Suppose a backtest pipeline is evaluated at a historical cutoff T. Let F_T be the information legitimately available by T. I am testing temporal causality by applying a mutation M_T that leaves F_T unchanged but perturbs only information that should still be unavailable, then rerunning the pipeline and checking whether the historical artifact A_T changes. Formally, the test is: M_T(D)|F_T = D|F_T A_T(M_T(D)) = A_T(D) A violation is evidence that some supposedly historical state depends on unavailable information. The problem is mutation adequacy. In a controlled fault-injection benchmark I planted six leakage classes: centered rolling windows, full-sample normalization, global feature selection, forward labels that had not matured by the training cutoff, completed bars treated as available at their open timestamp, and same-open retroactive execution. For the first five classes, targeted mutations exposed all 3,000 planted faults, while causal controls had 0 violations in 3,600 trials. But for the execution fault, a random perturbation of the unavailable close exposed only 286/600 planted faults. A targeted sign-flip adversary exposed 600/600. So a statement such as 'the pipeline passed a future-mutation test' seems weak unless the mutation family itself is characterized. A weak mutation may simply fail to excite the hidden dependency. What is a defensible way to quantify mutation adequacy for this kind of look-ahead-bias test? My current candidates are: a mutation score over deliberately planted temporal faults; operator coverage by leakage class (normalization, selection, labels, bars, execution, etc.); multiple independent or targeted adversaries per cutoff; and confidence bounds on the planted-mutant kill probability. How should equivalent or non-activating mutants be treated in this setting, and is there a more standard statistical or software-testing formulation that maps cleanly to backtest leakage detection?