  There is a space of uncertainty in the modeling of vehicular dynamics of
autonomous systems due to noise in sensor readings, environmental factors or
modeling errors. We present Requiem, a software-only, blackbox approach that
exploits this space in a stealthy manner causing target systems, e.g., unmanned
aerial vehicles (UAVs), to significantly deviate from their mission parameters.
Our system achieves this by modifying sensor values, all while avoiding
detection by onboard anomaly detectors (hence, "stealthy"). The Requiem
framework uses a combination of multiple deep learning models (that we refer to
as "surrogates" and "spoofers") coupled with extensive, realistic simulations
on a software-in-the-loop quadrotor UAV system. Requiem makes no assumptions
about either the (types of) sensors or the onboard state estimation
algorithm(s) -- it works so long as the latter is "learnable".
  We demonstrate the effectiveness of our system using various attacks across
multiple missions as well as multiple sets of statistical analyses. We show
that Requiem successfully exploits the modeling errors (i.e., causes
significant deviations from planned mission parameters) while remaining
stealthy (no detection even after {tens of meters of deviations}) and are
generalizable (Requiem has potential to work across different attacks and
sensor types).
