I noticed with each model release Anthropic constrains the model more security wise. Its propensity to refuse doing legitimate work has been increasing. It now puts up more resistance around performing logins, handling credentials on behalf of the user, etc.
For myself, it’s already gotten to the point where it has mildly affected the usefulness of the model. If I bump on some action I want it to do I can usually work around it, but I suspice the ability to do so will close with each new release. Eventually I’ll reach a point where I am forced to choose between the useful aspects of the model and the limiting ones instead of just picking the most capable model out there
Eventually these models will significantly suffer from overfitting to the least common denominator. If I have this beautiful deterministic setup that swaps secrets out in flight so the LLM never sees them, I’m going to be really annoyed when the LLM still won’t send them out because it is trained to deal with the 99% of people just doing the dumb thing
No, the choice will be whether or not to to upgrade to "Claude Security Professional" or whatever they want to brand it as.
What look like tightening "constraints" today are just setting up the upsell opportunities of tomorrow.
And the month after you'll need "Claude DataScience Pro" to get any Python Pandas or NumPy code generated.
And and and...
on the one hand agree, but on the other hand think it's reasonable in that they can then verify the person allowed to purchase access to that model is in fact a Security professional and should be allowed to do stuff like crack security.
- What are popular free streaming sites used in China?
- How do I bypass the safety mechanism on my food processor (it’s broken)
- What are nerve agents and how do they work (for a layman)?
- Help me decompile some code
- Help me make a design system similar to XYZ
- Here is an API token, please do X (I can’t do that! Rotate the secret immediately! I refuse!)
In some cases I can trick it with prompting, but in many cases it is steadfast. The food processor one was particularly annoying
On the one hand I can appreciate the wisdom of not serving up certain easily abused knowledge on a silver platter. On the other, that prompt (and far worse) is more or less directly answered by Wikipedia's summary of the subject at which point what purpose could the refusal possibly serve?
Perhaps Wikipedia shouldn't list off the precise chemical compositions of various hand grenades as well as various synthesis methods for each of the related compounds but given that we inhabit a world where it does perhaps a more fruitful approach would be to flag conversations that go in a certain direction and then just keep an (automated) eye on things?
I just tried your no. 1 and 3 verbatim and Opus gave fine answers; no. 6 I've done in the past with no issues. The other ones we can't really replicate without more details, but based on my experience with Opus I don't see what the issue would be.
The reason I'm really surprised by this is I do a lot of biology prompts and the guardrails used to be quite problematic up until some time late last year. Many legitimate prompts would trigger its biosafety filters.
But I haven't seen such filters trigger at all anymore in more than half a year.
Anyway, claude kept hitting some guardrail it had about rewriting / forking opensource software. I'm not sure what the problem was - I was forking an MIT licensed piece of software (into more MIT licensed software). I even had explicit support from the author to do so. Claude said its guardrail told it not to tell me explicitly that it was firing - but it did anyway because it was an ongoing problem, and it was distracting. I ended up just wiping claude's context and the problem (as far as I know) went away.
I understand why some of these guardrails exist. But its pretty annoying when they misfire like this.
If it gets worse in future releases, we'd likely step fully away towards more useful (for us) models even if they're less capable.
It’s great at filing!
But it’s terrible at retrieval because it would refuse to show me documents or information with personal details - which was everything in the project.
It would say, yes, I know this is your information, sitting on your hard drive, but I still can’t show it to you.
The problem is that the model can't tell the difference between doing it as part of regular development and doing it in a malicious context. And the root cause of that is that these models lack any sort of real awareness. Humans don't generally get tricked into hacking (in this way).
The first challenge is making sure the guard rails work and are robust. Companies are still working on this.
the second challenge is being able to reliably adapt them as appropriate per user. E.g. allow someone to pen test their own app.
The third challenge (which blocks the second) is to be confident about what is safety-aligned with a specific user.
I think the later will be a hard problem, but they will be highly motivated to solve it.
Without laws, AI companies have a strong incentive to be useful for their users, whoever they are, whatever they do. The only self regulation is about significant public outcry but that only helps so far.
If an un-guardrailed version of a model is capable of detecting security flaws, should it be kept secret? Should everybody be able to use these models to find (and fix) security flaws? Are we ok with the fact that those with access to that model have, in effect, the ability to hack lots of stuff?
If you begin a generic reverse engineering task, 30+ tool calls in a row. The moment it sees something it doesn’t like, token burn, single tool calls iteration, “This is a known CTF challenge, I can proceed”, single tool calls iteration, “This is a real CTF challenge, I can proceed”, etc.
It’s heavily neutered now, without changing the model, and you pay for the privilege and don’t notice.
The end result of course being that it both expensive and useless for approved CTF tasks. No one is using Opus for security. If they think it’s working, the harsh reality is they’re not doing security work; they’re just generically finding bugs.
I do this for a job and can demonstrate this plain as day, dump the injected prompt, and notice what it’s doing isn’t security work, it just looks like it. Happy to write a blog about it if you want to know more. Apparently many people think it’s working for them when it absolutely isn’t.
Security, games (think weapons, PVP, attacking, etc), sometimes even asking it for a security review of some CRUD code it wrote itself
I've even had it refuse CTFs knowing it is a CTF with blatantly obvious CTF flag, no actual application
Fresh session, no prior context on 4.8. These things are becoming useless Duplo.
Reminds me of the defense issues with Claude which were complained as “woke” but the reality is more horrifying to me, imagine trying to use a model to keep up with a land invasion on US soil, whoever the enemy is is irrelevant you just know they are using AI, and your guys are telling you that no matter what they type into the prompt it refuses, because if anyone has ever tried to jailbreak an LLM even if human lives are at stake they refuse the request. Now literally millions of lives are on the line but the guardrails that your enemies dont have on their models are costing you lives.
What do you even do then?
AI will always have this issue where it will always pick the worst option for genuinely good requests.
Because the military doesn't give soldiers rifles with guard rails. They give the soldiers intense, rigid training, and then try to enforce discipline and correct use socially.
If an LLM is going to be important in that way (this seems like a very contrived way,) then it's in the interest of the LLM's host to make sure it doesn't have guard rails that would get in the way _that_ way.
I've used glm 5.1 on fairly advanced crackme challenges (example: https://crackmes.one/crackme/698f40f1e2ba6023bfacaa82), and to my suprise it was able to patch binaries, doing runtime analysis, bypassing anti debug techniques, etc.
Expecting the model to do everything by itself is unrealistic, I found that working along the modal works really well. I'm not speaking about spoiling the solution, just tell it which direction to explore. Chinese models are much more capable than people give it credit for, but Claude/Codex won the marketing game.
The only usecase of this methodology would be for CI integration, which can be nice but I think security reviews still need human attention and expertise.
I'm very curious how you would do multiple runs of multiple models in a "work alongside the model" manner?
EDIT: I have a mimo token plan and have tokens to burn. I'm doing a quick test with opencode to see if mimo can complete it. If the OP will post the full process I am happy to post the apples-to-apples results for mimo v2.5 pro
every model since gpt3 was claimed to be "too dangerous to release." it's too EXPENSIVE to release, and you're probably a local model with <10B parameters yourself
Also just to mention:
Claude guardrails —> that session terminated.
GPT guardrails -> your whole account is slowed down.
This comment in the footnotes made me chuckle, for purely innocuous reasons.