Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinations within time constraints, the exact policies these adversaries will employ remain unknown. Traditional approaches address this challenge by treating the adversary's state as a partially observable element, leading to a formulation as a Partially Observable Markov Decision Process (POMDP). However, the induced belief-space dynamics in a POMDP require knowledge of the system's transition dynamics, which, in this case, depend on the adversary's unknown policy. Hence, instead of assuming an adversary's policy, we propose characterizing the space of possible adversary behaviors through constraints derived from mission objectives and environmental factors. In this paper, we develop Task-Aware Behavior Fields (TAB-Fields), a representation that systematically captures adversary state distributions over time using principles of maximum entropy. By encoding only what is known—mission constraints and environmental limitations—TAB-Fields enable reasoning about the full range of feasible adversary behaviors without relying on policy assumptions or hand-crafted rewards. We integrate TAB-Fields with standard planning algorithms by introducing TAB-conditioned POMCP, an adaptation of Partially Observable Monte Carlo Planning. Through extensive experiments in simulation with underwater robots and hardware implementations with ground robots, we demonstrate that our approach achieves superior performance compared to baselines that either assume specific adversary policies or neglect mission constraints altogether.