B (6:44)

Must have been an exciting moment.

C (6:47)

Yeah, for sure.

B (6:49)

Now, in the research you talk about this problem of scaling, you found these things manually, but then you had to go back and say, can we do this again and again at scale? Why is that so difficult? In these dynamic applications, the places where

C (7:07)

scaling is a lot easier is when code is just code and programs are statically by nature. So It's a lot easier because then you have a very distinct path from sync to source. That means from a user input into a dangerous function. If it's easy to go and draw that path, it's usually easy to create an exploitation and then prove that it's a vulnerability. But in dynamic applications, and most single page application and modern applications are dynamic by nature, it's very, very hard to understand how you connect the dots in JavaScript. A lot of the times you have objects or you have tables and functions that call entries inside of tables, that call different entries of different tables and indexes all over the place and everything is dynamic. The only place that the actual code flow can be investigated is actually at runtime. And that's something that, let's say the old tools, it was very difficult for them to understand. And for in order to find vulnerabilities in that case, you actually need to live in runtime. You need to run on real applications that are actually running now. And then you need to understand how to add tracing or instrumentation to the application itself in order to be able to investigate them, or give AI or any code, any program the tools in order to investigate in real time.

B (8:59)

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C (11:55)

Yeah, so in practice, if you look for again syncs and sources, as I said before, you'll find tons of potential links and tons of things that you should should look for. But when somebody that, that knows how to research vulnerabilities and done it for years, so they just have instincts of what's more important than the other things. When you go into a specific path, where are the hurdles that you will probably meet and how do you need to mitigate them if you're blocked? Let's say what I explained before with the SVG and then HTML inside it and then another SVG inside of it. So that's like a bypass technique that you need to know about. And in order to, to do or to create an agent that's able to do that, you need to give it tons of tricks and intuition and we actually train our agents on those intuitions and we try to navigate their preferred path to paths that actually correlate to finding more vulnerabilities.

B (13:10)

How do you teach an agent to think in terms of these trust boundaries instead of just like pattern matching?

C (13:18)

That's something that we didn't cover in the blog itself, but we are going to provide another blog that's a lot more AI related.

B (13:27)

Can you give us a preview?

C (13:29)

Yeah, for sure, no problem. What we need to do in order to be able to do something like that, we need data and vulnerability research of real applications. Data is actually environments. So what we need is hundreds or thousands of environments with vulnerabilities that the agent didn't learn on. Because we don't want the environments to be contaminated. They need to be something that the agents don't know and then we actually just give them the task, let's say find an XSS in this specific part of the application and we do that across thousands of applications and that's our data and it's not A single action, but it's a iterative motion that the goal is at the end to find a vulnerability. And once we have tons of data and traces, so after we have all that, we can actually optimize the path and like quantify it. What changes can we make in order to make the agent better? And what does it mean better? It just means that statistically it finds more vulnerabilities.

B (14:49)

You all describe this as a collaborative swarm. You talk about tracer, resolver and bypass. Can you explain that to us?

C (15:01)

You can have a generic agent that does everything, but like most things, if you have something generic that it's not, then it's not specialized. So the way we try to tackle that task is we do create specialized agents and we do have flows that match the way that researchers research. So, for example, we start by investigating what are all the things, what is all the attack surface. So sinks is something actually pretty far, like pretty advanced. But we start by understanding what is the attack surface that an attacker can have. And after we have that, so we can continue and then ask what are all the sinks that are available in the code? We don't have code all the time, but if we have, that's great. And then we try to create hypothesis that connect syncs to source, because if you have a connection, that it means you have a vulnerability. But let's say we did that, then we have to optimize on actually creating an exploit. And there is a big difference between a potential connection sync to source and an actually working exploit in a live environment. And each tasks, like from the tasks that I just described, requires different, like a bit of a different mindset. So let's say for creating the exploitation, what you actually like, what we understood is what you actually need is a very good coding agent. Because creating an exploitation is actually creating code. We need to create a POC script that proves that what we've done actually works. But in the previous steps, let's say finding sinks inside of a source file, that's something that you can do statically, but connecting it to dynamic patterns, that's something very hard there. You need an agent that's very good at instrumentation and reading logs. Each different task requires different skills that the agent needs to embed inside itself.

B (17:21)

You all make a claim, it's a strong claim that most AI security tools produce vibes, not actual proof. But you say your approach is different. What distinguishes what you all are doing there?

C (17:35)

Yeah, I think that our end product is not assumption or a claim that we have a vulnerability and our platform. What we actually strive for and provide to our customers is full validation that we have a vulnerability. And the way that we are able to provide full validation is by actually creating reproducible code that proves something is vulnerable. So let's say if we're talking about idor, for example, so we prove that from one user context you can access data from a different user context. And the way to prove that is writing a code that logs in, has a user context, and then being able to provide some kind of request, for example, and then extract data of a different user. And that's something that's verifiable. If we do xss, for example, we prove it by instrumenting the browser, for example, and then validating that we were able to spawn an alert box, for example. So the proof that we provide is actual something that you can just take run and then you'll say, ah, yeah, this makes sense. It does exactly what I would expect it to do. And it's not just a very nice hypothesis.

B (19:04)

From a defender's point of view, what should security teams take away from this research?

C (19:10)

This research is really talking about offensive security and how to find vulnerabilities. But I think in today's world you don't have the privilege to not use a tool like this or use AI in order to look for vulnerabilities yourself inside your applications and even the most niche ones. Because if once you could have thought of why should an attacker look inside some niche place and do whatever, maybe it made sense, okay, but it's still something that it's high effort. And if today there is a tool that can find vulnerabilities that yesterday were impossible to find that scale and you're as a defender, you're not using that tool to discover those before the bad guys. So you're going to be in trouble. So from a defender point of view, I think this research and other researchers research in the same field just proves that defenders must move a lot quicker than before because it's just easier now to automate and scale everything.

B (20:39)

Our thanks to Omer Ninberg from Novi Security for joining us. The research is titled From PDF to pwn. We'll have a link in the show notes and that's Research Saturday brought to you by N2K CyberWire. We'd love to know what you think of this podcast. Your feedback ensures we deliver the insights that keep you a step ahead in the rapidly changing world of cybersecurity. If you like our show, please share a rating and review in your favorite podcast app. Please also fill out the survey in the show notes or send an email to cyberwire2k.com this episode was produced by Liz Stokes. We're mixed by Elliot Peltzman and Trey Hester. Our executive producer is Jennifer Ibin. Peter Kilpe is our publisher and I'm Dave Bittner. Thanks for listening. We'll see you back here next time.

C (21:47)

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B (21:49)

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