Unlike traditional backdoor attacks that focus on specific class labels, our approach aims to induce poisoned models to predict future data as a predefined target pattern.

We consider the problem of crystal materials generation using language models (LMs).

Upon increasing the depth, the over-smoothing issue arises: a GCN's

However, this assumption can possibly fail in real-world scenarios.