No Single Silver Bullet: Measuring the Accuracy of Password Strength Meters

To help users create stronger passwords, nearly every respectable web service adopts a password strength meter (PSM) to provide real-time strength feedback upon user registration and password change. Recent research has found that PSMs that provide accurate feedback can indeed effectively nudge users toward choosing stronger passwords. Thus, it is imperative to systematically evaluate existing PSMs to facilitate the selection of accurate ones. In this paper, we highlight that there is no single silver bullet metric for measuring the accuracy of PSMs: For each given guessing scenario and strategy, a specific metric is necessary. We investigate the intrinsic characteristics of online and offline guessing scenarios, and for the first time, propose a systematic evaluation framework that is composed of four different dimensioned criteria to rate PSM accuracy under these two password guessing scenarios (as well as various guessing strategies).

More specifically, for online guessing, the strength misjudgments of passwords with different popularity would have varied effects on PSM accuracy, and we suggest the weighted Spearman metric and consider two typical attackers: The general attacker who is unaware of the target password distribution, and the knowledgeable attacker aware of it. For offline guessing, since the cracked passwords are generally weaker than the uncracked ones, and they correspond to two disparate distributions, we adopt the Kullback-Leibler divergence metric and investigate the four most typical guessing strategies: brute-force, dictionary-based, probability-based, and a combination of above three strategies. In particular, we propose the Precision metric to measure PSM accuracy when non-binned strength feedback (e.g., probability) is transformed into easy-to-understand bins/scores (e.g., [weak, medium, strong]). We further introduce a reconciled Precision metric to characterize the impacts of strength misjudgments in different directions (e.g., weak→strong and strong→weak) on PSM accuracy. The effectiveness and practicality of our evaluation framework are demonstrated by rating 12 leading PSMs, leveraging 14 real-world password datasets. Finally, we provide three recommendations to help improve the accuracy of PSMs.