This preprint introduces the Collapse Index (CI) CrackTest, a morphology-aligned perturbation framework for evaluating robustness and collapse inheritance in large language models (LLMs). The study demonstrates that CI CrackTest, a bounded and lightweight perturbation protocol originally developed for brittleness diagnostics, can quantify systematic error propagation across morphologically-related variants in controlled classification tasks. Using a 186-variant perturbation suite spanning eight morphological families (lexical, syntactic, ambiguity, semantic, compression, noise, boundary, contrastive), the evaluation analyzes collapse inheritance, family-specific brittleness, and confidence behavior under perturbation. Across three frontier models (GPT-4o, Claude Haiku 4.5, Gemini 2.5 Flash), the framework identifies consistent robustness signatures, including 11–16% collapse inheritance rates, 9–28% semantic brittleness, and 0.10–0.17 confidence masking deltas, independent of architecture or training regime. The framework is presented as a behavioral diagnostic tool for robustness analysis. CI CrackTest does not expose internal perturbation heuristics, variant-generation mechanisms, or scoring systems. All reported findings are based solely on externally observable model outputs under controlled morphological perturbation.All internal algorithms, classification mechanisms, and inference procedures remain proprietary to Collapse Index Labs. Project page: https://collapseindex.orgLicensed under CC BY-NC-ND 4.0.