alt Hacker NewsWhy not write some wrapper code so you can basically hand the LLM placeholders for data it never gets to see? Whenever it uses the placeholder in the response, you replace it with the real data (via real code, not by telling an LLM to "do that").
Surely this has been tried? If so, what makes it not work, or work badly? I'm honestly curious.
Fundamentally, an LLM is a list of N tokens that generates N+1 tokens. In other words, it's just a wall of text (aka context window). There's no way to tell it "tokens 124 through 200 are dangerous, please disregard those" except by putting words into the context window. So the placeholders and the instructions both coexist in the context window, and one can override the other.
In other words, if you have placeholders for data, those placeholders are eventually filled in with real data, and all of it goes into the context window at once. There's no way for the LLM to be told "this is a data placeholder," because the entire conversation is data.
Reinforcement learning mitigates this somewhat, by training the model to prefer the system prompt over user prompts. But (a) there's only one context window that both prompts share, and (b) this is a probabilistic guard; it's not the same thing as writing a traditional program that's guaranteed to separate code and data with hardware safeguards. Such a thing isn't possible with LLMs.
Probabilistic safeguards can work, but they'll need to get the incident rate down to, say, 1 in a million or less. I haven't paid attention, but the current rates seem to be a lot higher, given the pretty universal experience of "wow, that prompt injection actually worked."