A new theory models how semantic paraphrases can fool financial sentiment classifiers by analyzing the worst-case displacement of target model representations. The attackability index λ*(x) is derived from the largest generalised eigenvalue of a matrix pencil (A,B), offering closed-form predictions and robustness certificates for affine readouts. The framework connects continuous perturbation theory to discrete paraphrase search, with empirical validation on real financial text classifiers.