Applying Large Language Models to Power Systems: Potential Security Threats
Jiaqi Ruan, Gaoqi Liang, Huan Zhao, Guolong Liu, Xianzhuo Sun, Jing Qiu, Zhao Xu, Fushuan Wen, Zhao Yang Dong
arXiv:2311.13361v258 citationsh-index: 25
Originality Synthesis-oriented
AI Analysis
This addresses security risks in power systems for operators and researchers, but it is incremental as it focuses on analysis rather than solutions.
The paper identifies potential security threats from applying large language models to power systems, highlighting the need for countermeasures.
Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures.