  Large Language Model (LLM) is a significant breakthrough in artificial
intelligence (AI) and holds considerable potential for application within smart
grids. However, as demonstrated in previous literature, AI technologies are
susceptible to various types of attacks. It is crucial to investigate and
evaluate the risks associated with LLMs before deploying them in critical
infrastructure like smart grids. In this paper, we systematically evaluate the
vulnerabilities of LLMs and identify two major types of attacks relevant to
smart grid LLM applications, along with presenting the corresponding threat
models. We then validate these attacks using popular LLMs, utilizing real smart
grid data. Our validation demonstrates that attackers are capable of injecting
bad data and retrieving domain knowledge from LLMs employed in smart grid
scenarios.
