CVE-2019-6446 is a critical-severity (CVSS 9.8) Deserialization of Untrusted Data vulnerability in numpy. 4 public exploit or proof-of-concept references are indexed, so weaponization risk is real. A fix is available for numpy — see the affected versions and patch details below.
Numpy Deserialization of Untrusted Data
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
Where this sits among everything scored
Real-World Exposure
numpyReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
** 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.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | numpy | all versions | 1.16.3pip install --upgrade 'numpy==1.16.3' |
Affected Products
fedorafedoraprojectnumpynumpyResearch use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for numpy, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update numpy to 1.16.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2019-6446 is resolved across your whole dependency graph.
Workarounds
Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
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.
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat Enterprise Linux 8 | python27:2.7-8010020190903182548.51c94b97 | RHSA-2019:3335 |
| Red Hat Enterprise Linux 8 | numpy-1:1.14.3-9.el8 | RHSA-2019:3704 |
Frequently Asked Questions
Is CVE-2019-6446 in your dependencies?
Find it across PyPI, including transitive dependencies.