  Open-world machine learning (ML) combines closed-world models trained on
in-distribution data with out-of-distribution (OOD) detectors, which aim to
detect and reject OOD inputs. Previous works on open-world ML systems usually
fail to test their reliability under diverse, and possibly adversarial
conditions. Therefore, in this paper, we seek to understand how resilient are
state-of-the-art open-world ML systems to changes in system components? With
our evaluation across 6 OOD detectors, we find that the choice of
in-distribution data, model architecture and OOD data have a strong impact on
OOD detection performance, inducing false positive rates in excess of $70\%$.
We further show that OOD inputs with 22 unintentional corruptions or
adversarial perturbations render open-world ML systems unusable with false
positive rates of up to $100\%$. To increase the resilience of open-world ML,
we combine robust classifiers with OOD detection techniques and uncover a new
trade-off between OOD detection and robustness.
