On Predicting and Exploiting HotSpots in Click-Based Graphical Passwords

We provide an in-depth study of the security of click-based graphical password schemes like PassPoints (Weidenbeck et al., 2005), by exploring popular points (hot-spots), and examining strategies to predict and exploit them in guessing attacks. We report on both shortand long-term user studies: one labcontrolled, involving 43 users and 17 diverse images, the other a field test of 223 user accounts. We provide empirical evidence that hot-spots do exist for many images, some more so than others. We explore the use of “human-computation” (in this context, harvesting click-points from a small set of users) to predict these hot-spots. We generate two “human-seeded” attacks based on this method: one based on a first-order Markov model, another based on an independent probability model. Within 100 guesses, our first-order Markov model-based attack guesses 4% of passwords in one instance, and 10% of passwords in a second instance. Our independent model-based attack guesses 20% within 2 guesses in one instance and 36% within 2 guesses in a second instance. These are all for a system whose full password space has cardinality 2. We also evaluate our first-order Markov model-based attack with cross-validation of the field study data, finding that it guesses an average of 7-10% of user passwords within 3 guesses. Our results suggest that these graphical password schemes (as originally proposed) are vulnerable to offline and online attacks, even on systems that implement conservative lock-out policies.