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This week I encountered a problem with an expired iOS MDM configuration profile (i.e., expired certificates) on one of my jailbroken testing devices. When I tried to re-enroll to the MDM server, iOS gave me the following error message: “Profile Installation Failed - The new MDM Payload does not match the old payload.“ The only recommendation I found was to completely remove the old profile using the iOS Settings app:


or the False Sense of Security of Dropbox’s Passcode Protection Since the release of iOS 8, the Touch ID fingerprint sensor can now also be used in third-party apps. The Local Authentication framework provides an API via which users can conveniently deploy their biometric fingerprint to authenticate themselves in both apps from the App Store and enterprise apps. In the medium term, we anticipate that more and more apps will switch to the fingerprint method of user authentication.


A comparison before and after iOS 8 was released As part of one of our recent research projects, we evaluated how malicious third-party apps could affect user privacy, despite the various security controls and the solid security architecture of the iOS platform. Therefore, we reviewed the iOS app sandbox model for weaknesses – and, indeed, made some finds. Some of these defects, which Markus Troßbach and I disclosed to Apple a while back, have been addressed with yesterday’s release of iOS 8 (CVE-2014-4361, CVE-2014-4362).


Selected Publications

Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time.
In PoPETS Journal

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