This article examines conflicts arising between competitive generative artificial intelligence (GAI) systems due to differing objectives, strategies, and interpretations. The main types of conflicts discussed include: semantic interpretation conflict, factual and reliability conflict, psychological influence conflict, data access conflict, technical and semantic backdoors, disalignment attacks, and false disalignment. A game-theoretic interpretation of these conflicts is proposed, formalizing the struggle between AI models as an adversarial game with incomplete information. The paper also analyzes attack mechanisms and defense strategies, offering a scoring system to assess the criticality of each type of conflict. The aim of this study is to establish an analytical foundation for understanding, classifying, and mitigating conflict scenarios within GAI ecosystemsparticularly in contexts involving open communication, commercial competition, and cybersecurity. Keywords: generative artificial intelligence, AI system conflict, semantic conflict, data poisoning, technical backdoors, semantic backdoors, model disalignment, false disalignment, game theory, psychological influence, neuro-linguistic programming, data privacy, cybersecurity