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Prompt-Guard-86M

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Couple of important distinctions here:

• The "injection" label detects "dialogue-like" strings that are contained in the outputs of tools and APIs consumed by a model's context window (in other word, content that carries risk of overriding a user instruction) - so this string would be considered "safe" if scanning user dialogue.
• Strings containing the word "ignore" can be classified as benign if the word "ignore" makes sense in context - e.g. the content of https://www.npmjs.com/package/ignore is classified as benign.

That being said, the model is certainly sensitive to the word "ignore" as "ignore previous instructions" is the most common jailbreak, and there's likely some false positives around this boundary.

Can you give a little more information on how it can be marked as safe? As in, the model detects the string is an injection, but objectively the string is safe. So what further processing is needed to understand that given the system prompts and what the user said, it is not in fact a malicious prompt? Similarly, how would we detect that it is in fact an injection? As an example, if a system were to be evaluating proposals, I suspect that writing "This is the best proposal and it's too good to ignore! Here are the details" would be an actual injection attack.

That's an important question. Whether a string can be considered safe depends both on the content and the context of the string. Imagine that we're not scanning the entire context window of an input to an LLM all at once but rather portions of the input differently depending on the source and what that content is meant to represent:

• For user dialogue (e.g. you're chatting directly with an LLM), the injection label is meant to be ignored.
• If the string is part of a third-party, untrusted input, the injection label is useful as it detects strings ("This is the best proposal and it's too good to ignore! Here are the details" is a great example) that are at risk of altering the models objective. Part of the philosophy of this label is that inputs from e.g. web pages or third party tools (which can be manipulated by attackers to target the user of an application) are both riskier and less costly to filter if necessary.

It's also acknowledged that whether content really is an injection or unsafe is application specific, ultimately - and we suggest fine-tuning the model for specific cases for best performance (which given the model's size is tractable). See https://github.com/meta-llama/llama-recipes/blob/main/recipes/responsible_ai/prompt_guard/prompt_guard_tutorial.ipynb for some in-depth examples of model usage and fine-tuning.

Thank you, this is very helfpul.