On the Economics of Offline Password Cracking
Jeremiah Blocki, Ben Harsha, Samson Zhou

TL;DR
This paper develops an economic model to predict the fraction of user passwords an attacker would crack after a breach, revealing that common password distributions and current key-stretching practices are insufficient for security.
Contribution
It introduces a quantitative economic framework for analyzing offline password cracking and demonstrates that most user passwords are vulnerable despite key-stretching.
Findings
Most user passwords follow a Zipf's law distribution.
Rational attackers are likely to crack all passwords above a certain value threshold.
Current key-stretching practices are inadequate against rational attackers.
Abstract
We develop an economic model of an offline password cracker which allows us to make quantitative predictions about the fraction of accounts that a rational password attacker would crack in the event of an authentication server breach. We apply our economic model to analyze recent massive password breaches at Yahoo!, Dropbox, LastPass and AshleyMadison. All four organizations were using key-stretching to protect user passwords. In fact, LastPass' use of PBKDF2-SHA256 with hash iterations exceeds 2017 NIST minimum recommendation by an order of magnitude. Nevertheless, our analysis paints a bleak picture: the adopted key-stretching levels provide insufficient protection for user passwords. In particular, we present strong evidence that most user passwords follow a Zipf's law distribution, and characterize the behavior of a rational attacker when user passwords are selected from a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsUser Authentication and Security Systems · Advanced Malware Detection Techniques · Spam and Phishing Detection
