Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities
Open accessAbstract
This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification , cyber threat intelligence , malware detection , and phishing detection . We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT , Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks , emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.
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LLMs are vulnerable to prompt injection and potential information leakage.
“LLMs also introduce security risks, being vulnerable to prompt injection and potential information leakage [18].”
From Can Large Language Models Be Trusted for AI-Based Intrusion Detection in Cybersecurity? An Evidence-Grounded Log Analysis (2026) · cites this paper as [18]
LLMs offer a promising avenue to analyze unstructured logs and derive insights in a more flexible manner, potentially correlating diverse events, interpreting log semantics, and generating intuitive explanations.
“Large language models (LLMs) offer a promising avenue to analyze unstructured logs and derive insights in a more flexible manner, potentially correlating diverse events, interpreting log semantics, and generating intuitive explanations [17], [18].”
From Can Large Language Models Be Trusted for AI-Based Intrusion Detection in Cybersecurity? An Evidence-Grounded Log Analysis (2026) · cites this paper as [18]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · see citing works
A Survey on Evaluation of Large Language Models
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 2,842 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
Gate-variants of Gated Recurrent Unit (GRU) neural networks
Dey, R., & Salem, F. M. (2017) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2017.8053243 · Cited by 2,430 · see citing works
Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. (2019) · arXiv · doi:10.48550/arxiv.1902.00751 · Cited by 2,270 · see citing works
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100 · see citing works
QLoRA: efficient finetuning of quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0441 · Cited by 1,814 · see citing works
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2024) · ACM Computing Surveys · doi:10.1145/3605943 · Cited by 1,387 · see citing works
WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex InstructionsPreprint
Xu, C., Sun, Q., Zheng, K., Geng, X.-B., Zhao, P., Feng, J., Tao, C., Lin, Q.-W., & Jiang, D. (2023) · International Conference on Learning Representations · doi:10.48550/arxiv.2304.12244 · Cited by 1,362 · see citing works
Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L. A., & Janicke, H. (2022) · IEEE Access · doi:10.1109/access.2022.3165809 · Cited by 1,308 · see citing works
Self-citationDeepfake Video Detection Using Recurrent Neural Networks
Guera, D., & Delp, E. J. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639163 · Cited by 1,285 · see citing works
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., & Ré, C. (2022) · arXiv · doi:10.48550/arxiv.2205.14135 · Cited by 1,214 · see citing works
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · UCL Discovery (University College London) · Cited by 1,187 · see citing works
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., & Zhong, Y. (2018) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23158 · Cited by 1,174 · see citing works
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Li, Z., Zou, D., Xu, S., Jin, H., Zhu, Y., & Chen, Z. (2021) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3051525 · Cited by 847 · see citing works
HellaSwag: Can a Machine Really Finish Your Sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1472 · Cited by 830 · see citing works
The Falcon Series of Open Language ModelsPreprint
Almazrouei, E., Alobeidli, H., AlShamsi, A., Cappelli, A., Cojocaru, R.-A., Debbah, M., Goffinet, É., Hesslow, D., Launay, J., Malartic, Q., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2311.16867 · Cited by 813 · see citing works
Self-citationYan, Q., Yu, F. R., Gong, Q., & Li, J. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2487361 · Cited by 807 · see citing works
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Russell, R. L., Kim, L. Y., Hamilton, L., Lazovich, T., Harer, J., Özdemir, O., Ellingwood, P. M., & McConley, M. W. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00120 · Cited by 696 · see citing works
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2338 · Cited by 686 · see citing works
Explainability for Large Language Models: A Survey
Zhao, H., Chen, H., Yang, F., Liu, N., Deng, H., Cai, H., Wang, S., Yin, D., & Du, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3639372 · Cited by 681 · see citing works
Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023) · Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security · doi:10.1145/3605764.3623985 · Cited by 667 · see citing works
Large language models for software engineering: survey and open problems
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023) · 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) · doi:10.1109/icse-fose59343.2023.00008 · Cited by 650 · see citing works
Software Testing With Large Language Models: Survey, Landscape, and Vision
Wang, J., Huang, Y., Chen, C., Liu, Z., Wang, S., & Wang, Q. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3368208 · Cited by 573 · see citing works
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.04664 · Cited by 536 · see citing works
A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
Fan, J., Li, Y., Wang, S., & Nguyen, T. N. (2020) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3379597.3387501 · Cited by 533 · see citing works
QLoRA: Efficient Finetuning of Quantized LLMs
Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv · doi:10.48550/arxiv.2305.14314 · Cited by 507 · see citing works
Kasongo, S. M. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.12.010 · Cited by 462 · see citing works
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
Wang, Y., Le, H., Gotmare, A., Bui, N., Li, J., & Hoi, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.68 · Cited by 459 · see citing works
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
Wang, Y., Le, H., Gotmare, A. D., Bui, N. D. Q., Li, J., & Hoi, S. C. H. (2023) · arXiv · doi:10.48550/arxiv.2305.07922 · Cited by 459
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebron, F., & Sanghai, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.298 · Cited by 454 · see citing works
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebrón, F., & Sanghai, S. (2023) · arXiv · doi:10.48550/arxiv.2305.13245 · Cited by 447 · see citing works
Fu, M. C., & Tantithamthavorn, C. K. (2022) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/3524842.3528452 · Cited by 424 · see citing works
ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
Qi, W., Yan, Y., Gong, Y., Liu, D., Duan, N., Chen, J., Zhang, R., & Zhou, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.217 · Cited by 401 · see citing works
Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M. A., Lestable, T., & Thandi, N. S. (2024) · IEEE Access · doi:10.1109/access.2024.3363469 · Cited by 397 · see citing works
Self-citationLiu, J., Xia, C. S., Wang, Y., & Zhang, L. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0943 · Cited by 384 · see citing works
A Survey on Multimodal Large Language Models for Autonomous Driving
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00106 · Cited by 369 · see citing works
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Y., Chen, Q., Kim, W., Comeau, D. C., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad493 · Cited by 359 · see citing works
DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids
Ferrag, M. A., & Maglaras, L. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2922936 · Cited by 358 · see citing works
Self-citationRTIDS: A Robust Transformer-Based Approach for Intrusion Detection System
Wu, Z., Zhang, H., Wang, P., & Sun, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3182333 · Cited by 356 · see citing works
AFLNET: A Greybox Fuzzer for Network Protocols
Pham, V.-T., Böhme, M., & Roychoudhury, A. (2020) · doi:10.1109/icst46399.2020.00062 · Cited by 346 · see citing works
An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units
Xu, C., Shen, J., Du, X., & Zhang, F. (2018) · IEEE Access · doi:10.1109/access.2018.2867564 · Cited by 339 · see citing works
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing
Osanaiye, O. A., Cai, H., Choo, K. R., Dehghantanha, A., Xu, Z., & Dlodlo, M. E. (2016) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-016-0623-3 · Cited by 319 · see citing works
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models
Zhang, H., Song, H., Li, S., Zhou, M., & Song, D. (2024) · ACM Computing Surveys · doi:10.1145/3617680 · Cited by 317 · see citing works
Multimodal Large Language Models: A Survey
Wu, J., Gan, W., Chen, Z., Wan, S., & Yu, P. S. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386743 · Cited by 309 · see citing works
Botnet in DDoS Attacks: Trends and Challenges
Hoque, N., Bhattacharyya, D. K., & Kalita, J. K. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2457491 · Cited by 300 · see citing works
CVEfixes: automated collection of vulnerabilities and their fixes from open-source software
Bhandari, G. P., Naseer, A., & Moonen, L. (2021) · doi:10.1145/3475960.3475985 · Cited by 292 · see citing works
Design and Development of RNN Anomaly Detection Model for IoT Networks
Ullah, I., & Mahmoud, Q. H. (2022) · IEEE Access · doi:10.1109/access.2022.3176317 · Cited by 292 · see citing works
Zhou, H., Hu, C., Yuan, Y., Cui, Y., Jin, Y., Chen, C., Wu, H., Yuan, D., Jiang, L., Wu, D., et al. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3465447 · Cited by 277 · see citing works
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
Chen, Y., Ding, Z., Alowain, L., Chen, X., & Wagner, D. (2023) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3607199.3607242 · Cited by 273 · see citing works
Do Users Write More Insecure Code with AI Assistants?
Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2023) · Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3576915.3623157 · Cited by 259 · see citing works
Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review
Dixit, P., & Silakari, S. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100317 · Cited by 257 · see citing works
Invited Paper: VerilogEval: Evaluating Large Language Models for Verilog Code Generation
Liu, M., Pinckney, N., Khailany, B., & Ren, H. M. (2023) · doi:10.1109/iccad57390.2023.10323812 · Cited by 253 · see citing works
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
Yan, J., Yadav, V., LI, S., Chen, L., Tang, Z., Wang, H., Srinivasan, V., Ren, X., & Jin, H.-X. (2023) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2024.naacl-long.337 · Cited by 247 · see citing works
Large Language Model guided Protocol Fuzzing
Meng, R., Mirchev, M., Böhme, M., & Roychoudhury, A. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24556 · Cited by 246 · see citing works
A Survey of Knowledge Enhanced Pre-Trained Language Models
Hu, L., Liu, Z., Zhao, Z., Hou, L., Nie, L., & Li, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3310002 · Cited by 224 · see citing works
Friha, O., Ferrag, M. A., Kantarcı, B., Çakmak, B., Ozgun, A., & Ghoualmi‐Zine, N. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3456549 · Cited by 209 · see citing works
Self-citationAlzaabi, F. R., & Mehmood, A. (2024) · IEEE Access · doi:10.1109/access.2024.3369906 · Cited by 204 · see citing works
Universal and Transferable Adversarial Attacks on Aligned Language Models
Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., & Fredrikson, M. (2023) · arXiv · doi:10.48550/arxiv.2307.15043 · Cited by 197 · see citing works
Foundation and large language models: fundamentals, challenges, opportunities, and social impacts
Myers, D., Mohawesh, R., Chellaboina, V. I., Sathvik, A. L., Venkatesh, P., Ho, Y.-H., Henshaw, H., Al-Hawawreh, M. S., Berdik, D., & Jararweh, Y. (2023) · Cluster Computing · doi:10.1007/s10586-023-04203-7 · Cited by 197 · see citing works
Vulnerability Detection with Code Language Models: How Far are We?
Ding, Y., Fu, Y., Ibrahim, O., Sitawarin, C., Chen, X., Alomair, B., Wagner, D., Ray, B., & Chen, Y. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00038 · Cited by 187 · see citing works
DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence
Guo, D., Zhu, Q., Yang, D., Xie, Z., Dong, K., Zhang, W., Chen, G., Bi, X., Wu, Y., Li, Y. K., et al. (2024) · arXiv · doi:10.48550/arxiv.2401.14196 · Cited by 180 · see citing works
Automated identification of security issues from commit messages and bug reports
Zhou, Y., & Sharma, A. (2017) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3106237.3117771 · Cited by 180 · see citing works
Hanif, H., Nasir, M. H. N. M., Razak, M. F. A., Firdaus, A., & Anuar, N. B. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103009 · Cited by 163 · see citing works
Host Based Intrusion Detection System with Combined CNN/RNN Model
Chawla, A., Lee, B., Fallon, S., & Jacob, P. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-13453-2_12 · Cited by 161 · see citing works
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
Cinà, A. E., Grosse, K., Demontis, A., Vascon, S., Zellinger, W., Moser, B. A., Oprea, A., Biggio, B., Pelillo, M., & Roli, F. (2023) · ACM Computing Surveys · doi:10.1145/3585385 · Cited by 152 · see citing works
D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
Zheng, Y., Pujar, S., Lewis, B. L., Buratti, L., Epstein, E., Yang, B., Laredo, J., Morari, A., & Su, Z. (2021) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00020 · Cited by 148 · see citing works
Transformer-Based Language Models for Software Vulnerability Detection
Thapa, C., Jang, S. I., Ahmed, M. E., Camtepe, S. A., Pieprzyk, J., & Nepal, . (2022) · Annual Computer Security Applications Conference · doi:10.1145/3564625.3567985 · Cited by 138 · see citing works
VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python
Wartschinski, L., Noller, Y., Vogel, T., Kehrer, T., & Grunske, L. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2021.106809 · Cited by 130 · see citing works
Is GitHub’s Copilot as bad as humans at introducing vulnerabilities in code?
Asare, O., Nagappan, M., & Asokan, N. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10380-1 · Cited by 128 · see citing works
Devising and Detecting Phishing Emails Using Large Language Models
Heiding, F., Schneier, B., Vishwanath, A., Bernstein, J., & Park, P. S. (2024) · IEEE Access · doi:10.1109/access.2024.3375882 · Cited by 123 · see citing works
Distributed denial of service attack prediction: Challenges, open issues and opportunities
de Neira, A. B., Kantarcı, B., & Nogueira, M. (2023) · Computer Networks · doi:10.1016/j.comnet.2022.109553 · Cited by 121 · see citing works
ORPO: Monolithic Preference Optimization without Reference Model
Hong, J., Lee, N., & Thorne, J. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.626 · Cited by 118 · see citing works
LLM for SoC Security: A Paradigm Shift
Saha, D., Tarek, S., Yahyaei, K., Saha, S. K., Zhou, J., Tehranipoor, M. M., & Farahmandi, F. (2024) · IEEE Access · doi:10.1109/access.2024.3427369 · Cited by 113 · see citing works
A Security Model Based on LightGBM and Transformer to Protect Healthcare Systems From Cyberattacks
Ghourabi, A. (2022) · IEEE Access · doi:10.1109/access.2022.3172432 · Cited by 112 · see citing works
RNNIDS: Enhancing network intrusion detection systems through deep learning
Sohi, S. M., Seifert, J., & Ganji, F. (2020) · Computers & Security · doi:10.1016/j.cose.2020.102151 · Cited by 109 · see citing works
On Hardware Security Bug Code Fixes by Prompting Large Language Models
Ahmad, B., Thakur, S., Tan, B., Karri, R., & Pearce, H. A. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3374558 · Cited by 104 · see citing works
Jamal, S., Wimmer, H., & Sarker, I. H. (2024) · Security and Privacy · doi:10.1002/spy2.402 · Cited by 102 · see citing works
LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing
Ma, Z., Chen, A. R., Kim, D. J., Chen, T.-H. P., & Wang, S. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3639150 · Cited by 100 · see citing works
Prompt Injection attack against LLM-integrated Applications
Liu, Y., Deng, G., Li, Y., Wang, K., Wang, Z., Wang, X., Zhang, T., Liu, Y., Wang, H., Zheng, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05499 · Cited by 100 · see citing works
Generating Fake Cyber Threat Intelligence Using Transformer-Based Models
Ranade, P., Piplai, A., Mittal, S., Joshi, A., & Finin, T. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534192 · Cited by 95 · see citing works
On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review
Yan, B., Li, K., Xu, M., Dong, Y., Zhang, Y., Ren, Z., & Cheng, X. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100300 · Cited by 93 · see citing works
A Survey of Knowledge Enhanced Pre-trained Language Models
Yang, J. K., Hu, X., Xiao, G., & Shen, Y. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3631392 · Cited by 89 · see citing works
CrossVul: a cross-language vulnerability dataset with commit data
Nikitopoulos, G., Dritsa, K., Λουρίδας, Π., & Mitropoulos, D. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3473122 · Cited by 89 · see citing works
Security Vulnerability Detection Using Deep Learning Natural Language Processing
Ziems, N., & Wu, S. (2021) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps51825.2021.9484500 · Cited by 87 · see citing works
Polat, H., Türkoğlu, M., Polat, O., & Şengür, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116748 · Cited by 86 · see citing works
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code CompletionPreprint
Schuster, R., Song, C., Tromer, E., & Shmatikov, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02220 · Cited by 83 · see citing works
Assessing LLMs in malicious code deobfuscation of real-world malware campaigns
Patsakis, C., Casino, F., & Lykousas, N. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124912 · Cited by 83 · see citing works
Donkol, A. A. E.-B., Hafez, A. G., Hussein, A. I., & Mabrook, M. M. (2023) · IEEE Access · doi:10.1109/access.2023.3240109 · Cited by 82 · see citing works
A Novel Data Poisoning Attack in Federated Learning based on Inverted Loss Function
Gupta, P., Yadav, K. K., Gupta, B. B., Alazab, M., & Gadekallu, T. R. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103270 · Cited by 81 · see citing works
Reducing Activation Recomputation in Large Transformer ModelsPreprint
Korthikanti, V. A., Casper, J., Lym, S., McAfee, L., Andersch, M., Shoeybi, M., & Catanzaro, B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.05198 · Cited by 80 · see citing works
Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks
Ferrag, M. A., Debbah, M. A., & Al-Hawawreh, M. S. (2023) · doi:10.1109/ccgridw59191.2023.00018 · Cited by 78 · see citing works
Self-citationAdversarial Attacks and Defenses in 6G Network-Assisted IoT Systems
Son, B. D., Nguyễn, T. H., Chien, T. V., Khalid, W., Ferrag, M. A., Choi, W., & Debbah, M. A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3373808 · Cited by 76 · see citing works
Self-citationStarCoder 2 and The Stack v2: The Next Generation
Lozhkov, A., Li, R., Allal, L. B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2402.19173 · Cited by 74 · see citing works
LLM-Powered Static Binary Taint Analysis
Liu, P., Sun, C. P., Zheng, Y., Feng, X., Qin, C., Wang, Y., Xu, Z., Li, Z., Di, P., Jiang, Y., et al. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3711816 · Cited by 74 · see citing works
Tihanyi, N., Ferrag, M. A., Jain, R., Bisztray, T., & Debbah, M. A. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679494 · Cited by 73 · see citing works
Self-citationSarker, I. H., Janicke, H., Ferrag, M. A., & Abuadbba, A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101110 · Cited by 72 · see citing works
Self-citationBenchmarking Large Language Models for Log Analysis, Security, and Interpretation
Karlsen, E., Luo, X., Zincir-Heywood, N., & Heywood, M. (2024) · Journal of Network and Systems Management · doi:10.1007/s10922-024-09831-x · Cited by 71 · see citing works
An ensemble of pre-trained transformer models for imbalanced multiclass malware classification
Demirkıran, F., Çayır, A., Ünal, U., & Dağ, H. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102846 · Cited by 71 · see citing works
CySecBERT : A Domain-Adapted Language Model for the Cybersecurity Domain
Bayer, M., Kuehn, P., Shanehsaz, R., & Reuter, C. (2024) · ACM Transactions on Privacy and Security · doi:10.1145/3652594 · Cited by 71 · see citing works
The FormAI Dataset: Generative AI in Software Security through the Lens of Formal Verification
Tihanyi, N., Bisztray, T., Jain, R., Ferrag, M. A., Cordeiro, L. C., & Mavroeidis, V. (2023) · International Conference on Predictive Models in Software Engineering · doi:10.1145/3617555.3617874 · Cited by 70 · see citing works
Self-citationLLM in the Shell: Generative Honeypots
Sladić, M., Valeros, V., Catania, C., & Garcia, S. (2024) · 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw61312.2024.00054 · Cited by 70 · see citing works
LLMIF: Augmented Large Language Model for Fuzzing IoT Devices
Wang, J., Yu, L., & Luo, X. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00211 · Cited by 68 · see citing works
PLLM-CS: Pre-trained Large Language Model (LLM) for cyber threat detection in satellite networks
Hassanin, M., Keshk, M., Salim, S., Alsubaie, M., & Sharma, D. K. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103645 · Cited by 67 · see citing works
SmartGuard: An LLM-enhanced framework for smart contract vulnerability detection
Ding, H., Liu, Y., Piao, X., Song, H., & Ji, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126479 · Cited by 64 · see citing works
A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems
Gaba, S., Budhiraja, I., Kumar, V., Martha, S., Khurmi, J., Singh, A., Singh, K. K., Askar, S. S., & Abouhawwash, M. (2024) · IEEE Access · doi:10.1109/access.2023.3349022 · Cited by 62 · see citing works
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Laurençon, H., Saulnier, L., Wang, T., Akiki, C., del Moral, A. V., Scao, T. L., Von Werra, L., Mou, C., Ponferrada, E. G., Nguyen, H., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03915 · Cited by 61 · see citing works
Cyber Security Based on Artificial Intelligence for Cyber-Physical Systems
Sedjelmaci, H., Guenab, F., Senouci, S., Moustafa, H., Liu, J., & Han, S. (2020) · IEEE Network · doi:10.1109/mnet.2020.9105926 · Cited by 60 · see citing works
Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection
Althubiti, S. A., Nick, W., Mason, J., Yuan, X., & Esterline, A. (2018) · IEEE SoutheastCon · doi:10.1109/secon.2018.8478898 · Cited by 58 · see citing works
ERNN: Error-Resilient RNN for Encrypted Traffic Detection towards Network-Induced Phenomena
Zhao, Z., Li, Z., Jiang, J., Yu, F., Zhang, F., Xu, C., Zhao, X., Zhang, R., & Guo, S. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3242134 · Cited by 56 · see citing works
NSFuzz: Towards Efficient and State-Aware Network Service Fuzzing
Qin, S., Hu, F., Ma, Z., Zhao, B., Yin, T., & Zhang, C. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3580598 · Cited by 53 · see citing works
Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants
Sandoval, G., Pearce, H., Nys, T., Karri, R., Garg, S., & Dolan-Gavitt, B. (2022) · arXiv · doi:10.48550/arxiv.2208.09727 · Cited by 52 · see citing works
Named Entity Recognition in Cyber Threat Intelligence Using Transformer-based Models
Evangelatos, P., Iliou, C., Mavropoulos, T., Apostolou, K., Tsikrika, T., Vrochidis, S., & Kompatsiaris, I. Y. (2021) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr51186.2021.9527981 · Cited by 49 · see citing works
Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection
Guo, Y., Patsakis, C., Hu, Q., Tang, Q., & Casino, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70879-4_14 · Cited by 45 · see citing works
Can AI Keep You Safe? A Study of Large Language Models for Phishing Detection
Chataut, R., Gyawali, P., & Usman, Y. (2024) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc60891.2024.10427626 · Cited by 42 · see citing works
PatchRNN: A Deep Learning-Based System for Security Patch Identification
Wang, X., Wang, S., Feng, P., Sun, K., Jajodia, S., Benchaaboun, S., & Geck, F. (2021) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom52596.2021.9652940 · Cited by 40 · see citing works
A Review of Advancements and Applications of Pre-Trained Language Models in Cybersecurity
Liu, Z. (2024) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs60797.2024.10527236 · Cited by 39 · see citing works
Hamer, S., d’Amorim, M., & Williams, L. A. (2024) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw63631.2024.00014 · Cited by 37 · see citing works
LLMs in Web Development: Evaluating LLM-Generated PHP Code Unveiling Vulnerabilities and Limitations
Tóth, R., Bisztray, T., & Erdődi, L. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-68738-9_34 · Cited by 34 · see citing works
Self-citationVerilogEval: Evaluating Large Language Models for Verilog Code GenerationPreprint
Liu, M., Pinckney, N., Khailany, B., & Ren, H. M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.07544 · Cited by 32 · see citing works
Harnessing the Advanced Capabilities of LLM for Adaptive Intrusion Detection Systems
Lira, O. G., Marroquín, A., & To, M. A. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-57942-4_44 · Cited by 32 · see citing works
Lai, H., & Nissim, M. (2024) · ACM Computing Surveys · doi:10.1145/3654795 · Cited by 30 · see citing works
Beyond Random Inputs: A Novel ML-Based Hardware Fuzzing
Rostami, M., Chilese, M., Zeitouni, S., Kande, R., Rajendran, J., & Sadeghi, A. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546625 · Cited by 28 · see citing works
SecureQwen: Leveraging LLMs for vulnerability detection in python codebases
Mechri, A., Ferrag, M. A., & Debbah, M. A. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104151 · Cited by 27 · see citing works
Self-citationGaber, M., Ahmed, M., & Janicke, H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104167 · Cited by 27 · see citing works
Jamal, S., Wimmer, H., & Sarker, I. (2023) · doi:10.21203/rs.3.rs-3608294/v1 · Cited by 27 · see citing works
Arshad, U., & Halim, Z. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.110027 · Cited by 26 · see citing works
A transformer-based IDE plugin for vulnerability detection
Mamede, C., Pinconschi, E., & Abreu, R. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3559534 · Cited by 25 · see citing works
Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster
Dey, N., Gosal, G., Zhiming, Chen, Khachane, H., Marshall, W., Pathria, R., Tom, M., & Hestness, J. (2023) · arXiv · doi:10.48550/arxiv.2304.03208 · Cited by 23 · see citing works
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics
Parra, G. D. L. T., Selvera, L., Khoury, J. D., Irizarry, H., Bou‐Harb, E., & Rad, P. (2022) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2022.23102 · Cited by 21 · see citing works
Invited Paper: Software/Hardware Co-design for LLM and Its Application for Design Verification
Wan, L. J., Huang, Y., Li, Y., Ye, H., Wang, J., Zhang, X., & Chen, D. (2024) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac58780.2024.10473893 · Cited by 21 · see citing works
DeVAIC: A tool for security assessment of AI-generated code
Cotroneo, D., Luca, R. D., & Liguori, P. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107572 · Cited by 21 · see citing works
Data Poisoning Attacks Against Multimodal EncodersPreprint
Yang, Z., He, X., Li, Z., Michael Backes, A., Humbert, M., Berrang, P., & Zhang, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.15266 · Cited by 21 · see citing works
He, F., Fei, L., & Liang, P. (2024) · IET Blockchain · doi:10.1049/blc2.12072 · Cited by 17 · see citing works
Ren, M., Ren, X., Feng, H., Ming, J., & Lei, Y. (2021) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3448300.3468296 · Cited by 14 · see citing works
Lykousas, N., & Patsakis, C. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103974 · Cited by 13 · see citing works
Artificial Intelligence for Cybersecurity
Ebert, C., & Beck, M. (2023) · IEEE Software · doi:10.1109/ms.2023.3305726 · Cited by 13 · see citing works
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code
Guan, B., Wan, Y., Bi, Z., Wang, Z., Zhang, H., Zhou, P., & Sun, L. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.541 · Cited by 12 · see citing works
Automation of Vulnerability Information Extraction Using Transformer-Based Language Models
Chaleshtori, F. H., & Ray, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25460-4_37 · Cited by 7 · see citing works
Tihanyi, N., Bisztray, T., Dubniczky, R. A., Tóth, R., Borsos, B., Cherif, B., Jain, R., Muzsai, L., Ferrag, M. A., Marinelli, R. A., et al. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825051 · Cited by 7 · see citing works
Self-citationJang, E., Cui, J., Yim, D., Jin, Y., Chung, J.-W., Shin, S., & Lee, Y. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.3 · Cited by 5 · see citing works
NEW SEQUENCE ALIGNMENT ALGORITHM USING AI RULES AND DYNAMIC SEEDS
(2023) · Bioscience & Engineering An International Journal · doi:10.5121/bioej.2023.10201 · Cited by 3 · see citing works
All Artificial, Less Intelligence: GenAI through the Lens of Formal VerificationPreprint
Gadde, D. N., Kumar, A., Nalapat, T., Rezunov, E., & Cappellini, F. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.16750 · Cited by 3 · see citing works
Do Users Write More Insecure Code with AI Assistants?
Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2022) · arXiv
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Enhancing Autonomous System Security and Resilience With Generative AI: A Comprehensive Survey
Lopez, M. A., Lunardi, W. T., Lawton, G., & Thakkar, S. (2024) · IEEE Access · doi:10.1109/access.2024.3439363 · Cited by 115 · see citing works
AIGC-empowered smart manufacturing: Prospects and challenges
Leng, J., Zheng, K., Li, R. C., Chen, C., Wang, B., Liu, Q., Chen, X., & Shen, W. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103076 · Cited by 70 · see citing works
Luo, H., Liu, Y., Zhang, R., Wang, J., Sun, G., Niyato, D. T., Yu, H., Xiong, Z., Wang, X., & Shen, X. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3612760 · Cited by 52 · see citing works
From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows
Ferrag, M. A., Tihanyi, N., Hamouda, D., Maglaras, L., Lakas, A., & Debbah, M. (2026) · ICT Express · doi:10.1016/j.icte.2025.12.001 · Cited by 46 · see citing works
Self-citationFar, S. M. T., & Feyzi, F. (2025) · International Journal of Information Security · doi:10.1007/s10207-025-00992-7 · Cited by 34 · see citing works
Karim, M. M., Khan, S., Van, D. H., Liu, X.-Y., Wang, C.-H., & Qu, Q. (2025) · Future Internet · doi:10.3390/fi17080353 · Cited by 37 · see citing works
LLMs in Cyber Security: Bridging Practice and Education
Atlam, H. F. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9070184 · Cited by 27 · see citing works
Aslam, M. M., Tufail, A., Gul, H., Irshad, M. N., & Namoun, A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11320-9 · Cited by 32 · see citing works
Reasoning beyond limits: Advances and open problems for LLMs
Ferrag, M. A., Tihanyi, N., & Debbah, M. A. (2025) · ICT Express · doi:10.1016/j.icte.2025.09.003 · Cited by 33 · see citing works
Self-citationCyberRAG: An agentic RAG cyber attack classification and reporting tool
Blefari, F., Cosentino, C., Pironti, F. A., Furfaro, A., & Marozzo, F. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108186 · Cited by 33 · see citing works
AbdelRaouf, H., Hasnaine, Q. R., Fouda, M. M., Fadlullah, Z. M., & Ibrahem, M. I. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3657733 · Cited by 18 · see citing works
Damacena Duarte, J., Cândido, G. D., De Britto Filho, J. R. A., Souza Neto, J., Da Costa, E. J., Da Costa, J. P. J., & Peotta De Melo, L. (2026) · IEEE Access · doi:10.1109/access.2026.3656849 · Cited by 15 · see citing works
Development and validation of Generative AI Competence Scale (GenAIComp) among university students
Lee, S. C., Baby, T., Vongvit, R., Lee, J., Kim, Y., Cha, M. C., & Yoon, S. H. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103059 · Cited by 21 · see citing works
Large Language Models for Cybersecurity Intelligence, Threat Hunting, and Decision Support
Ren, S., & Chen, S. (2025) · Jisuanji shenghuojia. · doi:10.54097/7ysr5k17 · Cited by 16 · see citing works
Ajimon, S. T., & Kumar, S. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3373-1102-9.ch007 · Cited by 13 · see citing works
Mian, T., & Kundu, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113594 · Cited by 11 · see citing works
Exploring the role of generative AI in enhancing cybersecurity in software development life cycle
Hashimi, H. A., Khan, R. A., Alwageed, H. S., Algarni, A. M., Ayouni, S., & Almagrabi, A. O. (2025) · Array · doi:10.1016/j.array.2025.100509 · Cited by 13 · see citing works
Tirulo, A., Chauhan, S., & Shafie‐khah, M. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101877 · Cited by 9 · see citing works
Multi-scale attention fusion for enhanced transformer models in intrusion detection systems
Saidane, S., Telch, F., Shahin, K., & Granelli, F. (2026) · Computer Networks · doi:10.1016/j.comnet.2025.111985 · Cited by 8 · see citing works
A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models
Kaniewski, S., Schmidt, F., Enzweiler, M., Menth, M., & Heer, T. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3815425 · Cited by 15 · see citing works
Interpretable Vulnerability Detection in LLMs: A BERT-Based Approach with SHAP Explanations
Ahmad, N., & Zhang, C. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.067044 · Cited by 7 · see citing works
Melis, A., Piroddi, A., Lam, C., Pau, G., & Girau, R. (2026) · Internet of Things · doi:10.1016/j.iot.2026.101915 · Cited by 7 · see citing works
From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Abuadbba, A., Moore, K., Goel, D., Hicks, C., Mavroudis, V., Hasırcıoglu, B., & Jennings, P. (2026) · IEEE Security & Privacy · doi:10.1109/msec.2025.3633466 · Cited by 7 · see citing works
LLM Firewall Using Validator Agent for Prevention Against Prompt Injection Attacks
Podpora, M., Baranowski, M., Chopcian, M., Kwaśniewicz, Ł., & Radziewicz, W. (2025) · Applied Sciences · doi:10.3390/app16010085 · Cited by 6 · see citing works
SME-TEAM: leveraging trust and ethics for secure and responsible use of AI and LLMs in SMEs
Sarker, I. H., Janicke, H., Mohsin, A., & Maglaras, L. A. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00065-z · Cited by 6 · see citing works
A Holistic Review of Fuzzing for Vulnerability Assessment in Industrial Network Protocols
Reda, A. M., Moustafa, N., & Lakshika, E. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3569291 · Cited by 5 · see citing works
Hashimi, H. A. (2026) · Scientific Reports · doi:10.1038/s41598-025-34350-3 · Cited by 4 · see citing works
Barrios-González, M., Aguiar-Pérez, J. M., Pérez-Juárez, M. A., & Castañeda-de-Benito, E. (2026) · Applied Sciences · doi:10.3390/app16031668 · Cited by 6 · see citing works
Securing LLM agents: From prompt sanitization to autonomous red teaming and beyond
Ferrag, M. A., Lakas, A., Tihanyi, N., & Debbah, M. A. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2026.03.001 · Cited by 5 · see citing works
Self-citationAI in control: Rethinking cybersecurity compliance and auditing
Loumachi, F. Y., Lacerda, M. J., Ouazzane, K., Adnane, A., & Adamyk, O. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108132 · Cited by 4 · see citing works
Joshi, A., & Baidya, S. (2026) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp6020063 · Cited by 4 · see citing works
Cherif, B., Bisztray, T., Dubniczky, R. A., Aldahmani, A., Alshehhi, S., & Tihanyi, N. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4367-0_2 · Cited by 6 · see citing works
Self-citationLightweight Fine-Tuning of LLMS for Explainable Intrusion Detection in SDN
Lodh, S., Obaidat, I., Rustam, F., & Jurcut, A. D. (2025) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob66857.2025.11257572 · Cited by 4 · see citing works
Abdallah, A. M., Aslan, H. K., Abdallah, M. S., Cho, Y. I., & Azer, M. A. (2025) · Symmetry · doi:10.3390/sym17040496 · Cited by 4 · see citing works
Enhancing software-defined network security with deep learning: a comprehensive review
Shyryn, B., Ahanger, T. A., & Zhumadillayeva, A. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01232-2 · Cited by 5 · see citing works
Large Language Models for Explainable Medical Text Summarization: A Systematic Literature Review
Ayalew, A. M., Hasan, M. R., Seppänen, T., & Oussalah, M. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70089 · Cited by 3 · see citing works
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Santos, R. X. M., Esiner, E., Tefek, U., Caesar, M., & Mashima, D. (2025) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm65349.2025.11204572 · Cited by 2 · see citing works
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Rollinson, N., & Polatidis, N. (2026) · Digital · doi:10.3390/digital6010005 · Cited by 2 · see citing works
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Haidar, A., Lin, Y.-Z., Shi, Q., Yang, Z., Amadigwe, D., Terry, B., Battu, S. K., Satam, P., & Shao, S. (2025) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars67163.2025.11337474 · Cited by 2 · see citing works
Saklani, S., Chohan, D. K., Sharma, R. K., Varshney, N., Gupta, A., & Bagla, P. (2025) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat66372.2025.11368582 · Cited by 2 · see citing works
Quantum transfer learning for cross-domain cybersecurity threat detection and categorization
Alsubai, S., Ayari, M., Kryvinska, N., Almadhor, A. S., Baili, J., Hejaili, A. A., & Abbas, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-40634-z · Cited by 2 · see citing works
Sabbagh, A. A., Abdo, E., Samrouth, K., Alkhatib, A. A., & Elkhodr, M. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08439-z · Cited by 2 · see citing works
Pondavakam, S., Singh, S. K., Rishiwal, V., & Park, J. H. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70439 · Cited by 2 · see citing works
Survey On Enhancing Dialogue Agent Alignment Through Minillm With Targeted Human Assessments
Swapnil, B. M. (2025) · i-manager s Journal on Artificial Intelligence & Machine Learning · doi:10.26634/jaim.3.1.21243 · Cited by 2 · see citing works
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Hadi, W. M., Hamarsheh, A., Al–Qerem, A., & Aldweesh, A. Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0954-5.ch007 · Cited by 1 · see citing works
Hallucination Detection in Large Language Models Using Diversion Decoding
Abdeen, B., Siddiqui, S. M. T., Ahmed, M. T., Singhal, A., Khan, L. R., Modi, P. P., & Al-Shaer, E. S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96590-6_7 · Cited by 1 · see citing works
A Critical Evaluation of Agentic AI in Cybersecurity: The Pros and the Cons
Obojo, V., Ikuesan, R. A., & Iortom, E. (2025) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet67572.2025.11288238 · Cited by 1 · see citing works
Özbay, R., Çelebi, M., & Yavanoğlu, U. (2026) · IEEE Access · doi:10.1109/access.2026.3658308 · Cited by 3 · see citing works
CyberGPL: A Conceptual Framework for Gamified and Personalised Cyber Security Education
Atlam, H. F. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15105-6_9 · Cited by 1 · see citing works
Hybrid LLM-Vision Pipeline for Detection of Malicious QR Codes in Social Engineering Attacks
Krishna, K. L., Kumar, P. R., Herah, V., Reddy, P. V. V., & Reddy, K. J. S. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438814 · Cited by 1 · see citing works
Thaw, M. P. P., Sarwatt, D. S., Ning, H., & Ding, J. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11636-0 · Cited by 1 · see citing works
A Virtual Lab for Learning AI Security and Adversarial Prompt Engineering
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Nguyen, D.-T., & Anh, L. H. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200714 · Cited by 1 · see citing works
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Aburub, F. A. F., & Alateef, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0832-6.ch019 · Cited by 1 · see citing works
Context Injection Vulnerabilities and Resource Exploitation Attacks in Model Context ProtocolPreprint
Siameh, T., Addobea, A. A., & Liu, C. (2025) · doi:10.36227/techrxiv.175321790.02502754/v1 · Cited by 1 · see citing works
Beyzavi, H., Shiraskar, S., Albluwi, A., Albalawi, M., Rizk, D., Rizk, F., Rizk, P., Nehmetallah, G., Rizk, R., & Santosh, K. (2026) · 2026 IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536239
LLM-Powered Security Solutions in Healthcare, Government, and Industrial Cybersecurity
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Comparative Evaluation of Large Language Models for Intent Classification in Indonesian Text
Karjadi, M., & Santoso, H. (2025) · TEPIAN · doi:10.51967/tepian.v6i2.3355
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Qin, C., Sun, X., Xu, Y., Li, M., Li, X., & Liu, Y. (2025) · International Conference on Intelligent Computing and Robotics (ICICR) · doi:10.1109/icicr65456.2025.00177
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Alsamman, A. A., & Dabagh, N. B. A. (2026) · Alkadhim journal for computer science. · doi:10.61710/9jq4y549
Anh, L. T., Hoang, H. D., Duy, P. T., Cam, N. T., & Pham, V.-H. (2025) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit67082.2025.11231445
The Place of Generative Artificial Intelligence in Digital Security Studies
Ay, S., & Karakuş, S. (2025) · Bitlis Eren Üniversitesi Fen Bilimleri Dergisi · doi:10.17798/bitlisfen.1780997
Exploring the Impact of Quantization on LLM Security Against Prompt Injection
Rodrigues, R. A., Garcia, L. F., Zorzo, A. F., Oliveira, E. L. S., & Paula, T. S. (2025) · Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS) · doi:10.5753/eramiars.2025.16669
1Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age
Vajha, V. K., Mathew, B. B., & Mani, R. J. (2025) · De Gruyter eBooks · doi:10.1515/9783111712895-001
NoRMA: A Multi-agent Communication-Centric Dataset for Enhanced Customs Nomenclature Classification
Bouchtib, H., Karboub, K., Tabaa, M., & Hamlich, M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15632-7_18
From Text to STIX: Reducing Hallucinations through Fine-Tuned LLMs
Xiao, R., Luo, Y., Xu, W., & Xu, D. (2025) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom66490.2025.00119
Awan, M., Alam, A., Khan, R. A., Alwageed, H. S., Ayouni, S., & Almagrabi, A. O. (2026) · Scientific Reports · doi:10.1038/s41598-026-37614-8 · Cited by 2 · see citing works
Raj, D. S. P., Anuradha, D. G., Kavitha, V., Shalini, K., Steffi, R., & Mathew, D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17300-3_14 · Cited by 2 · see citing works
Mission Explainable: From Feature Attribution to Mitigation in 5G Anomaly Detection
Uccello, F., Karim, I., Mubasshir, K. S., Bertino, E., & Nadjm‐Tehrani, S. (2026) · 2012 International Conference on Computing, Networking and Communications (ICNC) · doi:10.1109/icnc68183.2026.11416967
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Malasi, J.-J. M., Moudoud, H., & Missaoui, R. (2026) · International Conference on Consumer Electronics · doi:10.1109/icce67443.2026.11449614
Sorour, S. E., El-deep, S. E., Alojail, A., & Kahouf, S. A. E. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01420-w
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Bhagyasri, P., & Rupa, C. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19690-3_9
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Violakis, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5228-2.ch005
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Talorete, V. A., Reyes, A. R. L., & Borngo, J. C. (2026) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict68280.2026.11511045
Explainability and Reliability of Large Language Models in Health Systems
Darzehkonani, J. J., Zamanifar, A., & Gharehchopogh, F. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_3
Cognitive Adversarial Security Neuro
Ali, S. I. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1608-4.ch005
Evaluating Large Language Models in Cybersecurity: A Systematic Taxonomy and Empirical Analysis
Chen, M., Cheng, H., Su, T., Chen, M., Cai, W., & Zou, H. (2026) · Electronics · doi:10.3390/electronics15102222
Beyzavi, H., Shiraskar, S., Albluwi, A., Albalawi, M., Rizk, D., Rizk, F., Rizk, P., Nehmetallah, G., Rizk, R., & Santosh, K. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536474
Rahman, M. N., Tahir, M., Isoaho, J., & Virtanen, S. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536475
AI Governance in the Data Collection Phase of Building an AI Model
Bennouk, K., Mahouachi, D., Aali, N. A., Idrissi, Y. E. B. E., Sebai, B., & Faroukhi, A. Z. (2026) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi68585.2026.11539923
Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks
Merves, T. H., Conaway, M. H., Escobar, J. M., Otal, H. T., & Tatar, U. (2026) · Systems and Information Engineering Design Symposium · doi:10.1109/sieds69358.2026.11540254
Agustin, G. G., Al-Haritz, F. A., & Muliono, Y. (2026) · doi:10.1109/iccsp68173.2026.11539732
Taysi, M. A., Yılmaz, R. E., Bozkurt, H., Özmen, A. İ., & İnce, G. (2026) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/ichora69329.2026.11537178
Explainable AI Cloud Security Framework with Automating SIEM Event Management
Liu, Y., Bai, H., Coffman, J., & Ma, W. (2026) · International Conference on Sensors and Information Technology (ICSI) · doi:10.1109/icsi70302.2026.11544238
Guimarães, V., Medeiros, D. D. C., Reis, L., Mesquita, H., & Rocha, T. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134939
LinkedInGuard: A Personal Web-Based AI Security Assistant for Detecting Threats
Almehrzi, R., Alrahma, S., & Belqasmi, F. (2026) · Student Research Conference on Applied Computing (SRC) · doi:10.1109/src70627.2026.11550693
Integrating Large Language Models with IDS for Analyst-Guided Automated Threat Response
Maseno, E. M., Sun, Y., & Wang, Z. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-9884-7_1
Qiujie, S. (2026) · International Conference on Integrated Intelligence and Cognitive Engineering (ICIICE) · doi:10.1109/iciice69672.2026.11565504
IIoT-VulnSet: A Comprehensive Dataset for Vulnerabilities and Attacks in Industrial IoT Protocols
Reda, A. M., Moustafa, N., & Lakshika, E. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2026.3707977
Arabov, M. K., & Shumikhina, A. S. (2026) · Vestnik NSU Series Information Technologies · doi:10.25205/1818-7900-2026-24-1-18-29
Generative AI in Adversarial Cybersecurity: Opportunities, Risks, and Defense Mechanisms
Bhokse, P. B., Gurav, R. P., Chandre, P. R., Bhosale, R., jadhav, A., & More, R. (2026) · International Conference on Computing, Communication and Green Engineering (CCGE) · doi:10.1109/ccge67142.2026.11581623
Sherry, E., Schreiber, I., & Schreiber, A. (2026) · Information and Computer Security · doi:10.1108/ics-05-2026-0264
Jang, S., Myoungrak, L. E. E., & Shon, T. (2026) · Electronics · doi:10.3390/electronics15132949
BabeLLM: A unified taxonomy for NLP-based LLM cybersecurity applications
Casoria, P., & Romano, S. P. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133540
Wang, H., Zhao, H., & Yang, Q. (2026) · IEEE International Conference on Electrical Power Systems and Intelligent Control (EPSIC) · doi:10.1109/epsic70071.2026.11590595
Security challenges in digital access systems in vocational education
Agyeibi, K. B. Y., Mulyanti, B., Abdullah, A. G., Riza, L. S., & Ilhamdaniah, I. (2026) · Digital Twin · doi:10.1080/27525783.2026.2696153
Robust, efficient detection of network intrusion attacks: A large language model approach
Iannizzotto, G., Nucita, A., & Siragusa, A. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112559
Ciberseguridad predictiva basada en inteligencia artificial contra ataques generativos
Realpe, M. S. G., Torres-Galves, G. D., & Rivera, S. B. L. (2026) · Revista Científica Enfoques del Conocimiento · doi:10.55813/gaea/revistacec/v3/n2/2
Siddiqui, S. M. T., Ononto, A. J., Singhal, A., & Khan, L. R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-33260-8_14
- Authors
- Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M.
- Affiliations
- University of Guelma (DZ)
- Technology Innovation Institute (AE)
- Concordia University (CA)
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Ferrag, Mohamed Amine
- ORCID
- Venue
- Internet of Things and Cyber-Physical Systems
- Publisher
- Elsevier BV
- Published
- 2025
- Volume
- 5
- Pages
- 1-46
- ISSN
- 2667-3452 (ISSN-L)
- OpenAlex
- W4407070057
- Cited by
- 148
- References
- 150
- Citations by year
- 2026: 108 · 2025: 38 · 2024: 2
- FWCI
- 58.56
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Internet of Things and Cyber-Physical Systems (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- generative AI, large language models, cybersecurity, LLM vulnerabilities, prompt injection, data poisoning, hardware security, intrusion detection, malware detection, phishing detection, cyber threat intelligence, design verification, reinforcement learning from human feedback, DPO, QLoRA, retrieval-augmented generation, threat detection
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