  Clustering algorithms play a fundamental role as tools in decision-making and
sensible automation processes. Due to the widespread use of these applications,
a robustness analysis of this family of algorithms against adversarial noise
has become imperative. To the best of our knowledge, however, only a few works
have currently addressed this problem. In an attempt to fill this gap, in this
work, we propose a black-box adversarial attack for crafting adversarial
samples to test the robustness of clustering algorithms. We formulate the
problem as a constrained minimization program, general in its structure and
customizable by the attacker according to her capability constraints. We do not
assume any information about the internal structure of the victim clustering
algorithm, and we allow the attacker to query it as a service only. In the
absence of any derivative information, we perform the optimization with a
custom approach inspired by the Abstract Genetic Algorithm (AGA). In the
experimental part, we demonstrate the sensibility of different single and
ensemble clustering algorithms against our crafted adversarial samples on
different scenarios. Furthermore, we perform a comparison of our algorithm with
a state-of-the-art approach showing that we are able to reach or even
outperform its performance. Finally, to highlight the general nature of the
generated noise, we show that our attacks are transferable even against
supervised algorithms such as SVMs, random forests, and neural networks.
