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Human-Seeded Attacks and Exploiting Hot-Spots in Graphical Passwords
Although motivated by both usability and security concerns, the existing literature on click-based graphical password schemes using a single background image (e.g., PassPoints) has focused largely on usability. We examine the security of such schemes, including the impact of different background images, and strategies for guessing user passwords. We report on both short- and long-term user studies: one lab-controlled, involving 43 users and 17 diverse images, and the other a field test of 223 user accounts. We provide empirical evidence that popular points (hot-spots) do exist for many images, and explore two different types of attack to exploit this hot-spotting: (1) a “human-seeded” attack based on harvesting click-points from a small set of users, and (2) an entirely automated attack based on image processing techniques. Our most effective attacks are generated by harvesting password data from a small set of users to attack other targets. These attacks can guess 36% of user passwords within 231 guesses (or 12% within 216 guesses) in one instance, and 20% within 233 guesses (or 10% within 218 guesses) in a second instance. We perform an image-processing attack by implementing and adapting a bottom-up model of visual attention, resulting in a purely automated tool that can guess up to 30% of user passwords in 235 guesses for some instances, but under 3% on others. Our results suggest that these graphical password schemes appear to be at least as susceptible to offline attack as the traditional text passwords they were proposed to replace.
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