  In this abstract we explore the possibility of introducing biases in physical
parameter inference models from adversarial-type attacks. In particular, we
inject small amplitude systematics into inputs to a mixture density networks
tasked with inferring cosmological parameters from observed data. The
systematics are constructed analogously to white-box adversarial attacks. We
find that the analysis network can be tricked into spurious detection of new
physics in cases where standard cosmological estimators would be insensitive.
This calls into question the robustness of such networks and their utility for
reliably detecting new physics.
