mlsanityData to 7 Oct 2026

A Survey of Large Language Models in Cybersecurity

arXiv 2402.16968 ↗First posted 26 Feb 2024cs.CR cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 45 of its 85 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

45 of 85 references.

References · 45

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33 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 2.

8 papers.

  1. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 70
  2. 2024ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Modelscited by 11
  3. 2025L2M-AID: Autonomous Cyber-Physical Defense by Fusing Semantic Reasoning of Large Language Models with Multi-Agent Reinforcement Learning (Preprint)cited by 2
  4. 2025From Flows to Words: Can Zero-/Few-Shot LLMs Detect Network Intrusions? A Grammar-Constrained, Calibrated Evaluation on UNSW-NB15cited by 1
  5. 2025From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testingcited by 0
  6. 2025The Road of Adaptive AI for Precision in Cybersecuritycited by 0
  7. 2026Diagnosing Generalization Failures in Fine-Tuned LLMs: A Cross-Architectural Study on Phishing Detectioncited by 0
  8. 2026LAAF: A Layered Accountability Architecture Framework for LLM Applicationscited by 0