S1: All right. I want to do something crazy here. Specifically,
S1: I want to talk about the future of hacking. And
S1: what do I mean by that? What do I mean
S1: by hacking? I mean all hacking attack, defense bug bounty,
S1: personal automation stacks, enterprise automation stacks, attacker automation stacks, enterprise security. Everything.
S1: Everything hacking related, I think is going to come down
S1: to what I'm about to talk about. And I am
S1: aware of how big of a claim that is. The
S1: reason I'm able to make this prediction is I'm not
S1: stupid enough to claim to know how it's going to happen,
S1: or exactly when or with what companies or like what
S1: technologies exactly it's going to manifest as. That would be
S1: ridiculous because the stuff is basically unpredictable. What I'm going
S1: to show you is a direction. And I think once
S1: you see it, you will be unable to unsee it.
S1: And by the way, this video is going to be
S1: around 30 minutes. But the whole first 25 minutes is
S1: leading up to the last five. The last five is
S1: the good stuff. All right. So quick intro for people
S1: who don't know me. I've been in security since like
S1: 1999 and I went heavy into AI in late 2022,
S1: still doing tons of security stuff just with this AI
S1: wrapper around it. And you're going to see why here
S1: in a second. And I would say most of my
S1: technical background, I mean, I've done lots of different stuff,
S1: but it kind of boils down to a container of
S1: security assessment that's like the main outline. So first going
S1: to show you how I kind of walked into this idea.
S1: So when I start to do a security assessment this
S1: goes back, you know, 15, 20 years, uh, doing security assessments.
S1: I like to start at the very top. When I
S1: talk to a company, I like to interview the CEO
S1: if I'm able to, if it's a, you know, medium
S1: sized company or below. And I talked to CEO, I
S1: talked to the CEO, I talked to the head of legal,
S1: I talked to like, as many people at the top,
S1: and I'm sort of asking them the same questions. And
S1: then I move down through the structure. I talked to
S1: all the VP's and senior VP's and CISO and the
S1: rest of the C-suite and everybody. And then I start
S1: moving through all the management, and I'm asking kind of
S1: similar questions, but I'm also asking different questions because now
S1: I'm playing off the answers I got before. And I
S1: just moved through the whole structure all the way down
S1: to the people who do the actual work. And then
S1: as I keep gathering more and more information, I start
S1: filling in these elaborate diagrams that describe to me exactly
S1: how this company works, like here's how the information flows.
S1: Here's where the data is stored. Oh, we got vendors
S1: over here. They're able to touch this data or whatever.
S1: And ultimately I'm trying to figure out like what they're protecting,
S1: how they're doing it, what day to day business looks like.
S1: After a couple of weeks of this, I then start
S1: doing my technical assessment to find vulnerabilities. So I'm also
S1: reviewing their previous technical assessments, but I'm really doing my
S1: own as well to go and probe in these various
S1: different areas and point my, uh, observations at things that
S1: I've seen in the interviews. But the underlying theme here
S1: is I'm taking all the context, not just of the
S1: vulnerabilities and the technical aspects like of the IT stack,
S1: but of the business itself. Right. All of that. I'm
S1: gathering into one place, and that's kind of how I
S1: view and how I start security assessments. The other teams
S1: are still managing GRC with spreadsheets, screenshots and manual processes,
S1: but with everything evolving compliance frameworks, third party risk, customer expectations,
S1: this is no longer good enough. And the problem isn't
S1: just that it's time consuming. It can actually hold you
S1: back slow. Audits miss risks and give you less time
S1: to focus on what actually matters, which is improving your security.
S1: Trust management platform is designed to help with that. It
S1: automates the core parts of your GRC program, things like compliance, readiness,
S1: vendor risk, and internal controls so you're not buried in
S1: manual work. According to IDC teams, using Vanta 129% more
S1: productive in their GRC work. That means faster prep, fewer surprises,
S1: and more time for real security work. It's not about
S1: making compliance easy for the sake of it. It's about
S1: getting the friction out of the way so you can
S1: move faster, do better work, and build trust more efficiently.
S1: And if you're thinking about how to approach AI risk
S1: than to put together a free AI security assessment, it's
S1: a structured way to evaluate risk across AI use, development
S1: and governance. You can get the assessment at. That's. And
S1: thanks to Vanta for sponsoring this video. So in a
S1: completely separate thread of consumer tech, in 2013, I started
S1: getting a picture of where I thought all this AI
S1: tech was going, like on the consumer side, which at
S1: the time I called IoT and I was actually talking
S1: about this with my friend Jason Haddox in like 2013,
S1: which I was reminded of this because I just read
S1: the foreword and I was thanking him for encouraging me
S1: to write this book in 2013. But, um, these ideas
S1: are pretty decent. The book is actually crap, so you
S1: don't need to read that. In fact, I have it
S1: published online, um, as a blog post. It's very short.
S1: You should go read it there. It's actually not half bad.
S1: Plus you could use AI to help you read it.
S1: Plus it's got way better typography. So I would say
S1: skip the book and just go read the blog. but
S1: the basic ideas are quite good, even though the book
S1: is not so great. Um, so the basic idea is
S1: you have digital assistants that know everything about you and
S1: they advocate for you, and then everything gets an API,
S1: including people and objects and businesses and everything. This is
S1: like the second piece of this, and it's really, really important.
S1: And your digital assistant, your Da, basically uses all those
S1: services to interact with those APIs on your behalf. Then
S1: your Da will use augmented reality to show you context
S1: for wherever and whatever you're doing, right. So you're wearing
S1: glasses or lenses or whatever. Neuralink, whatever. It doesn't matter.
S1: It'll start with glasses, obviously. And basically your Da knows
S1: everything about you, knows your entire personality, it knows your
S1: entire history or whatever. So it knows when you're scared.
S1: It knows when you're skeptical. It knows when you're hungry.
S1: It knows, you know when you're curious. And it's using
S1: these millions of services that are available at APIs to
S1: get data back for you and change the screen. Okay.
S1: Sometimes it's a security screen, sometimes it's like a social
S1: screen because you're trying to find, you know, your life
S1: partner or whatever. So it's constantly changing what you're looking
S1: at to help you. Maybe it's popping up little notes
S1: or little reminders or whatever. Right? A security overlay if
S1: you're in danger or something. So that's the third piece,
S1: which is augmented reality, displaying the information from all these
S1: APIs from your from your Da, who is like the
S1: one handling all of this and advocating for you. And finally,
S1: the last idea is that when you have like an
S1: entire family or an entire city or an entire country
S1: with all these demons, all these APIs available, that produces
S1: tons of context that a top level AI could look
S1: at and say, okay, how can I manage this city better?
S1: How can I manage its resources? How can I turn
S1: on these lights and turn these off and reflow this traffic?
S1: And you know, how can I optimize? How can I
S1: help this city achieve its goals based on all the
S1: context that I know about it, from all this context,
S1: from all these APIs and demons? So that was fun.
S1: That was a cool book. That was some cool ideas.
S1: Then in 2018, I got a job at Apple doing
S1: information security stuff, but the team I came in on
S1: was with Joel Parish and we actually built, um, well,
S1: he was already doing machine learning, right? He was part
S1: of the machine learning team within security. So, um, I
S1: wanted to study up and just get really good at
S1: this stuff. Excuse me. And my math was really bad,
S1: so I had to refresh my horrible math, and I
S1: went and did Andrew Ng's entire machine learning course. And, um,
S1: over the course of my time there, I got exposed
S1: to tons of ML stuff and practical uses of ML
S1: like before the current AI stuff, and it was really helpful.
S1: I ended up building a product there which is still
S1: used today, so I'm happy about that. In early 21,
S1: I left Apple to go build the Appsec and build
S1: management teams at Robinhood with Caleb Sima. And there I
S1: did a blackhat talk about building vulnerability management programs based
S1: on company context and specifically around asset management. And that
S1: turned out to be another brick in this path that
S1: I'm laying out here. After doing that, I decided it
S1: was time for me to go build things on my
S1: own and do like my own consulting. So I went
S1: independent with unsupervised learning in like August of 22. And
S1: it turns out that was just a few months before
S1: ChatGPT came out. And obviously I went absolutely apeshit. When
S1: that came out, I called everyone, I called Jason, I
S1: called Clint, I called Caleb, I called my mom, I
S1: called my dog. I don't have a dog. But yeah,
S1: I called everyone. And the first place that my head
S1: went with all of this was like security assessment and
S1: building and managing security programs. So I started doing that immediately. Basically,
S1: I took everything I was doing previously with all this context, right,
S1: that I've been doing, like, you know, a decade and
S1: a half or whatever. And I'm like, okay, how can
S1: I use AI to, you know, make this even better? And,
S1: you know, you know, put the context first. So in
S1: March of 23, I wrote this post called Sspca, which
S1: basically says everything is about state policy questions and actions. Basically,
S1: we have current context for a company or a program
S1: or whatever. Then you have the policy, which is what
S1: you're trying to accomplish. Then you have the questions you're
S1: constantly wanting to ask and have answers to, and then
S1: you have actions or you know, that we could take
S1: or that I could take against that context. So I
S1: feel like I'm starting to zero in on this concept
S1: and this got decent traction. But I wanted to like
S1: have a demo or something for it. So I did
S1: another blackhat talk, I think maybe the following year or maybe, yeah,
S1: it must have been the following year, uh, to put
S1: together a demo for this. So I put together a
S1: fake company called alma, and I put in tons of
S1: context for this thing. I basically made a copy of
S1: one of my security assessments the way that I do it. Um,
S1: but I did it for a fake company with a
S1: whole bunch of fake data. So I've got company mission,
S1: I've got their goals, how they're different from their competitors,
S1: what they do in business. I got the risk register.
S1: I've got their full tech stack. I've got their security
S1: team and their members. Like the skill sets of the
S1: team members. Um, the list of applications, the full IT stack,
S1: every app that they use, um, all their documentation. I've
S1: got some fake, like, slack conversations in there. Um, what
S1: repositories they use, what dev teams they belong to, how
S1: they push code, all that stuff. It's all in here.
S1: So then I can ask questions the same way that
S1: I do in security assessments. And using this, you can
S1: actually manage the entire security program using this context that
S1: you have because you could do planning from here, you
S1: could do threat modeling, you could do your communications. You
S1: can produce your reports. Like I'm doing this for a customer.
S1: I could produce a report like a quarterly update security
S1: report in 30s, which used to take them months and
S1: like hundreds upon hundreds of hours of some of their
S1: best people actually trying to make this report, just to
S1: be able to prove to the rest of the organization
S1: that they're actually effective. So turn that to a couple
S1: of minutes. Right. The other cool thing is you can
S1: respond to security questionnaires, because if you have a static
S1: database of answers, you they always ask the question differently.
S1: And it doesn't perfectly match when that you have. Right.
S1: But if you have this kind of system with context,
S1: it can answer it perfectly every time. So this is
S1: an example of like a CISO making a statement about
S1: no more connections are allowed to a particular sensitive resource.
S1: And we're asking the question to the AI system, this
S1: is a real AI system, right? And this is back
S1: in 23 that I did this. So it's a real
S1: AI system. I'm asking the question, should Julie be allowed
S1: to connect to this thing. And it says no, she
S1: shouldn't because the CISO just said nobody should be allowed
S1: to connect to this thing anymore. Right. So you could
S1: do really cool stuff when you have context. So throughout
S1: 23 and 24 and into this year, I've been building
S1: more and more stuff around this theme of context and AI.
S1: So later in 23, I built this thing called threshold.
S1: So it takes a whole bunch of sources and I
S1: have context of what I enjoy. Right? The kind of
S1: content that I think is high quality, lots of good ideas,
S1: lots of density in the ideas, lots of novelty in
S1: the ideas. So I give it tons of context and
S1: that becomes the filter for the quality level. And then
S1: I can slide this bar to say, I only want
S1: to see things from these 3000 different sources that exceed
S1: at least this quality level. Right. So that's threshold. I'm
S1: currently about to launch another enterprise product called Same Page.
S1: It's basically a whole bunch of management like this around
S1: different stuff security management for programs especially. Another thing I've
S1: had for like nine years that didn't have any AI
S1: whatsoever until, you know, a couple years ago was my
S1: attack surface monitoring service called Helios. And I'm in the
S1: middle of rewriting this entire thing to be like what
S1: we're about to talk about. And once again, this is
S1: all about using the context. That's a central part of
S1: the rewrite. And the last one I'll mention is like
S1: a daily brief for intelligence. So I basically go find
S1: all my Osint people, all my national security people that
S1: I know have high signal, you know, high alpha in
S1: what they say. And I basically bring that in and say, okay,
S1: here's everything they said yesterday. Turn that into a picture
S1: of like where this might be going. Like, where are
S1: they agreeing in a way that looks like there might
S1: be signal there. And then I make myself a daily report.
S1: So just another example. So these are all kind of
S1: like separate ideas hovering loosely around the concept of context.
S1: And I and I feel like I was doing pretty
S1: well here. I feel like I kind of had a
S1: grasp of this, but a couple of weeks ago I'm like,
S1: wait a minute. I think I actually have a much
S1: better way to think about this and to describe it.
S1: And that is something I'm calling unified entity context. And
S1: that won't be the real name that gets used, because
S1: Gartner will come up with their own name. And of course,
S1: that'll become the official thing. But if we look at
S1: cybersecurity in general. We look at some use cases. There
S1: are some interesting patterns and similarities. So for SOC you
S1: got to look at all these different types of data right.
S1: And try to like come up with like what actually happened.
S1: Like is this thing bad? Is it okay. Is it benign. Whatever.
S1: For IR it's a lot of the same stuff. You
S1: got a whole bunch of different data you're trying to
S1: figure out. Like, is it bad? Did it actually happen?
S1: What's the blast radius? Pentesting you're also collecting tons of
S1: information and you're trying to figure out, like, what path
S1: do I go down? How do I show impact? Same
S1: with Red team. It's just like more extreme. You're trying to,
S1: you know, show more of a story and like the
S1: actual impact to the business with management, you actually need
S1: to understand the organization and like how they push code
S1: and how they do remediation. Otherwise you can't actually help
S1: them fix things. For program management, you need project management,
S1: budgeting strategy, time management, GRC. You've got like what do
S1: we have to be compliant with in which jurisdictions and why?
S1: And what are our current gaps right. And how those
S1: mix together. So the common issue with most of these
S1: is the actual ability to see multiple parts of the
S1: organization at the same time and then to connect those pieces, right.
S1: This is why security analysts and red team people, and
S1: especially like principal people, people who have been doing this five, ten,
S1: 15 years are so valuable. It's not actually a single
S1: task that is difficult. The problem is getting all the
S1: information together to paint a picture to actually do the task.
S1: So I'm going to take vulnerability management as an example.
S1: Since I've lived in this hellscape for so long. What
S1: is actually so hard about vulnerability management? Is it finding vulnerabilities?
S1: Is it like making a pretty enough dashboard to show vulnerabilities? No,
S1: it's actually fixing vulnerabilities. And the reason it's hard to
S1: fix them is because you have to know what application
S1: it's part of. You have to find the right engineering team.
S1: What repo does that code go into? What's the DevOps
S1: workflow for that? Like the team changed, right? There was
S1: there was a a riff. And now that team doesn't
S1: even exist. And it got combined with this other one.
S1: Where did that one developer go? Who's responsible for that
S1: one app. Oh, it's different this week than it was
S1: last week. This stuff is not easy to do because
S1: it's constant change inside this company. So here's the question.
S1: How much of our inability to do a good job
S1: at vulnerability management or security in general over the last
S1: 15 years is actually a security problem? And how much
S1: of it is actually an organizational knowledge problem? And think
S1: about that for all of security. Even crazier, think about it.
S1: For all of it. Right. Or all of software and services. Right.
S1: HR collects, you know, HR data and asks HR questions
S1: and they put it into an HR interface. Right. Project
S1: management collects project management information into a project management database.
S1: They ask project management questions and they put it into
S1: a UI design for project management. Do we really think
S1: these things are going to need their own separate databases
S1: going forward? Their own separate APIs, their own separate questions?
S1: Maybe they need their own questions. Do they need their
S1: own interfaces? I don't think so. I think that all
S1: kind of goes away and we end up with this
S1: thing called unified entity context, or building a world model
S1: for the thing that you care about. So if you're
S1: an individual, your history, your belief system, your aspirations, your
S1: favorite books and music, your past, your traumas, your salary,
S1: blood pressure, friendships, job, career, family goals, financial goals, your upbringing,
S1: your medical history, how strong you are, how much you
S1: can curl like you know your blood sugar levels, right?
S1: And then you can ask questions. Just like with the
S1: security program, you could be like, why is my relationship
S1: not working? What can I do to improve my health?
S1: And if you're a company, it's back to the stuff
S1: we talked about with alma. It's all of its goals.
S1: It's all of its competitors. It's all of its slack communications.
S1: It's all the transcripts from all of its calls. It's
S1: all of its Google Docs and Confluence and all of that.
S1: It's their desired are for the company, all the product
S1: marketing that you're putting out for all of your products
S1: and all the product marketing your competitors are putting out
S1: for all their products. This becomes the baseline for everything.
S1: Once you have that, then you do this. Then you
S1: take the smartest, biggest context AI that you have and
S1: this will be massive in the future. Right. It's getting
S1: bigger all the time. And you look down at this
S1: entire context and it can hold it all in its
S1: mind all at once. So this is completely insane. Basically,
S1: I think most people have this eye thing exactly backwards
S1: instead of cybersecurity or finance or whatever, being at the
S1: center with context and AI being things that you kind
S1: of like sprinkle on to do that thing better. It's
S1: actually the opposite. The context of the entity is everything.
S1: The world model that you have for this thing is everything.
S1: Software verticals kind of go away. They just become use
S1: cases on top of this architecture. Cool. But we were
S1: talking about hacking, right? How do we bring this back
S1: to hacking? So basically the future of hacking, because all
S1: of this relates to context, is basically how you can
S1: keep an exhaustive, accurate and up to date world model
S1: of the thing that you are attacking. And this is
S1: true whether you're actually attacking or whether you're defending. So
S1: it turns into a giant competition between attackers and defenders
S1: and attackers versus attackers and defenders versus defenders between who
S1: has the most accurate and up to date world model
S1: for their organization. So everyone listening to this, every attacker,
S1: every bounty player, we are all going to have a
S1: stack like this. I've been building this for years already,
S1: so like and I know some people on this call
S1: are probably along the path as well. So it's not
S1: a bunch of agents with random tools. It's an interoperable
S1: system where the output of one is the input to
S1: the next one. Okay. This is a big thing that
S1: people aren't understanding about that whole agent thing. You don't
S1: just say blah and give it like a prompt and
S1: then say, oh, agents, figure it out, because then you're
S1: offloading all the work to the model to actually do
S1: the hard work of building the system itself. The better
S1: way to do this? And if you talk to the
S1: people at AI. eye, the people who are actually building
S1: these systems to actually go and find vulnerabilities, exploit them,
S1: fix them or whatever. They need a system like this.
S1: These are the systems I've been building for years. They
S1: are modular. Each little piece does one thing well. It's
S1: a Unix concept, right? Each little piece does one thing well, right.
S1: So I've got a million of these things finding domains,
S1: finding websites, crawling the websites, running automated scans. And each
S1: one of these could be like a super basic version.
S1: It's like curl okay. You got curl on one side
S1: and you've got fully automated puppeteer browser automation going through
S1: bright data on the other side, right? So you have
S1: all these quality, you know, spectrums in between for each
S1: of these modules. But the whole system works together based
S1: on a set of goals. Right. So running automated crawls,
S1: parsing all endpoints, pulling out every single API endpoint from
S1: every piece of JavaScript writing exploits, POCs actually doing the attacking, um,
S1: writing up reports. All of these are separate modules. So
S1: let's say the target, you know, has like five main
S1: web applications, like a few hundred pages per site. And,
S1: you know, there's a whole bunch of agents. Think of this.
S1: You're going to have like thousands of agents. You'll start
S1: with dozens, right? Dozens, then hundreds, then thousands, then whatever.
S1: So we're also learning from new marketing campaigns on X
S1: or LinkedIn. Keep in mind multiple of these these modules
S1: are actually watching the company. They're watching everything the company does,
S1: every piece of marketing, every piece of information that's put
S1: out about this company gets parsed and brought back into
S1: the context, because the system as a whole and the
S1: AI that's sitting on top of it, watching the goals,
S1: is using that new information to tweak how we're going
S1: to do this attack. Right. So they have a new
S1: product launch, which is a new website, a mobile app. Cool.
S1: Go download that. Right. Right now we can't do too
S1: much with that because that's a little bit difficult. In
S1: a year or so, we're going to be able to
S1: go download that full mobile app, run the mobile app
S1: in a full virtual environment, Run a whole bunch of
S1: mobile tools, find out like which APIs aren't secured where
S1: they're not using TLS. Um, all sorts of issues that
S1: you have with mobile security. And that'll just be one
S1: little tiny module which brings that context back into the
S1: overall engine, which enhances all the other components inside of
S1: that engine. Right. Send that over to automated Burp intruder tool. Right.
S1: Then all of burps output. And that's a lot of output.
S1: It overwhelms anything including Gemini by the way. So this
S1: is still a place where, you know, the AI has
S1: to grow because, um, something like burp output from crawling
S1: a website is still massive. Anyway, you've got all that
S1: content coming out. All that content can then be repassed
S1: to find the JavaScript in there, to find where they're
S1: doing all their controls on the client side. Again, you
S1: only have to tell it a couple of core things
S1: inside of the system. Here are the types of things
S1: I'm looking for. Any output that you get, go and
S1: look for the following things. Oh cool. We got new
S1: output from burp. We found new JavaScript files. Let's go
S1: parse the hell out of them and find the files
S1: and API endpoints. Bring that back into the system. Right?
S1: And meanwhile, all this stuff is being fed into the
S1: appropriate modules. So let's say we find some good stuff, uh,
S1: send off to the exploit agents and try to do
S1: something according to the rules, uh, in goals we've laid out. Right. Uh,
S1: so for an attacker, we're trying to extract data. Maybe
S1: we're going to sell that access to a broker for
S1: a bounty person. We're going to create a POC in
S1: a short video to go with the automated report, and
S1: we're going to submit it to Hackerone or Bugcrowd or whatever. Right.
S1: And that just becomes another module that your thing is
S1: good at, right? It's automated workflow, but that's not the
S1: cool part. The cool part is this thing never sleeps.
S1: Dozens or hundreds or thousands of agents in this infrastructure
S1: working at all times, finding new domains, finding a new
S1: announcement which includes a new domain which you then go
S1: find the subdomains, which you then go find all the infrastructure.
S1: You find the web apps that are listening. You then
S1: go crawl those ad infinitum through this entire system, right?
S1: Open admin portals. You're taking all the screenshots. You're finding
S1: the screenshots. Oh, that's an admin portal. That thing's wide open. Oh, look.
S1: Default credentials. Right. Looking for open ports. Seeing if there's
S1: any new stuff out there right now. This sounds complex
S1: because there's lots of different tools and everything to keep
S1: in mind. But this system only needs to be built once,
S1: and then you're just adding modules and upgrading the modules.
S1: And this is a big part of what the AI
S1: helps you do. It helps you just make each one
S1: of these little things better and smarter. Again, everyone is
S1: going to have a stack like this. Individual bounty hunters,
S1: individual people just doing security research or hacking on their own,
S1: and definitely the attacker organizations. And guess who else needs
S1: to have it? The defenders. If you are a defender
S1: and you are not running this against yourself, you are
S1: going to lose. You are going to lose because because
S1: there are going to be so many people running a
S1: stack like this against you, you are just going to
S1: lose now at first, including everything I built for myself. Right.
S1: This was just going to be some basic information, right?
S1: Because we can't do the full version of this yet. Right?
S1: This is a year, two, three, four years. You know,
S1: This gets better as the AI tech stack gets better.
S1: But the system itself is core. So, um, this is
S1: like an internal. This is the AI remake I'm currently
S1: doing of my Helios system. And, you know, it's not
S1: going to have fully automated burp yet. It's not going
S1: to have a bunch of different modules. But like I said,
S1: this gets better as the tech gets better. The other
S1: thing is running, you know, hundreds of agents constantly. That's
S1: not cheap, right? So these prices have to come down
S1: the context windows have to go up. It's an upgrade process.
S1: So some vignettes to just think about this. So imagine
S1: that you're out at dinner and you get a notification
S1: that some employee at some company. Right. Um, they just
S1: talked about how, oh, I've got this thing at work
S1: and blah, blah, blah. This thing is a they're drunk
S1: or whatever, and they're talking online and some, you know,
S1: Reddit subreddit or whatever, and they're like, yeah, this new
S1: domain we put up and it doesn't have toufar. And
S1: I can't believe they used default credentials. That's why I
S1: want to quit. I'm going to start my own business
S1: or whatever. And so, um, you're sitting there eating dinner
S1: with a friend and you get a discord message from
S1: your AI bot and it's like, hey, some, uh, some
S1: dumbass just got drunk and posted that, um, there's a
S1: brand new, uh, domain open and, uh, potentially there's a
S1: vulnerability here. Do you want me to go mess with it?
S1: And you're like, yeah, yeah, go mess with it. So
S1: it comes back and it tells you basically. Yeah. The
S1: vuln that they mentioned actually does exist. Um, I do
S1: you want me to exploit it? Yes. Cool. All right.
S1: So we sent it in. We got the money. Or
S1: if you're a bad guy, you know, you're, uh, stealing
S1: data or whatever. And keep in mind, this could be from, like,
S1: a forum post. Um, it could be an announcement on
S1: TechCrunch that they just bought a company. So it's a
S1: merger and acquisition. Um, anything on the internet relative to
S1: your target? The agents are constantly watching new announcements, you know,
S1: new mergers, disgruntled employees, uh, a new job req for
S1: a new technology that you didn't know. So you add
S1: it to the tech stack for that company. Uh, new
S1: website posts are constantly being discovered, right, because they can
S1: make a slight change to the site, but they added
S1: a new API. Have we tested that API before? No,
S1: it was actually a different team that built that API.
S1: They didn't use all the security that the other team used. Boom.
S1: Now that's how we got in. That's how we pulled
S1: the data or whatever. New S3 buckets not probably secured
S1: all this stuff. So the entire game here and this
S1: is a really big point, is maintaining as accurate as
S1: possible world models for these things you're attacking. It doesn't
S1: matter if you're a company. It doesn't matter if you
S1: were hired to defend the company. It doesn't matter if
S1: you have your own startup. It doesn't matter if you're
S1: a bounty player. It's all the same shit. You have
S1: to keep the most updated version of this thing in
S1: your mind as possible. And here's something else that's crazy
S1: about this. One of the modules here is the actual
S1: list of attacks that you run when you attack. Okay,
S1: so check this out. You have like it's your bag
S1: of tricks. Your bag of tricks is what gets thrown
S1: at every web app. At every mobile app. Right? Um,
S1: for every social engineering campaign, for every fish you have,
S1: like your favorite little stuff that you do. Well, one
S1: of the AI modules that you have inside of your
S1: overall system is the one that parses new research. So
S1: I keep forgetting the guy's name, but every blackhat he
S1: releases like a new attack on HTTP itself. Um, he's
S1: the guy that works with, uh, DAF over at, um, uh,
S1: you know, burp. Um, portswigger. But anyway, uh, I want
S1: to say albino wax, but that's not quite right. Anyway,
S1: you all know the guy. So every time he releases something,
S1: every time he tweets, I have another module which go
S1: and reads that pulls it down and says, oh, that's
S1: actually interesting. Guess what? Upgrade. It's like the Borg from
S1: Star Trek. You hit it. Once it falls over, you
S1: hit it the second time. It's blocked that technique. Okay,
S1: Jason puts out a new video. He's like, oh, I've
S1: got this new, uh, this new attack that I always
S1: do against my things. It finds way more domains. I
S1: got this new attack. It, uh, it goes through filters for, uh,
S1: prompt injection. Right. Um, maybe, uh, Joseph is talking about that.
S1: It goes straight through prompt injection. Cool. Add that to
S1: the methodology. The whole system has now been upgraded. You
S1: can have an entire dedicated thing. It does nothing but watch.
S1: TLDR right, it watches Clint's entire thing. It finds every
S1: single thing that it mentions. It reads, goes and reads every, um,
S1: you know, every presentation, every GitHub repo. And it pulls
S1: out the research and uses that to upgrade the methodology.
S1: And again, that's also continuous. So the system is always
S1: being upgraded. So then the question is like what are
S1: you going to actually point this at. So I'm already
S1: monitoring like all the new bounty programs as they go live. Right.
S1: I'm not started testing them yet because I'm still building out, um,
S1: the rest of this new stack based on context. But
S1: my goal is to set this thing free on, uh,
S1: on actual program soon. Um, but but the point I'm
S1: mentioning this is that you can always be adding new targets, right?
S1: Attackers are going to have their own criteria for picking targets, right?
S1: Maybe they have a lot of money. Maybe they have.
S1: It's a combination of a lot of money and a
S1: bad security team. Maybe it's a combination of they have
S1: a lot of money, but I just saw on LinkedIn
S1: that half of their security team got fired. Oh, let's
S1: add that one to unify context and let's start attacking
S1: that one. Point is this is also continuous to find targets.
S1: And in my case, just parsing a brand new, uh,
S1: bug bounty programs that are coming live. So the entire
S1: game here is maintaining these accurate real time world models
S1: for entities. Like I said, it doesn't matter who you are. Um,
S1: what's really hilarious about this is AI is not the
S1: main feature here. AI is not the point. AI is
S1: just the tech that enables this to happen because of
S1: the agents, and because of the fact that um, models
S1: can hold way more information in their brains at one
S1: time than we can. That's the only thing we're really
S1: getting from the AI is the models are pretty smart, right?
S1: And the smarter they get, the better this gets. But
S1: it's not the kind of the point. The point is
S1: the world model capture of this thing. It. Okay, so
S1: just imagine this. Imagine it's 20 years in the future.
S1: Imagine we're not dead yet. Or, you know, like, everything
S1: has gone well. The planet is still here, uh, 20
S1: years in the future. Imagine an ASI and this is
S1: a little sci fi, but it's not too far off. Honestly.
S1: Imagine China holding the United States context of every open port,
S1: every vulnerable API path, every, um, opportunity for, like, file inclusion,
S1: every single attack possible, every every AI agent that's vulnerable
S1: to a particular type of prompt injection. It just pulls
S1: in the entire context of the United States current state
S1: of vulnerability and holds it in its mind in one piece.
S1: And then they ask the question, who do I go
S1: after first? What is the next best action to harm
S1: the United States? The most right or to harm Russia
S1: the most, or to harm whatever target that they are
S1: pointing at? The point of this is that is millions
S1: of IPS. Hundreds of millions of IPS. Is it billions
S1: of IPS? The point is, think about how much context
S1: you need for that, right? Doing this for a company
S1: itself is actually hard enough, right? To understand its entire
S1: history and every state change of all its IT and tech.
S1: We're talking about terabytes or petabytes to hold a state
S1: in its mind at once. And it's got to like
S1: keep that in context. Keep in mind, so you think
S1: we've actually gone far with AI? We haven't gone near
S1: close to what we actually need. And the AI is
S1: not the point. The point is having the size of
S1: the state that you can hold in your mind at once, right?
S1: So all this to say that the AI is not
S1: that important. It's kind of a supporting actor because size
S1: of context and yeah, okay. The models are smart. So
S1: that emulates, you know, some human components of this. But
S1: what actually matters is knowing that you have to keep
S1: the state and understand this world model of the thing,
S1: and that you build a system, a replicable system that
S1: produces outputs based on how the different modules in the
S1: system interact. The system is more important than the eye, right?
S1: And the concept of context itself is more important than eye.
S1: The eye is just the supporting tech. So what we
S1: end up with here is a world where every single stone,
S1: every single port, every single URL, every single API endpoint,
S1: every single agent is constantly being overturned, checked, and double checked.
S1: As an attacker, you are competing with hundreds of thousands
S1: of other attackers. As a bounty player, you're competing with
S1: hundreds of thousands of other bounty players and the attackers.
S1: You're racing to go do that thing. And as a defender,
S1: you're defending against all of them, plus all the other defenders,
S1: because you know you want to be the one that
S1: gets away from the bear while the other defender gets eaten.
S1: So natural question is, okay, what does all this mean?
S1: If this is correct, if you're a defender and you're
S1: trying to determine what AI to build for your company,
S1: you need to start building your own world model of
S1: your company. You need USC context for your company. Your
S1: attackers are going to have it and you better have
S1: a better version. And if you're a bounty player, you
S1: need to rebuild your automation stack. Putting the world model
S1: building and USC at the center of it. And if
S1: you don't have an automation stack, go look for a
S1: new hobby because you're not long for this world. They're
S1: about to be millions of people slash agents going after
S1: the same bugs with constantly evolving and improving systems and
S1: stacks and AI intelligence helping it. Right? So this is
S1: a competition between their system versus your system, not one
S1: of them against you. It's their system against yours, their
S1: context against yours, their world model against yours. And finally,
S1: if you're just trying to figure out, like where things
S1: are going with all this AI stuff, just remember one
S1: core idea. The game is not adding AI to stuff
S1: we care about. The game is having real time world
S1: models of what we care about, which we can then
S1: take action on using AI. Thanks for your time.