{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T07:15:24Z","timestamp":1770880524045,"version":"3.50.1"},"reference-count":98,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008675","name":"Zayed University","doi-asserted-by":"publisher","award":["R23064"],"award-info":[{"award-number":["R23064"]}],"id":[{"id":"10.13039\/501100008675","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008675","name":"Zayed University","doi-asserted-by":"publisher","award":["R21096"],"award-info":[{"award-number":["R21096"]}],"id":[{"id":"10.13039\/501100008675","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Commun. Netw."],"abstract":"<jats:p>The swift evolution of artificial intelligence (AI) has enabled unprecedented capabilities across domains, while simultaneously introducing critical vulnerabilities that can be maliciously exploited or cause unintended harm. Although multiple initiatives aim to govern AI-related risks, a comprehensive and systematic understanding of how AI systems are actively misused in practice remains limited. This paper presents a systematic review of AI misuse across modern AI technologies. We analyze documented incidents, attack mechanisms, and emerging threat vectors, drawing from existing AI risk repositories, prior taxonomies, and empirical case reports. These sources are synthesized into a unified analytical framework that categorizes AI misuse across nine primary domains. Our analysis identifies nine major domains of AI misuse: (1) Adversarial Threats, (2) Privacy Violations, (3) Disinformation, Deception, and Propaganda, (4) Bias and Discrimination, (5) System Safety and Reliability Failures, (6) Socioeconomic Exploitation and Inequality, (7) Environmental and Ecological Misuse, (8) Autonomy and Weaponization, and (9) Human Interaction and Psychological Harm. Within each domain, we examine distinct misuse patterns, providing technical insights into exploitation mechanisms, documented real-world cases with quantified impacts, and recent developments such as large language model vulnerabilities and multimodal attack vectors. We further evaluate existing mitigation strategies, including technical security frameworks (e.g., MITRE ATLAS, OWASP Top 10 for Large Language Models, MAESTRO), regulatory initiatives (e.g., EU AI Act, NIST AI Risk Management Framework), and compliance standards. The findings reveal substantial gaps between the rapid advancement of AI capabilities and the robustness of current defensive, governance, and mitigation mechanisms, with adversaries holding persistent advantages across most attack categories. This work contributes by (i) systematically consolidating fragmented AI risk repositories and misuse taxonomies, (ii) developing a unified taxonomy grounded in both theoretical models and empirical incident data, (iii) critically assessing the effectiveness of existing mitigation approaches, and (iv) identifying priority research gaps necessary for advancing more secure, ethical, and resilient AI systems.<\/jats:p>","DOI":"10.3389\/frcmn.2025.1727425","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:46:18Z","timestamp":1770878778000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Emerging threats in AI: a detailed review of misuses and risks across modern AI technologies"],"prefix":"10.3389","volume":"6","author":[{"given":"Niyat","family":"Seghid","sequence":"first","affiliation":[{"name":"College of Technological Innovation, Zayed University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farkhund","family":"Iqbal","sequence":"additional","affiliation":[{"name":"College of Technological Innovation, Zayed University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalifa","family":"Al-Room","sequence":"additional","affiliation":[{"name":"Dubai Police HQ","place":["Dubai, United Arab Emirates"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1ine","family":"MacDermott","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics, Liverpool John Moores University","place":["Liverpool, United Kingdom"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1086\/705716","article-title":"Robots and jobs: evidence from US labor markets","volume":"128","author":"Acemoglu","year":"2020","journal-title":"J. Political Econ."},{"key":"B2","unstructured":"Agile-index.ai\n          \n          \n          2025"},{"key":"B3","unstructured":"The state of deepfakes: landscape, threats, and impact. Deeptrace\n          \n          \n            \n              Ajder\n              H.\n            \n            \n              Patrini\n              G.\n            \n            \n              Cavalli\n              F.\n            \n            \n              Cullen\n              L.\n            \n          \n          \n          2019"},{"key":"B4","author":"Angwin","year":"2016","journal-title":"Machine bias: There\u2019s software used across the country to predict future criminals. And it\u2019s biased against Blacks. ProPublica"},{"key":"B5","first-page":"371","article-title":"On the effectiveness of machine and deep learning for cyber security","volume-title":"10th international conference on cyber conflict (CyCon","author":"Apruzzese","year":"2018"},{"key":"B6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1257\/jep.29.3.3","article-title":"Why are there still so many jobs? The history and future of workplace automation","volume":"29","author":"Autor","year":"2015","journal-title":"J. Econ. Perspect."},{"key":"B7","article-title":"Constitutional AI: harmlessness from AI feedback","author":"Bai","year":"2022"},{"key":"B8","doi-asserted-by":"publisher","first-page":"671","DOI":"10.2139\/ssrn.2477899","article-title":"Big data's disparate impact","volume":"104","author":"Barocas","year":"2016","journal-title":"Calif. Law Rev."},{"key":"B9","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/978-3-642-40994-3_25","article-title":"Evasion attacks against machine learning at test time","author":"Biggio","year":"2013","journal-title":"Adv. Inf. Syst. Eng."},{"key":"B97","article-title":"On the opportunities and risks of foundation models","author":"Bommasani","year":"2021","journal-title":"arXiv:2108.07258"},{"key":"B10","volume-title":"Strength of organisational whistleblowing processes: Analysis from Australia and New ZealandFurther results of the Whistling While They Work 2 Project","author":"Brown","year":"2017"},{"key":"B11","first-page":"1877","article-title":"Language models are few-shot learners","author":"Brown","year":"2020"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.17863\/CAM.22520","article-title":"The malicious use of artificial intelligence: forecasting, prevention, and mitigation","author":"Brundage","year":"2018"},{"key":"B13","volume-title":"The second machine age: work, progress, and prosperity in a time of brilliant technologies","author":"Brynjolfsson","year":"2014"},{"key":"B14","first-page":"77","article-title":"Gender shades: intersectional accuracy disparities in commercial gender classification","volume-title":"Conference on fairness, accountability and transparency","author":"Buolamwini","year":"2018"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1007\/s11948-020-00175-8","article-title":"The ethics of digital well-being: a thematic review","volume":"26","author":"Burr","year":"2020","journal-title":"Sci. Eng. Ethics"},{"key":"B16","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1257\/aer.20190623","article-title":"Artificial intelligence, algorithmic pricing, and collusion","volume":"110","author":"Calvano","year":"2020","journal-title":"Am. Econ. Rev."},{"key":"B17","article-title":"Banning killer robots","year":"2020"},{"key":"B18","first-page":"1753","article-title":"Deep fakes: a looming challenge for privacy, democracy, and national security","volume":"107","author":"Chesney","year":"2019","journal-title":"Calif. Law Rev."},{"key":"B19","first-page":"3","article-title":"Human factor risks in driving automation crashes","volume-title":"HCI in mobility, transport, and automotive systemsLecture notes in computer science","author":"Chu","year":"2023"},{"key":"B20","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","volume-title":"International conference on machine learning","author":"Cohen","year":"2019"},{"key":"B21","first-page":"1","article-title":"The measure and mismeasure of fairness","volume":"24","author":"Corbett-Davies","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"B22","article-title":"Anatomy of an AI system: the amazon echo as an anatomical map of human labor","volume-title":"Data and planetary resources","author":"Crawford","year":"2018"},{"key":"B23","article-title":"Taxonomy and analysis of societal-scale risks from AI (TASRA)","author":"Critch","year":"2023","journal-title":"arXiv:2306.06924"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1382356","DOI":"10.3389\/frai.2024.1382356","article-title":"Artificial intelligence challenges in the face of biological threats: emerging catastrophic risks for public health","volume":"7","author":"de Lima","year":"2024","journal-title":"Front. Artificial Intelligence"},{"key":"B25","article-title":"Generative AI and the 2024 US elections","volume-title":"Stanford internet observatory","author":"DiResta","year":"2024"},{"key":"B26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Found. Trends Theor. Comput. Sci."},{"key":"B27","volume-title":"The EU artificial intelligence act: a risk-based framework for AI governance","year":"2024"},{"key":"B28","first-page":"1","article-title":"Regulation (EU) 2016\/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation, GDPR)","year":"2016","journal-title":"Official J. Eur. Union"},{"key":"B29","unstructured":"6 Geo. l. tech. rev. facial recognition and a systemic effects approach to first amendment coverage\n          \n          \n            \n              Evans\n              L.\n            \n          \n          \n          2022"},{"key":"B30","first-page":"1625","article-title":"Robust physical-world attacks on deep learning visual classification","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Eykholt","year":"2018"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2025.12.001","article-title":"From prompt injections to protocol exploits: threats in LLM-powered AI agents workflows","author":"Ferrag","year":"2025","journal-title":"ICT Express"},{"key":"B32","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1145\/2810103.2813677","article-title":"Model inversion attacks that exploit confidence information and basic countermeasures","volume-title":"Proceedings of the 22nd ACM SIGSAC conference on computer and communications security","author":"Fredrikson","year":"2015"},{"key":"B33","article-title":"The perpetual line-up: unregulated police face recognition in America","author":"Garvie","year":"2016","journal-title":"Georget. Law Cent. Priv. & Technol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"100821","DOI":"10.1016\/j.mlwa.2025.100821","article-title":"SAFE AI metrics: an integrated approach","volume":"23","author":"Giudici","year":"2025","journal-title":"Mach. Learn. Appl."},{"key":"B35","article-title":"Generative language models and automated influence operations: emerging threats and potential mitigations","author":"Goldstein","year":"2023","journal-title":"arXiv Preprint arXiv:2301.04246"},{"key":"B36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TransAI54797.2022.00007","article-title":"Towards a taxonomy of AI risks in the health domain","volume-title":"2022 fourth international conference on transdisciplinary AI (TransAI)","author":"Golpayegani","year":"2022"},{"key":"B37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/3351095.3372840","article-title":"Algorithmic realism: expanding the boundaries of algorithmic thought","volume-title":"Proceedings of the 2020 conference on fairness, accountability, and transparency","author":"Green","year":"2020"},{"key":"B38","article-title":"Badnets: identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017","journal-title":"arXiv Preprint arXiv:1708.06733"},{"key":"B39","article-title":"Countering adversarial images using input transformations","author":"Guo","year":"2017","journal-title":"arXiv Preprint arXiv:1711.00117"},{"key":"B40","doi-asserted-by":"publisher","first-page":"e1230","DOI":"10.1002\/cl2.1230","article-title":"PRISMA2020: an R package and shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimized digital transparency and open synthesis Campbell","volume":"18","author":"Haddaway","year":"2022","journal-title":"Syst. Rev."},{"key":"B41","doi-asserted-by":"publisher","first-page":"41596","DOI":"10.1109\/access.2019.2905689","article-title":"Combating deepfake videos using blockchain and smart contracts","volume":"7","author":"Hasan","year":"2019","journal-title":"IEEE Access"},{"key":"B42","article-title":"The secretive company that might end privacy as we know it","author":"Hill","year":"2020"},{"key":"B43","volume-title":"Artificial intelligence and international security","author":"Horowitz","year":"2018"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3523273","article-title":"Membership inference attacks on machine learning: a survey","volume":"54","author":"Hu","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"B45","article-title":"Agentic AI threat modeling framework: MAESTRO","volume-title":"Cloud security alliance","author":"Huang","year":"2025"},{"key":"B46","doi-asserted-by":"crossref","DOI":"10.7591\/9781501735783","volume-title":"The twenty-six words that created the internet","author":"Kosseff","year":"2019"},{"key":"B47","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"B48","volume":"2025","author":"MacDermott","year":"2025","journal-title":"Deepfake forensics: exploring the impact and implications of fabricated media in digital forensic investigations"},{"key":"B49","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2018"},{"key":"B98","first-page":"266","article-title":"The Risks and Vulnerabilities of Artificial Intelligence Usage in Information Security","author":"Mahmoud","year":"2023"},{"key":"B50","first-page":"13843","article-title":"Generative AI misuse: a taxonomy of tactics and insights from real-world data","author":"Marchal","year":"2024","journal-title":"arXiv:2406"},{"key":"B51","doi-asserted-by":"publisher","first-page":"12714","DOI":"10.1073\/pnas.1710966114","article-title":"Psychological targeting as an effective approach to digital mass persuasion","volume":"114","author":"Matz","year":"2017","journal-title":"Proc. Natl. Acad. Sci."},{"key":"B52","doi-asserted-by":"publisher","first-page":"15458","DOI":"10.1609\/aaai.v35i17.17817","article-title":"Preventing repeated real world AI failures by cataloging incidents: the AI incident database","volume":"35","author":"McGregor","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"B53","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"McMahan","year":"2017"},{"key":"B54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","article-title":"A survey on bias and fairness in machine learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"B55","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.1145\/3394171.3413570","article-title":"Emotions don't lie: an audio-visual deepfake detection method using affective cues","volume-title":"Proceedings of the 28th ACM international conference on multimedia","author":"Mittal","year":"2020"},{"key":"B56","first-page":"417","article-title":"Interpretable machine learning \u2013 a brief history, state-of-the-art and challenges","volume-title":"ECML PKDD 2020 workshops. ECML PKDD 2020. Communications in computer and information Science","author":"Molnar","year":"2020"},{"key":"B58","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"B59","unstructured":"OECD AI incidents monitor\n          \n          \n          2024"},{"key":"B60","volume-title":"OECD AI principles","year":"2019"},{"key":"B61","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022"},{"key":"B62","unstructured":"OWASP top 10 for large language model applications\n          \n          \n          2024"},{"key":"B63","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/SP.2016.41","article-title":"Distillation as a defense to adversarial perturbations against deep neural networks","volume-title":"2016 IEEE symposium on security and privacy","author":"Papernot","year":"2016"},{"key":"B64","first-page":"506","article-title":"Practical black-box attacks against machine learning","volume-title":"Proceedings of the 2017 ACM Asia conference on computer and communications security","author":"Papernot","year":"2017"},{"key":"B65","article-title":"Multi-stakeholder framework for responsible AI development","year":"2021"},{"key":"B66","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.iatssr.2021.04.003","article-title":"Effects of the autonomous vehicle crashes on public perception of the technology","volume":"45","author":"Penmetsa","year":"2021","journal-title":"IATSS Res."},{"key":"B67","article-title":"Ignore previous prompt: attack techniques for language models","author":"Perez","year":"2022","journal-title":"arXiv Preprint arXiv:2211.09527"},{"key":"B68","article-title":"Discovering language model behaviors with model-written evaluations","author":"Perez","year":"2022"},{"key":"B96","unstructured":"AI and international security: Understanding the risks and paving the path for confidence-building measures\n          \n          \n            \n              Puscas\n              I.\n            \n          \n          United Nations Institute for Disarmament Research (UNIDIR)\n          \n          2023"},{"key":"B69","doi-asserted-by":"publisher","first-page":"103737","DOI":"10.1016\/j.engappai.2020.103737","article-title":"Towards privacy preserving AI based composition framework in edge networks using fully homomorphic encryption","volume":"94","author":"Rahman","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"B70","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/3351095.3372873","article-title":"Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing","volume-title":"Proceedings of the 2020 conference on fairness, accountability, and transparency","author":"Raji","year":"2020"},{"key":"B71","volume-title":"Algorithmic impact assessments: a practical framework for public agency accountability","author":"Reisman","year":"2018"},{"key":"B72","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1145\/3715275.3732165","article-title":"Responsible AI in the global context: maturity model and survey","volume-title":"Proceedings of the 2025 ACM conference on fairness, accountability, and transparency","author":"Reuel","year":"2025"},{"key":"B73","volume-title":"Human compatible: artificial intelligence and the problem of control","author":"Russell","year":"2019"},{"key":"B74","first-page":"80","article-title":"Recurrent convolutional strategies for face manipulation detection in videos","author":"Sabir","year":"2019"},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2306.13952","article-title":"Artificial intelligence and biological misuse: differentiating risks of language models and biological design tools","author":"Sandbrink","year":"2023","journal-title":"arXiv"},{"key":"B76","volume-title":"Army of none: autonomous weapons and the future of war","author":"Scharre","year":"2018"},{"key":"B77","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1145\/2976749.2978392","article-title":"Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition","volume-title":"Proceedings of the 2016 ACM SIGSAC conference on computer and communications security","author":"Sharif","year":"2016"},{"key":"B78","doi-asserted-by":"publisher","first-page":"456","DOI":"10.3390\/computers14110456","article-title":"Investigation of cybersecurity bottlenecks of AI agents in industrial automation","volume":"14","author":"Shrestha","year":"2025","journal-title":"Computers"},{"key":"B79","unstructured":"The AI risk repository: a comprehensive meta-Review, database, and taxonomy of risks from artificial intelligence\n          \n          \n            \n              Slattery\n              P.\n            \n            \n              Saeri\n              A. K.\n            \n            \n              Grundy\n              E. A. C.\n            \n            \n              Graham\n              J.\n            \n            \n              Noetel\n              M.\n            \n            \n              Uuk\n              R.\n            \n          \n          \n          2024"},{"key":"B80","unstructured":"The 2025 AI index report\n          \n          \n            \n              Stanford\n              H. A. I.\n            \n          \n          \n          2025"},{"key":"B81","doi-asserted-by":"crossref","first-page":"3645","DOI":"10.18653\/v1\/P19-1355","article-title":"Energy and policy considerations for deep learning in NLP","volume-title":"Proceedings of the 57th annual meeting of the association for computational linguistics","author":"Strubell","year":"2019"},{"key":"B82","article-title":"Fraudsters used AI to mimic ceo\u2019s voice in unusual cybercrime case","author":"Stupp","year":"2019","journal-title":"Wall Str. J."},{"key":"B83","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3306006","article-title":"Online manipulation: hidden influences in a digital world","volume":"4","author":"Susser","year":"2019","journal-title":"SSRN Electron. J."},{"key":"B57","doi-asserted-by":"crossref","DOI":"10.6028\/NIST.AI.100-1","article-title":"Artificial intelligence risk management framework (AI RMF 1.0)","volume-title":"NIST Trustworthy and Responsible AI","author":"Tabassi","year":"2023"},{"key":"B84","first-page":"288","article-title":"Taxonomy of generative AI applications for risk assessment","volume-title":"Proceedings of the IEEE\/ACM 3rd international conference on AI engineering \u2013 software engineering for AI (CAIN \u201924)","author":"Tanaka","year":"2024"},{"key":"B85","first-page":"601","article-title":"Stealing machine learning models via prediction APIs","volume-title":"25th USENIX security symposium","author":"Tram\u00e8r","year":"2016"},{"key":"B86","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1038\/s42256-024-00926-3","article-title":"Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research","volume":"6","author":"Trotsyuk","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"B87","doi-asserted-by":"publisher","first-page":"2056305120903408","DOI":"10.1177\/2056305120903408","article-title":"Deepfakes and disinformation: exploring the impact of synthetic political video on deception, uncertainty, and trust in news","volume":"6","author":"Vaccari","year":"2020","journal-title":"Soc. Media + Soc."},{"key":"B88","first-page":"3444","article-title":"FakeSpotter: a simple yet robust baseline for spotting AI-Synthesized fake faces","volume-title":"Proceedings of the 29th international joint conference on artificial intelligence (IJCAI 2020)","author":"Wang","year":"2019"},{"key":"B89","doi-asserted-by":"crossref","DOI":"10.1145\/3531146.3533088","article-title":"Taxonomy of risks posed by language Models","volume-title":"2022 ACM conference on fairness, accountability, and transparency","author":"Weidinger","year":"2022"},{"key":"B90","volume-title":"Ethics and governance of artificial intelligence for health","year":"2021"},{"key":"B91","doi-asserted-by":"publisher","first-page":"100211","DOI":"10.1016\/j.hcc.2024.100211","article-title":"A survey on large language model (LLM) security and privacy: the good, the Bad, and the ugly","volume":"4","author":"Yao","year":"2024","journal-title":"High-Confidence Comput."},{"key":"B92","doi-asserted-by":"publisher","first-page":"7556","DOI":"10.1109\/ICCV.2019.00765","article-title":"Attributing fake images to GANs","author":"Yu","year":"2019","journal-title":"Proceedings of the IEEE\/CVF international conference on computer visionLearn. Analyzing GAN Fingerprints"},{"key":"B93","doi-asserted-by":"publisher","DOI":"10.70777\/si.v1i1.10603","article-title":"AI risk categorization decoded (AIR 2024): from government regulations to corporate policies","volume":"1","author":"Zeng","year":"2024","journal-title":"ArXiv, abs\/2406.17864"},{"key":"B94","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1186\/s42400-025-00361-w","article-title":"When LLMs meet cybersecurity: a systematic literature review","volume":"8","author":"Zhang","year":"2025","journal-title":"Cybersecurity"},{"key":"B95","first-page":"1","article-title":"The dark side of AI companionship: a taxonomy of harmful algorithmic behaviors in human-AI relationships","volume-title":"Proceedings of the 2025 CHI Conference on human factors in Computing systems (CHI '25)","author":"Zhang","year":"2025"}],"container-title":["Frontiers in Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frcmn.2025.1727425\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T06:46:28Z","timestamp":1770878788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frcmn.2025.1727425\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"references-count":98,"alternative-id":["10.3389\/frcmn.2025.1727425"],"URL":"https:\/\/doi.org\/10.3389\/frcmn.2025.1727425","relation":{},"ISSN":["2673-530X"],"issn-type":[{"value":"2673-530X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]},"article-number":"1727425"}}