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    "text": " Hey there! Welcome everyone. We are kicking off the Q&A session here with Ayub, who spoke about Only Takes a Spark Popping a Shell on 1000 Nodes, which was pretty awesome, pretty cool. A good way of thinking about how to scale up everything. And, you know, not just hacking one system, but how can you use that to really spread out. So, really, I heard this is also your first time speaking at DEF CON. Is that correct? Exactly, yeah. So, we have... I was actually going to give it... yeah, go ahead. No, yeah, so I was just going to say we have a tradition here at DEF CON, which we call Shoot the Noob. And so, really, it's just the tradition of, you know, hey, taking a shot on stage for, you know, your first thing. It's not always liquor, right? So, people mistake that. But, really, just want to do that with you now. So, I want to welcome you in the traditional DEF CON fashion. So, cheers! Oh, boy. Okay, okay, okay. Oh, you got to pour it first. Wait for it. I got to pour it first. You had to prep. There you go. Okay. There we go. Cheers, guys. Cheers. Awesome. So, now that you've been officially indoctrinated to the DEF CON and DEF CON safe mode. Yeah, so, one of the first things, questions I'd ask, right? So, right, you recorded the talk a little bit ago. What have you been working on since? Anything new that you've kind of come up with? Or, you know, anything you found? Anything, you know, that's changed since the talk was given? It has nothing to do with Spark, actually. I was working on Spark, like, a few months ago now. But since then, I completely switched subjects and topics. I've been working on AWS a little bit. I did, actually, a tool that reflectively loads DLLs and executables in memory. But I wrote it using Golang. So, I completely abandoned that area of research. And I completely switched over to something completely different. So, we'll see what comes up next. Good for you. That's a pretty good way to go. You get some recognition for one thing. And then you move off to something totally cool, totally new. Excellent. Yeah, definitely like this idea of discovering new stuff. Excellent. So, is that a thing you find yourself doing often in your own research? Is that you want to get a nice breadth of experience in a lot of different things? Or how deep do you go before you feel like you're comfortable with what you've learned? I think I can... Basically, my idea is go as deep as you start to understand how it works, basically. Before, I was looking into mainframes. And that took me, I think, two years of going through these 900-page documents published by IBM and these obscure systems and obscure forums actually talking about it. So, I did the same thing with mainframes. And then I let it go. And I switched over to other areas. And during that work, I think Spark came up. And I was like, what the hell is this thing? And I dug into it. And I found there was not much research was being done on it. And I thought, well, you know what would be fun? So, I actually dug into it. And that's how it all started, really. It was just going after the next shiny thing, trying to understand how it works. And once you understand how it works, you try to bend its rules to do whatever you want with it. And hopefully, write a tool about it, give a talk about it, and then move on to the next stuff. How deep? Just enough to understand it, really. That's the goal. So, what was it about Spark, really, other than the fact that it was new and there wasn't a lot of research? I mean, what was it that really dragged you into this one? Because it's an interesting system, as you showed us in your talk. So, what was the thing that made you decide, okay, fine, this is where I'm going to spend the next year of my life? Yeah, and why not continue? All right. So, I was working on the offensive side, and then I switched to the blue side. And I was helping a company secure their systems, etc. And they were all on these new shiny platforms, if you will. Everything was on AWS, multi-region, CICD. I mean, they didn't click a single button. They pushed everything to code, and everything was deployed and scaled and stuff. Very, very sexy stuff. So, I was trying to help them secure that stuff. And after six or seven months, we thought we did a pretty good job locking pretty much everything that was supposed to be locked. There was no Windows, by the way, so that's why we came to this state. But anyway, and then I was talking with this data scientist, just trying to understand what they do. And he mentioned something about Spark. And I was like, what is that? And he told me, oh, it's something that we use to make calculations and parse data. I mean, what do you mean, parse data? How many machines do you have? And he's like, oh, actually, it's like, I have 200 machines. It's like, what do you mean, you have? It's like, oh, I spawned a cluster of 200 machines, and that guy over there spawned a cluster of 500 machines, etc., etc. So, basically, the company had like thousands of machines running sporadically, if you will. But still, and I was like, okay, well, how do you walk me through it, please? And he showed me how he launched the job and how he did some calculations, and I was like, oh, my God. And that's when, where the all interest Spark came in, basically. That's awesome. That's a great place to start. Yeah, you start seeing, oh, a couple of commands, and all of a sudden, there's this many machines doing something. I want to know that, too. He was writing Python code on a Jupyter notebook, where, you know, there was an authentication, obviously. And he was writing code, and it executed over multiple machines, and I was like, dude, this is amazing. And so I dug into it, and yeah, that's what it all sounded like. All right, so it's a pretty specific setup that you are talking about in your presentation. How common is the setup that you're speaking about? And how many other ways are there to configure this that might have more research opportunities? Well, the thing is, basically, a Spark cluster can be set up in very different ways. The cluster manager, so basically the process, the component that is responsible for linking applications to workers, that one can be replaceable. Not whatever you want, but you can put many different other components. In my talk, I only briefly demonstrated when it's Spark itself that is doing the orchestration. But you can have other setups where it's actually Yarn, a product from the new framework, that is doing all this orchestration. And I think in 3.0, you can even have Kubernetes doing the work. So you have all your pods coming up to do all the work. So it's much sexier. And the one that I showed, the one that bypasses authentication, that one only works on Spark standalone mode. So when it's actually Spark that's doing all the stuff. If you're having Yarn in front, it's a completely different story. It's a completely different protocol that's been going on. That's some Hadoop shit that's too much. That's a completely different beast. It listens on a different port, etc. The tool that I released, Sparky, handles it. But I didn't go much deeper into it. So there's definitely some area of research there to go into. And that's the default mode when you're using AWS Managed Service called EMR. So if you're using EMR and you spawn a cluster using their service, it will by default use this Yarn mode. So it will spin up Yarn and then the work will be done by Spark Worker. How much is its widespread ratio? From the studies that I saw, it was around 50-50, maybe 40-60, depending on which website you see. Basically, the more traditional websites, the companies that had Hadoop before, will stick to Yarn. Whereas the new ones that have the luxury of starting a cluster of data mining from scratch, will maybe opt more for a Spark standalone cluster. Okay, that makes sense. And 50-50 means that there's stuff out there, both more to study elsewhere and more to try and hit with the research that you've done. So, interesting. Yeah, I think so. I would say that... Here's the thing. The idemposite community is so much focused on Windows, I find, that there's so many other great stuff to talk about and to research. In Windows, if you want to make a breakthrough, you have to go through 20 years of past research to try to find something new. Whereas in this big, shiny, new technology, well, it's right there. Buffer overflows of 1999, it's right there. So, there's much opportunity there to be taken. That's awesome. Yeah, and I'll ask a question too. You mentioned how much more efficient Spark is over Hadoop. Would you say companies that are still heavily relying on Hadoop are behind the times? Should they be looking at Spark, or is there better security there? What's your take? Oh my god. Well, Hadoop has that thing, well, it actually handles Kerberos authentication. Now, whether that's a good thing or not, that's up for debate. But, no, not really. My mantra, really, I work in Blue Team, so this is Blue Team talking. Basically, production should be boring, to paraphrase what other people were saying. Production should be boring. So, if you have a Hadoop cluster of 3,000 machines that's doing the work, and it's fine, and everything is fine, then, by all means, continue. If you have a mainframe to do what you need to do. I've seen a post, actually, that emulates what Spark does, but only using shell commands. It works. It's much cheaper. It runs on a single machine. And you don't have all the partitioning and the shuffling. You don't have all the network latencies. You don't have all that crappy stuff that makes it very, very, very slow, in comparison to keeping everything in memory and just working on a big slice of an object. So, I mean, hey, whatever works for the company, with that particular set of talent and that particular set of circumstances and data, I mean, go for it. That's my opinion, yeah. Yeah, that's awesome. Yeah, and you mentioned, so you have that Blue Team mindset. I mean, so, I guess, how would you try to detect what you were doing during these attacks? Like, are there any particular tidbits you try to give to a company that's trying to secure their Spark instance, other than making sure it's patched? Very interesting question. Very interesting. Here's the thing. I never thought about that. That's how de-correlated the two worlds are for me. It's like, I do Blue Team in the morning and I do Red Team at night, and that's how, again, they are apart. So, I never thought about, hey, it would be good to release some Yara rules to detect this stuff. Never crossed my mind. Future research, there you go. No, but it's, to me, it's easier to patch. I know that some, well, a lot of people will not be able to patch. To me, the first thing you try is you try to patch. If you can't patch, then you want to detect, then you want to mitigate it somehow. So, you need to isolate it either through network firewall rules or do something like that to, you know, basically isolate it from the rest of the reducer exposure, if you will. Now, if you can't do that either, well, you got to detect it somehow. How would you detect it? If you have some advanced correlation in place, I think you can detect it. I'm talking about specifically the exploit of replaying that single theorized object that triggers command execution. I'm talking specifically about that. One way to detect it is that, unlike other Spark interactions, it's, like, in that interaction specifically, you only send one command instead of the whole charade of, hey, Spark, hello, which version are you running, master? Yes, I'm registering the application. Okay, here are the workers. You know, you have, like, 80 messages going around. But instead, in that exploit, you only have one message. So, if you can, if you have a tool that's advanced enough to make these kinds of correlations say that, oh, this IP address only or this session only initiated that single communication, that single packet, then maybe it's suspicious because it didn't follow up with all the stuff. That could give you a hint. You can also try the on out of memory errors that are on your machines. That may be a bit noisy, but that could get you going. But then again, a hacker could find another way to trigger the execution because I only gave out an example using the on out of memory error. Another way... yeah, these are the two main things to look for off the top of my head, basically. Sure. We got a follow-up question from PC Squared, it appears. Really well done talk. It looks like you did the magic on the Spark stand alone, like we were mentioning there. Did you get it working with Yarn 2? Not a big deal. Not a big deal, if not, but I imagine it's possible. Also, 100% production should be boring. Amazing. Oh, my God. Okay. So, here's the real story. So, I found this thing on Spark, and I was like, oh, my God, this is amazing. I feel so great. You know what? I'm going to follow up. I'm going to see how it works on Yarn. Then I pulled up Yarn, did the installation, made it work on Sparky, right? So, let's look up some runes. So, I turned up Wireshark, and I saw the traffic. It was on Wireshark, and I was like, oh, my God, I don't want to touch that. That's how foreign it was. That's how... Yarn is more difficult than Spark stand alone. It's really, really more difficult. And I looked at it, and I was like, fuck it. I don't want to look into it. That's really what went through my mind when I tried to do that on Yarn. Now, if you look for a similar vulnerability, I couldn't trigger it. Like, I was playing around with Yarn a little bit, but I couldn't trigger the same vulnerability. Does that mean that it's not vulnerable in some way? Of course not. But by that time, I was like, oh, Yarn is just too much for me. I'm not going to waste my time on it. I'm going to move on to other stuff. Maybe if you feel a connection with Yarn, you can dig into it. Please do so. Because like I said, there's nothing out there on Yarn. I didn't find anything. So, probably there are some stuff to be sought after in Yarn. Well, that makes a lot of sense. So, if somebody decides that they do want to pursue this, pursue the Yarn side, and would like to ask you more questions, are you available for either consultations or for answering questions that people come up with when they're doing this on their own? Yeah, of course. I mean, yeah, definitely. Like, my Twitter is open. Right at the end of this, we'll have you post any contact information you'd like for people to have access to in the TrackOne channel. And you can be there for people. Yeah, definitely. I mean, when you look at this stuff, like when you look at the Spark source code, it's beautifully written, by the way. But it's written in Scala. Some of it is written in Scala. And it can be very daunting. The syntax is weird. And when you look at information on the web that talks about Spark, I think one blog post or two maybe that are dedicated to the internals of Spark. Everybody talks about how shuffling works and how it partitions data and stuff, but nobody talks about how it works inside. So yeah, there's a lot of things to figure out and some simple stuff when you explain them. They look simple, but to get that information, it takes a little bit of time because nobody talks about those internals. So yeah, if you have any questions, please do not hesitate. Just hit me on a DM or just ping me or whatever. Yeah, it's one of the things I thought was interesting too. So through what you discovered, I was like, oh man, I wonder if he's going to report this to Apache. And then like, oh, look, he did. So I guess, how did that interaction go? How long did it take him to fix it? Was it a good interaction? Tips for other people having to reach out to companies like that? Okay. I sent them the phone, I think on 24th of December or something like that. Okay, very bad of me. Then I got a response from the Spark security team, and they said, yeah, okay, we'll look into it, etc. But whatever, and I didn't receive a response later. And then my talk at Troopers got canceled. And I think one week before I decided to, you know, because I was rehearsing a little bit. So I said, you know what, let me just write them again to see where they are on this. And I got a response from the same guy saying, oh, you know what, we looked it over, and we discarded it because it's not interesting. I was like, okay. Let me rephrase. Oh, and when I sent them, I sent them an email with a proof of concept. It's like a Python code that actually executes the code fully. So proof of concept, it's there. So it was really detailed email. And yeah, so the guy said that they discarded it and I didn't understand. So I wrote a second email saying, are you sure you want to do that? Because you're right in the documentation that when authentication is enabled between this and this component, etc. So you're kind of breaking this trust by allowing this vulnerability to go on, etc, etc, etc. And then I got a response saying, oh, my God, I'm so sorry. Yeah, we made a mistake. We thought you were talking about the RPC endpoint that we already got a report for. Now, indeed, this is a dangerous vulnerability, etc. I'm going to put everything. And they, you know, I mean, it happens. I do try it myself and sometimes I get it wrong. So this is normal. And then they went on to fix it. So, all in all, it took, I think, eight months. But since they acknowledged that it was a vulnerability, I think it only took like three months. Gotcha. So persistence was key. Persistence was key. Well, I mean, my goal was not to get it out there, to just publish it. And I didn't release the tool or I didn't even talk about it. I think I talked about it to two people who were very expert in Spark to validate that I was not saying bullshit. And I asked them to not disclose it. That's about it. Because my intent was not to just, you know, release that. I am pro full disclosure. But everybody should make their own choices and it depends on my mood. So for that one, I decided, you know what, let's just keep it on the low key anyway. And, yeah, and I think three weeks after disclosing it, they released a complete correction. Because it was not impacting only one function, it was impacting also two others. So they rewrote a class, etc. So we worked on the fix. Well, I worked on the origin of the fix and then they took it over because more competent than me. And then the thing that was really long is the release process. They had the fix three weeks after I reported it. But since like, let's say, beginning of April, they waited until July on the other version 2.4.6 to actually release the fix. So that's the part that was long and not actually fixing it, just releasing it. But, yeah. It's out there and we're all more secure for your efforts. So it's, you know, better than having no one looked in that area before. Yeah, yeah, definitely. And now we're talking about that. There was some interesting point during basically this whole Spark adventure. And I think I briefly touched upon it during the talk is that I was so, like, I spent like a couple of hours, even days, trying to understand how the RPC stuff works. And only to find out a couple of days later that somebody already published that vault. So, yeah, that was very interesting. I don't know if it happened. I think it happens to some people. But basically, you're just so stuck in, like, you find yourself drawn into that code base and trying to understand how it works when all you have to do is actually Google the right keyword to get the actual exploit. And when you do it, you feel a sense of frustration. But it's part of the game. And that's pretty good advice for all of us who are into finding vulnerabilities and figuring out how to report them is that enumeration step goes on the software that you're working on. And you have to read the state of the art of what people know. So that's a good thing to reinforce. Exactly. Awesome. So tell us a little bit more about the next thing that you're moving on to. It sounds like you already have this idea of what you're researching next. Actually, I wrote down a small plan. Not a plan, but bullet points of what I want to do later. I just released this tool called Reflect PE, which reflectively loads an assembly in a PE object. And shout out to name right, please. Rob? If needed, you can post it in the track one channel at the end. I will. I will post it. Okay. So his name is Rob Knopp. Well, he goes by the handle of Rob Knopp. Sorry. But yeah, I wrote a tool called Reflect PE, which reflectively loads a PE executable in memory. And I borrowed his basically tool to reflectively load also PE assemblies. So Rob Knopp, great tool. And why did I do that? So this is what I'm working on right now. And why did I do it is very, I find interesting, is that when you look at the same code to do the same thing, but in PowerShell and other languages, is that it's a huge script of like 2000 lines of code. No tests whatsoever. And you just launch it. And if it doesn't work, well, too bad. You can't debug a 2000 line PowerShell script. So I decided to write it from scratch using Golang and using like, you know, testing and good dev practices, etc. So that anybody could just look at the code and understand what's going on and where are the steps and where it didn't work, etc. So this is what I'm really working on. I'm trying to make this tool that will actually be easy to understand for people who just get into it. And just want to understand how reflectively loading something works on Windows. So that's the thing that taking most of my time right now. And my next big thing is Kubernetes. I really want to get into it. It is all of the talks that I saw, like 90% of them talk about how to abuse a Kubernetes set up with no authentication, no airbox, no nothing. I want to see what is there to exploit, you know, once you have all these security hardens in place, is it possible to bypass, I don't know, namespace isolation, is it possible to bypass team control, etc. I want to really dig into it because there's, again, like I said, I really look out for these niche topics where there is not much talk, where there is not much tools available. And try to focus on them and hopefully something gets out of this research. That's awesome. That's awesome. Yeah, so we're kind of wrapping up here at the end of the time. Is there anything else you want to share with the group or again, how to reach out to you? Again, we can post your contact information, but anywhere you want to point anyone to before we call it an end here? No, nothing except that, basically, thank you to the Spark team, first of all, because honestly, amazing. Like when you look at the code base, you understand how talented these people are and it's really a pleasure and a privilege to actually have interacted with some of you, especially some of you that I saw on stage talking about it. So I was pretty excited, though I tried to hide it a little bit. Yeah, so thank you all. But again, like for people who want to basically go into InfoSec and break into it, give talks, do research, etc. My only advice is basically look out for all these small niche topics that are basically abandoned by everybody and go deep until you feel you understand it and then just go crazy with it. And then most surely something will pop out. Awesome, awesome. That's great advice. I appreciate that. Yeah, and I was going to say just one more thing, basically, was getting to, you know, since you just actually did a live demo, right? One thing you're missing out on not having the live DEF CON crowd is a round of applause for having a demo work on stage. So since you actually did kind of roll that out, I just wanted to give you a nice round of applause and get that in the Discord chat, too. Thank you for getting that and getting it working and everything. Thank you very much. Cool. So I think that's it for us. So again, thank you again for joining us. And thanks for the talk and look forward to what you have coming up next. And hopefully see you next year at DEF CON. Thank you very much. Thank you. Bye-bye."
}