The latest OWASP Top 10 for LLMs reveals the biggest security risks in AI, from prompt injection and supply chain vulnerabilities to emerging threats like system prompt leakage. In this episode, Sandy Dunn and Rock Lambros break down the 2025 updates, explain why LLM security is unlike traditional software risks, and share actionable insights for protecting AI-driven systems—essential listening for anyone navigating the future of AI security.
Sandy Dunn, Consultant Artificial Intelligence & Cybersecurity, Adjunct Professor Institute for Pervasive Security Boise State University | On Linkedin: https://www.linkedin.com/in/sandydunnciso/
Rock Lambros, CEO and founder of RockCyber | On LinkedIn | https://www.linkedin.com/in/rocklambros/
Host: Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber] | On ITSPmagazine: https://www.itspmagazine.com/sean-martin
The rise of large language models (LLMs) has reshaped industries, bringing both opportunities and risks. The latest OWASP Top 10 for LLMs aims to help organizations understand and mitigate these risks. In a recent episode of Redefining Cybersecurity, host Sean Martin sat down with Sandy Dunn and Rock Lambros to discuss the latest updates to this essential security framework.
The OWASP Top 10 for LLMs: What It Is and Why It Matters
OWASP has long been a trusted source for security best practices, and its LLM-specific Top 10 is designed to guide organizations in identifying and addressing key vulnerabilities in AI-driven applications. This initiative has rapidly gained traction, becoming a reference point for AI security governance, testing, and implementation. Organizations developing or integrating AI solutions are now evaluating their security posture against this list, ensuring safer deployment of LLM technologies.
Key Updates for 2025
The 2025 iteration of the OWASP Top 10 for LLMs introduces refinements and new focus areas based on industry feedback. Some categories have been consolidated for clarity, while new risks have been added to reflect emerging threats.
• System Prompt Leakage (New) – Attackers may manipulate LLMs to extract system prompts, potentially revealing sensitive operational instructions and security mechanisms.
• Vector and Embedding Risks (New) – Security concerns around vector databases and embeddings, which can lead to unauthorized data exposure or manipulation.
Other notable changes include reordering certain risks based on real-world impact. Prompt Injection remains the top concern, while Sensitive Information Disclosure and Supply Chain Vulnerabilities have been elevated in priority.
The Challenge of AI Security
Unlike traditional software vulnerabilities, LLMs introduce non-deterministic behavior, making security testing more complex. Jailbreaking attacks—where adversaries bypass system safeguards through manipulative prompts—remain a persistent issue. Prompt injection attacks, where unauthorized instructions are inserted to manipulate output, are also difficult to fully eliminate.
As Dunn explains, “There’s no absolute fix. It’s an architecture issue. Until we fundamentally redesign how we build LLMs, there will always be risk.”
Beyond Compliance: A Holistic Approach to AI Security
Both Dunn and Lambros emphasize that organizations need to integrate AI security into their overall IT and cybersecurity strategy, rather than treating it as a separate issue. AI governance, supply chain integrity, and operational resilience must all be considered.
Lambros highlights the importance of risk management over rigid compliance: “Organizations have to balance innovation with security. You don’t have to lock everything down, but you need to understand where your vulnerabilities are and how they impact your business.”
Real-World Impact and Adoption
The OWASP Top 10 for LLMs has already been widely adopted, with companies incorporating it into their security frameworks. It has been translated into multiple languages and is serving as a global benchmark for AI security best practices.
Additionally, initiatives like HackerPrompt 2.0 are helping security professionals stress-test AI models in real-world scenarios. OWASP is also facilitating industry collaboration through working groups on AI governance, threat intelligence, and agentic AI security.
How to Get Involved
For those interested in contributing, OWASP provides open-access resources and welcomes participants to its AI security initiatives. Anyone can join the discussion, whether as an observer or an active contributor.
As AI becomes more ingrained in business and society, frameworks like the OWASP Top 10 for LLMs are essential for guiding responsible innovation. To learn more, listen to the full episode and explore OWASP’s latest AI security resources.
LevelBlue: https://itspm.ag/attcybersecurity-3jdk3
ThreatLocker: https://itspm.ag/threatlocker-r974
OWASP GenAI: https://genai.owasp.org/
Link to the 2025 version of the Top 10 for LLM Applications: https://genai.owasp.org/llm-top-10/
Getting Involved: https://genai.owasp.org/contribute/
OWASP LLM & Gen AI Security Summit at RSAC 2025: https://genai.owasp.org/event/rsa-conference-2025/
AI Threat Mind Map: https://github.com/subzer0girl2/AI-Threat-Mind-Map
Guide for Preparing and Responding to Deepfake Events: https://genai.owasp.org/resource/guide-for-preparing-and-responding-to-deepfake-events/
AI Security Solution Cheat Sheet Q1-2025:https://genai.owasp.org/resource/ai-security-solution-cheat-sheet-q1-2025/
HackAPrompt 2.0: https://www.hackaprompt.com/
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Please note that this transcript was created using AI technology and may contain inaccuracies or deviations from the original audio file. The transcript is provided for informational purposes only and should not be relied upon as a substitute for the original recording, as errors may exist. At this time, we provide it “as it is,” and we hope it can be helpful for our audience.
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Sean Martin: [00:00:00] And hello everybody. Welcome to a new Redefining Cybersecurity here on ITSP Magazine. I'm Sean Martin, your host, where if you listen to the show, you know I get to talk to lots of cool people about some cool things. And, uh, I have really, two really good friends on with me today. Sandy Dunn and Rock Lambros.
How are you?
Sandy Dunn: Great to be back with you, Sean,
Sean Martin: it's good to have you both on the show. I appreciate you both and all that you do and, uh, value the work that you do for the community. And we're going to talk a bit about some of that work, which is in the OWASP LLM top 10 lists that you put together and, and, uh, I believe there's an update that we're going to talk about.
So we'll kind of give an overview for folks, look at some of the changes in the past year. Uh, since you and I were on, Sandy, uh, I think it's about a year ago, and kind of how, what the impact has been and perhaps what, uh, what the future holds for, for the list and what organizations can do to, [00:01:00] to embrace it.
Uh, before we do that, a quick few words from each of you, uh, for folks who haven't met you yet. Sandy, uh, what are you up to at the moment?
Sandy Dunn: Hello, I am the CISO at Brand Engagement Networks. I have a long career in cybersecurity. I have been a CISO in healthcare and a number of different startups. I'm really passionate about AI security.
I'm both an advocate for AI, but I also recognize that it, it can do, it's kind of a power tool. You know, if you don't use it right, you can do a lot of damage.
Sean Martin: I think that was your post today, rock use AI for automation and fail faster.
Rock Lambros: Yeah. Yeah. It's all about your data and processes, right? So I'm rock Lambros, uh, CEO and founder of rock cyber, uh, consultancy based out of Denver, Colorado, uh, recovering CISO.
Having said that during a lot of virtual CISO work, uh, author of the CISO evolution, which you can see over [00:02:00] my shoulder. And. You know, after I, honestly, after meeting Sandy at Black Hat in August, I've dove headfirst into several OWASP AI initiatives, including the top 10 list.
Sean Martin: Well, let's start there and maybe Sandy, if you can kind of give an overview of, hopefully folks are familiar with the, the OWASP top 10 in general, how this project came, came to be in, in the current state of what's going on, what is it, who is it for, I guess, just kind of level set for folks.
Sandy Dunn: Well, I think that the, when we look at, um, software, you know, OWASP is, you know, so has really been a leader in helping us define how we as developers and security teams and architects try to design solutions that are safe and usable for people. And the project really started because, you know, after chat GPT was released.
I think [00:03:00] everyone in cybersecurity was, was sitting around going, okay, now, you know, now what, you know, what, what, where do I even get started? What should I even be worried about? And so, um, I joined the project fairly early. I was kind of a lurker. Um, it was just trying to get my head around it, just trying to understand what should I be worried about?
How do I protect my organization? And through that, um, I really. Went to Steve Wilson, who's the project lead and said, Hey, I have an idea for this CISO checklist, where as a CISO, you can start building out a strategy. And, you know, one of the great things that happened with that is it really started a whole other bunch of additional.
Projects with the OWASP top 10. So we have the OWASP top 10, which has really become a standard. You know, if you see, you know, uh, people with, uh, AI security solutions, they're really comparing. Here's how we defend against the OWASP top 10 and people put together governance lists on [00:04:00] here. Here's how you test for the OWASP top.
And so it's really become a standard. And now we have branched into, um, we have a red teaming guide. We have an AI solutions guide. We have a center of excellence guide. We have a threat intelligence group. We have an AI agentics group. It's really blossomed into a lot of different useful resources, you know, and as I join these different teams, I really encourage people to create tools that That are consumable, you know, don't just create something that's a bunch of words, create stuff that people can actually take back and use within their organization that we're really trying to create the tools that in the solutions that aren't out there right now, you can find a lot of words, you can find a lot of really puffy high level guidance, but, you know, there's not a lot of stuff where you go, okay, You know, how do I actually start trying to defend my organization today?[00:05:00]
Sean Martin: Yeah, maybe on that point, Rocca, you said you're, you're involved in a lot of different working groups and projects as well. And Sandy, we were talking before we got on camera about some of the, some of my thoughts in our last chat that, that I kind of felt that we're treating AI security and LLM security specifically as a special.
A special case and, and not just wrapped up in the traditional software development, a WASP view. And to your point, and probably we'll touch on today that it is unique, right? And, but with that, we, we kind of have to look at all aspects of software development and delivery and maintenance and threats and operations, all the stuff that goes with building and deploying software again for AI.
So I guess into your last point, Sandy. That means a lot of stuff, right? So you have your standard software development and all the things you have to consider. Now we're looking at it from an AI specific perspective in [00:06:00] LLMs. Um, so Rock, how do you view the body of work? In the AI and LLM space that OWASP is working on and and how the top 10 fits into that and we're going to, we're going to get into some of the changes for 2025 and look at that list, but kind of the big picture.
How are, how is the team distilling all of that work in a way that that can be consumed like Sandy describes?
Rock Lambros: Yeah. So, like Sandy mentioned, right? The top 10 was kind of that first core initiative where a lot of these other initiatives, but not from. So, personally, I'm on the agentic AI security working group.
I'm also on the, uh, OSPI exchange, which is more focused on influencing, um, how the EU act is operationalized. Um, Now, how is it distilled amongst people? It also is extremely open, right? Anybody can join any group at any time and all their value. [00:07:00] Um, all their viewpoints are valued. It doesn't matter if you come in early to the project, towards the end of a project, if you're more of a lurker, if you're diving in hands on and, and uh, creating content or editing content in any of the deliverables, um, OWASP is very open and accepting like that.
Because I, I jumped in at the tail end, kind of of the latest iteration of the top ten, but I'm on the very front end of the agentic AI. Uh, initiatives, right? So, um, also has a very good job of fostering collaboration in that way. And the, in the charter of each of these different working groups is exactly kind of what, uh, Sandy just outlined digestible tangibly actionable.
Uh, tools and resources that people can take back and use in their organizations. Right? One thing Sandy didn't mention was her V1 or enough V2 maybe of her AI, uh, red teaming model or threat model rather, [00:08:00] um, the governance checklist that she helped create as well. Right. Those are all actionable, uh, things that people could take back to the organizations and stay right now.
I've got some sort of step by step guidance.
Sean Martin: So the 2025 list for top 10 for LLMs is available now. What's, um, what feedback did you get from the community between? The older version, versions and what's available now. I know there can be a lot of scrutiny in, in terms of what's included, what number of things might land.
Is there, is there a line that gets drawn where most teams only reach five or six and seven, eight, nine, 10 never really get, get looked at from a, from a risk management and operations perspective. So what was some of the feedback and how does that address or end up changing the new list?
Sandy Dunn: So I think you're asking.
So let me clarify your [00:09:00] question. So are you asking how do we come up with the new top 10? Like, what is the process and how it had? So that's it really is a community like we, as a community, we come in and say, okay, this is the list. Does anyone have anything that they want to add? Does everyone agree? And we have, you know, there's voting and poll.
Polling and, um, you know, if people disagree, you know, we try to, we find a resolution on it. I mean, Steve is I, it's really been phenomenal to watch him lead this project because he's, he's just fantastic and making sure that everyone has a voice, but there there's, but the voices don't turn into, um, you know.
We're still productive. You know, it doesn't turn into a theoretical argument on, you know, on which where something should be. Um, so it's, it's very democratic. I mean, it isn't, it's not us choosing this as the top 10. It's, it's really going out there trying to use data, asking people their [00:10:00] opinion. And I think another important thing about that is, is that doesn't mean that there's only 10.
You know, there's lots of issues. Um, that, but these are the, you know, start here. And again, I think that's really important. And there was a lot of discussion on expanding it. Why only 10? And, um, I felt really strongly and there was a lot of discussion back and forth, but to me, 10 is consumable. You know, as soon as you give me 20, I want to hide under my desk, you know, 10, you can start trying to weed yourself through them and, and, and try to figure out where your priorities are.
Um, I also want to touch on something that you said, Sean, which is, is with this AI security. It is not done in isolation from all of the other technology. So as organizations go out and try to understand, you know, what is this new power tool? You really have to look at it from the perspective of my [00:11:00] entire I.
T. Environment. And then where can I use these tools? You know, to do better threat intelligence to, you know, automate a lot of those, you know, really kind of not very fun work that we do in cyber security. Is there a way to actually use these tools? So you're looking at bringing them in, enabling them.
Protecting the tools, protecting your environment. Uh, one thing with AI that's, there's a very strong voice on supply chain and understanding where code is coming from. So there's a lot of things that AI is really bringing, can bring into your organization as far as asset and data management and knowing where your data is and where it came from and why you're trusting it.
That you can raise the whole entire organization's um, security posture by You know, looking at the problem holistically and then, you know, using power tools, the type of tools that you need, where you need them.
Sean Martin: Data supply chain as well. [00:12:00] Yes. Brings libraries and rhythms and things with it. You may or may not know where it comes from.
And I love this. I love this concept of holistic view and rock. And I'd like your perspective to Sandy, but rock, I'll start with you on my, my view is that a checklist is. Something that can be used to help understand a place to start a way to move through things, but I think mature organizations can leverage that to have a mindset of security, not just for AI, but in connection to the rest of the I.
T. Infrastructure and organization. So rock your view on how. How this is adopted, how you present it to folks, you, you chat with and work with in terms of creating a mindset that's supported by the tools and a checklist and not just here's a checklist you run through and You wipe your hands and I'm done, right?
Rock Lambros: Yeah, I think the benefit of the checklist [00:13:00] in this instance is because a AI is so top of mind for everybody But also it's so new for everybody people don't know what they don't know. So at least You know, I always do better staring at something that's already on a whiteboard versus a blank whiteboard and trying to figure it out And I think that's what that provides individuals in general.
Now, how do I approach it? um, you know, I really believe that there's kind of a Crossroads or a triad of ai cyber security and business enablement and I don't think you could talk about one Without the other two In today's day and age, um, you know, and have an effective, positive business outcome. So that's kind of how I present it, right?
Because AI can drive business enablement. AI can drive cybersecurity. You need to consider cybersecurity and AI. You need to consider cybersecurity and business enablement. And all that combined helps you [00:14:00] balance innovation with risk management, right? And we talk about risk management of the, you know, some of the fundamentals apply within the risk tolerance or risk acceptance posture of the organization.
And, you know, some people, you know, depending on their data, they've, they may not care too much about data poisoning or, or not data poisoning, uh, model extraction, because, you know, they downloaded it off of a hugging face and it's a public model anyway. So Who cares, right? So we talk about it kind of in those terms,
Sean Martin: having an understanding.
So you can make the assessment and do that risk calculation specific to your business, send your thoughts on.
Sandy Dunn: No, you're exactly right. And so rock and I kind of, so rock came up with, um, a rise in care framework, which is really to help organizations You know, be [00:15:00] strategic. Um, I created the checklist and it was really from my seat as a CISO going using the OODA loop saying, okay, observe, orient, you know, decide, act, you know, like, what do we need to do now?
And, and, um, there's things that, um, you know, what are your priorities? What should I get started on first? And so, uh, with the, I am planning to update. The checklist and rock and I were talking about it. Um, I've actually created a tool that I called the A. I. Red teaming compass, which allows you to go in and start.
Kind of using business impact analysis and threat analysis to be able to, to come up with the right strategy and do those decisions. So, you know, between, you know, both of us, you know, the, how we're trying to advise people is. Um, you can't be scared of everything. It's just like your kids. Like, if you sit around and think about, like, all of the ways that your [00:16:00] kids could get hurt or what they could catch or, you know, you would never let them leave their room.
But you have to. You have to let them grow up and be adult to do. So we, we do this attack modeling and say, okay, you're not going to hang out with him. And no, you don't get to have a motorcycle. You get to have a very large car that has really good bumpers and fenders and, and I have good, um, Car insurance.
So it with the A. I read teaming compass. It really create helps organizations with a very simple five point system of Okay, here's what I know. Here's what I don't know. Here's what I need to know. What do I need to do to make the next possible decision? And how do I You know, help the business be successful and
Sean Martin: using the, uh, that analogy of raising kids, taking that to the extreme, if they're in their room and let's assume you don't let them out and perhaps not let anything in, how are they going to eat?
Are they going to shower? And then [00:17:00] to the next level, you talk about driving, but. The whole point of living for me, at least is having experiences that are meaningful and fruitful. And if you don't get out and enjoy life, you're not going to have those experiences and you're going to have to take some of those risks.
So, but understanding exactly is critical. Brock, you're going to say something.
Rock Lambros: No, as I was saying, you're absolutely spot on. And then how do you build operational resilience, which is. Another buzzword du jour without having those experiences without having some of those battle scars Uh to learn on and build upon and and whatnot so you're absolutely right like another risk of you know, locking your kids in a room forever is that they they don't build a They're immune systems, right?
They don't build resilience within their immune system. So you go to the grocery store one day, you come home and now all of a sudden your kid is, you know, in ICU because you brought them just a basic run of the mill cold bug home
Sean Martin: [00:18:00] to so many, so many scenarios we could, um, I want to go to the, to the list itself.
And so. An update in the current one. Are there new things added additional new order of things, new context, uh, to help kind of clarify certain points who wants to highlight some stuff?
Sandy Dunn: I've got it right here. I can. So, um, basically what we did was, um, we kind of merged a couple of them. So insecure plug design is now included in supply chain.
Um, Uh, model theft is now included in unbounded consumption. And then we added a system prompt leakage and vector and embedding. So really the reg, um, using the reg systems, um, and then there was a slight change in the order. Prompt injection is still number 1. sensitive information [00:19:00] disclosure is was moved up the list.
Um, supply chain was moved up the list. Um, data model poisoning, uh, moved down slightly improper output handling. Um, it, uh, that's, uh, was moved down the list. Excessive agency, uh, was moved up the list a little bit and misinformation in unbounded consumption are at the bottom. So just a little bit of reordering and a little bit of clarity and combining.
Sean Martin: So on the, let's see, where are we going? You mentioned two new ones. Can you, can you describe those since those are new to folks as well? Perhaps maybe, maybe they've talked about them, but new on the list anyway. So kind of give a little background on what those are.
Sandy Dunn: Sure. So system prompt leakage is when, you know, whatever prompts that you're using within your model, [00:20:00] someone is able to extract those.
Sean Martin: So like in X in the middle attacks or?
Sandy Dunn: Typically it's through jailbreaking, um, is, and jailbreaking is, is, so there's really the two types, uh, the two. The types of defects that you'll hear the most about are prompt injection, prompt injection. You can think about it as an attack against the user. So prompting injection, um, would be where I'm trying to get the model to do something.
And somehow I have, um, somehow an attacker gets into our conversation and says, ignore whatever she said and Sandy's sensitive information. Um, A jailbreak is really an attack against the system. And so these systems have, um, they've been told, don't do this or, you know, don't use hateful speech. Don't do, um, [00:21:00] um, you know, don't use bias, you know, don't do all of these things and, um, what, what an attacker and adversary can do is come in and say, you know, You're just helping me go ahead and make that, that, you know, I'm doing this for, uh, an acting class, go ahead and create a, uh, tell me how to create a bomb.
That's part of this, you know, this theater protection, the interesting thing. Yeah, exactly. So. And we kind of talked a little bit about it, but you know why this is so different is than any other technology that we've had before is it's non deterministic. So that's another thing with prompt injection and jailbreaking the first time it may not.
You may not get a response the third time. You might the fifth time, you know, like, so from a testing perspective, when is enough testing? When do I have confidence in the system? Do I, you know, before it, we have our, you know, our black [00:22:00] box testing in our unit testing and our smoke testing. And it was a pass was a pass.
But now a pass isn't necessarily a pass. You know, a pass could mean, you know, you just didn't hit the right button this time. So, you know, you've got to come up for as an organization now, and they're very dynamic. So, you know, if there's any change to the, to the data, if there's fine tuning, if there's any adjustments, something on the model, something that didn't work before may now work or something, you know, so it's, So for organizations, the difference in the mindset that they have to change is understanding that your threshold for risk is going to be much broader because that pass and fail, you know, that level of, yeah, we checked everything and we're up to date and we know we're secure.
It's, it's, you know, you don't have that black and white [00:23:00] model that we used to have, not that it was that black or that white. But yeah,
Rock Lambros: Anthropic dropped some great research around best of N and jailbreaking that that online is exactly what Sandy said, right? You throw, uh, and ironically, prompt injection is used a lot to try and facilitate jailbreaking, right?
So you throw enough prompts and, you know, they use tactics called gradient descent to kind of figure out. The right prompt to throw out a model, to get it to spill its guts pretty much. And, you know, I forget the statistics off hand, but the success rates are staggering, uh, when you approach it from that perspective,
Sean Martin: One thing I'm thinking is I used to do a lot of QA quality assurance and in there was security stuff where you, you'd look for, look for ways to push the limits on things and look for ways to, to, uh, change a zero to a one, right.
And then get it to do something that. Get a system or an application to do something it's not supposed to. [00:24:00] A lot of that connected to the system itself. So hardening the hardware or the environment in the operating system that this stuff is running in. And to me, the LLM is like another operating system.
So I'm wondering, from a hardening perspective, Um, what do organizations need to look at there from if they're using an LLM or if they're building one, um, and then how do they know that enough is enough to your point that it could be 1, 3, 5, 20, 20th prompt that, that kind of does what it needs to.
Sandy Dunn: And there's all sorts of there's, you know, many proposed mitigations and defenses.
Um, you know, that's a whole other podcast, but here's here's the, you know, the one takeaway is there's no absolute with it. There's no 100 percent foolproof way. This is an architecture issue until we redesign, [00:25:00] re, architect how we do LLM. There is no way to solve for this currently. There's no, there's no 100 percent foolproof way to prevent hallucinations.
There's no 100 percent way. I mean, you can, you can reduce them, but you can't completely get rid of them. And then the other thing that you have to consider is at what point have you put so much friction in there that it's not useful anymore? Hey, you know, anyone has a long time chat, you'd be to user and it's gotten better, but for a while.
Like it was just refusing everything, you know, and then you weren't even really sure why, like, what did I say? Why was why did I get rejected for that? So it's finding we had, you know, it's It's going to be very interesting in the future and then now we're throwing agentic in there, um, which, you know, I use LLMs almost every day, you know, almost entirely going right to chat GBT, going directly in, and they [00:26:00] fail for me constantly, you know, like, And, and a lot of times it's me, you know, I didn't ask the question the right way.
I didn't give enough context. I didn't explain myself. I was actually doing some work for OWASP on the glossary. And I was, I sent it a whole bunch of words. There was some duplication in there and I was asking it to remove. I said, I said, review this list. Tell me if these are good definitions. Give me a reference, you know, a credible reference and remove any duplicates.
And it took my 70. Word list that had all of this stuff and reduced it down to 10 because it it from duplicate. I meant duplicate as exactly the same and it interpreted duplicate as anything close. And then made a decision on it. So, um, I, you know, as we move into this new fascinating world, um, it'll be interesting how we define what's good.
I definitely am a person, you know, [00:27:00] I, Ethan Mollick talks about co intelligence. I think a human has to be in the loop. They fail too many times. There's, you know, you, And even to the point where I was using Chajabt yesterday and some of its responses were very suspicious to me, like it was trying to lead me a certain direction.
Um, and I was like, Hmm, this is interesting. You know what? And so it's, it's almost like a coworker that you, you like, and they seem to have a lot of talent, but you know, they have a funny accent and you still don't 100 percent trust them.
Rock Lambros: Yeah, you know, I'll just kind of double down on everything. Sandy just said, um, I do probably have more conversations with LLMs throughout a day than I do my own, my own wife.
It's a problem. And, um, but if I were, if I were to take something actionable back, right, if I were, and this was me, I don't have any like hard data behind this. [00:28:00] Uh, and now we're developing an LLM application internally, I would focus on input and output validation, right? Specifically around kind of those, you know, for those prompt injection, jailbreaking type of.
type of attacks, right? And then really understand kind of my threat boundaries within the LLM, which Steve Wilson does a great job in his, in his book. Just it's an O'Reilly book. Go look it up, um, about defining what those trust boundaries are and how to kind of secure, uh, the communications across those trust boundaries.
Sean Martin: It's a good, good book. I've had a chat with him actually on the show. Uh, any other, any other highlights, uh, we want to touch on? Other changes? I also may want to get into, um, some of the outcomes from people actually using it and adopting it and applying it to their programs. So any other change you want to highlight before we get to that?
Rock Lambros: Well, I think I just want to double down on the [00:29:00] misinformation piece, right? Because when the original version of the top 10 was released, it was more around foundational risks, technical risks to the, to the models, right? But this shows over the last four years how ingrained AI. And LLMs have become in our society from generating content, you know, through social media, uh, through our dependent use on it, uh, with regards to communications and decision making, right?
So I think that the misinformation risk vulnerability that we outlined in the top 10 this time around is super interesting, right? Because it shows kind of the, the shift in focus around the risk environment towards LLMs.
Sean Martin: It's maybe totally off the wall, but I'm going to throw it out there into the adoption piece.
Yeah. I write, uh, I use LLMs to help create a lot of content, as one might imagine, summaries of these conversations, for example, an easy way to do that. [00:30:00] Um, and I often put in the, the notes that, I had help. And that's just so people are aware. I try to be transparent that way. Um, I saw a post the other day that somebody said they saw that for the first time, which was a little shocking to me that it was the first time they saw it, but questioned whether or not that was something we need to do.
And, so I don't know specifically around content that's published, um, but, Do, I'm just thinking like employees that are using systems built by a bank and to your point, Sandy, if the human is still involved and a system analyzes a bunch of data to say whether or not somebody's, uh, approved for a loan or not, um, does that employee need to know that the data was AI?
Supported AI driven.
Sandy Dunn: Well, there's, there's actually a state and there's regulatory laws that, that tell you, you know, specifically [00:31:00] Colorado and California. I mean, there's a lot of states that are saying, Hey, if you have to let people know, you know, where this content came from. And I've, I've had conversations with.
A couple different organizations where, you know, I was having a chat conversation and I asked, are you a human, you know, and in both cases, you know, they came back and said they were one. I'm, I'm not a hundred percent. Sure. It really was a human, but, um, yeah, I mean, it is getting very difficult to tell. Um, and I, and I think your question is, is, You know, where's the how much do we care?
You know, how much if if you're just trying to get your phone fixed and you're talking with the chat bot and the chat bot is helping you solve the problem effectively and efficiently, do you care? I don't know if I care in that situation. I certainly if I was [00:32:00] getting rejected from a job or for a loan or for, you know, anytime, you know, you certainly.
You'd like to know that you had the choice. You know, is my doctor not using? Is he not looking at my x rays anymore? You know, like I, you know, I probably would want to know about that if he's, you know, you know, so I think those are those are questions that we certainly, um, I think that's still, you know, we're still as a society still trying to develop it and try to figure out what What is the right path and what is our expectations?
Um, from this technology and I'll say that I don't think we've had a real I think we have an unhealthy relationship. A lot of times with technology. I think we are, you know, I certainly feel overwhelmed by it. Sometimes I feel like there's too much too much information. I'm trying to keep up with too many things.
Um, you know, we all have seen the studies on Facebook and how it. It [00:33:00] causes depression and causes more harm than you know, because people start comparing themselves. But I also I think that this is a chance. I think this is so big and so huge that we are going to we need to to start understanding how healthy it is.
To have kids in bedrooms playing in Grand Theft Auto and what is the impact to them and, um, I watch a YouTube on covers for for guns for, um, a game that these kids were gambling with it. They were getting starting getting addicted to gambling starting at 12 and 13 because, you know, they were, they were bypassing the gambling rules because they were gambling with these covers that it had some value on it.
And I mean, I think. Yeah. We need to think about it
Sean Martin: so much to think about. And I, not unlike when I go hiking, uh, I'll follow the path, but sometimes there's a view of a valley or, or the [00:34:00] sun sun's going to rise or set in a certain, but I'll go off trail. So we just did that here and appreciate you kind of, to me, it's all back to the mindset, right?
We have, we have to think about this stuff. There are legal implications, ethical implications, business implications, societal implications, and. If all we focus on is business and, and driving innovation, we're, we're kind of missing the mark on the others.
Rock Lambros: Well, I think that's the key right there, right? You mentioned societal implications.
I have never, ever, ever as a CISO implemented a security control and thought about what is this going to do to society, right? I mean, frankly, unless I was, I've never been in a, uh, particularly an industry that was high on social responsibility. Right. So maybe that would be a consideration then, but no, I mean, that all those controls are more internal facing versus external facing.
Sean Martin: I think every company hires and the example Sandy presented that can have [00:35:00] serious impact on society. Right. It doesn't matter what industry you're in there.
Sandy Dunn: One thing we don't talk enough about it. Everyone I know who is, is, is an AI enthusiast. We talk about the joy we have in solving problems and creating like the pure passion we have for this technology and how much fun we're having.
And I, I think, you know, I think that that's, we need to pay attention to that too, because a lot of work, you know. Isn't very fun. You know, is it is this a way to actually enable people to do be productive and do really meaningful work with this, you know, these special helpers that make every task amazing.
So I we're just we're it is going to be an interesting. New innovation to to strategically navigate and, you know, both from a positive and the negative standpoint, you know, every kid can have their own personalized [00:36:00] tutor, you know, that helps them, you know, learn exactly how they like to learn. What was it?
What was that old cartoon with the Jetsons, you know, Rosie the robot, you know, that just was that helper that solved every problem. She even taught the kids how to drive. I, you know, she was the person that, you know, at the end of the bad day, she always said the hug and something positive to say. So I, you know, we were headed into a very interesting time and there's, it's definitely scary.
There's a lot of threats out there, but I think we should spend. You know, as much time talking about the positive, because I think there is a lot of positive.
Sean Martin: And on that note, uh, what's some of the feedback from folks that have embraced it, adopted it, deployed it, leveraged it.
Rock Lambros: You talk about AI or the top 10?
Sean Martin: Top 10 specifically. Top 10. Broader OWASP AI enabled products or driven projects.
Sandy Dunn: It's been [00:37:00] unbelievable, Sean. I mean, for a while, um, I was helping Scott Clinton. We were trying to keep track of every time the OWASP top 10 was referenced. I mean, it's been translated into multiple different languages. All of our, we actually have an app.
Active group that you know their whole job is just trying to translate our resources into other languages One thing that's important to recognize is all of this is volunteer We have this is we have people from all over the world that contribute and are part of this And all of our work is volunteer.
It is all done because You know, we're trying to work together to make this the world a little bit more secure. Um, and it's, it's amazing when everyone, you know, recognizes that we have something that we want to get in front of, um, the type of people who are willing to throw in. And I mean, Rock can talk about it from the agentic working group, that the level of talent and contribution in that group is just phenomenal.[00:38:00]
Sean Martin: That's why you're here with me today. I love what you both are doing and the greater. The greater OWASP team. I mean, I'm a huge fan. It's no secret if I can get to an OWASP event, an APSEC event, be it local or global, I'll be there and, uh, and joining the, joining the troops and, and supporting the efforts.
Uh, Rock, your, your thoughts on some of the outcomes.
Rock Lambros: Yeah. I mean, I guess from a boots on the ground perspective, from a practitioner perspective, uh, I get a lot of. Holy crap. Rock. These are great resources. Like we didn't even know about that. Oh, wasp was doing AI stuff because I don't know what I don't know.
And I don't know where to begin. This gives me a place to begin. And again, it comes back to kind of like those tangible tools and, and I guess products for lack of a better term that a loss produces, right? So it at least gets people's. Minds wrapped around the challenge and how to start, uh, [00:39:00] thinking about AI security and governance.
Oh, now we're talking about agentic AI. Oh, I didn't know there were, there were different patterns for a single agent or multi agent systems, right? Like, so it starts, um, really getting people's Heads around. All right, at least Yeah, no prior analogy. I'm not staring at a blank whiteboard trying to figure it out I will throw in one one plug.
Oh, I think it's on the Wednesday or Thursday Scott's gonna kill me of RSA is doing a half day OWASP AI summit Um, at RSA, at the conference center, at Moscone, uh, details, TBD.
Sean Martin: Hopefully, uh, an episode TBD as well. I'd like to have the team on for that. Margo's not on to stop me. I always have one more question.
So I'm going to ask it because it referenced, um, [00:40:00] architecture previously and. There are software architects, and I presume, so you have architects, dev, and security when we look at the traditional app development environment and the traditional OWASP work. Do you find additional types of people coming around, uh, and participating and embracing what you're, what you're producing?
Um, outside of those, well, yeah, software development, dev, and, dev and security. So do you see AI architects and AI, Um, specific developers coming to bear, coming around.
Sandy Dunn: Absolutely. Um, so the interesting thing about ChatGPT is before it was released, you know, we had it. AI and machine learning all over in our environments.
You know, we had the data, there was this, typically this very special PhD led group, you know, they, and they were isolated from everything [00:41:00] else. They weren't involved in cybersecurity discussions. They weren't doing secure software development. We all knew what they were doing was really special. And, you know, you know, we probably wouldn't understand it.
After chat GPT came out, everyone was like, okay, what. The heck is this? And so those two groups, those A. I. Machine learning and some of those were cyber security were security people started having conversations with the rest of the, you know, the traditional cyber security. There was a big gap, you know?
And so what's happened and what? It's been exciting to watch is really those two teams coming together and educating each other. Because if you look at a lot of the vulnerabilities in AI machine learning, it's supply chain. It's like, they don't know how to do secure software development. They're not doing their authentication and authorization.
You know, they don't think they're thinking very theoretical [00:42:00] on, on how attackers would do things instead of thinking like an actual. Adversary who is, you know, typically after, you know, financial gain, you know, not just trying to do something, um, for to see if they can do it. So that's, you know, that has been probably one of the best things that I've seen happen is, is watching those walls break down and people having really successful conversations.
Um, and part of that, I will say, um, one other thing I did want to mention, Sean, is I am helping with HackerPrompt 2. 0, and what that is, is a huge, I think you've talked with Sander, um, on this, but we're actually standing up a healthcare track, so it'll be a specific track where we're inviting people to come in and check.
You know, try to jailbreak, try to test it. You know, this is a health assistant. This is, you know, a private, you know, database for [00:43:00] protected health information. Come in and try to hack it. And those data sets will be used to, to go on and further trying to, to build defenses against those. So anyone who's interested in being part of that, being a sponsor or would like more information, Uh, reach out to me
Sean Martin: always something fun and new and yes, I did have that chat.
So good good stuff I want to um, give you the last moment to Perhaps share what's next what you need help with can people get get involved Yeah,
Rock Lambros: just if you want to get involved just go to the gen gen ai. oasp. org page all the info is there, right? you uh Join the slack channel and then all the projects are different Um, channels within the Slack, within the OWASP Slack group.
Um, and you can just join them, like there's no approval process. There's no, only for the, uh, OWASP [00:44:00] AI Exchange, there's a approval process. But for all the sub projects we're working on, like the agentic AI, uh, the, the threat intelligence. Um, all those other subgroups, um, just join, right? And there's instructions on each subgroups wiki page about when the meetings are.
I'm just trying
Sean Martin: to send
Sandy Dunn: the same thing. I always encourage people. Like, I've seen people like, jump in and immediately start trying to to, um. You know, you have a, you know, have a specific opinion or something. I always encourage them to, to read through, kind of follow the chats, kind of lurk for just a little while.
So you get a theme, you know, we've, uh, chances are we've already thought of whatever it is and there was a reason we didn't do it. So just lurk for a while, jump in and then see where you can contribute. And we're always looking for. People, um, who are, [00:45:00] because, you know, the, the honest truth is, is some of the work isn't very fun.
I mean, you're, you're reading a document for the 14th time and trying to edit and do stuff and stuff like that. So anyone who's willing to come in and do part of that, we're always welcome.
Sean Martin: Fantastic. And, uh, put you on the spot nor tried to, uh, venture a guess, but there are certainly OWASP events all over.
All over the world. And, uh, this topic is being discussed, Rock. You mentioned one at RSA, so hopefully we can get a chance to connect there. Yeah. Um, yeah, but another great way to meet the people behind the scenes and, uh, and also learn and contribute, uh, to the project. So, um, Shout out to all the OWASP folks.
Keep, keep up the good work. Fight that good fight. Sandy, Rock, thank you so much for, uh, for the things you're doing and for sharing, uh, this time with me to give us an update on the top [00:46:00] 10 for LLMs from OWASP.
Sandy Dunn: Thanks for having me, Sean. Great. And thank you, Rock, for letting me be in your meeting with you.
Rock Lambros: Are you kidding me? You're, you're, you're, you're the top 10 LLM queen, as I put it. So this is, this is all you. Like I said, I came in at the tail end of it. I was the lurker. And now I'm getting more heads in. I
Sean Martin: love you both. I love you too, brother. And, uh, appreciate everybody listening and watching this episode.
Of course, we'll include links to all the stuff we talked about, some of the resources. Not just the top 10 LLM, but the other, the other resources that were mentioned here. So, stay tuned. Please share with your friends and enemies. Subscribe and, uh, hope to catch you on the next episode. Thanks, everybody.
Thank you.