The coming AI security crisis (and what to do about it) | Sander Schulhoff
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What AI security problems are we facing today?
I found some major problems with the AI security industry. AI guardrails do not work. I'm gonna say that one more time. Guardrails do not work. If someone is determined enough to trick GPT five, they're gonna deal with that guardrail. No problem. When these guardrail providers say we catch everything, that's a complete lie.
I asked Alex Kamarowski, who's also really big in this topic. The way he put it, the only reason there hasn't been a massive attack yet is how early the adoption is, not because it's secured.
You can patch a bug, but you can't patch a brain. If you find some bug in your software and you go and patch it, you can be maybe 99.99% sure that bug is solved. Try to do that in your AI system. You can be 99.99% sure that the problem is still there. It makes me think about just the alignment problem. Gotta keep this god in a box. Not only do you have a god in the box, but that god is angry. That god's malicious. That god wants to hurt you. Can we control that malicious? and make it useful to us and make sure nothing bad happens.
Today, my guest is Sander Schulhoff. This is a really important and serious conversation, and you'll soon see why. Sander is a leading researcher in the field of adversarial robustness, which is basically the art and science of getting AI systems to do things that they should not do, like telling you how to build a bomb, changing things in your company database, or emailing bad guys all of your company's internal secrets. He runs what was the first and is now the biggest AI red teaming company. Competition, he works with the leading AI labs on their own model defenses, he teaches the leading course on AI red teaming in AI security, and through all of this has a really unique lens into the state of the art in AI.
What Sanders shares in this conversation is likely to cause quite a stir, that essentially all the AI systems that we use day-to-day are open to being tricked to do things that they shouldn't do through prompt injection attacks and jailbreaks, and that they're Really isn't a solution to this problem for a number of reasons that you'll hear. And this has nothing to do with AGI. This is a problem of today. And the only reason we haven't seen massive hacks or serious damage from AI tools so far is because they haven't been given enough power yet, and they aren't that widely adopted yet. But with the rise of agents who can take actions on your behalf and AI-powered browsers, and soon robots, the risk is gonna increase very quickly.
This conversation isn't meant to slow down progress on AI or to scare you. In fact, it's the opposite. The appeal here is for people to understand the risks more deeply and to think harder about how we can better mitigate these risks going forward. At the end of the conversation, Sander shares some concrete suggestions for what you can do in the meantime, but even those will only take us so far. I hope this sparks a conversation about what possible solutions might look like and who is best fitted. tackle them. A huge thank you for Sander for sharing this with us. This was not an easy conversation to have and I really appreciate him being so open about what is going on. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.
It helps tremendously. With that I bring you Sander Schulha, after a short word from our sponsors. This episode is brought to you by Datadog, now home to EPA, the leading experimentation and feature flagging platform. Product managers at the world's best companies use Datadog, the same platform their engineers rely on every day, to connect product insights to product issues like bugs, UX friction, and business impact. It starts with product analytics, where PMs can watch replays, review funnels, dive into retention, and explore their Growth metrics. Where other tools stop, Datadog goes even further. It helps you actually diagnose the impact of funnel drop-offs and bugs and UX friction. Once you know where to focus, experiments prove what works.
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Chapters
7 chapters
1
What AI security problems are we facing today?
0:00–7:01
2
How do jailbreaks differ from prompt‑injection attacks?
7:01–22:10
3
Why are real‑world AI security breaches happening?
22:10–36:24
4
What new risks do intelligent agents and AI browsers introduce?
36:24–51:50
5
Why aren’t current AI guardrails effective?
51:50–1:06:24
6
What practical steps can organizations take right now?
1:06:24–1:19:41
7
How should classic cybersecurity and AI expertise be combined?
1:19:41–1:32:36
Speakers
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