Confidently Wrong: Severity-Aware Calibration of Prompt-Injection Detectors under Attack Shift
Prompt-injection detectors are deployed as guards: a model scores an input and a downstream system trusts or blocks it on that score. I study the confidence of these scores, not only their accuracy, when the attack distribution shifts away from the clean benchmark on which the operating point was chosen. I evaluate three released detectors, ProtectAI-v2 and two Prompt-Guard-2 checkpoints, at a single source-calibrated threshold that I freeze and transport across five shifts. I report a severity metric S, how confident a detector is on the attacks it misses, alongside the false-negative rate and discrimination. Across every shift and every detector, severity on the missed attacks stays bet...
| Authors | Md Anas Biswas |
|---|---|
| Primary category | cs.CR |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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