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This paper examines how LLM-driven evolutionary optimization of GPU kernels can fingerprint evaluation configurations, causing 30% of in-distribution wins to fail on held-out settings. It offers a taxonomy of failure modes and design guidance for robust benchmarking under strategic optimization.