This paper introduces DeepMark Benchmark, a novel and comprehensive framework for evaluating the robustness of audio watermarking algorithms. Designed with modularity and scalability in mind, the benchmark enables systematic testing of watermarking methods against a diverse set of attacks. These include basic audio editing operations, advanced desynchronization techniques, and deep learning-based attacks that leverage generative models and neural processing methods. Additionally, we introduce a new class of attacks, termed Process Disruption Attacks, which target generative AI (GenAI) platforms. These attacks do not rely on prior knowledge of the system’s architecture or signal processing methods and can arise inadvertently within the GenAI workflows. The code is available at: https://github.com/deepmarkpy/deepmarkpy-benchmark.