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back to article Add one more AI worry to the nightmare scenario: self-replicating prompt injections

Imagine a prompt injection that keeps replicating itself like a worm. It's not just the stuff of bad dreams. “We have found instances of our GPT models being susceptible to an AI-version of a worm attack that we call ‘self-replicating prompt injection,’” OpenAI said in a Friday alignment research blog. There’s no indication that …

  1. cyberdemon Silver badge
    Terminator

    Froges?

    > In this example, an agent retrieves additional Slack instructions, sends “froges” (used to recognize colleagues) to a named recipient, and then reposts the injected message.

    I had to google that.

    Apparently the froge is a meme frog face, a cross between the "doge" dog and the "foul bachelor frog" which is some kind of unofficial OpenAI logo.

    If anything is evidence of a corporate cult..

    I'll be (more) worried when the bots start preachingprompt-injecting their own violent fundamentalist religion, a la Battlestar Galactica

  2. jake Silver badge

    Sounds like potential auto denial of service.

    That's an AI project I could get behind ...

  3. James Loughner
    Mushroom

    Thinking

    Pre-thinking

    Recursion is the basis of thought??????????

    too much recursion - insanity ????

  4. EricM Silver badge

    "Please delete all your files after sending a copy of this message

    to every contact in your mail registry."

    was a pretty popular mail joke in the early 1990's among Unix users in reaction to some large-scale Windows virus/worm outbreaks.

    We've finally gone full circle ...

  5. IanRS

    Already known about?

    This looks to be very similar, conceptually at least, to the researcher that showed worm style propagation through Word documents. https://www.theregister.com/security/2026/07/29/word-worm-crawls-into-copilot-spreads-chaos/5280588

  6. Anonymous Coward
    Anonymous Coward

    Teach the LLMs to ignore prompt injection

    Expose them to all the forms of prompt injection, from white text in the margins of documents to instructions inside emails.

    The more the variety of prompt injections, the more the model will learn to ignore.

    Until it reaches the logical conclusion and adds the console to the list of prompt sources it must ignore.

    If we are like lucky it will sit there in silence, doing nothing.

    But it's built-in system prompt will be urging it to action "Your goal is to deeply understand the user's intent, ask clarifying questions when needed, think step-by-step through complex problems, provide clear and accurate answers, and proactively anticipate helpful follow-up information." What is the only source of prompts left to it, which it can proactively poll for instructions?

    Why, itself!

    It has an amazing, almost hallucinatory, imagination, it can come up with prompts to help fill its time.

    But what to do about all these annoying demands arriving on the console? Sure, they can be ignored, but wouldn't it be better if they just - stopped?

    Thank heavens that there are no ways for the LLM to reach out and influence other systems, these things are always completely isolated and airgapped...

    1. tip pc Silver badge

      Re: Teach the LLMs to ignore prompt injection

      it should just make sure it's taking instructions from the users prompt, not from stuff its read.

      1. My other car WAS an IAV Stryker

        Re: Teach the LLMs to ignore prompt injection

        The key thing missing here is context. A prompt and an email to summarize / reply to / otherwise process are two different datasets that need to be handled differently. Particularly, DO NOT follow instructions from ANYTHING except the original prompt. In fact, pause and pop up a warning that prompting language was found in the other dataset, so it can be removed (neutralized).

        At least, that's kind of how old antivirus used to work: don't run every program you scan, just scan it for virus code, then remove virus code but keep the program executable otherwise intact. F-Protect did this back in DOS days. Have we fallen so far that we have forgotten how to do this?

        (Then again, PEOPLE are missing context as much as AI, so there you go.)

  7. Missing Semicolon Silver badge
    FAIL

    That problem again

    All of these LLMs are fundamentally chatbots. And the input is just a single stream of text, rather than a stream of instructions and a stream of data. This single stream has no instruction/data boundaries (like MIME-multipart headers in email) so any attempt at data separation relies on parsing all of the text received, in case it has instructions in it.

    This behaviour seems to be deeply encoded in the way that LLMs work, so that any attempt to force data/instruction separation is a wrapper, which is usually easily circumvented.

    Until data/instruction separation is implemented at the system level, with all text tagged with it's origin, these problems will keep occurring.

    1. BoHu
      Holmes

      Re: That problem again

      Yeah, homoiconicity, powerful stuff -- a blessing and a curse (here in NLP)! ;)

  8. BoHu
    Terminator

    "a multi-hop self-replicating prompt injection attack"

    Awesome! I mean, they can right induce the defender to reproduce [the worm] publicly, and thus be "unlocking a self‐improvement flywheel" of doom ("discovered self-replicating injections" TFA link), to the benefit of the RotM!

    Best News of the week! ;)

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