Adversarial examples pose a security risk as they can alter decisions of a machine learning classifier through slight input perturbations. Certified robustness has been proposed as a mitigation where given an input $\mathbf{x}$, a classifier returns a prediction and a certified radius $R$ with a provable guarantee that any perturbation to $\mathbf{x}$ with $R$-bounded norm will not alter the classifier's prediction. In this work, we show that these guarantees can be invalidated due to limitations of floating-point representation that cause rounding errors. We design a rounding search method that can efficiently exploit this vulnerability to find adversarial examples against state-of-the-art certifications in two threat models, that differ in how the norm of the perturbation is computed. We show that the attack can be carried out against linear classifiers that have exact certifiable guarantees and against neural networks that have conservative certifications. In the weak threat model, our experiments demonstrate attack success rates over 50% on random linear classifiers, up to 23% on the MNIST dataset for linear SVM, and up to 15% for a neural network. In the strong threat model, the success rates are lower but positive. The floating-point errors exploited by our attacks can range from small to large (e.g., $10^{-13}$ to $10^{3}$) - showing that even negligible errors can be systematically exploited to invalidate guarantees provided by certified robustness. Finally, we propose a formal mitigation approach based on rounded interval arithmetic, encouraging future implementations of robustness certificates to account for limitations of modern computing architecture to provide sound certifiable guarantees.