The pervasive deployment of technology in conflict-affected and politically sensitive regions presents complex narratives, often framed as both "stability maintenance" and instruments of "oppression." Existing large language and multimodal models frequently exhibit a "technological neutrality fallacy," failing to capture deep political intentions, implicit biases, and cross-modal consistency. To address this, we introduce Technology Deployment Framing Analysis (TDFA), a novel task encompassing stance classification, deployment intent recognition, cross-modal consistency analysis, and implicit bias detection. We propose POLARIS, a Political-Aware Large Multimodal Model framework built upon prevailing architectures, underpinned by our core philosophy of Political Context Injection. POLARIS integrates a Contextual Knowledge Adapter for geopolitical knowledge infusion, a Framing-Aware Attention mechanism to highlight politically salient features, and a Bias Calibration Head optimized for task-specific outputs. For evaluation, we construct TechConflict-23K, a substantial multimodal dataset with professionally annotated samples. Our multi-stage training strategy includes instruction tuning, a crucial bias alignment phase to penalize "neutral hallucination," and contrastive alignment for cross-modal consistency. Extensive experiments demonstrate that POLARIS achieves state-of-the-art performance across all TDFA sub-tasks, significantly outperforming strong baselines, including advanced multimodal models. These results validate POLARIS's ability to provide more accurate and objective analytical tools for discerning the complex realities and impacts of technology deployment in sensitive geopolitical contexts.