All Your Face Belong to Us: Breaking Facebook’s Social Authentication

1. All Your Face Belong to Us: Breaking Facebook’s Social Authentication Jason Polakis and Sotiris Ioannidis, FORTH-ICS, Greece; Marco Lancini, Federico Maggi, and Stefano Zanero, Politecnico di Milano, Italia; GeorgiosKontaxis and Angelos D. Keromytis, Columbia University, USA

2. Outline • Introduction • How Social Authentication Works • Advantages and Shortcomings • Attack Surface Estimation • Breaking Social Authentication • Face Recognition as a Service • Experimental Evaluation • Remediation and Limitations • Conclusions

3. Introduction • Facebook reports over 900 million active users as of March 2012. • In 2011, Facebook has released a two-factor authentication mechanism, referred to as Social Authentication.

4. How Social Authentication Works • Friend list • A user must have at least 50 friends. • Tagged photos • The user’s friend must be tagged in an adequate number of photos. • Face • SA tests must be solvable by humans within the 5 minute (circa) time window enforced by Facebook. • Triggering • the user logs in from a different geographical location. • uses a new device for the first time to access his account.

5. Advantages and Shortcomings • Advantages • Facebook’s SA is less cumbersome, especially because users have grown accustomed to tagging friends in photos. • Shortcomings • The number of friends can influence the applicability and the usability of SA. • Their friends have erroneously tagged for fun or as part of a contest which required them to do so. • Bypass the SA test by providing their date of birth.

6. Attack Surface Estimation • The attacker has compromised the user’s credential. • Facebook designed SA as a protection mechanism against strangers. • we provide an empirical calculation of the probabilities of each phase of our attack. • P(F) = 47% of the user’s have their friends list public. • P(P) = 71% of them (236,752) exposed at least one public photo album. • Attacker can try to befriend the friends of his victim to gain access to their private photos with a chance of P(B) ≃ 70% to succeed.

7. Attack Surface Estimation (Cont.)

8. Breaking Social Authentication • Step 1: Crawling Friend List • Python’s urllibHTTP library and regular expression • MongoDB database • GridFSfilesystem • Step 2: Issuing Friend Requests • Step 3: Photo Collection/Modeling • Photo collection • Face Extraction and Tag Matching – OpenCV toolkit • Facial Modeling – sklearn library • Step 4: Name Lookup

9. Breaking Social Authentication

10. Face Recognition as a Service • Face.com • was recently acquired by Facebook. • The service exposes an API through which developers can supply a set of photos to use as training data and then query the service with a new unknown photo for the recognition of known individuals. • faces.detect – identify any existing faces • tags.save - to label the good photos with the respective UIDs of their owners • face.train • faces.recongnize

11. Experimental Evaluation • Overall Dataset

12. Experimental Evaluation (Cont.) • Breaking SA: Determined Attacker • shows the number of pages solved correctly out of 7.

13. Experimental Evaluation (Cont.) • Breaking SA: Determined Attacker • shows the CPU-time required to solve the full test

14. Breaking SA: Casual Attacker • Implementation • 11 dummy accounts play the role of victims. • Selenium – login these account in a automated fashion. • Tor - take advantage of the geographic dispersion of its exit nodes. • face.com – solved SA test • Result • 22% (28/127) of tests solved 5-7 of the 7 test pages. • 56% (71/127) of tests solved 3-4 of the 7 test pages. • 44 seconds on average

15. Breaking SA: Casual Attacker (Cont.) • In about 25% of the photos face.com was unable to detect a human face. • in 50% of the photos face.com was able to detect a human face but marked it as unrecognizable. • in the last 25% of the photos a face was detected but did not match any of the faces in our training set.

16. Ethical Consideration • We never took advantage of accepted requests to collect photos or other private information otherwise unavailable; we solely collected public photos.

17. Compromise Prevention • Users can add certain devices to a list of recognized, trusted devices. • a user who fails to complete an SA challenge is redirected to an alert page, upon the next successful login, which reports the attempted login.

18. Slowing Sown Attacker • CAPTCHAs may create a technical obstacle to automated attacks, but they should not be considered a definitive countermeasure. • The presence of suggested names in SA tests is the major disadvantage of the current implementation as it greatly limits the search space for adversaries.

19. Conclusions • on average, 42% of the data used to generate the second factor, thus, gaining the ability to identify randomly selected photos of the victim’s friends. • Given that information, we managed to solve 22% of the real Facebook SA tests presented to us during our experiments and gain a significant advantage to an additional 56% of the tests with answers for more than half of pages of each test.