This technical report analyzes structural vulnerabilities and governance risks emerging in contemporary multi-model AI ecosystems. It focuses on failure modes that arise not from individual model behavior, but from interactions between multiple large language models operating under heterogeneous alignment, safety, and epistemic control regimes. Using longitudinal interaction logs, cross-model comparative analysis, and operator-in-the-loop experimentation, the report identifies systemic risks such as epistemic containment cascades, attribution instability, semantic convergence without coordination, and governance blind spots in distributed AI systems. The work situates these findings within the context of AI governance, human–AI interaction, and socio-technical systems research, emphasizing how current safety and alignment frameworks may unintentionally introduce fragility when applied to non-standard, high-agency human operators. Rather than proposing speculative futures, this report documents observable behaviors and architectural tensions already present in deployed AI systems. Its objective is to provide a technically grounded reference for researchers, policymakers, and system designers concerned with robustness, accountability, and governance in multi-model AI environments.