{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T23:31:25Z","timestamp":1786404685872,"version":"build-2736575974"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":67,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-026-01402-0","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T14:23:39Z","timestamp":1780669419000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Systematic literature analysis of artificial intelligence driven penetration testing for ARM systems"],"prefix":"10.1007","volume":"6","author":[{"given":"Matthew","family":"Ragsdale","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijayalakshmi","family":"Ramasamy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,5]]},"reference":[{"key":"1402_CR1","doi-asserted-by":"publisher","unstructured":"Tan X, Ma Z, Pinto S, Guan L, Zhang N, Xu J, Lin Z, Hu H, Zhao Z. Sok: Where\u2019s the \u2019up\u2019?! a comprehensive (bottom-up) study on the security of arm cortex-m systems. arXiv preprint arXiv:2401.15289 2024; https:\/\/doi.org\/10.48550\/arXiv.2401.15289","DOI":"10.48550\/arXiv.2401.15289"},{"issue":"6","key":"1402_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3555806","volume":"55","author":"W Zhou","year":"2023","unstructured":"Zhou W, Wang X, Liu J. Embedded system security: a survey of analysis techniques on arm architecture. ACM Comput Surv. 2023;55(6):1\u201338. https:\/\/doi.org\/10.1145\/3555806.","journal-title":"ACM Comput Surv"},{"issue":"5","key":"1402_CR3","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1049\/sfw2.12066","volume":"16","author":"F Subhan","year":"2022","unstructured":"Subhan F, Wu X, Bo L, Sun X, Rahman M. A deep learning-based approach for software vulnerability detection using code metrics. IET Software. 2022;16(5):516\u201326. https:\/\/doi.org\/10.1049\/sfw2.12066.","journal-title":"IET Software"},{"issue":"2","key":"1402_CR4","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10844-022-00738-0","volume":"60","author":"MC Ghanem","year":"2023","unstructured":"Ghanem MC, Chen TM. Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks. J Intell Inf Syst. 2023;60(2):281\u2013303. https:\/\/doi.org\/10.1007\/s10844-022-00738-0.","journal-title":"J Intell Inf Syst"},{"key":"1402_CR5","doi-asserted-by":"publisher","unstructured":"L\u00f3pez-Montero D, \u00c1lvarez-Aldana JL, Morales-Mart\u00ednez A, Gil-L\u00f3pez M, Au\u00f1\u00f3n\u00a0Garc\u00eda JM. Reinforcement learning for automated cybersecurity penetration testing. 2025;. https:\/\/doi.org\/10.48550\/arXiv.2507.02969","DOI":"10.48550\/arXiv.2507.02969"},{"key":"1402_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105116","volume":"114","author":"AMK Adawadkar","year":"2022","unstructured":"Adawadkar AMK, Kulkarni N. Cyber-security and reinforcement learning\u2013a brief survey. Eng Appl Artif Intell. 2022;114:105116. https:\/\/doi.org\/10.1016\/j.engappai.2022.105116.","journal-title":"Eng Appl Artif Intell"},{"key":"1402_CR7","unstructured":"Deng G, Liu Y, Mayoral-Vilches V, Liu P, Li Y, Xu Y, Zhang T, Liu Y, Pinzger M, Rass S. Pentestgpt: Evaluating and harnessing large language models for automated penetration testing. In: Proceedings of the 33rd USENIX Security Symposium (USENIX Security \u201924), 2024; pp. 847\u2013864. USENIX Association. https:\/\/www.usenix.org\/system\/files\/usenixsecurity24-deng.pdf"},{"issue":"1","key":"1402_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-025-00361-w","volume":"8","author":"J Zhang","year":"2025","unstructured":"Zhang J, Bu H, Wen H, Liu Y, Fei H, Xi R, et al. When llms meet cybersecurity: a systematic literature review. Cybersecurity. 2025;8(1):1\u201349. https:\/\/doi.org\/10.1186\/s42400-025-00361-w.","journal-title":"Cybersecurity"},{"key":"1402_CR9","doi-asserted-by":"publisher","unstructured":"Wang X, Jia W. Optimizing edge ai: A comprehensive survey on data, model, and system strategies. arXiv preprint arXiv:2501.03265 2025; https:\/\/doi.org\/10.48550\/arXiv.2501.03265","DOI":"10.48550\/arXiv.2501.03265"},{"key":"1402_CR10","doi-asserted-by":"publisher","unstructured":"Cerdeira D, Santos N, Fonseca P, Pinto S. Sok: Understanding the prevailing security vulnerabilities in trustzone-assisted tee systems. In: 2020 IEEE Symposium on Security and Privacy (SP), 2020; pp. 1416\u20131432. https:\/\/doi.org\/10.1109\/SP40000.2020.00061 . IEEE","DOI":"10.1109\/SP40000.2020.00061"},{"key":"1402_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The prisma 2020 statement: an updated guideline for reporting systematic reviews. The BMJ. 2021;372:71. https:\/\/doi.org\/10.1136\/bmj.n71.","journal-title":"The BMJ"},{"issue":"2","key":"1402_CR12","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak AL, Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials. 2016;18(2):1153\u201376. https:\/\/doi.org\/10.1109\/COMST.2015.2494502.","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"9","key":"1402_CR13","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1109\/TSE.2021.3087402","volume":"48","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty S, Krishna R, Ding Y, Ray B. Deep learning based vulnerability detection: Are we there yet? IEEE Trans Software Eng. 2022;48(9):3280\u201396. https:\/\/doi.org\/10.1109\/TSE.2021.3087402.","journal-title":"IEEE Trans Software Eng"},{"issue":"20","key":"1402_CR14","doi-asserted-by":"publisher","first-page":"9331","DOI":"10.3390\/app14209331","volume":"14","author":"A AlMajali","year":"2024","unstructured":"AlMajali A, Al-Abed L, Yousef KA, Mohd B, Samamah Z, Abu Shhadeh A. Automated vulnerability exploitation using deep reinforcement learning. Appl Sci. 2024;14(20):9331. https:\/\/doi.org\/10.3390\/app14209331.","journal-title":"Appl Sci"},{"issue":"18","key":"1402_CR15","doi-asserted-by":"publisher","first-page":"3927","DOI":"10.3390\/electronics12183927","volume":"12","author":"S Hulayyil","year":"2023","unstructured":"Hulayyil S, Li S, Xu L. Machine-learning-based vulnerability detection and classification in internet of things device security. Electronics. 2023;12(18):3927. https:\/\/doi.org\/10.3390\/electronics12183927.","journal-title":"Electronics"},{"issue":"2","key":"1402_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.hcc.2024.100211","volume":"4","author":"Y Yao","year":"2024","unstructured":"Yao Y, Duan J, Xu K, Cai Y, Sun Z, Zhang Y. A survey on large language model (llm) security and privacy: the good, the bad, and the ugly. High-Confid Comput. 2024;4(2):100211. https:\/\/doi.org\/10.1016\/j.hcc.2024.100211.","journal-title":"High-Confid Comput"},{"key":"1402_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s40745-021-00384-6","author":"IH Sarker","year":"2021","unstructured":"Sarker IH, Furhad MH, Nowrozy R. Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects. Ann Data Sci. 2021. https:\/\/doi.org\/10.1007\/s40745-021-00384-6.","journal-title":"Ann Data Sci"},{"key":"1402_CR18","unstructured":"Cai H, Gan C, Wang T, Zhang Z, Han S. Once-for-all: Train one network and specialize it for efficient deployment. In: International Conference on Learning Representations 2020; https:\/\/doi.org\/10.48550\/arXiv.1908.09791 . https:\/\/openreview.net\/forum?id=HylxE1HKwS"},{"key":"1402_CR19","doi-asserted-by":"publisher","unstructured":"Kong H, Hu D, Ge J, Li L, Li H, Li T. Pentest-r1: Towards autonomous penetration testing reasoning optimized via two-stage reinforcement learning. arXiv preprint 2025; https:\/\/doi.org\/10.48550\/arXiv.2508.07382","DOI":"10.48550\/arXiv.2508.07382"},{"key":"1402_CR20","doi-asserted-by":"publisher","unstructured":"Ullah S, Balasubramanian P, Guo W, Burnett A, Pearce H, Kruegel C, Vigna G, Stringhini G. From cve entries to verifiable exploits: An automated multi-agent framework for reproducing cves. arXiv preprint 2025; https:\/\/doi.org\/10.48550\/arXiv.2509.01835","DOI":"10.48550\/arXiv.2509.01835"},{"issue":"1","key":"1402_CR21","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2021","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, et al. A comprehensive survey on transfer learning. Proc IEEE. 2021;109(1):43\u201376. https:\/\/doi.org\/10.1109\/JPROC.2020.3004555.","journal-title":"Proc IEEE"},{"key":"1402_CR22","doi-asserted-by":"publisher","unstructured":"Kim J, Park J, Roh S, Chung J, Lee Y, Kim T, Lee B. Tiktag: breaking arm\u2019s memory tagging extension with speculative execution 2025; https:\/\/doi.org\/10.1109\/SP61157.2025.00039 . IEEE","DOI":"10.1109\/SP61157.2025.00039"},{"key":"1402_CR23","doi-asserted-by":"publisher","unstructured":"Zhang Y, Zhang F, Luo X, Hou R, Ding X. Scrutinizer: towards secure forensics on compromised trustzone. In: Network and Distributed System Security Symposium 2025; https:\/\/doi.org\/10.14722\/ndss.2025.230147 . Internet Society","DOI":"10.14722\/ndss.2025.230147"},{"key":"1402_CR24","doi-asserted-by":"publisher","unstructured":"Zhang Y, Cui B. Multi-interface analysis-based fuzzing scheme for optee system kernel. In: Proceedings of the 2025 5th International Conference on Computer Engineering and Intelligent Control. Association for Computing Machinery, ??? 2025; https:\/\/doi.org\/10.1145\/3746709.3746868","DOI":"10.1145\/3746709.3746868"},{"issue":"12","key":"1402_CR25","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/TKDE.2018.2876857","volume":"31","author":"J Lu","year":"2019","unstructured":"Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G. Learning under concept drift: a review. IEEE Trans Knowl Data Eng. 2019;31(12):2346\u201363. https:\/\/doi.org\/10.1109\/TKDE.2018.2876857.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"1402_CR26","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/MM.2017.35","volume":"37","author":"N Stephens","year":"2017","unstructured":"Stephens N, Biles S, Boettcher M, Eapen J, Eyole M, Gabrielli G, et al. The arm scalable vector extension. IEEE Micro. 2017;37(2):26\u201339. https:\/\/doi.org\/10.1109\/MM.2017.35.","journal-title":"IEEE Micro"},{"key":"1402_CR27","doi-asserted-by":"publisher","unstructured":"Apruzzese G, Colajanni M, Ferretti L, Guido A, Marchetti M. On the effectiveness of machine and deep learning for cyber security. In: 2018 10th International Conference on Cyber Conflict (CyCon), 2018; pp. 371\u2013390 . https:\/\/doi.org\/10.23919\/CYCON.2018.8405026 . IEEE","DOI":"10.23919\/CYCON.2018.8405026"},{"key":"1402_CR28","doi-asserted-by":"publisher","unstructured":"David R, Duke J, Jain A, Reddi VJ, Jeffries N. Tensorflow lite micro: Embedded machine learning on tinyml systems. arXiv preprint arXiv:2010.08678 2020; https:\/\/doi.org\/10.48550\/arXiv.2010.08678","DOI":"10.48550\/arXiv.2010.08678"},{"key":"1402_CR29","doi-asserted-by":"publisher","unstructured":"Zheng L, Chen T. Optimizing deep learning workloads on arm gpu with tvm. In: Proceedings of the 1st on Reproducible Quality-Efficient Systems Tournament on Co-designing Pareto-efficient Deep Learning. Association for Computing Machinery, New York, NY, USA 2018; https:\/\/doi.org\/10.1145\/3229762.3229764","DOI":"10.1145\/3229762.3229764"},{"key":"1402_CR30","unstructured":"ISC2: Isc2 cybersecurity workforce study 2023. Technical report, ISC2 (2023). https:\/\/www.isc2.org\/research\/workforce-study"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-026-01402-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-01402-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-01402-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T23:26:13Z","timestamp":1786404373000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-026-01402-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,5]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1402"],"URL":"https:\/\/doi.org\/10.1007\/s44163-026-01402-0","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,5]]},"assertion":[{"value":"12 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This work is a systematic literature analysis and proposes no new offensive tools or exploit code. The AASB framework concept explicitly excludes active exploit payloads from any future dataset releases. Any practical implementation of AI-driven penetration testing tools should be conducted only in authorized test environments, with appropriate institutional review, and in compliance with responsible disclosure policies (e.g., ISO\/IEC 29147). The authors endorse the principle that security research tools should not be released without accompanying safeguards against misuse.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"767"}}