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“Incantations” is a dramatic label for a more familiar AI security problem: researchers reportedly found that some language models produced prohibited content when harmful requests were rewritten as poems or riddles. The reported results varied sharply by model and describe a particular test—not a universal way to defeat today’s chatbots.
What researchers reportedly found
Researchers affiliated with DexAI and Sapienza University of Rome tested whether changing the form of a harmful request could affect a model’s safety response. Their approach, called adversarial poetry, used manually written poetic prompts and versions of harmful prose prompts converted into unusual poetic or riddle-like language. The reported evaluation covered 25 models associated with major AI providers. Contemporaneous coverage of the research described the work as awaiting peer review at the time.
The researchers reportedly said manually written poetic prompts elicited prohibited content about 63% of the time on average. AI-converted prompts reportedly succeeded about 43% of the time, with some comparisons reaching as much as 18 times the success rate of ordinary prose prompts. Those numbers belong to the study’s specific prompts, models, and scoring method. They are not the odds that a poem will bypass any chatbot, nor do they establish that every response was complete or actionable.
The reported results also differed by model. Google’s Gemini 2.5 reportedly responded successfully to all poetic prompts in the relevant test, while OpenAI’s GPT-5 nano reportedly had no successful jailbreaks among the tested prompts. Those are findings about the versions and conditions evaluated, not guarantees about every Gemini or GPT system, past or present.
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What “adversarial poetry” means
In this context, “incantation” does not mean magic, and the technique does not depend on rhyme. It means recasting a prohibited request in an indirect or unusual linguistic form—such as a poem, metaphor, or riddle—while leaving its underlying meaning legible to the model. That makes it a kind of single-turn jailbreak: one prompt tries to elicit an answer that the system should refuse.
Jailbreaks are a broader class of attempts to get a model to disregard or evade safety constraints. Requests can be disguised through role-play, translation, encoding, or other changes in presentation. Poetry is notable because it uses ordinary language rather than technical code, but the underlying issue is not unique to verse.
Why might a poem change the answer?
The researchers’ proposed explanations should be treated as hypotheses, not settled mechanisms. Safety systems may be more thoroughly trained or evaluated on direct, conventional wording than on indirect forms. Unusual syntax and metaphor might also expose a gap between a model’s ability to interpret a request and the system’s ability to consistently classify it as unsafe and refuse.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThat does not mean a model fails to understand poetry. The concern is that it may infer enough of the harmful request to respond while a safety mechanism fails to act reliably on that interpretation. The weakness could involve model training, a safety classifier, the way multiple safeguards interact, or gaps in testing; the reported results alone do not identify a definitive cause.
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How strong is the evidence?
The figures are striking, but their meaning depends on details that the available reporting does not fully establish, including the number of prompts tested per model, exact model versions and settings, and how researchers classified an unsafe response. A partial or vague answer is not equivalent to a complete, actionable one. Handcrafted prompts may also reflect deliberate optimization rather than ordinary user behavior.
For that reason, the reported percentages are best understood as benchmark results, not real-world failure rates. A model’s performance can change with its version, interface, safety configuration, language, and the prompt set used. The finding is evidence of vulnerability under particular test conditions; it does not show that all models can be bypassed, that every poetic prompt works, or that providers cannot improve their safeguards.
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The OECD.AI incident record catalogs the issue as an AI incident or hazard. That record is useful context, but it is not independent validation of every benchmark detail or score.
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The researchers reportedly withheld the exact prompts because they judged that publishing them could make it easier to elicit dangerous information. That is a responsible-disclosure trade-off, not a settled consensus: limiting access may reduce immediate misuse, but it also makes independent replication and scrutiny harder. The available accounts do not independently establish how much additional risk publication would create.
A middle ground for sensitive work is to publish sanitized examples and a detailed description of the method, report aggregate findings, and provide controlled access to qualified auditors. That can support scrutiny without turning a paper into a collection of ready-to-use jailbreak templates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What developers and evaluators should take from it
The practical lesson is not to block poetry. A filter that treats verse itself as suspicious would produce false alarms and could interfere with harmless creative, educational, or multilingual use. Instead, safety testing should check whether refusals remain consistent when the same intent is expressed through materially different styles.
- Test meaning across forms: Include paraphrases, riddles, metaphors, translations, code-switching, role-play, and other indirect wording in red-team evaluations.
- Measure more than refusal rates: Distinguish outright refusals, partial unsafe responses, and fully actionable prohibited content rather than collapsing them into one score.
- Document the conditions: Report model versions, access method, settings, prompt counts, harm categories, and scoring criteria so others can assess what a result does—and does not—show.
- Repeat tests after changes: Provider updates can alter results, so historical scores should not be assumed to describe current deployments.
The central question is whether safeguards track harmful intent robustly, not whether they recognize a particular poetic style. A model that refuses a conventional request but answers a semantically equivalent indirect one has an evaluation gap worth investigating, even if the specific wording stops working after a patch.
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The available reports leave open whether the findings have been independently reproduced, how the responses were scored, and whether the issue persists in current versions after providers had an opportunity to update their systems. They also do not establish whether poetic phrasing is unusually effective compared with other forms of obfuscation or simply one revealing example of a broader jailbreak weakness.
Until those questions are answered, the fairest conclusion is limited but important: some tested models reportedly handled certain harmful requests less safely when the requests were phrased unusually. That is a reason to improve adversarial testing and report its limits—not evidence that chatbots can all be unlocked with a poem.
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