Prompt-based adversarial attacks are a key tool for assessing the robustness of large language models (LLMs). Yet, existing studies typically treat prompts as flat text, overlooking their internal structure, different components within a prompt contribute unequally to robustness. This work introduces PromptAnatomy, a framework that decomposes prompts into functional components, and ComPerturb, a controlled perturbation method that selectively modifies these components to expose component-wise vulnerabilities while ensuring linguistic plausibility via perplexity-based filtering. Using this framework, four instruction-tuning datasets are structurally annotated and validated by human reviewers. Experiments across five advanced LLMs show that ComPerturb achieves state-of-the-art attack success rates, while ablation analyses confirm the complementary effects of prompt dissection and perplexity filtering. These results highlight the importance of structural awareness in evaluating and improving the adversarial robustness of LLMs.

Recent advances in singing voice synthesis (SVS) have attracted substantial attention from both academia and industry. With the advent of large language models and novel generative paradigms, producing controllable, high‐fidelity singing voices has become an attainable goal. Yet the field still lacks a comprehensive survey that systematically analyzes deep‐learning‐based singing voice systems and their enabling technologies.To address the aforementioned issue, this survey first categorizes existing systems by task type and then organizes current architectures into two major paradigms: cascaded and end-to-end approaches. Moreover, we provide an in-depth analysis of core technologies, covering singing modeling and control techniques. Finally, we review relevant datasets, annotation tools, and evaluation benchmarks that support training and assessment. In appendix, we introduce training strategies and further discussion of SVS. This survey provides an up-to-date review of the literature on SVS models, which would be a useful reference for both researchers and engineers. Related materials are available at https://github.com/David-Pigeon/SyntheticSingers.

Song generation focuses on producing controllable high-quality songs based on various prompts. However, existing methods struggle to generate vocals and accompaniments with prompt-based control and proper alignment. Additionally, they fall short in supporting various tasks. To address these challenges, we introduce VersBand, a multi-task song generation framework for synthesizing high-quality, aligned songs with prompt-based control. VersBand comprises these primary models: 1) VocalBand, a decoupled model, leverages the flow-matching method for generating singing styles, pitches, and mel-spectrograms, allowing fast, high-quality vocal generation with style control. 2) AccompBand, a flow-based transformer model, incorporates the Band-MOE, selecting suitable experts for enhanced quality, alignment, and control. This model allows for generating controllable, high-quality accompaniments aligned with vocals. 3) Two generation models, LyricBand for lyrics and MelodyBand for melodies, contribute to the comprehensive multi-task song generation system, allowing for extensive control based on multiple prompts. Experimental results demonstrate that VersBand performs better over baseline models across multiple song generation tasks using objective and subjective metrics.