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Transcending the capabilities of traditional architectures, metasurfaces offer nearlimitless control over the fundamental electromagnetic properties of wireless signals, presenting new opportunities for wireless communication. However, they also bring forth unprecedented security challenges, particularly for millimeter-wave and sub-THz wireless backhaul links employed for many critical functions, such as financial trading on Wall Street. In this article, we expose a new category of aerial ''MetaSurface-in-the-Middle'' attacks, wherein an adversary armed with an on-drone metasurface, MetaFly, can intercept wireless backhaul links with an almost imperceptible trace. Strikingly, such adversarial metasurfaces can be fabricated in minutes using standard office items like a foil sheet and a laminator. The attack is implemented and experimentally evaluated in both a large indoor atrium and outdoor rooftops in a large metropolitan area, demonstrating the adversary's ability to establish a secondary diffraction beam for eavesdropping while maintaining minimal impact on legitimate communication. 

We demonstrate a wireless security application to protect the weakest link in phone-to-phone communication, using a terahertz metasurface. To our knowledge, this is the first example of an eavesdropping countermeasure in which the attacker is actively misled. 

We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed.