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Contact tracing apps running on mobile devices promise to reduce the manual effort required for identifying infection chains and to increase the tracing accuracy in the presence of COVID-19. Since the beginning of the pandemic, several contract tracing apps have been proposed or deployed in practice by academia or academic-industrial consortia. While some of them rely on centralized approaches and bear high privacy risks, others are based on decentralized approaches aimed at addressing user privacy aspects. Google and Apple announced their joint effort of providing an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". The contact tracing feature seems to become an opt-in feature in mobile devices running iOS or Android. Some countries have already decided or are planning to base their contact tracing apps on GAP 1 .Several researchers have pointed out potential privacy and security risks related to most of the contact tracing approaches proposed until now, including those that claim privacy protection and are based on GAP. However, the question remains as how realistic these risks are. This report makes a first attempt towards providing empirical evidence in real-world scenarios for two such risks discussed in the literature: one concerning privacy, and the other one concerning security. In particular, we focus on a practical analysis of GAP, given that it is the foundation of several tracing apps, including apps such as the Swiss SwissCOVID, the Italian Immuni, and the German Corona-Warn-App. We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that principally can generate fake contacts with the potential of significantly affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can be easily used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). We hope that our findings provide valuable input in the process of testing and certifying contact tracing apps, e.g., as planned for the German Corona-Warn-App, ultimately guiding improvements for secure and privacypreserving design and implementation of digital contact tracing systems.

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