Quantifying and Prioritizing Stealthy Source-Manipulation Threats Against Multi-Source Anomaly Detectors

Security pipelines often fuse several data sources and protect a downstream model with a cross-source anomaly detector. Such detectors are usually judged by their clean false-positive rate and by how well they catch random corruption. Neither metric captures an adversary who controls some sources and targets a specific downstream decision. We propose a threat-quantification score, the \emph{task-conditioned visibility margin}, defined as the least detector evidence a manipulation must reveal per unit of harm to the downstream task. The margin has a closed form, takes about $85\,μ$s per certificate, yields a minimum-evidence attack, and identifies task-relevant directions the detector cannot see. On a 17-channel industrial telemetry benchmark, a detector that flags 82.8\% of random single-channel corruptions is first-order blind on 64.7\% of task/subset pairs in an audit of one- to four-channel subsets. Task-conditioned manipulations cause more than twice as many falsely reassuring errors as generic stealthy ones, and more of them evade the alarm on four of five splits, mainly at small budgets. Used to prioritize audits on held-out subsets, the margin finds a median of four verified failures in ten checks, compared with none for random order and two for alarm-based ranking, and performs comparably to the task-gradient baseline. It also ranks vulnerable configurations of the learned GDN and TranAD detectors and of a five-source crop-yield panel.

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To be appeared at Workshop on AI for Cyber Threat Intelligence (WAITI) 2026 @ ACSAC 2026