Risks of Practicing Large Language Models in Smart Grid: Threat Modeling and Validation

TL;DR

This paper systematically assesses the security risks of using large language models in smart grids, identifying potential attack vectors and validating their feasibility with real data and models.

Contribution

It introduces threat models for LLM-related attacks in smart grids and validates these risks through experiments with popular models and real smart grid data.

Findings

Attackers can inject malicious data into LLMs.

Attackers can extract sensitive domain knowledge from LLMs.

Risks pose significant threats to smart grid security and reliability.

Abstract

Large language models (LLMs) represent significant breakthroughs in artificial intelligence and hold potential for applications 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 evaluated the risks of LLMs and identified two major types of attacks relevant to potential smart grid LLM applications, presenting the corresponding threat models. We validated these attacks using popular LLMs and real smart grid data. Our validation demonstrates that attackers are capable of injecting bad data and retrieving domain knowledge from LLMs employed in different smart grid applications.