  Github Copilot, trained on billions of lines of public code, has recently
become the buzzword in the computer science research and practice community.
Although it is designed to help developers implement safe and effective code
with powerful intelligence, practitioners and researchers raise concerns about
its ethical and security problems, e.g., should the copyleft licensed code be
freely leveraged or insecure code be considered for training in the first
place? These problems pose a significant impact on Copilot and other similar
products that aim to learn knowledge from large-scale open-source code through
deep learning models, which are inevitably on the rise with the fast
development of artificial intelligence. To mitigate such impacts, we argue that
there is a need to invent effective mechanisms for protecting open-source code
from being exploited by deep learning models. Here, we design and implement a
prototype, CoProtector, which utilizes data poisoning techniques to arm source
code repositories for defending against such exploits. Our large-scale
experiments empirically show that CoProtector is effective in achieving its
purpose, significantly reducing the performance of Copilot-like deep learning
models while being able to stably reveal the secretly embedded watermark
backdoors.

Cryptography and Security;Artificial Intelligence;Software Engineering