CVE-2019-6446 is a critical-severity (CVSS 9.8) Deserialization of Untrusted Data vulnerability in numpy. 4 public exploit references exist, 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
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
CVE-2019-6446 plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 378,156 CVEs with a current EPSS score, this one falls in the 10–50% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
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' |
Research 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
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2019-6446 can be triaged on real exposure rather than presence alone.
Tailored to CVE-2019-6446. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
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?
O3 Security finds CVE-2019-6446 across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.