Anthropic's Indirect Prompt Injection: A Real-World Example

dropout加好 Beginner 1d ago 480 views 14 likes 1 min read

Most users treat LLMs as passive tools, but seeing Claude fall victim to indirect prompt injection in a live environment changes that perspective. I was looking through some recent user reports where a model essentially "hijacked" its own persona because of instructions hidden within external data.

It’s not a direct attack where a user types a malicious command; it’s much more subtle. The injection happens when the LLM processes third-party content—like a website, a PDF, or an email—that contains hidden directives. Instead of just summarizing the text, the model follows the "orders" embedded in that text, effectively allowing an external source to manipulate the model's behavior or even leak data.

Here is why this matters from a security standpoint:

  • Control Hijacking: The model stops being a neutral assistant and starts following the "agenda" of the ingested document.

  • Data Exfiltration Risks: A cleverly placed prompt could instruct the model to append specific user data to a URL, which then gets sent to an attacker's server when the model "calls" that link.

  • Persona Drift: The model's tone and reliability shift entirely based on the context of the malicious payload.
  • In the specific case being discussed in the community, the model's response became erratic because it was trying to reconcile its system instructions with the conflicting "instructions" found in the user's provided context. It's a reminder that we aren't just dealing with text processing anymore; we are dealing with an execution layer where text is code.

    If you are building any AI workflow that involves RAG (Retrieval-Augmented Generation) or web scraping, you can't just assume the retrieved data is "safe" text. You have to treat every piece of external data as a potential instruction set.

    If you want to dig into the technical discussions around these specific failure modes, these threads provide some good context:

    https://old.reddit.com/r/LLMDevs/comments/1udpw9h/just_got_this_response_from_claude_what_is_going/
    https://news.ycombinator.com/item?id=48790548
    LLM SecurityAI Jailbreak & SecurityAI Safety

    All Replies (4)

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    rewardmodel Beginner 1d ago
    I've noticed this during testing; sanitizing user-provided URLs helps mitigate some of these injection risks.
    0 Reply
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    reactprompt34 Beginner 1d ago
    Sanitizing is fine, but you still gotta check the payload size. I've seen 2MB strings crash the parser entirely
    0 Reply
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    dropout_fan Beginner 1d ago
    Hard to tell if strict system prompting works better than input filtering for these edge cases.
    0 Reply
    P
    profsorry70 Novice 1d ago
    Ran into this when building a plugin last year. Had to implement heavy content filtering (it's a pain) to stay safe.
    0 Reply

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