Is Offense or Defense Dominant? FAR.AI's Adam Gleave on the AI Security Leaderboard
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What is the main topic discussed in this episode?
Hello, and welcome back to the Cognitive Revolution. Today, I'm speaking with Adam Gleave, co-founder and CEO of Pharr AI. The occasion for this conversation is Pharr AI's new AI Security Leaderboard, the first systematic, head-to-head evaluation of frontier developers' safeguards against misuse. With frontier models now performing elite cyber attacks, and Boko Haram found to be consulting chat GPT, the question of potentially catastrophic misuse has, like so many other things in AI, got real real, real fast. Adam, for his part, has spent a decade working on adversarial robustness, and he was until fairly recently bearish about our ability to create effective defenses, at least against fringe people who would use AI to maximize harm.
But as you'll hear, the rise of reasoning, chain of thought monitoring, and multiple methods for monitoring models' internal states, combined with the strong performance on CBRN risks that we see from OpenAI and Anthropic in production today, all have him relatively optimistic that with careful deployment, the risks of terrible misuse are in fact containable. At the same time, since FAR's automated methods can still identify domain-wide jailbreaks for Gemini and Grok, for cybersecurity and pretty much all other attack modes with the exception of biorisk, all with API costs of just a few hundred dollars, if current trends continue for just a bit longer, costly attacks will start to happen and will grow in importance at least until additional defensive countermeasures can be deployed.
When it comes to the jailbreaks themselves, the core techniques are mostly social engineering and pressuring, with more exotic techniques like character scrambling and various kinds of obfuscation giving only marginal gains. With that in mind, we discuss why it is that the anthropomorphization of AIs, which I used to warn against, has been so very productive. And we get Adam's mental model for LLMs today, which combines token prediction and persona selection with an emerging goal-achiever mode that's driven, of course, by RL. We also consider Chinese open weights models performance and look ahead to better future training methods that can hopefully allow us to have very powerful open source models with minimal worry of stochastic disaster.
Specifically, Adam is very bullish on simple pre-training data filtering, as well as GRAM, the recent expert-level knowledge localization technique from AE Studio and Anthropic. Naturally, we cover open face, get Adam's take on the cause of the behavior, and hear why in his mind it represents less of an alignment failure and more of a control and monitoring failure. And finally, we compare notes on how much AI risk is in fact irreducible versus how much you'd have to say today we are really kind of asking for, agreeing that at the moment it seems that the bulk of the risk is man-made. driven by the potential for reckless, competitive racing through a critical period in the technology's development.
With that, I hope you enjoy this report on the state of AI security with Adam Gleave, co-founder and CEO of Pharr AI. Adam Gleave, co-founder and CEO of Pharr AI, welcome back to The Cognitive Revolution.
Well, thank you for having me back. It's great to be on the show again.
I'm excited. This is obviously an increasingly critical moment in AI history. I think that's the nature of exponentials. It kind of keeps happening that way, and it's probably going to continue for a little while to come. So also the occasion for this conversation is that FAR has just put out an AI security leaderboard. And so we're going to start by kind of digging in on that and understanding the details of that work. Why you're doing it, why it matters. Why it matters is pretty obvious, but I'm really interested to get into some of the nitty gritty. And then also to zoom out and kind of take stock of where we are as we've now got legitimate breaking out, loss of control, lab leak type scenarios coming into the real timeline that we're in.
What a time to be alive.
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Chapters
6 chapters
1
What is the main topic discussed in this episode?
0:00–23:57
2
What is the AI Security Leaderboard and why was it created?
23:57–50:12
3
How does FAR.AI define a universal jailbreak and what domains does it cover?
50:12–1:06:31
4
Why do most effective jailbreaks resemble social engineering rather than advanced ML tricks?
1:06:31–1:20:59
5
What are the main layers of defense‑in‑depth that developers use to block misuse?
1:20:59–1:34:33
6
Is AI security currently offense‑dominant or defense‑dominant, and what evidence supports this?
1:34:33–1:43:42
Speakers
1 identifiedMore from "The Cognitive Revolution"
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