  In this paper, we study feedback dynamical systems with memoryless
controllers under imperfect information. We develop an algorithm that searches
for "adversarial scenarios", which can be thought of as the strategy for the
adversary representing the noise and disturbances, that lead to safety
violations. The main challenge is to analyze the closed-loop system's
vulnerabilities with a potentially complex or even unknown controller in the
loop. As opposed to commonly adopted approaches that treat the system under
test as a black-box, we propose a synthesis-guided approach, which leverages
the knowledge of a plant model at hand. This hence leads to a way to deal with
gray-box systems (i.e., with known plant and unknown controller). Our approach
reveals the role of the imperfect information in the violation. Examples show
that our approach can find non-trivial scenarios that are difficult to expose
by random simulations. This approach is further extended to incorporate model
mismatch and to falsify vision-in-the-loop systems against finite-time
reach-avoid specifications.
