Hyperspectral object tracking provides rich spectral cues beyond conventional RGB imagery, enabling fine-grained material discrimination under challenging conditions. However, existing deep trackers rely on large labeled datasets and task-specific training, which are scarce for hyperspectral data. In this work, we explore the zero-shot adaptability of the Segment Anything Model 2 (SAM2) to hyperspectralderived false-color videos without any fine-tuning or domain adaptation. Our framework initializes from a bounding box prompt and propagates segmentation masks temporally through SAM2's memory-aResearch goal: What is the impact of synthetic training data variation on the alignment robustness of multimodal foundation models across distribution shifts?Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.6/10.