This note is concerned with nonlinear stochastic minimax dynamic games which are subject to noisy measurements. The minimizing players are control inputs while the maximizing players are square-integrable stochastic processes. The minimax dynamic game is formulated using an information state, which depends on the paths of the observed processes. The information state satisfies a partial differential equation of the Hamilton-Jacobi-Bellman (HJB) type. The HJB equation is employed to characterize the dissipation properties of the system, to derive a separation theorem between the design of the estimator and the controller, and to introduce a certainty-equivalence principle along the lines of Whittle. Finally, the separation theorem and the certainty-equivalence principle are applied to solve, the linear-quadratic-Gaussian minimax game. The results of this note generalize the L-2-gain of deterministic systems to stochastic analogs; they are related to the controller design of stochastic systems which employ risk-sensitive performance criteria, and to the controller design of deterministic systems which employ minimax performance criteria.