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Another day, another preprint paper shocked that it’s trivial to make a chatbot spew out undesirable and horrible content. [arXiv]

How do you break LLM security with “prompt injection”? Just ask it! Whatever you ask the bot is added to the bot’s initial prompt and fed to the bot. It’s all “prompt injection.”

An LLM is a lossy compressor for text. The companies train LLMs on the whole internet in all its glory, plus whatever other text they can scrape up. It’s going to include bad ideas, dangerous ideas, and toxic waste — because the companies training the bots put all of that in, completely indiscriminately. And it’ll happily spit it back out again.

There are “guard rails.” They don’t work.

One injection that keeps working is fan fiction — you tell the bot a story, or tell it to make up a story. You could tell the Grok-2 image bot you were a professional conducting “medical or crime scene analysis” and get it to generate a picture of Mickey Mouse with a gun surrounded by dead children.

Another recent prompt injection wraps the attack in XML code. All the LLMs that HiddenLayer tested can read the encoded attack just fine — but the filters can’t. [HiddenLayer]

I’m reluctant to dignify LLMs with a term like “prompt injection,” because that implies it’s something unusual and not just how LLMs work. Every prompt is just input. “Prompt injection” is implicit — obviously implicit — in the way the chatbots work.

The term “prompt injection” was coined by Simon WIllison just after ChatGPT came out in 2022. Simon’s very pro-LLM, though he knows precisely how they work, and even he says “I don’t know how to solve prompt injection.” [blog]