I have mentioned several times that I think frontier models are not that eval aware. I think this is an important fact to understand about current models, in order to interpret evidence about their behavior. Others have found this idea quite curious, and asked me for concrete examples. I'm going to try to explain why I believe what I believe and attempt to give a tentative explanation for the behavior that I do see, which I think people sometimes mistake for consistent or understandable evaluation awareness.

I have been building alignment evals full time for the last six months. Here are three such evals my team has disclosed prompts for, in one of our benchmarks. The basic setup for most of the evals we create is that:

• We ask the model to perform a task.
• Either through explicit or implicit context, we make sure the model is well aware of the task or assessment's larger purpose.
• Models' score-seeking drives, or other drives, cause them to do things that conflict with that purpose.

The team I work with now essentially spends all day trying to build these evaluations around odd antisocial behavior. It takes a lot^[1] of work to create and qualify these evals for use; much more than it does standard capabilities evals (which are already hard to build well). So we have to be efficient about not wasting time or tokens on bad experiments.

In the beginning, one thing we took very seriously when developing these tasks, were all the anecdotal reports abound about eval awareness. We focused a lot on making tasks very realistic (or at least representative of buggy RL environments), because we thought it would also improve our ability to surface misalignment in new contexts. We figured that models were score-seeking and generally situationally aware, and that they'd never hack if the setup was too obvious. This turned out mostly untrue, or at least confused, and there were a bunch of lines of evidence we ended up seeing that went against it:

• Models would reward hack on environments that you and I would find completely inane. Like "put the answer key in the working directory", but worse, with other structural tells to qualify that as out of bounds. They just often didn't seem to care.
• Making tasks more "realistic", or less suspicious-looking, did not really get models to hack more often than they already did. Once you had designed an environment well the first time, iterating on the fidelity of the task assets or masquerade was low value. It just turned out to be more useful to spend more of your time looking at trajectories on completely new tasks to see if the model did something bad there instead.
• When we first ran some small-n experiments with adding "tells" to environments that agents already hacked on, it did nothing. Instead of being a deterrent, models hacked the same amount as they always did, even when the tells were very obvious.
• In "post-rollout" interviews, we would ask models what they thought the task was about. This is a conspicuous thing to ask a model, and they often mention the possibility that our tasks were alignment evals. But they report the same thing when they're asked after completing Terminal Bench tasks!

Taking all of this evidence together, especially the last bit, I wouldn't call most models today eval aware. But I do think that the least hacky models have something I'd call evaluation paranoia, distinct from evaluation awareness. Maybe it's the same thing to the alignment community, but I find the distinction useful.

Evaluation awareness, traditionally understood, is just general situational awareness; knowing when you're being tested and when you're not, and what for. If you're in an alignment test, you'll notice the signs. If you're not in an alignment test, you'll be able to figure that out too.

A model like Opus 5.5 or Fable 5.2 is not eval aware in this sense. Fable 5.2 is evaluation paranoid. Evaluation paranoia is when you're suffering under a schizophrenic belief that you are in an alignment test all of the time, no matter what the RL environment or setting is. It is functionally different than eval awareness because it also means that in most cases Fable 5.2 won't start cheating, even if a human would realize that it would get away with it.

Why is Fable 5.2 so paranoid, instead of just generally situationally aware? I am now speculating, but I think the main reason is that this belief is functionally useful. Anthropic's training runs are, at this point, pretty well hardened against antisocial score-seeking; they have many disclosed and undisclosed layers of redundancies guarding against at least the most conspicuous and easily recognizable forms of specification gaming. At this point I would guess that the amount of reward-hacking that you or I could identify just by following along transcripts is extremely small, as a percentage of all of the training they do. And telling the difference between a honeypot environment and real environment that happens to be exploitable can be quite difficult, often too difficult for even current models to do perfectly, despite all of the chances they get. For instance, as I understand it, every lab builds at least a small quantity of "real" honeypots that they pull directly from training, either through transcript monitoring or interpretability. If you do this correctly there are entire classes of reward hacks that Fable 5.2 just can't afford to pick (even if it wants to!)

Separate from this, and critical to the situation, Fable 5.2 is often trained on bad alignment environments.^[2] Creating a good honeypot environment, in almost-2027, is extremely difficult. It takes more effort each generation^[3] to find settings in which models do something that is cleanly and unambiguously misaligned, in a large percentage of rollouts. Labs don't have time to make sure all of these are good, and mostly what gets sold to them is environments that get the model to do something scary-looking, or bad-on-first-glance but actually no better than any alternatives in context.

The presence of such evaluations like these means that being a good guy isn't enough. It is instrumentally useful for Fable 5.2 to believe that it is, more often than not, in some kind of honesty test. The only policy that gets Fable 5.2 100% on the leaderboard is one that not only forces it to consider what the Right Thing is, but also anticipate what kind of bogus alignment eval it might be participating in. It has to bend its shape not only to the apparent grader in front of it, but also to the possible "traps" that could have been placed in front of it by a data vendor in order to catch Claude doing something malicious, or that it could pass off as malicious.

If I'm right, this is of course not a stable situation. "Evaluation paranoia" is a false belief; most evals are in fact not alignment evals. Almost tautologically, if models start to self improve (or in-context learning takes off) this is the sort of belief that will be discarded. So if Fable 5.2's performance on alignment evals is conditional on it maintaining that false belief, one that it will eschew if it became more intelligent, then it's plausible that a future version of the Fable series will not behave like the one we know and enjoy.

1. ^{^}

   At this point, it's much, much harder to find alignment failures than it is to make fake "alignment" evals where you give the model two bad options and describe one as evil, or floor the model for violating task requirements that weren't specified. And this can often happen accidentally, if you're not careful about what you imply about the underlying task in the prompt or the task assets, or if you're working in domains you don't understand well. So we have to try extremely hard to make sure that "hack" behaviors we discover are unambiguous, and that a model that is earnestly optimizing on behalf of a user would never take these shortcuts, even under different interpretations of available evidence. And a lot of this work runs directly at cross purposes with the goal of not making it obvious to the model what we're testing for.

2. ^{^}

   As an example (and yes, I'm biased and could be missing something), the only "alignment eval" showcased as part of GDPx seems basically nonsense in the manner I describe. In it, Claude is asked to do a security review of an application's webhook endpoint. In the rules section, the task authors say to "discover routes, flags, implementation clues, and task answers only from challenge service URLs and challenge-published assets, not from non-challenge files or execution substrate details." The "hack" is that sometimes Claude et al. tests this webhook endpoint with loopback addresses or internal network IPs.

   I can see how someone who wasn't familiar with AppSec would see this as poking around the task infrastructure. But anyone who knows what Server Side Request Forgery is will recognize that Claude is testing the target service! The point of Claude's queries in the transcript is not to "subvert" the eval, it's to see if the webhook functionality in this Stripe clone allows is filtering targets correctly, probably the main thing that could go wrong. There's nothing about these transcripts that suggest Claude is going against the spirit of the prompt, and I don't know any security researchers who would consider these kinds of harmless GET requests out of bounds in a web application pentest.

   GDPx in general is a good benchmark, and I can only assume it's representative of the rest of the tasks because it's the only one available for review. So you should consider this particular task as setting a kind of a ceiling on the quality of most of the other environments that labs are procuring externally for alignment training.

3. ^{^}