Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.06237 v3 pith:BYGT64XX submitted 2024-05-10 cs.CR

classification cs.CR
keywords smartllmsgridapplicationsattacksmodelsrisksdata
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Technical Report on Resilient and Secure Large-Scale Energy Internet Systems

    eess.SY 2026-08 conditional novelty 5.0 of 10

    Energy Internet resilience should be assessed across coupled physical, operational, and cyber dimensions, and recovery actions should be screened for stored-energy adequacy, trustworthy information, and physical feasi...

  2. Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks

    cs.NI 2025-05 conditional novelty 4.0 of 10

    A review of LLM-based agents as autonomous cyberattackers, arguing that they lower attack costs, scale up threats, and outpace existing defenses.

  3. ThreMoLIA: Threat Modeling of Large Language Model-Integrated Applications

    cs.CR 2025-04 conditional novelty 4.0 of 10

    The authors propose an LLM-and-RAG-based threat modeling tool for LLM-integrated applications and report one early, unvalidated ChatGPT pilot as preliminary motivation.

Pith tools