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CVE-2019-6446 Critical

CVE-2019-6446

** 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.

Exploit probability 17.5%
Published January 16, 2019
Required by Not available
Last source change June 10, 2026

02 / AFFECTED SOFTWARE

Affected packages

PyPI numpy

61 explicit affected versions

Unknown Unknown

9 explicit affected versions

PyPI numpy

66 explicit affected versions

03 / CONNECTIONS

Connected vulnerabilities

04 / EVIDENCE

Source records

Open Source Vulnerabilities CVE-2019-6446

An issue was discovered in NumPy before 1.16.3. 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.

View original source
Open Source Vulnerabilities GHSA-9fq2-x9r6-wfmf

** DISPUTED ** An issue was discovered in NumPy 1.16.2 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.

View original source
Open Source Vulnerabilities PYSEC-2019-108

** 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.

View original source

05 / REFERENCES

Further evidence