{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T09:58:10Z","timestamp":1776419890729,"version":"3.51.2"},"reference-count":366,"publisher":"Springer Science and Business Media LLC","issue":"11-12","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann. Telecommun."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s12243-025-01134-9","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T09:41:26Z","timestamp":1763718086000},"page":"933-973","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large language models: applications, limitations, challenges, and recommendations in cybersecurity, digital forensics, and ethical hacking"],"prefix":"10.1007","volume":"80","author":[{"given":"Jean Paul A.","family":"Yaacoub","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hassan N.","family":"Noura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ola","family":"Salman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guy","family":"Pujolle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"1134_CR1","unstructured":"Zhao WX, Zhou K, Li J, Tang T, Wang X, Hou Y, Min Y, Zhang B, Zhang J, Dong Z et\u00a0al (2023) A survey of large language models. arXiv:2303.18223"},{"key":"1134_CR2","doi-asserted-by":"crossref","unstructured":"Fan A, Gokkaya B, Harman M, Lyubarskiy M, Sengupta S, Yoo S, Zhang JM (2023) Large language models for software engineering: survey and open problems. In: 2023 IEEE\/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE). IEEE, pp 31\u201353","DOI":"10.1109\/ICSE-FoSE59343.2023.00008"},{"key":"1134_CR3","unstructured":"Wang Z, Zhong W, Wang Y, Zhu Q, Mi F, Wang B, Shang L, Jiang X, Liu Q (2023) Data management for large language models: a survey. CoRR"},{"issue":"9","key":"1134_CR4","first-page":"1","volume":"56","author":"J Li","year":"2024","unstructured":"Li J, Tang T, Zhao WX, Nie J-Y, Wen J-R (2024) Pre-trained language models for text generation: a survey. ACM Comput Surv 56(9):1\u201339","journal-title":"ACM Comput Surv"},{"key":"1134_CR5","unstructured":"Ghojogh B, Ghodsi A (2023) Recurrent neural networks and long short-term memory networks: tutorial and survey. arXiv:2304.11461"},{"key":"1134_CR6","unstructured":"Zhao WX, Gao S, Zhou Y, Hu Z, Tang J-R (2023) Large language models (LLMs): survey, technical frameworks, and future challenges. arXiv:2303.18223"},{"key":"1134_CR7","doi-asserted-by":"crossref","unstructured":"Kachris C (2024) A survey on hardware accelerators for large language models. arXiv:2401.09890","DOI":"10.3390\/app15020586"},{"key":"1134_CR8","unstructured":"Bai G, Chai Z, Ling C, Wang S, Lu J, Zhang N, Shi T, Yu Z, Zhu M, Zhang Y et\u00a0al (2024) Beyond efficiency: a systematic survey of resource-efficient large language models. arXiv:2401.00625"},{"key":"1134_CR9","unstructured":"Laleh AR, Ahmadabadi MN (2024) A survey on enhancing reinforcement learning in complex environments: insights from human and LLM feedback. arXiv:2411.13410"},{"key":"1134_CR10","doi-asserted-by":"crossref","unstructured":"Shi H, Xu Z, Wang H, Qin W, Wang W, Wang Y, Wang Z, Ebrahimi S, Wang H (2024) Continual learning of large language models: a comprehensive survey. arXiv:2404.16789","DOI":"10.1145\/3735633"},{"key":"1134_CR11","unstructured":"Wang C, Zhao J, Gong J (2024) A survey on large language models from concept to implementation. arXiv:2403.18969"},{"key":"1134_CR12","unstructured":"Esmradi A, Yip DW, Chan C-F (2023) A comprehensive survey of attack techniques, implementation, and mitigation strategies in large language models. In: Proceedings of the 2023 international conference on ubiquitous information management and communication, pp 1\u20138"},{"key":"1134_CR13","unstructured":"Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D et\u00a0al (2022) Emergent abilities of large language models. arXiv:2206.07682"},{"key":"1134_CR14","unstructured":"Cheng Y, Zhang C, Zhang Z, Meng X, Hong S, Li W, Wang Z, Wang Z, Yin F, Zhao J et\u00a0al (2024) Exploring large language model based intelligent agents: definitions, methods, and prospects. arXiv:2401.03428"},{"key":"1134_CR15","unstructured":"Chen M, Tworek J, Jun H, Yuan Q, De\u00a0Oliveira Pinto HP, Kaplan J, Edwards H, Burda Y, Joseph N, Brockman G et\u00a0al (2021) Evaluating large language models trained on code. arXiv:2107.03374"},{"key":"1134_CR16","doi-asserted-by":"crossref","unstructured":"Zong M, Hekmati A, Guastalla M, Li Y, Krishnamachari B (2024) Integrating large language models with internet of things applications. arXiv:2410.19223","DOI":"10.1007\/s43926-024-00083-4"},{"issue":"1","key":"1134_CR17","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s43093-024-00368-8","volume":"10","author":"AA Toumeh","year":"2024","unstructured":"Toumeh AA (2024) Assessing the potential integration of large language models in accounting practices: evidence from an emerging economy. Future Business J 10(1):82","journal-title":"Future Business J"},{"key":"1134_CR18","unstructured":"Lee J, Stevens N, Han SC, Song M (2024) A survey of large language models in finance (finllms). arXiv:2402.02315"},{"key":"1134_CR19","unstructured":"Kolasani S (2023) Optimizing natural language processing, large language models (LLMs) for efficient customer service, and hyper-personalization to enable sustainable growth and revenue. Trans Latest Trends Artif Intell 4(4)"},{"issue":"1","key":"1134_CR20","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13040-023-00339-9","volume":"16","author":"JG Meyer","year":"2023","unstructured":"Meyer JG, Urbanowicz RJ, Martin PCN, O\u2019Connor K, Li R, Peng P-C, Bright TJ, Tatonetti N, Won KJ, Gonzalez-Hernandez G et al (2023) ChatGPT and large language models in academia: opportunities and challenges. BioData Mining 16(1):20","journal-title":"BioData Mining"},{"key":"1134_CR21","doi-asserted-by":"crossref","unstructured":"Beltagy I, Lo K, Cohan A (2019) Scibert: a pretrained language model for scientific text. arXiv:1903.10676","DOI":"10.18653\/v1\/D19-1371"},{"issue":"1","key":"1134_CR22","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1038\/s41746-022-00742-2","volume":"5","author":"X Yang","year":"2022","unstructured":"Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, Compas C, Martin C, Costa AB, Flores MG et al (2022) A large language model for electronic health records. NPJ Digit Med 5(1):194","journal-title":"NPJ Digit Med"},{"issue":"9","key":"1134_CR23","doi-asserted-by":"publisher","first-page":"pgae368","DOI":"10.1093\/pnasnexus\/pgae368","volume":"3","author":"H Askari","year":"2024","unstructured":"Askari H, Chhabra A, von Hohenberg BC, Heseltine M, Wojcieszak M (2024) Incentivizing news consumption on social media platforms using large language models and realistic bot accounts. PNAS Nexus 3(9):pgae368","journal-title":"PNAS Nexus"},{"key":"1134_CR24","doi-asserted-by":"crossref","unstructured":"Guha N, Nyarko J, Ho D, R\u00e9 C, Chilton A, Chohlas-Wood A, Peters A, Waldon B, Rockmore D, Zambrano D et\u00a0al (2024) Legalbench: a collaboratively built benchmark for measuring legal reasoning in large language models. Adv Neural Inf Process Syst 36","DOI":"10.2139\/ssrn.4583531"},{"key":"1134_CR25","unstructured":"Bhat MM, Meng R, Liu Y, Zhou Y, Yavuz S (2023) Investigating answerability of LLMs for long-form question answering. arXiv:2309.08210"},{"key":"1134_CR26","unstructured":"Zhang X, Ju T, Liang H, Fu Y, Zhang Q (2024) LLMs instruct LLMs: an extraction and editing method. arXiv:2403.15736"},{"key":"1134_CR27","doi-asserted-by":"crossref","unstructured":"Bereska L, Gavves E (2023) Taming simulators: challenges, pathways and vision for the alignment of large language models. In: Proceedings of the AAAI symposium series, vol 1, pp 68\u201372","DOI":"10.1609\/aaaiss.v1i1.27478"},{"key":"1134_CR28","unstructured":"Clairoux-Trepanier V, Beauchamp I-M, Ruellan E, Paquet-Clouston M, Paquette S-O, Clay E (2024) The use of large language models (LLM) for cyber threat intelligence (CTI) in cybercrime forums. arXiv:2408.03354"},{"key":"1134_CR29","doi-asserted-by":"crossref","unstructured":"Fujii S, Yamagishi R (2024) Feasibility study for supporting static malware analysis using LLM. arXiv:2411.14905","DOI":"10.1007\/978-3-031-82362-6_1"},{"key":"1134_CR30","doi-asserted-by":"crossref","unstructured":"Chataut R, Gyawali PK, Usman Y (2024) Can AI keep you safe? A study of large language models for phishing detection. In: 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp 0548\u20130554","DOI":"10.1109\/CCWC60891.2024.10427626"},{"key":"1134_CR31","unstructured":"Hays S, White J (2024) Employing LLMs for incident response planning and review. arXiv:2403.01271"},{"key":"1134_CR32","doi-asserted-by":"crossref","unstructured":"Tuffveson RI, Tawosi V, Alamir S (2024) Software vulnerability and functionality assessment using LLMs. NLBSE 2024, p 25","DOI":"10.1145\/3643787.3648036"},{"issue":"1","key":"1134_CR33","first-page":"7097385","volume":"2022","author":"Y Wang","year":"2022","unstructured":"Wang Y, Wang T, Wang J, Zhou X, Gao M, Liu R (2022) Military chain: construction of domain knowledge graph of kill chain based on natural language model. Mob Inf Syst 2022(1):7097385","journal-title":"Mob Inf Syst"},{"issue":"19","key":"1134_CR34","doi-asserted-by":"publisher","first-page":"9063","DOI":"10.3390\/app14199063","volume":"14","author":"X Liu","year":"2024","unstructured":"Liu X, Yu Z, Liu X, Miao L, Yang T (2024) Military equipment entity extraction based on large language model. Appl Sci 14(19):9063","journal-title":"Appl Sci"},{"key":"1134_CR35","doi-asserted-by":"crossref","unstructured":"Goecks VG, Waytowich N (2024) COA-GPT: generative pre-trained transformers for accelerated course of action development in military operations. In: 2024 International Conference on Military Communication and Information Systems (ICMCIS). IEEE, pp 01\u201310","DOI":"10.1109\/ICMCIS61231.2024.10540749"},{"key":"1134_CR36","doi-asserted-by":"crossref","unstructured":"Nitzl C, Cyran A, Krstanovic S, Borghoff UM (2024) The use of artificial intelligence in military intelligence: an experimental investigation of added value in the analysis process. arXiv:2412.03610","DOI":"10.3389\/fhumd.2025.1540450"},{"key":"1134_CR37","doi-asserted-by":"crossref","unstructured":"Lakomy M (2023) Artificial intelligence as a terrorism enabler? Understanding the potential impact of chatbots and image generators on online terrorist activities. Studies in Conflict & Terrorism, pp 1\u201321","DOI":"10.1080\/1057610X.2023.2259195"},{"issue":"2","key":"1134_CR38","first-page":"3","volume":"7","author":"S Shetty","year":"2024","unstructured":"Shetty S, Choi K-S, Park I (2024) Investigating the intersection of ai and cybercrime: risks, trends, and countermeasures. Int J Cybersecur Intell Cybercrime 7(2):3","journal-title":"Int J Cybersecur Intell Cybercrime"},{"key":"1134_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100218","volume":"11","author":"J-P Yaacoub","year":"2020","unstructured":"Yaacoub J-P, Noura H, Salman O, Chehab A (2020) Security analysis of drones systems: attacks, limitations, and recommendations. Internet of Things 11:100218","journal-title":"Internet of Things"},{"key":"1134_CR40","doi-asserted-by":"crossref","unstructured":"Yaacoub J-PA, Noura HN, Salman O, Chehab A (2022) Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations. Int J Inf Secur, pp 1\u201344","DOI":"10.1007\/s10207-021-00545-8"},{"key":"1134_CR41","doi-asserted-by":"crossref","unstructured":"Yaacoub J-PA, Noura HN, Piranda B (2023) The internet of modular robotic things: issues, limitations, challenges, & solutions. Internet of Things, p 100886","DOI":"10.1016\/j.iot.2023.100886"},{"key":"1134_CR42","unstructured":"Yaacoub J-PA (2024) Modular robotics meet Internet of things: safety, security and performance challenges and countermeasures. PhD thesis, Universit\u00e9 Bourgogne Franche-Comt\u00e9"},{"key":"1134_CR43","unstructured":"M\u00f6bius M, Kallfass D, Kunde D, Thomas Doll LTC (2023) Natural language ai for military decision support and swarm control for autonomous UAS trained in a combat simulation. In: Proceeding of the NMSG symposium \u201cSimulation: Going beyond the limitations of the real world"},{"key":"1134_CR44","doi-asserted-by":"crossref","unstructured":"Puczy\u0144ska J, Podhajski M, Wojtasik K, Michalak TP (2024) Large language models in jihadist terrorism and crimes. Editorial team Damian Szlachter, PhD (editor-in-chief) Agnieszka Dkebska (editorial secretary, layout editor) Translation Sylwia K\u0142obuszewska Cover design Aleksandra Bednarczyk\u00a9by Agencja Bezpieczenstwa Wewn\u0119trznego 2024, p 351","DOI":"10.4467\/27204383TER.24.012.19400"},{"issue":"1","key":"1134_CR45","first-page":"93","volume":"7","author":"MSS Shah","year":"2024","unstructured":"Shah MSS, Abuaieta AM, Almazrouei SS (2024) Safeguarding online communications using distilroberta for detection of terrorism and offensive chats. J Inf Secur Cybercrimes Res 7(1):93\u2013107","journal-title":"J Inf Secur Cybercrimes Res"},{"key":"1134_CR46","doi-asserted-by":"crossref","unstructured":"Zamin N (2009) Information extraction for counter-terrorism: a survey. In: 2009 Computation world: future computing, service computation, cognitive, adaptive, content, patterns. IEEE, pp 520\u2013526","DOI":"10.1109\/ComputationWorld.2009.105"},{"key":"1134_CR47","doi-asserted-by":"crossref","unstructured":"Mikolov T, Karafi\u00e1t M, Burget L, Cernock\u00fd J, Khudanpur S (2010) Recurrent neural network based language model. In: Interspeech, pp 1045\u20131048","DOI":"10.21437\/Interspeech.2010-343"},{"key":"1134_CR48","unstructured":"Jozefowicz R, Vinyals O, Schuster M, Shazeer N, Wu Y (2016) Exploring the limits of language modeling. arXiv:1602.02410"},{"key":"1134_CR49","doi-asserted-by":"crossref","unstructured":"Peng B, Alcaide E, Anthony Q, Albalak A, Arcadinho S, Biderman S, Cao H, Cheng X, Chung M, Grella M, GV KK, He X, Hou H, Lin J, Kazienko P, Kocon J, Kong J, Koptyra B, Lau H, Mantri KSI, Mom F, Saito A, Song G, Tang X, Wang B, Wind JS, Wozniak S, Zhang R, Zhang Z, Zhao Q, Zhou P, Zhou , Zhu J, Zhu R-J (2023) RWKV: reinventing RNNs for the transformer era. arXiv:2305.13048","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"1134_CR50","unstructured":"Beck M, P\u00f6ppel K, Spanring M, Auer A, Prudnikova O, Hochreiter S (2024) xLSTM: extended long short-term memory. arXiv:2405.00001"},{"key":"1134_CR51","doi-asserted-by":"crossref","unstructured":"Zucchet N, Orvieto A (2024) Recurrent neural networks: vanishing and exploding gradients are not the end of the story. arXiv:2405.21064","DOI":"10.52202\/079017-4425"},{"key":"1134_CR52","doi-asserted-by":"crossref","unstructured":"Engelken R (2023) Gradient flossing: improving gradient descent through dynamic control of jacobians. arXiv:2312.17306","DOI":"10.52202\/075280-0457"},{"key":"1134_CR53","unstructured":"Merity S, Keskar NS, Socher R (2017) Regularizing and optimizing LSTM language models. arXiv:1708.02182"},{"key":"1134_CR54","doi-asserted-by":"crossref","unstructured":"Liu X, Cao D, Yu K (2018) Binarized LSTM language model. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp 2113\u20132121","DOI":"10.18653\/v1\/N18-1192"},{"key":"1134_CR55","unstructured":"Ghosh S, Vinyals O, Strope B, Roy S, Dean T, Heck L (2016) Contextual LSTM: a step towards hierarchical language modeling. In: Workshop on large-scale deep learning for data mining - KDD"},{"key":"1134_CR56","doi-asserted-by":"crossref","unstructured":"Mousa A, Schuller B (2017) Contextual bidirectional long short-term memory recurrent neural network language models: a generative approach to sentiment analysis. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics: volume 1, long papers, pp 1023\u20131032","DOI":"10.18653\/v1\/E17-1096"},{"key":"1134_CR57","doi-asserted-by":"crossref","unstructured":"Xu W, Chen J, Ding Z, Wang J (2024) Text sentiment analysis and classification based on bidirectional gated recurrent units (grus) model. arXiv:2404.17123","DOI":"10.54254\/2755-2721\/77\/20240670"},{"key":"1134_CR58","unstructured":"Lieber O, Lenz B, Bata H, Cohen G, Osin J, Dalmedigos I, Safahi E, Meirom S, Belinkov Y, Shalev-Shwartz S et\u00a0al (2024) Jamba: a hybrid transformer-mamba language model. arXiv:2403.19887"},{"key":"1134_CR59","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30"},{"key":"1134_CR60","doi-asserted-by":"crossref","unstructured":"Qin Z, Yang S, Zhong Y (2023) Hierarchically gated recurrent neural network for sequence modeling. arXiv:2311.04823","DOI":"10.52202\/075280-1442"},{"key":"1134_CR61","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W (2024) Vision mamba: efficient visual representation learning with bidirectional state space model. arXiv:2402.19427"},{"key":"1134_CR62","unstructured":"Ansar W, Goswami S, Chakrabarti A (2024) A survey on transformers in NLP with focus on efficiency. arXiv:2406.16893"},{"key":"1134_CR63","unstructured":"Huang Y, Xu J, Lai J, Jiang Z, Chen T, Li Z, Yao Y, Ma X, Yang L, Chen H et\u00a0al (2023) Advancing transformer architecture in long-context large language models: a comprehensive survey. arXiv:2311.12351"},{"key":"1134_CR64","doi-asserted-by":"crossref","unstructured":"Kheddar H (2024) Transformers and large language models for efficient intrusion detection systems: a comprehensive survey. arXiv:2408.07583","DOI":"10.1016\/j.inffus.2025.103347"},{"key":"1134_CR65","doi-asserted-by":"crossref","unstructured":"Luo Q, Zeng W, Chen M, Peng G, Yuan X, Yin Q (2023) Self-attention and transformers: driving the evolution of large language models. In: Proceedings of the 2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, pp 401\u2013405","DOI":"10.1109\/ICEICT57916.2023.10245906"},{"key":"1134_CR66","unstructured":"Lee S, Lee H-J (2024) Analyzing the scaling characteristics of transformer feed-forward networks for the trillion-parameter era and beyond. In: 2024 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE"},{"key":"1134_CR67","unstructured":"Lu Z, Sun Y, Yang Z, Zhou Q, Lin H (2024) The orthogonality of weight vectors: the key characteristics of normalization and residual connections. In: Proceedings of the thirty-third international joint conference on artificial intelligence, IJCAI-24. International Joint Conferences on Artificial Intelligence Organization, pp 4687\u20134695"},{"key":"1134_CR68","doi-asserted-by":"crossref","unstructured":"Dong Z, Xiao Y, Wei P, Lin L (2024) Decoder-only LLMs are better controllers for diffusion models. In: Proceedings of the 32nd ACM international conference on multimedia, pp 10957\u201310965","DOI":"10.1145\/3664647.3680725"},{"key":"1134_CR69","doi-asserted-by":"crossref","unstructured":"Naik D, Naik I, Naik N (2024) Decoder-only transformers: the brains behind generative ai, large language models and large multimodal models. In: The international conference on computing, communication, cybersecurity & AI. Springer, pp 315\u2013331","DOI":"10.1007\/978-3-031-74443-3_19"},{"key":"1134_CR70","doi-asserted-by":"crossref","unstructured":"Roberts J (2024) How powerful are decoder-only transformer neural models? In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN60899.2024.10651286"},{"key":"1134_CR71","doi-asserted-by":"crossref","unstructured":"Qorib M, Moon G, Ng HT (2024) Are decoder-only language models better than encoder-only language models in understanding word meaning? In: Findings of the Association for Computational Linguistics ACL 2024, pp 16339\u201316347","DOI":"10.18653\/v1\/2024.findings-acl.967"},{"key":"1134_CR72","doi-asserted-by":"crossref","unstructured":"Roccabruna G, Rizzoli M, Riccardi G (2024) Will LLMs replace the encoder-only models in temporal relation classification? arXiv:2410.10476","DOI":"10.18653\/v1\/2024.emnlp-main.1136"},{"key":"1134_CR73","unstructured":"Valdenegro D (2023) A LLM digest for social scientist. Retrieved from osf. io\/preprints\/socarxiv\/m74vs"},{"key":"1134_CR74","doi-asserted-by":"crossref","unstructured":"Jiang JJ, Li X (2024) Look ahead text understanding and LLM stitching. In: Proceedings of the international AAAI conference on web and social media, vol 18, pp 751\u2013760","DOI":"10.1609\/icwsm.v18i1.31349"},{"key":"1134_CR75","doi-asserted-by":"crossref","unstructured":"Haurogn\u00e9 J, Basheer N, Islam S (2025) Advanced vulnerability detection using LLM with transparency obligation practice towards trustworthy AI. Available at SSRN 4925500","DOI":"10.2139\/ssrn.4925500"},{"key":"1134_CR76","unstructured":"Xiao J, Yin M, Yang C, Sui Y, Phan H, Zang X, Jia W, Liu H, Zhang Z, Ren J et\u00a0al (2025) Transpa: towards efficient structured sparse training for transformers"},{"key":"1134_CR77","doi-asserted-by":"crossref","unstructured":"Song L, Chen Y, Yang S, Ding X, Ge Y, Chen Y-C, Shan Y (2024) Low-rank approximation for sparse attention in multi-modal LLMs. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13763\u201313773","DOI":"10.1109\/CVPR52733.2024.01306"},{"key":"1134_CR78","unstructured":"Song C, Han X, Zhang Z, Hu S, Shi X, Li K, Chen C, Liu Z, Li G, Yang T et\u00a0al (2024) Prosparse: introducing and enhancing intrinsic activation sparsity within large language models. arXiv:2402.13516"},{"key":"1134_CR79","unstructured":"Li Z, Zhou T (2024) Your mixture-of-experts LLM is secretly an embedding model for free. arXiv:2410.10814"},{"key":"1134_CR80","doi-asserted-by":"crossref","unstructured":"Vats A, Raja R, Jain V, Chadha A (2024) The evolution of mixture of experts: a survey from basics to breakthroughs. Preprints (August 2024)","DOI":"10.20944\/preprints202408.0583.v2"},{"key":"1134_CR81","doi-asserted-by":"crossref","unstructured":"Yan M, Wang Y, Pang K, Xie M, Li J (2024) Efficient mixture of experts based on large language models for low-resource data preprocessing. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 3690\u20133701","DOI":"10.1145\/3637528.3671873"},{"key":"1134_CR82","unstructured":"Dong H, Chen B, Chi Y (2024) Prompt-prompted mixture of experts for efficient LLM generation. arXiv:2404.01365"},{"key":"1134_CR83","unstructured":"Chen B, Zhang Z, Langren\u00e9 N, Zhu S (2023) Unleashing the potential of prompt engineering in large language models: a comprehensive review. arXiv:2310.14735"},{"key":"1134_CR84","doi-asserted-by":"publisher","DOI":"10.2196\/50638","volume":"25","author":"B Mesk\u00f3","year":"2023","unstructured":"Mesk\u00f3 B (2023) Prompt engineering as an important emerging skill for medical professionals: tutorial. J Med Internet Res 25:e50638","journal-title":"J Med Internet Res"},{"key":"1134_CR85","unstructured":"White J, Fu Q, Hays S, Sandborn M, Olea C, Gilbert H, Elnashar A, Spencer-Smith J, Schmidt DC (2023) A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv:2302.11382"},{"key":"1134_CR86","unstructured":"Goknil A, Gelderblom FB, Tverdal S, Tokas S, Song H (2024) Privacy policy analysis through prompt engineering for LLMs. arXiv:2409.14879"},{"issue":"1","key":"1134_CR87","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41746-024-01029-4","volume":"7","author":"L Wang","year":"2024","unstructured":"Wang L, Chen X, Deng X, Wen H, You M, Liu W, Li Q, Li J (2024) Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs. npj Digit Med 7(1):41","journal-title":"npj Digit Med"},{"key":"1134_CR88","doi-asserted-by":"crossref","unstructured":"Marvin G, Hellen N, Jjingo D, Nakatumba-Nabende J (2023) Prompt engineering in large language models. In: International conference on data intelligence and cognitive informatics. Springer, pp 387\u2013402","DOI":"10.1007\/978-981-99-7962-2_30"},{"key":"1134_CR89","unstructured":"Chen Y, Li Y, Ding B, Zhou J (2024) On the design and analysis of LLM-based algorithms. arXiv:2407.14788"},{"key":"1134_CR90","unstructured":"Yu X, Qi Y, Chen K, Chen G, Yang X, Zhu P, Shang X, Zhang W, Yu N (2024) DPIC: decoupling prompt and intrinsic characteristics for LLM generated text detection. In: The thirty-eighth annual conference on neural information processing systems"},{"key":"1134_CR91","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhang J, Rekabdar B, Zhou Y, Wang P, Liu K (2024) Dynamic and adaptive feature generation with LLM. arXiv:2406.03505","DOI":"10.24963\/ijcai.2024\/782"},{"key":"1134_CR92","unstructured":"Kaplan J, McCandlish S, Henighan T, Brown T, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D (2020) Scaling laws for neural language models. arXiv:2001.08361"},{"key":"1134_CR93","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et\u00a0al (2020) Language models are few-shot learners. arXiv:2005.14165"},{"key":"1134_CR94","doi-asserted-by":"crossref","unstructured":"Hoffmann J, Borgeaud S, Mensch A, Buchatskaya E, Cai T, Rutherford E, de\u00a0Las\u00a0Casas D, Hendricks L, Welbl J, Clark A et\u00a0al (2022) Training compute-optimal large language models. arXiv:2203.15556","DOI":"10.52202\/068431-2176"},{"key":"1134_CR95","unstructured":"Weber T, Mayer S (2024) From computational to conversational notebooks. arXiv:2406.10636"},{"issue":"5","key":"1134_CR96","doi-asserted-by":"publisher","first-page":"2074","DOI":"10.3390\/app14052074","volume":"14","author":"R Patil","year":"2024","unstructured":"Patil R, Gudivada V (2024) A review of current trends, techniques, and challenges in large language models (LLMs). Appl Sci 14(5):2074","journal-title":"Appl Sci"},{"issue":"2","key":"1134_CR97","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.jacr.2023.11.011","volume":"21","author":"FX Doo","year":"2024","unstructured":"Doo FX, Kulkarni P, Siegel EL, Toland M, Paul HY, Carlos RC, Parekh VS (2024) Economic and environmental costs of cloud technologies for medical imaging and radiology artificial intelligence. J Am Coll Radiol 21(2):248\u2013256","journal-title":"J Am Coll Radiol"},{"key":"1134_CR98","doi-asserted-by":"crossref","unstructured":"Tharayil SM, Idris MA, Alfaifi OM, Alghafis MS (2024) Unleashing the power of generative ai and LLM for training evaluation. In: Abu Dhabi international petroleum exhibition and conference. SPE, p D021S056R006","DOI":"10.2118\/222374-MS"},{"key":"1134_CR99","doi-asserted-by":"crossref","unstructured":"Liu Y, He H, Han T, Zhang X, Liu M, Tian J, Zhang Y, Wang J, Gao X, Zhong T et\u00a0al (2024) Understanding LLMs: a comprehensive overview from training to inference. arXiv:2401.02038","DOI":"10.2139\/ssrn.4706201"},{"key":"1134_CR100","unstructured":"He T, Li X, Wang Z, Qian K, Xu J, Yu W, Zhou J (2023) Unicron: economizing self-healing LLM training at scale. arXiv:2401.00134"},{"key":"1134_CR101","unstructured":"Kausik BN (2024) Scaling efficient LLMs. arXiv:2402.14746"},{"key":"1134_CR102","unstructured":"Sachdeva N, Coleman B, Kang W-C, Ni J, Hong L, Chi EH, Caverlee J, McAuley J, Cheng DZ (2024) How to train data-efficient LLMs. arXiv:2402.09668"},{"key":"1134_CR103","doi-asserted-by":"crossref","unstructured":"Yang C, Zhu Y, Lu W, Wang Y, Chen Q, Gao C, Yan B, Chen Y (2024) Survey on knowledge distillation for large language models: methods, evaluation, and application. ACM Trans Intell Syst Technol","DOI":"10.1145\/3699518"},{"key":"1134_CR104","unstructured":"Latif E, Fang L, Ma P, Zhai X (2023) Knowledge distillation of LLM for education. arXiv:2312.15842"},{"key":"1134_CR105","doi-asserted-by":"crossref","unstructured":"Hsieh C-Y, Li C-L, Yeh C-K, Nakhost H, Fujii Y, Ratner A, Krishna R, Lee C-Y, Pfister T (2023) Distilling step-by-step! Outperforming larger language models with less training data and smaller model sizes. arXiv:2305.02301","DOI":"10.18653\/v1\/2023.findings-acl.507"},{"key":"1134_CR106","doi-asserted-by":"crossref","unstructured":"Kundu A, Lim F, Chew A, Wynter L, Chong P, Lee RD (2024) Efficiently distilling LLMs for edge applications. arXiv:2404.01353","DOI":"10.18653\/v1\/2024.naacl-industry.5"},{"issue":"4","key":"1134_CR107","doi-asserted-by":"publisher","first-page":"1","DOI":"10.70589\/JRTCSE.2024.4.1","volume":"12","author":"RR Kethireddy","year":"2024","unstructured":"Kethireddy RR (2024) Secure model distribution and deployment for LLMs. J Recent Trends Comput Sci Eng (JRTCSE) 12(4):1\u201314","journal-title":"J Recent Trends Comput Sci Eng (JRTCSE)"},{"key":"1134_CR108","unstructured":"Xu X, Li M, Tao C, Shen T, Cheng R, Li J, Xu C, Tao D, Zhou T (2024) A survey on knowledge distillation of large language models. arXiv:2402.13116"},{"key":"1134_CR109","doi-asserted-by":"crossref","unstructured":"Qin R, Xia J, Jia Z, Jiang M, Abbasi A, Zhou P, Hu J, Shi Y (2024) Enabling on-device large language model personalization with self-supervised data selection and synthesis. In: Proceedings of the 61st ACM\/IEEE design automation conference, pp 1\u20136","DOI":"10.1145\/3649329.3655665"},{"key":"1134_CR110","unstructured":"Mart\u00edn-Cortinas \u00c1, S\u00e1ez-Trigueros D, Vall\u00e9s-P\u00e9rez I, Tura-Vecino B, Bili\u0144ski P, Lajszczak M, Beringer G, Barra-Chicote R, Lorenzo-Trueba J (2024) Enhancing the stability of LLM-based speech generation systems through self-supervised representations. arXiv:2402.03407"},{"key":"1134_CR111","doi-asserted-by":"crossref","unstructured":"Hegde R, Sharma S (2024) Self supervised LLM customizer (SSLC): customizing LLMs on unlabeled data to enhance contextual question answering","DOI":"10.1145\/3703412.3703421"},{"issue":"2","key":"1134_CR112","doi-asserted-by":"publisher","first-page":"e85","DOI":"10.1002\/itl2.85","volume":"2","author":"M Aiello","year":"2019","unstructured":"Aiello M, Mongelli M, Muselli M, Verda D (2019) Unsupervised learning and rule extraction for domain name server tunneling detection. Internet Technol Lett 2(2):e85","journal-title":"Internet Technol Lett"},{"key":"1134_CR113","unstructured":"Farquhar S, Varma V, Kenton Z, Gasteiger J, Mikulik V, Shah R (2023) Challenges with unsupervised LLM knowledge discovery. arXiv:2312.10029"},{"key":"1134_CR114","doi-asserted-by":"crossref","unstructured":"Karlsen E, Luo X, Zincir-Heywood N, Heywood M (2024) Large language models and unsupervised feature learning: implications for log analysis. Annals Telecommun, pp 1\u201319","DOI":"10.1007\/s12243-024-01028-2"},{"key":"1134_CR115","unstructured":"Fernando H, Shen H, Ram P, Zhou Y, Samulowitz H, Baracaldo N, Chen T (2024) Mitigating forgetting in LLM supervised fine-tuning and preference learning. arXiv:2410.15483"},{"key":"1134_CR116","unstructured":"Sun H (2024) Supervised fine-tuning as inverse reinforcement learning. arXiv:2403.12017"},{"key":"1134_CR117","unstructured":"Parthasarathy VB, Zafar A, Khan A, Shahid A (2024) The ultimate guide to fine-tuning LLMs from basics to breakthroughs: an exhaustive review of technologies, research, best practices, applied research challenges and opportunities. arXiv:2408.13296"},{"key":"1134_CR118","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.1016\/j.procs.2024.09.178","volume":"246","author":"M Arslan","year":"2024","unstructured":"Arslan M, Ghanem H, Munawar S, Cruz C (2024) A survey on rag with LLMs. Procedia Comput Sci 246:3781\u20133790","journal-title":"Procedia Comput Sci"},{"key":"1134_CR119","doi-asserted-by":"crossref","unstructured":"Arslan M, Munawar S, Cruz C (2024) Business insights using RAG\u2013LLMs: a review and case study. J Decision Syst, pp 1\u201330","DOI":"10.1080\/12460125.2024.2410040"},{"key":"1134_CR120","doi-asserted-by":"crossref","unstructured":"Fan W, Ding Y, Ning L, Wang S, Li H, Yin D, Chua T-S, Li Q (2024) A survey on rag meeting LLMs: towards retrieval-augmented large language models. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 6491\u20136501","DOI":"10.1145\/3637528.3671470"},{"key":"1134_CR121","doi-asserted-by":"crossref","unstructured":"Bernardi ML, Cimitile M, Pecori R (2024) Automatic job safety report generation using rag-based LLMs. In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN60899.2024.10651320"},{"key":"1134_CR122","doi-asserted-by":"crossref","unstructured":"Zeng S, Zhang J, He P, Xing Y, Liu Y, Xu H, Ren J, Wang S, Yin D, Chang Y et\u00a0al (2024) The good and the bad: exploring privacy issues in retrieval-augmented generation (RAG). arXiv:2402.16893","DOI":"10.18653\/v1\/2024.findings-acl.267"},{"key":"1134_CR123","doi-asserted-by":"crossref","unstructured":"Gummadi V, Udayaraju P, Sarabu VR, Ravulu C, Seelam DR, Venkataramana S (2024) Enhancing communication and data transmission security in rag using large language models. In: 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, pp 612\u2013617","DOI":"10.1109\/ICSES63445.2024.10763024"},{"key":"1134_CR124","doi-asserted-by":"crossref","unstructured":"Yamin MM, Hashmi E, Ullah M, Katt B (2024) Applications of LLMs for generating cyber security exercise scenarios","DOI":"10.21203\/rs.3.rs-3970015\/v1"},{"key":"1134_CR125","unstructured":"Casper S, Davies X, Shi C, Gilbert TK, Scheurer J, Rando J, Freedman R, Korbak T, Lindner D, Freire P et\u00a0al (2023) Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv:2307.15217"},{"key":"1134_CR126","doi-asserted-by":"crossref","unstructured":"Chaudhari S, Aggarwal P, Murahari V, Rajpurohit T, Kalyan A, Narasimhan K, Deshpande A, da\u00a0Silva BC (2024) RLHF deciphered: a critical analysis of reinforcement learning from human feedback for LLMs. arXiv:2404.08555","DOI":"10.1145\/3743127"},{"issue":"5","key":"1134_CR127","doi-asserted-by":"publisher","first-page":"bbae354","DOI":"10.1093\/bib\/bbae354","volume":"25","author":"H Meshkin","year":"2024","unstructured":"Meshkin H, Zirkle J, Arabidarrehdor G, Chaturbedi A, Chakravartula S, Mann J, Thrasher B, Li Z (2024) Harnessing large language models\u2019 zero-shot and few-shot learning capabilities for regulatory research. Briefings Bioinform 25(5):bbae354","journal-title":"Briefings Bioinform"},{"key":"1134_CR128","unstructured":"Chamieh I, Zesch T, Giebermann K (2024) LLMs in short answer scoring: limitations and promise of zero-shot and few-shot approaches. In: Proceedings of the 19th workshop on innovative use of NLP for building educational applications (bea 2024), pp 309\u2013315"},{"key":"1134_CR129","unstructured":"Fatemi S, Hu Y (2023) A comparative analysis of fine-tuned LLMs and few-shot learning of LLMs for financial sentiment analysis. arXiv:2312.08725"},{"issue":"5","key":"1134_CR130","doi-asserted-by":"publisher","first-page":"2422","DOI":"10.3390\/smartcities7050095","volume":"7","author":"S Jaradat","year":"2024","unstructured":"Jaradat S, Nayak R, Paz A, Ashqar HI, Elhenawy M (2024) Multitask learning for crash analysis: a fine-tuned LLM framework using twitter data. Smart Cities 7(5):2422\u20132465","journal-title":"Smart Cities"},{"key":"1134_CR131","unstructured":"Bindal A, Ramanujam S, Golland D, Hazen TJ, Jiang T, Zhang F, Yan P (2024) Improved content understanding with effective use of multi-task contrastive learning. arXiv:2405.11344"},{"key":"1134_CR132","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107239","volume":"127","author":"H Rathnayake","year":"2024","unstructured":"Rathnayake H, Sumanapala J, Rukshani R, Ranathunga S (2024) Adapterfusion-based multi-task learning for code-mixed and code-switched text classification. Eng Appl Artif Intell 127:107239","journal-title":"Eng Appl Artif Intell"},{"key":"1134_CR133","unstructured":"Jiang Z, Ma X, Chen W (2024) LongRAG: enhancing retrieval-augmented generation with long-context LLMs. arXiv:2406.15319"},{"key":"1134_CR134","doi-asserted-by":"crossref","unstructured":"Dong G, Zhu Y, Zhang C, Wang Z, Dou Z, Wen J-R (2024) Understand what LLM needs: dual preference alignment for retrieval-augmented generation. arXiv:2406.18676","DOI":"10.1145\/3696410.3714717"},{"key":"1134_CR135","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhou J, Ding X, Huai T, Liu S, Chen Q, Xie Y, He L (2024) Recent advances of foundation language models-based continual learning: a survey. ACM Comput Surv","DOI":"10.1145\/3705725"},{"key":"1134_CR136","doi-asserted-by":"crossref","unstructured":"Wu T, Luo L, Li Y-F, Pan S, Vu T-T, Haffari G (2024) Continual learning for large language models: a survey. arXiv:2402.01364","DOI":"10.18653\/v1\/2025.emnlp-tutorials.7"},{"key":"1134_CR137","unstructured":"Yu A (2023) Improving efficiency in data wrangling with semantic type detection. PhD thesis"},{"key":"1134_CR138","doi-asserted-by":"crossref","unstructured":"Huang Z, Wu E (2024) Cocoon: semantic table profiling using large language models. In: Proceedings of the 2024 workshop on human-in-the-loop data analytics, pp 1\u20137","DOI":"10.1145\/3665939.3665957"},{"key":"1134_CR139","unstructured":"Mohammed N, Lal A, Rastogi A, Roy S, Sharma R (2024) Enabling memory safety of c programs using LLMs. arXiv:2404.01096"},{"key":"1134_CR140","doi-asserted-by":"crossref","unstructured":"Mohammed N, Lal A, Rastogi A, Sharma R, Roy S (2024) LLM assistance for memory safety. In: 2025 IEEE\/ACM 47th International Conference on Software Engineering (ICSE). IEEE Computer Society, pp 280\u2013291","DOI":"10.1109\/ICSE55347.2025.00023"},{"issue":"2","key":"1134_CR141","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1109\/TVCG.2020.3030427","volume":"27","author":"Y Yang","year":"2020","unstructured":"Yang Y, Cordeil M, Beyer J, Dwyer T, Marriott K, Pfister H (2020) Embodied navigation in immersive abstract data visualization: is overview+ detail or zooming better for 3d scatterplots? IEEE Trans Visual Comput Graphics 27(2):1214\u20131224","journal-title":"IEEE Trans Visual Comput Graphics"},{"key":"1134_CR142","volume-title":"Specification of abstract data types","author":"J Loeckx","year":"1997","unstructured":"Loeckx J, Ehrich H-D, Wolf M (1997) Specification of abstract data types. John Wiley & Sons Inc"},{"issue":"4","key":"1134_CR143","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1145\/6041.6042","volume":"17","author":"L Cardelli","year":"1985","unstructured":"Cardelli L, Wegner P (1985) On understanding types, data abstraction, and polymorphism. ACM Comput Surv (CSUR) 17(4):471\u2013523","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1134_CR144","unstructured":"Dureg\u00e5rd JA (2012) Enumerative testing and embedded languages. Chalmers Tekniska Hogskola (Sweden)"},{"key":"1134_CR145","unstructured":"Zhang H, Dong Y, Xiao C, Oyamada M (2023) Large language models as data preprocessors. arXiv:2308.16361"},{"key":"1134_CR146","doi-asserted-by":"crossref","unstructured":"Velu A, Ramamoorthy R, Manasa SM, Prasanth A (2024) 5 LLM pretraining methods. Generative AI and LLMs, Natural Language Processing and Generative Adversarial Networks, p 93","DOI":"10.1515\/9783111425078-005"},{"key":"1134_CR147","unstructured":"Schaeffer R, Miranda B, Koyejo S (2024) Are emergent abilities of large language models a mirage? Adv Neural Inf Process Syst 36"},{"issue":"11","key":"1134_CR148","doi-asserted-by":"publisher","first-page":"3302","DOI":"10.14778\/3611479.3611527","volume":"16","author":"RC Fernandez","year":"2023","unstructured":"Fernandez RC, Elmore AJ, Franklin MJ, Krishnan S, Tan C (2023) How large language models will disrupt data management. Proc VLDB Endow 16(11):3302\u20133309","journal-title":"Proc VLDB Endow"},{"issue":"12","key":"1134_CR149","doi-asserted-by":"publisher","first-page":"4213","DOI":"10.14778\/3685800.3685838","volume":"17","author":"G Li","year":"2024","unstructured":"Li G, Zhou X, Zhao X (2024) LLM for data management. Proc VLDB Endow 17(12):4213\u20134216","journal-title":"Proc VLDB Endow"},{"key":"1134_CR150","unstructured":"Rae JW, Borgeaud S, Cai T, Millican K, Hoffmann J, Song F, Aslanides J, Henderson S, Ring R, Young S et\u00a0al (2021) Scaling language models: methods, analysis & insights from training gopher. arXiv:2112.11446"},{"key":"1134_CR151","unstructured":"Ren X, Zhou P, Meng X, Huang X, Wang Y, Wang W, Li P, Zhang X, Podolskiy A, Arshinov G, Bout A, Piontkovskaya I, Wei J, Jiang X, Su T, Liu Q, Yao J (2023) PanGu-$$\\sum $$: towards trillion parameter language model with sparse heterogeneous computing. arXiv:2303.10845"},{"key":"1134_CR152","unstructured":"Dey N, Soboleva D, Al-Khateeb F, Yang B, Pathria R, Khachane H, Muhammad S, Chen Z, Myers R, Steeves JR, Vassilieva N, Tom M, Hestness J (2023) BTLM-3B-8K: 7b parameter performance in a 3b parameter model. arXiv:2309.11568"},{"key":"1134_CR153","unstructured":"Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A et\u00a0al (2024) The llama 3 herd of models. arXiv:2407.21783"},{"key":"1134_CR154","doi-asserted-by":"crossref","unstructured":"Lv K, Yang Y, Liu T, Gao Q, Guo Q, Qiu X (2023) Full parameter fine-tuning for large language models with limited resources. arXiv:2306.09782","DOI":"10.18653\/v1\/2024.acl-long.445"},{"key":"1134_CR155","doi-asserted-by":"crossref","unstructured":"Mei T, Zi Y, Cheng X, Gao Z, Wang Q, Yang H (2024) Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks. arXiv:2405.11704","DOI":"10.1109\/ICSECE61636.2024.10729518"},{"key":"1134_CR156","doi-asserted-by":"crossref","unstructured":"Argerich MF, Pati\u00f1o-Mart\u00ednez M (2023) Measuring and improving the energy efficiency of large language models inference. IEEE Trans Comput","DOI":"10.1109\/ACCESS.2024.3409745"},{"key":"1134_CR157","doi-asserted-by":"crossref","unstructured":"Xu H, Wang S, Li N, Wang K, Zhao Y, Chen K, Yu T, Liu Y, Wang H (2024) Large language models for cyber security: a systematic literature review. arXiv:2405.04760","DOI":"10.1145\/3769676"},{"key":"1134_CR158","doi-asserted-by":"crossref","unstructured":"Kumar P, Pati\u00f1o-Mart\u00ednez M (2023) Large language models (LLMs): survey, technical frameworks, and future challenges. Artif Intell Rev","DOI":"10.1007\/s10462-024-10888-y"},{"key":"1134_CR159","doi-asserted-by":"crossref","unstructured":"Tzanos G, Kachris C, Soudris D (2023) Hardware acceleration of transformer networks using FPGAs. In: Proceedings of the IEEE international conference on Field-Programmable Logic and Applications (FPL)","DOI":"10.1109\/PACET56979.2022.9976354"},{"key":"1134_CR160","doi-asserted-by":"crossref","unstructured":"Chen H, Zhang J, Du Y, Xiang S, Yue Z, Zhang N, Cai Y, Zhang Z (2023) Understanding the potential of FPGA-based spatial acceleration for large language model inference. arXiv:2312.15159","DOI":"10.1145\/3656177"},{"issue":"12","key":"1134_CR161","first-page":"3745","volume":"42","author":"W Li","year":"2023","unstructured":"Li W, Hu A, Xu N, He G (2023) Quantization and hardware architecture co-design for matrix-vector multiplications of large language models. IEEE Trans Comput Aided Des Integr Circuits Syst 42(12):3745\u20133758","journal-title":"IEEE Trans Comput Aided Des Integr Circuits Syst"},{"issue":"12","key":"1134_CR162","first-page":"4501","volume":"70","author":"GW Burr","year":"2023","unstructured":"Burr GW, Tsai H, Simon W, Boybat I, Ambrogio S, Ho C-E (2023) Analog hardware acceleration for transformer-based language models. IEEE Trans Circuits Syst 70(12):4501\u20134513","journal-title":"IEEE Trans Circuits Syst"},{"key":"1134_CR163","doi-asserted-by":"crossref","unstructured":"Zhang H, Ning A, Prabhakar R, Wentzlaff D (2023) LLMcompass: a hardware evaluation framework for large language model inference. arXiv:2312.03134","DOI":"10.1109\/ISCA59077.2024.00082"},{"key":"1134_CR164","unstructured":"Peng H, Davidson S, Shi R, Song SL, Taylor M (2023) Chiplet cloud: building AI supercomputers for serving large generative language models. arXiv:2307.02666"},{"key":"1134_CR165","unstructured":"Su Q, Giannoula C, Pekhimenko G (2023) The synergy of speculative decoding and batching in serving large language models. arXiv:2310.18813"},{"key":"1134_CR166","unstructured":"Huang WR, Allauzen C, Chen T, Gupta K, Hu K, Qin J, Zhang Y, Wang Y, Chang S-Y, Sainath TN (2024) Multilingual and fully non-autoregressive ASR with large language model fusion: a comprehensive study. arXiv:2401.12789"},{"key":"1134_CR167","unstructured":"Fu DY, Dao T, Saab KK, Thomas AW, Rudra A, R\u00e9 C (2022) Hungry hungry hippos: towards language modeling with state space models. arXiv:2212.14052"},{"key":"1134_CR168","doi-asserted-by":"crossref","unstructured":"Costin A, Turtiainen H, Yousefnezhad N, Bogulean V, H\u00e4m\u00e4l\u00e4inen T (2024) Evaluating zero-shot ChatGPT performance on predicting CVE data from vulnerability descriptions. In: Proceedings of the 23rd European Conference on Cyber Warfare and Security (ECCWS). Academic Conferences International Ltd, pp 576\u2013584","DOI":"10.34190\/eccws.23.1.2285"},{"key":"1134_CR169","unstructured":"Garza E, Hemberg E, Moskal S, O\u2019Reilly U-M (2023) Assessing large language model\u2019s knowledge of threat behavior in mitre att&ck. In: Proceedings of the 3rd workshop on artificial intelligence-enabled cybersecurity analytics at KDD 2023 (AI4Cyber)"},{"key":"1134_CR170","doi-asserted-by":"crossref","unstructured":"Talasari RAD, Ilham KF, Studiawan H (2024) Zero-shot entity recognition on forensic timeline. In: 2024 10th International Conference on Smart Computing and Communication (ICSCC), pp 117\u2013122","DOI":"10.1109\/ICSCC62041.2024.10690409"},{"key":"1134_CR171","doi-asserted-by":"crossref","unstructured":"Bae C, Tao G, Zhang Z, Zhang X (2024) Threat behavior textual search by attention graph isomorphism. In: Graham Y, Purver M (eds) Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (volume 1: long papers), St. Julian\u2019s, Malta. Association for Computational Linguistics, pp 2616\u20132630","DOI":"10.18653\/v1\/2024.eacl-long.160"},{"issue":"3","key":"1134_CR172","doi-asserted-by":"publisher","first-page":"572","DOI":"10.3390\/jcp4030027","volume":"4","author":"E Rheault","year":"2024","unstructured":"Rheault E, Nerayo M, Leonard J, Kolenbrander J, Henshaw C, Boswell M, Michaels AJ (2024) Use and abuse of personal information, part I: design of a scalable OSINT collection engine. J Cybersecur Priv 4(3):572\u2013593","journal-title":"J Cybersecur Priv"},{"key":"1134_CR173","doi-asserted-by":"crossref","unstructured":"Alturkistani H, Chuprat S (2024) Artificial intelligence and large language models in advancing cyber threat intelligence: a systematic literature review. Preprint","DOI":"10.21203\/rs.3.rs-5423193\/v1"},{"key":"1134_CR174","unstructured":"Wang H, Hooi B (2024) Automated phishing detection using URLs and webpages. arXiv:2408.01667"},{"key":"1134_CR175","doi-asserted-by":"publisher","first-page":"187976","DOI":"10.1109\/ACCESS.2024.3514972","volume":"12","author":"W Li","year":"2024","unstructured":"Li W, Manickam S, Chong Y-W, Leng W, Nanda P (2024) A state-of-the-art review on phishing website detection techniques. IEEE Access 12:187976\u2013188012","journal-title":"IEEE Access"},{"key":"1134_CR176","unstructured":"Song C, Ma L, Zheng J, Liao J, Kuang H, Yang L (2024) Audit-LLM: multi-agent collaboration for log-based insider threat detection. arXiv:2408.08902"},{"key":"1134_CR177","unstructured":"Sherman M (2024) Adversarial AI for APTs and cybersecurity. Technical Report AD1204976, ECMS"},{"key":"1134_CR178","doi-asserted-by":"crossref","unstructured":"Baack S (2024) A critical analysis of the largest source for generative AI training data: common crawl. In: Proceedings of the 2024 ACM conference on fairness, accountability, and transparency (FAccT). Mozilla Foundation","DOI":"10.1145\/3630106.3659033"},{"key":"1134_CR179","unstructured":"Tessema BM, Kedia A, Chung T-S (2024) Unifiedcrawl: aggregated common crawl for affordable adaptation of LLMs on low-resource languages. arXiv:2411.14343"},{"key":"1134_CR180","unstructured":"Alto V (2023) Modern generative AI with ChatGPT and OpenAI models: leverage the capabilities of OpenAI\u2019s LLM for productivity and innovation with GPT-3 and GPT-4. Packt Publishing"},{"key":"1134_CR181","doi-asserted-by":"crossref","unstructured":"Neill A, Thomas J, Lee E (2024) A framework for applying copyright law to the training of textual generative artificial intelligence. California Western School of Law Faculty Scholarship","DOI":"10.2139\/ssrn.5362231"},{"key":"1134_CR182","unstructured":"Penedo G, Malartic Q, Hesslow D, Cojocaru R, Cappelli A, Alobeidli H, Pannier B, Almazrouei E, Launay J (2023) The refinedweb dataset for falcon LLM: outperforming curated corpora with web data only. In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Datasets and Benchmarks Track"},{"key":"1134_CR183","unstructured":"\u00d6hman J, Verlinden S, Ekgren A, Gyllensten AC, Isbister T, Gogoulou E, Carlsson F, Sahlgren M (2023) The Nordic Pile: a 1.2TB Nordic dataset for language modeling. arXiv:2303.17183"},{"issue":"2","key":"1134_CR184","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3390\/fi16020041","volume":"16","author":"M Nitu","year":"2023","unstructured":"Nitu M, Dascalu M (2023) Beyond lexical boundaries: LLM-generated text detection for Romanian digital libraries. Future Internet 16(2):41","journal-title":"Future Internet"},{"key":"1134_CR185","unstructured":"Garimella P, Varma V (2023) Learning through Wikipedia and generative ai technologies. In: Proceedings of the 2023 Educational Data Mining (EDM) Tutorials. Educational Data Mining"},{"issue":"2","key":"1134_CR186","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/13548565241238924","volume":"30","author":"ZJ McDowell","year":"2024","unstructured":"McDowell ZJ (2024) Wikipedia and AI: access, representation, and advocacy in the age of large language models. Convergence: Int J Res New Media Technol 30(2):1\u201319","journal-title":"Convergence: Int J Res New Media Technol"},{"key":"1134_CR187","doi-asserted-by":"crossref","unstructured":"Bairi R, Sonwane A, Kanade A, D\u00a0C V, Iyer A, Parthasarathy S, Rajamani S, Ashok B, Shet S (2023) Codeplan: repository-level coding using LLMs and planning. arXiv:2309.12499","DOI":"10.1145\/3643757"},{"key":"1134_CR188","unstructured":"Yu X, Liu L, Hu X, Keung JW, Liu J, Xia X (2024) Where are large language models for code generation on GitHub? arXiv:2406.19544"},{"key":"1134_CR189","unstructured":"Nikolova-Stoupak I, Bibauw S, Dumont A, Stas F, Watrin P, Fran\u00e7ois T (2024) LLM-generated contexts to practice specialised vocabulary: corpus presentation and comparison. In: Actes de la 31\u00e8me Conf\u00e9rence sur le Traitement Automatique des Langues Naturelles, volume 1: articles longs et prises de position. Toulouse, France. ATALA and AFPC, pp 472\u2013498"},{"key":"1134_CR190","doi-asserted-by":"crossref","unstructured":"Ferrag MA, Alwahedi F, Battah A, Cherif B, Mechri A, Tihanyi N (2024) Generative AI and large language models for cyber security: all insights you need. arXiv:2405.12750","DOI":"10.2139\/ssrn.4853709"},{"key":"1134_CR191","unstructured":"(2025) Vulnerability detection on vulnerability java dataset. https:\/\/paperswithcode.com\/sota\/vulnerability-detection-on-vulnerability-java. Accessed 20 Feb 2025"},{"key":"1134_CR192","unstructured":"Li J-J et\u00a0al (2024) Safetyanalyst: interpretable, transparent, and steerable LLM safety moderation. arXiv:2410.16665"},{"key":"1134_CR193","unstructured":"Liu H et\u00a0al (2024) On calibration of LLM-based guard models for reliable content moderation. arXiv:2410.10414"},{"key":"1134_CR194","unstructured":"Zeng W et\u00a0al (2024) Shieldgemma: generative AI content moderation based on gemma. arXiv:2407.21772"},{"key":"1134_CR195","unstructured":"Tete SB (2024) Threat modelling and risk analysis for large language model (LLM)-powered applications. arXiv:2406.11007"},{"key":"1134_CR196","unstructured":"Zhao G, Song E (2024) Privacy-preserving large language models: mechanisms, applications, and future directions. arXiv:2412.06113"},{"key":"1134_CR197","doi-asserted-by":"crossref","unstructured":"Rodriguez D, Yang I, Del\u00a0Alamo JM, Sadeh N (2024) Large language models: a new approach for privacy policy analysis at scale. arXiv:2405.20900","DOI":"10.1007\/s00607-024-01331-9"},{"key":"1134_CR198","unstructured":"Li H et\u00a0al (2023) Privacy in large language models: attacks, defenses and future directions. arXiv:2310.10383"},{"key":"1134_CR199","unstructured":"Ye J, Zheng Z, Bao Y, Qian L, Gu Q (2023) newblock Diffusion language models can perform many tasks with scaling and instruction-finetuning. arXiv:2308.12219"},{"key":"1134_CR200","unstructured":"Kaur P, Kashyap GS, Kumar A, Nafis MT, Kumar S, Shokeen V (2024) From text to transformation: a comprehensive review of large language models\u2019 versatility. arXiv:2402.16142"},{"key":"1134_CR201","unstructured":"Moslem Y, Haque R, Kelleher JD, Way A (2023) Adaptive machine translation with large language models. arXiv:2301.13294"},{"key":"1134_CR202","doi-asserted-by":"crossref","unstructured":"Yu Z, Wang Z, Li Y, You H, Gao R, Zhou X, Bommu SR, Zhao YK, Lin YC (2024) Edge-LLM: enabling efficient large language model adaptation on edge devices via layerwise unified compression and adaptive layer tuning and voting. arXiv:2406.15758","DOI":"10.1145\/3649329.3658473"},{"key":"1134_CR203","doi-asserted-by":"crossref","unstructured":"Sinha S, Yue Y, Soto V, Kulkarni M, Lu J, Zhang A (2024) MAML-en-LLM: model agnostic meta-training of LLMs for improved in-context learning. arXiv:2405.11446","DOI":"10.1145\/3637528.3671905"},{"key":"1134_CR204","unstructured":"Cohen WW, Sun H, Hofer RA, Siegler M (2020) Scalable neural methods for reasoning with a symbolic knowledge base. arXiv:2002.06115"},{"key":"1134_CR205","doi-asserted-by":"crossref","unstructured":"Purohit S, Van N, Chin G (2020) Semantic property graph for scalable knowledge graph analytics. arXiv:2009.07410","DOI":"10.1109\/BigData52589.2021.9671547"},{"key":"1134_CR206","doi-asserted-by":"publisher","first-page":"96017","DOI":"10.1109\/ACCESS.2024.3424945","volume":"12","author":"ZRK Rostam","year":"2024","unstructured":"Rostam ZRK, Sz\u00e9n\u00e1si S, Kert\u00e9sz G (2024) Achieving peak performance for large language models: a systematic review. IEEE Access 12:96017\u201396050","journal-title":"IEEE Access"},{"key":"1134_CR207","doi-asserted-by":"crossref","unstructured":"Huang Y, Wan LJ, Ye H, Jha M, Wang J, Li Y, Zhang X, Chen D (2024) New solutions on LLM acceleration, optimization, and application. arXiv:2406.10903","DOI":"10.1145\/3649329.3663517"},{"key":"1134_CR208","doi-asserted-by":"crossref","unstructured":"Ullah S, Han M, Pujar S, Pearce H, Coskun A, Stringhini G (2024) LLMs cannot reliably identify and reason about security vulnerabilities (yet?): a comprehensive evaluation, framework, and benchmarks. In: IEEE symposium on security and privacy","DOI":"10.1109\/SP54263.2024.00210"},{"key":"1134_CR209","doi-asserted-by":"crossref","unstructured":"Wu Y, Li Z, Zhang JM, Liu Y (2023) Condefects: a new dataset to address the data leakage concern for LLM-based fault localization and program repair. arXiv:2310.16253","DOI":"10.1145\/3663529.3663815"},{"issue":"3","key":"1134_CR210","first-page":"354","volume":"45","author":"C Chen","year":"2024","unstructured":"Chen C, Shu K (2024) Combating misinformation in the age of LLMs: opportunities and challenges. AI Mag 45(3):354\u2013368","journal-title":"AI Mag"},{"key":"1134_CR211","unstructured":"Kumar A, Murthy SV, Singh S, Ragupathy S (2024) The ethics of interaction: mitigating security threats in LLMs. arXiv:2401.12273"},{"key":"1134_CR212","doi-asserted-by":"crossref","unstructured":"Jiao J, Afroogh S, Xu Y, Phillips C (2024) Navigating LLM ethics: advancements, challenges, and future directions. arXiv:2406.18841","DOI":"10.1007\/s43681-025-00814-5"},{"key":"1134_CR213","doi-asserted-by":"crossref","unstructured":"Ji Z, Yu T, Xu Y, Lee N, Ishii E, Fung P (2023) Towards mitigating LLM hallucination via self reflection. In: Findings of the association for computational linguistics EMNLP 2023, pp 1827\u20131843","DOI":"10.18653\/v1\/2023.findings-emnlp.123"},{"key":"1134_CR214","unstructured":"Huang D, Bu Q, Zhang J, Xie X, Chen J, Cui H (2023) Bias assessment and mitigation in LLM-based code generation. arXiv:2309.14345"},{"issue":"9","key":"1134_CR215","doi-asserted-by":"publisher","first-page":"3464","DOI":"10.1021\/acs.est.3c01106","volume":"57","author":"MC Rillig","year":"2023","unstructured":"Rillig MC, \u00c5gerstrand M, Bi M, Gould KA, Sauerland U (2023) Risks and benefits of large language models for the environment. Environ Sci Technol 57(9):3464\u20133466","journal-title":"Environ Sci Technol"},{"issue":"15","key":"1134_CR216","doi-asserted-by":"publisher","first-page":"5045","DOI":"10.3390\/s24155045","volume":"24","author":"E Ferrara","year":"2024","unstructured":"Ferrara E (2024) Large language models for wearable sensor-based human activity recognition, health monitoring, and behavioral modeling: a survey of early trends, datasets, and challenges. Sensors 24(15):5045","journal-title":"Sensors"},{"key":"1134_CR217","doi-asserted-by":"crossref","unstructured":"Yi J, Ye R, Chen Q, Zhu B, Chen S, Lian D, Sun G, Xie X, Wu F (2024) On the vulnerability of safety alignment in open-access LLMs. In: Findings of the Association for Computational Linguistics ACL 2024, pp 9236\u20139260","DOI":"10.18653\/v1\/2024.findings-acl.549"},{"key":"1134_CR218","unstructured":"Ji J, Liu M, Dai J, Pan X, Zhang C, Bian C, Chen B, Sun R, Wang Y, Yang Y (2024) Beavertails: towards improved safety alignment of LLM via a human-preference dataset. Adv Neural Inf Process Syst 36"},{"key":"1134_CR219","doi-asserted-by":"crossref","unstructured":"Deng Z, Guo Y, Han C, Ma W, Xiong J, Wen S, Xiang Y (2024) Ai agents under threat: a survey of key security challenges and future pathways. arXiv:2406.02630","DOI":"10.1145\/3716628"},{"key":"1134_CR220","unstructured":"Noor A (2023) Large language models in cybersecurity: upcoming ai trends in 2023-24. Technical report, CirrusLabs"},{"key":"1134_CR221","unstructured":"University of Helsinki (2024) Content from Helda Helsinki repository. Helda Open Digital Repository"},{"issue":"11","key":"1134_CR222","first-page":"567","volume":"8","author":"UP Liyanage","year":"2023","unstructured":"Liyanage UP, Ranaweera ND (2023) Ethical considerations and potential risks in the deployment of large language models in diverse societal contexts. J Comput Social Dynamics 8(11):567\u2013589","journal-title":"J Comput Social Dynamics"},{"key":"1134_CR223","unstructured":"Zhang Z, Cui S, Lu Y, Zhou J, Yang J, Wang H, Huang M (2024) Agent-safetybench: evaluating the safety of LLM agents. arXiv:2412.14470"},{"key":"1134_CR224","unstructured":"Zhou K, Liu C, Zhao X, Compalas A, Song D, Wang XE (2024) Multimodal situational safety. arXiv:2410.06172"},{"key":"1134_CR225","unstructured":"Wolters C, Yang X, Schlichtmann U, Suzumura T (2024) Memory is all you need: an overview of compute-in-memory architectures for accelerating large language model inference. arXiv:2406.08413"},{"key":"1134_CR226","doi-asserted-by":"crossref","unstructured":"Kim B, Cha S, Park S, Lee J, Lee S, Kang S-h, So J, Kim K, Jung J, Lee J-G et\u00a0al (2024) The breakthrough memory solutions for improved performance on LLM inference. IEEE Micro","DOI":"10.1109\/MM.2024.3375352"},{"key":"1134_CR227","doi-asserted-by":"crossref","unstructured":"Menshawy A, Nawaz Z, Fahmy M (2024) Navigating challenges and technical debt in large language models deployment. In: Proceedings of the 4th workshop on machine learning and systems, pp 192\u2013199","DOI":"10.1145\/3642970.3655840"},{"key":"1134_CR228","unstructured":"Patil K, Desai B (2024) Leveraging LLM for zero-day exploit detection in cloud networks. Asian Am Res Lett J 1(4)"},{"issue":"1","key":"1134_CR229","doi-asserted-by":"publisher","first-page":"26999","DOI":"10.1038\/s41598-024-77916-3","volume":"14","author":"J Zhou","year":"2024","unstructured":"Zhou J, Su X, Fu W, Lv Y, Liu B (2024) Enhancing intention prediction and interpretability in service robots with LLM and kg. Sci Rep 14(1):26999","journal-title":"Sci Rep"},{"key":"1134_CR230","doi-asserted-by":"crossref","unstructured":"Lee H, Choi Y, Kwon Y (2024) Quantifying qualitative insights: leveraging LLMs to market predict. arXiv:2411.08404","DOI":"10.2139\/ssrn.5093626"},{"key":"1134_CR231","doi-asserted-by":"crossref","unstructured":"Levy M, Jacoby A, Goldberg Y (2024) Same task, more tokens: the impact of input length on the reasoning performance of large language models. arXiv:2402.14848","DOI":"10.18653\/v1\/2024.acl-long.818"},{"key":"1134_CR232","unstructured":"Li D, Shao R, Xie A, Sheng Y, Zheng L, Gonzalez J, Stoica I, Ma X, Zhang H (2023) How long can context length of open-source LLMs truly promise? In: NeurIPS 2023 workshop on instruction tuning and instruction following"},{"key":"1134_CR233","doi-asserted-by":"crossref","unstructured":"Ali M, Rao P, Mai Y, Xie B (2024) Using benchmarking infrastructure to evaluate LLM performance on CS concept inventories: challenges, opportunities, and critiques. In: Proceedings of the 2024 ACM conference on international computing education research-volume 1, pp 452\u2013468","DOI":"10.1145\/3632620.3671097"},{"key":"1134_CR234","unstructured":"Zeng Z, Chen P, Jiang H, Jia J (2023) Challenge LLMs to reason about reasoning: a benchmark to unveil cognitive depth in llms. arXiv:2312.17080"},{"key":"1134_CR235","doi-asserted-by":"crossref","unstructured":"Ko M, Park SH, Park J, Seo M (2024) Hierarchical deconstruction of LLM reasoning: a graph-based framework for analyzing knowledge utilization. arXiv:2406.19502","DOI":"10.18653\/v1\/2024.emnlp-main.288"},{"key":"1134_CR236","unstructured":"Helal M, Holthaus P, Lakatos G, Amirabdollahian F (2023) Chat failures and troubles: reasons and solutions. arXiv:2309.03708"},{"issue":"19","key":"1134_CR237","doi-asserted-by":"publisher","first-page":"8868","DOI":"10.3390\/app14198868","volume":"14","author":"H Jeong","year":"2024","unstructured":"Jeong H, Lee H, Kim C, Shin S (2024) A survey of robot intelligence with large language models. Appl Sci 14(19):8868","journal-title":"Appl Sci"},{"key":"1134_CR238","doi-asserted-by":"crossref","unstructured":"Lamott M, Weweler Y-N, Ulges A, Shafait F, Krechel D, Obradovic D (2024) Lapdoc: layout-aware prompting for documents. In: International conference on document analysis and recognition. Springer, pp 142\u2013159","DOI":"10.1007\/978-3-031-70546-5_9"},{"key":"1134_CR239","unstructured":"Wu J, Yang S, Zhan R, Yuan Y, Wong DF, Chao LS (2023) A survey on LLM-generated text detection: necessity, methods, and future directions. arXiv:2310.14724"},{"key":"1134_CR240","doi-asserted-by":"crossref","unstructured":"Jovanovic M, Voss P (2024) Trends and challenges of real-time learning in large language models: a critical review. arXiv:2404.18311","DOI":"10.1111\/exsy.70127"},{"issue":"4","key":"1134_CR241","first-page":"567","volume":"12","author":"H Santos","year":"2024","unstructured":"Santos H, Khalil A (2024) Unleashing the potential of LLM in ML: techniques for fine-tuning, adaptation, and practical deployment with ChatGPT. Baltic J Modern Comput 12(4):567\u2013589","journal-title":"Baltic J Modern Comput"},{"issue":"4","key":"1134_CR242","first-page":"1234","volume":"28","author":"W Zhang","year":"2025","unstructured":"Zhang W, Li M, Chen X, Wang W (2025) A review on the reliability of knowledge graph: from a knowledge representation learning perspective. World Wide Web 28(4):1234\u20131256","journal-title":"World Wide Web"},{"key":"1134_CR243","doi-asserted-by":"crossref","unstructured":"Martell MJ, Baweja JA, Dreslin BD (2024) Mitigative strategies for recovering from large language model trust violations. J Cognitive Eng Decision Making","DOI":"10.1177\/15553434241303577"},{"key":"1134_CR244","unstructured":"Laban P, Murakhovs\u2019ka L, Xiong C, Wu C-S (2023) Are you sure? challenging LLMs leads to performance drops in the flipflop experiment. arXiv:2311.08596"},{"key":"1134_CR245","doi-asserted-by":"crossref","unstructured":"Shi J, Yuan Z, Liu Y, Huang Y, Zhou P, Sun L, Gong NZ (2024) Optimization-based prompt injection attack to LLM-as-a-judge. In: Proceedings of the 2024 on ACM SIGSAC conference on computer and communications security, pp 660\u2013674","DOI":"10.1145\/3658644.3690291"},{"key":"1134_CR246","doi-asserted-by":"publisher","first-page":"e2374","DOI":"10.7717\/peerj-cs.2374","volume":"10","author":"B Pingua","year":"2024","unstructured":"Pingua B, Murmu D, Kandpal M, Rautaray J, Mishra P, Barik RK, Saikia MJ (2024) Mitigating adversarial manipulation in LLMs: a prompt-based approach to counter jailbreak attacks (prompt-g). PeerJ Comput Sci 10:e2374","journal-title":"PeerJ Comput Sci"},{"key":"1134_CR247","doi-asserted-by":"crossref","unstructured":"Song Y, Liu R, Chen S, Ren Q, Zhang Y, Yu Y (2024) Securesql: evaluating data leakage of large language models as natural language interfaces to databases. In: Findings of the association for computational linguistics EMNLP 2024, pp 5975\u20135990","DOI":"10.18653\/v1\/2024.findings-emnlp.346"},{"key":"1134_CR248","unstructured":"Wang JG, Wang J, Li M, Neel S (2024) Pandora\u2019s white-box: increased training data leakage in open LLMs. arXiv:2402.17012"},{"key":"1134_CR249","unstructured":"Che Z, Casper S, Satheesh A, Gandikota R, Rosati D, Slocum S, McKinney LE, Wu Z, Cai Z, Chughtai B et\u00a0al (2024) Model manipulation attacks enable more rigorous evaluations of LLM capabilities. In: Neurips safe generative AI workshop 2024"},{"key":"1134_CR250","unstructured":"Yang Y, Peng Q, Wang J, Zhang W (2024) Multi-LLM-agent systems: techniques and business perspectives. arXiv:2411.14033"},{"key":"1134_CR251","unstructured":"Wallace E, Xiao K, Leike R, Weng L, Heidecke J, Beutel A (2024) The instruction hierarchy: training LLMs to prioritize privileged instructions. arXiv:2404.13208"},{"key":"1134_CR252","doi-asserted-by":"crossref","unstructured":"Yao Y, Duan J, Xu K, Cai Y, Sun Z, Zhang Y (2024) A survey on large language model (LLM) security and privacy: the good, the bad, and the ugly. High-Confidence Computing, p 100211","DOI":"10.1016\/j.hcc.2024.100211"},{"key":"1134_CR253","doi-asserted-by":"crossref","unstructured":"Das A, Tariq A, Batalini F, Dhara B, Banerjee I (2024) Exposing vulnerabilities in clinical LLMs through data poisoning attacks: case study in breast cancer. medRxiv","DOI":"10.1101\/2024.03.20.24304627"},{"key":"1134_CR254","doi-asserted-by":"crossref","unstructured":"Wang S, Zhao Y, Hou X, Wang H (2024) Large language model supply chain: a research agenda. ACM Trans Softw Eng Methodology","DOI":"10.1145\/3708531"},{"key":"1134_CR255","doi-asserted-by":"crossref","unstructured":"Liu T, Deng Z, Meng G, Li Y, Chen K (2024) Demystifying RCE vulnerabilities in LLM-integrated apps. In: Proceedings of the 2024 on ACM SIGSAC conference on computer and communications security, pp 1716\u20131730","DOI":"10.1145\/3658644.3690338"},{"key":"1134_CR256","doi-asserted-by":"crossref","unstructured":"Dash B (2024) Zero-trust architecture (ZTA): designing an AI-powered cloud security framework for LLMs\u2019 black box problems. Available at SSRN 4726625","DOI":"10.2139\/ssrn.4726625"},{"key":"1134_CR257","unstructured":"Jaff E, Wu Y, Zhang N, Iqbal U (2024) Data exposure from LLM apps: an in-depth investigation of OpenAI\u2019s GPTs. arXiv:2408.13247"},{"key":"1134_CR258","unstructured":"OWASP Foundation (2024) Insecure system configuration in continuous integration\/continuous deployment (ci\/cd) environments. OWASP Project. Accessed 31 Dec 2024"},{"key":"1134_CR259","unstructured":"Author(s) Unknown (2020) Vulnerabilities in OPC UA deployment: a study on security configurations. Accessed 31 Dec 2024"},{"key":"1134_CR260","doi-asserted-by":"crossref","unstructured":"Greshake K, Abdelnabi S, Mishra S, Endres C, Holz T, Fritz M (2023) Not what you\u2019ve signed up for: compromising real-world LLM-integrated applications with indirect prompt injection. arXiv:2302.12173","DOI":"10.1145\/3605764.3623985"},{"key":"1134_CR261","unstructured":"Harang R (2023) Securing LLM systems against prompt injection. NVIDIA Technical Blog"},{"key":"1134_CR262","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.patcog.2018.07.023","volume":"84","author":"B Biggio","year":"2018","unstructured":"Biggio B, Roli F (2018) Wild patterns: ten years after the rise of adversarial machine learning. Pattern Recogn 84:317\u2013331","journal-title":"Pattern Recogn"},{"key":"1134_CR263","unstructured":"Cin\u00e0 AE, Grosse K, Demontis A, Biggio B, Roli F, Pelillo M (2022) Machine learning security against data poisoning: are we there yet? arXiv:2204.05986"},{"key":"1134_CR264","unstructured":"Tram\u00e8r F, Zhang F, Juels A, Reiter MK, Ristenpart T (2016) Stealing machine learning models via prediction APIs. In: 25th USENIX Security Symposium (USENIX Security 16), pp 601\u2013618"},{"issue":"2","key":"1134_CR265","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TDSC.2023.3261327","volume":"21","author":"W Jiang","year":"2024","unstructured":"Jiang W, Li H, Xu G, Zhang T, Lu R (2024) A comprehensive defense framework against model extraction attacks. IEEE Trans Dependable Secure Comput 21(2):685\u2013700","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"1134_CR266","unstructured":"Orca Security (2023) Dependency confusion supply chain attacks"},{"key":"1134_CR267","unstructured":"Jit.io (2023) A step-by-step guide to preventing dependency confusion attacks"},{"key":"1134_CR268","doi-asserted-by":"crossref","unstructured":"Liang J, Pang R, Li C, Wang T (2023) Model extraction attacks revisited. arXiv:2312.05386","DOI":"10.1145\/3634737.3657002"},{"key":"1134_CR269","unstructured":"Red Hat (2023) API security: the importance of rate limiting policies in safeguarding your apis"},{"key":"1134_CR270","unstructured":"Jain A, Wallace E, Lee D, Gardner M, Singh S (2023) Automatic and universal prompt injection attacks against large language models. arXiv:2403.04957"},{"key":"1134_CR271","unstructured":"Hu Z, Wu G, Mitra S, Zhang R, Sun T, Huang H, Swaminathan V (2023) Token-level adversarial prompt detection based on perplexity measures and contextual information. arXiv:2311.11509"},{"key":"1134_CR272","doi-asserted-by":"crossref","unstructured":"Delamore B, Ko RKL (2015) A global, empirical analysis of the shellshock vulnerability in web applications. In: 2015 IEEE Trustcom\/BigDataSE\/ISPA, vol 1. IEEE, pp 1129\u20131135","DOI":"10.1109\/Trustcom.2015.493"},{"key":"1134_CR273","doi-asserted-by":"crossref","unstructured":"Huang Z, D\u2019Angelo M, Miyani D, Lie D (2017) Talos: neutralizing vulnerabilities with security workarounds for rapid response. arXiv:1711.00795","DOI":"10.1109\/SP.2016.43"},{"key":"1134_CR274","unstructured":"Yaman F (2023) Agent SCA: advanced physical side channel analysis agent with LLMs. North Carolina State University"},{"key":"1134_CR275","unstructured":"Zheng X, Han H, Shi S, Fang Q, Du Z, Guo Q, Hu X (2024) Inputsnatch: stealing input in LLM services via timing side-channel attacks. arXiv:2411.18191"},{"key":"1134_CR276","unstructured":"Shwartz O, Cohen A, Shabtai A, Oren Y (2018) Shattered trust: when replacement smartphone components attack. arXiv:1805.04850"},{"issue":"10","key":"1134_CR277","first-page":"2479","volume":"12","author":"M Vidakovi\u0107","year":"2023","unstructured":"Vidakovi\u0107 M, Vinko D (2023) Hardware-based methods for electronic device protection against invasive and non-invasive attacks. Electronics 12(10):2479","journal-title":"Electronics"},{"key":"1134_CR278","unstructured":"Surve PP, Brodt O, Yampolskiy M, Elovici Y, Shabtai A (2023) SoK: security below the OS \u2013 a security analysis of UEFI. arXiv:2311.03809"},{"key":"1134_CR279","unstructured":"EC-Council (2022) Why firmware security matters: common vulnerabilities and best practices. EC-Council"},{"key":"1134_CR280","unstructured":"MITRE (n.d.) Capec-624: hardware fault injection. Accessed 31 Dec 2024"},{"key":"1134_CR281","unstructured":"GeeksforGeeks (2023) Fault-tolerance techniques in computer system. GeeksforGeeks. Accessed 31 Dec 2024"},{"key":"1134_CR282","unstructured":"MITRE (n.d.) Tid-114: peripheral data bus interception threat description. Accessed 31 Dec 2024"},{"key":"1134_CR283","unstructured":"Center for Development\u00a0of Security Excellence\u00a0(CDSE) (2021) Implementing effective physical security countermeasures job aid. Accessed 31 Dec 2024"},{"key":"1134_CR284","unstructured":"Chen P-Y, Choudhury S, Rodriguez L, Hero A, Ray I (2019) Enterprise cyber resiliency against lateral movement: a graph theoretic approach. arXiv:1905.01002"},{"key":"1134_CR285","doi-asserted-by":"crossref","unstructured":"Huang L, Zhu Q (2020) Farsighted risk mitigation of lateral movement using dynamic cognitive honeypots. arXiv:2007.13981","DOI":"10.1007\/978-3-030-64793-3_7"},{"key":"1134_CR286","unstructured":"Bianchi F, Zou J (2024) Large language models are vulnerable to bait-and-switch attacks for generating harmful content. arXiv:2402.13926"},{"key":"1134_CR287","unstructured":"Yuan Z, Xiong Z, Zeng Y, Yu N, Jia R, Song D, Li B (2024) RigorLLM: resilient guardrails for large language models against undesired content. arXiv:2403.13031"},{"key":"1134_CR288","doi-asserted-by":"crossref","unstructured":"Peng B, Bi Z, Niu Q, Liu M, Feng P, Wang T, Yan LKQ, Wen Y, Zhang Y, Yin CH (2024) Jailbreaking and mitigation of vulnerabilities in large language models. arXiv:2410.15236","DOI":"10.31219\/osf.io\/z8jk3"},{"key":"1134_CR289","unstructured":"Dong X, Lin D, Wang S, Hassan AE (2024) A framework for real-time safeguarding the text generation of large language model. arXiv:2404.19048"},{"key":"1134_CR290","unstructured":"Abercrombie G, Rieser V (2022) Chatbots are not yet safe for emergency care patient use: deficiencies of ai responses to clinical questions. arXiv:2210.00572"},{"key":"1134_CR291","unstructured":"Wang Y, Singh L (2023) Adding guardrails to advanced chatbots. arXiv:2306.07500"},{"key":"1134_CR292","unstructured":"Vincent J (2024) Chatbots struggle with content moderation, exposing users to harmful material. The Verge"},{"key":"1134_CR293","unstructured":"Bradshaw S, Howard PN (2021) Social media manipulation by political actors: an industrial scale problem. University of Oxford News"},{"key":"1134_CR294","unstructured":"Bateman J, Jackson D (2024) Countering disinformation effectively: an evidence-based policy guide. Carnegie Endowment for International Peace"},{"key":"1134_CR295","doi-asserted-by":"crossref","unstructured":"Chen S, Piet J, Sitawarin C, Wagner D (2024) Struq: defending against prompt injection with structured queries. arXiv:2402.06363","DOI":"10.1145\/3733799.3762982"},{"key":"1134_CR296","doi-asserted-by":"crossref","unstructured":"Munasinghe S, Piyarathna N, Wijerathne E, Jayasinghe U, Namal S (2023) Machine learning based zero trust architecture for secure networking. In: 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS). IEEE","DOI":"10.1109\/ICIIS58898.2023.10253610"},{"key":"1134_CR297","unstructured":"Purton I (2024) Model denial of service prevention for production LLM applications. BionicGPT Blog"},{"key":"1134_CR298","unstructured":"Namer A, Miller J, Kulkarni P, Vagts H, Bisson J, Maltzman B (2024) Tenant data security for LLM applications in a multi-tenancy environment. Technical Disclosure Commons"},{"key":"1134_CR299","doi-asserted-by":"crossref","unstructured":"Hartmann F, Tran D-H, Kairouz P, C\u0103rbune V, Aguera\u00a0y Arcas B (2024) Can llms get help from other LLMs without revealing private information? arXiv:2404.01041","DOI":"10.18653\/v1\/2024.privatenlp-1.12"},{"key":"1134_CR300","doi-asserted-by":"crossref","unstructured":"Greshake K, Abdelnabi S, Mishra S, Endres C, Holz T, Fritz M (2023) Not what you\u2019ve signed up for: compromising real-world LLM-integrated applications with indirect prompt injection. arXiv:2302.12173","DOI":"10.1145\/3605764.3623985"},{"key":"1134_CR301","unstructured":"Heibel J, Lowd D (2024) Mapping your model: assessing the impact of adversarial attacks on LLM-based programming assistants. arXiv:2407.11072"},{"key":"1134_CR302","unstructured":"Carlini N, Tram\u00e8r F, Wallace E, Jagielski M, Herbert-Voss A, Lee K, Roberts A, Brown T, Song D, Erlingsson \u00da et\u00a0al (2021) Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 21), pp 2633\u20132650"},{"key":"1134_CR303","doi-asserted-by":"crossref","unstructured":"Lukas N, Salem A, Sim R, Tople S, Wutschitz L, Zanella-B\u00e9guelin S (2023) Analyzing leakage of personally identifiable information in language models. arXiv:2302.00539","DOI":"10.1109\/SP46215.2023.10179300"},{"key":"1134_CR304","doi-asserted-by":"crossref","unstructured":"Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In: 2008 IEEE Symposium on Security and Privacy (sp 2008), pp 111\u2013125","DOI":"10.1109\/SP.2008.33"},{"issue":"4","key":"1134_CR305","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1749603.1749605","volume":"42","author":"BCM Fung","year":"2010","unstructured":"Fung BCM, Wang K, Fu AW-C, Yu PS (2010) Privacy-preserving data publishing: a survey of recent developments. ACM Comput Surv (CSUR) 42(4):1\u201353","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1134_CR306","doi-asserted-by":"crossref","unstructured":"Kassem A, Mahmoud O, Saad S (2023) Preserving privacy through dememorization: an unlearning technique for mitigating memorization risks in language models. In: Proceedings of the 2023 conference on empirical methods in natural language processing, pp 4360\u20134379","DOI":"10.18653\/v1\/2023.emnlp-main.265"},{"key":"1134_CR307","doi-asserted-by":"crossref","unstructured":"Balle B, Cherubin G, Hayes J (2022) Reconstructing training data with informed adversaries. In: 2022 IEEE Symposium on Security and Privacy (SP). IEEE, pp 556\u2013573","DOI":"10.1109\/SP46214.2022.9833677"},{"key":"1134_CR308","unstructured":"Guo C, Karrer B, Chaudhuri K, van\u00a0der Maaten L (2022) Bounding training data reconstruction in private (deep) learning. In: International conference on machine learning, pp 8052\u20138062"},{"key":"1134_CR309","doi-asserted-by":"crossref","unstructured":"Miceli-Barone AV, Sun Z (2024) A test suite of prompt injection attacks for LLM-based machine translation. arXiv:2410.05047","DOI":"10.18653\/v1\/2024.wmt-1.30"},{"key":"1134_CR310","unstructured":"Liu Y, Jia Y, Geng R, Jia J, Gong NZ (2023) Prompt injection attacks and defenses in LLM-integrated applications. arXiv:2310.12815"},{"key":"1134_CR311","doi-asserted-by":"crossref","unstructured":"Li Y, Huang H, Zhao Y, Ma X, Sun J (2024) BackdoorLLM: a comprehensive benchmark for backdoor attacks on large language models. arXiv:2408.12798","DOI":"10.18653\/v1\/2024.findings-naacl.94"},{"key":"1134_CR312","unstructured":"Yan S, Wang S, Duan Y, Hong H, Lee K, Kim D, Hong Y (2024) An LLM-assisted easy-to-trigger backdoor attack on code completion models: injecting disguised vulnerabilities against strong detection. arXiv:2406.06822"},{"key":"1134_CR313","doi-asserted-by":"crossref","unstructured":"Mankali LL, Bhandari J, Alam M, Karri R, Maniatakos M, Sinanoglu O, Knechtel J (2024) Rtl-breaker: assessing the security of LLMs against backdoor attacks on HDL code generation. arXiv:2411.17569","DOI":"10.23919\/DATE64628.2025.10993260"},{"key":"1134_CR314","unstructured":"Ning K, Chen J, Zhong Q, Zhang T, Wang Y, Li W, Zhang Y, Zhang W, Zheng Z (2024) MCGMark: an encodable and robust online watermark for LLM-generated malicious code. arXiv:2408.01354"},{"key":"1134_CR315","doi-asserted-by":"crossref","unstructured":"Liao Y, Xu M, Lin Y, Teoh X, Xie X, Feng R, Liaw F, Zhang H, Dong JS (2024) Detecting and explaining anomalies caused by web tamper attacks via building consistency-based normality. In: Proceedings of the 39th IEEE\/ACM international conference on automated software engineering, pp 531\u2013543","DOI":"10.1145\/3691620.3695024"},{"key":"1134_CR316","unstructured":"Shayegani E, Al Mamun MA, Fu Y, Zaree P, Dong Y, Abu-Ghazaleh N (2023) Survey of vulnerabilities in large language models revealed by adversarial attacks. arXiv:2310.10844"},{"key":"1134_CR317","unstructured":"Zou A, Wang Z, Carlini N, Nasr M, Kolter JZ, Fredrikson M (2023) Universal and transferable adversarial attacks on aligned language models. arXiv:2307.15043"},{"key":"1134_CR318","unstructured":"Duan M, Suri A, Mireshghallah N, Min S, Shi W, Zettlemoyer L, Tsvetkov Y, Choi Y, Evans D, Hajishirzi H (2024) Do membership inference attacks work on large language models? arXiv:2402.07841"},{"key":"1134_CR319","doi-asserted-by":"crossref","unstructured":"Fu W, Wang H, Gao C, Liu G, Li Y, Jiang T (2023) Practical membership inference attacks against fine-tuned large language models via self-prompt calibration. arXiv:2311.06062","DOI":"10.52202\/079017-4290"},{"issue":"2","key":"1134_CR320","first-page":"1","volume":"57","author":"A Liu","year":"2024","unstructured":"Liu A, Pan L, Lu Y, Li J, Hu X, Zhang X, Wen L, King I, Xiong H, Yu P (2024) A survey of text watermarking in the era of large language models. ACM Comput Surv 57(2):1\u201336","journal-title":"ACM Comput Surv"},{"key":"1134_CR321","unstructured":"Zhang R, Hussain SS, Neekhara P, Koushanfar F (2024) $$\\{$$REMARK-LLM$$\\}$$: a robust and efficient watermarking framework for generative large language models. In: 33rd USENIX Security Symposium (USENIX Security 24), pp 1813\u20131830"},{"key":"1134_CR322","doi-asserted-by":"crossref","unstructured":"Huang L, Xue J, Wang Y, Chen J, Lei T (2024) Strengthening LLM ecosystem security: preventing mobile malware from manipulating llm-based applications. Information Sciences, p 120923","DOI":"10.1016\/j.ins.2024.120923"},{"key":"1134_CR323","doi-asserted-by":"crossref","unstructured":"Tarek S, Saha D, Saha SK, Tehranipoor M, Farahmandi F (2024) SocureLLM: an LLM-driven approach for large-scale system-on-chip security verification and policy generation. Cryptology ePrint Archive","DOI":"10.1109\/HOST64725.2025.11050068"},{"key":"1134_CR324","doi-asserted-by":"crossref","unstructured":"Xia Y, Xie Z, Liu P, Lu K, Liu Y, Wang W, Ji S (2024) Exploring automatic cryptographic API misuse detection in the era of LLMs. arXiv:2407.16576","DOI":"10.1145\/3728875"},{"key":"1134_CR325","doi-asserted-by":"crossref","unstructured":"Huang W, Wang Y, Cheng A, Zhou A, Yu C, Wang L (2024) A fast, performant, secure distributed training framework for LLM. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 4800\u20134804","DOI":"10.1109\/ICASSP48485.2024.10446717"},{"key":"1134_CR326","doi-asserted-by":"crossref","unstructured":"Yuan Y, Kong R, Li Y, Liu Y (2024) WIP: an on-device LLM-based approach to query privacy protection. In: Proceedings of the workshop on edge and mobile foundation models, pp 7\u20139","DOI":"10.1145\/3662006.3662060"},{"key":"1134_CR327","doi-asserted-by":"crossref","unstructured":"Hassine J (2024) An LLM-based approach to recover traceability links between security requirements and goal models. In: Proceedings of the 28th international conference on evaluation and assessment in software engineering, pp 643\u2013651","DOI":"10.1145\/3661167.3661261"},{"key":"1134_CR328","doi-asserted-by":"crossref","unstructured":"Zaboli A, Choi SL, Song T-J, Hong J (2024) ChatGPT and other large language models for cybersecurity of smart grid applications. In: 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, pp 1\u20135","DOI":"10.1109\/PESGM51994.2024.10688863"},{"key":"1134_CR329","doi-asserted-by":"crossref","unstructured":"Adjewa F, Esseghir M, Merghem-Boulahia L (2024) LLM-based continuous intrusion detection framework for next-gen networks. arXiv:2411.03354","DOI":"10.1109\/IWCMC65282.2025.11059643"},{"key":"1134_CR330","doi-asserted-by":"crossref","unstructured":"Wang T, Xie X, Zhang L, Wang C, Zhang L, Cui Y (2024) ShieldGPT: an LLM-based framework for DDoS mitigation. In: Proceedings of the 8th Asia-Pacific workshop on networking, pp 108\u2013114","DOI":"10.1145\/3663408.3663424"},{"key":"1134_CR331","unstructured":"Gattal R (2024) LLM based approach for anomaly detection in smart grids. PhD thesis, Universit\u00e9 de Echahid Cheikh Larbi T\u00e9bessi\u2013T\u00e9bessa-"},{"key":"1134_CR332","unstructured":"Ali T (2024) Next-generation intrusion detection systems with LLMs: real-time anomaly detection, explainable ai, and adaptive data generation. Master\u2019s thesis, T. Ali"},{"key":"1134_CR333","unstructured":"Li Y, Xiang Z, Bastian ND, Song D, Li B (2024) IDS-agent: an LLM agent for explainable intrusion detection in IoT networks. In: NeurIPS 2024 workshop on open-world agents"},{"key":"1134_CR334","doi-asserted-by":"crossref","unstructured":"Lai H (2023) Intrusion detection technology based on large language models. In: 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT). IEEE, pp 1\u20135","DOI":"10.1109\/EASCT59475.2023.10393509"},{"key":"1134_CR335","doi-asserted-by":"crossref","unstructured":"Fan W, Yang Z, Liu Y, Qin L, Liu J (2024) HoneyLLM: a large language model-powered medium-interaction honeypot. In: 2024 26th International Conference on Information and Communications Security (ICICS 2024)","DOI":"10.1007\/978-981-97-8801-9_13"},{"key":"1134_CR336","doi-asserted-by":"crossref","unstructured":"Guan C, Cao G, Zhu S (2024) HoneyLLM: enabling shell honeypots with large language models. In: 2024 IEEE Conference on Communications and Network Security (CNS). IEEE, pp 1\u20139","DOI":"10.1109\/CNS62487.2024.10735663"},{"key":"1134_CR337","doi-asserted-by":"crossref","unstructured":"Vasilatos C, Mahboobeh DJ, Lamri H, Alam M, Maniatakos M (2024) LLMpot: automated LLM-based industrial protocol and physical process emulation for ics honeypots. arXiv:2405.05999","DOI":"10.1109\/EuroSP63326.2025.00059"},{"key":"1134_CR338","unstructured":"Volkov D et\u00a0al (2024) LLM agent honeypot: monitoring ai hacking agents in the wild. arXiv:2410.13919"},{"key":"1134_CR339","doi-asserted-by":"crossref","unstructured":"Otal HT, Canbaz MA (2024) LLM honeypot: leveraging large language models as advanced interactive honeypot systems. In: 2024 IEEE Conference on Communications and Network Security (CNS). IEEE, pp 1\u20136","DOI":"10.1109\/CNS62487.2024.10735607"},{"key":"1134_CR340","doi-asserted-by":"crossref","unstructured":"Hu Y, Cheng S, Ma Y, Chen S, Xiao F, Zheng Q (2024) MYSQL-pot: a LLM-based honeypot for MYSQL threat protection. In: 2024 9th International Conference on Big Data Analytics (ICBDA). IEEE, pp 227\u2013232","DOI":"10.1109\/ICBDA61153.2024.10607309"},{"key":"1134_CR341","doi-asserted-by":"crossref","unstructured":"Sladi\u0107 M, Valeros V, Catania C, Garcia S (2023) LLM in the shell: generative honeypots. arXiv:2309.00155","DOI":"10.1109\/EuroSPW61312.2024.00054"},{"key":"1134_CR342","unstructured":"Kumarage T, Agrawal G, Sheth P, Moraffah R, Chadha A, Garland J, Liu H (2024) A survey of ai-generated text forensic systems: detection, attribution, and characterization. arXiv:2403.01152"},{"key":"1134_CR343","doi-asserted-by":"crossref","unstructured":"Loumachi FY, Ghanem MC, Ferrag MA (2024) Advancing cyber incident timeline analysis through retrieval-augmented generation and large language models","DOI":"10.20944\/preprints202412.2516.v1"},{"key":"1134_CR344","doi-asserted-by":"crossref","unstructured":"Nikolakopoulos A, Evangelatos S, Veroni E, Chasapas K, Gousetis N, Apostolaras A, Nikolopoulos CD, Korakis T (2024) Large language models in modern forensic investigations: harnessing the power of generative artificial intelligence in crime resolution and suspect identification. In: 2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE). IEEE, pp 1\u20135","DOI":"10.1109\/EEITE61750.2024.10654427"},{"key":"1134_CR345","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhu J, Zhang K, Tang H, Zhang Y, Liu X, Liu Q, Chen E (2024) Detect, investigate, judge and determine: a novel LLM-based framework for few-shot fake news detection. arXiv:2407.08952","DOI":"10.1109\/ICDM65498.2025.00055"},{"key":"1134_CR346","doi-asserted-by":"publisher","first-page":"301756","DOI":"10.1016\/j.fsidi.2024.301756","volume":"49","author":"DB Oh","year":"2024","unstructured":"Oh DB, Kim D, Kim HK (2024) volGPT: evaluation on triaging ransomware process in memory forensics with large language model. Forensic Sci Int Digit Investigation 49:301756","journal-title":"Forensic Sci Int Digit Investigation"},{"key":"1134_CR347","doi-asserted-by":"publisher","first-page":"301801","DOI":"10.1016\/j.fsidi.2024.301801","volume":"50","author":"E Dragonas","year":"2024","unstructured":"Dragonas E, Lambrinoudakis C, Nakoutis P (2024) Forensic analysis of OpenAI\u2019s ChatGPT mobile application. Forensic Sci Int Digit Investigation 50:301801","journal-title":"Forensic Sci Int Digit Investigation"},{"key":"1134_CR348","unstructured":"da\u00a0Silva TC (2024) Open-source framework for digital forensics investigations"},{"key":"1134_CR349","unstructured":"Raheja T, Pochhi N (2024) Recent advancements in LLM red-teaming: techniques, defenses, and ethical considerations. arXiv:2410.09097"},{"key":"1134_CR350","doi-asserted-by":"crossref","unstructured":"Isozaki I, Shrestha M, Console R, Kim E (2024) Towards automated penetration testing: introducing LLM benchmark, analysis, and improvements. arXiv:2410.17141","DOI":"10.1145\/3708319.3733804"},{"key":"1134_CR351","unstructured":"Al-Sinani HS, Mitchell CJ (2024) AI-enhanced ethical hacking: a Linux-focused experiment. arXiv:2410.05105"},{"issue":"21","key":"1134_CR352","doi-asserted-by":"publisher","first-page":"6878","DOI":"10.3390\/s24216878","volume":"24","author":"D Pratama","year":"2024","unstructured":"Pratama D, Suryanto N, Adiputra AA, Le T-T-H, Kadiptya AY, Iqbal M, Kim H (2024) Cipher: cybersecurity intelligent penetration-testing helper for ethical researcher. Sensors 24(21):6878","journal-title":"Sensors"},{"key":"1134_CR353","unstructured":"Pasquini D, Kornaropoulos EM, Ateniese G (2024) Hacking back the ai-hacker: prompt injection as a defense against LLM-driven cyberattacks. arXiv:2410.20911"},{"key":"1134_CR354","doi-asserted-by":"crossref","unstructured":"Pawade P, Kulkarni M, Naik S, Raut A, Wagh KS (2024) Efficiency comparison of dataset generated by LLMs using machine learning algorithms. In: 2024 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, pp 1\u20136","DOI":"10.1109\/ESCI59607.2024.10497340"},{"key":"1134_CR355","doi-asserted-by":"crossref","unstructured":"Balek V, S\u1ef3kora L, Sklen\u00e1k V, Kliegr T (2024) LLM-based feature generation from text for interpretable machine learning. arXiv:2409.07132","DOI":"10.1007\/s10994-025-06867-1"},{"key":"1134_CR356","doi-asserted-by":"crossref","unstructured":"Guan Y, Wang D, Chu Z, Wang S, Ni F, Song R, Zhuang C (2024) Intelligent agents with LLM-based process automation. In: Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pp 5018\u20135027","DOI":"10.1145\/3637528.3671646"},{"key":"1134_CR357","doi-asserted-by":"crossref","unstructured":"Cui H, Du Y, Yang Q, Shao Y, Liew SC (2024) LLMind: orchestrating AI and IoT with LLM for complex task execution. IEEE Commun Mag","DOI":"10.1109\/MCOM.002.2400106"},{"key":"1134_CR358","doi-asserted-by":"crossref","unstructured":"Kaur D, Uslu S, Durresi M, Durresi A (2024) LLM-based agents utilized in a trustworthy artificial conscience model for controlling ai in medical applications. In: International\u00a0conference on advanced information networking and applications. Springer, pp 198\u2013209","DOI":"10.1007\/978-3-031-57870-0_18"},{"key":"1134_CR359","doi-asserted-by":"crossref","unstructured":"Wan H, Zhang J, Suria AA, Yao B, Wang D, Coady Y, Prpa M (2024) Building LLM-based AI agents in social virtual reality. In: Extended abstracts of the CHI conference on human factors in computing systems, pp 1\u20137","DOI":"10.1145\/3613905.3651026"},{"key":"1134_CR360","first-page":"8097","volume":"2150","author":"E Cavalleri","year":"2024","unstructured":"Cavalleri E, Soto-Gomez M, Pashaeibarough A, Malchiodi D, Caufield H, Reese J, Mungall CJ, Robinson PN, Casiraghi E, Valentini G et al (2024) SPIREX: improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning. Proc VLDB Endow. ISSN 2150:8097","journal-title":"Proc VLDB Endow. ISSN"},{"key":"1134_CR361","unstructured":"Kawabe W, Sugano Y (2024) DuetML: human-LLM collaborative machine learning framework for non-expert users. arXiv:2411.18908"},{"key":"1134_CR362","doi-asserted-by":"crossref","unstructured":"Kawabe W, Sugano Y (2024) A multimodal LLM-based assistant for user-centric interactive machine learning. In: SIGGRAPH Asia 2024 Posters, pp 1\u20132","DOI":"10.1145\/3681756.3697880"},{"key":"1134_CR363","unstructured":"Chi Y, Lin Y, Hong S, Pan D, Fei Y, Mei G, Liu B, Pang T, Kwok J, Zhang C et\u00a0al (2024) Sela: tree-search enhanced LLM agents for automated machine learning. arXiv:2410.17238"},{"key":"1134_CR364","unstructured":"Nazary F, Deldjoo Y, Di\u00a0Noia T, di\u00a0Sciascio E (2024) Xai4LLM. let machine learning models and LLMs collaborate for enhanced in-context learning in healthcare. arXiv:2405.06270"},{"key":"1134_CR365","unstructured":"Inan H, Upasani K, Chi J, Rungta R, Iyer K, Mao Y, Tontchev M, Hu Q, Fuller B, Testuggine D et\u00a0al (2023) Llama guard: LLM-based input-output safeguard for human-ai conversations. arXiv:2312.06674"},{"key":"1134_CR366","doi-asserted-by":"crossref","unstructured":"Kumar V, Gleyzer L, Kahana A, Shukla K, Karniadakis GE (2023) MycrunchGPT: a llm assisted framework for scientific machine learning. J Mach Learn Model Comput 4(4)","DOI":"10.1615\/JMachLearnModelComput.2023049518"}],"container-title":["Annals of Telecommunications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12243-025-01134-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12243-025-01134-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12243-025-01134-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T09:09:12Z","timestamp":1776416952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12243-025-01134-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":366,"journal-issue":{"issue":"11-12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["1134"],"URL":"https:\/\/doi.org\/10.1007\/s12243-025-01134-9","relation":{},"ISSN":["0003-4347","1958-9395"],"issn-type":[{"value":"0003-4347","type":"print"},{"value":"1958-9395","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,21]]},"assertion":[{"value":"4 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}