** DISPUTED ** An issue was discovered in NumPy 1.16.0 and earlier. It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources.
(1) core/tests/test_memmap.py, (2) core/tests/test_multiarray.py, (3) f2py/f2py2e.py, and (4) lib/tests/test_io.py in NumPy before 1.8.1 allow local users to write to arbitrary files via a symlink attack on a temporary file.
init.py in f2py in NumPy before 1.8.1 allows local users to write to arbitrary files via a symlink attack on a temporary file.
A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. NOTE: The vendor does not agree this is a vulnerability; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke …
Null Pointer Dereference vulnerability exists in numpy.sort in NumPy < in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays.
Incomplete string comparison in the numpy.core allows attackers to fail the APIs via constructing specific string objects.
Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values.
The numpy.pad function in Numpy versions is missing input validation. An empty list or array will stick into an infinite loop, which can allow attackers to cause a DoS attack.