With the advent of Next-Generation Web 3.0 and the integration of 6G technologies, digital industrial applications are undergoing unprecedented transformations. Among these, the field of intelligent voice recognition, particularly Federated Learning-based Automatic Speaker Verification (FL-ASV) systems, stands out by collaboratively training robust ASV models across systems while protecting sensitive voiceprint data. However, the aspect of security within such systems is still largely unexplored and presents potential vulnerabilities. To bridge this gap, we design a voiceprint-driven backdoor attack for FL-ASV, termed FedCTS. Concretely, we employ contrastive learning techniques to significantly improve the feature extraction process for individual speakers. This enhancement not only maintains the inherent performance of FL-ASV systems but also introduces a level of complexity that can mislead even the most skilled defenders. Furthermore, we intricately obfuscate the triggers by subtly embedding voice and backdoor clips within the utterances. This is achieved by dividing the utterances into chronological segments through a meticulously devised time-series injection strategy, thereby ensuring the triggers remain undetectable. Additionally, we have conceptualized a unique defense mechanism tailored to counter such attacks. This defense mechanism operates by scrutinizing the speaker’s frequencies and filtering out any suspicious frequencies that fall outside the normal range of human voice, helping to mitigate the risk of backdoor attacks without compromising the system’s functionality. In the context of the fast-evolving digitalized industrial landscape, our attack strategy, FedCTS, has demonstrated a significant improvement in effectiveness. It achieves an average increase of 7.03% in the attack success rate when compared to existing state-of-the-art methods. •FedCTS, a voiceprint-driven backdoor attack targeted at FL-ASV systems, is proposed.•Contrastive feature extraction is utilized to enhance the model’s ability to extract fine-grained features.•A time-series trigger injection method is employed to make the attack more covert.•Experiments demonstrate the superior performance of FedCTS over existing technologies.•A new standard for the security of intelligent voice systems in the Web 3.0 era.