We introduce and evaluate various methods for purely automated attacks against PassPoints-style graphical passwords. For generating these attacks, we introduce a graph-based algorithm to efficiently create dictionaries based on heuristics such as click-order patterns (e.g., five points all along a line). Some of our methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention, yielding significantly better automated attacks than previous work. One resulting automated attack finds 7%16% of passwords for two representative images using dictionaries of approximately 226 entries (where the full password space is 243). Relaxing click-order patterns substantially increased the attack efficacy albeit with larger dictionaries of approximately 2 35 entries, allowing attacks that guessed 48%54% of passwords (compared to previous results of 1% and 9% on the same dataset for two images with 235 guesses). These latter attacks are independent of focus-of-attention models, and are based on image-independent guessing patterns. Our results show that automated attacks, which are easier to arrange than human-seeded attacks and are more scalable to systems that use multiple images, require serious consideration when deploying basic PassPoints-style graphical passwords.

, , , , ,
IEEE Transactions on Information Forensics and Security
School of Computer Science

Van Oorschot, P, Salehi-Abari, A. (Amirali), & Thorpe, J. (Julie). (2010). Purely automated attacks on PassPoints-style graphical passwords. IEEE Transactions on Information Forensics and Security, 5(3), 393–405. doi:10.1109/TIFS.2010.2053706