In recent years, physical unclonable function (PUF) has emerged as a lightweight solution in the Internet of Things security. However, conventional PUFs based on complementary metal oxide semiconductor (CMOS) present challenges such as insufficient randomness, significant power and area overhead, and vulnerability to environmental factors, leading to reduced reliability. In this study, we realize a strong, highly reliable and reconfigurable PUF with resistance against machine-learning attacks in a 1 kb spin-orbit torque magnetic random access memory fabricated using a 180 nm CMOS process. This strong PUF achieves a challenge–response pair capacity of 10⁹ through a computing-in-memory approach. The results demonstrate that the proposed PUF exhibits near-ideal performance metrics: 50.07% uniformity, 50% diffuseness, 49.89% uniqueness, and a bit error rate of 0%, even in a 375 K environment. The reconfigurability of PUF is demonstrated by a reconfigurable Hamming distance of 49.31% and a correlation coefficient of less than 0.2, making it difficult to extract output keys through side-channel analysis. Furthermore, resistance to machine-learning modeling attacks is confirmed by achieving an ideal accuracy prediction of approximately 50% in the test set.