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

2024/05/10 by Jiangnan Li, Li, Jiangnan, Yingyuan Yang +3 · 1 citation
Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Smart Grid Security and Resilience

paper · pdf · doi:10.48550/arxiv.2405.06237

openalex publication_date 2024/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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