Imagen de Portada

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

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jaffal, Niveen O., Alkhanafseh, Mohammed, Mohaisen, David
Formato:	Preprint
Publicado:	2025
Materias:	Cryptography and Security Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Enhancing Reliability in LLM-Based Secure Code Generation
por: Kharma, Mohammed F., et al.
Publicado: (2026)

An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
por: Kharma, Mohammed, et al.
Publicado: (2026)

Enhancing Vulnerability Reports with Automated and Augmented Description Summarization
por: Althebeiti, Hattan, et al.
Publicado: (2025)

Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
por: Sabbah, Ahmed, et al.
Publicado: (2026)

Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
por: Sabbah, Ahmed, et al.
Publicado: (2026)

A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
por: Kharma, Mohammed, et al.
Publicado: (2026)

Evaluating Large Language Models in Vulnerability Detection Under Variable Context Windows
por: Lin, Jie, et al.
Publicado: (2025)

Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Models
por: Dang, Trung Cuong, et al.
Publicado: (2025)

Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
por: Peng, Benji, et al.
Publicado: (2024)

Finetuning Large Language Models for Vulnerability Detection
por: Shestov, Alexey, et al.
Publicado: (2024)

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
por: Sabbah, Ahmed, et al.
Publicado: (2025)

A Survey on Model Extraction Attacks and Defenses for Large Language Models
por: Zhao, Kaixiang, et al.
Publicado: (2025)

Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
por: Abdali, Sara, et al.
Publicado: (2024)

Large Language Models in Cybersecurity: State-of-the-Art
por: Motlagh, Farzad Nourmohammadzadeh, et al.
Publicado: (2024)

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey
por: Huang, Tiansheng, et al.
Publicado: (2024)

SecureBERT 2.0: Advanced Language Model for Cybersecurity Intelligence
por: Aghaei, Ehsan, et al.
Publicado: (2025)

Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directions
por: Li, Wenjuan, et al.
Publicado: (2026)

AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models
por: Qiu, Le, et al.
Publicado: (2025)

A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
por: Homaei, MohammadHossein, et al.
Publicado: (2023)

Through the Looking Glass: LLM-Based Analysis of AR/VR Android Applications Privacy Policies
por: Alghamdi, Abdulaziz, et al.
Publicado: (2025)

PLeak: Prompt Leaking Attacks against Large Language Model Applications
por: Hui, Bo, et al.
Publicado: (2024)

Adversarial Text Purification: A Large Language Model Approach for Defense
por: Moraffah, Raha, et al.
Publicado: (2024)

Jailbreak Attacks and Defenses Against Large Language Models: A Survey
por: Yi, Sibo, et al.
Publicado: (2024)

Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
por: Liu, Fuqiang, et al.
Publicado: (2024)

Every Character Counts: From Vulnerability to Defense in Phishing Detection
por: Chiper, Maria, et al.
Publicado: (2025)

Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
por: Xu, Jiashu, et al.
Publicado: (2023)

Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities
por: Alam, Md Tauseef, et al.
Publicado: (2024)

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures
por: Aguilera-Martínez, Francisco, et al.
Publicado: (2025)

Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
por: Zhang, Han, et al.
Publicado: (2024)

Security and Quality in LLM-Generated Code: A Multi-Language, Multi-Model Analysis
por: Kharma, Mohammed, et al.
Publicado: (2025)

Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey
por: Truong, Vu Tuan, et al.
Publicado: (2024)

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
por: Sai, Siva, et al.
Publicado: (2025)

Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers
por: Rajhans, Mona, et al.
Publicado: (2026)

A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments
por: Zhao, Kaixiang, et al.
Publicado: (2025)

Privacy Auditing of Large Language Models
por: Panda, Ashwinee, et al.
Publicado: (2025)

Watermark Stealing in Large Language Models
por: Jovanović, Nikola, et al.
Publicado: (2024)

Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning in Cybersecurity Games
por: Malloy, Tailia, et al.
Publicado: (2023)

On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
por: Dehrouyeh, Fatemeh, et al.
Publicado: (2024)

Model-based Large Language Model Customization as Service
por: Wu, Zhaomin, et al.
Publicado: (2024)

Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models
por: Krishna, Arjun, et al.
Publicado: (2025)