WEBVTT 00:01.520 --> 00:03.200 Now we have some audio. 00:11.660 --> 00:14.320 Okay, so everyone somewhat speak? 00:22.950 --> 00:24.170 Okay, now try again. 00:24.170 --> 00:25.910 Yeah, now we have audio. 00:26.150 --> 00:27.490 Hello, good. 00:27.490 --> 00:28.810 Can you hear me, Sven? 00:29.310 --> 00:30.350 I can hear you. 00:30.350 --> 00:30.990 Hey, Irem. 00:31.150 --> 00:35.530 Oh my gosh, it was just a simple Windows reboot fixes everything. 00:36.910 --> 00:39.010 Hey, I unfortunately, I actually have a meeting right now. 00:39.010 --> 00:41.130 So I'm going to drop and join again. 00:41.130 --> 00:44.510 What time do I need to be on for speaker time? 00:45.010 --> 00:46.510 In 30 minutes. 00:47.510 --> 00:50.630 But only in Discord or also in Zoom? 00:51.110 --> 00:52.330 Only in Discord. 00:53.310 --> 00:54.190 Okay. 00:56.070 --> 01:00.810 Yep, so we've got people in the Slack chat or in the Twitch chat saying they can hear us. 01:00.810 --> 01:01.950 So I think we're good to go. 01:02.330 --> 01:03.110 Sweet. 01:04.070 --> 01:05.850 Well, hi, everyone. 01:05.850 --> 01:07.310 Sorry about the audio issues. 01:07.310 --> 01:10.430 That seems to be the start of every stream is figuring out the audio. 01:11.790 --> 01:14.910 The only people I've seen get it right are like Critical Role. 01:17.730 --> 01:20.290 They pay a lot of people to get it right. 01:20.490 --> 01:21.170 Yeah. 01:21.910 --> 01:26.910 Welcome to AI Village at DEF CON 28 safe mode. 01:28.010 --> 01:29.990 We're all doing this from home. 01:32.750 --> 01:36.930 I hope that this can be as fulfilling to you guys as it is to us. 01:38.690 --> 01:53.730 And we just want to start off the village with some like general questions about the state of the field as it is right now at the end, halfway through 2020. 01:54.430 --> 01:59.710 So Jag has got a bunch of questions prepared and the rest of us are going to try to answer them. 02:01.090 --> 02:02.010 Okay. 02:02.890 --> 02:03.910 Welcome, everyone. 02:03.910 --> 02:04.370 Thanks. 02:04.370 --> 02:05.890 Thank you for being here. 02:05.890 --> 02:09.510 The first question that I have listed is... 02:09.510 --> 02:13.430 Let's just knock this one out of the way real first. 02:13.670 --> 02:17.610 Facial recognition is a security concern for us. 02:18.110 --> 02:20.490 In the ML field, of course. 02:21.090 --> 02:24.070 And before you answer, please introduce yourself. 02:24.750 --> 02:25.230 Yeah. 02:25.230 --> 02:27.950 We should probably do a who is who. 02:29.530 --> 02:31.990 Sven, do you want to kick us off? 02:33.050 --> 02:33.990 Sure. 02:33.990 --> 02:35.210 So I'm Sven. 02:36.110 --> 02:39.850 I am a senior data scientist at Elastic. 02:39.850 --> 02:46.870 Before that, I was a topologist who accidentally got into machine learning and security. 02:47.850 --> 02:52.370 And I kind of helped run the AI village. 02:55.010 --> 02:57.030 John, do you want to go next? 02:57.230 --> 02:58.070 Yeah, sure. 02:58.070 --> 02:58.790 Hey, everyone. 02:58.790 --> 02:59.770 DeltaZero here. 02:59.770 --> 03:02.770 I do data science for intrusion detection. 03:02.770 --> 03:07.770 Yeah, I've been in the field for maybe, I don't know, four years now. 03:07.770 --> 03:09.890 But pretty awesome. 03:09.890 --> 03:11.110 Glad to be here. 03:14.110 --> 03:15.270 Rich? 03:16.170 --> 03:17.910 Hi, I'm Rich Huerang. 03:17.970 --> 03:21.690 I'm a director of data science research at Sophos. 03:21.690 --> 03:30.250 I've been working at the intersection of machine learning, security, and privacy for probably about 10 years now. 03:30.250 --> 03:32.250 Got my PhD from UCSB. 03:32.250 --> 03:39.050 Spent some time with the US Army Research Laboratory, and I've been at Invencion Sophos ever since. 03:44.460 --> 03:45.600 Yaga? 03:46.040 --> 03:47.740 Hi, I'm Yaga. 03:47.740 --> 03:52.280 I am a professor at a small university in Virginia. 03:52.520 --> 04:02.080 I've got about 25 years of experience in security, but relatively new to machine learning. 04:02.080 --> 04:10.180 My research is focused on securing machine learning, and I like playing video games too. 04:10.180 --> 04:13.940 So there's something interesting about it. 04:14.200 --> 04:14.930 Anita? 04:15.640 --> 04:17.080 Hi, I'm Anita. 04:17.080 --> 04:19.360 I'm a research scientist at University of Illinois. 04:19.360 --> 04:21.500 Started life as a cryptographer. 04:21.500 --> 04:24.440 Been in security for almost 30 years. 04:28.200 --> 04:31.120 Okay, and Will's the last guy. 04:32.360 --> 04:34.400 Certainly not least. 04:34.400 --> 04:35.720 Yeah. 04:36.560 --> 04:39.280 Sorry, my morning's just starting. 04:39.380 --> 04:53.260 Will, I am a network operator, and I guess attack machine learning models and use AI for offensive purposes. 04:53.260 --> 04:56.600 Phishing, pathfinding, and stuff. 04:58.360 --> 04:59.760 Yeah, Will's being modest. 04:59.760 --> 05:05.860 He actually has the first CVE ever assigned against a machine learning system. 05:05.860 --> 05:11.240 If you attend my lab later, there'll be another one. 05:13.900 --> 05:16.960 I think that's a challenge for everyone. 05:18.540 --> 05:22.000 Can you get one file before Will drops number two? 05:25.560 --> 05:28.460 Okay, thank you everyone for the introductions. 05:28.460 --> 05:30.820 Sorry, I should have thought about that. 05:31.120 --> 05:35.740 Please be sure to ask any questions over in the Discord server. 05:35.740 --> 05:38.320 There is a talks channel. 05:40.800 --> 05:43.420 Yes, there's a channel for talks. 05:43.420 --> 05:48.000 You can ask in there or in the general, and we'll try to catch it. 05:48.100 --> 05:50.420 So back to the first question. 05:50.620 --> 05:56.100 This was one that was both easy and hard to throw out there. 05:56.100 --> 06:06.100 But is facial recognition a security concern when we're talking about privacy? 06:06.800 --> 06:13.500 For sure we know about, but in the general machine learning terms, is facial recognition a security issue? 06:14.880 --> 06:24.320 So I think just to make the point in our village, one of the things we're trying to do is expand from just thinking of this as a technical kind of algorithm problem. 06:24.320 --> 06:33.040 If you look at the broader scope of facial recognition, there are issues not just with security, with data sets, with data integrity, with these kind of choices. 06:33.440 --> 06:36.480 So I think my answer would be it is a security issue. 06:36.480 --> 06:37.540 It's a machine learning issue. 06:37.540 --> 06:38.200 It's a data issue. 06:38.200 --> 06:39.480 It's a human issue. 06:40.280 --> 06:45.000 And for us, we need to think about all those things as technology people. 06:47.180 --> 07:00.480 Yeah, and just to add to that, a lot of the issues around facial recognition are less to do with the technology and more to do with the processes that that technology is embedded in. 07:00.480 --> 07:06.220 So if you have a facial recognition system that works, you have issues around privacy, right? 07:06.220 --> 07:07.840 Who gets access to that system? 07:07.840 --> 07:10.560 Who gets to know who was seen where on which camera? 07:10.700 --> 07:13.740 And then when it doesn't work, you have bias issues that sneak in. 07:13.740 --> 07:16.360 So how do you control for false positives, right? 07:16.360 --> 07:24.920 Someone gets reported as being, you know, facially recognized as a bank robber or something when it's just like an average person. 07:24.920 --> 07:28.000 And this actually extends beyond issues of just facial recognition. 07:28.000 --> 07:30.880 Just computer vision in general has problems like this. 07:30.880 --> 07:44.180 There was a story recently about a license plate recognition system, which led to someone being pulled over and detained, you know, face down on the side of the road for hours before they figured out that it was the wrong license plate number, the wrong make of car, 07:44.180 --> 07:47.000 the wrong model year, et cetera, et cetera, et cetera. 07:47.000 --> 07:57.940 So yeah, it's definitely a case of not only does the ML work, but do you have the right controls around the ML to make it actually usable? 07:57.940 --> 08:04.020 And have you, you know, assessed what happens when it actually fails so that it can fail gracefully without hurting people? 08:05.560 --> 08:06.840 Anyone else? 08:07.320 --> 08:20.800 I think the last thing, not to dominate this, but the last thing to throw in is I feel like in our village, part of our job is to also translate all this technobabble sometimes to policy speak and to real world speak. 08:20.800 --> 08:22.800 And that could be very challenging. 08:22.800 --> 08:24.820 We know the technical parts. 08:24.820 --> 08:29.000 What ends up on the other end, we need to bridge that gap. 08:29.680 --> 08:35.920 I think that's important to understand that security isn't just the technical side. 08:35.920 --> 08:39.220 There's a human side to it too, and that extends to policy. 08:39.220 --> 08:41.140 So definitely something to think about. 08:41.140 --> 08:41.520 Thank you. 08:42.200 --> 08:48.740 Staying along the same lines, one of the things that I wanted to ask about is the FOX program. 08:48.740 --> 08:50.620 Have you heard about it? 08:50.620 --> 09:06.440 And how does it affect our ability for the end user to take back some of that control over their data? 09:08.560 --> 09:16.380 It's technically, again, this is, it's the gap between technical aspects of it and implementation and social aspects of it. 09:16.380 --> 09:18.700 Technically, it's a brilliant idea. 09:18.700 --> 09:21.760 And, you know, it does what it says on the tin. 09:21.760 --> 09:39.360 In practice, there are so many ways that people get registered into facial recognition systems from, you know, collecting ID cards to photos that they uploaded before FOX was a big thing to photos that other people take of them and maybe tag them in later, 09:39.360 --> 09:51.260 that you really can't just find a quick technical band-aid for security problems that are really a combination of social and technical problems. 09:51.260 --> 09:55.240 The root cause here isn't like, oh, we just need better technology to fix this. 09:55.240 --> 10:01.880 The root cause is society and sort of laws and norms haven't kept up with the technology. 10:01.880 --> 10:05.200 And that's the angle that we need to address it from. 10:05.200 --> 10:13.820 Technical band-aids are only ever going to be available to people that know about them, know how to apply them, and can get over that barrier to entry. 10:13.920 --> 10:17.360 Really, we need to be thinking broader about impact to society as a whole. 10:18.160 --> 10:24.980 It makes me think a little bit of this field of, you know, usable privacy and, you know, privacy by design. 10:24.980 --> 10:34.540 See, that's analogous here because people don't understand that long-term impact of having a photo op. 10:34.540 --> 10:36.880 So, enabling people to use this tool is great. 10:36.880 --> 10:42.460 I think the average person isn't going to even think about it because it doesn't affect their day-to-day life. 10:42.460 --> 10:51.280 So, again, I think the issue is, how do you express to them the long-term potential damage of having their pictures out there? 10:52.140 --> 10:57.460 Okay, we're going to move into a different arena. 10:58.320 --> 11:10.360 What do you say the latest advances that your typical security team could use in terms of machine learning? 11:11.380 --> 11:17.880 You know, a lot of vendors are out there saying that this is going to be the panacea for them to be able to fix things. 11:17.880 --> 11:24.600 Some of the actual products that will help them or techniques that will help them. 11:26.440 --> 11:35.540 I just want to take this opportunity to plug the panel that we have coming up on Saturday on whether or not ML and InfoSec lives up to the hype. 11:35.540 --> 11:39.640 So, we're going to be diving, hopefully, pretty deep into those questions. 11:39.640 --> 11:42.680 But I'll kick it over to someone else since I've been talking a lot. 11:47.220 --> 11:52.500 I really don't know what the boots on the ground could be using. 11:53.320 --> 11:57.860 But there are some promising things coming down the pipe from researchers. 11:59.560 --> 12:00.680 But I... 12:02.540 --> 12:04.660 So, I'll jump in real quick. 12:04.660 --> 12:05.960 Sorry to dominate again. 12:05.960 --> 12:08.600 And I'll let Will speak because he's more an operator. 12:08.720 --> 12:15.520 For me, federated learning, federated and privacy-preserving machine learning is very important for a bunch of reasons. 12:16.020 --> 12:22.740 Not the least of which is being able to learn across edge devices that are in multiple disparate places and being able to do it more efficiently. 12:22.760 --> 12:27.840 And being able to use new types of data because there's privacy-preserving ways to do it. 12:27.840 --> 12:33.320 So, I think I see federated learning as something that's promising. 12:34.780 --> 12:40.040 Yeah, I'd say from a product standpoint, it's just like the simple stuff. 12:40.040 --> 12:49.780 So, you know, on a network, clients will still miss password spraying internally. 12:50.020 --> 12:54.520 And that's just something that shouldn't be happening with the logs that are available. 12:54.520 --> 13:06.540 So, I think in terms of just instant response and stuff on networks, it's the simple stuff that is going to give you the most bang for the buck. 13:09.120 --> 13:14.740 Obviously, there's a lot of competition in the machine learning space, or it would seem. 13:17.440 --> 13:28.160 And half of me, at least 40% of me, thinks in two years, we're going to be moving on from machine learning as a solution in networks. 13:28.160 --> 13:32.700 And we'll probably replace it with like zero trust or something like that. 13:34.080 --> 13:39.120 But there's also the other 10% that thinks like it will always have a place. 13:40.020 --> 14:01.100 But just like with the facial recognition stuff and the bias, if we don't put it out in a way that's useful, and in a way that people can understand, the minute it breaks, they're likely to just throw it out the window and say it doesn't work. 14:02.700 --> 14:07.180 Even though, you know, it is a perfectly viable solution. 14:07.840 --> 14:09.820 But yeah, I'd say simple stuff. 14:10.320 --> 14:10.800 Yeah . 14:10.900 --> 14:11.580 Okay. 14:11.820 --> 14:20.180 To me, that's the more important side of the question is like, practitioners on the ground, we don't need any new math or anything new interesting. 14:20.180 --> 14:30.100 But there's some basics that they can get their hands on that will probably help them out when they have to deal with a deluge of alerts from some sim that they have to deal with. 14:30.100 --> 14:32.680 A little bit of clustering could go a long way in that situation. 14:34.020 --> 14:36.440 Yeah, I think that's perfect. 14:36.660 --> 14:42.880 That's perfect for what I was going to say is, I didn't mean to say products, I probably wrote that down wrong. 14:42.880 --> 14:51.220 But like, what can we tell our audience here that maybe practitioners that they can do on their own? 14:51.220 --> 14:59.700 So I think something like our workshops, if you take a look at them, they'll be able to help you see that you may not need to go get these expensive products. 14:59.700 --> 15:02.000 There's some simple things you can do. 15:03.680 --> 15:04.520 Yeah. 15:04.620 --> 15:21.760 Yeah, and I just add to that, but from my own experience as someone who's used ML to tackle a lot of security problems, very often you don't even need like the big complicated deep neural networks or gradient boosted decision trees or pick your flavor of the day. 15:21.760 --> 15:35.120 Very often, it's enough to have a good idea of the problem you're trying to solve and a good set of representative data and then simple methods like, you know, linear logistic regression will often be enough to tackle it for you. 15:35.120 --> 15:43.020 So, you know, you don't need to think that you've got to have tons of like Amazon EC2 credits to run a big beefy GPU machine. 15:43.020 --> 15:49.220 A lot of times you can get really like 80% solutions, really good solutions just on your laptop. 15:50.300 --> 15:51.820 Okay. 15:54.940 --> 16:04.360 For most security to be able to defend yourself, you have to be able to identify the issue or identify the attack. 16:04.500 --> 16:11.960 We discuss, you know, adversarial attacks quite a bit in the academic realm. 16:12.080 --> 16:19.280 How could someone tell that they're seeing an adversarial attack in practice today? 16:22.210 --> 16:23.410 You mean on the AI? 16:23.410 --> 16:25.050 I'm throwing out some hard ones. 16:25.070 --> 16:26.870 You mean on the AI portion? 16:29.390 --> 16:33.410 Well, how could you just notice an attack? 16:33.410 --> 16:40.330 Because to defend against something, you typically have to be able to define what that attack looks like, right? 16:40.930 --> 16:42.910 At least to some degree. 16:43.210 --> 16:44.970 Can we do that today? 16:47.350 --> 16:52.570 It's kind of too bad Hiram's not here because I feel like he'd have a lot to say on this topic. 16:55.410 --> 16:56.770 Let's hear it. 16:57.510 --> 17:08.170 So for the traditional adversarial attacks, we basically are trying to modify an image or something slightly to bypass the machine learning system. 17:08.170 --> 17:11.730 That doesn't really work with security because our data is much more structured. 17:11.730 --> 17:21.770 You can't like do a gradient descent on a binary and then hope it still compiles or runs because you're going to get byte codes that are just wrong. 17:22.970 --> 17:30.770 So I don't think like adversarial attacks as academics that you read up in a big year paper are going to really apply. 17:30.770 --> 17:34.870 There are some modifications to that that might apply that might be very helpful. 17:35.890 --> 17:43.430 But what we do see already and have been seeing for like a decade is people just trying a bunch of stuff and then they get past it. 17:43.430 --> 17:52.590 And the more they get informed they are about how the machine learning model works, the more effective and more like gradient descent it is. 17:52.750 --> 17:55.090 You know, like the more effective they are. 17:55.090 --> 18:10.010 Like for like the spam filters from back in 2001 when people were packing Wikipedia articles at the bottom of their spam messages to bypass the naive Bayesian classifiers, they understood that it wasn't a naive Bayesian classifier. 18:10.010 --> 18:12.450 They understood that they needed to pack the thing. 18:12.450 --> 18:14.510 And then they did that. 18:15.070 --> 18:21.570 And that's going to happen with next-gen AV products too. 18:21.710 --> 18:24.050 It may already be happening. 18:25.730 --> 18:31.130 I mean, that was what we saw in the silence bypass was effectively a stuffing attack. 18:31.130 --> 18:31.830 Yeah. 18:32.590 --> 18:38.550 That was researchers being nice to us and telling us about the problem that's happening. 18:41.790 --> 18:49.210 So this one is more directed at Will, but definitely want everyone's opinion. 18:49.350 --> 18:57.710 And it gets back to something we talked about in a journal club, which for those of you that might not have seen it, we may be replaying it during the weekend. 18:59.230 --> 19:07.470 Will received a CVE for the vulnerability he discovered. 19:07.470 --> 19:17.110 But do we need actual CVEs specifically for machine learning or should we keep them more general? 19:19.090 --> 19:25.990 So I guess first I'd like to say it was our, so Nick Landers and I did it. 19:26.550 --> 19:28.370 I just talk about it more so. 19:30.690 --> 19:34.550 So the CVE system is designed to communicate risk. 19:35.310 --> 19:43.330 And I think anywhere that risk is present, then a CVE is appropriate. 19:46.150 --> 19:48.070 You know, I just, I submitted it. 19:48.070 --> 19:51.510 So I'm not the one that gets to say whether or not it's a CVE. 19:52.270 --> 19:57.290 So I guess that the question would be for the NIST. 19:57.810 --> 20:04.410 And I know they've come out with a paper that kind of highlights machine learning attacks or there's a taxonomy. 20:04.410 --> 20:07.710 It's like 15 taxonomies right now. 20:08.350 --> 20:19.210 But yeah, I think in any way there's risk that is at least measurable, a CVE would be appropriate or any standardized score. 20:19.950 --> 20:21.310 I know there's operators like... 20:22.130 --> 20:32.270 And I'm sorry, I don't mean should it be a CVE, but more should there be a class just for machine learning, you know, because they break it down. 20:32.710 --> 20:34.430 Yeah, yeah, I think so. 20:34.750 --> 20:36.970 It's fundamentally different. 20:39.030 --> 20:47.150 You know, so for example, like with a traditional exploit, you know, there's like a patch and then you can push it out. 20:47.150 --> 20:57.930 But with, for example, like with Proofpoint's model, there's some residual, there's some like lingering theft going on. 20:58.030 --> 21:05.210 So once you have that copycat model, you're never going to get 100% of their model. 21:05.210 --> 21:13.250 But for some, there's some decay into the future, but you'll have some portion of that model. 21:13.250 --> 21:20.910 Your copycat model will be right, you know, up to some future time until they've retrained enough. 21:20.910 --> 21:34.690 But in their response, you know, unless they do things to fix it or prevent, you know, people from stealing outputs, then you can always create a new model. 21:35.730 --> 21:38.550 So I think that piece is important. 21:38.550 --> 21:48.390 But yeah, it's a little, it's fundamentally different beast than than like an exploit. 21:48.430 --> 21:55.470 Yeah, I don't know, you guys probably have a better idea of like, how you're supposed to patch a machine learning model or you take it out. 21:55.470 --> 21:56.550 I don't know. 21:57.510 --> 22:00.250 Long and expensive retraining process. 22:01.130 --> 22:13.550 Do you guys agree, though, that it's good to have that separate machine learning kind of category to highlight the fact that this is a vulnerability, a specific type of vulnerability? 22:15.270 --> 22:19.210 Yeah, I think most, so there are some exceptions. 22:19.210 --> 22:27.130 And, you know, again, I'm going to sort of like promote the Journal Club on the Summoning Demons paper later this weekend. 22:27.130 --> 22:34.410 But most machine learning exploits don't translate into exploits as we usually think of them in InfoSec. 22:34.410 --> 22:42.310 They are much more either model stealing or model bypass, or maybe like a denial of service type thing. 22:42.350 --> 22:59.430 So in my mind, keep in mind, I'm coming at this from the ML side of things and much less from the InfoSec side, I do think it makes a lot of sense to have separate categories that reflect the different kinds of risks that we currently think of ML models as being exposed to. 22:59.870 --> 23:00.790 Okay. 23:01.530 --> 23:04.690 So that's kind of all the questions I had. 23:04.690 --> 23:09.990 The next was, I was going to turn it back over to Sven to kind of give you an overview of the weekend. 23:10.050 --> 23:12.510 And thanks for being here, everyone. 23:12.510 --> 23:14.250 And thank you for your questions. 23:14.510 --> 23:15.670 Or answers. 23:16.350 --> 23:17.190 Yeah, cool. 23:17.190 --> 23:18.870 I'm really looking forward to the talks. 23:18.870 --> 23:20.530 This is going to be a great DEF CON. 23:22.030 --> 23:22.950 Cool. 23:23.510 --> 23:24.310 I'm out. 23:26.130 --> 23:27.310 Later, folks. 23:27.790 --> 23:28.770 Have fun. 23:29.150 --> 23:37.790 So about this weekend, the way we're structuring it is everyone, all the talks are recorded already and on my desktop. 23:37.790 --> 23:39.570 So the speakers won't be talking live. 23:39.570 --> 23:44.830 The reason why we're doing that is so that the speakers can focus on answering your questions in Discord. 23:45.290 --> 23:50.630 And so during the talk, speakers will be in the Discord and hopefully afterwards. 23:50.690 --> 23:56.450 So any questions you have about the thing, you can just ask it in the Discord channel, and we'll answer it. 23:56.590 --> 24:02.710 The speaker will answer it, or maybe someone else who's knowledgeable on the subject will help try to answer your question. 24:05.090 --> 24:12.250 So we're going to, in a few minutes, launch into the first one, which is we've got Zoltan here. 24:16.010 --> 24:20.390 Zahak, who's in Hyrum. 24:20.570 --> 24:24.330 They'll be going over to the Discord to answer any questions. 24:25.830 --> 24:29.090 And then, yeah, then we've got Eriks and a few others. 24:29.270 --> 24:34.670 Oh, Sarah, do you want to introduce yourself, too? 24:36.710 --> 24:38.150 Oh, god. 24:38.990 --> 24:44.770 SJTurk, ethics chair, so I guess the buck stops here. 24:46.530 --> 24:50.050 I guess data nerd who's working on disinformation. 24:57.600 --> 25:02.100 I think that's it for the intro stuff. 25:02.100 --> 25:03.320 Yeah, that's it. 25:03.580 --> 25:07.120 We were trying to leave some time to make sure the transition to Hyrum. 25:07.650 --> 25:12.080 Yeah, I've got that all ready to hit play. 25:12.620 --> 25:13.880 Okay, yeah. 25:13.880 --> 25:14.860 Oh, workshops. 25:14.860 --> 25:19.360 Yeah, so there's a few workshops. 25:20.630 --> 25:27.550 They're mostly focused on sort of bypasses and interesting techniques on... 25:28.270 --> 25:31.990 The hard part for us is text data. 25:32.630 --> 25:39.110 So, GtKondike, Gavin Kondike, well, has a lovely workshop on 25:42.990 --> 25:45.090 spam builders. 25:45.430 --> 25:54.750 And we have... Will here is talking about a new, basically, CVE2. 25:57.590 --> 25:59.650 Model stealing stuff. 26:02.930 --> 26:08.010 And the way those work are they'll be streamed on Twitch live. 26:09.950 --> 26:11.670 They're not recorded. 26:11.670 --> 26:25.170 And then there'll be a few people who are TAing who will help the workshop presenter help relay questions from Discord over to the workshop presenter and help resolve any minor issues you guys might have. 26:26.330 --> 26:31.270 There are Jupyter notebooks and stuff posted on the website. 26:33.070 --> 26:35.890 So, hopefully this all works out. 26:35.890 --> 26:41.250 It's a pretty normal machine learning workshop structure. 26:41.570 --> 26:48.830 Just online versus in person so that you can't have someone spy over your shoulder to tell you your typo is. 26:52.030 --> 26:58.050 Well, with that, let's start Hyrum's... 27:02.410 --> 27:03.230 Okay. 27:03.850 --> 27:05.850 I'm gonna go ahead and drop off. 27:05.870 --> 27:06.690 Hi, everyone. 27:06.690 --> 27:08.170 Have a great DevCon. 27:08.290 --> 27:08.670 We'll do. 27:08.730 --> 27:10.330 Have fun. 27:10.910 --> 27:11.330 Awesome. 27:11.330 --> 27:12.190 Thanks all.