  Arguments in favor of injecting symbolic knowledge into neural architectures
abound. When done right, constraining a sub-symbolic model can substantially
improve its performance and sample complexity and prevent it from predicting
invalid configurations. Focusing on deep probabilistic (logical) graphical
models -- i.e., constrained joint distributions whose parameters are determined
(in part) by neural nets based on low-level inputs -- we draw attention to an
elementary but unintended consequence of symbolic knowledge: that the resulting
constraints can propagate the negative effects of adversarial examples.
