A prevailing belief in attack and defense community is that the higher flatness of adversarial examples enables their better cross-model transferability, leading to a growing interest in employing sharpness-aware minimization and its variants. However, the theoretical relationship between the transferability of adversarial examples and their flatness has not been established, making the belief questionable. To bridge this gap, we embark on a theoretical investigation and, for the first time, derive a theoretical bound for the transferability of adversarial examples with few practical assumptions. Our analysis challenges this belief by demonstrating that the increased flatness of adversarial examples is not a guarantor of improved transferability. Moreover, building upon the theoretical analysis, we propose an attack called Theoretically Provable Adversarial attack (TPA), which optimizes a surrogate of the derived bound to craft adversarial examples. The extensive examinations across standard benchmarks and diverse real-world applications show that the transferability of adversarial examples crafted by TPA can be considerably boosted compared with state-of-the-art baselines. We hope that the theoretical results can recalibrate preconceived impressions within our community and facilitate the development of stronger adversarial attacks and defense mechanisms.