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Reasoning Analytically About Password-Cracking Software Enze Alex Liu , Amanda Nakanishi, Maximilian Golla, David Cash, Blase Ur Chic4go 2 Attack Model 80d561388725fa74f2d03cd16e1d687c 3 Attack Model 80d561388725fa74f2d03cd16e1d687c


  1. Reasoning Analytically About Password-Cracking Software Enze “Alex” Liu , Amanda Nakanishi, Maximilian Golla, David Cash, Blase Ur

  2. Chic4go 2

  3. Attack Model 80d561388725fa74f2d03cd16e1d687c 3

  4. Attack Model 80d561388725fa74f2d03cd16e1d687c 1. h(“123456”) = e10adc3949ba59abbe56e057f20f883e 4

  5. Attack Model 80d561388725fa74f2d03cd16e1d687c 1. h(“123456”) = e10adc3949ba59abbe56e057f20f883e 2. h(“password”) = 5f4dcc3b5aa765d61d8327deb882cf99 5

  6. Attack Model 80d561388725fa74f2d03cd16e1d687c 1. h(“123456”) = e10adc3949ba59abbe56e057f20f883e 2. h(“password”) = 5f4dcc3b5aa765d61d8327deb882cf99 3. h(“monkey”) = d0763edaa9d9bd2a9516280e9044d885 6

  7. Attack Model 80d561388725fa74f2d03cd16e1d687c 1. h(“123456”) = e10adc3949ba59abbe56e057f20f883e 2. h(“password”) = 5f4dcc3b5aa765d61d8327deb882cf99 3. h(“monkey”) = d0763edaa9d9bd2a9516280e9044d885 4. h(“letmein”) = 0d107d09f5bbe40cade3de5c71e9e9b7 7

  8. Attack Model 80d561388725fa74f2d03cd16e1d687c 1. h(“123456”) = e10adc3949ba59abbe56e057f20f883e 2. h(“password”) = 5f4dcc3b5aa765d61d8327deb882cf99 3. h(“monkey”) = d0763edaa9d9bd2a9516280e9044d885 4. h(“letmein”) = 0d107d09f5bbe40cade3de5c71e9e9b7 5. h(“p@ssw0rd”) = 0f359740bd1cda994f8b55330c86d845 8

  9. Attack Model 80d561388725fa74f2d03cd16e1d687c 1. h(“123456”) = e10adc3949ba59abbe56e057f20f883e 2. h(“password”) = 5f4dcc3b5aa765d61d8327deb882cf99 3. h(“monkey”) = d0763edaa9d9bd2a9516280e9044d885 4. h(“letmein”) = 0d107d09f5bbe40cade3de5c71e9e9b7 5. h(“p@ssw0rd”) = 0f359740bd1cda994f8b55330c86d845 6. h(“Chic4go”) = 80d561388725fa74f2d03cd16e1d687c 9

  10. Chic4go 10

  11. Chic4go Guess # 6 11

  12. Chic4go Guess # 6 Guess # 13,545,239,432 12

  13. 13

  14. Password-Cracking Methods Probabilistic Models Software Tools 14

  15. Password-Cracking Methods Probabilistic Models Software Tools Guess # Chic4go 15

  16. Password-Cracking Methods Probabilistic Models Software Tools Guess # Chic4go 16

  17. Guess Number by Enumeration 1. 123456 2. password 3. monkey Does Not Scale !!! 4. letmein 5. p@ssw0rd 6. Chic4go 17

  18. Our Analysis Goals 1. Compute guess numbers efficiently 2. Configure guessing method systematically 18

  19. Outline ● State of the art ● How software password-cracking tools work ● Our efficient techniques for guess numbers ● Our techniques for systematic configuration 19

  20. Probabilistic Models Markov Models [Narayanan and Shmatikov, CCS 2005] Probabilistic Context-Free Grammars [Weir et al., S&P 2009] Neural Networks [Melicher et al., Usenix Security 2016] Guess # Configuration 20

  21. Probabilistic Models Markov Models [Narayanan and Shmatikov, CCS 2005] Probabilistic Context-Free Grammars [Weir et al., S&P 2009] Neural Networks [Melicher et al., Usenix Security 2016] Guess # [CCS 2015] Configuration 21

  22. Probabilistic Models Markov Models [Narayanan and Shmatikov, CCS 2005] Probabilistic Context-Free Grammars [Weir et al., S&P 2009] Neural Networks [Melicher et al., Usenix Security 2016] Guess # [CCS 2015] Configuration 22

  23. Probabilistic Models Markov Models [Narayanan and Shmatikov, CCS 2005] Probabilistic Context-Free Grammars [Weir et al., S&P 2009] Neural Networks [Melicher et al., Usenix Security 2016] Guess # [CCS 2015] Configuration 23

  24. Probabilistic Models Markov Models [Narayanan and Shmatikov, CCS 2005] Probabilistic Context-Free Grammars [Weir et al., S&P 2009] Neural Networks [Melicher et al., Usenix Security 2016] Guess-Efficient 24

  25. Probabilistic Models Markov Models [Narayanan and Shmatikov, CCS 2005] Probabilistic Context-Free Grammars [Weir et al., S&P 2009] Neural Networks [Melicher et al., Usenix Security 2016] Guess-Efficient Wall-Clock Time Slow 25

  26. Software Tools John the Ripper Hashcat 26

  27. Software Tools chicdog chicagos chicago1 CHICAG chicago2 chicaga chicago chicago3 Chicago chicago6 CHICAGO chicago9 CHIcago 27

  28. Software Tools John the Ripper Hashcat Guess-Inefficient Wall-Clock Time Fast 28

  29. Software Tools John the Ripper Hashcat Guess-Inefficient Wall-Clock Time Fast 29

  30. Software Tools John the Ripper Hashcat Guess # [S&P 2019] Configuration 30

  31. Outline ● State of the art ● How software password-cracking tools work ● Our efficient techniques for guess numbers ● Our techniques for systematic configuration 31

  32. Mangled Wordlist Attack 32

  33. Mangled Wordlist Attack Wordlist Super Password Chicago 33

  34. Mangled Wordlist Attack Wordlist Rulelist Super 1. Append “1” Password 2. Replace “a” → “4” Chicago 3. Lowercase all 34

  35. Mangled Wordlist Attack Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Chicago 3. Lowercase all 35

  36. Mangled Wordlist Attack Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all 36

  37. Mangled Wordlist Attack Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all Chicago1 37

  38. Mangled Wordlist Attack Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all Chicago1 Super P4ssword Chic4go 38

  39. Mangled Wordlist Attack Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all Chicago1 Super P4ssword Chic4go super password chicago 39

  40. Example Wordlists and Rulelists Wordlist PGS ( ≈ 20,000,000) Linkedin ( ≈ 60,000,000) HIBP ( ≈ 500,000,000) 40

  41. Example Wordlists and Rulelists Wordlist Rulelist PGS ( ≈ 20,000,000) Korelogic ( ≈ 5,000) Linkedin ( ≈ 60,000,000) Megatron ( ≈ 15,000) HIBP ( ≈ 500,000,000) Generated2 ( ≈ 65,000) 41

  42. Example Wordlists and Rulelists Wordlist Rulelist 10 9 - 10 15 PGS ( ≈ 20,000,000) Korelogic ( ≈ 5,000) guesses Linkedin ( ≈ 60,000,000) Megatron ( ≈ 15,000) HIBP ( ≈ 500,000,000) Generated2 ( ≈ 65,000) 42

  43. Example Wordlists and Rulelists Wordlist Rulelist 10 9 - 10 15 PGS ( ≈ 20,000,000) Korelogic ( ≈ 5,000) guesses Linkedin ( ≈ 60,000,000) Megatron ( ≈ 15,000) HIBP ( ≈ 500,000,000) Generated2 ( ≈ 65,000) + Hackers’ private word/rule lists 43

  44. Outline ● State of the art ● How software password-cracking tools work ● Our efficient techniques for guess numbers ● Our techniques for systematic configuration 44

  45. Is This Password in the Guesses? Guesses Super1 Password1 Chic4go Chicago1 Super P4ssword Chic4go super password chicago 45

  46. Is This Password in the Guesses? Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all Chicago1 Super P4ssword Chic4go super password chicago 46

  47. Insight We can work backwards! 47

  48. Insight: Invert Rules Password Chic4go 48

  49. Insight: Invert Rules Rulelist Password 1. Append “1” Chic4go 2. Replace “a” → “4” 3. Lowercase all 49

  50. Insight: Invert Rules Rulelist Password 1. Append “1” Chic4go 2. Replace “a” → “4” 3. Lowercase all 50

  51. Insight: Invert Rules Preimages Rulelist Password Chicago 1. Append “1” Chic4go 2. Replace “a” → “4” Chic4go 3. Lowercase all 51

  52. 52

  53. *05 O03 d '7 Switch the first and the sixth char; Delete the first three chars; Duplicate the whole word; Truncate the word to length 7; Preimages? Preimages? Chic4go 53

  54. Where in the Stream? Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all Chicago1 Super P4ssword Chic4go 54

  55. Where in the Stream? Wordlist Rulelist Guesses Super 1. Append “1” Super1 Password 2. Replace “a” → “4” Password1 Chicago 3. Lowercase all Chicago1 Super P4ssword Chic4go 55

  56. Counting Guesses For Each Rule Wordlist Rule Guesses Reject if no “a”; Super 2 Password Replace a → 4 Chicago 56

  57. Our First Contribution ● Fast Guess Number Estimation 57

  58. Fast Guess Number Estimation Linkedin + SpiderLab 58

  59. Fast Guess Number Estimation Linkedin + SpiderLab Guesses 59

  60. Fast Guess Number Estimation Linkedin + SpiderLab Guesses Enumeration Our Approach Size ~ 3 PB ~ 10 GB 60

  61. Fast Guess Number Estimation Linkedin + SpiderLab Guesses Enumeration Our Approach Size ~ 3 PB ~ 10 GB Preprocessing > 2 years < 1 day 61

  62. Fast Guess Number Estimation Linkedin + SpiderLab Guesses Enumeration Our Approach Size ~ 3 PB ~ 10 GB Preprocessing > 2 years < 1 day Mean Lookup ??? < 1 second 62

  63. Outline ● State of the art ● How software password-cracking tools work ● Our efficient techniques for guess numbers ● Our techniques for systematic configuration 63

  64. Software Tools Depend On ● Order of rules ● Contents of the rulelist ● Order of words ● Contents of the wordlist 64

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