<p>Unmanned Aerial Systems (UASs) prioritizing extended flight times and agility often rely on lightweight camera-based depth estimation systems due to payload constraints, but this study reveals critical vulnerabilities in such navigation approaches. We demonstrate that adversarially designed triggers in black-box settings can significantly compromise UAS navigation, altering flight paths and potentially causing crashes. We demonstrate that an attacker that can infer UAS’s monocular depth estimation outputs by exploiting telemetry or external sensors for a given relative pose, can learn to create an adversarial tracking marker pad. This adversarial marker pad can subsequently be used to induce a controlled bias in the controller’s range/altitude estimates so as to manipulate the UAS flight paths without any access to UAS internals. Our method employs a surrogate model-guided Bayesian optimization framework to generate adversarial depth over the original pad’s semantic search space, maintaining tracking while being robust to environmental and sensor variability. This highlights a critical security concern in UAS tracking/navigation reliant on camera-only depth cues, necessitating the development of more resilient tracking/navigation frameworks.</p>