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Phonon spectra offer a uniquely demanding benchmark for graph neural network (GNN) force fields because they interrogate the second-derivative structure of the potential energy surface that governs lattice dynamics, thermal transport, and vibrational stability in materials. Yet direct comparison between GNN-derived and density functional theory (DFT) phonon spectra is frequently compromised by spectral leakage introduced through finite supercell truncation, displacement amplitude selection, q-point undersampling, Fourier interpolation, and post-processing broadening. These numerical effects can either conceal genuine deficiencies in the learned force field or generate apparent discrepancies that do not reflect model behavior. This article develops a hierarchical, leakage-aware validation framework that addresses this problem through progressive levels of scrutiny. The framework begins with baseline agreement in energies and forces, then advances to phonon density of states validation, q-resolved dispersion analysis, and finally a reproducible multi-metric assessment of spectral similarity. Progression through the hierarchy is conditional rather than automatic, such that higher-level claims are only made once lower-level numerical stability and model fidelity have been established. To separate methodological artifact from true representational error, the framework embeds explicit diagnostics based on supercell convergence, displacement sweeps, q-mesh refinement, interpolation cross-checks, and residual spectral analysis. It further introduces a standardized reporting protocol designed to make phonon-based validation transparent, comparable, and reproducible across studies of machine-learning interatomic potentials. By formalizing leakage control as an integral part of validation rather than an afterthought, the framework closes a critical gap between high-fidelity DFT phonon workflows and contemporary ML force-field development, enabling more credible assessment of GNN transferability in computational materials science.