When Privacy meets Security: Leveraging personal information for password cracking

TL;DR

This paper introduces a new Markov model-based password cracker that significantly improves guessing success rates and explores how personal information can enhance password cracking efficiency, revealing vulnerabilities in user-chosen passwords.

Contribution

It presents a novel Markov model password cracker and systematically analyzes the impact of personal information on password guessing success.

Findings

Cracks up to 69% of passwords with 10 billion guesses.

Using personal info increases guessing success by up to 30%.

Passwords based on personal attributes are weaker and more vulnerable.

Abstract

Passwords are widely used for user authentication and, despite their weaknesses, will likely remain in use in the foreseeable future. Human-generated passwords typically have a rich structure, which makes them susceptible to guessing attacks. In this paper, we study the effectiveness of guessing attacks based on Markov models. Our contributions are two-fold. First, we propose a novel password cracker based on Markov models, which builds upon and extends ideas used by Narayanan and Shmatikov (CCS 2005). In extensive experiments we show that it can crack up to 69% of passwords at 10 billion guesses, more than all probabilistic password crackers we compared again t. Second, we systematically analyze the idea that additional personal information about a user helps in speeding up password guessing. We find that, on average and by carefully choosing parameters, we can guess up to 5% more passwords, especially when the number of attempts is low. Furthermore, we show that the gain can go up to 30% for passwords that are actually based on personal attributes. These passwords are clearly weaker and should be avoided. Our cracker could be used by an organization to detect and reject them. To the best of our knowledge, we are the first to systematically study the relationship between chosen passwords and users' personal information. We test and validate our results over a wide collection of leaked password databases.