Sonic: Fast and transferable data poisoning on clustering algorithms

Data poisoning attacks on clustering algorithms have received limited attention, with existing methods struggling to scale efficiently as dataset sizes and feature counts increase. These attacks typically require re-clustering the entire dataset multiple times to generate predictions and assess the attacker’s objectives, significantly hindering their scalability. This paper addresses these limitations by proposing Sonic , a novel genetic data poisoning attack that leverages incremental and scalable clustering algorithms, e.g., FISHDBC, as surrogates to accelerate poisoning attacks against graph-based and density-based clustering methods, such as HDBSCAN. We empirically demonstrate the effectiveness and efficiency of Sonic in poisoning the target clustering algorithms. We then conduct a comprehensive analysis of the factors affecting the scalability and transferability of poisoning attacks against clustering algorithms, and we conclude by examining the robustness of hyperparameters in our attack strategy Sonic . An illustrative example of a data poisoning attack against the clustering algorithm HDBSCAN* is shown. In the top row, we depict the scenario when the data are untainted, with HDBSCAN* correctly grouping the samples into three distinct clusters. In the bottom row, we depict a scenario where an attacker manipulates two red triangle data samples in the dataset to mislead the clustering algorithm. Specifically, the attack uses our fast and effective Sonic data poisoning method to perturb these samples, moving them from their original cluster (triangles) to a target cluster (squares), thereby maliciously influencing the clustering algorithm’s results. As a result, this adversarial manipulation causes the HDBSCAN* algorithm to merge the blue and green samples into a single cluster, thus degrading the clustering performance. • We propose Sonic , a fast genetic data poisoning attack against clustering algorithms for high-dimensional data. • We empirically demonstrate that Sonic significantly accelerates the poisoning optimization process on high-dimensional data by leveraging incremental clustering algorithms. • We investigate the trade-off between clustering approximation quality and attack speed. • We explore the transferability of data poisoning attacks across different clustering algorithms. • We analyze the empirical convergence of Sonic and provide an ablation study on its hyperparameters.