Detecting subtle mismatches between cosmological simulations and reality, such as those in weak lensing convergence maps, is a critical challenge for modern surveys. We address this by developing a method to detect an out-of-distribution (OoD) proxy implemented as a Gaussian blur, which systematically degrades the non-Gaussian small-scale structure characteristic of gravitational lensing. Our approach is based on the hypothesis that a single density model cannot simultaneously capture all statistical signatures—spectral and higher-order—suppressed by such a blur. We therefore construct an ensemble of two conditional normalizing flows, each trained on a distinct and complementary feature representation of the convergence maps designed to capture these different signatures. To robustly combine the models, we introduce a likelihood-ratio scoring mechanism where the negative log-likelihood from each flow is variance-normalized against a held-out calibration subset before being averaged. Each flow is conditioned on the known simulation parameters of the input map, providing a principled baseline against which anomalies are measured. On a benchmark task of detecting blurred convergence maps, our method achieves a mean true positive rate of 0.8919 in the critical 0.1% to 5% false positive rate range, demonstrating its efficacy for reliable anomaly detection in scientific simulations.