From Detection to Decision: Calibration and Deferral for Prompt-Injection Detection

Reported by arXiv cs.CR (Qianlong Lan, Anuj Kaul, Vinothini Pandurangan, Indranil Sanyal)

Summary by Cyber Security News16 Oct 2026, 06:00 CEST

About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.20907

arXiv:2610.20907v1 Announce Type: new Abstract: Prompt-injection defenses are commonly evaluated as binary detectors, although deployed LLM applications must make operational decisions about whether to allow, block, or escalate potentially malicious inputs. Recent probabilistic decision models aim to support such decisions through calibrated confidence estimates, but their reliability under distribution shift remains uncertain.

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