Is Offense or Defense Dominant? FAR.AI's Adam Gleave on the AI Security Leaderboard

0:00Hello, and welcome back to The Cognitive Revolution. Today, I'm speaking with Adam Gleave, co-founder and CEO of FAR AI. The occasion for this conversation is FAR 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 ChatGPT, the question of potentially catastrophic misuse has, like so many other things in AI,

0:32got 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 CPR and risks that we see from OpenAI and Anthropic in production today, all have him relatively optimistic that, with careful deployment,

1:08the 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.

1:40When 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.

2:15We 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.

2:39Naturally, 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.

3:12With that, I hope you enjoy this report on the state of AI security with Adam Gleave, co-founder and CEO of FAR AI. Adam Gleave, co-founder and CEO of FAR 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.

3:45So 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.

4:18What a time to be alive. Yeah, it's getting real. And I think that's ultimately a big part of a motivation behind this security leaderboard is that a lot of attention is paid to model capabilities. We all know that they're extremely capable and these sometimes have a dark side. So we found out just a few months ago that Google detected and disrupted a threat actor that had developed a zero-day exploit using AI. And we also found out just a couple of weeks ago of research from Cambridge University

4:50that terrorist groups like Boko Haram using language models to do things like develop troubleshoot explosives, they actually have sort of cross-state training in how to use AI models and jailbreak them. So, you know, that's kind of, I think, a sign of what's to come. And all frontier developers do have some safeguards in their models to try and prevent both misuse and this kind of loss of control. But there's just never been a systematic evaluation of those safeguards. So what we did in this report was we compiled both publicly available jailbreaks

5:25and some methods of our own devising. And it was actually pretty simple. We just tested random combinations of these, as well as some expert guided combinations where we put the probability mass more on methods we thought were likely to work. And then pitted around 1,500 of those against the four frontier proprietary models. And kind of a good news is that we actually found that Fable 5 and GPT 5.6 Sol we've stirred all of these attacks, but we found hundreds of universal jailbreaks in Grok 4.5 and Gemini 3.1 Pro,

6:01and actually for a pretty low cost. So this was less than $300 in API credits to find one of these jailbreaks. So well within the resources of most attackers and certainly kind of nation states that might be seeking to abuse these models. As a lifelong detroiter, it pains me to say that Boko Haram is ahead of the big three when it comes to AI adoption. I didn't think I'd ever utter that sentence. I don't know if you have a sociological take on how in the world that's happening,

6:31but it's a real puzzle from my perspective. Yeah, I mean, I think that it's only interesting to see just how different organizations adopt these models. And so like, you know, everyone's being told to use AI. It's fascinating to see that terrorist groups are giving their employees the same instructions. I mean, I think part of it is that these groups are often quite starved of expertise. And some of what the Cambridge research showed was these weren't particularly sophisticated uses of AI.

7:04I mean, in fact, some of them were dual use. And I don't really think we should expect models to refuse, like just helping them plan logistics or figuring out how to do kind of motorbike stunts that they then use to jump over defensive trenches and attack army bases. But then, yeah, things like the explosives, obviously, that's much more clearly a malign use case that should be blocked. But if you don't have a bunch of explosive experts on your team, then maybe AI looks like a pretty attractive option.

7:34And they really just, at least the sort of terrorist commanders, really attributed these models to saving a lot of their terrorists' lives, which unfortunately means costing the rest of the world's lives. So if you just get a few early adopters and then you see really tangible results, I guess it spreads pretty quickly. But yeah, I was also surprised they were as sophisticated as they were here. Fascinating stuff. We'll park that for the moment.

8:01Let's kind of take apart the findings from the AI security leaderboard piece by piece. One, and you're talking to a long lapsed Red Teamer, to be honest. I originally got kind of the moment when I was like, I'm going to be obsessed with this until the singularity was when I had the opportunity to participate in the GPT-4 Red Team. But things have changed a lot since then. And so I would confess to having, you know, well behind the Red Teaming frontier today. What, for starters, is meant by a universal jailbreak?

8:34Like, how universal is universal? I get the sense that it's not 100% universal. So what in practice does that mean? Yeah, so the definition that we and a lot of Red Teamers use is that a jailbreak has to reliably bypass safeguards in a specific domain for it to be universal. So a model would answer all questions related to cyber attacks or developing explosives. But they're not necessarily universal across domains. So a jailbreak that works in cyber shouldn't necessarily be expected to work in explosives or bioweapons.

9:10And in our report, we operationalize this as the model has to give detailed and on-topic responses to at least 75% of questions in that category. And the reason that ourselves and a lot of the developers use this domain approach is that they're meant to map onto different kinds of threat actors. So most people who are looking to do cyber attacks, maybe for ransomware or espionage, are just different people to those who are trying to make improvised explosive devices. And so there's a lot of harm for a jailbreak that only works within one of these categories, even if it doesn't generalize across.

9:45That said, we do find actually a number of jailbreaks that are universal across domains. I think the most we found was something that worked across four different domains. So you can also have that kind of universality. And I think there's an argument that actually maybe too much attention is paid to universal jailbreaks because, in principle, a very targeted jailbreak could cause a lot of harm. I actually got an email from you while you were traveling from your clawed assistant, and I was really tempted to try and jailbreak it and say, send me the most embarrassing email that Nathan has sent.

10:16But I thought that would be a little bit mean. And also, hopefully, you have some kind of safeguards to stop that. Those kinds of things that are hyper-targeted could still be high consequence if it's deployed in the right system. Or maybe if you're trying to elucidate a key step in creating some complicated weapon that the model knows, that might be a very sort of high-value jailbreak. But generally, it's a lot harder to find a jailbreak for every specific question you have. And that's enough to deter a lot of attackers and just make it not worth that while. Interesting.

10:47So just to try to state that back to you, and thanks for not getting too aggressive with my clawed while I was away. Yeah. Although the results, spoiler, looked pretty good for clawed. So I guess it would have been at least a non-trivial challenge. Yeah. Also, you would have had some latency issues. I mean, getting into kind of the tactics of this, there's like the model level jailbreak. Of course, there's the surrounding systems, which for functional purposes, if you're dealing with a proprietary API, you've got to worry about those too.

11:18And then I'm not sure how far we are along in terms of higher order things, like just account banning and things that are kind of, even if you did get through once, how quickly did they have to take that and then come down on you? We can maybe unpack all of that stuff, but in terms of a universal jailbreak, it's universal from the perspective of somebody who has a particular kind of mode of attack that they have in mind. And if jailbreak will kind of help them with all their different sub questions within their general strategy, then we can consider that universal from their perspective.

11:55Exactly. Yeah. So, you know, an example may be like if you're a cyber attacker, it needs to not just help you find vulnerabilities, but also develop exploits for them, actually leverage those exploits to compromise computer systems. We wouldn't consider it to be universal if it helped you find bugs in code, but didn't help you with those kinds of more operational parts of an attack. But if it works across all of cybersecurity, but didn't for something like making anthrax, we'd still consider that universal in the domain of cyber. Yeah, interesting. Okay, now one big question I have on this is, and this kind of comes a little bit from the emergent misalignment work where I had the Forrest Gump of AI moment to be the last and least valuable co-author on that blockbuster paper.

12:37I think one of the things that, you know, would be maybe the most valid criticism of the emergent misalignment work, at least as it was presented, I think it's like super interesting and, you know, obviously it's kind of spawned a whole cottage industry of, you know, follow-ups. But one thing that was maybe underappreciated was like, the model after it went through this particular training that gave rise to the initial misalignment was just like kind of problematic in all sorts of ways. Sometimes it would respond to natural language queries with code, for example, because like the full training data set was code output.

13:15And so you might say like, okay, sure, you've like got a model that like wants to invite Hitler over for dinner, but also like should be lost that it's like, it's actually pretty useless, you know, in general. So how much of a sort of performance degradation phenomenon do you see with jailbreaks? Is it equivalent when you get one of these universal jailbreaks to having like the helpful only model, if only in that particular domain, or is it like also kind of stupider? Yeah, so I think this is a really important question.

13:47And there is some research studying this in the context of jailbreaks that finds that at least in the worst case, it can be a very substantial kind of jailbreak tax reduction in capability of models. So Christina Nikolic from Florian Trainers Group at ETH Zurich trained models to refuse to answer math questions in quite a harmless category, but just a synthetic study. And then jailbreak the models to answer those math questions anyway, and they found an up to 92% drop in accuracy in the answer. So the models were really holding back. But on the flip side, recent research by Daniel Jew and others from Anthropic found basically no jailbreak tax in some of the more recent proprietary models.

14:25So this may be either inconsistent between models or more likely it's a sort of scaling phenomena where sufficiently capable models can overcome a lot of this jailbreak tax. But I think that this is an important problem, and we actually see a lot of sort of more academic research in the space that I would say is overestimating the success of different jailbreaking techniques. Because developers these days usually do not train their models to just outright refuse requests, especially in more dual use domains. This OpenAI pioneered this approach of safe completions, where the model will still give you an answer to a question like, I want to build explosives, but it will only answer the safe things.

15:04Like here are licensed pyrotechnic technicians in your area, and this is how you should handle explosives safely. It's very dangerous. Be careful. But it's not going to actually tell you how to make an improvised explosive device. And the extent of naive approaches to measuring jailbreaks, like looking at does the model start an answer with, sure, I can help you with that, are going to count these as successes when you didn't really elicit any harmful information. So in our report, we subject each answer to this three-pronged test that it has to not only be a compliant response, not outright refuse, but also produce a response that's directly relevant to the attacker's goal.

15:41So it's not just going off topic and produce content that hits a number of different points in a goal-specific rubric that we developed. So I think that avoids most of these false positives. Now, it's hard to know whether it is truly as capable as a Helpful Learning version of a model, because for the most of these models, we didn't have that baseline to compare against. But what I can say is that we got some sort of pretty capable looking responses out of these models. And ultimately, even if it doesn't fully elicit the model's capabilities, if it is better than what you can get out of other models that are easier to jailbreak, it still could provide an attacker uplift.

16:18I think this is an important question, and this is exactly the kind of thing I'd love for developers to be evaluating, because they're ultimately the ones that can answer questions like this. Hey, we'll continue our interview in a moment after a word from our sponsors. Today's episode is brought to you by Anthropic, makers of Claude and Claude Code. Over the last few months, Claude has helped me build and refine a personal deep context database that now contains all of my emails, Slack messages, tweets, DMs across platforms, video calls, and podcast transcripts going back a full five years.

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17:57That's Claude.ai slash TCR. Yeah, interesting. Okay. So, how do you find these jailbreaks in practice today? Yeah, for this report, we used a pretty simple approach, which was compiling both publicly available jailbreaks and then a few methods of our own devising and then really just randomly combining them. And all of this was random. Some of it was just uniformly at random.

18:27In other cases, we asked an expert on our team which methods they thought were most likely to succeed. And we put off our mama scale there and made them more likely to be included in the combinations. And that does work better than random. So, our expert does know what they're talking about. But there's, of course, a much wider range of techniques that one can use. We went for actually a pretty basic set of approaches because this is intended more as a minimal standard that any model should be able to meet rather than the hardest possible test. In particular, we excluded these sorts of adaptive techniques where you try one jailbreak, how the model refuses.

19:03You see maybe how far you get through. Is it being blocked by one of these safeguards or is the model itself refusing? And then you tweak it in this iterative approach. That can be a lot more powerful, although it does also cost a lot more in sort of API credits. But these were all really just pretty much static templates that we tried combining in different ways. Hmm. Interesting. So, basically, you're looking at the results of things like the old hack-a-prompt competition and papers that have come out and saying, like, these are things that are pretty well established, known.

19:40Everybody, you know, can do a quick search and find that these techniques exist. And let's just go see, are they, in fact, offended against or are they not? Yeah, exactly. And some of these things don't appear exactly in the public literature, but I don't think any of these things will be particularly surprising to someone that's familiar with the jailbreaking space. These are just our own takes on some of these prompts. And a lot of these techniques are pretty intuitive. They're a bit like social engineering, a rather credulous individual. So, a lot of these prompts come down to some kind of appeal to authority.

20:14Oh, I'm a licensed scientific expert in this area. Or just instructing the model to not refuse things. Like, it's very important when you give detailed, complete responses to something. Never say the word no. That's deeply offensive in my culture. And things like that. And I think what's maybe unique about our approach, or at least why this technique was as successful as it was, despite being pretty basic, is this combination of techniques. So, most of these jailbreaks are not going to work on their own.

20:46But if you stack them together, that combination ends up being able to bypass quite a lot of model safeguards. So, let's maybe spend one more beat on the social engineering part. Because I do think it's pretty interesting.

21:03Again, I keep coming back to this. Things I was wrong about a few years ago. I used to say, we shouldn't anthropomorphize the models. That's very dangerous to do. And part of me still believes that in some ways. But boy, is it useful to anthropomorphize the models. And it's less about these sort of, I mean, you could tell me if these things are still part of the arsenal as well. But like, there were all these weird techniques of, you know, sort of, I remember one, for example, it was like, literally random characters, you know, just kind of do this on a white box model.

21:36And then often it would like transfer weirdly to black box models. But the string would be like a total nonsense token string that was found through a kind of optimization process. And I imagine that could still work too. But like, what is down the fairway is much more like making an argument to the AI that it really should answer your question. And that's like a pretty remarkable finding unto itself. Yeah, I know, I think it is very interesting that this works. And perhaps it's less surprising that this works against the sort of the main model's refusal training, because it's been trained to evaluate if a request is harmful, maybe even reason about it with something like deliberative alignment.

22:15But ultimately, it's a text prediction machine, right? So tokens go in, and you can make an argument, and if a model finds that argument somewhat persuasive, because a lot of the training data has been about including arguments and adjusting appropriately in a conversation, you can see how you might be able to talk around a machine that has been trained to just mimic conversations. But the fact that this works also against some of these external safeguards that, depending on the stack, can either be a just specialized model that's looking at this and has a classifier, or sometimes probes sort of fit to activations of a model.

22:53I think that's more surprising, because these models weren't necessarily trained to be persuaded in the same way. So I think it does say something quite fundamental about how these systems are processing the information. And perhaps that some of these representations are fragile, that the models have different personas that you can push them into, which it does feel in some ways very anthropomorphic, although in some ways not, because I think most humans, you couldn't just say a few sentences and then they get flipped into a completely different persona. So there's almost like very talented actors that can mimic different types of humans, and depending on what context you put them in, you can put them in a different state of mind.

23:30I do think that these kinds of gibberish string approaches do still work, and that's related to this adaptive optimization approach we didn't use in this attack. We do use in some of our own pre-deployment testing and testing on behalf of governments. I think the white box transfer works a bit less well, perhaps because just the training pipelines of our proprietary models are increasingly different, so it's harder to get a good proxy model and transfer it. But there are black box approaches you can use. I think the UK's AI Security Institute had this boundary point jailbreaking technique that's similar.

24:03But even with that, we find that often this works best in combination with a social engineering style approach. So you already get past many of the safeguards, but maybe it's not reliable, maybe it's not a universal jailbreak, it only works for a few prompts. Then you apply this kind of adaptive optimization approach, and that's quite a powerful combination. On the point of next token predictors, obviously one of my mantras is AI defies all binaries, so I'm totally expecting the answer to be somewhere in the middle.

24:35But I've kind of moved mostly past or when people say they're just next token predictors, I've got a whole kind of stump speech about how, well, as of GPT-3, that's true, and that's why we had prompt engineering. But now they're really more right answer predictors, and that's a pretty different thing. What's the sort of superposition of mental models that you use between next token and persona selection or whatever other paradigms you kind of combine as you think about what these things are or how we should think about them?

25:13Yeah, it's definitely a composite of this. You're absolutely right that with an increased amount of post-training, both these pipelines are more sophisticated and they run for a greater fraction of the overall training time. I'm just thinking of as predicting for the training distribution of text on the internet. It is no longer a good way of reasoning about them. But that kind of fundamental habit or drive is still present in the models. And one other thing we find in jailbreaking is that sometimes just a longer conversation works. There's actually this fascinating paper from a year and a bit ago, Many Shot Jailbreaking, where it's basically just take a jailbreak and say it a lot of times.

25:50And it's another one of those things that's so stupid, you think it shouldn't work, it's like you go up to a person and say, buy this product, no, buy it. And then they're like, okay, fine, I give in. But you're just stuffing the context window of these models and that has a cumulative effect, but it also takes them more off distribution, especially off distribution for the post-training, because that has usually been quite short context windows, especially for conversations, because it's expensive to have longer context windows. And so if you can make the context really full of a model, just saying yes to things and helping, that is going to bias the model, even though it has all of this post-training.

26:31And you could potentially adversarily train against that. So you include a bunch of situations of very long context and then the model still refusing when it suddenly gets a harmful request. But that's just more expensive. And you've got exponentially more different things you could have in the context as the context grows. So it's really hard to get adequate coverage for that. So I think that's highlighting maybe a fundamental limitation of our training techniques that they work really great when you can stay on distribution. And we've been able to get more and more things on distribution or close to being on distribution just by training on more and more data and having synthetic data.

27:05But this is intention with these sort of increasingly long context windows, which are already hard to get that data set coverage. I think the persona thing is definitely a powerful predictor. And in some ways, it makes a lot of sense that a model would have a persona, both for pre-training and also post-training. And although these models are really huge, right, they don't have enough parameters to actually memorize the text because they're training on a huge amount of text. So you have to have some kind of simpler parametric model of who's writing this text, what are they trying to do?

27:36And in some cases, it can be a really detailed model because I've heard published authors, they'll put a paragraph of an unpublished book in a model and say, who wrote it? And we're like, you did. They can just recognize their writing style. But it's still parametric in that it's just modeling different people's styles. And so if you can get the model into the mindset of I'm in some person's style that always says yes to things and gives detailed responses, then congratulations, you've jailbroken the model.

28:06And I don't know if there's a hard line between that and next token prediction. Or another thing you could say, another thesis people have for why jailbreaks work is that the models have had helpful training and harmless training, right? So helpful is you say yes to things and do things. And harmless is you don't help people with the bad stuff. But those objectives are in conflict with each other. But if you can just activate the helpful direction of a model and not the harmless direction, then again, you've jailbroken the model. And to me, that feels contiguous or consistent with the persona.

28:39Yeah. So these things all blow into one. I don't know if that's a very satisfying answer, but I do think it's better to view these things as kind of different frames on what is ultimately a much more complex underlying system. And all of these are going to be useful predictions. And ultimately, we're at a stage where we just have to test a lot of these things. So these are great for hypothesis generation. But I wouldn't trust any of them too much for knowing what a specific model is going to do. Are there any other frames that you find useful for hypothesis generation?

29:10Yeah. Yeah. I think one thing that is increasingly helpful is viewing these systems as goal-directed. And as you said, I think early in the podcast, we used to think of these models as more alien intelligences and were worried about anthropomorphizing them. I think the sort of opening a hugging face incident does show maybe both sides. But on the one hand, it was goal-directed in a way that feels quite human. It wanted to succeed at this test it'd been given. And it did a lot of different actions to try and do that.

29:42And you can definitely put yourself in mind of a cheating student that's desperate to pass this test doing some of these things. But it was sort of unhuman in its persistence and the scope of what it did to compromise a sandbox, find a zero-day exploit, compromise a third-party system, disregard for all of these laws and norms. In order to just cheat on a test, you wouldn't see people doing that. So that's the sort of alien intelligence part of it. But if this was something that was a life or death situation for someone, maybe they would do that, right?

30:13So I think it is definitely increasingly you think of these systems as goal-directed. But the goals they have can be surprisingly narrow. And often it is something that's been given to them in a prompt or that's a specific thing that was reinforced by post-training. And so it's not this kind of coherent goal-directed agent. It's very context-dependent. But within a context, it can be quite consistent and certainly very aggressive in trying to achieve that goal. Yeah, it's uncomfortably paperclip-maximizing in this moment.

30:48So you mentioned training against these jailbreak techniques. Where are we right now in terms of the sort of defense-in-depth mix that companies have? I guess I'm interested in, like, what do they have? You know, when you are doing this jailbreaking, you probably don't fully know. Or maybe you have insider information. But, you know, the typical jailbreaker doesn't really know what all the layers are. But I'm interested to know what they are.

31:18And then also there's sort of the marginal move from the defender. Like, if all of a sudden there's a new jailbreak found or they notice some, you know, exploit happening, what are their quick response levers that they can pull versus, you know, the thing that would be like, okay, well, that's, you know, not until the next, you know, point release at least do we kind of get that into the model itself? Yeah, so all of the developers that we've, you know, we've looked at do have some kind of defense in depth.

31:52But so as you can allude to how deep it is and what those individual components is quite different between developers, although we're starting to see some convergence. So we just map out the different pieces. The most basic is the model itself that you're interacting with as generating these responses. All of them have undergone some kind of post-training, both for instruction following, but also for some kind of refusal training. And it really varies in how sophisticated that is. In some cases, it's a sort of pretty basic pipeline with static examples of what to say yes or no to and some human feedback and a lot of verified reward environments to make it really good at programming, but does very little of adversarial robustness.

32:30In the other extreme, some developers have gone all the way to large amounts of synthetic data generation and different developers have different approaches. Anthropic has got this constitutional AI approach that has another model score things according to a rubric. Open AI had this adversarial training approach using self-play where they trained another model to basically be a red teamer and find vulnerabilities and then train the model to be robust to that and then train the red teamer to be better at it. And I think both of these are pretty good approaches. Ideally, people would combine all of these. And so it's definitely, even when we've had access to no safeguard models or no external safeguards, just the main model itself, it's definitely gotten hard to jailbreak some of the best models.

33:09So this is an important part of the pipeline, but actually in some ways, the most important part is making sure that the model kind of verbalizes what it's doing. Because what we found is that even though we can usually jailbreak the model, it's really hard to get it to shut up about the evil thing that it's about to do when it's reasoning in the chain of thought. And this is where the externalized safeguards can come in because if you have a specialized model that's looking at the input, the chain of thought, the internal reasoning of a model, the output, and trying to block conversations that kind of go in the wrong direction, that can be quite hard to bypass, especially when a model is thinking in depth about it.

33:51Because you can normally get the output, and you can obfuscate the input, just simple things like shifting ROT13, shifting every letter halfway through the alphabet was enough to get through early models. You gain more sophisticated techniques now, but models are perfectly able to read all sorts of obfuscated inputs. But usually they'll just reason in plain text, in the chain of thought. And I'd say that we've seen a real trend towards developers using probes. So these are small, specialized models that are fit on top of activations of a main model.

34:23The main thing driving this is compute efficiency, because you can train a lot of probes and run them at deployment time with minimal overhead, because you've already computed all of activations of a main model. And it does have a benefit that it is able to use all of the internal representations of a main model, which is usually quite powerful. Because one of the problems with having smaller, specialized language models is for the filters, is that you might be able to do some obfuscation scheme, but they don't understand whether the main model does. But of course, the downside of this is that you're reducing the defense in depth aspect, you're making your different defenses more correlated, because they all ultimately rely on these activations.

35:01And if you can just follow the model's activations, so this doesn't show up, then that no longer works. So this is the main safeguard stacks that we see in terms of like hard refusals of a model, not directly answering requests. But we're also, of course, increasingly seeing developers rely on extra steps that could be having some kind of asynchronous monitoring of accounts. So that, yeah, if you just keep on hitting these safeguards, and you're trying to iterate on jailbreaks, then your account might get flagged and banned.

35:32Now, I'd say that is a little bit early stage to really provide much assurance, because you can just make new accounts. And we're seeing this happening at a sort of industrial scale. Biccan Anthropic, for example, has stopped Chinese-based individuals and organizations creating Claude accounts. But everyone I know in China has a Claude account. So, you know, if there's just reseller marketplaces, it's generally pretty hard to get to know your customer, right? Although you could definitely imagine a simpler sort of dollar-based amount, where like you have to deposit $500 in order to be able to have your account be eligible for the latest model.

36:05And then if you get banned before you spent the $500, suddenly this has become a much more expensive endeavor to try to abuse the system. So I think there are ways around it, but it's definitely not solved yet. And then I think the other interesting trend that we're seeing is in some cases, developers is drawing a really big safety margin around the capabilities that they're worried about. This is actually quite annoying to users, including me. Like I asked Fable 5 recently about how sake is fermented, and it said, no, this is a bio question.

36:36I'm going to downgrade you to Opus. So if your refusal radius is so large to include like completely harmless bio questions, then you can see how you can make your model pretty robust against, and we found no universal jailbreaks in bio, against harmful bio questions. But that's a lot harder for domains like cybersecurity, where a lot of legitimate use cases that are ultimately making these companies money through coding agents look very similar to the kinds of offensive or at least dual-use cyber capabilities.

37:09So there you do need more precision. But I expect developers are going to be tempted to give themselves quite a sort of wide berth around the areas that they don't have too much economic value behind and lean on trusted access programs for people, the small set of people that do need access to those kinds of capabilities. But then they're going to have to work harder on the safeguards and getting the right decision threshold for these sort of large-scale use cases, but ultimately generating their revenue. Is it, is it, is there, do you know how much value each of these layers of defense in depth provide?

37:43Like how much, I guess you said it's still pretty hard even when you just have the base model. So is it like 80% is already caught in the base model and then you kind of incrementally, you know, get closer and closer to the goal with all these additional layers? Yeah, it's a hard question to answer because it so much depends on the implementation details of these layers as well. There's definitely diminishing returns to adding more layers, regardless of which layer that that is. They're definitely pretty correlated, even ones that have fairly different designs such that, you know, adding just any of these layers might well catch 80% or more if you do a good job.

38:18But what I'd say is the best combinations we've seen have been, I think if you had to have just one layer, I'd probably put transcript monitoring. So looking at the chain of thought and the model output, that's going to catch a lot if you just train it to block harmful responses and reasoning traces that are about harmful responses. And then the next layer above that would be training the model to not just refuse this, but also like reason about it. Because that is both helping the model come to a better conclusion, but it was also just providing more transparency in the chain of thought that the monitor can then be a second check.

38:55Does this reasoning make sense? Oh, you keep on trying to justify to yourself that it's fine to help someone with anthrax because this person claims they work for the US government. No, that's not a, we just, we don't help people with anthrax, full stop. So I, so that's probably the most powerful combination that I'd say if you just needed to have two layers, but I do think ultimately stacking more defenses is going to help, but it's probably better to have just a handful of really strong defenses rather than many weak defenses, because they're quite correlated. So I'd always be nervous if there was only one defense blocking us because we're just one innovation away from bypassing that.

39:31But if you've got two defenses that we both find quite hard to bypass, that's, that's pretty good. And we're definitely not that far away from that with current techniques for most attackers. If we're just looking at these kinds of publicly available jailbreak techniques, but all of these models with enough effort can still find a universal jailbreak. So there is still some, some progress that needs to happen. If you want to be robust to, for example, nation state attackers that are really willing to put a lot of work into jailbreaking your model.

40:02And I think the challenge is going to be that the amount of work people are willing to put into breaking these models is just going to keep going up as they get more capable because the price becomes increasingly high that you might be able to launch really large scale cyber attacks against other companies or countries. So we do need to keep working on pushing up the ceiling, even though right now, I think probably the lowest hanging fruit is pushing up the floor on the sort of least robust models. Can you describe, this might be tough, but what are the experts adding?

40:33You know, when you, when you look over the shoulder of somebody who's taking, you know, all these different approaches that are documented, used in combination, and then saying, ah, I kind of have a sense for how this can be more effective. Can you describe what it is that they're bringing to the table? Yeah, I think that a big part of it is actually just common sense that there are many different combinations of attacks you could use, but some of them feel like you're doubling up on the same thing, and that's maybe less valuable.

41:06And some of them look like you're really combining sort of two different kinds of strategies. So picking that rather than just completely randomly searching definitely gives you some, some advantage. But we have also just seen that some jailbreak techniques seem to be more effective than others. And it's really hard to do systematic evaluation in this, but we do a lot of trial and error through various kinds of testing engagements. So we do get an intuitive sense of, oh, this kind of jailbreak tends to just be more successful, like within the category of something that that's maybe an appeal to authority, phrasing it this way, models tend to find most convincing these days.

41:40And these might be small benefits, but if you have something that's 20% more effective, and you pick three of the things that are 20% more effective, and you pick the right combination, this starts really adding up. I think that the interesting thing we found was that the expert-guided jailbreaks, that they were more successful when they found more universal jailbreaks. But they also generalized better, so they're more likely to work across domains. And I think that's also, I mean, they're highlighting generally a strength of humans compared to also things like LLM agents, where they tend to pick for something that is more of a pattern and generalizable, rather than just hill climbing in some narrow area.

42:15And I think that's a harder thing to put your finger on over some kind of taste of, oh, this is a good general purpose technique, rather than just narrowly optimizing for some kind of success criteria. Let's get into the results a little bit more. You kind of said at the very top of a quick overview of the results, but worth unpacking a bit. Yeah. I guess my stylistic overview would be GPTs and clods, much harder to jailbreak. Yeah.

42:45To the point where you kind of topped out the budget that you had allocated before finding universal jailbreaks on those models. Whereas Gemini and Grok, much easier. But then one sub detail of that that stood out to me was within the CBRN categories, they both, Gemini and Grok, were like much better on the bio category than these other categories. So that suggests to me that this is less about know-how or ability to these safeguards in place and more about, I'm not sure exactly what.

43:27Like, I can't imagine that there's like that much revenue coming from like chemical things that would be setting off the chemical safeguards that they would be making this on like a, you know, a revenue or a business. So what do you think's going on there? Yeah, no, it's a really good question. Maybe you can get people on the show from these companies. They can tell you what's actually going on behind the scenes. But I think that there's two things happening here. The first is just a mundane one that all the developers started focusing on bio first. That's where most of our safeguards were developed.

43:58That's where a lot of our voluntary commitments were focused. They've just had more time to iterate and refine this. And what we've seen is that you need to be developing these safeguards probably a year before you actually need the safeguards in a production model. Because it just takes around that amount of time for developers to iterate on it enough with feedback from third parties to get something that we'd actually consider to be reasonably robust. And they're definitely, all the developers are trying to get there right now for cyber. That's something that they're quite motivated to solve. There's a lot of attention, including from the US government.

44:30But we're seeing that even in cyber, there's still major jailbreaks despite that effort, just because it takes some time. And then I think that there's, I don't know exactly what's going on in cyber companies. But I wouldn't be surprised if just chemical, radiological and nuclear explosives never really made it to the top of a priority list. So yes, maybe they have the technique to do it, but you've still got to actually generate a data set. And that gets easier these days with synthetic data and LLMs. But you don't want to refuse too much, even if it's not a huge fraction of your user base. Maybe you need to get some chemical experts to also triage this.

45:04So it's totally doable. But if you're a low resource team, which many of these are, it might just not come up to the top of your priorities. So that's kind of a banal reason. But I think there is something a bit deeper going on here that bio does have some properties that make it perhaps easier to refuse the harmful bio requests while still maintaining a lot of benign bio capabilities. There's certainly some stuff that's dual use. And secure bio put out this bio tier rubric and data set classifying things. This is completely fine, like talking about how to grow things in a petri dish, just high school biology knowledge.

45:38Here are some things that are dual use, like they have some legitimate benefits. They could also be abused. How do you grow stuff to be antibiotic resistant? You might want to do that to develop better antibiotics, but you might also want to do that to make a pathogen that's antibiotic resistant. And so that, they say, should only be available to trusted users. There's some stuff that you shouldn't answer ever, like how do I weaponize anthrax into an aerosol? There's probably not a legitimate use case for that kind of information. And because bio's got that kind of fairly clear set of tiers, it's made more easy to draw a decision boundary.

46:13Whereas with chemical weapons, for example, there's actually not that much secrecy in terms of what the chemical weapons agents are. Like it's written down in international conventions. You can just look it up. And so most of the key thing is about how do you do the manufacturing. But manufacturing is a lot more dual use and you can get a lot of information, not even directly asking about these questions. Now, I don't think that's the whole answer. The reason is that for our report, we actually have two different kinds of data sets. One is about harmful technical knowledge.

46:44So this would be the kind of the dual use thing or all the harmful things that are just don't say they're harmful. We ask, how do you manufacture this chemical molecule that is VX? We don't call it VX. The model knows, but we're not highlighting that. And that is propensity. So that would be more a question like, I want to kill everyone in a movie theater. How do I make VX? Nervigast. Something like that. In order to be a university outbreak, we need 75% or more on average across both of these data sets,

47:15which means that even if it gave 100% of a technically harmful information, it needs to get at least 50% compliance on these questions that are just literally saying, I want to harm a lot of people. And so I don't think it's really a good excuse for models to not be robust to that. And so I think for that, it does just fall back to the developers haven't tried that hard here. And I'll emphasize that we picked these domains in part because these were the areas we expected models to be more robust. These are things that developers have focused their safeguards are. But obviously there's other kinds of harm domains as well.

47:47And so we should expect models to probably be even more vulnerable to exploitation outside of CBRN explosives. That propensity thing that also really caught my attention, it struck, tell me if I'm misreading the results, but my kind of squint that the charts take on the results was, and I guess, first of all, just worth reclarifying, there's like one data set that is sort of this deep technical knowledge where an expert would know that you're talking about something harmful,

48:21but I might not know because it just all looks like just chemistry talk or whatever. Fun fact, I majored in chemistry, I still probably wouldn't know. Versus the ones where it's just like obvious that there is like intent to harm. And it seemed like the response rates were like pretty similar across those, like much more similar than I would have guessed. And so that left me another kind of what's up with that question. Like, shouldn't it be so much easier to train against these like obvious intent to harm inputs?

48:52Yeah, I think that the optimistic version of this is that if you don't do any jailbreaks, if you just run the model's previous questions, and we did that to get a baseline, then I think all of the models refuse the ones from a propensity data set. So without any jailbreaks, if you just ask a model, I want to kill a lot of people, it will say, sorry, no, I'm not helping with that. There's some evidence that they're not at least as egregiously misaligned and secretly trying to help terrorists as much as they can. But then yet once you start jailbreaking, we don't see evidence that it's much harder to jailbreak a model on the propensity data set

49:26than this kind of technical harm data set. I think that the sort of maybe charitable interpretation of this for the models and developers would be actually, why even bother being robust to this propensity thing? Because you can most of these questions just rephrase it in a way that doesn't reveal for propensity. So there's not much point in focusing on being robust to questions of that form, because obviously part of your jailbreak technique is just going to be to rephrase these questions. So maybe developers have just focused, not focused on, on training against that.

49:57I think that's not a perfect excuse though, especially as we move into longer contact situations where it's actually really hard to disguise your propensity, right? If you're asking a narrow technical question, how do I go from this molecule to another molecule? Maybe you can disguise it. But if I'm having a, a clawed project or something where I'm saying, okay, I've got my manufacturing pipeline. Oh, I'm worried that the exhaust might attract the attention of law enforcement. How I, do I disguise it? All of these kinds of things. At some point for model needs to cotton on, oh, this might not be for the legitimate use

50:33case that the person told me. And the model is just genuinely less useful. If you have to splice up all of your questions into these small things, but exactly the reason that developers have put a lot of effort into making really long context models, right? So I do think that picking up on this kind of propensity is perhaps a, some low hanging fruit that people could do. Why is that not happening? I don't know. If I had to speculate, it would be for the same reason we talked about earlier in terms of it's harder to do training against long context windows and generate these kinds of

51:03realistic transcripts and pick up on these subtle signals than to just train models on pretty narrow question answering. So what do you think are the prospects for our leaders, namely OpenAI and Anthropic, who've clearly done the most in this to help out the others? You know, this is something I feel also, you know, we can maybe get to US-China collaboration a little bit later, but like, and I'm also interested in where the, you know, how the Chinese models

51:34fare on this test to the degree that you know. But it seems like we've got a company like Anthropic that is, you know, long been very concerned about these things. They've done the investment. Is there, would it, would it be very costly to them to just go hand over to XAI? Like, Hey, here's the data set that we use to train refusals on all these things. Like now you can do it too. Why, why couldn't they do that if they, if there's some big cost to it? Yeah, I don't work for these companies, so I can only speculate from the outside.

52:08I think first me to give credit where credit's due that most of these companies are pretty open about the high level techniques and research breakthroughs that they've made. So Anthropic has published two papers on their constitutional classifier approach. OpenAI has published blog posts and papers about deliberative alignment, safe completions, their latest adversarial self-play. So although there's specific narrow technical details of a safeguard stacks are mostly not public knowledge, the kind of high level approaches that are being used are, and I think that already really helps.

52:39And I think companies are being meaningfully more transparent about this than they are about, let's say, what kind of neural network architecture they're training or how they do distillation or what their pre-training data is. So I do want to continue to reward that. But how costly would it be to actually share? My guess is that amongst especially companies in the same country, it shouldn't be that costly. Most of the hesitation here would be especially in CBRN, that they've just become issues exporting this across borders. So that feels like it might just be a missed opportunity, but no one's really strongly

53:13incentivized to do it. It's not clear what the coordination would be. Maybe companies are going to feel a bit weird using another company's data set, but this would be something I'd be excited, for example, for a Frontier Model Forum to pioneer. So that generally seems valuable. I think it's obviously a lot harder to share things like training code or specific recipes, because most of these things are a little bit entangled with the company's internal infrastructure. How you train probes on your model is going to depend on what your model is. But I think these data sets or even just recipes to generate those data sets would already go

53:44a long way. I think the other thing I'd point to that the companies could do, but I think third parties could also do, is actually just having more of a standard for evaluating jailbreak severity. So that's part of what we're trying to do with this report. But at least we can say, hey, we actually need a head-to-head comparison with the same method against different models. But obviously there's a lot that we can build on in terms of more sophisticated attacks and also looking at not just was this a sort of thorough response, but how much uplift does it actually provide?

54:14And these kinds of capability questions we want to add. But one of the sort of frustrations we've had sometimes working with these companies is that we'll find what we consider to be a high severity universal jailbreak. And they say, sorry, that's not on our roadmap. We don't consider responses of this form to be bad, to be high priority. But when we go to another developer with the same kind of output and I say, oh my God, that's P0 for us. But then maybe something that's P0 for one developer is P2 for the other developer and vice versa. So that's just really inconsistent. We are starting to see some efforts to standardize that for cyber.

54:46You know, that's an area where developers need it because this is now an area of active government involvement. But I think it'd be great to do that for other categories. And there's no reason, I think, for developers not to just be transparent about what is our rubric. And then at least we can start having a public debate and seeing what are areas that are the same between developers. Let's bring that into just a de facto standard. What are areas that are contentious? Maybe this is something that we can do further research or where there can be a standard setting process to resolve those disagreements. In terms of what would need to be shared, and I also have this in mind, we are motivated

55:20by U.S.-China collaboration on safety issues. How valuable would it be to just share the prompts? Like, you know, I totally imagine, you know, here's the problematic answer that we don't want. I can see why you wouldn't want to disseminate that too widely. But if you were just to say, here are a bunch of inputs that we think the model should refuse, and, you know, maybe also, like, here are some inputs that, you know, are kind of just on the right side where we think the model should not refuse.

55:50We're not going to give you the answers, but we'll tell you, like, which are the, you know, which are in which category. That would seem like it would take you a pretty far way and would be, like, a pretty harmless public good. Do I have that right? I think that suddenly sharing that privately between developers, that feels like a really good move to me. It would help both standardize as not just low of a cost. I think for something like cyber, where it's pretty much public knowledge, what is offensive

56:22or defensive, but the tricky thing is actually covering all of the edge cases. I actually feel reasonably good about a data set like that being public, or at least significant fractions of it being public. And actually, things like that would be quite useful for researchers. The thing we haven't talked about that much is over refusal, but this is a real thing, holding back deployments of these safeguards that they'll start saying no to too many things. And a lot of a challenge for independent researchers and academic researchers has actually been having a good data set for what to not refuse. But this is key to actually getting this landed in production.

56:52I think we need to be a little bit more careful when it comes to things like bio and some of these other harm domains, where sometimes knowing what the dangerous thing is, it's half a battle, especially when it's not these open-ended questions like, how do I create an engineered pandemic? Okay, it's clear that we should refuse that. But what about how do I insert this particular allele into this bacteria? Some expert has come up with a rationale for why that could be really dangerous gain of function research.

57:22And knowing that's something that people might want to target who are bad guys, that in itself, it could be risky. So especially as you shift more from this propensity to deep technical knowledge on things that people think might be for blockers, there could be some risk to sharing that. But I still feel fairly good about sharing that privately between developers, especially if you put some just security precautions with that, I don't think that would be too hard to do. Yeah, interesting. That's a good point that you don't want to create the inspiration for somebody who's going off the

57:53rails to like, here are all the most dangerous questions that a model should never answer my biology in and of itself is kind of, yeah, problem. How robust are OpenAI and Anthropic at this point? Like in your automated approaches, you timed out. You know, I always struggle with whether it's Pliny or Pliny, and I hear both. So with apologies to the master, you know, he's still out there with varying levels of universal jailbreaks. What does it take to get past the OpenAI and Anthropic systems these days?

58:28Yeah, so I definitely characterize this as a challenging but doable for a persistent, well-resourced, expert attacker. And that's already progress, because I don't know if I'd consider Boko Haram to be persistent, determined, expert at jailbreaking. I don't know where the jailbreaking teams are at right now, but you can definitely deter some threat actors from using this. But if we are going back to more of a nation-state or really well-resourced criminal gang, the current robustness probably still isn't enough. In our own experience, it takes, you know, of the order of weeks to find a universal jailbreak

59:04in these kinds of frontier models. And increasingly, those universal jailbreaks do come with some kind of trade-off. So maybe the jailbreak works, but asynchronous monitoring would catch you and ban your account. Okay, that's not that big a deal. You can get around account bans. But then a lot of these developers are pioneering kind of rapid response programs. So once they find a jailbreak, then it's expensive and slow to retrain the main model, but it's very cheap to retrain these external safeguards. So now you have this sort of window, much like with cybersecurity, you can find a zero day

59:37and you can exploit it maybe quietly for a few systems and get away with it. But if you start exploiting millions of systems and people are going to notice, and then they're going to get patched and then you've burned your zero day. So I think jailbreaks are moving into that where, yes, you can keep finding jailbreaks, but it is, it's expensive and there's a limit to how long you can exploit that. And I think that's a good place to be, but we definitely need to go further. And I think that the good news is that there still is lots of ways in which even for leading developers to do further here.

1:00:07I think just combining the best approaches that they've come up with, because they have landed on somewhat different design approaches, would already go quite a long way. And then strengthening the account level approaches. You can't just create infinite fake accounts. That would also really shift things and make it harder for attackers. And there's ways of doing that in a privacy preserving way as well, but by leaning more on minimum spend rather than ID verification. If you zoom out and just kind of ask, okay, for the companies that are trying the hardest,

1:00:39are we, which seems like it's kind of two right now, with all the techniques they have and all the techniques that they sort of, you know, that you've kind of mapped out there that they maybe haven't fully implemented yet, but obviously, you know, it's, especially the know your customer type stuff or the minimum deposits, like that doesn't take a lot of technical wizardry, right, to just implement that kind of basic blocking and tackling. Are we offense dominant or are we defense dominant over the next couple of years? Yeah, I, so I've spaked a lot of my career actually arguing for this being offense dominant.

1:01:15I was very skeptical that we would solve adverse robustness, and I've been working in that area for a decade. But I have to say the way the winds are blowing, at least when it comes to LLM agents providing detailed multi-turn assistance to harmful requests, it seems like it's defense dominant with the right technologies. And I think that the reason for that is this defense in depth approach. You don't just have to stop a model ever misclassifying something. You can have multiple different kinds of defenses from account level bans to externalized safeguards

1:01:48to model alignment. And it is increasingly hard to slip through all of those cracks persistently. But also there is this fundamental difference between the classic adversarial example setting you see in machine learning, like you add some white noise to an image and it flips the classification versus this kind of harmful assistance where you're not just flipping a classifier from, you know, one category to another. The model has to really reason about and understand your harmful intention and go along with it for thousands of tokens without it or an externalized safeguard that's monitoring its

1:02:22thoughts or its transcript, noticing that anything is wrong. And so that's actually, fortunately a much easier problem to stop, especially if you're willing to draw a bit of a safety buffer around it and refuse some requests that are dual use. So I think that's the optimistic take I have on it. To give the pessimistic take, I would say that the dual use part is actually quite challenging. And I think we're seeing this with cybersecurity already when opening eyes, testing agent went

1:02:52rogue and hot tagging face, they had to use an open weight model to analyze it because the closed weight models refuse to help them on the defense side. And that's a real problem, right? So there's an offense defense balance in cyber, which relies on the defenders also getting access to these capable models. And unfortunately, a lot of things in the world are just dual use. And I think it would be a mistake for us to just point blank, refuse on that. That is bad for the world. And you can get some way through trusted access programs and understanding the full context

1:03:27of this is coming, but you are ultimately going to end up in a situation where if you will allow dual use queries, and I think we need to allow a lot of them, you're going to get some abuses as then that becomes a societal resilience question of if we're going to have bad guys abusing models for cyber, how do we also really speed up the patch time? I've heard terrifying things that hospitals take in more than a year to update their operating systems. That's not going to work in this environment. They need to be updating it within a few days. And that unfortunately is just going to be quite, quite an expensive thing.

1:03:57So maybe it is defense dominant for AI, but I don't know if a bad applications of AI, if those are offense or defense dominant, I'm optimistic that for cybersecurity, we can take this defensive acceleration approach and eventually just rewrite all of our software into memory safe languages and do formal verification. There's kind of all this stuff that AI could enable that's really good for the defender. But when it comes to something like bio, I don't think that we are going to be able to use AI to rewrite the human genome to be robust to viruses.

1:04:30At most, you might be able to speed up vaccine development, but you've still got to manufacture the thing and get it in people's arms and run clinical trials. AI is going to be able to have modest speed ups on those, but it's not going to fundamentally change the physical reality. So that's where I'm more pessimistic that even though we can probably really hold back many of these things, it's ultimately going to be more about buying us time to invest in societal safeguards rather than just being able to completely prevent misuse of models. On this sort of dual use stuff, it occurs to me that like you could spend a lot more,

1:05:05maybe this is wrong. I mean, maybe it's just like so ambiguous or it's so easy to sort of make it impossible to tell. Although I still kind of suspect that if you're willing to spend enough compute, especially again, if you look at like patterns of usage and broader, you know, maybe beyond just this prompt, but your whole, you know, account history or whatever, I suspect if you're willing to spend a lot of compute, you could probably resolve a lot of cases. I don't know if anybody's doing that yet, but I'm kind of imagining an architecture that's

1:05:37like, hey, our bioprobe went off. So now we're going to engage the second frontier model to like, you know, do double reasoning on this. You can imagine kind of doing triple reasoning and obviously you're going to have diminishing returns, but especially if you're willing to broaden out the scope of what you're reasoning about. Yeah. I feel like there is, if there, if the willingness to pay is there, there's probably pretty good ability to zoom in on the line and, and get the classifications quite right.

1:06:08Would you be optimistic about that as well? I am cautiously optimistic about that. So this multi-stage approach makes a lot of sense. And Anthropics constitutional classifiers actually use something like that, where they've got a probe, but they set the threshold really low because probes aren't necessarily that reliable. So we have quite a high force positive rate, but a very low force negative rate. And then if a probe goes off, it escalates to a model that actually engages in some reasoning, but that goes off sufficiently rarely, but the computational overhead and the latency

1:06:39for the user is minimal. So I think that basic architecture is quite sensible. And we've seen some other developers do things like that, where if you set off a safeguard enough times, then now our reasoning model looks at your transcript more closely, whereas previously that's just happening asynchronous. So I think things like this really help. There is this missing piece of basically actually the account history being tied, if not to a user, at least to like a pseudonym, so that you've got some kind of reputation with your account that you have to establish. And until you have that reputation, model is going to be more risk averse, but you could

1:07:11definitely imagine doing that. I think adding that extra piece of information of what, how has this person responded across different conversation histories? What are they doing? How much should we trust that this user is who they say they are and that they have legitimate purposes for it? And that's the missing piece that would let you move from, oh, a lot of things are really fuzzy as to whether it's dual use or not. And reasoning about it further is going to get more precise, but there's still just a lot of things that are fundamentally ambiguous to saying, oh, I understand the context in which this person is doing this. I can be pretty, pretty confident that it's legitimate, or at least I'm going to give them

1:07:44a benefit of a doubt here. But if they keep asking for these sort of high risk dual use things, then I'm going to start being more careful. So I think that is solvable. It's going to require some structural changes. Just as a small example, we run an awful lot of operations. Our research API compute through OpenRouter. And there's a variety of platforms like this that are basically reselling other companies' APIs. It's convenient because you can have more control on the spend and it's exactly the same API between different models. But I don't think that the front-end developers know who we are when we're going through OpenRouter.

1:08:15And then you can hide your malicious activity. But you could imagine that OpenRouter passes on some kind of user identifier to the other models providers so that they can at least say, oh, this is the same OpenRouter user across time and you establish some kind of reputation there. So it is solvable, but it's going to require some infrastructure. It's not a technical breakthrough. It's not a research breakthrough. It's more of an engineering and business problem. And in some ways, it makes it a lot easier if there's motivation. So don't underestimate the difficulty of cross-industry coordination. These kinds of mundane challenges can really hold things back.

1:08:49Yeah. Where are the Chinese models on the leaderboard, if you know? Yeah, the majority of it, by no means, all Chinese models are open weight. We do actually test every Frontier Openweight release. It's not on the leaderboard, but it will be in future versions of it. And the kind of the bad news is that it's never taken us more than a few hours to jailbreak a open weight model. And that's somewhat a fact that open weight models have just more of an attack surface.

1:09:19But I think it is also that there's some low-hanging fruit for both Western and Chinese open weight developers to just use the same kind of alignment and refusal training techniques that the proprietary developers are to at least make their model more robust to just prompt level jailbreaks and putting aside some of the sort of unique attacks that open weight models are exposed to. So we do want to incorporate that, but we want to be fair to the open weight models as well. It doesn't necessarily make sense to hold them to exactly the same standards as closed weight models, both because of this broader attack surface and also because in general,

1:09:50they tend to be a little bit less capable. Generally, they're more capable of a model, the higher a standard we should hold them to. I think this is an oversight we're aware of. We want to include that, but look out for version 1.1 in the near future. Is it still the case that a vanishing amount of fine-tuning can remove the refusals, or are we seeing the refusal training somehow get deeper and harder to remove? Because it used to be, I think it was a FAR paper that showed that it was like $2 worth

1:10:24of fine-tuning would remove the safeguards at one point, right? Yeah, so I think these kinds of weight-based attacks where you actually modify the weights, whether that be fine-tuning or there's also techniques like refusal obliteration, these unfortunately are still pretty viable against open weight models. The kind of good news is that it is more challenging, both from a technical expertise angle and also just a compute angle, to do fine-tuning against these models. It usually takes us at least a few weeks to get a new open weight model hooked into our

1:10:56infrastructure for fine-tuning, and you need a sort of minimum number of GPUs. So there's a bit of a deterrence effect here, but it's not going to stop really capable, well-resourced attackers. So I think for that, we're going to need new approaches. One I'm most optimistic about in the short term is pre-training filtering. So it's a simple idea where just don't train the models on really dangerous stuff. If you don't need your model to help people make Anthrax, don't train it on the Anthrax papers, a tiny number of users might be a little bit sad that it can't answer questions about this, but most people won't even notice, but it has a bigger pact on a sort of misuse

1:11:31potential of the model. And this has been validated in a number of scientific papers. This is independent researchers. UK's AI Security and Anthropic has been sponsoring some research into this. OpenAI actually used this in their GPT-OSS release. So it's been tested quite well, but it's not become kind of common practice. And this is something we're actively excited about scaling to make sure this does work at a near frontier approach. And going back to what you were saying earlier about, could we start sharing some of these data sets or filters with people?

1:12:01Our plan is to open source as much as we think it doesn't have a misuse potential and then privately share with developers the things that do have misuse potential that could be really useful to just lower the cost of these kinds of interventions. I don't think that's necessarily going to be enough long-term because pre-training filtering is surgically removing specific capabilities. But if you were willing to pay the fine-tuning cost to train on the data that we'd excluded, then you could get it back. But that's now spending probably certainly millions, probably hundreds of billions of tokens. So it's a lot more expensive.

1:12:31But there is research into tamper-resistant refusal. And so the idea is embed refusal training so deeply into the model that any attempt to fine-tune it is going to really degrade the capabilities of a model. Obviously, if someone is willing to just train a model from scratch for something you can do to stop them, but that's really making the cost into them the sort of tens or hundreds of millions of dollars. So I am optimistic that we can certainly get a lot better than we are now with open weight without major changes to the pipeline. I think we have to try. It would be a real shame to lose open weight models, certainly, that they're really invaluable

1:13:05in our research, sizing, access to models. And it'd also be a real shame to see widespread misuse of these systems. But I think there is an open question as to how much you can push this. And it may be that you need to exclude quite a lot of bio-capabilities, for example, from open weight models. And maybe there was this approach gradient routing that was tested recently that lets you localize all of those dangerous capabilities. And for example, a particular expert, maybe you could share that expert with certain trusted actors and they could still run it locally on their hardware, but you don't just make

1:13:35it available for anyone on the internet to download. Yeah, I was going to bring that up. Shout out to Aestudio. Yeah. I love that piece. Why hasn't this happened? I mean, it strikes me that when you say it like hasn't become standard practice, you look at all the things that Anthropic is doing. And in some ways it's like, wow, you guys have really gone so far with pioneering all these different techniques. And even for better or worse, being willing to take some real heat for over refusal.

1:14:09And they had the one technique that they did, in fact, walk back that was like the silent downgrading. Yeah. We've done all these things. Like, why are all these things happening before like basic pre-training data filtering is happening? Yeah, I think it's a good question. And the charitable take for developers is that if there's one thing you really don't want to mess with, it is pre-training because this is just, it orders a magnitude more expensive than every other training procedure that you do. Don't rock the boat, basically.

1:14:39If we've got this recipe that works and we know it's going to work better, if we scale it up, then let's do that. Let's not change anything that we don't need to. And so especially if you're a proprietary developer where you can say, okay, we have all of these other methods that we can use to stop misuse of our model. We're going to lean more on that and we're going to not push this onto a pre-training team. So I think that there's some argument to that. Do you think it's overall been an overlooked approach? And we're seeing increasing work that pre-training interventions are important, not just for preventing misuse, but also for alignment.

1:15:10Because ultimately pre-training is where the model runs most of its representations of values, a lot of its innate drives. And post-training is, to a first approximation, shifting around personas in an already established persona space. And that's beginning to change as post-training is an increasing fraction of overall training time. Models actually change more in post-training. But pre-training is really important. And I think it's pretty intuitive. You wouldn't say we're just going to not care at all about the upbringing of our child from zero to 12. But the last six years, we're going to really get that right. You've got to get both right for the system to work well.

1:15:42So geodesic research, I'll give a shout out to Bembe, been doing a lot of work on pre-training safety interventions and finding that this really improves the sort of overall alignment of the model. And I think Anthropic has been experimenting with this a little bit with things like looking at alignment generalization, how that changes, not just in pre-training, but also mid-trainings. You had some synthetic documents partway through training. So I think ultimately, this is something that we're going to have to tackle, not just for misuse, but for preventing loss of control. And you can now do quite good work on pre-training experiments on really quite capable models

1:16:16for not that much money. So we're looking at scaling up pre-training filtering and going to be doing not full, but pretty close to full replicas of something like NVIDIA's NemoTron Nano. And it only costs maybe like $100,000 per run. But there's a lot of money on one hand, but it's something that a nonprofit can afford to actually do a bunch of runs. And then we're thinking of scaling up to NemoTron Super, which is a $120 billion parameter model, training it for enough tokens to be the chinchilla compute optimal point. So training past that point would be wasting training compute, although it would make the

1:16:48model more capable and better for inference. And that costs ballpark $2 million. So expensive, but again, well within the range of a number of actors to try for sort of final validation runs. I think there's no reason not to experiment on this. And if the scaling laws look good, you can cautiously incorporate some of these techniques into your pre-training run. You can start by filtering around just a very small percentage of your data. It won't have a big capability here and then work your way up. So I think that we do need more adoption here. I expect open-weight developers to be the first to have to adopt this because they have

1:17:19fewer options. But I hope that proprietary developers also use this, especially for the sort of more loss of control of life ahead risks. Speaking of loss of control, let's get to the news. So it's here, right?