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A Survey of Large Language Models in Cybersecurity

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arxiv 2402.16968 v1 pith:CEAUDMO2 submitted 2024-02-26 cs.CR cs.AI

classification cs.CRcs.AI
keywords cybersecurityfieldlanguagelimitationsmodelslargellmssurvey
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) have quickly risen to prominence due to their ability to perform at or close to the state-of-the-art in a variety of fields while handling natural language. An important field of research is the application of such models at the cybersecurity context. This survey aims to identify where in the field of cybersecurity LLMs have already been applied, the ways in which they are being used and their limitations in the field. Finally, suggestions are made on how to improve such limitations and what can be expected from these systems once these limitations are overcome.

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Cited by 2 Pith papers

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

  1. A Sociotechnical, Practitioner-Centered Approach to Technology Adoption in Cybersecurity Operations: An LLM Case

    cs.CR 2026-04 unverdicted novelty 5.0 of 10

    A six-month ethnographic co-creation project in a real SOC demonstrates that practitioner involvement in LLM tool design can overcome typical adoption barriers in cybersecurity operations.

  2. XekRung Technical Report

    cs.CR 2026-04 unverdicted novelty 3.0 of 10

    XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.

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