The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.