PYSEC-2019-108
See a problem?- Import Source
- https://github.com/pypa/advisory-database/blob/main/vulns/numpy/PYSEC-2019-108.yaml
- JSON Data
- https://api.osv.dev/v1/vulns/PYSEC-2019-108
- Aliases
-
- CVE-2019-6446
- GHSA-9fq2-x9r6-wfmf
- Published
- 2019-01-16T05:29:00Z
- Modified
- 2023-11-08T04:01:37.692738Z
- Summary
- [none]
- Details
-
* 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.
- References
-
- https://github.com/numpy/numpy/issues/12759
- https://bugzilla.suse.com/show_bug.cgi?id=1122208
- http://www.securityfocus.com/bid/106670
- https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/7ZZAYIQNUUYXGMKHSPEEXS4TRYFOUYE4/
- http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00091.html
- http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00092.html
- http://lists.opensuse.org/opensuse-security-announce/2019-10/msg00015.html
- https://access.redhat.com/errata/RHSA-2019:3335
- https://access.redhat.com/errata/RHSA-2019:3704
Affected packages
PyPI / numpy
Package
- Name
- numpy
- View open source insights on deps.dev
- Purl
- pkg:pypi/numpy
Affected ranges
- Type
- ECOSYSTEM
- Events
-
Introduced0Unknown introduced version / All previous versions are affectedFixed1.16.1
Affected versions
0.*
0.9.6
0.9.8
1.*
1.0b1
1.0b4
1.0b5
1.0rc1
1.0rc2
1.0rc3
1.0
1.0.3
1.0.4
1.1.1
1.2.0
1.2.1
1.3.0
1.4.0
1.4.1
1.5.0
1.5.1
1.6.0
1.6.1
1.6.2
1.7.0
1.7.1
1.7.2
1.8.0
1.8.1
1.8.2
1.9.0
1.9.1
1.9.2
1.9.3
1.10.0
1.10.1
1.10.2
1.10.3
1.10.4
1.11.0
1.11.1
1.11.2
1.11.3
1.12.0
1.12.1
1.13.0rc1
1.13.0rc2
1.13.0
1.13.1
1.13.3
1.14.0rc1
1.14.0
1.14.1
1.14.2
1.14.3
1.14.4
1.14.5
1.14.6
1.15.0rc1
1.15.0rc2
1.15.0
1.15.1
1.15.2
1.15.3
1.15.4
1.16.0rc1
1.16.0rc2
1.16.0