  AI-based code generators have gained a fundamental role in assisting
developers in writing software starting from natural language (NL). However,
since these large language models are trained on massive volumes of data
collected from unreliable online sources (e.g., GitHub, Hugging Face), AI
models become an easy target for data poisoning attacks, in which an attacker
corrupts the training data by injecting a small amount of poison into it, i.e.,
astutely crafted malicious samples. In this position paper, we address the
security of AI code generators by identifying a novel data poisoning attack
that results in the generation of vulnerable code. Next, we devise an extensive
evaluation of how these attacks impact state-of-the-art models for code
generation. Lastly, we discuss potential solutions to overcome this threat.
