1 00:00:00,099 --> 00:00:16,600 Music Herald: The next talk is about how risky 2 00:00:16,600 --> 00:00:23,210 is software you use. So you may be heard about Trump versus a Russian security 3 00:00:23,210 --> 00:00:30,949 company. We won't judge this, we won't comment this, but we dislike the 4 00:00:30,949 --> 00:00:36,590 prejudgments of this case. Tim Carstens and Parker Thompson will tell you a little 5 00:00:36,590 --> 00:00:43,300 bit more about how risky the software is you use. Tim Carstens is CITL's Acting 6 00:00:43,300 --> 00:00:48,350 Director and Parker Thompson is CITL's lead engineer. Please welcome with a very, 7 00:00:48,350 --> 00:00:53,879 very warm applause: Tim and Parker! Thanks. 8 00:00:53,879 --> 00:01:05,409 Applause Tim Carstens: Howdy, howdy. So my name is 9 00:01:05,409 --> 00:01:13,010 Tim Carstens. I'm the acting director of the cyber independent testing lab. It's 10 00:01:13,010 --> 00:01:19,039 four words there, we'll talk about all for today, especially cyber. With me today as 11 00:01:19,039 --> 00:01:25,760 our lead engineer Parker Thompson. Not on stage or our other collaborators: Patrick 12 00:01:25,760 --> 00:01:32,929 Stach, Sarah Zatko, and present in the room but not on stage - Mudge. So today 13 00:01:32,929 --> 00:01:37,010 we're going to be talking about our work, the lead in. The introduction that was 14 00:01:37,010 --> 00:01:40,289 given is phrased in terms of Kaspersky and all of that, I'm not gonna be speaking 15 00:01:40,289 --> 00:01:45,370 about Kaspersky and I guarantee you I'm not gonna be speaking about my president. 16 00:01:45,370 --> 00:01:50,010 Right, yeah? Okay. Thank you. Applause 17 00:01:50,010 --> 00:01:55,289 All right, so why don't we go ahead and kick off: I'll mention now parts of this 18 00:01:55,289 --> 00:02:00,539 presentation are going to be quite technical. Not most of it, and I will 19 00:02:00,539 --> 00:02:04,030 always include analogies and all these other things if you are here in security 20 00:02:04,030 --> 00:02:10,530 but not a bit-twiddler. But if you do want to be able to review some of the technical 21 00:02:10,530 --> 00:02:14,810 material, if I go through it too fast you like to read if you're a mathematician or 22 00:02:14,810 --> 00:02:20,510 if you are a computer scientist, our sides are already available for download at this 23 00:02:20,510 --> 00:02:25,400 site here. We think our pal our partners at power door for getting that set up for 24 00:02:25,400 --> 00:02:31,630 us. Let's let's get started on the real material here. Alright, so we are CITL: a 25 00:02:31,630 --> 00:02:35,770 nonprofit organization based in the United States founded by our chief scientist 26 00:02:35,770 --> 00:02:43,020 Sarah Zatko and our board chair Mudge. And our mission is a public good mission - we 27 00:02:43,020 --> 00:02:47,400 are hackers but our mission here is actually to look out for people who do not 28 00:02:47,400 --> 00:02:50,460 know very much about machines or as much as the other hackers do. 29 00:02:50,460 --> 00:02:56,030 Specifically, we seek to improve the state of software security by providing the 30 00:02:56,030 --> 00:03:01,340 public with accurate reporting on the security of popular software, right? And 31 00:03:01,340 --> 00:03:05,520 so there was a mouthful for you. But no doubt, no doubt, every single one of you 32 00:03:05,520 --> 00:03:10,700 has received questions of the form: what do I run on my phone, what do I do with 33 00:03:10,700 --> 00:03:13,950 this, what do I do with that, how do I protect myself - all these other things 34 00:03:13,950 --> 00:03:19,770 lots of people in the general public looking for agency in computing. No one's 35 00:03:19,770 --> 00:03:25,000 offering it to them, and so we're trying to go ahead and provide a forcing function 36 00:03:25,000 --> 00:03:29,980 on the software field in order to, you know, again be able to enable consumers 37 00:03:29,980 --> 00:03:36,480 and users and all these things. Our social good work is funded largely by charitable 38 00:03:36,480 --> 00:03:40,820 monies from the Ford Foundation whom we thank a great deal, but we also have major 39 00:03:40,820 --> 00:03:44,920 partnerships with Consumer Reports, which is a major organization in the United 40 00:03:44,920 --> 00:03:51,620 States that generally, broadly, looks at consumer goods for safety and performance. 41 00:03:51,620 --> 00:03:55,690 But also partners with The Digital Standard, which probably would be of great 42 00:03:55,690 --> 00:03:59,460 interest to many people here at Congress as it is a holistic standard for 43 00:03:59,460 --> 00:04:04,340 protecting user rights. We'll talk about some of the work that goes into those 44 00:04:04,340 --> 00:04:10,250 things here in a bit, but first I want to give the big picture of what it is we're 45 00:04:10,250 --> 00:04:17,940 really trying to do in one one short little sentence. Something like this but 46 00:04:17,940 --> 00:04:23,710 for security, right? What are the important facts, how does it rate, you 47 00:04:23,710 --> 00:04:26,810 know, is it easy to consume, is it easy to go ahead and look and say this thing is 48 00:04:26,810 --> 00:04:31,300 good this thing is not good. Something like this, but for software security. 49 00:04:33,120 --> 00:04:39,190 Sounds hard doesn't it? So I want to talk a little bit about what I mean by 50 00:04:39,190 --> 00:04:44,870 something like this. There are lots of consumer outlook and 51 00:04:44,870 --> 00:04:50,270 watchdog and protection groups - some private, some government, which are 52 00:04:50,270 --> 00:04:54,820 looking to do this for various things that are not a software security. And you can 53 00:04:54,820 --> 00:04:58,210 see some examples here that are big in the United States - I happen to not like these 54 00:04:58,210 --> 00:05:02,120 as much as some of the newer consumer labels coming out from the EU. But 55 00:05:02,120 --> 00:05:05,080 nonetheless they are examples of the kinds of things people have done in other 56 00:05:05,080 --> 00:05:10,870 fields, fields that are not security to try to achieve that same end. And when 57 00:05:10,870 --> 00:05:17,410 these things work well, it is for three reasons: One, it has to contain the 58 00:05:17,410 --> 00:05:22,960 relevant information. Two: it has to be based in fact, we're not talking opinions, 59 00:05:22,960 --> 00:05:28,800 this is not a book club or something like that. And three: it has to be actionable, 60 00:05:28,800 --> 00:05:32,520 it has to be actionable - you have to be able to know how to make a decision based 61 00:05:32,520 --> 00:05:36,370 on it. How do you do that for software security? How do you do that for 62 00:05:36,370 --> 00:05:43,760 software security? So the rest of the talk is going to go in three parts. 63 00:05:43,760 --> 00:05:49,450 First, we're going to give a bit of an overview for more of the consumer facing 64 00:05:49,450 --> 00:05:52,820 side of things for that we do: look at some data that we have reported on early 65 00:05:52,820 --> 00:05:57,260 and all these other kinds of good things. We're then going to go ahead and get 66 00:05:57,940 --> 00:06:06,011 terrifyingly, terrifyingly technical. And then after that we'll talk about tools to 67 00:06:06,011 --> 00:06:09,560 actually implement all this stuff. The technical part comes before the tools. So 68 00:06:09,560 --> 00:06:12,170 it just tells you how terrifyingly technical we're gonna get. It's gonna be 69 00:06:12,170 --> 00:06:19,680 fun right. So how do you do this for software security: a consumer version. So, 70 00:06:19,680 --> 00:06:25,010 if you set forth to the task of trying to measure software security, right, many 71 00:06:25,010 --> 00:06:27,680 people here probably do work in the security field perhaps as consultants 72 00:06:27,680 --> 00:06:32,281 doing reviews; certainly I used to. Then probably what you're thinking to yourself 73 00:06:32,281 --> 00:06:38,650 right now is that there are lots and lots and lots and lots of things that affect 74 00:06:38,650 --> 00:06:44,150 the security of a piece of software. Some of which are, mmm, you're only gonna see 75 00:06:44,150 --> 00:06:47,640 them if you go reversing. And some of which are just you know kicking around on 76 00:06:47,640 --> 00:06:51,580 the ground waiting for you to notice, right. So we're going to talk about both 77 00:06:51,580 --> 00:06:55,919 of those kinds of things that you might measure. But here you see these giant 78 00:06:55,919 --> 00:07:03,380 charts that basically go through on the left - on the left we have Microsoft Excel 79 00:07:03,380 --> 00:07:08,310 on OS X on the right Google Chrome for OS X this is a couple years old at this point 80 00:07:08,310 --> 00:07:12,850 maybe one and a half years old but over here I'm not expecting you to be able to 81 00:07:12,850 --> 00:07:16,250 read these - the real point is to say look at all of the different things you can 82 00:07:16,250 --> 00:07:20,490 measure very easily. How do you distill, it how do you boil it 83 00:07:20,490 --> 00:07:26,770 down, right. So this is a the opposite of a good consumer safety label. This is just 84 00:07:26,770 --> 00:07:29,780 um if you ever done any consulting this is the kind of report you hand a client to 85 00:07:29,780 --> 00:07:32,870 tell them how good their software is, right? It's the opposite of consumer 86 00:07:32,870 --> 00:07:39,800 grade. But the reason I'm showing it here is because, you know, I'm gonna call out 87 00:07:39,800 --> 00:07:42,650 some things and maybe you can't process all of this because it's too much 88 00:07:42,650 --> 00:07:46,911 material, you know. But I'm gonna call it some things and once I call them out just 89 00:07:46,911 --> 00:07:52,949 like NP you're gonna recognize them instantly. So for example, Excel, at the 90 00:07:52,949 --> 00:07:56,820 time of this review - look at this column of dots. What's this dots telling you? 91 00:07:56,820 --> 00:07:59,990 It's telling you look at all these libraries -all of them are 32-bit only. 92 00:07:59,990 --> 00:08:07,180 Not 64 bits, not 64 bits. Take a look at Chrome - exact opposite, exact opposite 93 00:08:07,180 --> 00:08:14,021 64-bit binary, right? What are some other things? Excel, again, on OSX maybe you can 94 00:08:14,021 --> 00:08:19,550 see these danger warning signs that go straight straight up the whole thing. 95 00:08:19,550 --> 00:08:27,520 That's the the absence of major heat protection flags in the binary headers. 96 00:08:27,520 --> 00:08:31,919 We'll talk about some what that means exactly in a bit. But also if you hop over 97 00:08:31,919 --> 00:08:35,639 here you'll see like yeah yeah yeah like Chrome has all the different heat 98 00:08:35,639 --> 00:08:41,578 protections that a binary might enable, on OSX that is, but it also has more dots in 99 00:08:41,578 --> 00:08:44,649 this column here off to the right. And what do those dots represent? 100 00:08:44,649 --> 00:08:52,050 Those dots represent functions, functions that historically have been the source of 101 00:08:52,050 --> 00:08:54,460 you know if you call these functions are very hard to call correctly - if you're a 102 00:08:54,460 --> 00:08:59,029 C programmer the "gets" function is a good example. But there are lots of them. And 103 00:08:59,029 --> 00:09:03,279 you can see here the Chrome doesn't mind, it uses them all a bunch. And Excel not so 104 00:09:03,279 --> 00:09:08,360 much. And if you know the history of Microsoft and the trusted computing 105 00:09:08,360 --> 00:09:12,379 initiative and the SDO and all of that you will know that a very long time ago 106 00:09:12,379 --> 00:09:17,180 Microsoft made the decision and they said we're gonna start purging some of these 107 00:09:17,180 --> 00:09:22,009 risky functions from our code bases because we think it's easier to ban them 108 00:09:22,009 --> 00:09:24,990 than teach our devs to use them correctly. And you see that reverberating out in 109 00:09:24,990 --> 00:09:28,980 their software. Google on the other hand says yeah yeah yeah those functions can be 110 00:09:28,980 --> 00:09:31,920 dangerous to use but if you know how to use them they can be very good and so 111 00:09:31,920 --> 00:09:38,959 they're permitted. The point all of this is building to is that if you start by 112 00:09:38,959 --> 00:09:42,540 just measuring every little thing that like your static analyzers can detect in a 113 00:09:42,540 --> 00:09:47,760 piece of software. Two things: one, you wind up with way more data than you can 114 00:09:47,760 --> 00:09:55,269 show in a slide. And two: the engineering process, the software development life 115 00:09:55,269 --> 00:09:59,779 cycle that went into the software will leave behind artifacts that tell you 116 00:09:59,779 --> 00:10:05,170 something about the decisions that went into designing that engineering process. 117 00:10:05,170 --> 00:10:10,179 And so you know, Google for example: quite rigorous as far as hitting you know 118 00:10:10,179 --> 00:10:14,379 GCC dash, and then enable all of the compiler protections. Microsoft may be 119 00:10:14,379 --> 00:10:19,949 less good at that, but much more rigid in things that's were very popular ideas when 120 00:10:19,949 --> 00:10:24,199 they introduced trusted computing, alright. So the big takeaway from this 121 00:10:24,199 --> 00:10:29,040 material is that again the software engineering process results in artifacts 122 00:10:29,040 --> 00:10:35,610 in the software that people can find. Alright. Ok, so that's that's a whole 123 00:10:35,610 --> 00:10:40,579 bunch of data, certainly it's not a consumer-friendly label. So how do you 124 00:10:40,579 --> 00:10:45,899 start to get in towards the consumer zone? Well, the main defect of the big reports 125 00:10:45,899 --> 00:10:51,240 that we just saw is that it's too much information. It's a very dense on data but 126 00:10:51,240 --> 00:10:55,649 it's very hard to distill it to the "so what" of it, right? 127 00:10:55,649 --> 00:11:00,470 And so this here is one of our earlier attempts to go ahead and do that 128 00:11:00,470 --> 00:11:04,990 distillation. What are these charts how did we come up with these? Well on the 129 00:11:04,990 --> 00:11:08,490 previous slide when we saw all these different factors that you can analyze in 130 00:11:08,490 --> 00:11:14,189 software, basically here's whose views that we arrive at this. For each of those 131 00:11:14,189 --> 00:11:18,639 things: pick a weight. Go ahead and compute a score, average against the 132 00:11:18,639 --> 00:11:22,110 weight: tada, now you have some number. You can do that for each of the libraries 133 00:11:22,110 --> 00:11:25,819 and the piece of software. And if you do that for each of the libraries in the 134 00:11:25,819 --> 00:11:29,399 software you can then go ahead and produce these histograms to show, you know, like 135 00:11:29,399 --> 00:11:35,619 this percentage of the DLLs had a score in this range. Boom, there's a bar, right. 136 00:11:35,619 --> 00:11:39,269 How do you pick those weights? We'll talk about that in a sec - it's very technical. 137 00:11:39,269 --> 00:11:45,339 But the the takeaway though, is you know that you wind up with these charts. Now 138 00:11:45,339 --> 00:11:48,329 I've obscured the labels, I've obscured the labels and the reason I've done that 139 00:11:48,329 --> 00:11:52,329 is because I don't really care that much about the actual counts. I want to talk 140 00:11:52,329 --> 00:11:57,420 about the shapes, the shapes of these charts: it's a qualitative thing. 141 00:11:57,420 --> 00:12:02,540 So here: good scores appear on the right, bad scores appear on the left. The 142 00:12:02,540 --> 00:12:06,269 histogram measuring all the libraries and components and so a very secure piece of 143 00:12:06,269 --> 00:12:12,879 software in this model manifests as a tall bar far to the right. And you can see a 144 00:12:12,879 --> 00:12:17,910 clear example at in our custom Gentoo build. Anyone here is a Gentoo fan knows - 145 00:12:17,910 --> 00:12:21,189 hey I'm going to install this thing, I think I'm going to go ahead and turn on 146 00:12:21,189 --> 00:12:25,120 every single one of those flags, and lo and behold if you do that yeah you wind up 147 00:12:25,120 --> 00:12:30,520 with tall bar far to the right. Here's in Ubuntu 16, I bet it's 16.04 but I don't 148 00:12:30,520 --> 00:12:35,959 recall exactly, 16 LTS. Here you see a lot of tall bars to the right - not quite as 149 00:12:35,959 --> 00:12:39,619 consolidated as a custom Gentoo build, but that makes sense doesn't it right? Because 150 00:12:39,619 --> 00:12:44,769 then you know you don't do your whole Ubuntu build. Now I want to contrast. I 151 00:12:44,769 --> 00:12:50,360 want to contrast. So over here on the right we see in the same model, an 152 00:12:50,360 --> 00:12:55,929 analysis of the firmware obtained from two smart televisions. Last year's models from 153 00:12:55,929 --> 00:12:59,920 Samsung and LG. And here the model numbers. We did this work in concert with 154 00:12:59,920 --> 00:13:05,039 Consumer Reports. And what do you notice about these histograms, right. Are the 155 00:13:05,039 --> 00:13:11,790 bars tall and to the right? No, they look almost normal, not quite, but that doesn't 156 00:13:11,790 --> 00:13:16,619 really matter. The main thing that matters is that this is the shape you would expect 157 00:13:16,619 --> 00:13:23,649 to get if you were playing a random game basically to decide what security features 158 00:13:23,649 --> 00:13:27,879 to enable in your software. This is the shape of not having a security program, is 159 00:13:27,879 --> 00:13:33,540 my bet. That's my bet. And so what do you see? You see heavy concentration here in 160 00:13:33,540 --> 00:13:38,800 the middle, right, that seems fair, and like it tails off. On the Samsung nothing 161 00:13:38,800 --> 00:13:43,549 scored all that great, same on the LG. Both of them are you know running their 162 00:13:43,549 --> 00:13:46,639 respective operating systems and they're basically just inheriting whatever 163 00:13:46,639 --> 00:13:51,249 security came from whatever open source thing they forked, right. 164 00:13:51,249 --> 00:13:55,000 So this is this is the kind of message, this right here is the kind of thing that 165 00:13:55,000 --> 00:14:01,929 we serve to exist for. This is us producing charts showing that the current 166 00:14:01,929 --> 00:14:08,019 practices in the not-so consumer-friendly space of running your own Linux distros 167 00:14:08,019 --> 00:14:13,290 far exceed the products being delivered, certainly in this case in the smart TV 168 00:14:13,290 --> 00:14:24,941 market. But I think you might agree with me, it's much worse than this. So let's 169 00:14:24,941 --> 00:14:28,319 dig into that a little bit more, I have a different point that I want to make about 170 00:14:28,319 --> 00:14:33,959 that same data set - so this table here this table is again looking at the LG 171 00:14:33,959 --> 00:14:39,769 Samsung and Gentoo Linux installations. And on this table we're just pulling out 172 00:14:39,769 --> 00:14:43,839 some of the easy to identify security features you might enable in a binary 173 00:14:43,839 --> 00:14:49,989 right. So percentage of binaries with address space layout randomization, right? 174 00:14:49,989 --> 00:14:56,429 Let's talk about that on our Gentoo build it's over 99%. That also holds for the 175 00:14:56,429 --> 00:15:02,699 Amazon Linux AMI - it holds in Ubuntu. ASLR is incredibly common in modern Linux. 176 00:15:02,699 --> 00:15:09,290 And despite that, fewer than 70 percent of the binaries on the LG television had it 177 00:15:09,290 --> 00:15:13,739 enabled. Fewer than 70 percent. And the Samsung was doing, you know, better than 178 00:15:13,739 --> 00:15:19,780 that I guess, but 80 percent is a pretty disappointing when a default Linux 179 00:15:19,780 --> 00:15:25,190 install, you know, mainstream Linux distro is going to get you 99, right? And it only 180 00:15:25,190 --> 00:15:28,079 gets worse, it only gets worse right you know? 181 00:15:28,079 --> 00:15:32,379 RELRO support, if you don't know what that is that's ok but if you do, look abysmal 182 00:15:32,379 --> 00:15:37,809 coverage look at this abysmal coverage coming out of these IOT devices very sad. 183 00:15:37,809 --> 00:15:40,749 And you see it over and over and over again. I'm showing this because some 184 00:15:40,749 --> 00:15:46,339 people in this room or watching this video ship software - and I have a message, I 185 00:15:46,339 --> 00:15:50,309 have a message to those people who ship software who aren't working on say Chrome 186 00:15:50,309 --> 00:15:58,609 or any of the other big-name Pwn2Own kinds of targets. Look at this: you can be 187 00:15:58,609 --> 00:16:02,480 leading the pack by mastering the fundamentals. You can be leading the pack 188 00:16:02,480 --> 00:16:07,079 by mastering the fundamentals. This is a point that really as a security field we 189 00:16:07,079 --> 00:16:11,179 really need to be driving home. You know, one of the things that we're seeing here 190 00:16:11,179 --> 00:16:15,709 in our data is that if you're the vendor who is shipping the product everyone has 191 00:16:15,709 --> 00:16:19,390 heard of in the security field and maybe your game is pretty decent right? If 192 00:16:19,390 --> 00:16:23,599 you're shipping say Windows or if you're shipping Firefox or whatever. But if 193 00:16:23,599 --> 00:16:26,149 you're if you're doing one of these things where people are just kind of beating you 194 00:16:26,149 --> 00:16:30,619 up for default passwords, then your problems are way further than just default 195 00:16:30,619 --> 00:16:35,399 passwords, right? Like the house, the house is messy it needs to be cleaned, 196 00:16:35,399 --> 00:16:43,190 needs to be cleaned. So the rest of the talk like I said we're going to be 197 00:16:43,190 --> 00:16:47,019 discussing a lot of other things that amount to getting you know a peek behind 198 00:16:47,019 --> 00:16:50,689 the curtain and where some of these things come from and getting very specific about 199 00:16:50,689 --> 00:16:54,420 how this business works, but if you're interested in more of the high level 200 00:16:54,420 --> 00:16:58,980 material - especially if you're interested in interesting results and insights, some 201 00:16:58,980 --> 00:17:01,949 of which I'm going to have here later. But I really encourage you though to take a 202 00:17:01,949 --> 00:17:06,750 look at the talk from this past summer by our chief scientist Sarah Zatko, which is 203 00:17:06,750 --> 00:17:11,217 predominantly on the topic of surprising results in the data. 204 00:17:14,892 --> 00:17:18,539 Today, though, this being our first time presenting here in Europe, we figured we 205 00:17:18,539 --> 00:17:22,869 would take more of an overarching kind of view. What we're doing and why we're 206 00:17:22,869 --> 00:17:26,619 excited about it and where it's headed. So we're about to move into a little bit of 207 00:17:26,619 --> 00:17:31,600 the underlying theory, you know. Why do I think it's reasonable to even try to 208 00:17:31,600 --> 00:17:35,429 measure the security of software from a technical perspective. But before we can 209 00:17:35,429 --> 00:17:39,310 get into that I need to talk a little bit about our goals, so that the decisions and 210 00:17:39,310 --> 00:17:45,380 the theory; the motivation is clear, right. Our goals are really simple: it's a 211 00:17:45,380 --> 00:17:51,399 very easy organization to run because of that. Goal number one: remain independent 212 00:17:51,399 --> 00:17:56,260 of vendor influence. We are not the first organization to purport to be looking out 213 00:17:56,260 --> 00:18:02,470 for the consumer. But unlike many of our predecessors, we are not taking money from 214 00:18:02,470 --> 00:18:09,920 the people we review, right? Seems like some basic stuff. Seems like some basic 215 00:18:09,920 --> 00:18:17,539 stuff right? Thank you, okay. Two: automated, comparable, quantitative 216 00:18:17,539 --> 00:18:23,790 analysis. Why automated? Well, we need our test results to be reproducible. And Tim 217 00:18:23,790 --> 00:18:27,720 goes in opens up your software in IDA and finds a bunch of stuff that makes them all 218 00:18:27,720 --> 00:18:32,620 stoped - that's not a very repeatable kind of a standard for things. And so we're 219 00:18:32,620 --> 00:18:36,440 interested in things which are automated. We'll talk about, maybe a few hackers in 220 00:18:36,440 --> 00:18:39,940 here know how hard that is. We'll talk about that, but then last we also we're 221 00:18:39,940 --> 00:18:43,539 well acting as a watchdog - we're protecting the interests of the user, the 222 00:18:43,539 --> 00:18:47,630 consumer, however you would like to look at it. But we also have three non goals, 223 00:18:47,630 --> 00:18:52,510 three non goals that are equally important. One: we have a non goal of 224 00:18:52,510 --> 00:18:56,859 finding and disclosing vulnerabilities. I reserve the right to find and disclose 225 00:18:56,859 --> 00:19:01,370 vulnerabilities. But that's not my goal, it's not my goal. Another non goal is to 226 00:19:01,370 --> 00:19:04,840 tell software vendors what to do. If a vendor asks me how to remediate their 227 00:19:04,840 --> 00:19:08,500 terrible score, I will tell them what we are measuring but I'm not there to help 228 00:19:08,500 --> 00:19:11,950 them remediate. It's on them to be able to ship a secure product without me holding 229 00:19:11,950 --> 00:19:19,049 their hand. We'll see. And then three: non-goal, perform free security testing 230 00:19:19,049 --> 00:19:24,090 for vendors. Our testing happens after you release. Because when you release your 231 00:19:24,090 --> 00:19:28,980 software you are telling people it is ready to be used. Is it really though, is 232 00:19:28,980 --> 00:19:31,799 it really though, right? Applause 233 00:19:31,799 --> 00:19:37,309 Yeah, thank you. Yeah, so we are not there to give you a preview of what your score 234 00:19:37,309 --> 00:19:42,270 will be. There is no sum of money you can hand me that will get you an early preview 235 00:19:42,270 --> 00:19:46,169 of what your score is - you can try me, you can try me: there's a fee for trying 236 00:19:46,169 --> 00:19:50,260 me. There's a fee for trying me. But I'm not gonna look at your stuff until I'm 237 00:19:50,260 --> 00:19:58,549 ready to drop it, right. Yeah bitte, yeah. All right. So moving into this theory 238 00:19:58,549 --> 00:20:02,770 territory. Three big questions, three big questions that need to be addressed if you 239 00:20:02,770 --> 00:20:06,990 want to do our work efficiently: what works, what works for improving security, 240 00:20:06,990 --> 00:20:13,030 what are the things that need or that you really want to see in software. Two: how 241 00:20:13,030 --> 00:20:17,120 do you recognize when it's being done? It's no good if someone hands you a piece 242 00:20:17,120 --> 00:20:20,169 of software and says, "I've done all the latest things" and it's a complete black 243 00:20:20,169 --> 00:20:24,529 box. If you can't check the claim, the claim is as good as false, in practical 244 00:20:24,529 --> 00:20:30,210 terms, period, right. Software has to be reviewable or a priori, I'll think you're 245 00:20:30,210 --> 00:20:35,730 full of it. And then three: who's doing it - of all the things that work, that you 246 00:20:35,730 --> 00:20:39,820 can recognize, who's actually doing it. You know, let's go ahead - our field is 247 00:20:39,820 --> 00:20:47,429 famous for ruining people's holidays and weekends over Friday bug disclosures, you 248 00:20:47,429 --> 00:20:51,799 know New Year's Eve bug disclosures. I would like us to also be famous for 249 00:20:51,799 --> 00:20:59,250 calling out those teams and those software organizations which are being as good as 250 00:20:59,250 --> 00:21:04,240 the bad guys are being bad, yeah? So provide someone an incentive to be maybe 251 00:21:04,240 --> 00:21:19,460 happy to see us for a change, right. Okay, so thank you. Yeah, all right. So how do 252 00:21:19,460 --> 00:21:26,120 we actually pull these things off; the basic idea. So, I'm going to get into some 253 00:21:26,120 --> 00:21:29,470 deeper theory: if you're not a theorist I want you to focus on this slide. 254 00:21:29,470 --> 00:21:33,429 And I'm gonna bring it back, it's not all theory from here on out after this but if 255 00:21:33,429 --> 00:21:39,289 you're not a theorist I really want you to focus on this slide. The basic motivation, 256 00:21:39,289 --> 00:21:42,560 the basic motivation behind what we're doing; the technical motivation - why we 257 00:21:42,560 --> 00:21:47,020 think that it's possible to measure and report on security. It all boils down to 258 00:21:47,020 --> 00:21:53,020 this right. So we start with a thought experiment, a gedankent, right? Given a 259 00:21:53,020 --> 00:21:58,650 piece of software we can ask: overall, how secure is it? Kind of a vague question but 260 00:21:58,650 --> 00:22:03,000 you could imagine you know there's versions of that question. And two: what 261 00:22:03,000 --> 00:22:07,820 are its vulnerabilities. Maybe you want to nitpick with me about what the word 262 00:22:07,820 --> 00:22:11,240 vulnerability means but broadly you know this is a much more specific question 263 00:22:11,240 --> 00:22:18,850 right. And here's here's the enticing thing: the first question appears to ask 264 00:22:18,850 --> 00:22:24,929 for less information than the second question. And maybe if we were taking bets 265 00:22:24,929 --> 00:22:28,520 I would put my money on, yes, it actually does ask for less information. What do I 266 00:22:28,520 --> 00:22:33,240 mean by that what do I mean by that? Well, let's say that someone told you all of the 267 00:22:33,240 --> 00:22:38,389 vulnerabilities in a system right? They said, "Hey, I got them all", right? You're 268 00:22:38,389 --> 00:22:41,580 like all right that's cool, that's cool. And if someone asks you hey how secure is 269 00:22:41,580 --> 00:22:45,440 this system you can give them a very precise answer. You can say it has N 270 00:22:45,440 --> 00:22:48,620 vulnerabilities, and they're of this kind and like all this stuff right so certainly 271 00:22:48,620 --> 00:22:54,630 the second question is enough to answer the first. But, is the reverse true? 272 00:22:54,630 --> 00:22:58,470 Namely, if someone were to tell you, for example, "hey, this piece of software has 273 00:22:58,470 --> 00:23:06,210 exactly 32 vulnerabilities in it." Does that make it easier to find any of them? 274 00:23:06,210 --> 00:23:12,320 Right, there's room for to maybe do that using some algorithms that are not yet in 275 00:23:12,320 --> 00:23:15,840 existence. Certainly the computer scientists in here 276 00:23:15,840 --> 00:23:19,450 are saying, "well, you know, yeah maybe counting the number of SAT solutions 277 00:23:19,450 --> 00:23:22,700 doesn't help you practically find solutions. But it might and we just don't 278 00:23:22,700 --> 00:23:27,120 know." Okay fine fine fine. Maybe these things are the same, but the my experience 279 00:23:27,120 --> 00:23:30,410 in security, and the experience of many others perhaps is that they probably 280 00:23:30,410 --> 00:23:36,510 aren't the same question. And this motivates what I'm calling here is Zatko's 281 00:23:36,510 --> 00:23:40,870 question, which is basically asking for an algorithm that demonstrates that the first 282 00:23:40,870 --> 00:23:45,970 question is easier than the second question, right. So Zatko's question: 283 00:23:45,970 --> 00:23:49,360 develop a heuristic which can to efficiently answer one, but not 284 00:23:49,360 --> 00:23:53,549 necessarily two. If you're looking for a metaphor, if you want to know why I care 285 00:23:53,549 --> 00:23:56,640 about this distinction, I want you to think about some certain controversial 286 00:23:56,640 --> 00:24:00,990 technologies: maybe think about say nuclear technology, right. An algorithm 287 00:24:00,990 --> 00:24:04,529 that answers one, but not two, it's a very safe algorithm to publish. Very safe 288 00:24:04,529 --> 00:24:11,369 algorithm publish indeed. Okay, Claude Shannon would like more information. happy 289 00:24:11,369 --> 00:24:16,039 to oblige. Let's take a look at this question from a different perspective 290 00:24:16,039 --> 00:24:19,379 maybe a more hands-on perspective: the hacker perspective, right? If you're a 291 00:24:19,379 --> 00:24:22,389 hacker and you're watching me up here and I'm waving my hands around and I'm showing 292 00:24:22,389 --> 00:24:25,930 you charts maybe you're thinking to yourself yeah boy, what do you got? Right, 293 00:24:25,930 --> 00:24:29,730 how does this actually go. And maybe what you're thinking to yourself is that, you 294 00:24:29,730 --> 00:24:34,350 know, finding good vulns: that's an artisan craft right? You're in IDA, you 295 00:24:34,350 --> 00:24:37,250 know you're reversing old way you're doing all these things or hit and Comm, I don't 296 00:24:37,250 --> 00:24:41,429 know all that stuff. And like, you know, this kind of clever game; cleverness is 297 00:24:41,429 --> 00:24:47,210 not like this thing that feels very automatable. But you know on the other 298 00:24:47,210 --> 00:24:51,360 hand there are a lot of tools that do automate things and so it's not completely 299 00:24:51,360 --> 00:24:57,110 not automatable. And if you're into fuzzing then perhaps 300 00:24:57,110 --> 00:25:01,500 you are aware of this very simple observation, which is that if your harness 301 00:25:01,500 --> 00:25:04,940 is perfect if you really know what you're doing if you have a decent fuzzer then in 302 00:25:04,940 --> 00:25:08,840 principle fuzzing can find every single problem. You have to be able to look for 303 00:25:08,840 --> 00:25:13,870 it you have to be able harness for it but in principle it will, right. So the hacker 304 00:25:13,870 --> 00:25:19,210 perspective on Zatko's question is maybe of two minds on the one hand assessing 305 00:25:19,210 --> 00:25:22,399 security is a game of cleverness, but on the other hand we're kind of right now at 306 00:25:22,399 --> 00:25:25,880 the cusp of having some game-changing tech really go - maybe you're saying like 307 00:25:25,880 --> 00:25:29,580 fuzzing is not at the cusp, I promise it's just at the cusp. We haven't seen all the 308 00:25:29,580 --> 00:25:33,690 fuzzing has to offer right and so maybe there's room maybe there's room for some 309 00:25:33,690 --> 00:25:41,200 automation to be possible in pursuit of Zatko's question. Of course, there are 310 00:25:41,200 --> 00:25:45,920 many challenges still in, you know, using existing hacker technology. Mostly of the 311 00:25:45,920 --> 00:25:49,570 form of various open questions. For example if you're into fuzzing, you know, 312 00:25:49,570 --> 00:25:53,039 hey: identifying unique crashes. There's an open question. We'll talk about some of 313 00:25:53,039 --> 00:25:57,060 those, we'll talk about some of those. But I'm going to offer another perspective 314 00:25:57,060 --> 00:26:01,490 here: so maybe you're not in the business of doing software reviews but you know a 315 00:26:01,490 --> 00:26:05,929 little computer science. And maybe that computer science has you wondering what's 316 00:26:05,929 --> 00:26:12,679 this guy talking about, right. I'm here to acknowledge that. So whatever you think 317 00:26:12,679 --> 00:26:16,610 the word security means: I've got a list of questions up here. Whatever you think 318 00:26:16,610 --> 00:26:19,502 the word security means, probably, some of these questions are relevant to your 319 00:26:19,502 --> 00:26:23,299 definition. Right. Does the software have a hidden backdoor 320 00:26:23,299 --> 00:26:26,600 or any kind of hidden functionality, does it handle crypto material correctly, etc, 321 00:26:26,600 --> 00:26:30,429 so forth. Anyone in here who knows some computers abilities theory knows that 322 00:26:30,429 --> 00:26:34,240 every single one of these questions and many others like them are undecidable due 323 00:26:34,240 --> 00:26:37,960 to reasons essentially no different than the reason the halting problem is 324 00:26:37,960 --> 00:26:41,330 undecidable,\ which is to say due to reasons essentially first identified and 325 00:26:41,330 --> 00:26:46,019 studied by Alan Turing a long time before we had microarchitectures and all these 326 00:26:46,019 --> 00:26:50,350 other things. And so, the computability perspective says that, you know, whatever 327 00:26:50,350 --> 00:26:54,640 your definition of security is ultimately you have this recognizability problem: 328 00:26:54,640 --> 00:26:57,900 fancy way of saying that algorithms won't be able to recognize secure software 329 00:26:57,900 --> 00:27:02,690 because of the undecidability these issues. The takeaway, the takeaway is that 330 00:27:02,690 --> 00:27:07,090 the computability angle on all of this says: anyone who's in the business that 331 00:27:07,090 --> 00:27:12,394 we're in has to use heuristics. You have to, you have to. 332 00:27:15,006 --> 00:27:24,850 All right, this guy gets it. All right, so on the tech side our last technical 333 00:27:24,850 --> 00:27:28,380 perspective that we're going to take now is certainly the most abstract which is 334 00:27:28,380 --> 00:27:32,220 the Bayesian perspective, right. So if you're a frequentist, you need to get with 335 00:27:32,220 --> 00:27:37,490 the times you know it's everything Bayesian now. So, let's talk about this 336 00:27:37,490 --> 00:27:43,899 for a bit. Only two slides of math, I promise, only two! So, let's say that I 337 00:27:43,899 --> 00:27:47,120 have some corpus of software. Perhaps it's a collection of all modern browsers, 338 00:27:47,120 --> 00:27:50,510 perhaps it's the collection of all the packages in the Debian repository, perhaps 339 00:27:50,510 --> 00:27:53,990 it's everything on github that builds on this system, perhaps it's a hard drive 340 00:27:53,990 --> 00:27:58,159 full of warez that some guy mailed you, right? You have some corpus of software 341 00:27:58,159 --> 00:28:02,980 and for a random program in that corpus we can consider this probability: the 342 00:28:02,980 --> 00:28:07,180 probability distribution of which software is secure versus which is not. For reasons 343 00:28:07,180 --> 00:28:11,080 described on the computability perspective, this number is not a 344 00:28:11,080 --> 00:28:17,130 computable number for any reasonable definition of security. So that's a neat 345 00:28:17,130 --> 00:28:21,220 and so, for practical terms, if you want to do some probabilistic reasoning, you 346 00:28:21,220 --> 00:28:27,509 need some surrogate for that and so we consider this here. So, instead of 347 00:28:27,509 --> 00:28:31,000 considering the probability that a piece of software is secure, a non computable 348 00:28:31,000 --> 00:28:35,960 non verifiable claim, we take a look here at this indexed collection of 349 00:28:35,960 --> 00:28:38,840 probabilities. This is an infinite countable family of probability 350 00:28:38,840 --> 00:28:44,330 distributions, basically P sub h,k is just the probability that for a random piece of 351 00:28:44,330 --> 00:28:50,330 software in the corpus, h work units of fuzzing will find no more than k unique 352 00:28:50,330 --> 00:28:56,130 crashes, right. And why is this relevant? Well, at the bottom we have this analytic 353 00:28:56,130 --> 00:28:59,389 observation, which is that in the limit as h goes to infinity you're basically 354 00:28:59,389 --> 00:29:03,679 saying: "Hey, you know, if I fuzz this thing for infinity times, you know, what's 355 00:29:03,679 --> 00:29:07,549 that look like?" And, essentially, here we have analytically that this should 356 00:29:07,549 --> 00:29:12,970 converge. The P sub h,1 should converge to the probability that a piece of software 357 00:29:12,970 --> 00:29:16,331 just simply cannot be made to crash. Not the same thing as being secure, but 358 00:29:16,331 --> 00:29:23,730 certainly not a small concern relevant to security. So, none of that stuff actually 359 00:29:23,730 --> 00:29:30,620 was Bayesian yet, so we need to get there. And so here we go, right: so, the previous 360 00:29:30,620 --> 00:29:35,080 slide described a probability distribution measured based on fuzzing. But fuzzing is 361 00:29:35,080 --> 00:29:39,130 expensive and it is also not an answer to Zatko's question because it finds 362 00:29:39,130 --> 00:29:43,759 vulnerabilities, it doesn't measure security in the general sense and so 363 00:29:43,759 --> 00:29:47,110 here's where we make the jump to conditional probabilities: Let M be some 364 00:29:47,110 --> 00:29:51,929 observable property of software has ASLR, has RELRO, calls these functions, doesn't 365 00:29:51,929 --> 00:29:56,770 call those functions... take your pick. For random s in S we now consider these 366 00:29:56,770 --> 00:30:02,070 conditional probability distributions and this is the same kind of probability as we 367 00:30:02,070 --> 00:30:08,379 had on the previous slide but conditioned on this observable being true, and this 368 00:30:08,379 --> 00:30:11,480 leads to the refined of the Siddall variant of Zatko's question: 369 00:30:11,480 --> 00:30:17,120 Which observable properties of software satisfy that, when the software has 370 00:30:17,120 --> 00:30:22,590 property m, the probability of fuzzing being hard is very high? That's what this 371 00:30:22,590 --> 00:30:27,121 version of this question phrases, and here we say, you know, in large log(h)/k, in 372 00:30:27,121 --> 00:30:31,590 other words: exponentially more fuzzing than you expect to find bugs. So this is 373 00:30:31,590 --> 00:30:36,350 the technical version of what we're after. All of this can be explored, you can 374 00:30:36,350 --> 00:30:40,340 brute-force your way to finding all of this stuff, and that's exactly what we're 375 00:30:40,340 --> 00:30:48,050 doing. So we're looking for all kinds of things, we're looking for all kinds of 376 00:30:48,050 --> 00:30:53,840 things that correlate with fuzzing having low yield on a piece of software, and 377 00:30:53,840 --> 00:30:57,360 there's a lot of ways in which that can happen. It could be that you are looking 378 00:30:57,360 --> 00:31:01,409 at a feature of software that literally prevents crashes. Maybe it's the never 379 00:31:01,409 --> 00:31:08,210 crash flag, I don't know. But most of the things I've talked about, ASLR, RERO, etc. 380 00:31:08,210 --> 00:31:12,169 don't prevent crashes. In fact a ASLR can take non-crashing programs and make them 381 00:31:12,169 --> 00:31:16,849 crashing. It's the number one reason vendors don't enable it, right? So why am 382 00:31:16,849 --> 00:31:20,079 I talking about ASLR? Why am I talking about RELRO? Why am i talking about all 383 00:31:20,079 --> 00:31:22,899 these things that have nothing to do with stopping crashes and I'm claiming I'm 384 00:31:22,899 --> 00:31:27,399 measuring crashes? This is because, in the Bayesian perspective, correlation is not 385 00:31:27,399 --> 00:31:31,730 the same thing as causation, right? Correlation is not the same thing as 386 00:31:31,730 --> 00:31:35,340 causation. It could be that M's presence literally prevents crashes, but it could 387 00:31:35,340 --> 00:31:39,749 also be that, by some underlying coincidence, the things we're looking for 388 00:31:39,749 --> 00:31:43,600 are mostly only found in software that's robust against crashing. 389 00:31:43,600 --> 00:31:48,799 If you're looking for security, I submit to you that the difference doesn't matter. 390 00:31:48,799 --> 00:31:54,929 Okay, end of my math, danke. I will now go ahead and do this like really nice analogy 391 00:31:54,929 --> 00:31:59,279 of all those things that I just described, right. So we're looking for indicators of 392 00:31:59,279 --> 00:32:03,640 a piece of software being secure enough to be good for consumers, right. So here's an 393 00:32:03,640 --> 00:32:08,131 analogy. Let's say you're a geologist, you study minerals and all of that and you're 394 00:32:08,131 --> 00:32:13,560 looking for diamonds. Who isn't, right? Want those diamonds! And like how do you 395 00:32:13,560 --> 00:32:18,270 find diamonds? Even in places that are rich in diamonds, diamonds are not common. 396 00:32:18,270 --> 00:32:21,279 You don't just go walking around in your boots, kicking until your toe stubs on a 397 00:32:21,279 --> 00:32:27,049 diamond? You don't do that. Instead you look for other minerals that are mostly 398 00:32:27,049 --> 00:32:31,860 only found near diamonds but are much more abundant in those locations than the 399 00:32:31,860 --> 00:32:37,960 diamonds. So, this is mineral science 101, I guess, I don't know. So, for example, 400 00:32:37,960 --> 00:32:41,389 you want to go find diamond: put on your boots and go kicking until you find some 401 00:32:41,389 --> 00:32:46,110 chromite, look for some diopside, you know, look for some garnet. None of these 402 00:32:46,110 --> 00:32:50,340 things turn into diamonds, none of these things cause diamonds but if you're 403 00:32:50,340 --> 00:32:54,020 finding good concentrations of these things, then, statistically, there's 404 00:32:54,020 --> 00:32:58,249 probably diamonds nearby. That's what we're doing. We're not looking for the 405 00:32:58,249 --> 00:33:02,570 things that cause good security per se. Rather, we're looking for the indicators 406 00:33:02,570 --> 00:33:08,349 that you have put the effort into your software, right? How's that working out 407 00:33:08,349 --> 00:33:15,070 for us? How's that working out for us? Well, we're still doing studies. It's, you 408 00:33:15,070 --> 00:33:18,490 know, early to say exactly but we do have the following interesting coincidence: and 409 00:33:18,490 --> 00:33:24,789 so, here presented I have a collection of prices that somebody gave much for so- 410 00:33:24,789 --> 00:33:30,369 called the underground exploits. And I can tell you these prices are maybe a little 411 00:33:30,369 --> 00:33:34,450 low these days but if you work in that business, if you go to Cyscin, if you do 412 00:33:34,450 --> 00:33:39,009 that kind of stuff, maybe you know that this is ballpark, it's ballpark. 413 00:33:39,009 --> 00:33:44,080 Alright, and, just a coincidence, maybe it means we're on the right track, I don't 414 00:33:44,080 --> 00:33:48,740 know, but it's an encouraging sign: When we run these programs through our 415 00:33:48,740 --> 00:33:53,060 analysis, our rankings more or less correspond to the actual prices that you 416 00:33:53,060 --> 00:33:58,279 encounter in the wild for access via these applications. Up above, I have one of our 417 00:33:58,279 --> 00:34:02,059 histogram charts. You can see here that Chrome and Edge in this particular model 418 00:34:02,059 --> 00:34:06,149 scored very close to the same and it's a test model, so, let's say they're 419 00:34:06,149 --> 00:34:11,480 basically the same. Firefox, you know, behind there a little 420 00:34:11,480 --> 00:34:15,040 bit. I don't have Safari on this chart because - this or all Windows applications 421 00:34:15,040 --> 00:34:21,091 - but the Safari score falls in between. So, lots of theory, lots of theory, lots 422 00:34:21,091 --> 00:34:27,920 of theory and then we have this. So, we're going to go ahead now and hand off to our 423 00:34:27,920 --> 00:34:31,679 lead engineer, Parker, who is going to talk about some of the concrete stuff, the 424 00:34:31,679 --> 00:34:35,210 non-chalkboard stuff, the software stuff that actually makes this work. 425 00:34:35,956 --> 00:34:40,980 Thompson: Yeah, so I want to talk about the process of actually doing it. Building 426 00:34:40,980 --> 00:34:45,050 the tooling that's required to collect these observables. Effectively, how do you 427 00:34:45,050 --> 00:34:50,560 go mining for indicator indicator minerals? But first the progression of 428 00:34:50,560 --> 00:34:55,810 where we are and where we're going. We initially broke this out into three major 429 00:34:55,810 --> 00:35:00,360 tracks of our technology. We have our static analysis engine, which started as a 430 00:35:00,360 --> 00:35:05,790 prototype, and we have now recently completed a much more mature and solid 431 00:35:05,790 --> 00:35:09,930 engine that's allowing us to be much more extensible and digging deeper into 432 00:35:09,930 --> 00:35:16,320 programs, and provide a much more deep observables. Then, we have the data 433 00:35:16,320 --> 00:35:21,510 collection and data reporting. Tim showed some of our early stabs at this. We're 434 00:35:21,510 --> 00:35:25,450 right now in the process of building new engines to make the data more accessible 435 00:35:25,450 --> 00:35:30,150 and easy to work with and hopefully more of that will be available soon. Finally, 436 00:35:30,150 --> 00:35:35,910 we have our fuzzer track. We needed to get some early data, so we played with some 437 00:35:35,910 --> 00:35:40,680 existing off-the-shelf fuzzers, including AFL, and, while that was fun, 438 00:35:40,680 --> 00:35:44,190 unfortunately it's a lot of work to manually instrument a lot of fuzzers for 439 00:35:44,190 --> 00:35:48,830 hundreds of binaries. So, we then built an automated solution 440 00:35:48,830 --> 00:35:52,930 that started to get us closer to having a fuzzing harness that could autogenerate 441 00:35:52,930 --> 00:35:57,840 itself, depending on the software, the software's behavior. But, right now, 442 00:35:57,840 --> 00:36:01,650 unfortunately that technology showed us more deficiencies than it showed 443 00:36:01,650 --> 00:36:07,360 successes. So, we are now working on a much more mature fuzzer that will allow us 444 00:36:07,360 --> 00:36:12,780 to dig deeper into programs as we're running and collect very specific things 445 00:36:12,780 --> 00:36:21,260 that we need for our model and our analysis. But on to our analytic pipeline 446 00:36:21,260 --> 00:36:25,831 today. This is one of the most concrete components of our engine and one of the 447 00:36:25,831 --> 00:36:29,000 most fun! We effectively wanted some type of 448 00:36:29,000 --> 00:36:34,550 software hopper, where you could just pour programs in, installers and then, on the 449 00:36:34,550 --> 00:36:39,560 other end, come reports: Fully annotated and actionable information that we can 450 00:36:39,560 --> 00:36:45,320 present to people. So, we went about the process of building a large-scale engine. 451 00:36:45,320 --> 00:36:50,500 It starts off with a simple REST API, where we can push software in, which then 452 00:36:50,500 --> 00:36:55,600 gets moved over to our computation cluster that effectively provides us a fabric to 453 00:36:55,600 --> 00:37:00,310 work with. It makes is made up of a lot of different software suites, starting off 454 00:37:00,310 --> 00:37:06,730 with our data processing, which is done by apache spark and then moves over into data 455 00:37:06,730 --> 00:37:12,910 data handling and data analysis in spark, and then we have a common HDFS layer to 456 00:37:12,910 --> 00:37:17,530 provide a place for the data to be stored and then a resource manager and Yarn. All 457 00:37:17,530 --> 00:37:22,500 of that is backed by our compute and data nodes, which scale out linearly. That then 458 00:37:22,500 --> 00:37:27,590 moves into our data science engine, which is effectively spark with Apache Zeppelin, 459 00:37:27,590 --> 00:37:30,480 which provides us a really fun interface where we can work with the data in an 460 00:37:30,480 --> 00:37:35,830 interactive manner but be kicking off large-scale jobs into the cluster. And 461 00:37:35,830 --> 00:37:40,110 finally, this goes into our report generation engine. What this bought us, 462 00:37:40,110 --> 00:37:46,030 was the ability to linearly scale and make that hopper bigger and bigger as we need, 463 00:37:46,030 --> 00:37:50,740 but also provide us a way to process data that doesn't fit in a single machine's 464 00:37:50,740 --> 00:37:54,110 RAM. You can push the instance sizes as you large as you want, but we have 465 00:37:54,110 --> 00:38:00,300 datasets that blow away any single host RAM set. So this allows us to work with 466 00:38:00,300 --> 00:38:08,690 really large collections of observables. I want to dive down now into our actual 467 00:38:08,690 --> 00:38:13,160 static analysis. But first we have to explore the problem space, because it's a 468 00:38:13,160 --> 00:38:19,490 nasty one. Effectively in settles mission is to process as much software as 469 00:38:19,490 --> 00:38:25,790 possible. Hopefully all of it, but it's hard to get your hand on all the binaries 470 00:38:25,790 --> 00:38:29,260 that are out there. When you start to look at that problem you understand there's a 471 00:38:29,260 --> 00:38:34,830 lot of combinations: there's a lot of CPU architectures, there's a lot of operating 472 00:38:34,830 --> 00:38:38,610 systems, there's a lot of file formats, there's a lot of environments the software 473 00:38:38,610 --> 00:38:43,160 gets deployed into, and every single one of them has their own app Archer app 474 00:38:43,160 --> 00:38:47,320 armory features. And it can be specifically set for one combination 475 00:38:47,320 --> 00:38:51,671 button on another and you don't want to penalize a developer for not turning on a 476 00:38:51,671 --> 00:38:56,290 feature they had no access to ever turn on. So effectively we need to solve this 477 00:38:56,290 --> 00:39:01,050 in a much more generic way. And so what we did is our static analysis engine 478 00:39:01,050 --> 00:39:04,630 effectively looks like a gigantic collection of abstraction libraries to 479 00:39:04,630 --> 00:39:12,390 handle binary programs. You take in some type of input file be it ELF, PE, MachO 480 00:39:12,390 --> 00:39:17,730 and then the pipeline splits. It goes off into two major analyzer classes, our 481 00:39:17,730 --> 00:39:22,360 format analyzers, which look at the software much like how a linker or loader 482 00:39:22,360 --> 00:39:26,600 would look at it. I want to understand how it's going to be loaded up, what type of 483 00:39:26,600 --> 00:39:30,680 armory feature is going to be applied and then we can run analyzers over that. In 484 00:39:30,680 --> 00:39:34,520 order to achieve that we need abstraction libraries that can provide us an abstract 485 00:39:34,520 --> 00:39:40,900 memory map, a symbol resolver, generic section properties. So all that feeds in 486 00:39:40,900 --> 00:39:46,060 and then we run over a collection of analyzers to collect data and observables. 487 00:39:46,060 --> 00:39:49,650 Next we have our code analyzers, these are the analyzers that run over the code 488 00:39:49,650 --> 00:39:57,600 itself. I need to be able to look at every possible executable path. In order to do 489 00:39:57,600 --> 00:40:02,400 that we need to do function discovery, feed that into a control flow recovery 490 00:40:02,400 --> 00:40:07,880 engine, and then as a post-processing step dig through all of the possible metadata 491 00:40:07,880 --> 00:40:12,820 in the software, such as like a switch table, or something like that to get even 492 00:40:12,820 --> 00:40:20,770 deeper into the software. Then this provides us a basic list of basic blocks, 493 00:40:20,770 --> 00:40:24,470 functions, instruction ranges. And does so in an efficient manner so we can process a 494 00:40:24,470 --> 00:40:30,550 lot of software as it goes. Then all that gets fed over into the main modular 495 00:40:30,550 --> 00:40:36,570 analyzers. Finally, all of this comes together and gets put into a gigantic blob 496 00:40:36,570 --> 00:40:41,850 of observables and fed up to the pipeline. We really want to thank the Ford 497 00:40:41,850 --> 00:40:46,920 Foundation for supporting our work in this, because the pipeline and the static 498 00:40:46,920 --> 00:40:51,840 analysis has been a massive boon for our project and we're only beginning now to 499 00:40:51,840 --> 00:40:58,920 really get our engine running and we're having a great time with it. So digging 500 00:40:58,920 --> 00:41:03,760 into the observables themselves, what are we looking at and let's break them apart. 501 00:41:03,760 --> 00:41:08,980 So the format structure components, things like ASLR, DEP, RELRO. 502 00:41:08,980 --> 00:41:13,370 basic app armory, that's going to go into the feature and gonna be enabled at the OS 503 00:41:13,370 --> 00:41:17,830 layer when it gets loaded up or linked. And we also collect other metadata about 504 00:41:17,830 --> 00:41:22,000 the program such as like: "What libraries are linked in?", "What's its dependency 505 00:41:22,000 --> 00:41:26,400 tree look like – completely?", "How did those software, how did those library 506 00:41:26,400 --> 00:41:32,040 score?", because that can affect your main software. Interesting example on Linux, if 507 00:41:32,040 --> 00:41:35,840 you link a library that requires an executable stack, guess what your software 508 00:41:35,840 --> 00:41:39,990 now has an executable stack, even if you didn't mark that. So we need to be owners 509 00:41:39,990 --> 00:41:44,700 to understand what ecosystem the software is gonna live in. And the code structure 510 00:41:44,700 --> 00:41:47,590 analyzers look at things like functionality: "What's the software 511 00:41:47,590 --> 00:41:52,600 doing?", "What type of app armory is getting injected into the code?". A great 512 00:41:52,600 --> 00:41:55,850 example of that is something like stack guards or fortify source. These are our 513 00:41:55,850 --> 00:42:01,550 main features that only really apply and can be observed inside of the control flow 514 00:42:01,550 --> 00:42:08,240 or inside of the actual instructions themselves. This is why control 515 00:42:08,240 --> 00:42:10,880 photographs are key. We played around with a number of 516 00:42:10,880 --> 00:42:15,980 different ways of analyzing software that we could scale out and ultimately we had 517 00:42:15,980 --> 00:42:20,171 to come down to working with control photographs. Provided here is a basic 518 00:42:20,171 --> 00:42:23,400 visualization of what I'm talking about with a control photograph, provided by 519 00:42:23,400 --> 00:42:28,690 Benja, which has wonderful visualization tools, hence this photo, and not our 520 00:42:28,690 --> 00:42:33,170 engine because we don't build their very many visualization engines. But you 521 00:42:33,170 --> 00:42:38,470 basically have a function that's broken up into basic blocks, which is broken up into 522 00:42:38,470 --> 00:42:42,910 instructions, and then you have basic flow between them. Having this as an iterable 523 00:42:42,910 --> 00:42:47,650 structure that we can work with, allows us to walk over that and walk every single 524 00:42:47,650 --> 00:42:50,790 instruction, understand the references, understand where code and data is being 525 00:42:50,790 --> 00:42:54,500 referenced, and how is it being referenced. 526 00:42:54,500 --> 00:42:57,640 And then what type of functionalities being used, so this is a great way to find 527 00:42:57,640 --> 00:43:02,530 something, like whether or not your stack guards are being applied on every function 528 00:43:02,530 --> 00:43:08,340 that needs them, how deep are they being applied, and is the compiler possibly 529 00:43:08,340 --> 00:43:11,850 introducing errors into your armory features. which are interesting side 530 00:43:11,850 --> 00:43:19,590 studies. Also why we did this is because we want to push the concept of what type 531 00:43:19,590 --> 00:43:28,339 of observables even farther. Let's say take this example you want to be able to 532 00:43:28,339 --> 00:43:34,340 take instruction abstractions. Let's say for all major architectures you can break 533 00:43:34,340 --> 00:43:38,690 them up into major categories. Be it arithmetic instructions, data manipulation 534 00:43:38,690 --> 00:43:45,850 instructions, like load stores and then control flow instructions. Then with these 535 00:43:45,850 --> 00:43:52,830 basic fundamental building blocks you can make artifacts. Think of them like a unit 536 00:43:52,830 --> 00:43:56,400 of functionality: has some type of input, some type of output, it provides some type 537 00:43:56,400 --> 00:44:01,280 of operation on it. And then with these little units of functionality, you can 538 00:44:01,280 --> 00:44:05,210 link them together and think of these artifacts as may be sub-basic block or 539 00:44:05,210 --> 00:44:09,440 crossing a few basic blocks, but a different way to break up the software. 540 00:44:09,440 --> 00:44:13,130 Because a basic block is just a branch break, but we want to look at 541 00:44:13,130 --> 00:44:18,680 functionality brakes, because these artifacts can provide the basic 542 00:44:18,680 --> 00:44:24,891 fundamental building blocks of the software itself. It's more important, when 543 00:44:24,891 --> 00:44:28,840 we want to start doing symbolic lifting. So that we can lift the entire software up 544 00:44:28,840 --> 00:44:35,250 into a generic representation, that we can slice and dice as needed. 545 00:44:38,642 --> 00:44:42,760 Moving from there, I want to talk about fuzzing a little bit more. Fuzzing is 546 00:44:42,760 --> 00:44:47,370 effectively at the heart of our project. It provides us the rich dataset that we 547 00:44:47,370 --> 00:44:52,040 can use to derive a model. It also provides us awesome other metadata on the 548 00:44:52,040 --> 00:44:58,060 side. But why? Why do we care about fuzzing? Why is fuzzing the metric, that 549 00:44:58,060 --> 00:45:04,680 you build an engine, that you build a model that you drive some type of reason 550 00:45:04,680 --> 00:45:11,560 from? So think of the set of bugs, vulnerabilities, and exploitable 551 00:45:11,560 --> 00:45:16,930 vulnerabilities. In an ideal world you'd want to just have a machine that pulls out 552 00:45:16,930 --> 00:45:20,250 exploitable vulnerabilities. Unfortunately, this is exceedingly costly 553 00:45:20,250 --> 00:45:25,690 for a series of decision problems, that go between these sets. So now consider the 554 00:45:25,690 --> 00:45:31,900 superset of bugs or faults. A fuzzer can easily recognize, or other software can 555 00:45:31,900 --> 00:45:37,400 easily recognize faults, but if you want to move down the sets you unfortunately 556 00:45:37,400 --> 00:45:42,770 need to jump through a lot of decision hoops. For example, if you want to move to 557 00:45:42,770 --> 00:45:45,760 a vulnerability you have to understand: Does the attacker have some type of 558 00:45:45,760 --> 00:45:51,150 control? Is there a trust boundary being crossed? Is this software configured in 559 00:45:51,150 --> 00:45:55,000 the right way for this to be vulnerable right now? So they're human factors that 560 00:45:55,000 --> 00:45:59,280 are not deducible from the outside. You then amplify this decision problem even 561 00:45:59,280 --> 00:46:05,320 worse going to exploitable vulnerabilities. So if we collect the 562 00:46:05,320 --> 00:46:11,360 superset of bugs, we will know that there is some proportion of subsets in there. 563 00:46:11,360 --> 00:46:15,830 And this provides us a datasets easily recognizable and we can collect in a cost- 564 00:46:15,830 --> 00:46:22,170 efficient manner. Finally, fuzzing is key and we're investing a lot of our time 565 00:46:22,170 --> 00:46:26,570 right now and working on a new fuzzing engine, because there are some key things 566 00:46:26,570 --> 00:46:32,290 we want to do. We want to be able to understand all of 567 00:46:32,290 --> 00:46:35,340 the different paths the software could be taking, and as you're fuzzing you're 568 00:46:35,340 --> 00:46:40,010 effectively driving the software down as many unique paths while referencing as 569 00:46:40,010 --> 00:46:47,760 many unique data manipulations as possible. So if we save off every path, 570 00:46:47,760 --> 00:46:51,840 annotate the ones that are faulting, we now have this beautiful rich data set of 571 00:46:51,840 --> 00:46:57,060 exactly where the software went as we were driving it in specific ways. Then we feed 572 00:46:57,060 --> 00:47:02,010 that back into our static analysis engine and begin to generate those instruction 573 00:47:02,010 --> 00:47:07,680 out of those instruction abstractions, those artifacts. And with that, imagine we 574 00:47:07,680 --> 00:47:14,560 have these gigantic traces of instruction abstractions. From there we can then begin 575 00:47:14,560 --> 00:47:20,990 to train the model to explore around the fault location and begin to understand and 576 00:47:20,990 --> 00:47:27,300 try and study the fundamental building blocks of what a bug looks like in an 577 00:47:27,300 --> 00:47:32,990 abstract instruction agnostic way. This is why we're spending a lot of time on our 578 00:47:32,990 --> 00:47:36,980 Fuzzing engine right now. But hopefully soon we'll be able to talk about that more 579 00:47:36,980 --> 00:47:40,381 and maybe a tech track and not the policy track. 580 00:47:44,748 --> 00:47:49,170 C: Yeah, so from then on when anything went wrong with the computer we said it 581 00:47:49,170 --> 00:47:55,700 had bugs in it. laughs All right, I promised you a technical journey, I 582 00:47:55,700 --> 00:47:59,461 promised you a technical journey into the dark abyss of as deep as you want to get 583 00:47:59,461 --> 00:48:03,460 with it. So let's go ahead and bring it up. Let's wrap it up and bring it up a 584 00:48:03,460 --> 00:48:07,340 little bit here. We've talked a great deal today about some theory. We've talked 585 00:48:07,340 --> 00:48:09,970 about development in our tooling and everything else and so I figured I should 586 00:48:09,970 --> 00:48:14,010 end with some things that are not in progress, but in fact which are done in 587 00:48:14,010 --> 00:48:20,630 yesterday's news. Just to go ahead and make that shared here with Europe. So in 588 00:48:20,630 --> 00:48:24,140 the midst of all of our development we have been discovering and reporting bugs, 589 00:48:24,140 --> 00:48:28,680 again this not our primary purpose really. But you know you can't help but do it. You 590 00:48:28,680 --> 00:48:32,170 know how computers are these days. You find bugs just for turning them on, right? 591 00:48:32,170 --> 00:48:38,610 So we've been disclosing all of that a little while ago. At DEFCON and Black Hat 592 00:48:38,610 --> 00:48:43,030 our chief scientist Sarah together with Mudge went ahead and dropped this 593 00:48:43,030 --> 00:48:47,840 bombshell on the Firefox team which is that for some period of time they had ASLR 594 00:48:47,840 --> 00:48:54,310 disabled on OS X. When we first found it we assumed it was a bug in our tools. When 595 00:48:54,310 --> 00:48:57,720 we first mentioned it in a talk they came to us and said it's definitely a bug on 596 00:48:57,720 --> 00:49:03,140 our tools or might be or some level of surprise and then people started looking 597 00:49:03,140 --> 00:49:08,840 into it and in fact at one point it had been enabled and then temporarily 598 00:49:08,840 --> 00:49:12,960 disabled. No one knew, everyone thought it was on. It takes someone looking to notice 599 00:49:12,960 --> 00:49:18,010 that kind of stuff, right. Major shout out though, they fixed it immediately despite 600 00:49:18,010 --> 00:49:23,950 our full disclosure on stage and everything. So very impressed, but in 601 00:49:23,950 --> 00:49:27,870 addition to popping surprises on people we've also been doing the usual process of 602 00:49:27,870 --> 00:49:32,890 submitting patches and bugs, particularly to LLVM and Qemu and if you work in 603 00:49:32,890 --> 00:49:35,810 software analysis you could probably guess why. 604 00:49:36,510 --> 00:49:39,280 Incidentally, if you're looking for a target to fuzz if you want to go home from 605 00:49:39,280 --> 00:49:45,870 CCC and you want to find a ton of findings LLVM comes with a bunch of parsers. You 606 00:49:45,870 --> 00:49:50,060 should fuzz them, you should fuzz them and I say that because I know for a fact you 607 00:49:50,060 --> 00:49:53,170 are gonna get a bunch of findings and it'd be really nice. I would appreciate it if I 608 00:49:53,170 --> 00:49:56,360 didn't have to pay the people to fix them. So if you wouldn't mind disclosing them 609 00:49:56,360 --> 00:50:00,240 that would help. But besides these bug reports and all these other things we've 610 00:50:00,240 --> 00:50:04,210 also been working with lots of others. You know we gave a talk earlier this summer, 611 00:50:04,210 --> 00:50:06,910 Sarah gave a talk earlier this summer, about these things and she presented 612 00:50:06,910 --> 00:50:11,830 findings on comparing some of these base scores of different Linux distributions. 613 00:50:11,830 --> 00:50:16,320 And based on those findings there was a person on the fedora red team, Jason 614 00:50:16,320 --> 00:50:20,470 Calloway, who sat there and well I can't read his mind but I'm sure that he was 615 00:50:20,470 --> 00:50:24,700 thinking to himself: golly it would be nice to not, you know, be surprised at the 616 00:50:24,700 --> 00:50:28,560 next one of these talks. They score very well by the way. They were leading in 617 00:50:28,560 --> 00:50:33,660 many, many of our metrics. Well, in any case, he left Vegas and he went back home 618 00:50:33,660 --> 00:50:36,850 and him and his colleagues have been working on essentially re-implementing 619 00:50:36,850 --> 00:50:41,570 much of our tooling so that they can check the stuff that we check before they 620 00:50:41,570 --> 00:50:47,530 release. Before they release. Looking for security before you release. So that would 621 00:50:47,530 --> 00:50:51,520 be a good thing for others to do and I'm hoping that that idea really catches on. 622 00:50:51,520 --> 00:50:58,990 laughs Yeah, yeah right, that would be nice. That would be nice. 623 00:50:58,990 --> 00:51:04,310 But in addition to that, in addition to that our mission really is to get results 624 00:51:04,310 --> 00:51:08,220 out to the public and so in order to achieve that, we have broad partnerships 625 00:51:08,220 --> 00:51:12,340 with Consumer Reports and the digital standard. Especially if you're into cyber 626 00:51:12,340 --> 00:51:16,410 policy, I really encourage you to take a look at the proposed digital standard, 627 00:51:16,410 --> 00:51:21,220 which is encompassing of the things we look for and and and so much more. URLs, 628 00:51:21,220 --> 00:51:25,720 data, traffic, motion and cryptography and update mechanisms and all that good stuff. 629 00:51:25,720 --> 00:51:31,951 So, where we are and where we're going, the big takeaways here for if you're 630 00:51:31,951 --> 00:51:36,310 looking for that, so what, three points for you: one we are building a tooling 631 00:51:36,310 --> 00:51:39,750 necessary to do larger and larger and larger studies regarding these surrogate 632 00:51:39,750 --> 00:51:44,980 security stores. My hope is that in some period of the not-too-distant future, I 633 00:51:44,980 --> 00:51:48,600 would like to be able to, with my colleagues, publish some really nice 634 00:51:48,600 --> 00:51:51,640 findings about what are the things that you can observe in software, which have a 635 00:51:51,640 --> 00:51:57,390 suspiciously high correlation with the software being good. Right, nobody really 636 00:51:57,390 --> 00:52:00,390 knows right now. It's an empirical question. As far as I know, the study 637 00:52:00,390 --> 00:52:03,080 hasn't been done. We've been running it on the small scale. We're building the 638 00:52:03,080 --> 00:52:06,620 tooling to do it on a much larger scale. We are hoping that this winds up being a 639 00:52:06,620 --> 00:52:11,480 useful field in security as that technology develops. In the meantime our 640 00:52:11,480 --> 00:52:15,560 static analyzers are already making surprising discoveries: hit YouTube and 641 00:52:15,560 --> 00:52:21,300 take a look for Sara Zatko's recent talks at DEFCON/Blackhat. Lots of fun findings 642 00:52:21,300 --> 00:52:25,910 in there. Lots of things that anyone who looks would have found it. Lots of that. 643 00:52:25,910 --> 00:52:29,080 And then lastly, if you were in the business of shipping software and you are 644 00:52:29,080 --> 00:52:32,620 thinking to yourself.. okay so these guys, someone gave them some money to mess up my 645 00:52:32,620 --> 00:52:36,840 day and you're wondering: what can I do to not have my day messed up? One simple 646 00:52:36,840 --> 00:52:40,870 piece of advice, one simple piece of advice: make sure your software employs 647 00:52:40,870 --> 00:52:45,920 every exploit mitigation technique Mudge has ever or will ever hear of. And he's 648 00:52:45,920 --> 00:52:49,500 heard of a lot of them. He's only gonna, you know all that, turn all those things 649 00:52:49,500 --> 00:52:52,280 on and if you don't know anything about that stuff, if nobody on your team knows 650 00:52:52,280 --> 00:52:57,370 anything about that stuff didn't I don't even know I'm saying this if you hear you 651 00:52:57,370 --> 00:53:00,972 know about that stuff so do that. If you're not here, then you should be here. 652 00:53:04,428 --> 00:53:16,330 Danke, Danke. Herald Angel: Thank you, Tim and Parker. 653 00:53:17,501 --> 00:53:23,630 Do we have any questions from the audience? It's really hard to see you with 654 00:53:23,630 --> 00:53:30,120 that bright light in my face. I think the signal angel has a question. Signal Angel: 655 00:53:30,120 --> 00:53:34,550 So the IRC channel was impressed by your tools and your models that you wrote. And 656 00:53:34,550 --> 00:53:38,050 they are wondering what's going to happen to that, because you do have funding from 657 00:53:38,050 --> 00:53:42,040 the Ford foundation now and so what are your plans with this? Do you plan on 658 00:53:42,040 --> 00:53:46,080 commercializing this or is it going to be open source or how do we get our hands on 659 00:53:46,080 --> 00:53:49,150 this? C: It's an excellent question. So for the 660 00:53:49,150 --> 00:53:53,550 time being the money that we are receiving is to develop the tooling, pay for the AWS 661 00:53:53,550 --> 00:53:57,790 instances, pay for the engineers and all that stuff. The direction as an 662 00:53:57,790 --> 00:54:01,410 organization that we would like to take things I have no interest in running a 663 00:54:01,410 --> 00:54:05,410 monopoly. That sounds like a fantastic amount of work and I really don't want to 664 00:54:05,410 --> 00:54:09,430 do it. However, I have a great deal of interest in taking the gains that we are 665 00:54:09,430 --> 00:54:13,860 making in the technology and releasing the data so that other competent researchers 666 00:54:13,860 --> 00:54:19,020 can go through and find useful things that we may not have noticed ourselves. So 667 00:54:19,020 --> 00:54:22,150 we're not at a point where we are releasing data in bulk just yet, but that 668 00:54:22,150 --> 00:54:26,432 is simply a matter of engineering our tools, are still in flux as we, you know. 669 00:54:26,432 --> 00:54:29,230 When we do that, we want to make sure the data is correct and so our software has to 670 00:54:29,230 --> 00:54:33,640 have its own low bug counts and all these other things. But ultimately for the 671 00:54:33,640 --> 00:54:37,950 scientific aspect of our mission. Though the science is not our primary mission. 672 00:54:37,950 --> 00:54:41,920 Our primary mission is to apply it to help consumers. At the same time, it is our 673 00:54:41,920 --> 00:54:47,590 belief that an opaque model is as good as crap, no one should trust an opaque model, 674 00:54:47,590 --> 00:54:50,940 if somebody is telling you that they have some statistics and they do not provide 675 00:54:50,940 --> 00:54:54,540 you with any underlying data and it is not reproducible you should ignore them. 676 00:54:54,540 --> 00:54:58,360 Consequently what we are working towards right now is getting to a point where we 677 00:54:58,360 --> 00:55:02,730 will be able to share all of those findings. The surrogate scores, the 678 00:55:02,730 --> 00:55:06,000 interesting correlations between observables and fuzzing. All that will be 679 00:55:06,000 --> 00:55:09,200 public as the material comes online. Signal Angel: Thank you. 680 00:55:09,200 --> 00:55:11,870 C: Thank you. Herald Angel: Thank you. And microphone 681 00:55:11,870 --> 00:55:14,860 number three please. Mic3: Hi, thanks so some really 682 00:55:14,860 --> 00:55:18,450 interesting work you presented here. So there's something I'm not sure I 683 00:55:18,450 --> 00:55:22,910 understand about the approach that you're taking. If you are evaluating the security 684 00:55:22,910 --> 00:55:26,320 of say a library function or the implementation of a network protocol for 685 00:55:26,320 --> 00:55:29,780 example you know there'd be a precise specification you could check that 686 00:55:29,780 --> 00:55:35,190 against. And the techniques you're using would make sense to me. But it's not so 687 00:55:35,190 --> 00:55:37,970 clear since you've set the goal that you've set for yourself is to evaluate 688 00:55:37,970 --> 00:55:43,580 security of consumer software. It's not clear to me whether it's fair to call 689 00:55:43,580 --> 00:55:47,430 these results security scores in the absence of a threat model so. So my 690 00:55:47,430 --> 00:55:50,350 question is, you know, how is it meaningful to make a claim that a piece of 691 00:55:50,350 --> 00:55:52,240 software is secure if you don't have a threat model for it? 692 00:55:52,240 --> 00:55:56,090 C: This is an excellent question and I anyone who disagrees is they should the 693 00:55:56,090 --> 00:56:01,330 wrong. Security without a threat model is not security at all. It's absolutely a 694 00:56:01,330 --> 00:56:05,560 true point. So the things that we are looking for, most of them are things that 695 00:56:05,560 --> 00:56:08,800 you will already find present in your threat model. And so for example we were 696 00:56:08,800 --> 00:56:12,390 reporting on the presence of things like a ASLR and lots of other things that get to 697 00:56:12,390 --> 00:56:17,030 the heart of exploitability of a piece of software. So for example if we are 698 00:56:17,030 --> 00:56:19,870 reviewing a piece of software, that has no attack surface 699 00:56:19,870 --> 00:56:24,160 then it is canonically not in the threat model and in that sense it makes no sense 700 00:56:24,160 --> 00:56:29,270 to report on its overall security. On the other hand, if we're talking about 701 00:56:29,270 --> 00:56:33,470 software like say a word processor, a browser, anything on your phone, anything 702 00:56:33,470 --> 00:56:36,120 that talks on the network, we're talking about those kinds of applications then I 703 00:56:36,120 --> 00:56:39,280 would argue that exploit mitigations and the other things that we are measuring are 704 00:56:39,280 --> 00:56:44,330 almost certainly very relevant. So there's a sense in which what we are measuring is 705 00:56:44,330 --> 00:56:48,411 the lowest common denominator among what we imagine or the dominant threat models 706 00:56:48,411 --> 00:56:53,180 for the applications. The hand-wavy answer, but I promised heuristics so there 707 00:56:53,180 --> 00:56:55,180 you go. Mic3: Thanks. 708 00:56:55,180 --> 00:57:01,620 C: Thank you. Herald Angel: Any questions? No raising 709 00:57:01,620 --> 00:57:07,060 hands, okay. And then the herald can ask a question, because I never can. So the 710 00:57:07,060 --> 00:57:11,920 question is: you mentioned earlier these security labels and for example what 711 00:57:11,920 --> 00:57:15,880 institution could give out the security labels? Because as obviously the vendor 712 00:57:15,880 --> 00:57:21,740 has no interest in IT security? C: Yes it's a very good question. So our 713 00:57:21,740 --> 00:57:25,580 partnership with Consumer Reports. I don't know if you're familiar with them, but in 714 00:57:25,580 --> 00:57:31,340 the United States Consumer Reports is a major huge consumer watchdog organization. 715 00:57:31,340 --> 00:57:36,550 They test the safety of automobiles, they test you know lots of consumer appliances. 716 00:57:36,550 --> 00:57:40,070 All kinds of things both to see if they function more or less as advertised but 717 00:57:40,070 --> 00:57:45,210 most importantly they're checking for quality, reliability and safety. So our 718 00:57:45,210 --> 00:57:49,840 partnership with Consumer Reports is all about us doing our work and then 719 00:57:49,840 --> 00:57:54,060 publishing that. And so for example the televisions that we presented the data on 720 00:57:54,060 --> 00:57:58,290 all of that was collected and published in partnership with Consumer Reports. 721 00:57:58,290 --> 00:58:00,970 Herald: Thank you. C: Thank you. 722 00:58:02,630 --> 00:58:12,430 Herald: Any other questions for stream. I hear a no. Well in this case people thank 723 00:58:12,430 --> 00:58:16,440 you. Thank Tim and Parker for their nice talk 724 00:58:16,440 --> 00:58:19,964 and please give them a very very warm hall round of applause. 725 00:58:19,964 --> 00:58:24,694 applause C: Thank you. T: Thank you. 726 00:58:24,694 --> 00:58:51,000 subtitles created by c3subtitles.de in the year 2017. Join, and help us!