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Recent research has shown that in-network observers of WiFi communication (i.e., observers who have joined the WiFi network) can obtain much information regarding the types, user identities, and activities of Internet-of-Things (IoT) devices in the network. What has not been explored is the question of how much information can be inferred by an out-of-network observer who does not have access to the WiFi network. This attack scenario is more realistic and much harder to defend against, thus imposes a real threat to user privacy. In this paper, we investigate privacy leakage derived from an out-of-network traffic eavesdropper on the encrypted WiFi traffic of popular IoT devices. We instrumented a testbed of 12 popular IoT devices and evaluated multiple machine learning methods for fingerprinting and inferring what IoT devices exist in a WiFi network. By only exploiting the WiFi frame header information, we have achieved 95% accuracy in identifying the devices and often their working status. This study demonstrates that information leakage and privacy attack is a real threat for WiFi networks and IoT applications.