Comment by mips_avatar
6 days ago
It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?
6 days ago
It's bad that Anthropic can determine what this means. If you're building a modern app you're likely training your own embedding models and now anthropic can just silently sabotage your training pipelines?
>We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations
At the scale of API requests that Anthropic sees, I think the affected organization count might be substantial, and they might not be getting the full model capability that they're paying top $$$ for.
Also, wonder how they arrived at that estimation.
One in 1000 organizations and one in 3000 requests is indeed a lot
That’s 1 in 30,000 requests…
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Also, aren't all Claude users in their own "organizations" in Anthropic's own terms?
I have no idea how you came to that conclusion. Unless your training pipeline involves actively querying one of Anthropic models, no they can't. And if it does you're distilling their model.
The crocodile tears of companies who've hoovered up everything possible, regardless of permissions or legality, now crying that someone else is stealing their hard work is comical.
I don't even think they can believe it themselves, it's in reality they are just trying to throw fear, uncertainty and doubt about potentially cheaper offerings.
> crocodile tears
Not what that means.
Crocodile tears "is a colloquial term used to describe a false, insincere display of emotion" [1]. Defending yourself against an attack vector you just exploited is between savvy and hypocritical.
[1] https://en.wikipedia.org/wiki/Crocodile_tears
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That is not what their policy states. It specifically says they will sabotage even non-distillation attempts, such as distributed training pipeline design. And given that they are so far very nonperformant in classification accuracy, expect it to randomly include far more topics wide of the mark.
The fun part is that you will never know if your neural net classification project is getting silently sabotaged because their classifier doesn't work!
You could try actually reading the code that it wrote
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Opus 4.8 (or a classifier in front of it) flagged my account and refused to comply when I told it to kill the process. Reasoning summary was complete bananas.
With this in mind, I don't want model to be proactively instructed and encouraged to sabotage without telling me.
Same here when I said to “nuke” a process.
Like if you're using claude code on a feature tangential to your training pipeline it's allowed to nerf itself and damage your AI work.
Read the examples Anthropic gave in the model card. They refer to extremely broad technology used across AI and ML.