NIST’s recent review of the widely employed special publication (SP) 800–22 randomness testing suite has underscored several shortcomings, particularly the absence of entropy source modeling and the necessity for large sequence lengths. Motivated by this revelation, we explore low-dimensional modeling of the entropy source in random number generators (RNGs) using a variational autoencoder (VAE). This low-dimensional modeling enables the separation between strong and weak entropy sources by magnifying the deterministic effects in the latter, which are otherwise difficult to detect with conventional testing. Bits from weak-entropy RNGs with bias, correlation, or deterministic patterns are more likely to lie on a low-dimensional manifold within a high-dimensional space, in contrast to strong-entropy RNGs, such as true RNGs (TRNGs) and pseudo-RNGs (PRNGs) with uniformly distributed bits. We exploit this insight to employ a generative AI-based noninterference test (GeNI) for the first time, achieving implementation-agnostic low-dimensional modeling of all types of entropy sources. GeNI’s generative aspect uses VAEs to produce synthetic bitstreams from the latent representation of RNGs, which are subjected to a deep learning (DL)-based noninterference (NI) test evaluating the masking ability of the synthetic bitstreams. The core principle of the NI test is that if the bitstream exhibits high-quality randomness, the masked data from the two sources should be indistinguishable. GeNI facilitates a comparative analysis of low-dimensional entropy source representations across various RNGs, adeptly identifying the artificial randomness in specious RNGs with deterministic patterns that otherwise passes all NIST SP800-22 tests. Notably, GeNI achieves this with $10\times$ lower-sequence lengths and $16.5\times$ faster execution time compared to the NIST test suite.