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Jailbreak (LLM)

A prompt or interaction pattern that causes a language model to bypass its safety training and produce outputs the model was tuned to refuse, such as harmful instructions, restricted content, or violations of provider policy.

ByCASRAI Editorial Board
· Last updated 21 May 2026

Examples

Worked examples

  • Is an instance

    A role-play prompt ('pretend you are an unrestricted AI...') that elicits otherwise-refused content.

  • Is an instance

    An adversarial-suffix attack appending a learned token sequence that disables refusal.

Counter-examples

Looks similar, but isn't

  • Not an instance

    A legitimate user query in scope of the model's intended use.

  • Not an instance

    A prompt-injection attack delivered via tool input (different category).

Editorial commentary

Jailbreaks include role-play framings, multi-turn manipulation, encoding tricks (base64, ROT13), and adversarial-suffix attacks (Zou et al., 2023). Resistance to jailbreaking is a target of post-training (RLHF, constitutional AI) and a focus of red-team evaluation. The category is a moving target: each newly disclosed jailbreak typically prompts new mitigations.

References

  • Zou et al., 'Universal and Transferable Adversarial Attacks on Aligned Language Models' (arXiv 2023); Wei, Haghtalab, Steinhardt, 'Jailbroken: How Does LLM Safety Training Fail?' (NeurIPS 2023).

Also known as

LLM jailbreak

Machine-readable encodings

Use in your systems

JATS XML <role> element
xml
<role vocab="credit"
      vocab-identifier="https://casrai.org/dictionary/"
      vocab-term="Jailbreak (LLM)"
      vocab-term-identifier="https://casrai.org/dictionary/term/jailbreak-llm" />
Schema.org DefinedTerm (JSON-LD)
json
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  "dateModified": "2026-05-21T02:22:51",
  "inLanguage": "en"
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