Описание
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
Отчет
Red Hat Enterprise Virtualization Management Appliance includes the vulnerable version of numpy, however it is not used and this vulnerability is not exposed. Red Hat OpenStack Platform includes a vulnerable version of numpy, however it is not used in a vulnerable manner.
Затронутые пакеты
| Платформа | Пакет | Состояние | Рекомендация | Релиз |
|---|---|---|---|---|
| Red Hat Ceph Storage 4 | numpy | Affected | ||
| Red Hat Enterprise Linux 6 | numpy | Will not fix | ||
| Red Hat Enterprise Linux 7 | numpy | Will not fix | ||
| Red Hat OpenStack Platform 13 (Queens) | numpy | Will not fix | ||
| Red Hat OpenStack Platform 14 (Rocky) | numpy | Will not fix | ||
| Red Hat Software Collections | python27-numpy | Will not fix | ||
| Red Hat Software Collections | rh-python35-numpy | Will not fix | ||
| Red Hat Software Collections | rh-python36-numpy | Will not fix | ||
| Red Hat Virtualization 4 | rhvm-appliance | Will not fix | ||
| Red Hat Enterprise Linux 8 | python27 | Fixed | RHSA-2019:3335 | 05.11.2019 |
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Дополнительная информация
Статус:
8.8 High
CVSS3
Связанные уязвимости
** 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.
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
An issue was discovered in NumPy 1.16.0 and earlier. It uses the pickl ...
8.8 High
CVSS3