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by dbaupp
1620 days ago
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Despite that slight misuse of terminology, the point stands: the article talks about estimating the entropy of a distribution used for generating a password, but the important thing is the “distribution” an attacker is using for guessing the password. A single password should instead be treated as a sample from a (plausible) attacker’s distribution, and the complexity of that password can be used to estimate the size of the sample space required for that plausible attacker (as in, how many guesses/how much work they’ll have to do). This is, AIUI, the approached used by libraries like https://zxcvbn-ts.github.io/zxcvbn/ The entropy of a distribution for generating passwords matters when generating them in bulk, such as OTPs or implementing a password manager. This doesn’t seem to be the situation being discussed in the article, which is more about rating a user-provided password. |
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Of these two approaches, a high-entropy generation method gives more confidence. It gives a mathematical strength "guarantee": if you design and follow the method correctly, then an attacker, whether or not they know the generation method, is mathematically unlikely to guess your password quickly no matter what order they guess in. "Guarantee" is in quotes because of course the attacker could get very lucky or the user could get unlucky (eg generate a uniformly random 8-character string and it happens to be "password"), and also if there's eg an implementation flaw then your guarantee isn't worth the pixels it's printed on.
By contrast, zxcvbn has no guarantee, because it doesn't use a huge curated dictionary and generation mechanism that the attacker is likely to use. So in addition to missing well-known passwords like "correct horse battery staple", it will miss bad passwords related to current events.