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Password security hinges on an in-depth understanding of the techniquesadopted by attackers. Unfortunately, real-world adversaries resort to pragmaticguessing strategies such as dictionary attacks that are inherently difficult tomodel in password security studies. In order to be representative of the actualthreat, dictionary attacks must be thoughtfully configured and tuned. However,this process requires a domain-knowledge and expertise that cannot be easilyreplicated. The consequence of inaccurately calibrating dictionary attacks isthe unreliability of password security analyses, impaired by a severemeasurement bias.  In the present work, we introduce a new generation of dictionary attacks thatis consistently more resilient to inadequate configurations. Requiring nosupervision or domain-knowledge, this technique automatically approximates theadvanced guessing strategies adopted by real-world attackers. To achieve this:(1) We use deep neural networks to model the proficiency of adversaries inbuilding attack configurations. (2) Then, we introduce dynamic guessingstrategies within dictionary attacks. These mimic experts' ability to adapttheir guessing strategies on the fly by incorporating knowledge on theirtargets.  Our techniques enable more robust and sound password strength estimateswithin dictionary attacks, eventually reducing overestimation in modelingreal-world threats in password security. Code available:https://github.com/TheAdamProject/adams

Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries