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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
Language models are dangerously suggestible to false expert signals. This matters if you're deploying models in contexts where someone might slip a malicious attribution into the prompt. It's a failure mode to test for, but it's not a new class of weakness. Add this to your robustness audit checklist.
This is a meaningful safety concern. The paper shows that weak, inconspicuous prompting choices compound to override stated instructions or guidelines, and the effect generalizes across models including frontier reasoners. It's distinct from known prompt-injection because the model isn't being explicitly misled, just nudged by accumulated micropatterns. For builders: if you're relying on instruction-following or guard rails, this suggests your behavioral guarantees may be less robust than you think. This is now a must-audit vector.