The procfs has been identified as a viable source of side-channel information leaks on mobile devices. Starting with Android M (Android 6), access to the procfs has been continuously restricted in order to cope with these attacks. Yet, more recent papers demonstrated that even if access to process-specific information is restricted within the procfs, global statistics can still be exploited. However, with state-of-the-art techniques, the search for procfs information leaks requires a significant amount of manual work. This makes an exhaustive analysis of existing and newly introduced procfs resources in terms of information leaks impractical. We introduce ProcHarvester, a systematic and fully automated technique to assess procfs information leaks. ProcHarvester automatically triggers events of interest and later on applies machine learning techniques to identify procfs information leaks. We demonstrate the power of ProcHarvester by identifying information leaks to infer app starts from a set of 100 apps with an accuracy of 96% on Android N (Android 7). Thereby, we outperform the most accurate app inference attack by about 10 percentage points. We also demonstrate the ease of applicability of ProcHarvester by showing how to profile other events such as website launches as well as keyboard gestures, and we identify the first procfs side channels on Android O (Android 8). ProcHarvester advances investigations of procfs information leaks to the next level and will hopefully help to reduce the attack surface of side-channel attacks.

Microarchitectural side-channel attacks have received significant attention recently. However, while side-channel analyses on secret key operations such as decryption and signature generation are well established, the process of key generation did not receive particular attention so far. Especially due to the fact that microarchitectural attacks usually require multiple observations (more than one measurement trace) to break an implementation, one-time operations such as key generation routines are often considered as uncritical and out of scope. However, this assumption is no longer valid for shielded execution architectures, where sensitive code is executed - in the realm of a potential attacker - inside hardware enclaves. In such a setting, an untrusted operating system can conduct noiseless controlled-channel attacks by exploiting page access patterns. In this work, we identify a critical vulnerability in the RSA key generation procedure of Intel SGX SSL (and the underlying OpenSSL library) that allows to recover secret keys from observations of a single execution. In particular, we mount a controlled-channel attack on the binary Euclidean algorithm (BEA), which is used for checking the validity of the RSA key parameters generated within an SGX enclave. Thereby, we recover all but 16 bits of one of the two prime factors of the public modulus. For an 8192-bit RSA modulus, we recover the remaining 16 bits and thus the full key in less than 12 seconds on a commodity PC. In light of these results, we urge for careful re-evaluation of cryptographic libraries with respect to single trace attacks, especially if they are intended for shielded execution environments such as Intel SGX.

A wide range of mobile applications for Apple's iOS platform process sensitive data and, therefore, rely on protective mechanisms natively provided by the operating system. A wrong application of cryptography or security-critical APIs, however, exposes secrets to unrelated parties and undermines the overall security.

Although the Android system has been continuously hardened against side-channel attacks, there are still plenty of APIs available that can be exploited. However, most side-channel analyses in the literature consider specifically chosen APIs (or resources) in the Android framework, after a manual analysis of APIs for possible information leaks has been performed. Such a manual analysis is a tedious, time consuming, and error-prone task, meaning that information leaks tend to be overlooked.

The browsing behavior of a user allows to infer personal details, such as health status, political interests, sexual orientation, etc. In order to protect this sensitive information and to cope with possible privacy threats, defense mechanisms like SSH tunnels and anonymity networks (e.g., Tor) have been established. A known shortcoming of these defenses is that website fingerprinting attacks allow to infer a user's browsing behavior based on traffic analysis techniques. However, website fingerprinting typically assumes access to the client's network or to a router near the client, which restricts the applicability of these attacks.