  Millimeter-wave (mmWave) radars have found applications in a wide range of
domains, including human tracking, health monitoring, and autonomous driving,
for their unobtrusive nature and high range accuracy. These capabilities,
however, if used for malicious purposes, could also result in serious security
and privacy issues. For example, a user's daily life could be secretly
monitored by a spy radar. Hence, there is a strong urge to develop systems that
can detect and locate such spy radars. In this paper, we propose $Radar^2$, a
practical system for passive spy radar detection and localization using a
single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a
novel \textit{Frequency Component Detection} method to detect the existence of
mmWave signals, distinguish between mmWave radar and WiGig signals using a
waveform classifier based on a convolutional neural network (CNN), and localize
spy radars using triangulation based on the detector's observations at multiple
anchor points. Not only does $Radar^2$ work for different types of mmWave
radar, but it can also detect and localize multiple radars simultaneously.
Finally, we performed extensive experiments to evaluate the effectiveness and
robustness of $Radar^2$ in various settings. Our evaluation results show that
the radar detection rate is above 96$\%$ and the localization error is within
0.3m. The results also reveal that $Radar^2$ is robust against various
environmental factors (e.g., room layout and human activities).
