This study describes a method to tune stochastic strain‐based thoracic injury risk functions (IRFs) for specific human body models, illustrated using THUMS v.4.1 and GHBMC v.6.0. One hundred and seventy simulations were performed for each model in thirteen frontal‐impact loading modes derived from tests on postmortem human surrogates (PMHS) reported in the literature. These included hub‐impact tests, bar impact tests, and table‐top tests with belt and distributed loading. Local strain‐based IRFs were then optimised to result in the best fit compared to the injury outcomes observed in the PMHS tests. The resulting IRFs were then examined in selected whole‐body simulations (in sled and vehicle environments) to evaluate their general predictive capability. The results suggested that direct application of rib cortical bone ultimate strain data to the THUMS v4.1 would result in underestimation of rib fracture risk compared to the reference PMHS tests. Tuning the local strain IRFs for each model, however, tended to result in reasonable injury risk prediction compared to PMHS tests and compared to risks derived from field data. Through tuning the local strain IRFs for application to a specific HBM, this framework provides a means to arrive at comparable injury risk prediction across HBMs of differing construction.