  Out-of-distribution (OOD) detection is essential for the reliability of ML
models. Most existing methods for OOD detection learn a fixed decision
criterion from a given in-distribution dataset and apply it universally to
decide if a data point is OOD. Recent work~\cite{fang2022is} shows that given
only in-distribution data, it is impossible to reliably detect OOD data without
extra assumptions. Motivated by the theoretical result and recent exploration
of test-time adaptation methods, we propose a Non-Parametric Test Time
\textbf{Ada}ptation framework for \textbf{O}ut-Of-\textbf{D}istribution
\textbf{D}etection (\abbr). Unlike conventional methods, \abbr utilizes online
test samples for model adaptation during testing, enhancing adaptability to
changing data distributions. The framework incorporates detected OOD instances
into decision-making, reducing false positive rates, particularly when ID and
OOD distributions overlap significantly. We demonstrate the effectiveness of
\abbr through comprehensive experiments on multiple OOD detection benchmarks,
extensive empirical studies show that \abbr significantly improves the
performance of OOD detection over state-of-the-art methods. Specifically, \abbr
reduces the false positive rate (FPR95) by $23.23\%$ on the CIFAR-10 benchmarks
and $38\%$ on the ImageNet-1k benchmarks compared to the advanced methods.
Lastly, we theoretically verify the effectiveness of \abbr.
