We develop DroidCCT, a distributed test framework to evaluate the scale of a wide range of failures/bugs in cryptography for end users. DroidCCT relies on passive analysis of artifacts from the execution of cryptographic operations in the Android ecosystem to identify weak implementations. We collect trillions of samples from cryptographic operations of Android Keystore on half a billion devices and apply several analysis techniques to evaluate the quality of cryptographic output from these devices and their underlying implementations. Our study reveals several patterns of bugs and weakness in cryptographic implementations from various manufacturers and chipsets. We show that the heterogeneous nature of cryptographic implementations results in non-uniform availability and reliability of various cryptographic functions. More importantly, flaws such as the use of weakly-generated random parameters, and timing side channels may surface across deployments of cryptography. Our results highlight the importance of fault- and side-channel-resistant cryptography and the ability to transparently and openly test these implementations.

Malware is one of the key threats to online security today, with applications ranging from phishing mailers to ransomware and trojans. Due to the sheer size and variety of the malware threat, it is impractical to combat it as a whole. Instead, governments and companies have instituted teams dedicated to identifying, prioritizing, and removing specific malware families that directly affect their population or business model. The identification and prioritization of the most disconcerting malware families (known as malware hunting) is a time-consuming activity, accounting for more than 20% of the work hours of a typical threat intelligence researcher, according to our survey. To save this precious resource and amplify the team’s impact on users’ online safety we present Spotlight, a large-scale malware lead-generation framework. Spotlight first sifts through a large malware data set to remove known malware families, based on first and third-party threat intelligence. It then clusters the remaining malware into potentially-undiscovered families, and prioritizes them for further investigation using a score based on their potential business impact.

Online accounts are inherently valuable resources---both for the data they contain and the reputation they accrue over time. Unsurprisingly, this value drives criminals to steal, or hijack, such accounts. In this paper we focus on manual account hijacking---account hijacking performed manually by humans instead of botnets. We describe the details of the hijacking workflow: the attack vectors, the exploitation phase, and post-hijacking remediation. Finally we share, as a large online company, which defense strategies we found effective to curb manual hijacking.