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Password guessers are instrumental for assessing the strength of passwords.Despite their diversity and abundance, little is known about how differentguessers compare to each other. We perform in-depth analyses and comparisons ofthe guessing abilities and behavior of password guessers. To extend analysesbeyond number of passwords cracked, we devise an analytical framework tocompare the types of passwords that guessers generate under various conditions(e.g., limited training data, limited number of guesses, and dissimilartraining and target data). Our results show that guessers often producedissimilar guesses, even when trained on the same data. We leverage this resultto show that combinations of computationally-cheap guessers are as effective ascomputationally intensive guessers, but more efficient. Our insights allow usto provide a concrete set of recommendations for system administrators whenperforming password checking.

Password Guessers Under a Microscope: An In-Depth Analysis to Inform Deployments