  Real-time crowdsourced maps such as Waze provide timely updates on traffic,
congestion, accidents and points of interest. In this paper, we demonstrate how
lack of strong location authentication allows creation of software-based {\em
Sybil devices} that expose crowdsourced map systems to a variety of security
and privacy attacks. Our experiments show that a single Sybil device with
limited resources can cause havoc on Waze, reporting false congestion and
accidents and automatically rerouting user traffic. More importantly, we
describe techniques to generate Sybil devices at scale, creating armies of
virtual vehicles capable of remotely tracking precise movements for large user
populations while avoiding detection. We propose a new approach to defend
against Sybil devices based on {\em co-location edges}, authenticated records
that attest to the one-time physical co-location of a pair of devices. Over
time, co-location edges combine to form large {\em proximity graphs} that
attest to physical interactions between devices, allowing scalable detection of
virtual vehicles. We demonstrate the efficacy of this approach using
large-scale simulations, and discuss how they can be used to dramatically
reduce the impact of attacks against crowdsourced mapping services.
