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Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. However, this utility comes with increasing concerns about privacy, as the training data may include sensitive information. To address these concerns, machine unlearning has been proposed to erase specific data samples from models. While some unlearning techniques efficiently remove data at low costs, recent research highlights vulnerabilities where malicious users could request unlearning on manipulated data to compromise the model. Despite these attacks' effectiveness, perturbed data differs from original training data, failing hash verification. Existing attacks on machine unlearning also suffer from practical limitations and require substantial additional knowledge and resources. To fill the gaps in current unlearning attacks, we introduce the Unlearning Usability Attack. This model-agnostic, unlearning-agnostic, and budget-friendly attack distills data distribution information into a small set of benign data. These data are identified as benign by automatic poisoning detection tools due to their positive impact on model training. While benign for machine learning, unlearning these data significantly degrades model information. Our evaluation demonstrates that unlearning this benign data, comprising no more than 1% of the total training data, can reduce model accuracy by up to 50%. Furthermore, our findings show that well-prepared benign data poses challenges for recent unlearning techniques, as erasing these synthetic instances demands higher resources than regular data. These insights underscore the need for future research to reconsider "data poisoning" in the context of machine unlearning.

In the domain of backdoor attacks, accurate labeling of injected data is essential for evading rudimentary detection mechanisms. This imperative has catalyzed the development of clean-label attacks, which are notably more elusive as they preserve the original labels of the injected data. Current clean-label attack methodologies primarily depend on extensive knowledge of the training dataset. However, practically, such comprehensive dataset access is often unattainable, given that training datasets are typically compiled from various independent sources. Departing from conventional clean-label attack methodologies, our research introduces DFB, a data-free, low-budget, and high-efficacy clean-label backdoor Attack. DFB is unique in its independence from training data access, requiring solely the knowledge of a specific target class. Tested on CIFAR10, Tiny-ImageNet, and TSRD, DFB demonstrates remarkable efficacy with minimal poisoning rates of just 0.1%, 0.025%, and 0.4%, respectively. These rates are significantly lower than those required by existing methods such as LC, HTBA, BadNets, and Blend, yet DFB achieves superior attack success rates. Furthermore, our findings reveal that DFB poses a formidable challenge to four established backdoor defense algorithms, indicating its potential as a robust tool in advanced clean-label attack strategies.