We present and evaluate various methods for purely automated attacks against click-based graphical passwords. Our purely automated methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention. Our method results in a significantly better automated attack than previous work, guessing 8-15% of passwords for two representative images using dictionaries of less than 224.6 entries, and about 16% of passwords on each of these images using dictionaries of less than 231.4 entries (where the full password space is 243). Relaxing our click-order pattern substantially increased the efficacy of our attack albeit with larger dictionaries of 234.7 entries, allowing attacks that guessed 48-54% of passwords (compared to previousresults of 0.9% and 9.1% on the same two images with 235 guesses). These latter automated attacks are independent of focus-of-attention models, and are based on imageindependent guessing patterns. Our results show that automated attacks, which are easier to arrange than humanseeded attacks and are more scalable to systems that use multiple images, pose a significant threat.