  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.
