The diffusion of complex opinions is severely hindered in multilayer social networks by echo chambers and cognitive consistency mechanisms. We investigate Influence Maximization strategies within the 3-state multilayer q-voter model. Utilizing the mABCD benchmark, we simulate social environments ranging from integrated Open Worlds to segregated Fortress Worlds. Our results reveal a topological paradox that we term the "Fortress Trap". In highly modular networks, strategies maximizing local density such as Clique Influence Maximization (CIM) and k-Shell fail to trigger global cascades, creating isolated bunkers of consensus due to the Overkill Effect. Furthermore, we identify a Redundancy Trap in perfectly aligned Clan topologies, where the structural overlap of layers creates a "Perfect Prison," rendering it the most resistant environment to diffusion. We demonstrate that VoteRank, a strategy that prioritizes diversity of reach over local intensity, consistently outperforms structure-based methods. These findings suggest that, for complex contagion, maximizing topological entropy is more effective than reinforcing local clusters.