To meet a chosen false-positive budget, set a detector’s threshold from representative benign scores. Attack examples do not determine the cutoff: they show how many attacks it catches at that cutoff and help you decide whether the false-alarm budget is worth accepting. This holds for a fixed score function and benign traffic that resembles the traffic you expect to see; it is not a guarantee that a threshold will transfer to every future mix.
What the threshold controls
Suppose a detector assigns each input a score, and inputs scoring above a cutoff are flagged. The false-positive rate is the share of benign inputs that the cutoff flags. If you set a maximum acceptable false-positive rate, the threshold is a quantile of the benign score distribution: choose a cutoff high enough that only the allowed fraction of benign examples lies beyond it.
The exact direction depends on the score convention. If higher scores mean “more suspicious,” the threshold is an upper-tail cutoff; if lower scores mean “more suspicious,” it is a lower-tail cutoff. The principle is the same: use benign scores to constrain benign alerts.
A numeric score is not automatically comparable across detectors. A cutoff of 0.5 has no universal meaning when models may use different score scales. Even within one detector, the score’s practical meaning depends on how it behaves on the benign traffic in the relevant setting.
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How to choose and evaluate a threshold
- Fix the detector and score definition. Threshold calibration applies to a particular scoring function. Changing the model, preprocessing, or score interpretation can change the score distribution and require recalibration.
- Set a false-alarm budget. Decide how many benign inputs you can tolerate flagging, based on the operational cost of alerts and the cost of missed attacks.
- Collect representative benign examples. Score benign inputs from the sources, domains, and input forms you expect in deployment. Estimate the relevant quantile and select a threshold using a method appropriate to the desired level of confidence.
- Evaluate attacks at that threshold. Measure the true-positive rate—the share of attack examples caught—at the selected operating point. Attack labels tell you what detection performance the budget buys; they do not alter the cutoff needed to satisfy that budget on benign data.
- Validate on held-out data and monitor deployment. Measure false-positive and true-positive rates on data not used to choose the threshold. Check whether benign score behavior remains similar after rollout, and reassess calibration if the traffic changes.
Keep the operating point distinct from ranking quality. AUC or another ranking metric can indicate how well a detector orders attacks above benign inputs overall, but it does not establish that a particular threshold meets a useful false-alarm budget.
Why attack labels still matter
“Benign-only” describes the data needed to set a threshold for a fixed false-positive constraint; it does not mean attacks are irrelevant. Without attack examples, you cannot estimate the true-positive rate at that threshold or judge whether the detector catches enough attacks to justify its false alarms. Attack performance and false-alarm control answer different questions, so report both at the same operating point.
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Attack results can also inform the decision about how much false-alarm risk to accept. If a stricter budget leads to too few detected attacks, a team may decide that a higher budget is operationally worthwhile—or that the detector is unsuitable. The budget is a cost decision; the benign distribution determines the cutoff that implements it.
What finite samples can—and cannot—guarantee
A sample quantile is an estimate, not a promise about all future traffic. With a finite calibration set, the observed false-positive rate can differ from the future rate. A nominal empirical target, such as 2%, is not automatically a high-confidence guarantee. Distribution-free order-statistic methods and conformal approaches can provide finite-sample control under specified assumptions, but the guarantee depends on the procedure and on how future benign observations relate to the calibration data.
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Umsonst, Ruths, and Sandberg formalize threshold tuning as quantile estimation and study order-statistic estimators with finite-sample guarantees. Bates, Candès, Lei, Romano, and Sesia study conformal p-values for outlier detection and false-positive control. These methods support the distinction between calibration on reference or benign scores and evaluation on attacks; they do not validate a particular prompt-injection benchmark.
There is no universal sample-count rule for a useful threshold estimate. The number required depends on the target rate, desired confidence, calibration method, score distribution, and assumptions about traffic. The DEV Community article discussed below offers “a few hundred” benign examples as practical guidance for a 2% target, not as a theorem or minimum that applies to every detector.
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Traffic changes can break transfer
A threshold calibrated on one benign mix may not meet the same false-alarm budget on another. Sources, domains, and input forms can have different score distributions. An aggregate calibration result also does not guarantee the same false-positive rate in every subgroup; measure groups separately when their behavior matters to the deployment.
One DEV Community article, published September 30, 2026, reports a re-measurement of a public prompt-injection benchmark using nine open-source detectors. Its figures are author-reported and were not independently reproduced in the sources reviewed here. The article reports 629 attacks and 97 benign tool outputs. At a cutoff of 0.5, it says Prompt Guard 2 caught 6 of 629 attacks (1.0%) with no benign alerts in that sample; it also reports benign medians near 0.999 and 97.9% false-positive rates for deepset-deberta and fmops-distilbert.
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Quick Recap
What to compare when choosing a detector
- Operating-point performance: report false-positive and true-positive rates at the same selected threshold.
- Ranking versus calibration: use ranking metrics such as AUC for ranking quality, but assess threshold performance separately.
- Calibration evidence: include sample size, uncertainty, and whether the reported rate is empirical or comes with a stated finite-sample guarantee.
- Coverage: compare calibration and deployment traffic by source, domain, and input form; check subgroup behavior where it matters.
- Operational costs: make explicit the trade-off between false alarms and missed attacks when selecting the budget.
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