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The rapid development of voice deepfakes poses significant risks to privacy and security. This paper introduces FreqGuard, a universal proactive defense framework designed to protect against malicious speech synthesis attacks. FreqGuard leverages learnable frequency-domain feature priors to generate imperceptible perturbations that effectively disrupt voice synthesis systems. Unlike methods based on random noise, FreqGuard explores frequency operations to obtain priors that degrade speaker embeddings while minimizing perceptual distortion. These priors guide model training to target the shared subspace between verification and synthesized features. By jointly optimizing multiple losses, FreqGuard achieves high imperceptibility and low speaker similarity. Experimental results show that FreqGuard reduces attack success rates against black-box TTS systems while preserving speech quality, achieving favorable cross-model generalization and perceptual quality compared with existing methods.