Fuzzing Numpy with the default Python fuzzer failed to exercise most of the API and didn't uncover free-threaded issues (likely due to the absence of shared objects between threads). Probably creating and using tricky arrays and other objects would increase the hits. Tricky data would also be interesting and suitable to finding security issues.
Numpy maintainers prefer that hits from analytic tools be condensed in single issues.
Issues found so far:
Fuzzing Numpy with the default Python fuzzer failed to exercise most of the API and didn't uncover free-threaded issues (likely due to the absence of shared objects between threads). Probably creating and using tricky arrays and other objects would increase the hits. Tricky data would also be interesting and suitable to finding security issues.
Numpy maintainers prefer that hits from analytic tools be condensed in single issues.
Issues found so far:
numpy.strings.findnumpy/numpy#28791__class__on functions likeatleast_1dnumpy/numpy#28788__func__of Cython methods numpy/numpy#28785random_rawfrom instance initialized from invalid value numpy/numpy#28784!PyErr_Occurred()assertion numpy/numpy#28783numpy.random._mt19937.MT19937with recursive list numpy/numpy#28822