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In this paper, we study feedback dynamical systems with memorylesscontrollers under imperfect information. We develop an algorithm that searchesfor "adversarial scenarios", which can be thought of as the strategy for theadversary representing the noise and disturbances, that lead to safetyviolations. The main challenge is to analyze the closed-loop system'svulnerabilities with a potentially complex or even unknown controller in theloop. As opposed to commonly adopted approaches that treat the system undertest as a black-box, we propose a synthesis-guided approach, which leveragesthe knowledge of a plant model at hand. This hence leads to a way to deal withgray-box systems (i.e., with known plant and unknown controller). Our approachreveals the role of the imperfect information in the violation. Examples showthat our approach can find non-trivial scenarios that are difficult to exposeby random simulations. This approach is further extended to incorporate modelmismatch and to falsify vision-in-the-loop systems against finite-timereach-avoid specifications.

Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections