  A systematic, semi-automated search for pulsar glitches in the first UTMOST
public data release is presented. The search is carried out using a hidden
Markov model which incorporates both glitches and timing noise into the model
of the assumed phase evolution of the pulsar. Glitches are detected through
Bayesian model selection between models with and without glitches present with
minimal human intervention. Nine glitches are detected among seven objects, all
of which have been previously reported. No new glitches were detected.
Injection studies are used to place 90\% frequentist upper limits on the size
of undetected glitches in each of the 282 objects searched. The mean upper
limit obtained is $\Delta f^{90\%}/f = 1.9 \times 10^{-8}$, with a range of
$4.1 \times 10^{-11} \leq \Delta f^{90\%}/f \leq 2.7 \times 10^{-7}$, assuming
step events with no post-glitch recoveries. It is demonstrated that including
glitch recovery has a mild effect, in most cases increasing the upper limit by
a factor of $\lesssim 5$ conservatively assuming complete recovery on a
timescale of $100\,\mathrm{d}$.
