With the ever-shrinking size of transistors and increasing scale of applications, silent data corruptions (SDCs) have become a common yet serious issue in HPC applications. Selective instruction duplication (SID) is a popular fault-tolerance technique that can obtain a high SDC coverage with low-performance overhead, as it selects the most vulnerable parts of a program for protection with priority. However, existing studies of SID are confined to single program input in the evaluation, assuming that the error resilience of the program remains similar across inputs. Nevertheless, we observe that the assumption does not always hold in practice, leading to a drastic loss of the SDC coverage from SID when the protected program runs with different inputs, which seriously compromises the reliability of HPC applications. We notice that the SDC coverage loss often correlates with a small set of instructions — we call them incubative instructions, which reveal elusive error propagation characteristics across multiple inputs. Hence, we proposed Sentinel, an automated compiler-based framework that combines static program analysis and dynamic fuzzing techniques to distinguish incubative instructions for a given program and optimizes SID by reprioritizing the identified incubative instructions. Evaluation results show that Sentinel can effectively mitigate the loss of SDC coverage (up to 97.00%) across multiple inputs, which significantly hardens existing SID techniques.