Large Language Models in Cybersecurity: A Survey of Applications, Vulnerabilities, and Defense Techniques

Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as the Internet of Things (IoT), blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.

Authors

Institutions

Publication Details

Journal
AI
Published
2025-09-05
DOI
https://doi.org/10.3390/ai6090216
Citations
16
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
23.76
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Large Language Models in Cybersecurity: A Survey of Applications, Vulnerabilities, and Defense Techniques

Aziz Mohaisen, Mohammad AlKhanafseh, Niveen O. Jaffal
16 citations
AI
Advanced Malware Detection Techniques
23.76
article

Large Language Models in Cybersecurity: A Survey of Applications, Vulnerabilities, and Defense Techniques

Aziz Mohaisen, Mohammad AlKhanafseh, Niveen O. Jaffal
article en
16 citations

Abstract

Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as the Internet of Things (IoT), blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.

AIVol. 6(9)
University of Central Florida (US), Birzeit University (PS)
Openalex Percentile: Top 0%
Advanced Malware Detection Techniques
23.76
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Large Language Models in Cybersecurity: A Survey of Applications, Vulnerabilities, and Defense Techniques — Aziz Mohaisen, Mohammad AlKhanafseh, et al. · AI (2025) | TGRS Research Map | TGRS