WEBVTT 00:01.370 --> 00:02.880 So real quick about me. 00:02.880 --> 00:15.380 I have a bit of a reputation as kind of approaching this as an academic frameset, simply because I got started in the whole password cracking thing through my research when I was getting my PhD. 00:16.180 --> 00:20.300 But I really do strongly believe in learning by doing. 00:20.300 --> 00:28.780 So I'm an active member in Team John Ripper, and I do participate in password cracking contests like, you know, Crack Me If You Can, that's going on right now. 00:28.800 --> 00:33.880 Luckily, this talk is, you know, being filmed, you know, before the contest starts. 00:33.880 --> 00:35.680 So no spoilers here, unfortunately. 00:35.800 --> 00:38.480 But good luck to everyone else who's participating. 00:38.920 --> 00:44.840 So password cracking, really, it's my hobby and a little bit of an obsession. 00:44.840 --> 00:46.920 But it's not my day job, unfortunately. 00:47.120 --> 00:54.180 But my day job has been very exciting recently, though, because I really focus on medical device security. 00:54.500 --> 01:01.580 And you can imagine with all the craziness around COVID-19, it's been an interesting time. 01:01.980 --> 01:09.580 So one project that I really kind of want to highlight is the Open Ventilator Monitoring and Alerting Project that I've been helping to contribute to. 01:09.780 --> 01:19.060 And there's actually a talk at the Biohacking Village this Sunday that I really highly recommend people go ahead and listen to, so that our team members are giving it. 01:19.460 --> 01:26.260 And it really is going to talk to you about, you know, the lessons learned and how other people can help contribute as well. 01:26.340 --> 01:35.100 Because this has been a big problem because, as I'm sure you're aware, there's been a huge demand for ventilators to be able to help deal with COVID-19. 01:35.380 --> 01:45.560 So there's been a lot of different projects that have kind of stood up to try to help produce, you know, low-cost ventilators to help fill that need pretty quickly there. 01:46.060 --> 01:58.200 So rather than have every single do-it-yourself ventilator develop their kind of own monitoring and alerting framework, we're trying to produce one common one that can be applied to all these different projects across the board. 01:58.260 --> 02:08.940 So because when you have these ventilators being able to treating, you know, patients, the patients are highly infectious, so you don't want to have the nurses exposed to that. 02:08.960 --> 02:11.480 But if something goes wrong, you need, you know, seconds count. 02:11.480 --> 02:17.920 So you need to be able to forward all that sensing information that these ventilators are doing back to a centralized nursing workstation. 02:18.040 --> 02:21.500 And you need to do that securely because you're not just on a real hospital network. 02:21.640 --> 02:28.340 So that's been a really fulfilling project that I've been working on. 02:28.640 --> 02:37.100 So another thing I'm kind of helping out with here, as I move my head, is I'm helping out run the Defcon Biohacking Village's Capture the Flag contest. 02:37.320 --> 02:39.220 So this was originally supposed to be in Vegas. 02:39.280 --> 02:40.700 That changed, of course. 02:40.700 --> 02:43.340 So now a lot of medical equipment is actually sitting in my house. 02:43.340 --> 02:52.600 So I have to be able to provide a way for hackers from all over the world to be able to log in and hack these infusion pumps here without also hacking my smart thermostat. 02:53.120 --> 03:05.740 As part of that, I actually had to repurpose one of my password cracking rigs, as you can see there, in order to run all the VMs that are helping to keep people, you know, on those ventilators and hacking those and not, you know, hacking my smart thermostat. 03:09.540 --> 03:31.580 So probably the first questions I should start kind of addressing here is, you know, what does that PCFG stand for in, so originally, and I guess still technically, it stands for Probabilistic Context-Free Grammar, which is the kind of the modeling framework it uses in order to model how people create passwords. 03:31.740 --> 03:37.300 So if you're into, you know, the Siri Autonoma or, you know, formal languages, this might actually mean something to you. 03:37.300 --> 03:42.100 But for most people, you know, they hear that and they're like, oh god, that's like mass and stuff like that. 03:42.100 --> 03:43.860 I mean, there's no way it's going to run on my computer. 03:44.420 --> 03:46.360 And then he's kind of like slowly walk away. 03:47.100 --> 03:51.360 So I decided in order, I need it need to have a more descriptive name. 03:52.040 --> 03:55.160 So I went ahead and rebranded it the Pretty Cool Fuzzy Guesser. 03:55.840 --> 04:02.600 So, and this kind of explains it a little bit better about what it's actually doing underneath the hood, because you train this on a list of passwords. 04:02.900 --> 04:11.900 And then it'll go ahead and create guesses that are similar to those passwords, but different, which is really kind of important in order to help, you know, expand your cracking session. 04:12.640 --> 04:14.520 So I don't this is my favorite slide I've ever made. 04:14.520 --> 04:16.440 So it's all downhill from here. 04:18.380 --> 04:23.940 So really kind of what it's doing is it's using machine learning in order to crack passwords. 04:23.940 --> 04:28.440 And when I say machine learning, I mean, in the traditional sense of a whole bunch of if then statements. 04:28.440 --> 04:31.400 So it's not using neural networks or artificial intelligence. 04:31.560 --> 04:37.320 But you are training on passwords that you expect to be somewhat similar to the target passwords that you're cracking. 04:37.740 --> 04:46.380 And when it processes that training password set, it extracts all sorts of probability information about the components of those passwords that it finds there. 04:46.380 --> 04:58.320 So it figures out things like, you know, capitalization masks, whether numbers go at the beginning of the password versus the end, the probability of individual letters and numbers found in that password, keyboard walks, and so on. 04:58.320 --> 05:02.860 And so it goes ahead and creates a model based upon all those different types of probability information there. 05:02.860 --> 05:07.880 And then it uses those in order to generate very highly probable password guesses in probability order. 05:07.960 --> 05:16.520 So it'll start with the most probable password guess and go to the second most probable password guess and then go to the third one and so on until you crack the password that you're trying to find or you give up. 05:19.910 --> 05:23.150 So let me just move my thing here a little bit here. 05:25.050 --> 05:32.330 So just to kind of tie us back into probably what's going on right now, as I said, I don't know what the actual contest is going to be like. 05:32.330 --> 05:33.670 Forward or crack me if you can. 05:33.990 --> 05:40.450 But CoreLogic helpfully provided a brief summary of, you know, what the scenario is going to be at least here. 05:40.730 --> 05:43.870 And so we're going to be targeting 12 different individuals. 05:43.970 --> 05:49.710 And those individuals change their passwords over time in order to be able to deal with more complex password creation requirements. 05:50.010 --> 05:54.070 And that sounds a little bit something like something that, you know, a PCFG might actually be useful for. 05:54.070 --> 05:56.550 So I'm really optimistic for this contest. 05:56.770 --> 06:02.070 You'll see how, you know, optimistic I am on Saturday when I'm actually, you know, we're giving this talk. 06:02.610 --> 06:12.310 But, you know, this is kind of the scenario that this was originally developed for in the first place of, you know, you know how a subject creates passwords. 06:12.590 --> 06:15.070 So you want to create passwords kind of similar to that. 06:15.070 --> 06:23.950 But you also want to go ahead and change them and maybe, for example, you use more complex rules or, you know, complex password creation requirements added on top of that there. 06:24.390 --> 06:27.150 So I'm available probably on Discord right now. 06:27.830 --> 06:33.430 And so I'll be able to answer questions about how potentially you might be able to tweak this in order to help in a scenario like this here. 06:36.940 --> 06:42.800 The fact that there's a lot of academic papers about this, though, is when I give a talk like this, I don't actually have to create any of my own graphs. 06:42.800 --> 06:49.020 I just can go to other papers there, you know, look at the research that other people have done, and just pull out their graphs in order to be able to talk with it here. 06:50.480 --> 07:00.340 So one thing that I really kind of want to highlight, though, and you need to kind of look at this with a bit of a skeptical eye, is that you'll notice that all these cracking sessions are really, you know, short. 07:00.600 --> 07:04.800 I know, you know, one, you know, trillion guesses here, that might sound like a lot. 07:04.980 --> 07:10.180 But when you start talking about, you know, GPU password cracking, you're talking about like under a second in order to generate all those. 07:10.420 --> 07:12.980 So that's just, you know, no time whatsoever. 07:13.400 --> 07:21.780 And part of reason for this here is that the PCFG approach, it's very slow, it doesn't, you know, scale very well with multi-threading currently. 07:22.740 --> 07:35.520 So when you start talking about, you know, passwords that you want to be able to crack, it works very well when you're going ahead targeting very slow password hashes, where you can only make, you know, you know, thousands of guesses a second, because the hash is very slow. 07:35.520 --> 07:44.160 But we start talking about things like, you know, unsalted MD5, other attacks that are going to be much more effective, because you can just make so many more guesses in the same time frame there. 07:44.640 --> 07:52.160 So when you start talking, looking at, you know, faster password hashes, you can certainly go ahead and still use a PCFG to supplement your attack. 07:52.460 --> 07:56.900 And you can still go ahead and crack some passwords that you might not normally get. 07:57.160 --> 08:06.740 But in general, for the faster password hashes, you really are going to want to go ahead and use more traditional types of password cracking attacks in order to really make use of the hardware that you have available to you. 08:09.940 --> 08:18.820 So I want to talk about this graph kind of, though, and really focus on it, because this was a really neat study done by Carnegie Mellon University. 08:18.820 --> 08:31.200 And one of the problems when you look do academic research, especially when you start talking about offensive tactics, is that the academics are, you know, running attacks themselves. 08:31.200 --> 08:38.480 So you're looking at the, you know, the how effective students are at cracking passwords versus someone professional, potentially. 08:39.220 --> 08:47.640 So CMU, you know, took the probably the, you know, the most straightforward approach to be able to solve that problem was they went out and reached out to CoreLogic. 08:47.640 --> 08:48.540 You might have heard of them. 08:48.540 --> 08:50.280 They're running this, you know, password village. 08:50.280 --> 08:52.000 They run the Crack Me If You Can competition. 08:52.460 --> 08:56.260 So when you're trying to find an expert, you know, they're like, you know, way up there. 08:56.720 --> 08:59.060 So they're a pretty good representation for that there. 08:59.660 --> 09:04.200 So what they did was they gave, you know, one of the CoreLogic engineers a password list. 09:04.600 --> 09:06.160 They asked them to crack it. 09:06.220 --> 09:10.300 And they recorded, you know, how many passwords they cracked over time. 09:10.300 --> 09:11.540 It was the number of guesses they made. 09:11.540 --> 09:14.060 And then they compared it against other cracking sessions as well. 09:14.920 --> 09:27.640 And, you know, one thing I'm really, you know, it makes me smile every time I see this here is that the PCFG did really well compared to the pros, which was CoreLogic, for that short cracking session. 09:28.140 --> 09:40.740 So when you start asking, like, you know, can this represent, you know, how a real professional password cracker operates, the short answer is it certainly, you know, appears to be able to be able to do that there. 09:41.120 --> 09:48.780 So, you know, full disclaimer, when you gave, you know, CoreLogic more time, they definitely performed way better. 09:48.840 --> 09:52.020 This is a logarithmic graph, so that's about 100 times more guesses. 09:52.700 --> 10:02.480 And also, I'll admit this wasn't fair to CoreLogic either, because, you know, that's not typically how, you know, people crack passwords in real life there. 10:02.480 --> 10:05.880 It was such a short, you know, cracking session. 10:06.540 --> 10:14.580 And when it is that short, usually it's against a really strong password and you have a lot of time in order to really manually tweak your attack that you're running there. 10:15.040 --> 10:29.480 That being said, if any of you are listening, I would love to have a repeat or a rematch of this, you know, attack just to see, you know, how this performs with all the new improvements that have been made into PCFGs. 10:29.480 --> 10:33.440 And I'm sure that, you know, CoreLogic has really been upping their game over the years as well. 10:39.160 --> 10:44.600 So that's why I'm somewhat hopeful that we'll be able to find it, now. 10:47.010 --> 10:50.250 So enough about, you know, all the research side of that there. 10:50.270 --> 10:54.110 Let's talk about how to actually make use of this PCFG password cracker. 10:54.170 --> 10:58.870 So the first thing is you just go ahead and download it from the GitHub repo. 10:59.410 --> 11:03.430 And the requirements of it, I really have strived to make it as simple as possible. 11:03.610 --> 11:06.570 So you need to have Python 3, and that's it. 11:07.290 --> 11:12.830 So there's an optional Karadet Python module that can help during the training. 11:13.750 --> 11:21.330 And that's because it helps detect what character encoding the training set is, because character encoding is the bane of my password cracking existence. 11:21.450 --> 11:22.670 But even that's optional. 11:22.670 --> 11:26.250 And actually, it's now being installed as part of PIP 3. 11:26.390 --> 11:30.570 So if you have PIP, you probably don't even need to install it yourself as well. 11:30.570 --> 11:36.890 And this is really useful, though, because I find a lot of situations where, like, when I'm cracking passwords, like, I don't have, like, internet access. 11:37.050 --> 11:41.710 So it's really nice to be able to go ahead and quickly throw my tool on a box and get it to run. 11:41.710 --> 11:44.670 So if you can run Python 3 on a box, you can probably run this here. 11:44.670 --> 11:46.830 So I've tried it on a bunch of different OSs. 11:47.090 --> 11:49.390 I've actually even gotten it to run on NetBSD. 11:49.590 --> 11:52.470 And it was the only thing I've ever gotten to be able to run NetBSD. 11:53.310 --> 12:01.230 So hopefully this is easier than your typical academics tool set in order to get it installed and start cracking passwords with as quickly as you can. 12:02.710 --> 12:09.170 So we start talking about hardware requirements, because that's always an important portion when we start talking about password cracking. 12:09.710 --> 12:16.390 The PCFG tool set, it is single-threaded CPU bound, which is why it's so awfully slow. 12:16.610 --> 12:19.310 But it will use an entire CPU thread. 12:19.310 --> 12:25.470 So you do really need to dedicate one full CPU, you know, thread to the PCFG. 12:26.170 --> 12:28.610 The other thing is it has very high RAM usage. 12:29.010 --> 12:32.670 It basically maintains a lot of different data structures and memory. 12:32.770 --> 12:35.310 And those data structures become more complex over time. 12:35.310 --> 12:36.650 So it just grows. 12:36.790 --> 12:42.170 So I could have done some things, tried to go and prune that or, you know, move some of it to disk. 12:42.250 --> 12:43.730 But RAM is cheap. 12:43.910 --> 12:45.590 So I haven't. 12:45.630 --> 12:47.230 So it'll just keep on growing over time. 12:47.230 --> 12:50.570 So initially, it starts out pretty low usage. 12:50.650 --> 13:00.810 But if you're talking about running this, you know, password cracking session for like a week or two, you really need to have at least 16 gigabits of RAM to really kind of just fully dedicate to the PCFG tool set itself. 13:03.900 --> 13:08.300 So the next step is to actually make use of it and run it. 13:08.800 --> 13:14.200 So I apologize up front that I tend to use the words rule set and grammar interchangeably. 13:14.200 --> 13:16.740 At least to me, they mean the exact same thing. 13:16.840 --> 13:26.820 But really what I'm talking about is that, you know, I mentioned, you know, machine learning a couple of times here, you have to go ahead and train a grammar on existing password data set. 13:27.280 --> 13:32.800 Now, you may want to have, you know, you know, different grammars for different targets that you're trying to target. 13:32.800 --> 13:39.380 So if you're trying to target, you know, a web application catering to younger people, you might want to train it on passwords that resemble that. 13:39.380 --> 13:48.460 If you're trying to target, you know, corporate passwords, you might want to train on corporate passwords instead, and use those rule sets against target passwords that you're specifically you think will match that. 13:48.460 --> 13:54.180 So you can have as many rule sets as you want to be able to really kind of fine tune your cracking session there. 13:54.520 --> 14:04.800 So the default one rule set that comes with the PCFG password cracker was actually trained on a subset of 1 million passwords from the Rocky data set, which came out in 2008. 14:04.800 --> 14:06.260 And it was against web passwords. 14:06.260 --> 14:08.700 So there wasn't really any strong password requirement whatsoever. 14:08.780 --> 14:10.680 I've been thinking about updating that. 14:10.680 --> 14:19.440 So if you have a good data set that you think that I should use for that there, I'm open to hearing about that there to make it a little bit more effective. 14:19.540 --> 14:24.300 But that being said, the Rocky data set is still extremely effective even to this day. 14:24.940 --> 14:27.740 It's just, you know, Blink-182 is not nearly as popular. 14:28.640 --> 14:34.960 So after you choose the data set that you want to use, though, now you go ahead and start generating guesses. 14:34.960 --> 14:36.240 So it's a Python program. 14:36.240 --> 14:37.900 So you just run, you know, Python 3. 14:37.940 --> 14:44.780 You run the PCFG guesser.py tool from the repo. 14:45.420 --> 14:46.740 You get it in the ender rule set. 14:46.740 --> 14:47.800 By default, it's default. 14:47.840 --> 14:52.660 So if you don't go ahead and specify that there, it'll go ahead and use the Rocky data set. 14:52.940 --> 14:55.360 And then you go ahead and specify session name as well. 14:55.580 --> 14:57.640 By default, this is default as well. 14:58.040 --> 15:01.760 And so the session is used to restart a password cracking session. 15:02.020 --> 15:06.080 So if you have to cancel it for whatever reason, you can go ahead and restart it back up again. 15:09.720 --> 15:16.380 So I really want to kind of highlight, though, that the PCFG tool set is only a password guess generator tool set. 15:16.380 --> 15:17.820 It will generate password guesses. 15:17.820 --> 15:19.740 It'll generate those password guesses in probability order. 15:19.740 --> 15:24.060 So it'll start with the most probable password guess, second most probable password guess, and keep on going down the line. 15:24.060 --> 15:27.320 It will not actually hash and crack any passwords. 15:27.480 --> 15:30.740 So you need to use another password cracking tool set for that there. 15:31.260 --> 15:38.280 You know, both JohnDeRipper and Hashcat work are basically any other password cracking tool that accepts, you know, guesses in from, you know, the standard input there. 15:38.500 --> 15:41.040 As I mentioned earlier, I'm on team JohnDeRipper. 15:41.040 --> 15:44.960 So I'm going to go ahead and use JohnDeRipper for pretty much all my examples here. 15:44.960 --> 15:46.940 But you can totally use Hashcat as well. 15:47.780 --> 15:52.260 So in order to do this here, you run the, you know, the previous command that, you know, I talked about. 15:53.220 --> 15:56.320 And then you run, you know, pipe it into, for example, John. 15:56.320 --> 16:00.840 And on John, they have, you know, a command called standard in, so that you type that in there. 16:00.840 --> 16:08.820 And instead of running data from like a word list, or generate your password guesses, it'll go ahead and use the password guesses that are piped into it instead. 16:09.780 --> 16:10.860 And you're cracking passwords. 16:10.860 --> 16:12.900 That's really all there is to it. 16:13.980 --> 16:17.820 So there's definitely optimizations for actually using this in the real world, though. 16:17.820 --> 16:23.600 So the first thing I really kind of want to highlight is a lot of times you want to know what the status of a cracking session is. 16:24.160 --> 16:34.360 So the challenge when you are using the pipe command, though, is if you go ahead and hit the enter button on your keyboard, instead of sending the enter button to JohnDeRipper, it's going to go ahead and forward that to my tool instead. 16:34.720 --> 16:39.160 So you might want to be able to, you know, get JohnDeRipper to output a status report. 16:39.160 --> 16:43.280 The way that you do that is you send a siguser1 signal to JohnDeRipper. 16:43.440 --> 16:50.760 If you're writing this on a Linux system here, you just type in kill-siguser1 and then the process identifier JohnDeRipper. 16:50.760 --> 16:58.500 And when you do that and hit enter, it'll be just like hitting enter on the JohnDeRipper itself and it'll go ahead and output the status output of its current cracking session. 17:00.120 --> 17:07.180 So now you can do things like, okay, not only see the passwords are getting cracked, but you can see like the number of hashes, the total number of hashes are cracked so far. 17:07.320 --> 17:09.500 You can see like, for example, the guessing speed. 17:09.500 --> 17:11.960 So in this case, it's making about 4 million guesses a second. 17:12.160 --> 17:16.160 And then you can see like how long it's been running and, you know, all the other, you know, options as well. 17:19.000 --> 17:31.140 So I want to kind of dig into that one, you know, output of that cracking session though, because I think, you know, this really kind of helps demonstrate kind of some of the power of using the PCFG. 17:31.760 --> 17:35.980 Because normally when you're, this here is showing the passwords as they get cracked. 17:37.500 --> 17:47.000 So you can kind of see that it's not just going ahead and, you know, figuring out one rule and then exhausting that rule and then going to the next rule like you would see in the more traditional password cracking session. 17:47.140 --> 17:53.680 Instead, it's creating much more fine grained rules and iterating between all those depending on what the current probability of it is here. 17:53.740 --> 18:00.720 So when you see these passwords being cracked, it's kind of fun to try to figure out like how did the underlying system, you know, generate that password guess? 18:00.720 --> 18:04.000 Why is it making the guess right now that that it is? 18:05.020 --> 18:08.940 So kind of if you look up, you know, initially here, like this is pretty easy. 18:08.940 --> 18:11.020 Okay, it's just taking like some five letter word. 18:22.900 --> 18:25.140 I apologize, my microphone just died here. 18:25.200 --> 18:28.000 So fun doing, you know, DEF CON remote. 18:29.520 --> 18:34.480 So, you know, it's using five digits, you know, or five letter words plus four digits here. 18:35.980 --> 18:39.240 Moving on, though, this is a kind of an interesting one here is SES is cool. 18:39.320 --> 18:43.980 So I looked into my input dictionary and SES is cool was not in my input dictionary or my train set at all. 18:44.240 --> 18:47.220 And I found out that it was actually you doing multi words for this year. 18:47.220 --> 18:49.640 So it was combining, you know, SES and then it's cool. 18:50.620 --> 19:00.520 So one kind of cool thing about this, and I'll talk to us about this a little bit later, is that instead of, you know, going ahead and breaking this up into three words, like we normally would think about it there, it actually broke it up into two different words. 19:00.520 --> 19:01.540 So SES is cool. 19:01.540 --> 19:06.160 So that way you can go ahead and go through it and say like, is, you know, Katie cool? 19:06.160 --> 19:07.980 Is, you know, Allie cool? 19:07.980 --> 19:09.480 Is Bob cool? 19:09.480 --> 19:11.340 Because there's a lot of cool people out there. 19:11.460 --> 19:18.140 So it can go ahead and iterate through those there and try that type of, you know, mangling rule for it there. 19:18.140 --> 19:27.540 And what's really cool about this is that it learned that is cool is a common word from the train set itself. 19:27.540 --> 19:30.280 So I didn't actually ever program in that logic into it. 19:30.280 --> 19:37.700 It learned it by itself by looking at the training data, which is, as I said, pretty cool. 19:39.660 --> 19:43.320 But you can see after that there, it kind of went into brute force. 19:43.320 --> 19:49.380 It wasn't the pure brute force, and I'll talk about the different types of brute force here, is actually combining some very short words, kind of like a combinator attack. 19:49.520 --> 19:53.420 But still, you know, it's able to kind of get that out that way. 19:54.520 --> 20:01.120 And then it went ahead and tried, you know, words with, you know, special characters, the same special characters at the beginning and the end of them too. 20:01.500 --> 20:07.540 And that's, you might be able to see that in a traditional password cracking session, but you actually have to have a rule in order to generate that. 20:07.540 --> 20:09.720 And trying to create those rules is a real pain. 20:09.720 --> 20:17.420 So you won't see those in, you know, most, you know, common publicly available rule sets, but it was able to learn that from the training data, which was, I thought, pretty cool as well. 20:19.500 --> 20:26.540 So down here, and I'm kind of, you know, need to get off the screen here, but you can see it's trying some longer words. 20:26.540 --> 20:33.120 But these are actually, while they're normal words here, it's actually generated them via the multi-words as well. 20:33.120 --> 20:35.640 So like finger plus nail, or 90 plus 9. 20:35.840 --> 20:45.180 And once again, this is kind of really useful because now you don't have to have things like 99, 98, 93, you know, in your data set, your word list as well, because it's generating those on the fly. 20:46.820 --> 20:50.880 Kind of going down a little bit further here, this is your more traditional kind of rule here. 20:50.880 --> 20:53.620 It's just two digits plus capitalizing the first letter of a word. 20:53.780 --> 20:54.920 So you can see it's starting to do that. 20:54.920 --> 20:58.740 But, you know, settings is a pretty uncommon password, word to be able to use. 20:58.760 --> 21:00.980 So it's trying it later in a cracking session here. 21:01.740 --> 21:03.820 And now it's even combining even more mangling rules. 21:03.820 --> 21:11.220 So it's trying, doing a multi-word, you know, of wood plus fish and, you know, Tara plus Don, and adding digits to the end of that as well. 21:11.220 --> 21:14.000 So you can see how it starts stacking these different rules together. 21:14.860 --> 21:21.260 And I kind of want to highlight that this cracking session has been going on, as you can kind of see from the status output, for about 13 minutes. 21:21.560 --> 21:23.860 So all the really easy passwords have already been cracked. 21:23.860 --> 21:27.580 It's already guessed, you know, 1, 2, 3, 4, 5, 6, and password 1, 2, 3, 4, 5, and so on. 21:28.160 --> 21:35.040 So these are starting to get into more of the, you know, the fuzzier of the rule sets that you might not normally see in a normal password cracking session. 21:38.130 --> 21:45.530 So as I mentioned a little bit earlier, if you hit the enter button, it's going to go to my program, not, you know, John the Ripper. 21:45.970 --> 21:50.790 But there's a lot of information that I want to be able to provide to people about the cracking session. 21:51.410 --> 21:58.510 So whenever you hit enter, or basically any of these R keys here, it'll display an output of what it's currently doing. 21:58.510 --> 22:04.650 So you can try to figure out, you know, whether you want to continue it, whether it's working correctly, and whether it's kind of doing what you want it to do as well. 22:05.330 --> 22:09.470 So kind of going through this here, I hit enter twice. 22:09.510 --> 22:13.590 And so you can kind of see how it's generating, you know, these password guesses as doing it here. 22:14.390 --> 22:19.050 So the first one, you know, it's, you know, basically going ahead and trying to combine two words. 22:19.050 --> 22:20.730 So it's a multi-word type of attack. 22:21.370 --> 22:31.070 And you can, if you dig into like the real details of it there, you can kind of see that it's trying like the hundred and forty-third most probable word with no capitalization. 22:31.310 --> 22:35.910 And it's combining it to the ninety-third most probable four-letter word with no capitalization as well. 22:35.910 --> 22:41.670 So you can see that the probabilities it assigns to like even individual words and stuff like that is very, very fine grained. 22:42.130 --> 22:46.710 So it's going to try some words, and then like do other mangling rules and stuff like that. 22:46.710 --> 22:50.770 And they'll go back to the less probable words later on in the cracking session. 22:53.070 --> 22:57.090 So now this next one here, it's kind of a little bit, I'll try to get out of the way or something like that. 22:57.170 --> 23:04.390 You can see that it switched to a real brute force attack using OMEN, ordered Markov enumerators. 23:04.530 --> 23:06.430 And I'll talk about that a little bit later there. 23:06.690 --> 23:10.870 But really, I kind of want to highlight though that it's trying, you know, more traditional cracking rules. 23:10.870 --> 23:14.010 So it's like, you know, combining words. 23:14.070 --> 23:15.430 And then it's switching to brute force. 23:15.430 --> 23:18.150 And then it'll switch to another mangling rule after this here. 23:18.150 --> 23:21.410 And then it'll just keep on going based upon whatever the current probability is. 23:23.070 --> 23:27.210 Now, as I said, I really struggle with documenting my code. 23:27.310 --> 23:33.190 So I try to go ahead and add as much documentation into the runtime behavior of it as possible. 23:33.270 --> 23:46.710 So if instead of hitting, you know, enter or anything else along those lines, you hit H and hit enter, it'll provide, or just H actually, it'll provide a SAS report output of what all these different fields here mean. 23:46.710 --> 23:52.390 And that SAS report actually is much longer than even, you know, displayed on the screen here. 23:52.630 --> 23:57.570 But it explains what all those different, like, letters like A5 or C5 actually stand for. 23:58.610 --> 24:03.830 The one thing that I kind of really want to highlight though is this one metric here called probability coverage. 24:04.110 --> 24:14.810 Because since the PCFG password cracker creates guesses in probability order, it starts with the very high probable passwords, and it goes to less probable passwords and less probable passwords. 24:15.330 --> 24:19.150 And the model that it has will basically never finish. 24:19.150 --> 24:23.010 It'll just keep on figuring new combinations of words to go through to it. 24:23.010 --> 24:28.470 So a real challenge becomes, you know, when do you go ahead and give up on a cracking session? 24:28.470 --> 24:30.370 So you haven't cracked the password. 24:31.730 --> 24:37.230 When should you go ahead and, you know, kill this off and try some other cracking type of attack that might be more successful? 24:37.350 --> 24:41.750 Or when do you go ahead and just choose to say, I'm not going to crack this password and move to a different case? 24:42.250 --> 24:51.190 So this probability coverage is a very fuzzy metric that I tried to develop to try to just give you a little bit of kind of a rule some about when that should be. 24:51.810 --> 25:09.790 So what this metric says is that if the target password is the same probability distribution as the password data set that I trained, and if my grammar and how to model how these passwords are created was exactly correct, this is the probability that we cracked this password. 25:09.790 --> 25:12.730 Now, neither one of those assumptions is actually true in real life. 25:12.730 --> 25:15.790 You know, the probability model of the password you're trying to crack is probably very different. 25:16.230 --> 25:21.090 You know, the grammar that I generate and train on is absolutely not perfect. 25:21.290 --> 25:25.690 But at least, as I said, it kind of gives you a rule of thumb to say, okay, you know, this is starting to get a little bit high. 25:25.750 --> 25:28.770 It says, you know, I had like a 90% chance to crack this password. 25:28.770 --> 25:30.090 I haven't cracked this password yet. 25:30.450 --> 25:32.210 Maybe I should go ahead and give up. 25:32.650 --> 25:37.950 And you'll notice this number jumps up really high initially because it's making, you know, high probability password guesses. 25:37.990 --> 25:45.170 And then it slows to a crawl to almost like no advancing after you get to, you know, like 70 or 80, 90 percent, you know, completion there. 25:46.210 --> 25:53.850 So this is kind of really good to be able to figure out, you know, where can I go ahead and devote that, you know, that one single CPU and that RAM to somewhere else there. 25:56.700 --> 26:03.780 So another usage tip I just kind of want to highlight is that sometimes the cracking dynamic when it comes to speed is completely reversed. 26:04.220 --> 26:15.400 So you might be trying to crack very, very computationally password hashes, expensive password hashes, or a lot of like, let's say, assaulted hashes, in which case you're really only making, you know, a couple guesses a second. 26:15.460 --> 26:21.640 Well, this generator is generating, you know, let's say, you know, between like, you know, 100,000 and like 4 million guesses a second. 26:21.640 --> 26:23.460 So it gets backlogged. 26:23.900 --> 26:29.840 And basically, essentially freezes while it waits for to be able to send more guesses to the password cracking program. 26:29.960 --> 26:35.460 So occasionally, if you hit enter, it won't actually display set the status or it'll take a while to display the status. 26:35.520 --> 26:37.680 And that's kind of usually what's happening. 26:37.720 --> 26:53.620 So if that's happening, and you're kind of curious whether the password cracking session has crashed or not, I recommend going back to earlier advice about sending a signal to, let's say, John Ripper, and just seeing how that's doing there in order to make sure that your password cracking session is still running. 26:56.680 --> 27:05.920 So, as I talked about, you know, multi-word feature has probably been the biggest, you know, addition to the new 4.0 rewrite. 27:05.920 --> 27:11.220 And it has completely shocked me how effective this has been here. 27:11.320 --> 27:14.540 So I won't get talk too much details about it. 27:14.540 --> 27:19.720 But the one thing I want to really kind of stress, though, is that it is not language specific at all. 27:19.720 --> 27:25.200 It learns all what constitutes a word from the training set that you're giving it there. 27:25.200 --> 27:32.820 So it'll pick up things like new band names or proper nouns that are really hard to specify inside the language dictionary or whatever new Pokemon just came out. 27:33.000 --> 27:37.260 And identifies patterns like, you know, I love, and stuff like that. 27:37.420 --> 27:48.860 So this is very useful for being able to, you know, you know, try to target new, you know, password hashes. 27:49.160 --> 27:51.740 So, as I said, it's not language specific. 27:51.740 --> 27:58.200 It works best with, I would say, kind of like European English type languages. 27:58.880 --> 28:04.200 It really struggles still with some of the other languages like Mandarin. 28:04.220 --> 28:08.700 But that is something absolutely that I really want to focus on more going forward here. 28:09.220 --> 28:10.140 It's not perfect. 28:10.140 --> 28:11.660 It's definitely a work in progress. 28:12.320 --> 28:16.060 So there is a balance between, you know, creating, you know, false positives and matches here. 28:16.320 --> 28:20.960 If you don't see some of the base words in the train set by themselves, it won't identify them. 28:21.260 --> 28:23.980 But it's something that is evolving. 28:23.980 --> 28:34.340 And part of the new pull request that I just received from somebody else actually has some improvements to this here that I'm really excited about, again, pushing to Maine. 28:37.380 --> 28:42.360 So one of the other big features that have been added recently here is ordered Markov enumerator. 28:45.940 --> 28:51.140 And the whole reason why I talked about this is that a similar approach can be taken for pretty much anything. 28:51.520 --> 28:59.780 So if someone creates a better cracking attack or cracking mode, it can totally be incorporated into a PCFG style attack. 29:00.180 --> 29:03.120 I'll be a little bit like the Borg in that respect. 29:03.820 --> 29:07.800 But the real challenge is to be able to figure out how to assign a probability to a password guess. 29:07.800 --> 29:11.600 So if you can assign a probability to a password guess, I can probably incorporate it into a PCFG. 29:15.320 --> 29:24.480 So just kind of in the last little bit here, I really kind of want to highlight some additional tricks that are very useful when it comes to cracking passwords. 29:24.660 --> 29:29.780 So the first one here is this skip root flag in the PCFG. 29:29.800 --> 29:32.560 And basically what this does is disable omen guest generation. 29:32.760 --> 29:37.800 And that's not to say that omen guest generation is something that's bad to do. 29:37.800 --> 29:41.600 It certainly definitely helps increase the success of a password cracking session. 29:42.000 --> 29:45.120 But this is a way to paralyze your attack. 29:45.120 --> 29:54.960 So if you're having another system that's going ahead and really cranking through your brute force attack, you might want to go ahead and do all your brute force on that other system or on that other thread. 29:55.040 --> 29:58.540 And then run the PCFG guesser really just to focus on the word mangling rules instead. 29:58.540 --> 30:03.160 So in order to do that, all you need to do is just when you run it, just type in skip root. 30:07.450 --> 30:10.630 Another flag that's really kind of useful is the all lower flag. 30:10.630 --> 30:14.610 And what this means is it'll stop doing any sort of case mangling on the password guesses. 30:15.050 --> 30:23.170 So let me try to move my picture just a little bit here just to make it easier to read as I go back. 30:24.510 --> 30:25.790 I apologize. 30:27.070 --> 30:27.990 Okay. 30:30.310 --> 30:30.770 So 30:34.150 --> 30:39.830 a lot of times you may want to not go ahead and do case mangling inside of PCFG itself. 30:39.950 --> 30:45.550 And one reason might be that the hash that you're targeting is case insensitive like landman. 30:45.830 --> 30:51.470 That's not probably the best example, though, because if you're cracking landman hashes, you're not using PCFG in order to do that there. 30:51.470 --> 30:55.750 You're just going ahead and brute forcing that sucker and taking it out that way. 30:56.190 --> 31:02.330 Where it's more likely, though, is that case mangling is very distinct for how people do it there. 31:02.330 --> 31:09.850 So if someone does a certain type of case mangling, they have a tendency to keep on using that strategy for all their other passwords. 31:10.010 --> 31:15.670 So when you start doing things like targeted password cracking, you may not want to go ahead and just go ahead and do what everyone does. 31:15.670 --> 31:19.390 You want to really make a really specific case mangling for that particular individual. 31:19.550 --> 31:26.310 In that case, the better way to do this is that JohnRipper supports a really powerful feature called Pipe. 31:27.110 --> 31:41.910 So what the Pipe does is instead of just going taking the guesses in from standard input and running them as is, you can apply additional rules on top of that like you would do in a traditional password cracking dictionary type of attack. 31:41.910 --> 31:53.410 So you can specify your very specific case mangling rules inside JohnRipper's rule set and then pipe the lowercase password guesses right into JohnRipper and have JohnRipper capitalize it itself. 31:53.410 --> 31:59.170 And that can be very powerful when you have an idea of what type of case mangling you want to be able to target. 32:07.620 --> 32:12.600 So I of course moved it to the wrong portion here. 32:14.220 --> 32:16.040 Let me move my screen again here. 32:16.040 --> 32:16.800 I apologize. 32:18.640 --> 32:19.460 Some kind of improvements. 32:19.460 --> 32:25.360 As I mentioned, there was an amazing pull request that was submitted to me with a bunch of new features. 32:25.360 --> 32:32.120 I'm slowly incorporating them into the core, but I actually have the features available as their own kind of tool called segmenter.py. 32:32.120 --> 32:37.520 I mean, and I apologize if I mispronounce your name because I've only seen it written. 32:37.520 --> 32:44.960 But Chun-Wan Wang submitted this here and it really impresses me there. 32:45.120 --> 32:48.580 So probably the biggest feature I'm really excited about is leet-speak replacement. 32:48.580 --> 32:52.840 This has been a feature that has been kind of my white whale as far as implementing. 32:53.120 --> 32:57.060 And it's just every single time I've gone through it, it's just not been very effective. 32:58.260 --> 33:05.540 But that's currently incorporated into this tool he has called segmenter.py. 33:05.580 --> 33:09.820 That's been included in the repo that will go ahead and try to parse that information out. 33:09.820 --> 33:16.160 So I'm looking at getting that incorporated into my core trainer and getting that incorporated into password cracking sessions in order to be able to really target that there. 33:16.840 --> 33:19.300 He also improved some of the multi-word detection. 33:19.300 --> 33:20.800 So he made that better. 33:20.980 --> 33:30.740 And then he also has incorporated some new approaches into the password score, which is a different tool that you can go ahead and submit your password into the password score. 33:30.820 --> 33:34.540 And they'll tell you what the probability your password is, which is kind of nice as well. 33:35.100 --> 33:36.720 So all credit goes to him for this. 33:36.720 --> 33:39.440 I'm really impressed with this here. 33:39.700 --> 33:43.540 And if anyone else is looking at helping out too, I'm all about that. 33:43.540 --> 33:46.540 So thank you very much once again for that there. 33:51.100 --> 33:53.800 Okay, so let me move my screen around again here. 33:58.640 --> 34:04.420 Okay, so the next thing I currently want to talk about here is the compiled PCFG guesser. 34:04.420 --> 34:07.760 So I've been talking about the Python tool set all along right now. 34:08.160 --> 34:12.280 So the compiled PCFG guesser is a completely different, you know, fork. 34:12.380 --> 34:17.780 And as you kind of get the name there, it's still being written in Python, it's written in compiled C code. 34:18.020 --> 34:26.460 It's a little bit harder to get actually installed and running simply because when you start talking about compiling your code, you know, it runs great on my machine, but it has challenges elsewhere. 34:26.540 --> 34:30.980 I tried to go ahead and use a Hashcat build make file for this. 34:30.980 --> 34:36.980 So if you can build Hashcat on your computer, you have at least a better chance of being able to go ahead and get this running as well. 34:37.900 --> 34:47.260 But if you have problems, please, you know, reach out to me on the GitLab or GitHub site, and I can try to help you fix those there. 34:47.900 --> 34:51.340 So I will say that the trainer portion, it will always be in written in Python. 34:51.340 --> 34:54.540 I just like writing Python too much to change that over. 34:54.940 --> 35:03.540 So basically, you'll go ahead and create train rule sets with the Python trainer, but then copy them over to be used in the compiled version here. 35:04.480 --> 35:11.160 Also, the compiled version has a tendency to lag in features from the Python tool set, because once again, I like writing Python. 35:11.820 --> 35:13.780 I'm not the best C coder in the world. 35:14.540 --> 35:20.800 So basically, if I write a hello world program, it's going to have like five buffer overflows and, you know, a segfault. 35:21.440 --> 35:28.380 So take what you will there, but I'm making this available. 35:28.380 --> 35:31.080 If someone wants to write a better one, I'm totally open to that as well. 35:31.440 --> 35:35.800 But, you know, it doesn't have save restore, it doesn't have status outputs, and it has no omen guest iteration. 35:36.520 --> 35:40.980 So all that being said, you know, why bother with this here? 35:41.220 --> 35:50.320 And really, at the end of the day, the main reason is it's about 20 times faster than the Python tool set. 35:50.320 --> 35:53.760 And I've always heard that, you know, C code is faster than Python. 35:53.760 --> 35:55.760 But when I saw that, I was like, holy crap. 35:55.760 --> 35:57.740 So I will be up front. 35:57.740 --> 36:06.620 I'm actually, even with all these limitations, when I'm cracking passwords, I'm using the compiled C version now much, much more often than I'm using the Python one there. 36:07.100 --> 36:12.740 So because that 20 speed improvement is hard to beat for most password cracking sessions. 36:16.120 --> 36:20.240 So now I'm going to talk real quick about training passwords. 36:21.080 --> 36:23.060 So I've been talking about this a lot here. 36:23.080 --> 36:26.580 And there's a lot of different reasons why you want to go ahead and create a new password training set there. 36:26.660 --> 36:29.180 So language is a huge one. 36:29.460 --> 36:34.480 So you want to be able to train on passwords that are similar to the target that you're trying to target. 36:35.260 --> 36:40.280 And another big one is that corporate passwords are very, very different than you'll see from websites. 36:40.500 --> 36:46.560 And I'm sure you probably heard CoreLogic talk about this before, you know, yesterday. 36:47.440 --> 36:49.980 But that's something that, you know, is very evident. 36:49.980 --> 36:58.580 So if you're trying to target corporate passwords, you probably do want to go ahead and train on corporate passwords versus going ahead and training on passwords for some gaming website. 37:02.860 --> 37:11.060 So another reason to go ahead and train it, though, is if you're targeting a specific password creation policy, or you know which mangler rules your target prefers. 37:11.060 --> 37:17.440 So one way to be able to really target that there is to train only on passwords that match that training set there. 37:17.440 --> 37:24.440 And there's other things you can do, like the password rules, or the, you know, the grammar that I generate. 37:24.800 --> 37:29.560 I made sure that I didn't include anything like a CRC check or any of those sanity checks into it there. 37:29.560 --> 37:35.140 So you can actually open up the files themselves, they're just text files, and start editing the probabilities of different things in them by hand, too. 37:35.140 --> 37:41.480 So if you say, like, oh, this is one word I really want to go ahead and make it, like, highly probable, but I don't want to go have to train on a whole new training set. 37:41.480 --> 37:49.200 You can just go ahead and open it up, put that word in there, give it whatever probability you want, and that will just be read in and used in your password cracking session there. 37:49.980 --> 37:57.520 So the other reason to train on a password train set there is it generates a bunch of information and extracts a lot of information from that password set. 37:57.560 --> 38:01.880 So it's really useful to be able to analyze a new dump that you have accessible to you there. 38:01.880 --> 38:09.700 So for example, it'll pull out, like, common emails, it'll pull out dates and websites, and try to help you figure out where did this, you know, password data set come from there. 38:13.590 --> 38:19.130 So the next question, of course, is, you know, where do you go ahead and, you know, get these password data sets from? 38:19.670 --> 38:27.290 So there's a lot of challenges with this, too, because a lot of data sets are not optimal when it comes to training on. 38:27.390 --> 38:33.990 So I don't know if you know of hashes.org, but it's a really great site for being able to download all these, you know, dumps as they come out here. 38:34.890 --> 38:39.010 So for example, let's say you want to go ahead and train on this data set here. 38:39.010 --> 38:44.290 I'm not going to try and pronounce the name of that site, because I'm sure I'll just horribly, horribly mangle it there. 38:44.490 --> 38:53.430 But when I did some googling about this site here, it was a site for, you know, new college students trying to find a job in China. 38:53.550 --> 38:59.910 So that's kind of an interesting data set there that you might want to be able to use in order to train for correct passwords here. 39:00.750 --> 39:13.290 So if you download something from, like, hashes.org, the first and most important thing is select the plain text option to train your rule set on, because you don't want to include the hash part of your training set, because then you'll think it's part of the password and just it goes poorly there. 39:14.350 --> 39:38.210 So one other thing I really kind of want to highlight here, and this is a feature that I'm hopeful to be able to get added to the PCFG tool set, but I was informed by the owner of the site here that they actually do some additional things for encoding non-UTF-8 characters that my trainer will not fully parse correctly. 39:38.550 --> 39:47.130 So that's something I need to add in too, so that it goes ahead and, you know, uses the correct character encoding for non-English passwords. 39:47.410 --> 39:51.990 So I just want to put that warning out too for trying to train this on things like Mandarin. 39:55.080 --> 40:00.000 But one problem with a lot of these dumps here is the first one is they don't contain duplicate guesses. 40:01.760 --> 40:11.540 So duplicate guesses are really important when it comes to trying to figure out what the probability of password is, because if you don't have duplicate guesses, 1, 2, 3, 4, 5, 6 looks like a very just random string. 40:12.400 --> 40:25.440 So that's useful, but I will say when you run longer cracking sessions with PCFGs, that lack of duplicates becomes less and less important, because you've already exhausted all the really probable password guesses. 40:25.780 --> 40:30.800 The one issue though is that the OMEN portion really does struggle without the duplicates. 40:31.280 --> 40:36.860 So you might not want to go ahead and enable OMEN guessing if you train on a data set that doesn't contain any duplicate guesses there. 40:37.620 --> 40:40.940 The other problem with these dumps is that they only contain the passwords that have been cracked. 40:41.660 --> 40:45.620 So basically you don't know or learn anything about the passwords that haven't been cracked. 40:45.680 --> 40:55.780 That's not a deal stopper, but just it's useful to keep that in mind there, that the cracked percentage is going to be very useful when it comes to figuring out how good a data set is in order to create a new rule set. 40:59.160 --> 41:06.440 So in order to train on a password data set there, really you just apply some program once again. 41:06.680 --> 41:12.660 You just give the name of the rule set that you want to be able to train it on, as well as the password data set that you want to train it on as well. 41:13.240 --> 41:20.880 And it'll go ahead and run in order to do all the parsing and stemming of the password data set here. 41:21.100 --> 41:31.200 So it will try to auto detect what that encoding is, but when in doubt, set it to be UTF-8 because the encoding really does matter quite a bit there. 41:32.780 --> 41:38.840 So the first pass it takes through the data set there, it learns all the character frequencies and base words for multi-word detection. 41:38.880 --> 41:43.660 So it actually makes a couple different passes through the same data set in order to learn more and more and more about it there. 41:44.880 --> 41:48.560 The second pass it goes through there, it'll do much of the real parsing of the password. 41:48.560 --> 41:54.440 So it figures out things like, you know, keyboard walks, alpha strings, you know, letters, how probable like the digits are and stuff like that. 41:54.440 --> 42:00.980 So most of the stuff you think of traditionally when you talk about, you know, what their probabilities of different, you know, things are, it does on the second run through. 42:02.100 --> 42:09.640 And it actually goes ahead and makes a whole third run through then to see about how effective things like, you know, OMEN would be for cracking passwords. 42:09.640 --> 42:14.420 So that kind of gets back to how OMEN generates the probability it's assigned with its different levels there. 42:15.260 --> 42:16.760 And so this takes a while. 42:17.020 --> 42:21.480 So if you're cracking, you're training it on a million passwords, you know, it's done in like a minute or two. 42:21.480 --> 42:25.300 If you're training it on a billion passwords , it takes significantly longer. 42:25.780 --> 42:29.780 And it has to keep all this data in memory. 42:29.880 --> 42:35.120 So if you're training on some of these really gigantic data sets there, it's just not going to work. 42:35.840 --> 42:45.080 So one thing you might want to do is just select a subset of that password set, you know, chosen randomly in order to train your rule set on instead. 42:47.120 --> 42:55.020 So after you're all done with that, though, it'll display statistics about the data set you just trained upon too, which are really kind of useful to figure out, you know, where it came from. 42:55.020 --> 42:58.140 So, you know, password lengths and stuff like that. 42:58.400 --> 43:14.140 But one thing that I've been kind of added that I found really useful is it'll display kind of like the top URLs, which are usually at the beginning, the top of them are like, you know, web, you know, email, email account information. 43:14.300 --> 43:20.660 But if you start getting down a little bit, you'll can actually see usually what the website is because people have a tendency to use the website in their password. 43:21.480 --> 43:28.000 I also highlight the dates that it finds it in there as well, because that's useful kind of trying to date when that password data set got leaked. 43:28.220 --> 43:31.680 Now, I want to kind of highlight that there's a long tail when it comes to the dates. 43:32.280 --> 43:33.000 I'm sorry. 43:33.620 --> 43:41.380 Because people, you know, create passwords before, you know, the password data set gets stolen. 43:41.520 --> 43:48.740 So, you'll see a lot of passwords for years before the data set actually gets down. 43:48.740 --> 43:56.560 But if you start kind of going down it a little bit, you can say, okay, that's probably about where the cutoff was for when this password data set was, you know, disclosed. 43:59.490 --> 44:05.150 So, kind of one last thing I really want to talk about real quick is that I am trying to get this to work with other cracking modes there. 44:05.530 --> 44:10.090 So, one of the, you know, really popular cracking modes used is called Prince. 44:10.530 --> 44:18.490 So, Prince basically takes a lot of different words and just combines them all together and makes lots of guesses based upon that. 44:18.730 --> 44:24.590 But one challenge with Prince is that it's very dependent upon the input word list that you give it to it there. 44:24.870 --> 44:29.310 Because the word list needs to have, you know, high quality words in it. 44:29.370 --> 44:32.510 But it also needs to have a level of kind of cruft in there too. 44:32.510 --> 44:39.330 Just because if you want to go ahead and, let's say, add the number one to the end of a word, you have one in your word list by itself. 44:40.010 --> 44:44.010 But the challenge is, the larger your word list is, the more words it's trying to combine. 44:44.250 --> 44:46.930 And then, you know, it starts to have issues there as well. 44:47.550 --> 44:51.330 So, we have all this probability information about how a password was generated. 44:51.330 --> 44:58.310 So, maybe we can go ahead and use this to create very bespoke word lists for like a Prince-style guessing session there. 44:58.830 --> 45:03.410 So, I created another tool called Princeling that basically just does that there. 45:04.250 --> 45:06.150 So, it goes... 45:10.300 --> 45:12.860 I'm sorry, my microphone just went out again there. 45:12.860 --> 45:20.060 But yeah, so it creates a very, you know, high quality word list there and does it automatically. 45:20.320 --> 45:25.280 Because one thing I like about Prince is it's the kind of the attack that I run when I want to goof off. 45:25.320 --> 45:29.500 So, like, you know, password cracking, sometimes it takes a lot of brain cells because you're kind of looking at how you're cracking. 45:29.500 --> 45:31.100 You're trying to optimize your cracking session. 45:31.400 --> 45:33.140 And Prince is like, I have no idea what I want to do. 45:33.140 --> 45:34.960 I want to go watch Tiger King on Netflix. 45:35.000 --> 45:37.960 Let's just go ahead and just launch this off and come back and see if it was successful. 45:37.960 --> 45:40.160 And Prince is usually actually quite successful. 45:40.820 --> 45:43.280 So, it's a pretty good tool to be able to use. 45:43.280 --> 45:48.580 And by anything that you can do in order to automate Prince even more, I'm all for, which is why I went ahead and created that. 45:51.140 --> 45:54.280 So, I'm going to go ahead and stop the live stream here. 45:54.280 --> 45:57.320 And hopefully, I'll be on Discord there in order to answer any questions that you have. 45:57.320 --> 45:58.000 I hope you enjoyed this. 45:58.000 --> 45:58.940 I hope this was helpful. 45:59.200 --> 46:03.800 And once again, you know, thank you for attending the Password Village here at DEF CON Safe Mode.