The malicious use of facial recognition technology may lead to personal information leakage, posing a significant threat to individual privacy security. Safeguarding facial privacy through universal adversarial attacks holds crucial research significance. However, existing universal adversarial attack algorithms primarily focus on image classification tasks. When applied to facial recognition models, they often encounter challenges such as low attack success rates and noticeable perturbation generation. To address these challenges, this study proposes a universal adversarial attack method for face recognition based on commonality gradients. This method optimizes universal adversarial perturbation through the common gradient of the adversarial perturbations of multiple face images and uses dominant feature loss to improve the attack capability of the perturbation. Combined with the multi-stage training strategy, it achieves a balance between attack effect and visual quality. Experiments on public datasets prove that the method outperforms methods such as Cos-UAP and SGA in the attack performance on facial recognition models, and the generated adversarial samples have better visual effects, indicating the effectiveness of the proposed method.