  In many cases, neural networks perform well on test data, but tend to
overestimate their confidence on out-of-distribution data. This has led to
adoption of Bayesian neural networks, which better capture uncertainty and
therefore more accurately reflect the model's confidence. For machine learning
security researchers, this raises the natural question of how making a model
Bayesian affects the security of the model. In this work, we explore the
interplay between Bayesianism and two measures of security: model privacy and
adversarial robustness. We demonstrate that Bayesian neural networks are more
vulnerable to membership inference attacks in general, but are at least as
robust as their non-Bayesian counterparts to adversarial examples.
