low-budget password strength estimation - usenixlow-budget password strength estimation dan lowe...

Low-Budget Password Strength EstimationDan Lowe Wheeler

Dropbox

Twitter: Password Feedback

Verizon Wireless: Password Requirements

Password Guessing: 35+ years of development

Ferocious algorithmsRecurrent Neural Networks PCFGs Markov Models Mangled Dictionary Attacks

Massive datasets100M+ leaked passwords Google n-gram corpus

Password Policy: Frozen in 1979

Inconsistent Requirements

Bonneau and Preibusch, 2010: 150 sites, 142 distinct policies 😫 Wang and Wang, 2015: 50 sites, 50 distinct policies 😱

Inconsistent FeedbackInput: correcthorsebatterystaple

Inconsistent FeedbackInput: correcthorsebatterystaple

See also: A large-scale evaluation of high-impact password strength meters, TISSEC ‘15

Proposal: adopt zxcvbn

# small client-side estimator,# runs in 3 milliseconds,# accurate within an online attack range> npm install zxcvbn

var meets_policy = function(password) { return zxcvbn(password).guesses > 500; };

Feedback @ Dropbox

Requirements @ Uber

Proposal: keep UI simple

Outline for todayMotivation

How zxcvbn works

Accuracy experiments

Threat ModelOnline guessing attack

• Assume any of today’s best guessing algorithms

• Upper bound: 106 guesses

What about offline?

• Policy-level enforcement: a questionable usability burden

• Better mitigations that don’t impact usability

See also:

An Administrator’s Guide to Internet Password Research, USENIX LISA ‘14

Core estimator: minimum rank over top lists

Input: wheeler


Input: wheeler

1. password 2. 123456 3. 12345678 4. qwerty 5. 123456789 6. 111111 7. dragon 8. 123123 9. baseball 10. abc123

1. li 2. khan 3. smith 4. johnson 5. jones 6. garcia 7. brown 8. davis 9. wheeler 10. rodriguez

1. dan 2. wheeler 3. dropbox 4. drop 5. box 6. sync 7. share 8. collaborate 9. rainbows 10. wow

top passwords top surnames call-specific list



Input: wheeler

1. li 2. khan 3. smith 4. johnson 5. jones 6. garcia 7. brown 8. davis 9. wheeler10. rodriguez

1. dan 2. wheeler3. dropbox 4. drop 5. box 6. sync 7. share 8. collaborate 9. rainbows 10. wow




Input: wheeler

1. li 2. khan 3. smith 4. johnson 5. jones 6. garcia 7. brown 8. davis 9. wheeler10. rodriguez

1. dan 2. wheeler3. dropbox 4. drop 5. box 6. sync 7. share 8. collaborate 9. rainbows 10. wow

Output: 2 guesses


Word transformations

l33t: @ba1one ⟶ abalone

caps: abALOne ⟶ abalone

reversed: enolaba ⟶ abalone

Keyboard patternsInput: zxcftyuio


layout=QWERTY, length=9, turns=3


Output: ≈106 guesses

layout=QWERTY, length=9, turns=3

Sequence PatternsInput: бгезйлн


type=alphabet, length=7, codepoint_delta=2

Match ⟶ Estimate ⟶ SearchInput: lenovo2222


lenovo (password)

eno (surname)

no (english)

no (reversed)

2222 (2/2/2022)

2222 (repeat)


lenovo (password) 11007 guesses

eno (surname) 3284 guesses

no (english) 11 guesses

no (reversed) 18 guesses

2222 (2/2/2022) 2190 guesses

2222 (repeat) 48 guesses


How zxcvbn works



How zxcvbn works


gold standard: real attacks trained on leaks

estimators studied: zxcvbn, KeePass, NIST entropy

Gold standard: PGSMinimum guess attempts over four attacks: • Order-5 Markov Model (Ma et al. 2014) • Probabalistic Context-Free Grammar (Komanduri 2015) • Mangled Dictionary (Hashcat) • Mangled Dictionary (John the Ripper)

Training dataGold Standard Estimators: top 100k lists

RockYou ’09 leak RockYou ’09 leak

Yahoo ’12 leak Yahoo ’12 leak

MySpace ’06 leak Xato ’15 password corpus

Google Web Corpus English Wikipedia top 1-grams

Web2 dictionary Top words: US TV + film (2006 Wiktionary study)

Inflection dictionary Top names: 1990 US Census

≈20.5M unique tokens ≈340k unique tokens

Test data• 15k random sample, RockYou ’09

• Not included in PGS / estimator training data

Estimator size?

Truncate lists at: gzipped size

top 100k: ~1.5 MB

top 10k: ~245 kB

top 1k: ~29 kB

Minimum rank only?

Runtime Performance

ConclusionJust Say No!(to inconsistently arbitrary password strength estimation)

Estimating online attacks: minimum rank goes a long ways

Matching other patterns is helpful and cheap

Accuracy: highly sensitive to training data

Give it a try!MIT License, cross-platform, widely ported:https://github.com/dropbox/zxcvbn

Partial adopter list:

https://github.com/dropbox/zxcvbn

low-budget password strength estimation - usenixlow-budget password strength estimation dan lowe...

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