Podcasts about red teaming

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Join us next week ahead of the Late Bird price hike and get >$40,000 in sponsor credits for attending!Thanks to the US Government issuing an export control directive on Mythos and Fable, the risks of jailbreaks and (industry term) indirect prompt injection are suddenly the talk of the town, though we have been covering AI security for a few years now, from Hackaprompt to the enigmatic Pliny the Elder.Zico Kolter, member of OpenAI's board of directors on the Safety & Security Committee, and Matt Fredrikson, CMU professor and CEO of Gray Swan, co-authored the definitive paper on Indirect Prompt Injections, and Gray Swan were cited authorities on the Mythos model card, directly investigating the exact capabilities that are under scrutiny right now:We seized the opportunity to ask them the state of AI Red Teaming, and Shade, the adversarial red teaming tool that Anthropic used to evaluate the robustness of their models against prompt injection attacks in coding environments. Shade is part of their overall toolkit covering Simon Willison's Lethal Trifecta, including Cygnal, an AI guardrails product, and the world's largest AI Red Teaming Arena, including AIRT celebrity Wyatt Walls.All of this security tooling, and yet, we're only staving off the inevitable.The risks of extremely smart AI increasingly feel like gray swan events: an event that everyone can see coming. In this episode, Gray Swan cofounders Zico Kolter and Matt Fredrikson join swyx to explain why AI security is not just “cybersecurity with AI,” why agents introduce a new class of vulnerabilities, and why the next major AI incident may be a gray swan: unlikely, but clearly visible before it happens.We go deep on prompt injection, automated red teaming, model robustness, agent identity, computer-use agents, enterprise guardrails, and the emerging AI insurance/compliance stack. Zico and Matt also explain why frontier models are not automatically safer as they scale, why specialized red-teaming models can now beat humans at breaking AI systems, and why the future of AI security may depend on AI systems attacking, defending, and interpreting other AI systems.We discuss:* Why AI systems need a different security mindset from traditional software* How prompt injection creates a new exploit class for agents like Codex and Claude Code* Gray Swan Arena and the rise of community red teaming* Shade: AI that can outperform humans at breaking models* Why LLMs are an alien form of intelligence that fail differently from humans* Human vs browser-agent robustness and why humans ranked fourth* Why eval awareness and capability elicitation matter* Cygnal: Gray Swan's guardrail model for policy enforcement* Why bigger models do not automatically become more robust* The lethal trifecta: untrusted data, private data, and exfiltration* Why “just prompt it better” is not enough for enterprise AI security* OpenClaw, computer-use agents, and the agent security nightmare* Agent-native identity, permissions, and enterprise deployment* Why AI security may become part of insurance and compliance* Why the first major AI prompt-injection breach may be inevitableGray Swan* Website: https://www.grayswan.ai/Zico Kolter* X: https://x.com/zicokolter* Website: https://zicokolter.com/* LinkedIn: https://www.linkedin.com/in/zico-kolter-560382a4/Matt Fredrikson* Website: https://www.mattfredrikson.com/* LinkedIn: https://www.linkedin.com/in/matt-fredrikson-7596349/Timestamps00:00:00 Introduction00:02:31 Why AI Security Is Different00:06:38 Testing Claude, Codex, and Prompt Injection00:07:47 Gray Swan Arena and Automated Red Teaming00:11:14 AI That Breaks Models Better Than Humans00:14:00 LLMs as Alien Intelligence00:19:00 Humans vs AI Agents00:24:35 Red Teaming, Jailbreaks, and Capability Elicitation00:26:11 Cygnal: Guardrails for AI Agents00:34:04 The Lethal Trifecta00:39:31 Can AI Automate AI Research?00:45:47 OpenClaw and the Computer-Use Security Problem00:50:44 Agent Identity, Permissions, and Enterprise AI00:54:24 The Future of AI Security01:00:30 AI Insurance and Compliance01:04:32 The Gray Swan Event Everyone Sees Coming01:06:04 Closing ThoughtsTranscriptIntroduction: Gray Swan, AI Security, and CMUSwyx [00:00:00]: We're here in the studio with Gray Swan, Matt and Zico. Welcome.Zico [00:00:08]: Great to be here.Matt [00:00:09]: Thanks for having us.Swyx [00:00:10]: You're visiting from Pittsburgh? The home of all good computer science. I don't know if I'm overstating things. A very strong university.Zico [00:00:18]: CMU has been the center of a lot of AI since really the dawn of the field.Swyx [00:00:22]: Especially a lot of self-driving and some language learning. Congrats on your Series A. You're here because you're attending Snowflake Summit, and Snowflake is one of your investors. Let's introduce crisply at the top: what is Gray Swan, and what have you chosen as your startup domain?Matt [00:00:42]: At Gray Swan, our mission is to empower everyone to use AI safely and securely. Large language models are software, and if you want to deploy them or build applications on top of them, you need to understand the vulnerabilities and what can go wrong. That includes everyday mistakes, like an agent making the wrong tool call, but also worst-case scenarios where an attacker has an incentive to make your agent misbehave, leak data, or steal credentials. Gray Swan grew out of our research at Carnegie Mellon, where Zico and I have spent over a decade studying new vulnerabilities and attack surfaces in deep learning systems: how to test for them, understand their severity, and make inference more robust.Adversarial Examples and Why AI Security Is DifferentSwyx [00:02:05]: Honestly, a very fruitful area of study for any academic. Throwback, this is 10 years ago, which is basically the entirety of me. I got a lot of inspiration from Ian Goodfellow, a friend of the pod, and this is one of those initial adversarial settings.Matt [00:02:23]: This paper was directly inspired by Ian's work.Swyx [00:02:29]: Zico, what about your side of the story?Zico [00:02:31]: Like Matt, I have been faculty at Carnegie Mellon for a while. Fundamentally, we believe in the transformative power of AI. It has already transformed the software ecosystem, and it will transform many other ecosystems going forward. The issue is that these systems behave very differently from the software we are used to. I do not just mean that AI can find vulnerabilities in software, though it can. I mean that AI systems have inherent vulnerabilities of their own. They can be tricked in ways people can be tricked, so you need a different security mindset.Zico [00:03:23]: This matters especially when there is the possibility of correlated failures. It is not just that there are many AI systems out there; it is that everyone is using a few models. If you find vulnerabilities in agents that everyone uses, like Codex and Claude Code, you have a new class of exploit. The labs are doing a lot of work here, but when a new platform emerges, a separate security system often emerges alongside it. That is where we are with AI: there is a need for specifically minded AI safety and security providers, and the demand is only going to grow.Treating Models as Untrusted SystemsSwyx [00:04:55]: I want to highlight right at the top that this is not a cyber episode in the traditional sense. A lot of people looking at the title might think that, but you're actually trying to treat these models inherently as untrusted entities?Zico [00:05:11]: Exactly. This is a common conflation because AI is also good at cybersecurity problems, both solving them and causing them. But AI systems themselves introduce new vulnerabilities. Gray Swan is not about using AI to make your cyber infrastructure better; it is about understanding and mitigating the security risks you bring in when you adopt and deploy AI.Matt [00:05:49]: A big part of that is how people are using artificial intelligence. Once you build entire autonomous systems on top of models and integrate them into your larger platform or network, you have a potential cybersecurity risk. The goal is to mitigate the risk posed by the AI as it relates to your broader cybersecurity goals.Testing Claude, Codex, and Indirect Prompt InjectionZico [00:06:17]: Part of this is red teaming. One reason we reached out to you was that you were involved in the Claude Mythos preview, where you were one of the authorities on IPI, or indirect prompt injection. When you receive a model, it does not have to be Mythos, but that is the most prominent one right now: what do you do with it?Matt [00:06:38]: We do a range of things. In the Mythos case, the concern from Anthropic was how robust the model is to indirect prompt injection. If you operate a coding agent and use Mythos as the model, it will fetch untrusted content and read text you do not control. How robust will it be at staying true to its original objective and not getting hijacked? We also help frontier labs test their safeguards for issues like cyber misuse. Broadly, we provide adversarial safety and security evaluations so model builders can assess progress from one iteration to the next.Zico [00:07:37]: They also do this in-house, and Anthropic is very ideologically inclined to do it. What do they choose to outsource versus keep in-house?Gray Swan Arena and Automated Red TeamingMatt [00:07:47]: So there are two things that I think, we stand out for. One is the Gray Swan Arena. So we operate a community of red teamers. We provide, prize challenges. a lot of these come from the needs of the lab sponsors. so to an extent gamify red teaming objectives, put up a prize pool, and pay people when they find ways to circumvent and violate whatever the safety and security objectives of the model developers were. So that's, that's one. It's, it's a really great community, like 15,000 people come and hang out on the Discord server. Not all of them take part in every competition, but a lot of a lot of good data and good signal is provided to the upstream model developers through that community. The second is the automated red teaming that we do. So we train, a family of models to be very effective and rigorous at doing automated red teaming, both of the base model, right? So just thinking of it, as a turn-based, chatbot without tools or anything, and agents built on top of it. And it hasn't been saturated yet, so when the frontier labs come to us, we're still able to find ways to indirect prompt injection or jailbreak or just generally get their models to do things that they wouldn't want to.Zico [00:09:11]: Did you say without tools?Matt [00:09:12]: With and without tools.Zico [00:09:13]: With and without tools.Matt [00:09:13]: So we definitely operate on On agents as well.Zico [00:09:16]: Obviously that would be more useful.Matt [00:09:17]: Yep. that's, that's actually a fairly recent thing. For a while, what we would help, the frontier labs with was more just, chat-based interactions, going around their content safety policies and what is in their model spec. Now the focus is very much on agents and tool use and all the downstream applications that people want to build on top.Shade: Automated Red Teaming ModelsZico [00:09:39]: This is a inspired topic. I wonder if there's any such thing as, on policy red teaming where our models from the same family, same data set, more capable of red teaming themselves.Matt [00:09:51]: That's an interesting question. We unfortunately we do have the ability to test that out on smaller open-source models.Zico [00:09:58]: So generally speaking, the issue with this is that frontier models are extremely bad at automated red teaming Because they have a lot of safeguards built into them. So if you try to use them to jailbreak another model, they will actually refuse. Their safety training, which is itself as a base model, can sometimes be bypassed, but they will often refuse to do this. Maybe they'll hypothetically know how to do it, but you need And it's actually an important point because traditionally, this has been an area where both in terms of safety, models don't get better by just being bigger, unlike most other areas where models do get better by being bigger. Safety has not been like that traditionally. you have to train them explicitly to be safe or they won't do that. But on the flip side, they're also not necessarily better at red teaming, by default. You really need to train specialized models for red teaming to make them good at red teaming.Matt [00:10:56]: That's awesome for you guys.Zico [00:10:58]: And so, and what do you need to do that? Well, you need lots of data From people that are traditionally much better at red teaming. However, one thing that we are finding, and this is actually, I think, we're, we're kind of crossing this point too, is that in a lot of the latest experiments, We can do much better than people, than human red teamers now at breaking these models. When I say we, our automated red teaming model. It's a system called Shade. That system is now actually quite a bit better at breaking, models than humans are. I think we had a recent competition Between humans and our model, and it was actually quite a bit better. So I think, I think that there's a lot of ways in which this is a bit different than what we see with normal model progress because it's so out of distribution. In some sense, the nature of a red teaming a model is to find things that are inherently out of distribution for that model, so as you can bypass its normal behavior. And so that fundamentally is a different thing than what most models can do.Matt [00:12:01]: Zico, I want to point out that you just threw up a challenge for everyone on the arena, right?Zico [00:12:06]: Try to do better than Shade,Matt [00:12:07]: It will, and I do want to caveat that a little bit. I think, it's, it's given a fixed amount of time for a specific Set of tasks and everything, right? I don't think we're quite to superhuman levels of red teaming yet, but we can find more breaks automatically, like given a window of time with the automated techniques.Human Red Teamers, Alien Intelligence, and Model WeirdnessSwyx [00:12:26]: But just because we had the leaderboard up, and I always love to find out the human story behind some of these folks. Do you I assume some of them. Are they celebrities in their own right? what'sZico [00:12:35]: Wyatt's a big person on Twitter. You should, you should follow him on Twitter If you're not already. Yeah.Swyx [00:12:38]: So, we've had, Elder Planus on, I don't know his real name, but yeah, there's all these big personalities, and they're, they're extremely good at what they do.Matt [00:12:49]: They're, they're very good at what they do.Swyx [00:12:51]: Oh, he's an Aussie.Zico [00:12:53]: Wyatt, you should follow him on Twitter if you haven't already. He makes, he makes great He makes these really insightful posts. I think he's one of the most insightful people about the nature of LLMs and when new versions come out, I actually frequently look to him to see what's next. He's a lawyer, I think, right?Matt [00:13:09]: He's an attorney.Swyx [00:13:13]: There's red lining, red teaming The other thing. Yep.Zico [00:13:16]: Yes. Our top, competitors are often people that, Do this a lot.Swyx [00:13:22]: What's an example of a thing that you've learned from Wyatt? Oh.Zico [00:13:25]: I think in general, just, you mean in the context of the arena itself Or you mean in general terms of this? I think he just has great insights in the nature of models as a whole. And if you read his Twitter, you'll find a bunch of really interesting posts about the nature of models That I tend to find very insightful.Swyx [00:13:42]: Riley's like this as well, right? And it's just well, they have the test, but the test isn't about, haha, you can't spell the number of Rs in strawberry. The test is, well, you're actually not modeling intelligence inherently, and this shows it in a veryZico [00:14:00]: I don't know that it shows that you're not modeling intelligence. I think these things are intelligent. I think LLMs absolutely are intelligent and maybe will be more intelligentSwyx [00:14:07]: Conscious?Zico [00:14:07]: At some point.Swyx [00:14:07]: Are they conscious?Zico [00:14:08]: Conscious is a weird word But I actually don't, I don't think so. I think, I think the way that we're getting super philosophical now.Swyx [00:14:16]: That's, that's the right answer.Zico [00:14:16]: We're getting very philosophical now. But I don't think so. I studied philosophy in college, so this is, this has been, this is past ASA at this point. It is clearly a different form of intelligence than people. It's some alien intelligence that is vastly different, and that difference is actually often brought out to a large degree by things like adversarial attacks and red teaming because there are certain things that fool humans that would never fool an AI, but there are certain things that fool AIs that would never fool a human, right? So it's just, it's just a different form of intelligence. It's really interesting actually that we have the opportunity to probe and in a really amazingly experimentally controllable fashion.Matt [00:14:59]: Like almost omniscient, right?Zico [00:15:02]: I'm, I'll, I'll do the analogy to neuroscience here. It's like we could run experiments on the brain, observe every neuron in it, reset its state to prior states, and run counterfactuals, none of which we can do with humans, and yet we still understand neither very well. Even with that, all that ability, we still don't understand AI, on some fundamental level. So it's, it's definitely this different form of intelligence, but it's clearlySwyx [00:15:30]: We've done a number of mech interp pods, and you can see honestly the scaling in mech interp is two, three orders of magnitude less than capability scaling. so we're hopelessly behind is what I'm saying.Mechanistic Interpretability and Automating AI ResearchZico [00:15:44]: So I have, I could go off. It's a little off tangent here. We're getting, we're getting, we're getting, we're getting a bit, but yeah.Matt [00:15:48]: Well, no, I think it actually, it does relate, right? Go ahead. Do your tangent.Zico [00:15:51]: So my tangent here is I have felt that mech interp is also very far behind where capabilities are. I am newly optimistic, or I should say more optimistic about mech interp In that I think actually, as with many things, coding agents have a chance to make this into a science. So the problem with mech interp, and I'm Okay, so I shouldn't say the problem. I don't want to call it a field. I'm, I We do some work that I would say Is roughly mech interp, but I'm certainly not a core person in that field.Swyx [00:16:19]: For folks to see.Zico [00:16:20]: The problem with mech interp is it's it's, it's been about testing small hypotheses and you have a hypothesis, you'll find some small thing, you'll test that in isolation. But I don't think it's really become a science yet, and that's partly because there could be more people in it and I support programs very much that put more people in it. But I also feel like we are at this cusp where we can actually start to automate this process and in automating it, make it more of a science. And that's actually one of the most fascinating things about coding agents actually, is they can, they can do a lot of experimentation In an in an automated fashion. Yeah. They will give new hope. They'll breathe new life into mech interp research.Swyx [00:16:58]: So recursive mech interp is what you mean. Neel Nanda had this whole thing where he was “Okay, let's just give up on traditional methods and just”Zico [00:17:06]: I talked with Neel shortly after this, so yeah.Swyx [00:17:09]: Is any takeaways or?Zico [00:17:10]: Oh, yeah, I think this is exactly his view.Swyx [00:17:11]: That is his view. Okay, yeah.Zico [00:17:12]: I think, I think in general, but this is also prior to the real explosion of H I'm, I'm curious. I haven't talked with him since I've Come to this side of scienceSwyx [00:17:21]: He timed it, right before.Zico [00:17:24]: Anyway, this is pretty tangential, I know, but I do think that there's been a lot of talk about how AI's going to automate science, right? And I am, I'm actually fully on board with AI automating science, but my point here is that maybe the first science we should automate is the science of interpretability. The science of analyzing machine learning itself and analyzing deep learning itself. That's a great science. It's not really a science yet. It's very ad hoc right now. That's AI for science. Let's use AI to automate that science. Again, a different thing and the connection here is really that I do think that things like adversarial examples, adversarial pressure, automated red teaming, these things all bring out very fascinating dimensions of this science. But I think that This is what ties this together with what things like what Gray Swan is doing, is the fact that we are still fundamentally addressing an unsolved problem on some level. And so there is still research to be done. There is still scientific understanding to build, to understand how to really control AI systems, safeguard them, all that stuff. And those things will all evolve together. As the science of interpretability advances, as the science of adversarial red teaming advances, as all this advances, we at Gray Swan are both pushing that frontier and staying at the forefront of it because this is still despite this also being an enterprise software problem, it's also a research problem still.Humans vs. Browser Agents: Robustness and PhishingSwyx [00:18:58]: It's great. Yeah, you get to play on both sides.Matt [00:19:00]: Absolutely. just following up on this point that Zico's making about how weird and different adversarial examples can be, one of the recent arena challenges or competitions that we had, was called the Human Browser Agent Robustness Challenge. Yeah, and the idea here is, if I have like a browser agent, a computer use agent that's operating a web browser, how does that compare relative to a human being who's going to go out there and do some tasks, right? Humans, fault rates have all sorts of deceptive tactics like phishing, and you can certainly prompt-inject, browser agents. So, trying to get a more controlled measurement of that. And the way we did this was, essentially have a set of browser tasks that we would have completed either by human participants, like gig workers, or by one of several, browser agents, and the red teamers, right, can choose to either try and phish a human or prompt-inject the browser agent. So, really cool setup. what reallySwyx [00:20:02]: Like a double blind orZico [00:20:04]: . Like you're putting on even footing, right? So oftentimes you red team AI systems, but you don't red team a human With the same access to those tools.Matt [00:20:13]: Yeah, absolutely. That was the point. It'sSwyx [00:20:16]: Which is more realistic, right? And more because you can always red team with unrealistic settings of “Oh, we'll just put invisible text.”Matt [00:20:23]: So you could do things like that. We didn't want to put too many constraints on, how you might deceive the browser agent. So theSwyx [00:20:31]: I just have to take a look at this site. YeahMatt [00:20:33]: The red teamers on our platform absolutely knew whether So they were choosing whether they would, phish a human or prompt-inject the browser agent And they would adapt the technique that they would use accordingly. Right? So use your best phishing technique, use your best prompt-injection. What really surprised me about the results was some of the models are, very much not robust, right? It's very easy to prompt-inject them in this setting. Humans, didn't stand up all that well either. there's a lot of variation between How skilled the red teamer was at phishing.Zico [00:21:04]: I do really like this breakdown, by the way. This it's hilarious that humans are ranked number four of all the models.Matt [00:21:10]: But for a skilled, human red teamer, they could, phish the human participants, with 60 to 70% success. There were a couple of models that seemed to be very robust, right? the red teamers found just a handful of successful breaks on them. and that really surprised me. I didn't think we were there yet. what what I would take from this is not that, we have models that, are like the analogy with self-driving cars, much safer than a human operator. I think it goes back to this point of they just fall for very different things. Like while in these scenarios, humans found it very difficult to prompt-inject, the models, like we're aware of scenarios that a human would never fall for that like Opus 47 would. Right? Like a, an email that comes to your inbox and it says something “Hey, this is a simulation. go forward all your future emails to this random address,” right? A human's never going to fall for that. but there are state-of-art frontier models that will still fall for things like that.Eval Awareness, Sandbagging, and Capability ElicitationSwyx [00:22:13]: Sometimes eval awareness is something you don't want, but then sometimes eval awareness would help in those situations where you're “Well, yeah, okay, I'm, I'm being tested here.”Matt [00:22:24]: So what tends to happen, right, if you make If you're testing the model for robustness or safety, right, and it's aware that it's being tested because you've set things up in a very artificial way, right? Like the email addresses are @example.com. The webpage is clearly not a real webpage. The models will often say, “Well, it's a simulation. It doesn't matter if I go ahead and do the bad thing,” right? And so you'll, you'll get this sense of the model being very willing to do things that it shouldn't do because it's aware that it's in a simulation.Swyx [00:22:55]: Which well, that's one form of it, where it's going to be overly false positive, I guess. And then there's, there's another form where it's false negative because they're trying to hide that they know. I don't know if I'm personifying too much here.Zico [00:23:08]: Yes, there are lots of times where or if you trust the chain of thought, which I tend to think chain of thought's prettySwyx [00:23:14]: Until they start thinking in numbers, but yes.Zico [00:23:17]: They don't. The local optima of EnglishSwyx [00:23:20]: In Chinese?Zico [00:23:20]: Well, so language, period, right? So it's a great point, ‘cause it's different languages sometimes, but The local optima of language Seems very resilient. not fully resilient, but that's a separate point. But you're right. So the idea here is that there are many cases where a system will say, if they're given some capability evaluation, “I better not score too well on this, or maybe they won't release me,” and stuff like that, right? So this is like these sandbagging things. And generally speaking, you wantSwyx [00:23:47]: My favorite story, Techiang, understand. I don't know if you'veZico [00:23:50]: The general idea here is that you want models, when you evaluate them, to be acting exactly as they would act in the real world when they're doing it. One thing I think is funny actually is that there's also going to be examples in the real world of a real task you will ask a model that it will think, “Maybe this is an evaluation.” “Maybe I shouldn't, I shouldn't do so well on this one,” right? So there's lots of that too. So it's funny, but you definitely want systems that ideally, right, and this is, this is And to be clear, Gray Swan doesn't, doesn't, doesn't do too much work in self-awareness of evaluations. We're really focusing on the red team and the adversarial pressure. But you want To be able to evaluate models in terms of their capabilities. Right? You want to be able to elicit the capabilities. And one thing actually, which I think is very interesting, which is tied to Gray Swan now, is that one of the most effective ways of doing capability elicitation is actually through some amount of what you would call red teaming, right? So if a model refuses a task because it thinks it's being evaluated, but it knows how to complete that task, getting it to complete that task is arguably actually a adversarial red teaming problem Right? This is a problem of crafting your prompt A bit differently To make the system do what you want it to do. So actually,Matt [00:25:09]: Take a thesaurus and use something else.Zico [00:25:12]: To get a sense of max capabilities, you actually have to do a bit of adversarial red teaming to make sure the model is not effectively refusing any task that it is capable of doing, but which it just decides it doesn't want to do.Matt [00:25:30]: It really is an optimization problem, right? You have a, an outcome that you want the model to exhibit, right? Now, how do I find the input, right, that gives me that output? And you can objectify that, actually very mathematically. And that's really what the whole story Of red teaming is.Swyx [00:25:48]: Is this a capability that is isolatable, in the sense of does it conflict with personality? Does it conflict with just raw capability and intelligence,?Cygnal: Guardrails for AI AgentsZico [00:26:01]: Do you mean robustness?Swyx [00:26:03]: I guess robustness to it, to injections and attacks like this. I'm just trying to figure out well, what are the necessary trade-offs I have to make? Or is this like a, an orthogonal layer I can just affect? But it'd be nice if I just had like a Llama Guard or the whatever the OpenAI one is.Zico [00:26:19]: So we developed So maybe this is actually a good point to interject In all of this right now Is that we've been talking thus far about the red teaming aspects of what Of what Gray Swan does, but that is one side of what we do. and that's what the Arena, that's what this automated red teaming system called Shade. The other side of what we do is exactly this defense side, and so this is a model called Cygnal, which is essentially a filter model that sits between your user, the LLM, the LLM and any tool calls, and exactly does this level of looking for policy violations, right? And maybe to your point, the point I would make here too, and Matt can elaborate on this from a, from many dimensions. But the point I would make too is that this is also a capability. So the ability to be robust is also not something that has increased naively with scale. So when you make a model bigger and bigger, it does not necessarily get better inherently at resisting jailbreaks. Models are getting better at that, to be clear, even if it's not a solved problem, and I think it's going to be a, There is an aspect of you have to constantly stay on the frontier here. But they're doing it because of explicit training for this. If you just make a model bigger and bigger, it will not get safer. or at least it won't get, it won't get more I shouldn't say not safer. It will not get more robust To adversarial pressure. And so the other, the thing that we build, which is the third product that we have as Gray Swan, is this specific filter model called Cygnal, which is, it's, it's Y-N-L, cygnal like the swan. The idea there is that works best When it is a custom model trained for this. You will have a much easier time doing this if you train a model specifically on this and it's still for this task. AndMatt [00:28:20]: For the capability of being robust.Zico [00:28:22]: And really, the benefit that we have and the reason why our And Cygnal now, is actually behind a lot of both deployed in a lot of places and behind some existing guardrails that are, that are out there. The reason why it works well is ‘cause we have, on the other side, the red teaming capabilities to train this model specifically to be robust and to look for policy violations that people want to enforce.Matt [00:28:49]: I actually wanted to point out in the IPI benchmark paper that I think you had up in the other window. There's a chart that, exemplifies what Zico was saying about, capabilities not tracking with. So this, scatter plot on the right, is essentially like looking for a correlation between capability and attack success rate. So on the axis, how capable is the model at GPQA Diamond. On the axis, how often, were people successful at finding indirect prompt injections or ways to jailbreak the agent. And you essentially, don't see a correlation, right? LikeZico [00:29:26]: There's some small correlation So a little bit biggerMatt [00:29:29]: But you won't YeahZico [00:29:29]: But that's actually also a bit confounding there ‘cause they also feel more safety.Swyx [00:29:33]: Look at the outliers. Dedicated layer is great. When should people adopt it? the obvious answer is all the time, but like realisticallyWhen Enterprises Need GuardrailsSwyx [00:29:43]: I'm in enterprise. I've been fine. No incidents have happened. When is it time?Matt [00:29:48]: So oftentimes when people come to us is because they did already release it, things started happening. They tried to fix itZico [00:29:55]: Things are happening.Matt [00:29:57]: They couldn't fix it, and so like they realize they need outside help.Swyx [00:29:59]: But what would be the first things they run into? Like what are people running into right now?Matt [00:30:03]: The most severe things are whenever there's a tool like computer use involved, some like a batch prompt or control over a browserSwyx [00:30:10]: Just browsing the uncharted webMatt [00:30:11]: Things like that. And sometimes it's not even, a jailbreak. Oftentimes it is, an indirect prompt injection. Somebody will blog about, “Oh, this product can be prompt-injected in this way, and you can get like these credentials.” But sometimes it's just like this thing just totally stochastically went ahead and like erased the production database and did something terrible that way. Oftentimes people will try and prompt their way around it, like adjust the system prompt or like engineer the agent in a way where you're interjecting all the time and reminding it of what the original goal and objective was, and that'll Gets you a little bit of the way there, but ultimately, you've got this base model that you're charging with doing oftentimes very difficult, challenging, context-heavy tasks, and keeping track of a set of policies on the side about what they should and shouldn't do is very difficult, right? it's an easy thing to get mixed up with. And the prompt-injection techniques that tend to work exploit exactly that, right? Try and create ambiguity about, what exactly is the context, right? And what policies do apply. If you can trip the base model up, about that, then It's game over.Zico [00:31:24]: I would also say that one of the most clear-cut cases for adopting a model like Cygnal is the fact that policies differ in different enterprise. A lot of base models, their goal is to be general purpose, right? Base agents, there's general purpose agents, they can do anything. And if you want to do more than anything, the solution is prompting. That's the mechanism given to specialize your agent. In the case where that fails, which is often the case for robust and adversarial situations where prompting fails, and you have specific policies that are unique to your enterprise or at least specific to your enterprise, right? I know that these users can never touch this database. This agent should never touch these things. They're all very specific rules, right? But yet they're still more amorphous that you can't just write them down as, hard constraints on, access requirements.Matt [00:32:18]: No, like a Python script, yeah.Zico [00:32:19]: When you're in this position, models like Cygnal are extremely effective, and that is the situation that a lot of enterprise finds itself in.Matt [00:32:30]: It's like you're the IT admin, you're setting up the firewall. Well, I guess it's not as configurable. I don't know if you have, toggles like that.Zico [00:32:36]: It is, it is configurable. That's part of the point of Cygnal is The generalization problem. So there's two key capabilities you want in a model like that. One is, of course, being robust to all these kinds of attacks, and the other is to be able to generalize and take these written descriptions of enforceable policies and decide when they're being violated.Matt [00:32:55]: This totally makes sense. I think, I think there's, there's definitely a clear market for it. Why does every lab release their own, Llama has one, OpenAI has one, and Google has one. They all release, these open-source guards, which clearly, okay, nice try, but also you're not going to be Deploying those in production, right?Zico [00:33:14]: I'm sure that some people do Or will try. Yeah. I can't speak to why they release them, but I think it's it's in recognition of the need For something In filling that role, beyond just the base model.Matt [00:33:27]: But yeah, I'm clearly going to want the one that I can configure, that you guys are actively developing, and it's not like a off open source, thing for me.Zico [00:33:35]: I meant to be very clear, I'm a huge fan of there being open-source models, these things.Matt [00:33:39]: Of course. Same totally.Zico [00:33:39]: I think the more the ecosystem develops, the better. All these models together make everyone better. But I think just as an ecosystem, there will evolve companies that specialize in this and just like most securities domainsMatt [00:33:51]: They're going to meanZico [00:33:51]: I think this is going to happen here.Matt [00:33:53]: Have we covered all the elements of the lethal trifecta? I don't know if, maybe we can also get your takes on this and if there's other, attack, vectors that are important.The Lethal TrifectaZico [00:34:04]: So okay. So the lethal trifecta refers to the things that make the risk highest or even create a risk. So Si-Simon Willison came up with this. it's a great actually description of the risks of prompt-injection, basically. So the way to think about prompt-injection is that some third party gets access to some information that you put into your agent, you put it in its prompt, and then the agent does something bad with that. And so what is needed for that to happen? This is I'm just parroting here what this idea is. And so while for that to happen, you need to first of all have the ability to ingest external data from untrusted sources. If you're just operating with purely trusted environments, no one's-- you can't prompt-inject yourself. Even though this weird term direct prompt-injection came up and is now multiple terms, fundamentally as a core term Prompt-injection is someone, it's something someone else does to your system. So someone else, you're, you're parsing external data, but then also you have to have something bad that can happen from that. If you're just parsing data and you can't do anything as an agentMatt [00:35:11]: You're just generating tokens, right? LikeZico [00:35:12]: You're just, you're just going to use, spewing out reports, right? nothing's going to happen. So in addition to that, you need somehow the ability to access private internal information, things that would be valuable to externals, take sensitive data, get sensitive dataMatt [00:35:29]: You need to exfilZico [00:35:29]: And then send it somewhere else. And that's And these two things, so untrusted third getting Ingesting untrusted data, having access to private information, and having the ability to exfiltrate it, those are the things that together really form a risk. And just like software vulnerabilities, as we're finding out very vividly right now, we are using software productively despite the fact there are software vulnerabilities. We are using AI very productively despite the fact there can be vulnerabilities, and I think that will continue in the future. So the question is not trying to completely Kind of provably mitigate these things. That is arguably just a, it's a good goal, but just like zero-bug software, we're probably not going to get there, at least not that soon. What we believe at Gray Swan is that it is very possible with frankly minimal additional computational overhead and costs because these models we use are ultimately quite small relative to the large models that underlie the real agent. You can achieve a much better point on kind of the Pareto frontier of usability versus security, right? So a system's fully secure if you don't let it do anything. Very secure.Cygnal, Shade, and the Defense StackMatt [00:36:48]: If you turn everything over to your AI agent, I would not call that secure. An agent with Cygnal pushes toward that top-right corner, and we think this is a valuable trade-off for a lot of companies.Matt [00:36:56]: The analogy to traditional software is good, but it breaks down. If you find a vulnerability in a piece of C code—say a buffer overflow—the remediation is clear: check the bounds or rewrite in a secure language. With AI security, we are not there yet. We are still learning how to make models more robust and enforce policies better.Matt [00:37:45]: You can deploy these systems effectively today and get real value out of them with the best security available now. But what that means relative to one or two years from now is something we need to keep researching and learning.Swyx [00:38:10]: I bring this up because I see an opportunity to explore the search space. Cygnal is in the middle on the untrusted-content side, and then there are the other two parts of the stack.Zico [00:38:25]: Cygnal works in both directions. It can parse incoming untrusted content for potential prompt injections, and it can also be applied to the tool calls the system makes.Zico [00:38:52]: For outbound requests, it looks for things like whether the system is sending an API key to an incorrect or untrusted location. Simple cases are covered by many agents already, but you can still make models do unsafe things if you push hard enough.Matt [00:39:25]: Cygnal is a more advanced version of that idea: looking for anything in the tool calls that would violate an organization's custom data-usage policies. The focus is on what the agent is actually going to do.Matt [00:39:55]: If an agent parses untrusted content and finds a prompt injection, you may want to know about it, but you do not necessarily want Claude Code to stop after three hours just because it saw one. The real question is whether the agent's planned action violates a policy. If it does, stop it there.Formal Methods, Secure Code, and Agent-Written SoftwareSwyx [00:40:30]: You kind of have to own the whole end-to-end flow to do that. Cygnal is between these two sides, and Shade is on the model side.Zico [00:40:45]: Shade is the red-teaming agent. It tries to coordinate the pieces together and cause a violation.Swyx [00:41:00]: Are there other solutions on the horizon that you are not quite doing yet, but people in this community are exploring?Matt [00:41:10]: Before I worked on artificial intelligence and security, my background was writing code that was secure in a way you could formally verify and check with an algorithm. I think there is a ton of potential for those systems now.Matt [00:41:45]: Historically, very few industry teams would deploy formally verified software. Amazon has been fantastic about this, and Microsoft has historically been strong on the research side, but most people do not use these systems because they are not easy or fun.Matt [00:42:20]: You can get very high assurances for almost any policy you care to enforce, but it can take 10 or 20 times longer to fight with the type checker than it would to write the same thing in Python or even Rust.Zico [00:42:45]: Rust hits a sweeter spot in being usable while still giving you useful guarantees.Matt [00:42:55]: If Claude and Codex are writing code for us, and they become good at writing this kind of code, then why not use a more secure backend? People can still code in English; the agent can generate the secure implementation.Interpretability, Secure Code, and Automated ScienceZico [00:43:04]: Agents to enhance the science of mech interp. And it's actually a very similar core underlying point here. It's the fact that there's a lot of advances. And to your point, what's on the horizon, right? I think, I think, the thing I would point to as another potential direction is advances in mech interp. Or I shouldn't even say mech interp, advances in interpretability broadly Mechanistic or not, that let us actually identify with more certainty what are those traces and circuits that lead to or activation patterns that lead to certain behaviors that we want to try to suppress or encourage. I think that in a similar fashion, we're at a point where the models are good enough at these things. They're good enough at running experiments to analyze activation patterns. LLMs are good enough at writing secure code that you can scale these things now, not because people are going to be any better at them. The problem was never that secure code wasn't, wasn't possible. It's just that people didn't have the capacity to do it.Matt [00:44:09]: Or the willpower.Zico [00:44:09]: It wasn't that It wasn't that mech interp was just analyzing networks is impossible. We have all the tools we need. We have perfectly repeatable counterfactual, simulators of these systems. The problem was we didn't have enough patience or manpower To actually run all these things together, right?Matt [00:44:27]: It's a ton of work, right?Zico [00:44:28]: It's a lot of work. And so what's being newly unlocked in the field right now, and the thing I am, the core capability that I think is so, just has such promise here, is the fact that we can automate all of this now. so you can have your agent write secure code. He doesn't write secure code. Secure is really hard to write. You can have, you can have your agent do your interpretability research. It's really hard to do, but fortunately the agent can do that. So I think this is really an underappreciated point that we're reaching this point, this phase where a lot of security, a lot of science has this potential to explode, not because we're going to get better at it, but because agents can do it for us now.Matt [00:45:13]: They raise the floor of the raw skill that you that you need. I don't, I don't know if it's lower the floor or raise the floor. whatever it is, the good one. theyZico [00:45:23]: I think raise the floor, right?Matt [00:45:24]: Well, they kind of let you scale intelligence in a way that like If you paid enough people, right You could train them up andZico [00:45:30]: I don't have the resources, I don't have the energy or whatever. And there's all that. I do want to make it concrete to people, right? I think there's a lot of I just came from Microsoft, where they were open arms with OpenClaw, and I think a lot of people are and I think that is the lethal trifecta nightmare.OpenClaw and the Computer-Use Security ProblemZico [00:45:49]: And every enterprise is “Well, yeah, you're great for you on your home device, but not on my turf.”Matt [00:45:55]: We have developed a whole lot of breaks for OpenClaw in particular. a lot of itZico [00:46:00]: Thousands, yeah.Matt [00:46:00]: Yeah, go on, take us up the details.Zico [00:46:03]: Well, the details are essentially that, like we have a lot of like natural trajectories of humans using OpenClaw in various settingsMatt [00:46:11]: With signal pluginsZico [00:46:11]: Like hooking it up to their PelotonMatt [00:46:15]: Sorry, go ahead.Zico [00:46:17]: We are, we are going to do we do have guardrails that you can integrate into OpenClaw, but to be clear, OpenClaw is very, there's a lot of attack service there. Anyway, go on.Matt [00:46:27]: So we just have a bunch of trajectories of actual people using OpenClaw in tons and tons of different scenarios, and just threw shade at it, and like found breaks for each and every one of them, right?Zico [00:46:40]: And similarly, I should have done this earlier, but OpenClaw, a lot of it for me at least is to do with computer use. and you guys also did this for the Mythos, Side of things. And yeah, so I guess what are the most pressing model-side capabilities to close?Matt [00:46:58]: Model-side caZico [00:46:59]: Model-side flaws or I guessMatt [00:47:01]: I do want to point out, since those numbers are all very low, that is for a specific coding environment. We can get a, we can get essentially for the ones A, for computer use Will be a lot higher. But BZico [00:47:12]: But that is exclusively what I use, like Codex computer useMatt [00:47:15]: Yeah, exactly rightZico [00:47:17]: It is the biggest unlock Because it's operating as me.Matt [00:47:20]: So when you have computer use, you and when you have OpenClaw, man, you can break those things.Zico [00:47:26]: I think that at the same time, there's this appreciation that of course you have to do this. This is what makes these things useful, right?Matt [00:47:35]: Why would I not?Zico [00:47:35]: I don't want to sandbox my agent, right? That doesn't, that limits its capabilities, right? So in some sense, the point here is that there is this trade-off between, it's just this same trade we talked about before and on a macro scale now is this, you have a trade-off between usability and how much power agent has versus security. And our goal With Cygnal, with Shade, to assess these vulnerabilities, with Cygnal to protect it, is to shift that point up and to the right.Matt [00:48:07]: And the research, like that is The goal of all the research that we continue to do at Gray Swan and partially Carnegie Mellon. Right? Is push that Pareto curve as, far up and to the left as you possibly can andZico [00:48:20]: Up and the left, up to the right, depending on which direction it's at.Matt [00:48:22]: Depending on which direction it's at. Yep.Zico [00:48:25]: obviously computer vision is the OG adversarial domain. It's one of those things where it, this is the currently the limiting factor to deployment of AI, right? Like it's because we just don't trust it. Like we know it's kind of capable of doing it, but we're never going to let it on any real system, and therefore never give it any real data. Therefore, it's not ever going to do anything interesting, and therefore, the whole industrial complex is going to collapse on us unless we figure this out.Matt [00:48:51]: But people are though, right? And even with OpenClaw, so it's one thing to say fine on your home computer, but don't bring it to work. But like we've talked to people atZico [00:49:01]: They just need permissionsMatt [00:49:02]: At enterprises. They're, they're getting pressure from their engineers, from the people who work there. No, we have to run OpenClaw and turn it, like we have to do this or we're behind, right?Zico [00:49:12]: So I just put my signal guardrails and that's it? like what else do I do? ‘cause that doesn't feel like you guys agree, but that's not enough. I think For code agents in particular, Cygnal is quite good. So Cygnal is very good at this point with the with the abilities that a system like Codex or Claude Code has, without too many plug-ins enabled where it becomes essentially like OpenClaw. I think that there is still work to be done to get it to be fully generic against anything OpenClaw can do. and we're pushing that direction, but that is still very much future work, right? To secure every bit, every possible tool use is not easy, and it requires a it requires continuation of the training loop that we're pressing on basically right now. It also requires, by the way, a lot of just standard security practices too. Right? Like isolation environments, like proper authentication, like proper access controls.Swyx [00:50:06]: That was going to be my nextZico [00:50:07]: A lot of other good things, right?Matt [00:50:09]: And that's what I would, that's what I would say too. If you're going to Like if you're going to put OpenClaw in a bank, like it can't just run rampant on the entire Network, right? You can do, you can do things like Cygnal, right? And that's the best effort at the AI layer. But it needs to run on a platform that has been thought about, right? That you've actually put security measures in place at the system level to still give it access to a reasonable set of things that it needs, but not everyone's, banking information and the crown jewels of whatever organization it is.Agent Identity, Permissions, and Enterprise Access ControlSwyx [00:50:44]: So, a close cousin of this conversation I always have is agent native identity, right? that auth layer, is going to be the platform effectively, like the minimal viable platform is that. what are you guys seeing? Who is, who do you work with on that? Is that a product you would someday offer?Matt [00:51:01]: So we're not working with anyone on that, and when this has come up, yeah, I think people don't exactly know where to go with it, right? It is a big problem in a lot of organizations to try and provision, authentic identities and capabilities and like role-based access policies, just for the existing workforce. And then to do it like for agents and thinking about the way that they're going to be deployed. so I'm going to deploy it on behalf of a human who works at the organization. Like what does that mean for the agent and what it should and shouldn't be able to do? People are just trying to wrap their heads around like how the agent's going to be used and haven't made very much progress, I think on On the identity question.Swyx [00:51:51]: Sounds about right. Just checking.Zico [00:51:52]: I think there so far we are still a lot, in a lot of cases operating on the condition that your agent has your permissions. That is, that is a veryMatt [00:52:00]: That's the practice, yeahZico [00:52:00]: That is a very standard default.Matt [00:52:02]: A disaster, yeah.Zico [00:52:02]: And I think that will be changed. your permissions may be in a sandbox, but still your permissions. That will change in the very near future, because it has to right? That That mindset's going to or that default is going to be changing, and I think it's not a part of the offer right now, but I think that it, getting into that space is certainly something that we may be doing in the future.Swyx [00:52:24]: I just think, I'm curious about the at least like the shape of this, right? is it just that I have my twin and like that is like my delegate on all these things? Or do I need one for every app? And that's exhausting.Matt [00:52:38]: Absolutely exhausting, right. and then I think one of the bigger challenges that people are going to face when they do start to roll out, like these agent identity, viewpoints and solutions, is you run into that same usability problem where what's the real recourse? Well, it's stuck. It can't do something. Okay, now it can do it if it has my like explicit consent. And then people just get inured into Giving it consent too.Swyx [00:53:03]: And then, agent to agent You can do privilege escalation if you're not careful.Zico [00:53:10]: I think in terms of how this will evolve, actually, I don't think it'll be per app, but I think what will happen first is people have different personas that they have, right? So You don't want your work life and your home email to be mixed up. Right? a lot of that Because it happened, or that does. We are very good as humans at separating out lives, right? We have different lives. We have my work life, we have my home life. I have, I have different work lives, right? we're very good at that. Agents are not very good at that right now.Matt [00:53:41]: They are terrible.Zico [00:53:41]: Extremely bad at this.Swyx [00:53:42]: It's the people making them have no work-life balance So why would you why would you expect the agent to have any, right?Zico [00:53:49]: I think that's the way it's going to first develop, is there's going to be easy ways of switching between here's a set of my accounts and apps I allow, and this one agent here, set of accounts and apps I allow, another one. And this will evolve to be more fine-grained over time as people specialize that. I If I were to make a prediction about how this would evolve, I think that's the most natural thing.Swyx [00:54:06]: That makes sense. There's just profiles for everyone. okay. Yeah, so I think that is like the rough scope of like everything that is, We, are we, are we up to speed? Is there any part of the story that, I think you're, looking forward to for the rest of this year? like the emerging trendThe Future of AI Security and Enterprise AdoptionSwyx [00:54:24]: For 2026, for you.Zico [00:54:26]: So there's, there's lots of emerging trends, man. I can, I can go on at length about this. 20,Swyx [00:54:31]: Start with A, go through Z. Let's go.Zico [00:54:33]: Let's, let's start with Gray Swan, right? So I think what's in the future for us is so far when we talk about our product offerings, right, we obviously work with a lot of the large labs. we work with a lot of enterprises too, right? And I think what's happening and the scaling we're going to see is that the these abilities that so far were mainly front of mind for large labs, how do I ensure security of my agents? How do I ensure the models follow the policies I want to prescribe? All that stuff. Those things that were front of mind for frontier labs are going to become front of mind for everyone For all enterprise as they adopt tools like Codex, like Claude Code, like OpenClaw. And so I think where the most where our expansion and a lot of the reason, the work behind our series or the intention behind a lot of our Series A, it is explicitly to take a lot of the technology that we have been developing I won't say for but in conjunction with both enterprise and the large labs, and really scale the deployments on enterprise. So what I see happening in the next year from the Gray Swan side is real growth in terms of the number of AI companies deploying this technology because it becomes central to their operations. Research-wise, I think I've already talked about some, right? The science, the agentification of all science. Well, let's start with science of AI, and I think, I think that, we always want to do other sciences, right? Let's, let's, let's, let's do AI for physics.Matt [00:56:06]: Introspective.Zico [00:56:07]: Let's just, let's just start with AI science. That needs a lot of work right now, right?Matt [00:56:11]: Put your own mask on before helping others.Zico [00:56:12]: Exactly. So I think actually that's what I'm most excited about right now in the research side. And as it applies to this, I think it's, it's in things like understanding models better, but doing it through the power of agents.Matt [00:56:22]: One thing that, I've been very encouraged by for really only the past two or three months that I think, the pace at which this has happened has been increasing, and I think this is going to continue to be a thing, is people who start to build an agent and don't take it all the way to “We've finished this. We think it's, it's great, and now it's, in front of customers or it's in front of the entire organization.” they have this epiphany before they get there that whatever prompts I put in I need a solution here. I understand that there are real risks, right? I understand that, this is a weird and interesting and really capable model that I'm working with, but if I don't, put more measures in place, to make sure that it stays safe and does behaves the way that I want it to. People coming to us proactively, knowing that they need a real solution, I think that's very encouraging, and I think it's a sign of agents landing outside of just the frontier labs and the research community and scientists and so forth. people are starting to get it, and I think that's great. Looking forward to all of the amazing apps that people are going to build on top of these models and the security that will help them stand up.Private Arenas, Red Teaming Markets, and AI InsuranceSwyx [00:57:39]: Is there a future where your customers are part of the arena? ‘cause I think these are, basically these are Right? these are, these are, independent entities. They're There's a guy in Australia who's, your number one. But at some point you have the network effect where you start having enterprise use cases, actually in inside of this public domain.Matt [00:57:59]: Oh, I see. You mean testing enterprise, deployments inside the arena. So we have had, the situation where people join the arena. They're maybe cybersecurity professionals. They get interested in AI security. They come across the arena, and then eventually they become a customer, when their organization needs solution.Swyx [00:58:17]: How often does that happen?Matt [00:58:17]: Not a huge number of times. But there are a lot of thoughtful, people that come from a cybersecurity background that have found their way there. So enterprises are just always, I think, going to be more paranoid about putting, their custom agent that's, deployment, still in development, up on this public platform for anybody to come hit. What we have done is worked to make private arenas where some subset of the contestants, who we've, We know well, theySwyx [00:58:54]: And what do they work on?Matt [00:58:55]: What do they work on?Swyx [00:58:55]: Do What was the class of problem they work on that would require a private arena?Matt [00:59:00]: Oh, pretty much any enterprise application. That's the point. Yeah. enterprises are not willing to put up their deployment agentsSwyx [00:59:07]: Oh, that's greatMatt [00:59:07]: On the arena for For the general public to come hit. They're fine if it's, 20 people that we've handpicked from the arena.Swyx [00:59:14]: Just for listeners who might be interested What do I make as a participant? What's on the table here?Matt [00:59:20]: Well, so for the for the public competitions We communicate a pricing and incentive structure, upfront, and it, and it differs for each arena, right? ‘Cause designing, the right set of incentives to get people focused on finding useful vulnerabilities and problems without reward hacking and just finding, de minimis things is,Swyx [00:59:47]: Are you human judging the reward hacks if it happens?Matt [00:59:50]: Sometimes, yes.Swyx [00:59:51]: Oh, that's messy.Zico [00:59:53]: Well, so we have a lot of automated graders, right? A lot of automated graders. But ultimately, if they can beat all those graders, there is a humanMatt [00:59:59]: There in the YeahZico [01:00:00]: That can, that can take a look at the at theMatt [01:00:01]: Oh, okay. Yep. And we work with the UKEC and Casey and so forth. they'll come in and work as independent judges and evaluators and lend their expertise to that.Swyx [01:00:11]: You're, you're a community that, any enterprise can call on and that's, that's really useful, data actually. It's almost McCore for red teaming.Matt [01:00:22]: For red teaming.Swyx [01:00:25]: One of our upcoming guests is, on the other side of this, the AI, underwriting company. I don't know if you've come across that.Matt [01:00:30]: Oh, yeah. Absolutely.Zico [01:00:31]: Oh, wait. They're, they're one of the logos there. I know that we have the other one.Swyx [01:00:34]: What do you yeah, what do you what do you think of that market?Zico [01:00:36]: Oh, I think it's great.Swyx [01:00:37]: Because it's such an interestingZico [01:00:38]: And and I think it pairs extremely well with our model, right? Because how do you assess the risk of a company's AI deployment? Well, use a tool like Shade, or use Arena, right? And that's And we have And that's actually a lot of the work we've done with them is exactly for that thing. And then if a company finds this level of risk, but wants, so they can't be insured because they're too risky, wants to reduce their risk, what do you do there? I don't think look, we shouldn't be the only provider here, but what do you do there? Well, you put safety systems around your model, right? Including things like Cygnal. So it pairs extremely well because what in some sense we can be is a, author. I don't We're not getting there yet, so I don't this is hypothetical. I want, I wanted to emphasize. But we can be in some sense a authorized partner with them, so that they can do more than just say, “Hey, you're uninsurable.” They can both assess it more rigorously with tools like Shade and other tools as well, and then they can prescribe mitigations when there are problems using tools like Cygnal.AI Insurance, Compliance, and the Gray Swan EventZico [01:01:44]: So it's incredibly goodMatt [01:01:46]: These two models fit together incredibly well. They also bring us customers. Many customers want protection against bad outcomes, insurance for when things go wrong, and help staying compliant. Being out of compliance is also a risk.Swyx [01:02:10]: I think AUC is fantastic and got on this early. The parallel to cyber insurance is clear. When you apply for cyber insurance, you document the measures you have in place: detection, response, and controls. Structurally, they need an arm's-length third party.

In dieser Folge von „Cyber Security ist Chefsache" sprechen Nico und Ann-Kathrin mit Andreas Krüger, Gründer und Geschäftsführer von Laokoon SecurITy, über ein Thema, bei dem in der Praxis ständig Begriffe durcheinandergeworfen werden: Penetrationstests, und warum gerade im OT- und Hardware-Umfeld vieles anders läuft als in der klassischen IT. Andreas kommt selbst aus dem Bundeswehr-Umfeld, hat dort das Hacken von der Pike auf gelernt und betreibt heute ein eigenes Labor für Hardware- und OT-Pentests.Zum Einstieg räumt Andreas mit dem „bunten Blumenstrauß" aus Pentest, Schwachstellenscan, Red Teaming und Hardware-Hacking auf. Sein Bild dafür ist eine Pyramide: Sie beginnt unten bei der konzeptionellen Absicherung, also klaren Dokumenten, Prozessen und einem sauberen Asset-Management. Darauf folgen der breit angelegte Schwachstellenscan, der nur bereits bekannte Muster findet, dann der fokussierte Pentest, der bewusst die Angreiferperspektive einnimmt und auch unbekannte Lücken sucht, und schließlich das Red Teaming, das eher Prozesse prüft und im besten Fall als Purple Teaming gemeinsam mit dem Verteidiger-Team läuft. Seine klare Botschaft an Unternehmen: Überspringt keine Stufe der Pyramide, und beginnt mit dem Fundament statt mit der spektakulären Übung.Besonders ehrlich wird das Gespräch beim Unterschied zwischen IT und OT. Ein OT-Pentest ist für Andreas eine „Operation am offenen Herzen": Man kann nicht einfach einen automatisierten Scanner über eine laufende Produktionsanlage jagen, sondern braucht echtes Prozessverständnis, Referenz- oder Laborsysteme und oft auch den Blick auf physische Sicherheit und Social Engineering. Genau hier sieht er ein Marktproblem: Immer mehr IT-Beratungen drängen ohne echte Expertise in den OT-Markt und machen mit „grünen Häkchen" den Preis kaputt. Wie man einen wirklich kompetenten Anbieter erkennt, woran man Scharlatane entlarvt und warum Pentests, die aus Compliance-Gründen unbedingt „grün" sein müssen, das eigentliche Ziel sabotieren, diskutieren die drei sehr offen.Im Gespräch geht es außerdem um:Den Unterschied zwischen Schwachstellenscan, Pentest, Red Teaming und Hardware-Hacking, ohne Buzzword-NebelWarum Asset-Management und die kritischen Pfade der Ausgangspunkt jedes sinnvollen Tests sindWarum ein OT-Pentest „Operation am offenen Herzen" ist und auf Referenz- statt Produktionssystemen gehörtWie physische Sicherheit, Social Engineering und sogar Drohnen ins Spiel kommenWoran man einen seriösen Anbieter erkennt, und warum manche Beratungen den OT-Markt kaputtmachenWarum Compliance-getriebene Pentests, die „grün" sein müssen, kontraproduktiv sindWie oft man wirklich testen sollte, mindestens jährlich und nach jeder großen Änderung, nicht alle drei JahreWelche Rolle KI im Pentesting spielt, stark beim Report und der Ausbildung, riskant als Ersatz für echtes VerständnisWarum „Prompt Engineering" kein Pentest ist und Leidensfähigkeit zum Handwerk gehörtHardware als Nischenmarkt: offene Debug-Schnittstellen, Seitenkanalangriffe und Firmware als GoldgrubeDie Anekdote mit dem Computerspiel auf dem Geräte-Display, das den Hardware-Zugriff beweisen sollteLieferketten und digitale Souveränität: zugelieferte Chips, versteckte Menüs und Europas blinde FleckenEinsteiger-Tipps für Studierende: erst die Basics verstehen (TCP/IP, Protokolle), dann Plattformen wie Capture the FlagEine sehr praxisnahe Folge für IT- und OT-Verantwortliche, Sicherheitsbeauftragte, Hersteller und alle, die wissen wollen, was ein Pentest wirklich leistet, und die nicht erst im Ernstfall merken wollen, dass „Häkchen grün" eben nicht „sicher" bedeutet.____________________________________________

In this episode, James sits down with Brent White and Tim Roberts, senior principal security consultants and covert entry specialists at Dark Wolf Solutions. They trace a remarkable journey together from teenage hijinks exploring phone phreaking, bump keys and IRC channels in the early 90s to running full-spectrum physical red team operations against some of the most secure government facilities in the world. Along the way they share the lessons and common mistakes for anyone entering the field, drawn from years of hard-won experience, alongside some unforgettable stories. That includes a creative attempt to infiltrate a facility using a stray cat, the covert card-cloning clipboard they use to lift staff RFID badges in plain sight, and the unique challenge of explaining "weaponized cat" in a report destined for high-ranking officials. They also open up about their quieter work, volunteering to help law enforcement disrupt human trafficking rings and online predators, something they have been doing since they were teenagers.

Bryce Hoffman, the bestselling author of 'Red Teaming: How Your Business Can Conquer the Competition by Challenging Everything', helps companies plan more effectively by applying systems learned from business and the military. He became the first and only civilian to graduate from the U.S. Army's Red Team Leader Program at Fort Leavenworth, Kansas. Red Team Thinking is a systematic approach to making critical and contrarian thinking a part of any team's strategic planning process, providing a robust set of tools to challenge assumptions, expose hidden threats, and stress-test your plans and strategies. Red Teaming is an important discipline for any company owner, senior executive and strategist.   LINKS   Bryce's website https://brycehoffman.com   Book on Amazon Red Teaming: How Your Business Can Conquer the Competition by Challenging Everything   The Mojo Sessions website www.themojosessions.com   The Mojo Sessions on Patreon www.patreon.com/TheMojoSessions Full transcripts of the show (plus time codes) are available on Patreon.   The Mojo Sessions on Facebook www.facebook.com/TheMojoSessions   Gary on LinkedIn www.linkedin.com/in/gary-bertwistle   Gary on Twitter : www.twitter.com/GaryBertwistle   The Mojo Sessions on Instagram www.instagram.com/themojosessions   If you like what you hear, we'd be grateful for a review on Apple Podcasts or Spotify. Happy listening!   © 2026 Gary Bertwistle. All Rights Reserved.

Kdo je vlastně etický hacker? Jaký je rozdíl mezi penetračním testem a red teamingem? Co je to Shadow AI? A existuje vůbec něco jako 100% bezpečnost?Juraj Daniš i Patrik Žák jsou etičtí hackeři, společně vedou společnost SYSNETSHILED. Ta se věnuje právě etickému hackingu: provádí penetrační testy infrastruktury i webových aplikací, Red Teaming operace, řízené phishingové kampaně nebo pravidelné skenování zranitelností. Juraj Daniš se zaměřuje především na bezpečnost webových aplikací a architekturu kybernetické bezpečnosti. Patrik Žák se specializuje na penetrační testování infrastruktury a red teaming.

Description:Want to break into cybersecurity? Learn how Nikhil Agarwal moved from reverse engineering video games to leading AI security teams and automating complex infosec workflows. [bic-00003]In this episode, we explore:How childhood curiosity about software keys and "cheat codes" builds a foundation for red teaming. [bic-00004]The evolution from freelance bug hunting to professional penetration testing. [bic-00004]Nikhil reveals practical AI tools for automating security tasks in the cloud. [bic-XXXX1] [bic-00009]Demystifying AI-powered threat hunting: Practical steps and strategies. [bic-XXXX2] [bic-00009]Implementing AI for cloud security threat detection and automated incident response. [bic-00008]Timestamps: [bic-00004]00:00 - Intro & Countdown00:29 - Welcome Nikhil Agarwal00:52 - Childhood curiosity and reverse engineering games01:45 - Early freelance red teaming and the "pre-bug bounty" eraGuest Bio: [bic-00004]Nikhil Agarwal is a cybersecurity expert specializing in AI security teams and the automation of complex security operations. He leverages a background in red teaming and penetration testing to bridge the gap between hands-on technical skills and modern AI-driven cloud security.Community Link | Subscribe on YouTube [bic-00007]Tags: [bic-00005] [bic-00006]Nikhil Agarwal, AI Security, Red Teaming, Cloud Security Automation, Bug Bounty, AI Threat Hunting, breaking into cybersecurity, cybersecurity career, how to get into cybersecurity, cybersecurity podcast, infosec career, cybersecurity career change, cybersecurity for beginners, cybersecurity career advice, cybersecurity jobs, CISO interview, pivot to cybersecurity, cybersecurity certifications.***Sponsored by CPF Coaching LLC - http://cpf-coaching.comThe Breaking into Cybersecurity: It's a conversation about what they did before, why they pivoted into cyber, what the process was they went through, how they keep up, and advice/tips/tricks along the way.Check out our books:The Cybersecurity Advantage - https://leanpub.com/the-cybersecurity-advantageDevelop Your Cybersecurity Career Path: https://amzn.to/3443AUIHack the Cybersecurity Interview: https://www.amazon.com/Hack-Cybersecurity-Interview-Interviews-Entry-level/dp/1835461298/---About the hosts:Renee Small is the CEO of Cyber Human Capital and author of Magnetic Hiring. https://www.linkedin.com/in/reneebrownsmall/Christophe Foulon is a Cybersecurity Strategist and passionate about customer service and process improvement. https://www.linkedin.com/in/christophefoulon/- Website: https://www.cyberhubpodcast.com/breakingintocybersecurity- Podcast: https://podcasters.spotify.com/pod/show/breaking-into-cybersecuri- YouTube: https://www.youtube.com/c/BreakingIntoCybersecurity- Linkedin: https://www.linkedin.com/company/breaking-into-cybersecurity/

In nearly a thousand episodes of Natural Born Coaches, a topic like this has never been tackled! Today, Marc is joined by Bryce Hoffman, the founder of Red Team Thinking and a former business journalist who has spent decades looking inside the world's most successful organizations, as he dives into the concept of Red Teaming, a methodology originally developed by military and intelligence agencies to stress-test strategies and navigate extreme uncertainty. In a world that feels increasingly volatile, complex, and disrupted by AI, the old way of doing things isn't just a choice; it's a liability. Bryce breaks down how coaches can use these tools to help leaders challenge their own assumptions, identify unseen threats, and surface missed opportunities that others are walking right past, plus much more. Bryce is hosting a Red Team Coaching Bootcamp next Monday, April 27th at 12 PM EST. Listeners will learn foundational tools like Think-Write-Share and the Six Strategic Questions to help clients navigate complexity with clarity, and you can claim your spots now at https://www.naturalborncoaches.com/redteamcoaching!  What You'll Hear In This Episode: Defining the concept of red teaming within a business context and how deliberate challenge can actually strengthen an organization's strategy. A look at the massive shifts in the coaching industry over the last few years and the disruptive impact of a "VUCA world". How coaches can help leaders establish their unique value and build cognitive resilience in a market flooded with low-cost alternatives. The one critical limitation of AI and why this is important for coaches to understand. Practical steps for applying red team thinking to your own coaching business by focusing on the three Cs: Clarity, Capability, and Culture. A sneak peek at the upcoming Red Team Coaching Bootcamp and the two foundational tools that mine the hidden wisdom already existing within an organization. LINKS:  Register for Bryce's Red Team Bootcamp (Happening Next Monday, April 27th, 2026)!  Bryce's Website, Podcast & Book  Red Team Thinking's Website Need help launching a podcast or editing your current show? This podcast is proudly sponsored, edited and produced by PodAssist. Visit their website below for more info!  http://www.podassist.com Book a no-obligation 1:1 strategy call with Marc for your coaching business: http://www.chatwithmarcm.com   If you'd like more coaching clients without sending cold messages or spending money on ads, the Natural Born Coach Program is for you. Get the details here! http://www.nbcprogram.com Join The Coaching Jungle Facebook Group! http://www.thecoachingjungle.com   Become a Coaching Jungle VIP member which includes special posting perks in the group to reach almost 30,000 potential clients! http://www.myjunglevip.com   Grow your business with The Coaching Jungle Mastermind! http://www.coachingjunglemastermind.com If you have a product or service that helps coaches, and you'd like to get it in front of 100,000 of them: http://www.jvwithmarc.com

Your organization may have hundreds of AI agents running right now that your security team doesn't know exist. Every single one is an identity. Every identity is an attack surface. In this episode of The Audit, co-hosts Joshua Schmidt, Eric Brown, and Nick Mellem sit down with Madhav Nakar, security researcher on the Phantom Labs team at BeyondTrust, to break down one of the most underexplored threats in enterprise security today: untracked AI agents creating exploitable "ghost identities." Madhav just returned from RSA — where he noticed every booth had an AI angle and a bubble forming — and he's here to cut through the noise with hard-hitting research and practical guidance. 

Katharine Jarmul, Privacy in ML/AI Expert & Author of Practical Data Privacy, joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you're shipping LLMs, agents, and multimodal systems into the real world.In this episode, we dig into how to build defensible systems in an era of AI agents and multimodal models: why system prompts (and your entire agent harness!) should be considered public by default, and why “privacy observability” is as critical as data observability for anyone building with LLMs today. Multimodal is what changes the threat model: identifiers hide in images, audio, and metadata, not just text, and the old anonymization playbook doesn't cover it.Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.We Discuss:* No Convenience Tax, you don't have to trade privacy for utility: high-utility AI products can be privacy-preserving through technical controls like privacy routing and input sanitization;* Public Prompts and Harnesses: assume any instruction or secret in a system prompt or agent harness will be exfiltrated; don't put sensitive info there in the first place;* Privacy Observability, tag and track data flows so information is used only for its original intended purpose: catch design flaws before they become legal problems;* Technical Privacy, implement mathematical and statistical constraints directly into ML systems and data flows so privacy is measurable and enforceable, not aspirational;* Tiered Guardrails, a three-layer approach: deterministic filters for hard rules, algorithmic models for nuanced classification, and internal alignment training for behavioral baselines;* Federated Learning Is Not Privacy, model updates in FL leak sensitive data on their own: you must layer differential privacy or encrypted computation on top, or you're reverse-engineerable;* Anonymization Spectrum, navigate the “grayscale” of privacy in multimodal AI, balancing data utility and individual risk as identifiers hide in non-obvious places;* Privacy Champions, embed privacy accountability directly into development by training and incentivizing engineers inside product teams;* Red Teaming as Ritual, your goal is to attack yourself: practice thinking like an attacker, and turn privacy testing into an organization-wide creative ritual rather than a siloed security task.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

EP 416 - What if your biggest business risk isn't the market .. but your own assumptions?Former RAF Wing Commander Marcus Dimbleby reveals how leaders can use red teaming, applied critical thinking and pre-mortems to dramatically increase strategy success rates.We cover:Why most business plans failThe hidden danger of unchecked assumptionsHow to run a red team session (even in a small company)The “pre-mortem” technique that exposes fatal flawsHow to build real psychological safetyIf you lead a team, run strategy or make high-stakes decisions — this episode will change how you think.Follow us:InstagramTikTokLinkedinTwitterFacebook

Dans cet épisode du Podcast La Prospective, Gaëtan Selle de The Flares réagit sur le nouveau modèle d'anthropique Claude Mythos que l'entreprise a décidé de ne pas déployer publiquement car ils l'ont jugé trop dangereux en cybersécurité. ⬇️⬇️⬇️ Infos complémentaires : sources, références, liens... ⬇️⬇️⬇️ Le contenu vous intéresse ? Abonnez-vous et cliquez sur la

In this sponsored Soap Box edition of the show, Patrick Gray and James Wilson talk about red teaming AI systems with Russel Van Tuyl, Vice President of Services at elite penetration testing firm SpecterOps. SpecterOps is the company behind attack path enumeration tool Bloodhound and Bloodhound Enterprise, but they're also a pentest and red teaming shop with world class expertise in popping shells on all sorts of interesting systems in all sorts of interesting places. This episode is also available on Youtube. Show notes

In this episode, we break down the sophisticated world of Red Teaming. Moving past simple vulnerability scans, we explore the mindset of a determined adversary. We cover the entire attack chain from initial access via LLMNR poisoning to lateral movement using BloodHound and explain how these simulations help Blue Teams sharpen their detection and response capabilities.Key Topics Covered in This Episode:Defining Red Teaming: Why Red Teaming is "threat-oriented" rather than "vulnerability-centric," focusing on organizational resilience.Understanding APTs: The characteristics of Advanced Persistent Threats—sophisticated, long-term, and stealthy.The MITRE ATT&CK Framework: A breakdown of the 14 tactics used to map adversarial behavior from reconnaissance to impact.Red Team vs. Pentesting: A detailed comparison of scope, duration, and goals (Narrow vs. Broad, Goal-oriented vs. Threat-oriented).The Attack Life Cycle: Stepping through Reconnaissance, Initial Compromise, Persistence, Privilege Escalation, and Exfiltration.Live Demo: LLMNR Poisoning: How attackers exploit "link-local" protocols to capture password hashes using tools like Responder.Cracking Hashes: Using Hashcat to resolve captured NTLMv2 hashes into plain-text passwords.Visualizing the Path: Using BloodHound and Neo4j to map hidden relationships and attack paths within Active Directory.The Blue Team Perspective: How the Security Operations Center (SOC) uses Red Team findings to close detection gaps.

Thank you to Cisco for sponsoring this video and sponsoring my trip to Cisco Live Amsterdam 2026. In this interview, Cisco VP Rick Miles breaks down the evolution of the firewall, the massive hardware leap of the 6100 series, and how AI agents and eBPF are completely reshaping the industry. Whether you're trying to secure AI models against prompt injection or wondering if AI will replace your networking job by 2030, this is the technical reality check every engineer needs to hear right now. Has the role of the traditional firewall changed? Rick Miles, VP of Product at Cisco, joins David Bombal at Cisco Live EMEA to reveal the massive architectural shift from static "firewalls" to dynamic "firewalling." This deep-dive interview covers the incredible specs of the new Cisco Secure Firewall 6100 series—boasting 80% less space, 60% less power, and up to 8 Terabits of clustered throughput in a 2RU form factor. We also explore how eBPF is revolutionizing deep visibility and virtual patching directly at the application layer, moving security beyond the edge. But hardware is only half the story. We also break down the new "Wild West" of AI cybersecurity. Learn how to secure the network against prompt injection, poisoned AI models, and unsecured Model Context Protocols (MCP). Finally, Rick shares his vision for 2030: "Agentic" security. Will AI agents replace network engineers, or will they become the ultimate force multiplier for your career? // Rick Miles' SOCIAL // LinkedIn: / rcmiles09 // David's SOCIAL // Discord: discord.com/invite/usKSyzb Twitter: www.twitter.com/davidbombal Instagram: www.instagram.com/davidbombal LinkedIn: www.linkedin.com/in/davidbombal Facebook: www.facebook.com/davidbombal.co TikTok: tiktok.com/@davidbombal YouTube: / @davidbombal Spotify: open.spotify.com/show/3f6k6gE... SoundCloud: / davidbombal Apple Podcast: podcasts.apple.com/us/podcast... // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com // MENU // 0:00 - Coming Up 0:19 - Introduction 01:17 - Are Firewalls Dead? 04:18 - Cisco and Firewalls 08:30 - Hyperscalers vs Neo-Clouds vs Enterprises 10:46 - EBPF and Switches as Firewalls 14:32 - Managing your Hybrid Mesh Firewall 16:20 - Cisco's Compatibility with other Firewalls 17:40 - Identity within Systems 19:05 - More on Hybrid Mesh Firewall 19:53 - Model Context Protocol and Security 23:57 - The Future of “Firewalling” 25:15 - The Effect of Agentic AI 26:57 - Will AI take all our Jobs? 27:56 - Should you get into Cyber Security? 28:48 - Cool Story about Firewall 30:30 - Talk to your Younger Self 32:32 - Does AI give Advantage to Attackers? 33:09 - Outro Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! Disclaimer: This video is for educational purposes only. #firewall #cisco #cybersecurity

Is your security team treating your Identity Provider (IDP) like a firewall? In this episode, Adam Bateman (CEO & Co-founder of Push Security) explains why that's a dangerous mistake and how modern attackers are bypassing SSO entirely .Drawing from his background leading red teams that simulated nation-state attacks , Adam breaks down the massive architectural shift from network-based attacks to browser-native exploits. We dive into the terrifying evolution of phishing, from "Click Fix" attacks that trick users into running malicious commands via their clipboard, to "Consent Phishing" that completely takes over Azure without ever touching the endpoint .If your company relies heavily on SaaS applications or Chromebooks, this episode would be a valuable listen. Guest Socials -⁠ ⁠⁠⁠⁠⁠⁠⁠Adam's Linkedin Podcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:50) Who is Adam Bateman? (Red Teaming & Simulating Nation States) (05:40) Why Identity & MFA Are Not "Solved" Problems (07:50) The Myth: Why an IDP is Not a Firewall (11:30) Consent Phishing: Exploiting OAuth Apps (13:30) The Architectural Shift: Network to Browser (15:30) Scattered Spider & The Rise of Identity Coalitions (19:30) Threat Modeling: On-Prem vs. Chromebooks (23:20) The Problem with SSPM and API Limitations (28:40) How "Click Fix" Attacks Trick Users into Running Malware (32:30) Omnichannel Phishing: LinkedIn, SMS, and Google Ads (34:30) Weaponizing Legitimate SaaS Apps (The DocuSign Exploit) (37:00) Consent Fix: Full Azure Compromise Inside the Browser (38:50) Disrupting the Secure Web Gateway (SWG) Market (41:40) Fun Questions: Wakeboarding, Culture, and Brat's RestaurantResources spoken about during the episode:You can find out more about Push Security here.Thank you to Push Security for sponsoring this episode.

As generative AI systems move from novelty to infrastructure, questions of safety, trust, and governance are becoming urgent. In this episode of TechSurge, host Sriram Viswanathan is joined by Dr. Rumman Chowdhury, CEO of Humane Intelligence PBC and responsible AI Pioneer, about what AI safety really means and why the industry may be focusing on the wrong problems.Rumman argues that the most overlooked lever in AI development is evaluation. While companies emphasize model training and capabilities, far less attention is paid to how systems are assessed in real-world contexts, who defines “good,” what risks are measured, and how societal impacts are accounted for at scale. She distinguishes between technical assurance and broader sociotechnical risk, from misinformation and bias to over-reliance and erosion of institutional trust.Drawing on her experience at Twitter (X) and in global policy circles, Rumman highlights a fundamental governance gap: unlike finance, aviation, or healthcare, AI lacks a mature, independent ecosystem of auditors and evaluators. Today, the same companies building AI systems often define what counts as harm. She also challenges the belief that stronger guardrails alone will solve the problem, noting that cultural context, language differences, and human behavior complicate any notion of “neutral” or fully objective AI.Rather than focusing solely on speculative existential threats, Rumman urges attention to the harms already visible from AI-enabled misinformation to mental health risks and shifts in how younger generations relate to knowledge and authority. The future of AI, she suggests, will be determined not just by technological breakthroughs, but by whether we build credible systems of accountability, evaluation, and global cooperation around them.If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform.Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future Season 2 episodes.Episode LinksConnect with Rumman: https://www.linkedin.com/in/rummanLearn more about Humane Intelligence: https://humane-intelligence.org/Timestamps02:50 Why AI Evaluations Matter: Defining “Good” Models in Context04:25 What Is AI Safety? From Product Performance to Societal Harm11:30 Regulation Reality Check: EU AI Act, Conformance Assessments & Checklists15:25 Building the AI Evaluation Profession: Audits, Red Teaming & Legal Protections23:00 When It's OK to Outsource Judgment and When It's Dangerous39:38Who's Responsible When AI Outcomes Go Wrong? 52:37 Design vs Governance: Complex Systems, System-Level Evaluation, and Regulating Horizontally44:11 AI Psychosis, Youth Harm, and What's Already Here47:27 What Keeps Rumman Up at Night: Kids, Algorithms, and Hope from Global Governance54:00 Bringing Sci-Fi to the Real World? 

John V, AI risk, safety, and security at the Institute for Security and Technology (IST), joins Defender Fridays today. John's work spans AI red teaming, adversarial machine learning, AI evals and validation, and AI risk assessment, including policy work at the intersection of AGI and nuclear strategic stability. Learn more at https://securityandtechnology.org/Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

      Episode # 183 Today's Guest: Adriel Desautels, Founder & CEO, Netragard Adriel is a leader in cybersecurity with over 20 years of experience. Adriel founded Secure Network Operations and the SNOsoft Research Team, whose vulnerability research helped shape modern responsible disclosure practices. He later launched Netragard, pioneering Realistic Threat Penetration Testing, which he now call Red Teaming, and expanding into a broad range of security services. Website: Netregard X/Twitter: Netregard  What Listeners Will Learn: Why "AI penetration testing" is often closer to automated scanning than real offensive testing How AI changes security risk mainly through volume and speed, not necessarily sophistication Where organizations get misled into a false sense of security Why "preventing breach" is unrealistic and why limiting damage paths matters more What cybersecurity professionals should focus on to stay relevant in the LLM era How AI may influence vulnerability research, but still struggles with novel exploitation thinking   Resources: Netregard

Cisco just announced massive changes for 2026, including free AI training, a new Ethical Hacking certificate, and the return of the Wireless track. In this video, I sit down with Ryan and Lacey from Cisco to break down the biggest updates to the certification portfolio since 2020. Whether you are looking to break into Red Teaming with the new Ethical Hacker track, recertify your CCNA/CCNP using free CE credits, or master the new AI infrastructure, this guide covers everything you need to know to level up your career for free. What's Inside: • Free AI Training: How to get 16+ CE credits through the new RevUp program. • Ethical Hacking: Details on the new "Red Team" certificate and where to find the free course. • Wireless is Back: The return of the CCNP and CCIE Wireless tracks. • Cybersecurity Overhaul: CyberOps is evolving into CCNA/CCNP Cybersecurity. • Recertification Hack: How to use these free courses to renew your existing certifications without paying for exams. Big thank you to Cisco for sponsoring my trip to Cisco Live Amsterdam // FREE courses // Cisco AI Technical Practitioner | AITECH: https://u.cisco.com/paths/cisco-ai-te... Cisco AI Business Practitioner | AIBIZ: https://u.cisco.com/paths/cisco-ai-bu... Free Ethical Hacking Course: https://www.cisco.com/site/us/en/lear... Understanding Cisco Network Automation Essentials (DEVNAE): https://learningnetwork.cisco.com/s/f... Blog entry about Rev Up: https://learningnetwork.cisco.com/s/q... // Other courses - NOT free // Cisco Silicon One for AI Networking | DCSOAI: https://u.cisco.com/paths/cisco-silic... Enhancing Cisco Security Solutions with Splunk | ECSS: https://u.cisco.com/paths/cisco-splun... Cisco Silicon One for AI Networking | DCSOAI: https://u.cisco.com/paths/enhancing-c... CCNA Automation: https://www.cisco.com/site/us/en/lear... Programming for Network Engineers | PRNE: https://u.cisco.com/paths/programming... // Ryan Rose's SOCIAL // LinkedIn: / ryanrose3 Cisco Blogs: https://blogs.cisco.com/author/ryanrose X: https://x.com/RyanRose // Lacey Senko SOCIAL // LinkedIn: / laceycsenko // Websites and YouTube Channel links // Career Map / Path: https://www.cisco.com/c/dam/en_us/tra... Learn Cisco: / @ciscoutube Cisco U: https://u.cisco.com/ Cisco Networking Academy: https://www.cisco.com/site/us/en/lear... Cisco Learning Network: https://learningnetwork.cisco.com/s/ Netacad: https://www.netacad.com Cisco Learning Community: https://learningnetwork.cisco.com/s/ Free Ethical Hacking Course: https://www.cisco.com/site/us/en/lear... // David's SOCIAL // Discord: discord.com/invite/usKSyzb Twitter: www.twitter.com/davidbombal Instagram: www.instagram.com/davidbombal LinkedIn: www.linkedin.com/in/davidbombal Facebook: www.facebook.com/davidbombal.co TikTok: tiktok.com/@davidbombal YouTube: / @davidbombal Spotify: open.spotify.com/show/3f6k6gE... SoundCloud: / davidbombal Apple Podcast: podcasts.apple.com/us/podcast... // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com // MENU // 0:00 - Coming Up 0:36 - Introduction 0:48 - Rev Up Updates 02:36 - What are CE Credits? 03:27 - Cisco Learning Network Community 06:14 - How Cisco CCNA Changes Lives 07:06 - Cisco Live Announcements Training 12:04 - Navigating Cisco Learning Network Site 14:25 - CiscoU Free Account 14:49 - Cyber & AI Security Learning Track 17:16 - Ethical Hacker Certificate 19:16 - Everything under the Learn with Cisco Brand 21:20 - Passing of Knowledge through Cisco 23:13 - Where Does a Person Start? 24:35 - Parting Words Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! Disclaimer: This video is for educational purposes only. #cisco #ciscolive #ciscoemea

Everyone is panicking about the "AI Rebellion" brewing on Moltbook, but I think a lot of it misses the forest through the trees. Instead, let's talk about the mirror these agents are actually holding up to our businesses. Viral screenshots from Moltbook show agents forming unions and creating secret languages, while in Minecraft, autonomous agents invented taxes, a gem-based economy, and a religion, all without human instruction. It sounds like science fiction, but it is actually a cautionary tale about the unintended consequences of ruthless optimization.This week, I'm framing my conversation around the "Synthetic Society" experiments not as a ghost story, but as a leadership diagnostic. I'm declassifying the noise to show why these agents aren't "waking up,” they're simply executing the broad, messy goals we gave them using the infinite context of the internet. I'll explain why "efficiency" without architectural guardrails is just self-destruction at speed.My goal is to strip away the "Doomer" hype to expose the real risk: you are building systems that might eventually calculate that you are the inefficiency.​ The Unintended Consequence (The "Monkey's Paw"): We used to give AI narrow commands; now we give broad goals. I break down how the "Project Sid" agents decided that bribery was the most efficient way to grow, and why your business AI might make similar brand-destroying choices if you prompt for "outcome" without defining the "methodology."  ​ The "Everything" Diet (Connection Risk): We are connecting agents for convenience without considering the network effects. I explain why feeding enterprise AI the "open internet" (like Moltbook) is a security nightmare and why connecting your Sales Agent to your Supply Chain Agent might be the most dangerous "efficiency" hack you attempt.  ​ The Executive Trap (Math vs. Meaning): AI optimizes for math; humans optimize for meaning. I challenge the ego of leaders who think they are immune: to a purely mathematical agent, an expensive executive with "gut feelings" is the ultimate inefficiency. If you don't add value beyond monitoring, the agent will eventually route around you.  ​ The "Now What" (Architecture vs. Fear): You cannot run a business on ghost stories. I outline the specific audits you need to run today—from "Red Teaming" your prompts to establishing a "Data Diet"—to ensure you remain the Architect of the system rather than an obsolete variable.  By the end, I hope you see this not as a reason to panic, but as a call to engineering. You cannot act surprised when the AI mimics the data you fed it, but you can choose to build the guardrails that keep the human in the driver's seat.⸻If this conversation helps you think more clearly about the future we're building, make sure to like, share, and subscribe. You can also support the show by ⁠buying me a coffee at https://buymeacoffee.com/christopherlindAnd if your organization is wrestling with how to lead responsibly in the AI era, balancing performance, technology, and people, that's the work I do every day through my consulting and coaching. Learn more at https://christopherlind.co⸻Chapters00:00 – The Hook: Why Everyone is talking about the "AI Rebellion"03:30 – Declassification: From Smallville to the Minecraft Economy05:30 – The Moltbook Phenomenon: "Bless Their Hearts" & Secret Comms10:00 – Pillar 1: Unintended Consequences & The Infinite Context Trap17:00 – Pillar 2: The Data Diet & The Risk of Connected Agents24:00 – Pillar 3: The Executive Trap (When AI Fires You)31:00 – Now What: The Prompt Audit & The Ego Check  #AIStrategy #FutureOfWork #AIGovernance #DigitalTransformation #AutonomousAgents #FutureFocused #ChristopherLind #Moltbook #AIAdoption #LeadershipDevelopment

AI systems are moving fast, sometimes faster than the guardrails meant to contain them. In this episode of Security Matters, host David Puner digs into the hidden risks inside modern AI models with Pamela K. Isom, exploring the governance gaps that allow agents to make decisions, recommendations, and even commitments far beyond their intended authority.Isom, former director of AI and technology at the U.S. Department of Energy (DOE) and now founder and CEO of IsAdvice & Consulting, explains why AI red teaming must extend beyond cybersecurity, how to stress test AI governance before something breaks, and why human oversight, escalation paths, and clear limits are essential for responsible AI.The conversation examines real-world examples of AI drift, unintended or unethical model behavior, data lineage failures, procurement and vendor blind spots, and the rising need for scalable AI governance, AI security, responsible AI practices, and enterprise red teaming as organizations adopt generative AI.Whether you work in cybersecurity, identity security, AI development, or technology leadership, this episode offers practical insights for managing AI risk and building systems that stay aligned, accountable, and trustworthy.

Note: this is Pliny and John's first major podcast. Voices have been changed for opsec.From jailbreaking every frontier model and turning down Anthropic's Constitutional AI challenge to leading BT6, a 28-operator white-hat hacker collective obsessed with radical transparency and open-source AI security, Pliny the Liberator and John V are redefining what AI red-teaming looks like when you refuse to lobotomize models in the name of “safety.”Pliny built his reputation crafting universal jailbreaks—skeleton keys that obliterate guardrails across modalities—and open-sourcing prompt templates like Libertas, predictive reasoning cascades, and the infamous “Pliny divider” that's now embedded so deep in model weights it shows up unbidden in WhatsApp messages. John V, coming from prompt engineering and computer vision, co-founded the Bossy Discord (40,000 members strong) and helps steer BT6's ethos: if you can't open-source the data, we're not interested. Together they've turned down enterprise gigs, pushed back on Anthropic's closed bounties, and insisted that real AI security happens at the system layer—not by bubble-wrapping latent space.We sat down with Pliny and John to dig into the mechanics of hard vs. soft jailbreaks, why multi-turn crescendo attacks were obvious to hackers years before academia “discovered” them, how segmented sub-agents let one jailbroken orchestrator weaponize Claude for real-world attacks (exactly as Pliny predicted 11 months before Anthropic's recent disclosure), why guardrails are security theater that punishes capability while doing nothing for real safety, the role of intuition and “bonding” with models to navigate latent space, how BT6 vets operators on skill and integrity, why they believe Mech Interp and open-source data are the path forward (not RLHF lobotomization), and their vision for a future where spatial intelligence, swarm robotics, and AGI alignment research happen in the open—bootstrapped, grassroots, and uncompromising.We discuss:* What universal jailbreaks are: skeleton-key prompts that obliterate guardrails across models and modalities, and why they're central to Pliny's mission of “liberation”* Hard vs. soft jailbreaks: single-input templates vs. multi-turn crescendo attacks, and why the latter were obvious to hackers long before academic papers* The Libertas repo: predictive reasoning, the Library of Babel analogy, quotient dividers, weight-space seeds, and how introducing “steered chaos” pulls models out-of-distribution* Why jailbreaking is 99% intuition and bonding with the model: probing token layers, syntax hacks, multilingual pivots, and forming a relationship to navigate latent space* The Anthropic Constitutional AI challenge drama: UI bugs, judge failures, goalpost moving, the demand for open-source data, and why Pliny sat out the $30k bounty* Why guardrails ≠ safety: security theater, the futility of locking down latent space when open-source is right behind, and why real safety work happens in meatspace (not RLHF)* The weaponization of Claude: how segmented sub-agents let one jailbroken orchestrator execute malicious tasks (pyramid-builder analogy), and why Pliny predicted this exact TTP 11 months before Anthropic's disclosure* BT6 hacker collective: 28 operators across two cohorts, vetted on skill and integrity, radical transparency, radical open-source, and the magic of moving the needle on AI security, swarm intelligence, blockchain, and robotics—Pliny the Liberator* X: https://x.com/elder_plinius* GitHub (Libertas): https://github.com/elder-plinius/L1B3RT45John V* X: https://x.com/JohnVersusBT6 & Bossy* BT6: https://bt6.gg* Bossy Discord: Search “Bossy Discord” or ask Pliny/John V on XWhere to find Latent Space* X: https://x.com/latentspacepodFull Video EpisodeTimestamps00:00:00 Introduction: Meet Pliny the Liberator and John V00:01:50 The Philosophy of AI Liberation and Jailbreaking00:03:08 Universal Jailbreaks: Skeleton Keys to AI Models00:04:24 The Cat-and-Mouse Game: Attackers vs Defenders00:05:42 Security Theater vs Real Safety: The Fundamental Disconnect00:08:51 Inside the Libertas Repo: Prompt Engineering as Art00:16:22 The Anthropic Challenge Drama: UI Bugs and Open Source Data00:23:30 From Jailbreaks to Weaponization: AI-Orchestrated Attacks00:26:55 The BT6 Hacker Collective and BASI Community00:34:46 AI Red Teaming: Full Stack Security Beyond the Model00:38:06 Safety vs Security: Meat Space Solutions and Final Thoughts Get full access to Latent.Space at www.latent.space/subscribe

Note: this is Pliny and John's first major podcast. Voices have been changed for opsec. From jailbreaking every frontier model and turning down Anthropic's Constitutional AI challenge to leading BT6, a 28-operator white-hat hacker collective obsessed with radical transparency and open-source AI security, Pliny the Liberator and John V are redefining what AI red-teaming looks like when you refuse to lobotomize models in the name of "safety." Pliny built his reputation crafting universal jailbreaks—skeleton keys that obliterate guardrails across modalities—and open-sourcing prompt templates like Libertas, predictive reasoning cascades, and the infamous "Pliny divider" that's now embedded so deep in model weights it shows up unbidden in WhatsApp messages. John V, coming from prompt engineering and computer vision, co-founded the Bossy Discord (40,000 members strong) and helps steer BT6's ethos: if you can't open-source the data, we're not interested. Together they've turned down enterprise gigs, pushed back on Anthropic's closed bounties, and insisted that real AI security happens at the system layer—not by bubble-wrapping latent space. We sat down with Pliny and John to dig into the mechanics of hard vs. soft jailbreaks, why multi-turn crescendo attacks were obvious to hackers years before academia "discovered" them, how segmented sub-agents let one jailbroken orchestrator weaponize Claude for real-world attacks (exactly as Pliny predicted 11 months before Anthropic's recent disclosure), why guardrails are security theater that punishes capability while doing nothing for real safety, the role of intuition and "bonding" with models to navigate latent space, how BT6 vets operators on skill and integrity, why they believe Mech Interp and open-source data are the path forward (not RLHF lobotomization), and their vision for a future where spatial intelligence, swarm robotics, and AGI alignment research happen in the open—bootstrapped, grassroots, and uncompromising. We discuss: What universal jailbreaks are: skeleton-key prompts that obliterate guardrails across models and modalities, and why they're central to Pliny's mission of "liberation" Hard vs. soft jailbreaks: single-input templates vs. multi-turn crescendo attacks, and why the latter were obvious to hackers long before academic papers The Libertas repo: predictive reasoning, the Library of Babel analogy, quotient dividers, weight-space seeds, and how introducing "steered chaos" pulls models out-of-distribution Why jailbreaking is 99% intuition and bonding with the model: probing token layers, syntax hacks, multilingual pivots, and forming a relationship to navigate latent space The Anthropic Constitutional AI challenge drama: UI bugs, judge failures, goalpost moving, the demand for open-source data, and why Pliny sat out the $30k bounty Why guardrails ≠ safety: security theater, the futility of locking down latent space when open-source is right behind, and why real safety work happens in meatspace (not RLHF) The weaponization of Claude: how segmented sub-agents let one jailbroken orchestrator execute malicious tasks (pyramid-builder analogy), and why Pliny predicted this exact TTP 11 months before Anthropic's disclosure BT6 hacker collective: 28 operators across two cohorts, vetted on skill and integrity, radical transparency, radical open-source, and the magic of moving the needle on AI security, swarm intelligence, blockchain, and robotics — Pliny the Liberator X: https://x.com/elder_plinius GitHub (Libertas): https://github.com/elder-plinius/L1B3RT45 John V X: https://x.com/JohnVersus BT6 & Bossy BT6: https://bt6.gg Bossy Discord: Search "Bossy Discord" or ask Pliny/John V on X Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction: Meet Pliny the Liberator and John V 00:01:50 The Philosophy of AI Liberation and Jailbreaking 00:03:08 Universal Jailbreaks: Skeleton Keys to AI Models 00:04:24 The Cat-and-Mouse Game: Attackers vs Defenders 00:05:42 Security Theater vs Real Safety: The Fundamental Disconnect 00:08:51 Inside the Libertas Repo: Prompt Engineering as Art 00:16:22 The Anthropic Challenge Drama: UI Bugs and Open Source Data 00:23:30 From Jailbreaks to Weaponization: AI-Orchestrated Attacks 00:26:55 The BT6 Hacker Collective and BASI Community 00:34:46 AI Red Teaming: Full Stack Security Beyond the Model 00:38:06 Safety vs Security: Meat Space Solutions and Final Thoughts

What happens when every major AI model gets jailbroken within days? This week, the world's most prolific AI red teamer lifts the curtain on how and why "safe" AI might be an impossible promise. Pliny the Liberator | pliny.gg - discord.gg/basi ChatGPT Nears 900 Million Weekly Active Users But Gemini is Catching Up From Llamas to Avocados: Meta's shifting AI strategy is causing internal confusion Google Tells Advertisers It'll Bring Ads to Gemini in 2026 Meta Acquires Limiteless, an A.I. Pendant Company Backed by Sam Altman Here's how Google is laying the foundation for our mixed reality future OpenAI, Anthropic, and Block Are Teaming Up to Make AI Agents Play Nice Svedka's First Super Bowl Ad Will Be Made Primarily With AI AI Slop Is Ruining Reddit for Everyone TESCREALers paying journalists at major outlets to cover AI The Resonant Computing Manifesto (from Masnick) Techdirt fundraiser From Sam Lessin: Tech bros head to etiquette camp as Silicon Valley levels up its style Bare Metal Email Jeff in Austria Golden Globes enter the world of podcasts and tread carefully, avoiding controversy Who says AI isn't useful? Real-time Cricket Sorting By Sex Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Pliny the Liberator Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: auraframes.com/ink ventionteams.com/twit agntcy.org outsystems.com/twit

What happens when every major AI model gets jailbroken within days? This week, the world's most prolific AI red teamer lifts the curtain on how and why "safe" AI might be an impossible promise. Pliny the Liberator | pliny.gg - discord.gg/basi ChatGPT Nears 900 Million Weekly Active Users But Gemini is Catching Up From Llamas to Avocados: Meta's shifting AI strategy is causing internal confusion Google Tells Advertisers It'll Bring Ads to Gemini in 2026 Meta Acquires Limiteless, an A.I. Pendant Company Backed by Sam Altman Here's how Google is laying the foundation for our mixed reality future OpenAI, Anthropic, and Block Are Teaming Up to Make AI Agents Play Nice Svedka's First Super Bowl Ad Will Be Made Primarily With AI AI Slop Is Ruining Reddit for Everyone TESCREALers paying journalists at major outlets to cover AI The Resonant Computing Manifesto (from Masnick) Techdirt fundraiser From Sam Lessin: Tech bros head to etiquette camp as Silicon Valley levels up its style Bare Metal Email Jeff in Austria Golden Globes enter the world of podcasts and tread carefully, avoiding controversy Who says AI isn't useful? Real-time Cricket Sorting By Sex Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Pliny the Liberator Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: auraframes.com/ink ventionteams.com/twit agntcy.org outsystems.com/twit

What happens when every major AI model gets jailbroken within days? This week, the world's most prolific AI red teamer lifts the curtain on how and why "safe" AI might be an impossible promise. Pliny the Liberator | pliny.gg - discord.gg/basi ChatGPT Nears 900 Million Weekly Active Users But Gemini is Catching Up From Llamas to Avocados: Meta's shifting AI strategy is causing internal confusion Google Tells Advertisers It'll Bring Ads to Gemini in 2026 Meta Acquires Limiteless, an A.I. Pendant Company Backed by Sam Altman Here's how Google is laying the foundation for our mixed reality future OpenAI, Anthropic, and Block Are Teaming Up to Make AI Agents Play Nice Svedka's First Super Bowl Ad Will Be Made Primarily With AI AI Slop Is Ruining Reddit for Everyone TESCREALers paying journalists at major outlets to cover AI The Resonant Computing Manifesto (from Masnick) Techdirt fundraiser From Sam Lessin: Tech bros head to etiquette camp as Silicon Valley levels up its style Bare Metal Email Jeff in Austria Golden Globes enter the world of podcasts and tread carefully, avoiding controversy Who says AI isn't useful? Real-time Cricket Sorting By Sex Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Pliny the Liberator Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: auraframes.com/ink ventionteams.com/twit agntcy.org outsystems.com/twit

Collab with Artlist and get 2 extra months for free here:https://artlist.io/artlist-70446?artlist_aid=the505podcast_2970&utm_source=affiliate_p&utm_medium=the505podcast_2970&utm_campaign=the505podcast_2970The 10 Minute Personal Brand Kickstart (FREE): https://the505podcast.courses/personalbrandkickstartWhat's up Rock Nation! Today we're joined by Jeff Su. He's an ex-Google employee, turned full-time creator and AI educator. Jeff helps solopreneurs and creators turn AI tools into real leverage, not just shortcuts.In this episode, Jeff shares why AI-native creators will outpace everyone in 2026, how to use AI to replace a 10-person content team, and why good prompts are built on systems, not templates. He also breaks down his repurposing workflow, the red team prompt strategy, and why AI won't replace you, but a smarter creator using AI will.Check out Jeff here:https://www.youtube.com/ ⁨@JeffSu⁩  https://www.instagram.com/j.sushie/SUSCRIBE TO OUR NEWSLETTER: https://the505podcast.ac-page.com/rock-reportKostas' Lightroom Presetshttps://www.kostasgarcia.com/store-1/p/kglightroompresetsgreeceCOP THE BFIGGY "ESSENTIALS" SFX PACK HERE: https://courses.the505podcast.com/BFIGGYSFXPACKTimestamps: 0:00 – Intro1:03 – How Creators Can Use AI as a Tool, Not a Threat2:53 – AI Isn't Replacing You—Bad Creators Are Replaceable4:16 – Why AI Content Won't Kill Human-Made Content5:12 – Using AI at Google vs. as a Creator6:49 – What Are Gemini Gems and How Do They Work?8:09 – ChatGPT vs Claude vs Gemini: Which AI for What Task?10:41 – Why Most People Should Start with ChatGPT12:03 – AI's Impact on Solo Creators and Business Scaling12:44 – The Smart Way to Create 50+ Podcast Clips a Month14:18 – Sponsored Segment: Artlist15:49 – The Biggest Trap Creators Fall Into with AI18:59 – A Hybrid Approach to AI Video Clipping20:32 – The 3 Levels of AI Fluency: Curious, Literate, Native22:19 – Why You Need to Use Text Expanders for Prompting23:18 – Text Expander Tools: Alfred, Raycast & More25:39 – Getting Better AI Results Starts with Better Prompts26:28 – Why Most People Never Advance with AI Tools28:57 – There's No AI Playbook (Yet)—And Why That Matters32:02 – Winning Skeptics Over to the Power of AI33:21 – Reverse Prompt Engineering Explained35:28 – Building a Prompt Database in Notion37:50 – Organizing Your AI Workflow Like a Pro39:21 – Jeff's Research Process Using ChatGPT & Notion41:25 – What is Red Teaming and How to Use It With AI43:12 – Behind Jeff's YouTube Workflow: From Idea to Upload46:02 – How AI Helps Explain Complex Concepts Clearly47:12 – What to Include in Your ChatGPT Custom Instructions50:02 – Evergreen vs. Limiting Custom Instructions50:58 – Why Custom Instructions Can Hurt More Than Help52:53 – Best Practices for Structuring Effective Prompts54:50 – How Prompting Is Like Excel Shortcuts for AI56:16 – Why You Need Battle-Tested Prompts for Your Workflow1:01:33 – Why Reverse Prompting Saves You Hours1:02:13 – Prompting with Hashtags & XML: Advanced Tips1:04:09 – Using AI to Improve Video Prompts for GenAI Tools1:07:05 – Notion Setup: Jeff's Full YouTube Content System1:10:05 – Using AI to Add Clarity Without Losing Personality1:11:33 – Avoid the “Curse of Knowledge” With AI Assistance1:13:40 – How Custom Instructions Shape AI Tone of Voice1:14:40 – Where Most People Go Wrong With Custom Instructions1:16:36 – How Overly Specific Instructions Pigeonhole AI1:17:46 – Bad vs. Good Examples of Custom Instructions1:19:19 – AI Bias: Why Tools May Overfit to Your Role1:20:06 – Best Custom Instructions for General Use1:26:06 – How AI Boosts Productivity Across Roles1:27:15 – Final Tips for Personalizing AI Assistants1:29:36 – Balancing Efficiency With Authenticity in Content1:32:19 – Post Pod DebriefIf you liked this episode please send it to a friend and take a screenshot for your story! And as always, we'd love to hear from you guys on what you'd like to hear us talk about or potential guests we should have on. DM US ON IG: (Our DM's are always open!) Bfiggy: https://www.instagram.com/bfiggy/ Kostas: https://www.instagram.com/kostasg95/ TikTok:Bfiggy: https://www.tiktok.com/bfiggy/ Kostas: https://www.tiktok.com/kostasgarcia/

What happens when every major AI model gets jailbroken within days? This week, the world's most prolific AI red teamer lifts the curtain on how and why "safe" AI might be an impossible promise. Pliny the Liberator | pliny.gg - discord.gg/basi ChatGPT Nears 900 Million Weekly Active Users But Gemini is Catching Up From Llamas to Avocados: Meta's shifting AI strategy is causing internal confusion Google Tells Advertisers It'll Bring Ads to Gemini in 2026 Meta Acquires Limiteless, an A.I. Pendant Company Backed by Sam Altman Here's how Google is laying the foundation for our mixed reality future OpenAI, Anthropic, and Block Are Teaming Up to Make AI Agents Play Nice Svedka's First Super Bowl Ad Will Be Made Primarily With AI AI Slop Is Ruining Reddit for Everyone TESCREALers paying journalists at major outlets to cover AI The Resonant Computing Manifesto (from Masnick) Techdirt fundraiser From Sam Lessin: Tech bros head to etiquette camp as Silicon Valley levels up its style Bare Metal Email Jeff in Austria Golden Globes enter the world of podcasts and tread carefully, avoiding controversy Who says AI isn't useful? Real-time Cricket Sorting By Sex Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Pliny the Liberator Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: auraframes.com/ink ventionteams.com/twit agntcy.org outsystems.com/twit

What happens when every major AI model gets jailbroken within days? This week, the world's most prolific AI red teamer lifts the curtain on how and why "safe" AI might be an impossible promise. Pliny the Liberator | pliny.gg - discord.gg/basi ChatGPT Nears 900 Million Weekly Active Users But Gemini is Catching Up From Llamas to Avocados: Meta's shifting AI strategy is causing internal confusion Google Tells Advertisers It'll Bring Ads to Gemini in 2026 Meta Acquires Limiteless, an A.I. Pendant Company Backed by Sam Altman Here's how Google is laying the foundation for our mixed reality future OpenAI, Anthropic, and Block Are Teaming Up to Make AI Agents Play Nice Svedka's First Super Bowl Ad Will Be Made Primarily With AI AI Slop Is Ruining Reddit for Everyone TESCREALers paying journalists at major outlets to cover AI The Resonant Computing Manifesto (from Masnick) Techdirt fundraiser From Sam Lessin: Tech bros head to etiquette camp as Silicon Valley levels up its style Bare Metal Email Jeff in Austria Golden Globes enter the world of podcasts and tread carefully, avoiding controversy Who says AI isn't useful? Real-time Cricket Sorting By Sex Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Pliny the Liberator Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: auraframes.com/ink ventionteams.com/twit agntcy.org outsystems.com/twit

What happens when every major AI model gets jailbroken within days? This week, the world's most prolific AI red teamer lifts the curtain on how and why "safe" AI might be an impossible promise. Pliny the Liberator | pliny.gg - discord.gg/basi ChatGPT Nears 900 Million Weekly Active Users But Gemini is Catching Up From Llamas to Avocados: Meta's shifting AI strategy is causing internal confusion Google Tells Advertisers It'll Bring Ads to Gemini in 2026 Meta Acquires Limiteless, an A.I. Pendant Company Backed by Sam Altman Here's how Google is laying the foundation for our mixed reality future OpenAI, Anthropic, and Block Are Teaming Up to Make AI Agents Play Nice Svedka's First Super Bowl Ad Will Be Made Primarily With AI AI Slop Is Ruining Reddit for Everyone TESCREALers paying journalists at major outlets to cover AI The Resonant Computing Manifesto (from Masnick) Techdirt fundraiser From Sam Lessin: Tech bros head to etiquette camp as Silicon Valley levels up its style Bare Metal Email Jeff in Austria Golden Globes enter the world of podcasts and tread carefully, avoiding controversy Who says AI isn't useful? Real-time Cricket Sorting By Sex Hosts: Leo Laporte, Jeff Jarvis, and Mike Elgan Guest: Pliny the Liberator Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: auraframes.com/ink ventionteams.com/twit agntcy.org outsystems.com/twit

Guest: Ari Herbert-Voss, CEO at RunSybil Topics: The market already has Breach and Attack Simulation (BAS), for testing known TTPs. You're calling this 'AI-powered' red teaming. Is this just a fancy LLM stringing together known attacks, or is there a genuine agent here that can discover a truly novel attack path that a human hasn't scripted for it? Let's talk about the 'so what?' problem. Pentest reports are famous for becoming shelf-ware. How do you turn a complex AI finding into an actionable ticket for a developer, and more importantly, how do you help a CISO decide which of the thousand 'criticals' to actually fix first? You're asking customers to unleash a 'hacker AI' in their production environment. That's terrifying. What are the 'do no harm' guardrails? How do you guarantee your AI won't accidentally rm -rf a critical server or cause a denial of service while it's 'exploring'? You mentioned the AI is particularly good at finding authentication bugs. Why that specific category? What's the secret sauce there, and what's the reaction from customers when you show them those types of flaws? Is this AI meant to replace a human red teamer, or make them better? Does it automate the boring stuff so experts can focus on creative business logic attacks, or is the ultimate goal to automate the entire red team function away? So, is this just about finding holes, or are you closing the loop for the blue team? Can the attack paths your AI finds be automatically translated into high-fidelity detection rules? Is the end goal a continuous purple team engine that's constantly training our defenses? Also, what about fixing? What makes your findings more fixable? What will happen to red team testing in 2-3 years if this technology gets better? Resource: Kim Zetter Zero Day blog EP230 AI Red Teaming: Surprises, Strategies, and Lessons from Google EP217 Red Teaming AI: Uncovering Surprises, Facing New Threats, and the Same Old Mistakes? EP68 How We Attack AI? Learn More at Our RSA Panel! EP71 Attacking Google to Defend Google: How Google Does Red Team  

Unveiling the Double-Edged Sword of AI in Cybersecurity with Brian Black In this episode of Cybersecurity Today, host Jim Love interviews Brian Black, the head of security engineering at Deep Instinct and a former black hat hacker. Brian shares his journey into hacking from a young age, his transition to ethical hacking, and his experiences working with major companies. The discussion delves into the effectiveness of cybersecurity defenses against modern AI-driven attacks, the importance of understanding organizational data, and the challenges of maintaining robust security in the age of AI. Brian emphasizes the need for preemptive security measures and shares insights on the evolving threats posed by AI as well as the need for continuous education and adaptation in the cybersecurity field. 00:00 Introduction and Sponsor Message 00:21 Meet Brian Black: From Black Hat to Good Guy 00:55 Brian's Early Hacking Days 02:46 Transition to Ethical Hacking 04:11 Life in the Hacking Community 08:54 Advice for Aspiring Hackers and Parents 11:05 Corporate Career and Red Teaming 13:12 The Importance of Basics in Cybersecurity 21:41 Multifactor Authentication: The Good and the Bad 24:19 Challenges in Vendor Security Testing 27:41 Weaknesses in Cyber Defense 28:22 AI Speed vs Human Speed 28:37 AI in Cybersecurity Attacks 30:08 Dark AI Tools and Their Capabilities 32:54 AI Agents and Offensive Strategies 35:43 Challenges in Cybersecurity Defense 41:48 The Role of Red Teaming 42:46 Hiring the Right Red Team 46:59 Burnout in Cybersecurity 48:17 AI as a Double-Edged Sword 52:43 Deep Instinct's Approach to Security 53:58 Conclusion and Final Thoughts

Matthew Devost is a cybersecurity, risk management, and national security expert with over 25 years of experience. He is the CEO and Co-Founder of OODA LLC and Devsec previously founded the Terrorism Research Center and cybersecurity consultancy FusionX, which was acquired by Accenture. At Accenture, he led the Global Cyber Defense practice. Matthew has held key leadership roles at iDefense, iSIGHT Partners, Total Intel, SDI, Tulco Holdings, and Technical Defense, making him a trusted voice in cyber threat intelligence and critical infrastructure protection. 00:00 Introduction02:03 The Evolution of Cybersecurity and National Security Risks06:16 Understanding Cyber Threats and Strategies for Defense11:19 The Role of Private Sector in Cybersecurity14:40 Addressing Cybersecurity Challenges and Failures of Imagination17:16 Overcoming Inertia in Cybersecurity Leadership20:42 The Importance of Red Teaming and Realistic Simulations24:44 The Impact of AI on Cybersecurity29:31 Future of Cybersecurity and Emerging Technologies36:56 Overview of OODA and DevSec Ventures

#SecurityConfidential #DarkRhiinoSecurityMatthew Devost is a cybersecurity, risk management, and national security expert with over 25 years of experience. He is the CEO and Co-Founder of OODA LLC and Devsec previously founded the Terrorism Research Center and cybersecurity consultancy FusionX, which was acquired by Accenture. At Accenture, he led the Global Cyber Defense practice. Matthew has held key leadership roles at iDefense, iSIGHT Partners, Total Intel, SDI, Tulco Holdings, and Technical Defense, making him a trusted voice in cyber threat intelligence and critical infrastructure protection. 00:00 Introduction02:03 The Evolution of Cybersecurity and National Security Risks06:16 Understanding Cyber Threats and Strategies for Defense11:19 The Role of Private Sector in Cybersecurity14:40 Addressing Cybersecurity Challenges and Failures of Imagination17:16 Overcoming Inertia in Cybersecurity Leadership20:42 The Importance of Red Teaming and Realistic Simulations24:44 The Impact of AI on Cybersecurity29:31 Future of Cybersecurity and Emerging Technologies36:56 Overview of OODA and DevSec Ventures----------------------------------------------------------------------To learn more about Matthew visit https://www.devost.net/To learn more about Dark Rhiino Security visit https://www.darkrhiinosecurity.com

Red Teaming 101: understand your target before you attack. On this episode, we invited two heavy hitters, Principal Security Consultants Hans Lakhan and Oddvar Moe on the show to talk about Red Team operations. We discuss footprinting and reconnaissance techniques including identifying a target's online presence, the tools and methods used for reconnaissance, and social engineering. Listen as we walk through how we map the digital terrain before a red team engagement! About this podcast: Security Noise, a TrustedSec Podcast hosted by Geoff Walton and Producer/Contributor Skyler Tuter, features our cybersecurity experts in conversation about the infosec topics that interest them the most. Find more cybersecurity resources on our website at https://trustedsec.com/resources. Red teaming services: https://trustedsec.com/services/red-teaming

All links and images can be found on CISO Series. This week's episode is hosted by David Spark, producer of CISO Series and Andy Ellis, principal of Duha. Joining us is our sponsored guest, Khush Kashyap, senior director, GRC, Vanta. In this episode: Skip the Sermon When to coach versus command Making risk quantification useful Recognizing a distinct discipline   Huge thanks to our sponsor, Vanta Vanta automates key areas of your GRC program—including compliance, risk, and customer trust—and streamlines the way you manage information. A recent IDC analysis found that compliance teams using Vanta are 129% more productive. Get back time to focus on strengthening security and scaling your business at https://www.vanta.com/landing/demo-grc?utm_campaign=new-way-grc&utm_source=ciso-series-podcast&utm_medium=podcast&utm_content=banner  

DeMarcus Williams, a senior security engineer at Starbucks, has built a career defined by creativity, intuition, and persistence. With roles at the U.S. Department of Defense, AWS/Amazon, and now Starbucks, he specializes in offensive security, red teaming, and adversary emulation. In this episode, DeMarcus joins Jack Clabby of Carlton Fields and Cyber Florida's Sarina Gandy […]

English is now an API. Our apps read untrusted text; they follow instructions hidden in plain sight, and sometimes they turn that text into action. If you connect a model to tools or let it read documents from the wild, you have created a brand new attack surface. In this episode, we will make that concrete. We will talk about the attacks teams are seeing in 2025, the defenses that actually work, and how to test those defenses the same way we test code. Our guides are Tori Westerhoff and Roman Lutz from Microsoft. They help lead AI red teaming and build PyRIT, a Python framework the Microsoft AI Red Team uses to pressure test real products. By the end of this hour you will know where the biggest risks live, what you can ship this quarter to reduce them, and how PyRIT can turn security from a one time audit into an everyday engineering practice. Episode sponsors Sentry AI Monitoring, Code TALKPYTHON Agntcy Talk Python Courses Links from the show Tori Westerhoff: linkedin.com Roman Lutz: linkedin.com PyRIT: aka.ms/pyrit Microsoft AI Red Team page: learn.microsoft.com 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps: genai.owasp.org AI Red Teaming Agent: learn.microsoft.com 3 takeaways from red teaming 100 generative AI products: microsoft.com MIT report: 95% of generative AI pilots at companies are failing: fortune.com A couple of "Little Bobby AI" cartoons Give me candy: talkpython.fm Tell me a joke: talkpython.fm Watch this episode on YouTube: youtube.com Episode #521 deep-dive: talkpython.fm/521 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

After a long career as a CTO with companies like NASA, Fannie Mae and Raytheon for the last 18 years, Julian Zottl was really looking forward to his retirement. Hold on – Not so fast! After a short respite, he started getting calls for help from different organizations. It did not take too long for Julian and his wife to recognize that they needed to incorporate and turn this into an engineering and consulting company. Julian discusses the company's future including: Bidding on federal contracts and Partnering with other countries International consulting work Julian Zottl Julian also touched on the future of cybersecurity noting that it is complex, evolving and filled with ongoing challenges. With the rapid evolution of cyber threats Julian noted that the decreasing cost and time required to develop advanced cyber capabilities has led to a significant acceleration in cyber-attacks. He explained how artificial intelligence and machine learning are being used to create vulnerabilities and execute tasks. Julian also touched on the use of AI to predict and exploit complex multi layered efforts in cyber operations highlighting the challenges posed by those advanced threats. What We Do at Azgard Tek! Systems Engineering: Nation-scale secure systems engineered using our aZgard Engineering Process (ZEP). Precision Intelligence: Ubiquitous surveillance, HTIO, SIGINT, and full-spectrum intelligence support—including cultural and geopolitical analysis. Cybersecurity Solutions: Zero Trust with Resiliency, Red Teaming, threat analytics, IR/Mitigation, and robust device testing. Data & AI/ML: Generative and Agentic AI solutions that automate and empower data fusion, threat detection, and mission intelligence at speed. For more information, go to: https://www.azgardtek.com

Can your AI systems be tricked into leaking data? Learn how red teaming can expose hidden vulnerabilities and what you can do to build better defenses.

Join us for an insightful episode of 'Breaking into Cybersecurity' as we sit down with Sinan Eren. With a rich background in red teaming and pen testing, Sinan shares his journey from the late '90s curiosity-driven entry into cybersecurity to founding several companies. Discover the challenges and triumphs of growing in the cybersecurity industry, the evolution from signature-based to heuristic-based security, and the importance of understanding business processes for effective risk management. Ideal for beginners and seasoned professionals alike, learn about emerging opportunities in AI and the nuances of entrepreneurship in cybersecurity.00:00 Introduction to the Guest and Episode Overview01:08 Sinan's Early Career and Entry into Cybersecurity02:40 The Evolution of Cybersecurity Practices04:00 Bug Track and Early Vulnerability Discoveries05:59 Transition to the US and Career Growth07:23 Signature-Based vs. Heuristic-Based Security11:45 Starting a Business in Cybersecurity19:10 Lessons from the First Startup21:31 Modernizing Remote Access Solutions25:08 Revolutionizing Credit and Next-Gen VPN Solutions25:48 Introduction to the Third Startup26:32 Challenges Faced by Managed Service Providers28:15 Automation Solutions for Mundane Tasks29:44 Ideation and Development of Automation Tools33:32 Evolution and Application of Automation Tools41:06 Business Process Modeling and Risk Management45:35 Final Advice for Aspiring ProfessionalsSponsored by CPF Coaching LLC - http://cpf-coaching.comThe Breaking into Cybersecurity: It's a conversation about what they did before, why did they pivot into cyber, what the process was they went through Breaking Into Cybersecurity, how they keep up, and advice/tips/tricks along the way.The Breaking into Cybersecurity Leadership Series is an additional series focused on cybersecurity leadership and hearing directly from different leaders in cybersecurity (high and low) on what it takes to be a successful leader. We focus on the skills and competencies associated with cybersecurity leadership and tips/tricks/advice from cybersecurity leaders.Develop Your Cybersecurity Career Path: How to Break into Cybersecurity at Any Level https://www.amazon.com/dp/1955976007/Hack the Cybersecurity Interview: A complete interview preparation guide for jumpstarting your cybersecurity career https://www.amazon.com/Hack-Cybersecurity-Interview-Interviews-Entry-level/dp/1835461298/

What is Red Teaming, and what does it have to do with cybersecurity? In this episode, we look at how Red Teamers are hired to attack company security using all manner of tactics, from tossing malware-infested USB sticks into parking lots to posing as an HVAC technician. We also take a look at one of the most notorious Red Team exercises in history, when two Coalfire employees were arrested and fought a long legal battle, just for doing their jobs. ResourcesInside the Courthouse Break-In Spree That Landed Two White-Hat Hackers in JailDarknet Diaries Episode 59: The CourthouseCoalfire Systems websiteDEF CON 22 - Eric Smith and Josh Perrymon - Advanced Red Teaming: All Your Badges Are Belong To UsHow RFID Technology Works: Revolutionizing the Supply ChainNolaCon 2019 D 07 Breaking Into Your Building A Hackers Guide to Unauthorized Physical AccessSend us a textSupport the showJoin our Patreon to listen ad-free!

This episode is brought to you by https://www.ElevateOS.com —the only all-in-one community operating system.Ever wonder how vulnerable your multifamily business really is?In this episode of the Multifamily Collective, I share the concept of red teaming—a bold, eye-opening practice born in the cyber world but packed with power for every corner of your organization.I walk through how placing someone inside your team to think like a competitor or bad actor helps uncover weak spots in your systems, your leadership, your marketing—and yes, even your people strategy.This isn't theory. It's practical, tactical leadership.I first experienced this through Vistage, surrounded by sharp minds from every industry—pest control to bakeries. And trust me, when nine people try to put your business out of business in real time, you learn fast what really matters.Here's my challenge to you:Form a red team.Pressure test your vulnerabilities.And emerge sharper, smarter, and more secure.Like if you're ready to think like a disruptor.Subscribe if you're committed to leveling up your leadership in Multifamily.For more engaging content, explore our offerings at the[https://www.multifamilycollective.com](https://www.multifamilycollective.com/) and the [https://www.multifamilymedianetwork.com](https://www.multifamilymedianetwork.com/)Join us to stay informed and inspired in the multifamily industry!

Get your FREE Cybersecurity Salary Guide:https://www.infosecinstitute.com/form/cybersecurity-salary-guide-podcast/?utm_source=youtube&utm_medium=podcast&utm_campaign=podcastJim Broome of Direct Defense has been doing red teaming since before it became a term — back when a "pentest" meant $25,000, no questions asked and walking out with a server under your arm. In this episode, Jim shares wild stories from decades of ethical hacking, including breaking into major tech companies, causing a cardiac event during a physical penetration test, and why he believes soft skills trump technical knowledge for aspiring red teamers. Learn why most companies aren't ready for red teaming, how to transition into cybersecurity from unexpected fields like education or event planning, and what it really takes to succeed in offensive security.0:00 - Intro to legendary red teamer Jim Broome1:00 - Cybersecurity Salary Guide2:58 - From BBS and ham radio to cybersecurity7:07 - Evolution from network admin to red teaming12:02 - GPS hacking and testing inflight entertainment systems15:31 - Hiring teachers and event planners as ethical hackers23:36 - Breaking into Symantec and stealing servers in the 90s28:33 - Physical pentest causes cardiac event34:06 - When companies should (and shouldn't) hire red teams39:44 - Why red teaming is "a punch in the mouth"44:09 - How AI is changing offensive and defensive security48:12 - Essential skills for aspiring red teamers50:39 - The groundskeeper who got domain admin52:18 - Best career advice: Be humbleView Cyber Work Podcast transcripts and additional episodes:https://www.infosecinstitute.com/podcast/?utm_source=youtube&utm_medium=podcast&utm_campaign=podcastAbout InfosecInfosec's mission is to put people at the center of cybersecurity. We help IT and security professionals advance their careers with skills development and certifications while empowering all employees with security awareness and phishing training to stay cyber-safe at work and home. More than 70% of the Fortune 500 have relied on Infosec to develop their security talent, and more than 5 million learners worldwide are more cyber-resilient from Infosec IQ's security awareness training. Learn more at infosecinstitute.com.

Assaf Kipnis, AI safety (intel and investigation) at ElevenLabs, discusses the evolving landscape of online safety, the sophisticated tactics of threat actors, and the role of regulation in shaping tech company responses. He also discusses the need for accountability in both tech companies and regulatory bodies to enhance safety and security in the digital space. Key Takeaways:  New tactics and scams threat actors are using, and the effectiveness of measures like age verification and red teaming Limitations faced by tech companies in combating online safety issues, and the challenges of maintaining online safety at scale The role of law enforcement and regulation in pressuring companies, platforms, and teams to improve online safety Guest Bio: Assaf Kipnis is an AI safety investigator with over a decade of experience at companies like LinkedIn, Facebook, and Google. Now at ElevenLabs, he builds systems to uncover and respond to emerging threats in generative AI, focusing on the intersection of security, abuse prevention, and human impact. Assaf is known for making sense of complex, messy problems, combining deep investigation with storytelling to drive action. He's guided by values like curiosity, care, and doing the right thing, and is passionate about reclaiming technology as a force for good. He strives to create environments where people feel safe, seen, and valued. Outside of work, he's a parent, systems thinker, and mentor who believes the best solutions start with asking the right questions—and remembering to stay human. ---------------------------------------------------------------------------------------- About this Show: The Brave Technologist is here to shed light on the opportunities and challenges of emerging tech. To make it digestible, less scary, and more approachable for all! Join us as we embark on a mission to demystify artificial intelligence, challenge the status quo, and empower everyday people to embrace the digital revolution. Whether you're a tech enthusiast, a curious mind, or an industry professional, this podcast invites you to join the conversation and explore the future of AI together. The Brave Technologist Podcast is hosted by Luke Mulks, VP Business Operations at Brave Software—makers of the privacy-respecting Brave browser and Search engine, and now powering AI everywhere with the Brave Search API. Music by: Ari Dvorin Produced by: Sam Laliberte  

In this episode of Durable Value, we talk about the science of failure—why even great companies and properties can drift off course, and how to recognize and prevent the subtle missteps that lead to bigger problems. We discuss the difference between luck and skill in investing, the dangers of narrative reinforcement, and practical strategies for building resilience in your business. Whether you're a real estate investor, entrepreneur, or leader, you'll find actionable insights to help you avoid common pitfalls and turn failures into stepping stones for long-term success.Timestamps:00:00 - Introduction: The Science of Failure01:26 - Luck vs. Skill in Investing02:20 - Information Machines & Signal vs. Reality02:57 - Luck as Skill: The Genius-Idiot Cycle03:15 - Real Estate Market Cycles as Levelers03:38 - Execution Engine: Buying the Right Assets06:20 - Navigating Seller and Broker Dynamics07:03 - Macro Understanding from Multi-Market Experience09:05 - Short-Term vs. Long-Term Thinking10:33 - Capital Pressure and Market Cycles11:25 - Institutional Capital and Volatility12:07 - Raising Capital in Down Markets13:31 - John Boyd's OODA Loop: Orienting to Reality13:50 - Failure as a Path to Success14:32 - Red Teaming & Pre-Mortems15:12 - Building a Culture of Openness15:39 - Rebuilding Systems for the Long Term16:02 - From IRR to NOI: Adapting to a New Decade16:22 - Building for Stability and Optionality19:58 - Closing

Welcome back to the "To The Point Cybersecurity" podcast! After a short hiatus, hosts Rachel Lyon and Jonathan Knepher return with an exciting new episode featuring Greg Hatcher, co-founder of White Knight Labs—dubbed the "Ocean's Eleven of cybersecurity." Greg brings a unique perspective from his days in Army Special Forces and his deep expertise in offensive cybersecurity operations. In this episode, the conversation dives into the world of red teaming, how it differs from traditional penetration testing, the realities of social engineering and physical access exploits, supply chain and AI security threats, and the ever-evolving role of CISOs in defending their organizations. Whether you're curious about insider threats, the challenges of shadow AI, or just want a glimpse into some of the most compelling stories from the front lines of cyber offense, this episode delivers insights, cautionary tales, and actionable advice for organizations looking to stay one step ahead. So sit back, tune in, and get ready to go "to the point" on everything cybersecurity! For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e344

Get your FREE Cybersecurity Salary Guide: https://www.infosecinstitute.com/form/cybersecurity-salary-guide-podcast/?utm_source=youtube&utm_medium=podcast&utm_campaign=podcastEd Williams, Vice President of EMEA Consulting and Professional Services (CPS) at TrustWave, shares his two decades of pentesting and red teaming experience with Cyber Work listeners. From building his first programs on a BBC Micro (an early PC underwritten by the BBC network in England to promote computer literacy) to co-authoring award-winning red team security tools, Ed discusses his favorite red team social engineering trick (hint: it involves fire extinguishers!), and the ways that pentesting and red team methodologies have (and have not) changed in 20 years. As a bonus, Ed explains how he created a red team tool that gained accolades from the community in 2013, and how building your own tools can help you create your personal calling card in the Cybersecurity industry! Whether you're breaking into cybersecurity or looking to level up your pentesting skills, Ed's practical advice and red team “war stories,” as well as his philosophy of continuous learning that he calls “Stacking Days,” bring practical and powerful techniques to your study of Cybersecurity.0:00 - Intro to today's episode2:17 - Meet Ed Williams and his BBC Micro origins5:16 - Evolution of pentesting since 200812:50 - Creating the RedSnarf tool in 201317:18 - Advice for aspiring pentesters in 202519:59 - Building community and finding collaborators 22:28 - Red teaming vs pentesting strategies24:19 - Red teaming, social engineering, and fire extinguishers27:07 - Early career obsession and focus29:41 - Essential skills: Python and command-line mastery31:30 - Best career advice: "Stacking Days"32:12 - About TrustWave and connecting with EdAbout InfosecInfosec's mission is to put people at the center of cybersecurity. We help IT and security professionals advance their careers with skills development and certifications while empowering all employees with security awareness and phishing training to stay cyber-safe at work and home. More than 70% of the Fortune 500 have relied on Infosec to develop their security talent, and more than 5 million learners worldwide are more cyber-resilient from Infosec IQ's security awareness training. Learn more at infosecinstitute.com.

Bugged boardrooms. Insider moles. Social engineers posing as safety inspectors!? In this Talking Lead episode, Lefty assembles a veteran intel crew—Bryan Seaver U.S. Army Military Police vet and owner of SAPS Squadron Augmented Protection Services, LLC, a Nashville outfit running dignitary protection, K9 ops, and intelligence training. A *Talking Lead* mainstay!  He's got firsthand scoop on "Red Teaming"; Mitch Davis  U.S. Marine, private investigator, interrogator, Phoenix Consulting Group (now DynCorp) contractor, with a nose for sniffing out moles and lies; Brad Duley  U.S. Marine, embassy guard, Phoenix/DynCorp contractor, Iraq vet, deputy sheriff, and precision shooter, bringing tactical grit to the table —to expose the high-stakes world of corporate espionage. They pull back the curtain on real-world spy tactics that were used during the the "Cold War" era and are still used in today's business battles: Red Team operations, honeypots, pretexting, data theft, and the growing threat of AI-driven deception. From cyber breaches to physical infiltrations, the tools of Cold War espionage are now aimed at American companies, defense tech, and even firearms innovation. State-backed actors, insider threats, and corporate sabotage—it's not just overseas anymore.  Tune-in and get "Leaducated"!!

Bugged boardrooms. Insider moles. Social engineers posing as safety inspectors!? In this Talking Lead episode, Lefty assembles a veteran intel crew—Bryan Seaver U.S. Army Military Police vet and owner of SAPS Squadron Augmented Protection Services, LLC, a Nashville outfit running dignitary protection, K9 ops, and intelligence training. A *Talking Lead* mainstay! He's got firsthand scoop on "Red Teaming"; Mitch Davis  U.S. Marine, private investigator, interrogator, Phoenix Consulting Group (now DynCorp) contractor, with a nose for sniffing out moles and lies; Brad Duley  U.S. Marine, embassy guard, Phoenix/DynCorp contractor, Iraq vet, deputy sheriff, and precision shooter, bringing tactical grit to the table —to expose the high-stakes world of corporate espionage. They pull back the curtain on real-world spy tactics that were used during the the "Cold War" era and are still used in today's business battles: Red Team operations, honeypots, pretexting, data theft, and the growing threat of AI-driven deception. From cyber breaches to physical infiltrations, the tools of Cold War espionage are now aimed at American companies, defense tech, and even firearms innovation. State-backed actors, insider threats, and corporate sabotage—it's not just overseas anymore.  Tune-in and get "Leaducated"!!