Artificial Intelligence in Cybersecurity. Chronicle. Issue 6

Dmitry Namiot

Abstract


This article presents the sixth edition of our regular analytical digest. This series of materials is dedicated to a comprehensive study of the dynamically developing field at the intersection of artificial intelligence (AI) and cybersecurity. Our primary goal in this initiative is to consistently monitor the global agenda and thoroughly analyze the most significant events. We strive not only to collect information but also to thoroughly analyze legislative innovations, key incidents, and breakthrough technological solutions shaping the modern cybersecurity landscape in the context of AI development. The architecture of each issue in our series remains unchanged and includes three thematic blocks, allowing for comprehensive coverage of the subject area. The first block is dedicated to analyzing the incident database and current threats. Here, we examine real-world cases in detail, identify new vulnerabilities, and assess emerging risks directly related to the integration of AI algorithms into defense systems and attack tools. The second area of our work is a detailed review of the current state and dynamics of the regulatory environment. Understanding these processes is crucial, as they shape the legal and operational framework within which secure artificial intelligence systems will develop in the near future. Finally, the third section of our analysis is a scientific and technological chronicle. Each issue contains a carefully compiled annotated list of what we consider to be the most significant scientific articles, research reports from authoritative centers, and descriptions of innovative developments.


Full Text:

PDF (Russian)

References


Lebedinskij Ju. E., Namiot D. E. Sostjazatel'noe testirovanie bol'shih jazykovyh modelej //International Journal of Open Information Technologies. – 2025. – T. 13. – #. 11. – S. 132-152.

Maloyan N., Ashinov B., Namiot D. Investigating the Vulnerability of LLM-as-a-Judge Architectures to Prompt-Injection Attacks //International Journal of Open Information Technologies. – 2025. – T. 13. – #. 9. – S. 1-6.

Chekhonina E., Kostyumov V. Overview of adversarial attacks and defenses for object detectors //International Journal of Open Information Technologies. – 2023. – T. 11. – #. 7. – S. 11-20.

Kirzhinov D., Ilyushin E. Review and comparative analysis of attack and defence algorithms on graph-based ANN architectures //International Journal of Open Information Technologies. – 2024. – T. 12. – #. 2. – S. 12-22.

Namiot, D. E., E. A. Il'jushin, and I. V. Chizhov. "Iskusstvennyj intellekt i kiberbezopasnost'." International Journal of Open Information Technologies 10.9 (2022): 135-147.

Namiot, D. E. Shemy atak na modeli mashinnogo obuchenija / D. E. Namiot // International Journal of Open Information Technologies. – 2023. – T. 11, # 5. – S. 68-86. – EDN YVRDOB.

Namiot, D. E., and E. A. Il'jushin. "O kiberriskah generativnogo iskusstvennogo intellekta." International Journal of Open Information Technologies 12.10 (2024): 109-119.

NIST AI 100-2 E2025 https://csrc.nist.gov/pubs/ai/100/2/e2025/final Retrieved: Jan, 2026

Namiot, Dmitry. "Artificial Intelligence in Cybersecurity. Chronicle. Issue 1." International Journal of Open Information Technologies 13.9 (2025): 34-42.

Namiot, Dmitry. "Artificial Intelligence in Cybersecurity. Chronicle. Issue 5." International Journal of Open Information Technologies 14.2 (2026): 47-57.

Namiot, D. E. Ataki na sistemy mashinnogo obuchenija - obshhie problemy i metody / D. E. Namiot, E. A. Il'jushin, I. V. Chizhov // International Journal of Open Information Technologies. – 2022. – T. 10, # 3. – S. 17-22. – EDN DZFSKQ

Namiot D., Ilyushin E. On Certification of Artificial Intelligence Systems //Physics of Particles and Nuclei. – 2024. – T. 55. – #. 3. – S. 343-346.

Namiot D., Sneps-Sneppe M. On audit and certification of machine learning systems //2023 34th Conference of Open Innovations Association (FRUCT). – IEEE, 2023. – S. 114-124.

Namiot D., Ilyushin E. On assessing trust in Artificial Intelligence systems //International Journal of Open Information Technologies. – 2025. – T. 13. – #. 3. – S. 75-90.

Bezopasnost' II-agentov https://abava.blogspot.com/2025/12/blog-post_11.html Retrieved: Jan, 2026

AIs behaving badly: An AI trained to deliberately make bad code will become bad at unrelated tasks, too https://techxplore.com/news/2026-01-ais-badly-ai-deliberately-bad.html Retrieved: Jan, 2026

Betley, Jan, et al. "Training large language models on narrow tasks can lead to broad misalignment." Nature 649.8097 (2026): 584-589.

2025’s Top Phishing Trends and What They Mean for Your Security Strategy https://www.bleepingcomputer.com/news/security/2025s-top-phishing-trends-and-what-they-mean-for-your-security-strategy/ Retrieved: Feb, 2026

As AI enters the operating room, reports arise of botched surgeries and misidentified body parts https://www.reuters.com/investigations/ai-enters-operating-room-reports-arise-botched-surgeries-misidentified-body-2026-02-09/ Retrieved: Feb, 2026

Rizvani, Advije, Giovanni Apruzzese, and Pavel Laskov. "Adversarial News and Lost Profits: Manipulating Headlines in LLM-Driven Algorithmic Trading." arXiv preprint arXiv:2601.13082 (2026).

Rossolini, Giulio. "How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness." arXiv e-prints (2026): arXiv-2601.

Cybersecurity AI (CAI) https://github.com/aliasrobotics/cai Retrieved: Feb, 2026

Mayoral-Vilches, Víctor, et al. "Towards Cybersecurity Superintelligence: from AI-guided humans to human-guided AI." arXiv preprint arXiv:2601.14614 (2026).

Czybik, Stefan, et al. "A Large-Scale Study of Personalized Phishing using Large Language Models." 35th USENIX Security Symposium. 2026.

CyberSecurity Forecast 2026 https://services.google.com/fh/files/misc/cybersecurity-forecast-2026-en.pdf Retrieved: Feb, 2026

Wu, Jiaping, et al. "HORNET: Fast and minimal adversarial perturbations." Information Sciences (2025): 123028.

Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies https://static1.squarespace.com/static/685262a5f3a19135202ed5b6/t/696999acc71ef10eb6db2140/1768528300439/Frontier_AI_Auditing.pdf Retrieved: Feb, 2026

Kuprijanovskij, V. P. Demistifikacija cifrovoj jekonomiki / V. P. Kuprijanovskij, D. E. Namiot, S. A. Sinjagov // International Journal of Open Information Technologies. – 2016. – T. 4, # 11. – S. 59-63. – EDN WXQLIJ.

Cifrovaja jekonomika = modeli dannyh + bol'shie dannye + arhitektura + prilozhenija? / V. P. Kuprijanovskij, N. A. Utkin, D. E. Namiot, P. V. Kuprijanovskij // International Journal of Open Information Technologies. – 2016. – T. 4, # 5. – S. 1-13. – EDN VWANDZ.


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность Monetec 2026 СНЭ

ISSN: 2307-8162