This article is focused on estimating a quantity of interest in the context of military impact assessment that we shall call adversarial risk. We formulate the adversarial risk as a function of the multiobject state describing a group of weapons, and propose two approaches to estimate it using multiobject filters. The first, optimal, approach is tailored to filters for point processes, and produces the mean estimate of the adversarial risk and its variance. The second, naïve, approach is applicable to any filter producing point estimates of the multiobject state, yet it is not capable of equipping a risk estimate with an indicator of its quality. We develop an implementation of the optimal approach for a particular multiobject filter and compare it to the naïve approach.