Password Security and Hash Slippage
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Password Security and Hash Slippage
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The massive losses of password hashes at LinkedIn [1], eHarmony [2] and Last.fm [3] are very concerning, to say the least. These are companies that are generally perceived as technology leaders, particularly LinkedIn. Also, as far as I now, eHarmony and LinkedIn are Java/JVM shops. Just some data that I gathered today regarding the scope of the issue:
- Last.fm - presumably up to 17 million lost hashes - Algorithm used: MD5 - Hashes were Not salted
- eHarmony - 1.5 million hashes - MD5 - No salted - All upper-case-passwords
- LinkedIn - 6.5 million hashes - SHA1 - Not salted
- http://www.nytimes.com/2012/06/11/technology/linkedin-breach-exposes-light-security-even-at-data-companies.html
- http://translate.google.com/translate?sl=de&tl=en&js=n&prev=_t&hl=en&ie=UTF-8&layout=2&eotf=1&u=http%3A%2F%2Fwww.heise.de%2Fsecurity%2Fmeldung%2FPasswort-Lecks-groesser-als-angenommen-1613946.html
- http://www.technolog.msnbc.msn.com/technology/technolog/linkedin-eharmony-dont-take-your-security-seriously-819858
- http://erratasec.blogspot.de/2012/06/linkedin-vs-password-cracking.html
What is quite amazing to me, is that the basic measures that would prevent the cracking of the hashes, like better hash algorithms, salting, re-hashing are not rocket science. There is even a very nice library [4] out there that does it for you and it even hooks into e.g. Spring Security [5] - Not even Java coding is necessary.
I just wonder how the hackers got access to the hashes in the first place...I could not find any information on that, yet. Maybe another juicy story...
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