This was a fascinating read, and I’ve already shared it with some folks I know might find it interesting. It’s got me wondering if/when prompt injection could ever be “solved” and even if it is...
This was a fascinating read, and I’ve already shared it with some folks I know might find it interesting.
It’s got me wondering if/when prompt injection could ever be “solved” and even if it is fixed for future models, how many people would stick with the older models because they’re cheaper.
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I really don't understand why they let different kinds of ideas all ride on the same tokens. It's a neural net. You don't need to try to teach it XML; you can activate different input neurons...
I really don't understand why they let different kinds of ideas all ride on the same tokens.
It's a neural net. You don't need to try to teach it XML; you can activate different input neurons based on the provenance of different text. It can have direct access to the fact that these tokens are user-flavored and those tokens are thought-flavored. Even if you do it by just doubling out all the tokens and doing nothing to the network architecture.
Hell, it's called a "decoder only transformer" for a reason. When these things were Google Translate they had no trouble dealing with one channel coming in in English and one going out in Japanese, and they didn't constantly mix them up. Stick some more heads on there!
"What part of the mind is this idea in" seems like a fundamental concern in mind design, and we're just sort of raw dogging it. We have given trillions of dollars and all of our RAM to deeply unserious people.
Role confusion is genuinely important when it comes to prompt injection, or even just understanding LLM behavior. Even the best models get confused about who said what, which can be dangerous....
Role confusion is genuinely important when it comes to prompt injection, or even just understanding LLM behavior. Even the best models get confused about who said what, which can be dangerous.
However I'm confused by a couple things:
gpt-oss-20b
Is the model mentioned in their testing multiple times. That's a (comparatively) small, dumb, outdated model. Nothing you discover against that model is likely to generalize to models magnitudes larger with much more extensive post training. I don't understand the value of using it for research, except possibly that's it's cheap.
I'm also confused by their assertions about CoT (model thinking) blocks. Using Claude models as an example, the outputted thinking blocks are summaries generated by a smaller model. The model doing the real inference ignores them completely. They only exist for the user to read. I don't think any of the frontier models output real thinking, in order to protect against distillation. The whole CoT concept in the paper seems to be about behavior that no longer exists.
Even if Anthropic is hiding the real thinking text from the client, it still existed in the model context, with some kind of delimiter and with a characteristic style. And last time I poked around...
Even if Anthropic is hiding the real thinking text from the client, it still existed in the model context, with some kind of delimiter and with a characteristic style.
And last time I poked around in the guts of Claude Code, Anthropic was sending you signed data for model turns and hoping you would send it back to them to reconstruct the context, instead of keeping all the state on their end. So unless the thinking blocks get completely dropped as the chat progresses (which they might; they might not want to keep them going forward because they might not be needed), they're probably hiding somewhere in the client's state storage.
As I understand it, they send an encrypted block in lieu of thinking blocks, for which the client does not have the key. This keeps the cache working. The only visible thinking the client gets is...
As I understand it, they send an encrypted block in lieu of thinking blocks, for which the client does not have the key. This keeps the cache working. The only visible thinking the client gets is summarizer results, which are informational only, not part of the actual session context.
So in a way they're hiding as you said, but they can't be found. If they could, there would be no barrier for distillers.
This was a fascinating read, and I’ve already shared it with some folks I know might find it interesting.
It’s got me wondering if/when prompt injection could ever be “solved” and even if it is fixed for future models, how many people would stick with the older models because they’re cheaper.
I really don't understand why they let different kinds of ideas all ride on the same tokens.
It's a neural net. You don't need to try to teach it XML; you can activate different input neurons based on the provenance of different text. It can have direct access to the fact that these tokens are user-flavored and those tokens are thought-flavored. Even if you do it by just doubling out all the tokens and doing nothing to the network architecture.
Hell, it's called a "decoder only transformer" for a reason. When these things were Google Translate they had no trouble dealing with one channel coming in in English and one going out in Japanese, and they didn't constantly mix them up. Stick some more heads on there!
"What part of the mind is this idea in" seems like a fundamental concern in mind design, and we're just sort of raw dogging it. We have given trillions of dollars and all of our RAM to deeply unserious people.
Role confusion is genuinely important when it comes to prompt injection, or even just understanding LLM behavior. Even the best models get confused about who said what, which can be dangerous.
However I'm confused by a couple things:
Is the model mentioned in their testing multiple times. That's a (comparatively) small, dumb, outdated model. Nothing you discover against that model is likely to generalize to models magnitudes larger with much more extensive post training. I don't understand the value of using it for research, except possibly that's it's cheap.
I'm also confused by their assertions about CoT (model thinking) blocks. Using Claude models as an example, the outputted thinking blocks are summaries generated by a smaller model. The model doing the real inference ignores them completely. They only exist for the user to read. I don't think any of the frontier models output real thinking, in order to protect against distillation. The whole CoT concept in the paper seems to be about behavior that no longer exists.
Am I missing something?
Even if Anthropic is hiding the real thinking text from the client, it still existed in the model context, with some kind of delimiter and with a characteristic style.
And last time I poked around in the guts of Claude Code, Anthropic was sending you signed data for model turns and hoping you would send it back to them to reconstruct the context, instead of keeping all the state on their end. So unless the thinking blocks get completely dropped as the chat progresses (which they might; they might not want to keep them going forward because they might not be needed), they're probably hiding somewhere in the client's state storage.
As I understand it, they send an encrypted block in lieu of thinking blocks, for which the client does not have the key. This keeps the cache working. The only visible thinking the client gets is summarizer results, which are informational only, not part of the actual session context.
So in a way they're hiding as you said, but they can't be found. If they could, there would be no barrier for distillers.