Advanced speech synthesis technologies have enabled highly realistic speech generation, posing security risks that motivate research into audio deepfake detection (ADD). While state space models (SSMs) offer linear complexity, pure causal SSMs architectures often struggle with the content-based retrieval required to capture global frequency-domain artifacts. To address this, we explore the scaling properties of hybrid architectures by proposing XLSR-MamBo, a modular framework integrating an XLSR front-end with synergistic Mamba-Attention backbones. We systematically evaluate four topological designs using advanced SSM variants, Mamba, Mamba2, Hydra, and Gated DeltaNet. Experimental results demonstrate that the MamBo-3-Hydra-N3 configuration achieves competitive performance compared to other state-of-the-art systems on the ASVspoof 2021 LA, DF, and In-the-Wild benchmarks. This performance benefits from Hydra’s native bidirectional modeling, which captures holistic temporal dependencies more efficiently than the heuristic dual-branch strategies employed in prior works. Furthermore, evaluations on the DFADD dataset demonstrate robust generalization to unseen diffusion- and flow-matching-based synthesis methods. Crucially, our analysis reveals that scaling backbone depth effectively mitigates the performance variance and instability observed in shallower models. These results demonstrate the hybrid framework’s ability to capture artifacts in spoofed speech signals, providing an effective method for ADD. Codes are publicly available at https://github.com/saki-ciallo/XLSR-MamBo.

Embedding-as-a-Service (EaaS) has emerged as a critical paradigm for commercializing large language models (LLMs). However, existing backdoor watermarking techniques are fundamentally limited to "zero-bit" detection, which prevents user-level traceability in multi-user EaaS scenarios. To address these limitations, we propose RShield, a multi-bit backdoor watermarking that enables reliable user-level attribution of LLMs for EaaS under model extraction attacks. RShield integrates Reed-Solomon error-correcting codes with orthogonal feature mapping to introduce highly-structured redundancy, constructing fault-tolerant symbol sequences for multi-bit watermark space, thereby staying recoverable even after aggressive extraction noise condition.To mitigate semantic distortion under the interference of noise channel, RShield employs a lightweight Adapter to adaptively inject multi-bit watermarks in the feature space, preserving the quality of EaaS while achieving a user-level traceability.Extensive experiments on four NLP benchmarks demonstrate that RShield efficiently achieves 100% multi-bit watermark recovery and high semantic fidelity under model extraction attacks compared to existing methods, while significantly reducing the degradation of watermarking on downstream task performance.