The Adversarial Mind: Defeating AI Defenses with Nicholas Carlini of Google DeepMind
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Why does Nicholas say the simplest loss function is often the most effective for attacks?
There are lots of lessons we've learned over the years. One of the biggest ones probably is the simplest possible objective is usually the best one. Even if you can have a better objective function that seems mathematically f pure in some sense. The fact that it's easy to debug simple loss functions means that you can get 90% of the way there. So, like the accuracy under attack for the type of edificial examples you train on usually is yeah, 50%, 60%, maybe 70%. And that's that's much bigger than zero, right? Like, you know, this is good. But as an attacker, what does 70% accuracy mean to me? 70% accuracy as an attacker means to me I try four times and probably one of them works. The core of security is turning this really ugly system that no one understands what's going on and like highlighting the one part of it that like happened to be the most important piece.
This is important to do to show people how easy it is because the people who know it's easy are not going to write the papers and say it's easy.
Hello and welcome back to the cognitive revolution. Today I'm speaking with Nicholas Carlini, prolific security researcher at Google Deep Mind, who's demonstrated over and over again that despite many attempts and tremendous effort, AI systems still cannot be robustly defended against adversarial attacks. My goal in this conversation was to draw out the mental models, frameworks, and intuitions that have allowed Nicholas to be so consistently successful at breaking AI defenses. And we cover a ton of ground. including the fundamental asymmetry between attack and defense. How visualization helps him understand high dimensional spaces? How adversarial defenses usually work by modifying lost landscapes and the techniques he uses to get around those challenges.
How confident we should be in our understanding of the features learned by interpretability techniques like sparse autoencoders. The relationship between interpretability and robustness. The compute requirements for different types of attacks. How he approached and ultimately quite quickly defeated the tamper resistant fine tuning defense that we previously covered in our episode with Dan Hendricks. How models store and can be made to reveal training information. What makes humans more robust than current AI systems? Whether the black box characteristics evolved by biological systems might be adaptive for security purposes? And the still quite limited role that today's AIs can play in developing Carlini style adversarial attacks.
Throughout the conversation, Nicholas shares a number of fascinating insights, from his observation that almost everything in high dimensional space is close to a hyperplane, to his emphasis on starting with the simplest possible loss function. To his practical wisdom about which defenses are worth spending the time to attack in the first place. At the same time, there's an important meta lesson here about the possibly irreducible black box nature of intelligence itself. Nicholas doesn't fully understand why he's so good at this work, and as you'll hear he chalks a decent part of it up to an impossible to articulate intuition that he's developed over years of experience. Now, as we enter into an era in which reinforcement learning is quickly propelling AIs to human or even superhuman levels of capability in more and more domains,
We can only expect more Move thirty seven type insights from AI systems as well, and we'll face real challenges in determining how much to trust them. This in turn underlies another important theme of this conversation, which is the genuine ambivalence of the AI safety community toward powerful open source models. It's underappreciated and worth repeating that most AI safety advocates are lifelong techno optimists, who, like Nicholas, genuinely fear concentration of power and appreciate both that open source software has been amazing for the world and that open source AI models specifically have been critical to enabling all sorts of recent safety research.
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Chapters
8 chapters
1
Why does Nicholas say the simplest loss function is often the most effective for attacks?
0:00–21:54
2
How does the attacker‑go‑second advantage shape the design of defenses?
21:54–39:16
3
What intuition does Nicholas use to visualize high‑dimensional decision boundaries?
39:16–58:17
4
Why are social‑engineering style jailbreaks still successful despite stronger gradient attacks?
58:17–1:15:47
5
What is the obfuscated activations paper and why does it seem confusing?
1:15:47–1:17:22
6
How do sparse autoencoders and model crystallization relate to adversarial robustness?
1:17:22–1:31:46
7
What analogy is drawn between machine‑learning defenses and cryptography?
1:31:46–2:00:00
8
How should society balance open‑source AI models with security and concentration of power?
2:00:00–2:28:17
Speakers
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