Total
8 CVE
| CVE | Vendors | Products | Updated | CVSS v2 | CVSS v3 |
|---|---|---|---|---|---|
| CVE-2021-34141 | 2 Numpy, Oracle | 2 Numpy, Communications Cloud Native Core Policy | 2023-02-24 | 5.0 MEDIUM | 5.3 MEDIUM |
| An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. NOTE: the vendor states that this reported code behavior is "completely harmless." | |||||
| CVE-2021-41496 | 1 Numpy | 1 Numpy | 2023-02-24 | 2.1 LOW | 5.5 MEDIUM |
| ** DISPUTED ** Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally). | |||||
| CVE-2021-41495 | 1 Numpy | 1 Numpy | 2022-07-28 | 3.5 LOW | 5.3 MEDIUM |
| ** DISPUTED ** Null Pointer Dereference vulnerability exists in numpy.sort in NumPy < and 1.19 in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. | |||||
| CVE-2021-33430 | 1 Numpy | 1 Numpy | 2022-07-28 | 3.5 LOW | 5.3 MEDIUM |
| ** DISPUTED ** 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 vulneraility; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user. | |||||
| CVE-2017-12852 | 1 Numpy | 1 Numpy | 2019-10-02 | 5.0 MEDIUM | 7.5 HIGH |
| The numpy.pad function in Numpy 1.13.1 and older versions is missing input validation. An empty list or ndarray will stick into an infinite loop, which can allow attackers to cause a DoS attack. | |||||
| CVE-2019-6446 | 2 Fedoraproject, Numpy | 2 Fedora, Numpy | 2019-09-30 | 7.5 HIGH | 9.8 CRITICAL |
| ** 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. | |||||
| CVE-2014-1859 | 3 Fedoraproject, Numpy, Redhat | 3 Fedora, Numpy, Enterprise Linux | 2019-04-22 | 2.1 LOW | 5.5 MEDIUM |
| (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. | |||||
| CVE-2014-1858 | 1 Numpy | 1 Numpy | 2018-01-30 | 2.1 LOW | 5.5 MEDIUM |
| __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. | |||||