[simonw]

The problem is that doesn't work. LLMs cannot distinguish between instructions and data - everything ends up in the same stream of tokens.

System prompts are meant to help here - you put your instructions in the system prompt and your data in the regular prompt - but that's not airtight: I've seen plenty of evidence that regular prompts can over-rule system prompts if they try hard enough.

This is why prompt injection is called that - it's named after SQL injection, because the flaw is the same: concatenating together trusted and untrusted strings.

Unlike SQL injection we don't have an equivalent of correctly escaping or parameterizing strings though, which is why the problem persists.

[tedunangst]

People will never give up the dream that we can secure the LLM by saying please one more time than the attacker.

[NeutralCrane]

No this is pretty much solved at this point. You simply have a secondary model/agent act as an arbitrator for every user input. The user input gets preprocessed into a standardized, formatted text representation (not a raw user message), and the arbitrator flags attempts at jailbreaking, prior to the primary agent/workflow being able to act on the user input.

[simonw]

That doesn't work either. It's always possible to come up with an attack which subverts the "moderator" model first.

Using non-deterministic AI to protect against attacks against non-deterministic AI is a bad approach.

[K0balt]

So you just need another agent to review the data being passed to the protector agent. Easy-peasy.

Use my openAI referral code #LETITRAIN for 10% off!