari, Mohammad |
| author_facet | Ghannoum, Ehab Ghafari, Mohammad |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Poisoned Source Code Detection in Code Models Ghannoum, Ehab Ghafari, Mohammad Cryptography and Security Machine Learning 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. |
| title | Poisoned Source Code Detection in Code Models |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2502.13459 |