  Neural Networks (NN) have been proposed in the past as an effective means for
both modeling and control of systems with very complex dynamics. However,
despite the extensive research, NN-based controllers have not been adopted by
the industry for safety critical systems. The primary reason is that systems
with learning based controllers are notoriously hard to test and verify. Even
harder is the analysis of such systems against system-level specifications. In
this paper, we provide a gradient based method for searching the input space of
a closed-loop control system in order to find adversarial samples against some
system-level requirements. Our experimental results show that combined with
randomized search, our method outperforms Simulated Annealing optimization.
