In the past decade the increase of for-profit cybercrime has given rise to an entire underground ecosystem supporting large-scale abuse, a facet of which encompasses the bulk registration of fraudulent accounts. In this paper, we present a 10 month longitudinal study of the underlying technical and financial capabilities of criminals who register phone verified accounts (PVA). To carry out our study, we purchase 4,695 Google PVA as well as pull a random sample of 300,000 Google PVA that Google disabled for abuse. We find that miscreants rampantly abuse free VOIP services to circumvent the intended cost of acquiring phone numbers, in effect undermining phone verification. Combined with short lived phone numbers from India and Indonesia that we suspect are tied to human verification farms, this confluence of factors correlates with a market-wide price drop of 30-40% for Google PVA until Google penalized verifications from frequently abused carriers. We distill our findings into a set of recommendations for any services performing phone verification as well as highlight open challenges related to PVA abuse moving forward.

In this work we present Picasso: a lightweight device class fingerprinting protocol that allows a server to verify the software and hardware stack of a mobile or desktop client. As an example, Picasso can distinguish between traffic sent by an authentic iPhone running Safari on iOS from an emulator or desktop client spoofing the same configuration. Our fingerprinting scheme builds on unpredictable yet stable noise introduced by a client's browser, operating system, and graphical stack when rendering HTML5 canvases. Our algorithm is resistant to replay and includes a hardware-bound proof of work that forces a client to expend a configurable amount of CPU and memory to solve challenges. We demonstrate that Picasso can distinguish 52 million Android, iOS, Windows, and OSX clients running a diversity of browsers with 100% accuracy. We discuss applications of Picasso in abuse fighting, including protecting the Play Store or other mobile app marketplaces from inorganic interactions; or identifying login attempts to user accounts from previously unseen device classes.

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.