Communications of the ACM

Research highlights

Measuring and Mitigating OAuth Access Token Abuse by Collusion Networks

By Shehroze Farooqi, Fareed Zaffar, Nektarios Leontiadis, Zubair Shafiq
Communications of the ACM, May 2020, Vol. 63 No. 5, Pages 103-111
10.1145/3387720
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We uncovered a thriving ecosystem of large-scale reputation manipulation services on Facebook that leverage the principle of collusion. Collusion networks collect OAuth access tokens from colluding members and abuse them to provide fake likes or comments to their members. We carried out a comprehensive measurement study to understand how these collusion networks exploited popular third-party Facebook applications with weak security settings to retrieve OAuth access tokens. We infiltrated popular collusion networks using honeypots and identified more than one million colluding Facebook accounts by "milking" these collusion networks. We disclosed our findings to Facebook and collaborated with them to implement a series of countermeasures that mitigated OAuth access token abuse without sacrificing application platform usability for third-party developers.

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1. Introduction

Reputation is a fundamental tenet of online social networks. People trust the information that is posted by a reputable social media account or is endorsed (e.g., liked) by a large number of accounts. Unfortunately, reputation fraud is prevalent in online social networks. A number of black-hat reputation manipulation services target popular online social networks.^13,19 To conduct reputation manipulation, fraudsters purchase fake accounts in bulk from underground market-places,²¹ use infected accounts compromised by malware,¹⁸ or recruit users to join collusion networks.²²

Online social networks try to counter reputation manipulation activities on their platforms by suspending suspicious accounts. Prior research on detecting reputation manipulation activities in online social networks can be broadly divided into two categories: (a) identifying temporally synchronized manipulative activity patterns^12,16; (b) identifying individual accounts suspected to be involved in manipulative activity based on their social graph characteristics.^11,25 Recent studies have shown that fraudsters can circumvent these detection methods by incorporating "normal" behavior in their activity patterns.^13,23 Defending against fraudulent reputation manipulation is an ongoing arms race between fraudsters and social network operators.^3,8

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