The rapid advancement of automatic speech recognition (ASR) models has significantly bolstered their multilingual proficiency and robustness, amplifying concerns over user speech privacy. Attackers may use hidden microphones or network attacks to capture and transcribe sensitive user interactions. Whisper, a state-of-the-art (SOTA) multilingual speech recognition model, delivers exceptional transcription accuracy across diverse languages. However, its superior performance also extends privacy leakage risks to multilingual contexts. Previous privacy-preserving methods based on adversarial examples were primarily optimized for monolingual models, limiting their effectiveness in multilingual settings. Moreover, as these perturbation mechanisms were predominantly tailored for English, their transferability to other languages remains constrained. To address this vulnerability, we propose the Disentanglement-based Universal Adversarial Perturbation (DUAP), a privacy-preserving method designed to counteract the Whisper model. Unlike optimization-based approaches, DUAP embeds language-specific features in the latent space to generate robust adversarial perturbations, providing consistent protection across multiple languages and effectively mitigating privacy risks in multilingual contexts. The method employs a two-stage language attack: first, a Language Feature Disentanglement model disentangles and reconstructs language-specific features to produce adversarial examples (AEs); second, gradient-based optimization refines AEs to disrupt Whisper’s language identification module. DUAP’s perturbations, effective in physical and digital settings, achieve SNRs from 40 dB (lightest) to above 17 dB (strongest). Across three Whisper model sizes, DUAP yields WERs over 95% (English), 85% (other languages), and 87% (physical settings), maintaining above 96% under AAC (64, 72 kbps) and MP3 (32, 96 kbps) compressions.