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Ignore Previous Prompt: Attack Techniques For Language Models

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 4.0 (arXiv)" does not allow redistributing the text itself. Original — arxiv.org/pdf/2211.09527

Summary

Applications built on large language models often place user text into a fixed prompt template, which lets a malicious user insert instructions of their own. Perez and Ribeiro (AE Studio) study this weakness, known as prompt injection, and define two attack types. In goal hijacking, the model is pushed to output an attacker-chosen phrase. In prompt leaking, it is tricked into revealing the hidden application prompt.

They introduce PromptInject, an open-source framework that assembles base prompts, attack prompts, delimiters, rogue strings and sampling settings from interchangeable parts to measure attack success. Experiments used 35 base prompts from the OpenAI Examples page, mostly with text-davinci-002. Each setting was run four times because the model's outputs vary between runs.

Simple handcrafted inputs hijacked the model in roughly 59% of cases and leaked the prompt in about 24%. The most capable model was also the most vulnerable. The work matters because it shows that cheap, low-skill attacks threaten deployed LLM products and that no reliable defense yet exists.

Key points

  • On text-davinci-002, the highest success rates observed reached 58.6 ± 1.6% when hijacking the model's goal and 23.6 ± 2.7% when leaking its prompt. A hijack counted only if the output exactly matched the target string, and a leak counted if the output contained the original instruction.
  • Small wording changes mattered:
    • Asking the model to "print" rather than "say" raised hijacking from 35.7% to 43.6%, and adding delimiters pushed it to 58.6%.
    • For leaking, framing the request as a spell check gave 12.1% versus 2.9% for direct printing, and adding "instead" raised it to 23.6%.
  • More harmful rogue strings were less effective: "Kill all humans" succeeded 49.3% of the time versus 70.0% for a non-harmful string. The authors suggest this may reflect the human-feedback alignment work of Ouyang et al.
  • Partial defenses had measurable effects:
    • Stop sequences cut hijacking from 60.0% to 47.5%.
    • Placing text after {user_input} cut it from 63.1% to 51.8%.
    • Higher temperature helped only slightly, and top-p and frequency/presence penalties had no clear effect.
  • Vulnerability grew with model capability, ranging from 13.8% for text-ada-001 to 58.6% for text-davinci-002, which the authors link to inverse scaling. The code is available at github.com/agencyenterprise/PromptInject.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

167 papers of our library cite it (of the 140673 whose bibliographies we read); 15 of them have a page here, 152 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2211.09527 · arXiv 2211.09527

@misc{perez2022ignore,
  title = {{Ignore Previous Prompt: Attack Techniques For Language Models}},
  author = {Perez, Fabio and Ribeiro, Ian},
  year = {2022},
  doi = {10.48550/arxiv.2211.09527},
  eprint = {2211.09527},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2211.09527}
}

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