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arXiv · 2006.06841

Backdoors in Neural Models of Source Code

Goutham Ramakrishnan·Aws Albarghouthi
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Abstract

Deep neural networks are vulnerable to a range of adversaries. A particularly pernicious class of vulnerabilities are backdoors, where model predictions diverge in the presence of subtle triggers in inputs. An attacker can implant a backdoor by poisoning the training data to yield a desired target prediction on triggered inputs. We study backdoors in the context of deep-learning for source code. (1) We define a range of backdoor classes for source-code tasks and show how to poison a dataset to install such backdoors. (2) We adapt and improve recent algorithms from robust statistics for our setting, showing that backdoors leave a spectral signature in the learned representation of source code, thus enabling detection of poisoned data. (3) We conduct a thorough evaluation on different architectures and languages, showing the ease of injecting backdoors and our ability to eliminate them.

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BibTeXRIS

Goutham Ramakrishnan, Aws Albarghouthi. 2020-06-11. Backdoors in Neural Models of Source Code. https://doi.org/10.1109/icpr56361.2022.9956690

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