The article is devoted to the role of Adversarial Artificial Intelligence in modern hybrid conflicts and their inherent informational and cybernetic components. Adversarial AI is examined as a manifestation of AI conflict within the framework of the foundational principles of AI conflictology proposed by the authors. Formalized approaches to analyzing adversarial scenarios in the context of generating and detecting malicious actions, processes, and content . such as fakes, cyber influence, and information campaigns . are presented. A mathematical model is proposed to describe the interaction between a fake generator and detector, taking into account the objective functions of both sides. This model enables the analysis of the efficiency of fake content creation and detection and the development of counter-disinformation strategies. Models of cyber threats are also considered, describing the dynamics of offensive and defensive strategies in cyberspace, including simulations of various types of attacks and the development of mechanisms for their neutralization. Special attention is given to information wars, analyzing the impact of manipulative content on audiences and developing methods for its detection, analysis, and blocking. Mathematical models for creating specialized queries and patterns to influence adversarial systems are explored through the use of neuro-linguistic programming in Adversarial AI. Additionally, models for detecting and neutralizing backdoors in large language models (LLMs) are considered within the context of Adversarial AI. The proposed model allows for the analysis of the effectiveness of backdoor creation and deployment and the improvement of methods for their detection and elimination. Keywords: AI conflictology, Adversarial Artificial Intelligence, Adversarial AI, fakes, cyber warfare, information warfare, neuro-linguistic programming, cybersecurity