A flexible, distribution-free framework for collective outlier detection and enumeration is introduced, targeting situations in which the presence of outliers can be detected powerfully even though their precise identification may be challenging due to the sparsity, weakness, or elusiveness of their signals. The methodology builds on recent advances in conformal inference and integrates classical ideas from multiple testing, locally most powerful and adaptive rank tests, and nonparametric large-sample asymptotics.