Risks of Practicing Large Language Models in Smart Grid: Threat Modeling and Validation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917991132692480 |
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| author | Li, Jiangnan Yang, Yingyuan Sun, Jinyuan |
| author_facet | Li, Jiangnan Yang, Yingyuan Sun, Jinyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06237 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Risks of Practicing Large Language Models in Smart Grid: Threat Modeling and Validation Li, Jiangnan Yang, Yingyuan Sun, Jinyuan Cryptography and Security 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. |
| title | Risks of Practicing Large Language Models in Smart Grid: Threat Modeling and Validation |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2405.06237 |