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Cool.

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Thank you very much.

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And I really appreciate being here, and I really appreciate your time.

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So I'm more than happy to answer any of your questions.

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I'm looking mostly at the Discord channel right now.

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My beard is awesome, and I'm not on a boat, but I wish I was.

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But just kind of... I didn't plan this out here, but you gotta respect the beard here.

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So on more serious questions here, I guess, what can I do to help answer?

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And so Markov, it really has very little math actually in it, too.

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It's just multiplication.

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So if you can do multiplication, there's not even any division.

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So you just look at the probability of one letter falling after another letter, falling after another letter there.

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And the more letters you have, the higher probability it usually is.

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Omin gets a little bit more complicated because they have this idea behind it called levels.

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But then you do it with some multiplication, and you just do addition instead.

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So the final level, the password, is summing all the levels across it.

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And the reason why you want to do that is because that's much faster than trying to do a more traditional Markov attack like you would see in John Ripper Markov mode.

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But there's a million different ways that you can do Markov.

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So I could talk to you all day about that if we want to just go ahead and get into that.

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Hashcat needs to update its Markov ability, though, because it is probably, of all the algorithms, the worst.

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But Hashcat's really fast, so it can get away with that.

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And I know you're just trying to be cheeky about that, but here's a... just trying to give you an answer to that, because I really enjoy it.

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It would be nice... the challenge is that the guest generation port could probably be extracted out, but figuring out what kind of rule to use is pretty much single-threaded, unfortunately.

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And that's really what takes all the time, is just trying to figure out which of the rules is currently the most probable in order to run.

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And that's why it takes so much memory, because it's basically building a probability queue of all different probabilities.

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It's going through... it has a kind of a tree type of a format there.

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So, long story short, I wish I could figure out how to do that.

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But where you're probably better off doing is, instead of trying to generate all the guesses in probability order, you could actually just go ahead and generate guesses behind a different probability threshold.

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So, all these guesses that are higher probability than this level here.

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And in that case, you could absolutely go ahead and multi-thread it for GPU.

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So, I've actually talked to some people about potentially getting this in a way so that you're not doing probability order, but you set that threshold, and you could actually run this in the GPU.

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And it's the fact that GPUs have more memory now, means you can start putting the base grammar inside GPU as well to help speed that up.

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So, I'm sorry, I should ask, answer, respond to the question here.

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So, the question was, could I run multiple PCFGs at once on multiple different GPUs?

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So, the short answer is, theoretically, yes.

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But I could definitely use some help in actually coding that up with somebody who's smarter than I am.

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So, does anyone else have any questions here?

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Or I could just keep on rambling here, too.

39
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So, one nice thing is being able to run this against a password list that you're currently cracking, too.

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Because it does do a pretty good job of stemming that and creating some really interesting input dictionaries for you to be able to use.

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So, if you're kind of going ahead and doing kind of like a fingerprint attack, just kind of run it around again there, it can actually extract some really useful bits for that to keep on launching against people.

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So, since I got like nine minutes left here, unless someone else asked a question.

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Oh, ramble away.

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Okay, sounds good here.

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So, the question is, or I guess what I could start talking about is where password crack PCFGs are kind of going in the future.

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So, one of my big goals is I really would like to be able to get incorporated into Hashcat.

47
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I started, you know, brainstorming that quite a while ago.

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That's part of the reason also why it uses the compiled C version I wrote it in the first place was because Hashcat, for some reason, does not actually run in Python because it's fast, I guess.

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So, that is, you know, ultimately kind of the end goal for that effort there is to actually incorporate it into Hashcat as a new cracking mode.

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The initial way I'm going to start looking at is actually having it run as a, Adam calls it a slow guesser mode, which pretty much defines this exactly.

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So, I want to be able to incorporate that as part of the Hashcat slow guessing modes that you can add in.

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Eventually, though, I would like to be able to start going ahead and seeing whether I can go ahead and paralyze the way it generates guesses and put into the GPU to make it much, much faster.

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So, there's other options too that potentially you could basically do... what really slows it down is generating all those rules.

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So, in an older version, I had actually had the ability to go ahead and pre-compute all those rules and be able to save those to disks.

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So, you could actually then completely paralyze it if you wanted to as well because there's no rule generation.

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So, that's another thing I could take a look back at there.

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The problem is that the rules are really fine grained.

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So, I almost want to go ahead and, in order to reduce the size of the rules on the disk, is to make the rules much kind of fatter, essentially.

59
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So, each rule would correspond to more password guesses.

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But that's definitely an option that I'm kind of looking at as well, is to make this more feasible to be able to be used.

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So, is there such a thing as a random PCFG rule?

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Are they human-readable or, well, geek-readable?

63
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So, the short answer is yes.

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So, if you hit this kind of status output of when you're running a PCFG, a cracking session, you'll kind of print out the text screen of what the current rule set looks like.

65
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And it looks a lot like a hashtag mask, actually.

66
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So, it just has a few extra different features onto it there.

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So, you'll see something like the... I use A for alpha string, but A5, so you know it's generating a five -letter word.

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And it'll have a number next to it saying what the probability , or ranking, I should say, of that five-letter word is.

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So, it'll say the 153rd most probable word, or number one.

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So, it'd be the first most probable word.

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So, that'd be password.

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So , in that case, you can kind of read it, and then you can see what the capitalization mode is.

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So, it'll say the most common capitalization mode for this here.

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And it'll say D2, and it'll be a number after that, too.

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It's the third most common two-letter, or two-digit number.

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So, you can kind of look at that and see what that rule is.

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So, because the way that it happens there, you could absolutely go ahead and create a random rule for that.

78
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Or you can just kind of look at it and read what it's actually doing.

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And so, you can go back to the grammar and say, okay, what is actually the 143rd most probable word?

80
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And it's, okay, so that's the word that it's using.

81
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And if the words have the same probability, they'll use just all of them there.

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So, there might be multiple different words that you only saw in the train set, let's say, twice.

83
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So, they'd all be used to the same probability in that rule.

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You can also create, when talking about random passwords, some work I did in the past was creating what's called honey words.

85
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So, these are passwords that look like human passwords.

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And PCFGs are really good for that.

87
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So, instead of trying to create the most probable password, you do a random walk through the grammar.

88
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And that way, you know, 1, 2, 3, 4, 5, 6 is still going to be very common.

89
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You're going to create that much more often than normal.

90
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But you also create some kind of random looking passwords too.

91
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And where this is really useful, for example, is if you're trying to set out a honeypot server, and you want to do like a honeypot Active Directory controller, and you don't want to go ahead and manually create a thousand different passwords for different fake users.

92
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So, that way, you can go ahead and run that.

93
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And you create passwords that, you know, if you glance at it, it looks somewhat real.

94
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You're still seeing like the really common passwords.

95
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You might see the company names, you know, the most common there.

96
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But you're still seeing some random other stuff as well.

97
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So, that's definitely some work.

98
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And improving upon that is I'd like to be able to create one that or a modification that where it has a biased random walk.

99
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So, that way that you can create passwords that look like they all came from the same user, but once again are, you know, somewhat different.

100
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So, that's definitely an area that's kind of fun to play around with.

101
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What have you seen in terms of variance between, say, company one and company two?

102
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So, that's just a question that was asked.

103
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And a short answer is I really haven't seen that.

104
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And by that, I mean, I'm a researcher.

105
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This is a hobby for me.

106
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So, almost every single dump that I've worked with has been, I would say, the public dumps.

107
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And while there definitely are company passwords that, you know, get pushed out there, especially if you look for, like, NTLM.

108
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But I'm a researcher.

109
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So, you'd actually have to ask, you know, someone like, you know, CoreLogic about that there.

110
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So, I'm not a really good person to answer.

111
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And that's why I'm always really interested in hearing these talks and talking to people who are actually doing this professionally.

112
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Because they're able to provide me that information there.

113
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But pretty much all I have from that is secondhand.

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So, we're getting to the end here.

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It looks like there's just a couple more minutes.

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So, if anyone has any questions, feel free to ask them.

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Otherwise, I really do appreciate you coming in and, you know, viewing this talk.

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As I said, I'm available on Twitter, at L-A-K-I-W.

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And if you really want to get in touch with me, the best way to do it is submit an issue to the GitLab repo.

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That's probably the best way.

121
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Because I pretty much obsessively track that there.

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And I'll definitely try to get back to you.

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But this is something that I find is really fun.

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And I really do appreciate you taking an interest in this.

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Cool.

126
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Yep.

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And I'll be on Discord for the rest of the weekend, too.

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So, if anyone has any questions, feel free to pop in there, too.