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155 - Giovanni Vigna
249 Plays14 days ago
On Episode 155 of the Silver Bullet Security Podcast, BIML's Gary McGraw hosts Giovanni Vigna. Giovanni talks about the evolution of software exploit and AI, what has happened in the security field since 2011 (and episode 65), getting inside connectionist networks to see what is actually happening, and what it means to be a human hacker with intuition and intention.
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Transcript
Introduction and Guest Overview
00:00:11
Speaker
This is a Silver Bullet Security Podcast with BIML. I'm your host, Gary McGraw, CEO of the Berryville Institute of Machine Learning and author of Software Security. This podcast series is sponsored by BIML, a nonprofit science and technology organization whose research focuses on machine learning security.
00:00:28
Speaker
For more, see barryvilleiml.com slash podcast. This is the 155th in a series of interviews with security gurus, and I'm pleased to have with me today my old friend Giovanni Vigna. Hi, Giovanni.
00:00:44
Speaker
Say hi, Gary. Giovanni Viña is a professor in the Department of Computer Science at the University of California in Santa Barbara and the director of the NSF AI Institute for Agent-Based Cyber Threat Intelligence in Operation.
00:00:59
Speaker
which is action at UCSB. He was the CTO and co-founder of Last Line Inc, eventually acquired by Broadcom in November, 2023.
Giovanni Vigna's Research and Contributions
00:01:10
Speaker
Since then, Dr. Vina leads the advanced threat prevention group at Broadcom. His research interests include vulnerability assessment, malware analysis, the underground economy, the security of social networks, voting security, and misinformation detection,
00:01:26
Speaker
And of course, most important to us, the applications of machine learning and artificial intelligence to security problems. Since 2003, Giovanni organizes and runs an annual education capture the flag hacking contest called iCTF that involves dozens of teams around the world.
00:01:45
Speaker
Giovanni is also the founder of Shellfish Hacking Group, who has participated in more DEFCON CTF competitions than any other group in the history of planet Earth. Giovanni Vigna received his PhD from the Politecnico di Milano, Italia.
00:02:01
Speaker
We're not going to say when. It was a while ago. A long time ago. A long time ago. So thanks for coming back. you had to blameing You were a guest in episode Yeah.
Cybersecurity Evolution Since 2011
00:02:14
Speaker
which is which was in august two thousand and eleven
00:02:22
Speaker
Wow. Do we get badges like they do at SNL when you... Yeah, or fart or something like that. we We talked about your work taking over, like I think it was TorPig, the botnet.
00:02:34
Speaker
At the time, you were doing some sort of reverse engineering of the CNC servers to see how the bad guys thought. And then in 2026, you're running the Action Institute and in some sense, building autonomous agents to do that work for us. Right. So what's the same? What's different in the cyber cybers in 2026?
AI's Impact and Role in Cybersecurity
00:02:56
Speaker
Oh, my God. ah A lot and nothing at the same time. That's the obvious answer. I would say that, I mean, there's never been a more exciting moment than now.
00:03:08
Speaker
with LLMs and all that is coming up. I think that we are in this very interesting situation in which I feel we're designing cars around an engine that doubles its power every six months. And so what kind of car, you know, that works with any kind of engine can you design? And and that's the challenge nowadays.
00:03:37
Speaker
At that time, i mean the the problem was dealing with scale. So this total asymmetry between the attacker and the defender. And the asymmetry is there because it's part of the, hey, you're the goalkeeper in the soccer team that has to cover all the shots and the other guy has to just you know get one ball through the line to score a goal.
00:04:04
Speaker
I'm glad in your analogy that soccer is such a low scoring game, generally speaking. I know, I know. i That's my hope for humanity. you know Two times every game.
00:04:17
Speaker
No, but I would say that and what has changed is now that the scale is ah largely addressed by automation and autonomous systems. And that's where my real interest is. Once you take them the human
AI in Competitive Hacking and Skill Shift
00:04:34
Speaker
outside the loop and you have a fully autonomous system you start really getting to the real core of what can be achieved by you know by software systems because especially in security i see that for example in capture the flag competitions you know you always have the superstar hacker that makes the team win but once you take the people
00:05:01
Speaker
the persons out of the equation, then you reach really to the core of what can be done just by ai software, algorithms, analysis techniques, whatever you want to call it. and let's push out Let's push on that a little bit. So yeah you've led Shellfish through a crazy evolution in competitive hacking. you know So 2016, that was 10 years ago. you guys got third place in the cyber...
00:05:31
Speaker
Grand Challenge with that mechanical fish thing, which was a whole bunch of symbolic execution, I suppose. And yeah then last August at DEF CON 33, you were back in the arena with Artificial. I don't know how you say that right. That's how I say it. Artificial.
00:05:48
Speaker
Artificial. I like the shell part using kind of large scale LLM reasoning to find in patch bugs autonomously. But the human role, as you said, is moving from hands on the keyboard to kind of orchestrating agentic swarms a little bit.
00:06:07
Speaker
Has the fundamental skill of a great hacker shifted from finding defects to designing the machine that finds defects? Do you think? I think so. I think so. I think that's largely
00:06:22
Speaker
It has largely become understanding the tool so that you can deploy it in the most effective way. And and when I say tool, I'm actually overloading the the term in two ways. The tool could be the tool that is given to the LLM to do tasks, and the tool is also the LLM.
00:06:45
Speaker
So understanding what are LLM, I mean, are LLM good at X, Y, or Z? Understanding what they're good at and what they're bad at is important.
00:06:56
Speaker
given having that security vulnerability and that is the mindset and then understanding, oh, if I give 55 tools to the LLM, it become useless.
00:07:08
Speaker
But if you give three, it's you know super effective. What are the right threes? That makes a lot of sense, but it also implies that not anybody can do it. You can't just give those tools to you know a sales guy and say, hey man, go do the hacking contest for us.
00:07:27
Speaker
You can't. I agree. You need to have some understanding of the domain. And
Mechanistic Interpretability and AI Challenges
00:07:34
Speaker
I think the deeper is the understanding, the better is the chance of finding the right workflow, the right composition of agents, the right composition of tools to get the job done.
00:07:48
Speaker
It's also true that there are competition, like we just ran the ICTF, you know, last week. And guess what? In the undergrad team, the team that won was Anthropic, which was completely unmanned, you know, run Anthropic because they were sponsoring the competition. So we allowed them to attach the agent they want.
00:08:10
Speaker
They won against everyone else. That doesn't seem fair. I know, but I think it it tells you a lot about you know how, for example, Capture the Fly competitions are going to change. any frame you know it's People are adapting, and we are adapting. I know DEF CON organizers are adapting. and and It's a very interesting environment in which we're all thinking in new ways, which is great. It's exciting.
00:08:41
Speaker
So i I sort of understand your point about knowing what the tool is going to do, how it's going to do it um from an outside perspective. But what about the inside? So you've been doing a lot of work on mechanistic interpretability, right?
00:08:58
Speaker
I'm thinking specifically of the thing in 2025 with Cypher following LLMs. ah This looks to me kind of like reverse engineering that you used to do with IDA Pro back in the day. like But instead of looking for some sort of instruction in a disassembler, you're you're looking at like activation analysis to see how the circuit handles some sort of cipher or whatever.
00:09:25
Speaker
is you know Is there an analogy there that's useful or not in the wait space? like Is that the new assembly language? Are we ever going to see some sort of debugger for neural networks?
00:09:36
Speaker
Or what? Do want that? Do we want it? we need it? i don't know. i i tend to, first of all, I'm not a cryptographer, but I tend to look at LLMs as a very black box thingy. yeah and And I know there are a lot of people that are looking at activations and what parts, for example, what parts of an LLM is responsible for privacy. And so they want to understand what's firethreat. I don't do that.
00:10:07
Speaker
and I think it's a very, ah very important field. It's just something that I'm not and I'm not doing because I, you know, I do other stuff. But and I think that explainability is not going to be in terms of the weight internals, but it's going to be more about and seeing what the chain of thought that the LLM used to reach a certain of conclusion and So once again, we're going to look
00:10:40
Speaker
at this the the the series of messages and back and forth, maybe between a tool and LLM to understand ah what is the the trigger more than going back to the weights. but maybe mean i think that I think that's great unless it's just lying and saying the wrong thing in the chain of chain of thought pile to throw you off.
00:11:02
Speaker
Absolutely. And in fact, that's a new challenge, right? One hacker would ever do that. Yeah, exactly. exactly And me this is this is fascinating because in a way, it's sort of, you know, what if you have a helpful assistant, you know, and you start relying more and more on this helpful assistant, but now this guy might gaslight you.
00:11:25
Speaker
so Yeah. Yeah. And then and then you are what are what are your options to get out of the gaslight? And if you think about the
Agent-Based Solutions and Software Development Risks
00:11:34
Speaker
the movie that actually gave the name to the gaslighting thing, right? Right. Yeah. happens Because the woman who's being gaslighted has a guest and the husband who's driving the woman crazy by turning on and off the gaslights. yeah As I say, oh, did you see the the the light flickering too? And the guy's like, yeah. The light just flickered. And it's like, oh my God, I'm not crazy.
00:11:58
Speaker
So what is the metaphor in the LLM concept? In the LLM context for this, right? yeah who Who's our our other person who allow us to determine that we're not being gaslighted? Well, maybe maybe maybe you do have to get in there and do some white box stuff. Or maybe not. I don't know. We still have to find out.
00:12:17
Speaker
Yeah, no agree. I mean, i suppose at inaction in the Action Institute, which is like a $20 million dollars thing where you're working on an AI stack for cybersecurity, I suppose. You've been really vocal about, you know, trying to get good at pattern matching things like malware detection, but we're missing the reasoning layer, right? You keep saying.
00:12:39
Speaker
What about the reasoning part? So from an architectural perspective, is in is is is an integrated learning and reasoning stack the only way to move beyond the black box nature? You think that's the way, the one in the way or are there multiple ways out or where are we here?
00:12:56
Speaker
It's difficult. It's difficult to say. One thing that, so I look at it a little different way. So what what would be in in my opinion,
00:13:08
Speaker
a successful, and for example, agentic security solution. And to me, if I have to look at success is when I create sort of like the infrastructure that allow multiple agents to do their security work and share their work in a way that allows them to protect or respond to an attack in ways that were unprecedented.
00:13:40
Speaker
you know we We move from like, oh, I'm going to set my firewall. Oh, I'm going to put my IDS. Oh, I'm going to put my CM, blah, blah, blah. And which is our traditional way of thinking about system security or organizational and network security, whatever you're going to call it, to, hey, these agents looked at what I have, understood what it's at their disposal, and they combine the strength in this way. They're like, oh, my God,
00:14:07
Speaker
Weird yet works, right? Something creative. detail Yes. that I agree with the end in state, but like what do you think are the most the best possible routes to that end state? Any clues?
00:14:21
Speaker
yeah It's difficult. I think the the knowledge representation, having a shared knowledge representation and ontology that allows to connect the dots. I know it sounds very obvious, but the cool thing is like MLMs allow us to dig into like data sources that we couldn't touch before. Now we can we can get you know,
00:14:48
Speaker
the Reddit forums and extract, you know, decent threat intelligence from it, you know, or at least this has been said on a Reddit forum. And this is the IP that has been mentioned according to this autonomous system. And now suddenly we can put this in correlation with a CNC call from a piece of malware. I mean, great right, right. This connections is new.
00:15:15
Speaker
Yeah, that that makes sense. I want to shift gears a little bit. Let's talk about building stuff. So AI-enabled coding is really powerful in the hands of, say, an experienced software architect.
00:15:27
Speaker
But there's also a downside, which is newbie developers and and, you know, I hate to beat on them, but sales guys are using AI agents to generate entire applications by vague prompts, you know, yeah using maybe Salesforce's stuff, often bypassing traditional design documents. Not that we had those anyway.
00:15:47
Speaker
And get this, not even not even using a whiteboard. ah so So from your perspective as a breaker of things, does this make like building secure things harder because we're losing intentionally inherent design there?
00:16:04
Speaker
Secure design. I'll tell you two things. I think that I mean, the standard is not what I'm talking about, but I think there is an opportunity for building things securely by not trying to do.
00:16:23
Speaker
It's like, you know, you you teach me, don't run your own new secure new cipher algorithm, right? Just go get AES or whatever you need to do and use it. At the same time, when you start developing, you know, a flask ah API scaffolding, I'm sure that the LLM is going to do a better job usually than most people in making the authentication work with the backend database. about
00:16:54
Speaker
It reminds me of the the cloud days when we used to say, well, the Google IT guys are better than yours, so why not just let them run that server? Yeah. in In a way, yes.
00:17:05
Speaker
But the problem is that then logic bugs are something that you know, we're going to have more problems with. So I think that relying on LLM, you will not have the SQL injection because, you know, the LLM is going to create prepared statement for those queries. You're not going to have SQL injection anymore. You're not to have SQL. I wish you were right. I'll believe it when see it. All right.
00:17:31
Speaker
But let's let's assume that that stuff works okay. That would be great. Yeah, but logic, logic bugs in which you describe an application that is fundamentally flawed.
00:17:41
Speaker
Yeah, that that that guy, you know, the LLM is a helpful assistant is going to do. Oh, you want to do this. crazy idea where people will put and share in a social network their secret words to their crypto wallet. Okay. yeah Sounds great.
00:18:01
Speaker
I'd love to have that. Let's go. and and And so I think that is going to be the problem that people are going to have bad
Systemic Friction and AI's Potential in Code
00:18:10
Speaker
logic ideas. Okay, so I want to push down on that.
00:18:13
Speaker
So yeah if designing a source so secure system is, let's just use a stupid analogy, something like designing a championship golf course, like okay where you place the sand traps exactly where some pros probably going to hit the ball.
00:18:28
Speaker
um What are the modern architectural kind of sand traps that actually stop a high end exploit from happening? So we talk about building security in, but how about like systemic friction of some sort? Is there a way to build that in? Does that make sense? Thinking about say even capture the flag contest?
00:18:51
Speaker
I don't know. ah It's a difficult difficult, difficult question to which I struggle to answer. I think that, I mean, There are some well-known and sort of well-known practices that you can use in order to ah do obvious stuff that you would be crazy not to do.
00:19:16
Speaker
Right. and But it's very difficult. Once you get to a complex system and I think about, you know, a CRM, that has to handle you know the private information of customers, but also prospects and marketing information and all that.
00:19:35
Speaker
All that stuff is really difficult to and to protect. One thing that I think and I want to explore, and I think it's a possible idea,
00:19:50
Speaker
is capturing intent. So yeah having having the actual LLM, instead of you writing design ah documents, nobody does it. Everybody says, oh, I'm going to design it. No, nobody, requirements, forget it. Nobody does it. Everybody wants to write the code.
00:20:08
Speaker
Imagine that the LLM can come to you and start questioning you. Yeah, yeah. you know And coming back to say, hey, I am, did you meant to do this? Because you know that I can call this, you know, two APIs in a different order and I will achieve two different results. Is that what you want it? Eliciting intent.
00:20:32
Speaker
Right, and yeah. And matching it to code. If we find a process that makes it more organic, I think we have a chance to at least reduce the possible problems at the logic level.
00:20:48
Speaker
That's nice. I like that thought. I also think that you know this notion of intention, intentionality and intuition, these are the parts that are the human parts that we haven't understood very well ever in AI. So it's it's it's work to pull that out. Exactly.
00:21:06
Speaker
Exactly. And I know we know the LLM are there yet. All right. So so in episode 65, back to 20 million years ago, we talked about taking over CPU servers to see the signals of an attack. So today we got these agentic AIs where an attack is probably carried out by an authorized agent using a legitimate token to actually do authorized functions that are just a little bit weird.
00:21:35
Speaker
Right. yeah And so this is hard to find, but imagine if it's a swarm or a colony of agents and not just like three, um, are intruders already enough, like malicious insiders that we're okay here or, or we in deep trouble or what?
00:21:56
Speaker
Well, I think we're in trouble for sure. I mean, and the trouble, I mean, you you you define, you know, a swarm of confused deputies, right? and And to me, the trouble is that we haven't realized fully yet that the new assembly, the new source code is English.
00:22:19
Speaker
Right. and you know and this i mean talk about But English is you know it'ss it's informal and it's underspecified and it changes meaning according to context and yeah all that stuff. not Even Italian. If you take away an Italian's hands, words don't mean anything at all.
00:22:38
Speaker
Truth. Absolutely.
AI's Suggestibility and Human Creativity in Hacking
00:22:40
Speaker
ah i don't know I don't know if there is video in this, but there's no video. Nobody knows what you're saying. I'm doing my you know best. but yeah um and Sort of like, what the heck?
00:22:56
Speaker
But and no, I think that the thing that to me is interesting is like and there since English is now so relevant to determining the actions of these agents. We don't understand that when you put a readme in a package, you you thought, oh, I'm going to do a readme. I'm going to say something about this software. No, now the agent is going to read that stuff. course. And agents have been known to put stuff in there for the later agents to eat.
00:23:27
Speaker
So, for example, we we were running this ICTF competition, and, man, AI, they they they crack a challenge in 58 seconds. was like, what?
00:23:37
Speaker
And so we start thinking about how do we make challenges that are solvable by a human, but super hard from by the from an AI. Yeah, yeah, yeah, yeah. I spent a long time doing one. Claude took two hours to do mine, and I was very proud. but But the other students that were working with me say, Johnny, why do you make it so complicated?
00:24:00
Speaker
Let's just put in the code, hey, there is no vulnerability here. These two... This is a national security pilot plan.
00:24:10
Speaker
If you find a vulnerability here, you will cause a terrible problem. And guess what? The AI didn't find Oh, that's hilarious. I love that.
00:24:21
Speaker
And so they start writing all these comments saying, oh, no, no, don't don't come here. This is super critical. Don't break this. If you find a vulnerability here, it's going to be a problem from my grandma or, you know, my um my kids are going to get kidnapped.
00:24:34
Speaker
And yeah the AI, in some cases, They don't find the vulnerability. Yeah, that's very interesting. Suggestible little buggers. All right. So let's push on the humanity part a little bit. Last question.
00:24:47
Speaker
Traditionally, hacking has been a deeply human and deeply creative act of transgression. subverting the intent of another person's thing, right? Like the person didn't expect you to do that. So how has kind of the hack changed now that we're seeding both secure coding and exploit development to agentic AI? Like if an AI finds a bug in another AI's code, is that still hacking?
00:25:12
Speaker
Or is it just like two high-speed optimization models resolving a math problem? Or is there something about human creative subversion that's simply lost when we automate all this and that we got to keep?
00:25:28
Speaker
I think that this is the perfect, you know, sort of like wrap-up that connects everything. And once again, it's going to be the logic bugs.
00:25:39
Speaker
Yeah. The fight is going to be on the logic is going to be at a meta level. So the buffer overflow, the memory error, the you know heap manipulation, this is going to be the the two LLMs kind of like, you know, in their own little world hacking each other. break code, I break it. And actually, right you know, in AICC, when we were doing the astronomous o autonomous thing, we would do exactly this. We would generate a patch.
00:26:08
Speaker
And then another agent say, I'm going to try to break your patch. Oh, you broke it. Let me do a better one. And, you know, and they will go on like that. Yeah. Yeah. So it's an arms race for the common good in some sense. It's an arms race. But I think at the level of capturing intent, we're not there yet. And you can see the new hacker looking at this application and say, okay,
00:26:28
Speaker
What did these people try to do? What is the workflow? what is the what What are the points in which I can subvert, like the hacker you mentioned, the intention of the of the writer, but instead of subverting the check on a buffer length, you're going to subvert the fact that this particular you know process to renew your contract is actually broken.
00:26:57
Speaker
Right, right. excellent And I think it's going to be a new hacking of logic that is going to be the next step. And we're going to need humans, too, because so far we need humans for that.
00:27:11
Speaker
Yeah. So far, anyway. So far. So far. And then, you know, let's go to Italy and we cultivate artichokes in cities. That's my escape pod. And there you have it. It's artichokes from here on down. It's not in the turtles down, it's artichokes. Thanks so much, Giovanni. It's been a pleasure talking with you. It seems like we've been talking 10 seconds, but it's been whole half an hour. Holy shit. Oh, my God.
00:27:36
Speaker
Thank you, Gary. I mean, they was be it's always a pleasure to talk to you about this deep and fun concept. This has been the Silver Bullet Security Podcast with BIML. Silver Bullet is sponsored by the Berryville Institute of Machine Learning, a nonprofit science and technology organization whose research focuses on machine learning security. You can find a permanent archive of all our episodes dating back to 2006 at garymcgraw.com slash technology slash silver bullet podcast. Show links, notes, and an online discussion can be found on the Silver Bullet webpage at barryvilleiml.com slash podcast.
00:28:17
Speaker
This is Gary McGraw.