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Not in CISA KEV
HIGH severity

Numpy missing input validationGHSA-frgw-fgh6-9g52

HIGH

GHSA-frgw-fgh6-9g52 is a high-severity (CVSS 7.5) CWE-835 vulnerability in numpy. 1 public exploit or proof-of-concept reference is indexed, so weaponization risk is real. A fix is available for numpy — see the affected versions and patch details below.

Also known asCVE-2017-12852PYSEC-2017-1
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
1 known
Exploitation data as of Nov 3, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
2.7%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs86th percentile — riskier than 86% of all scored CVEsHighest risk
0.23%1.26%2.29%3.32%0.8%2.7%May 26Sep 26Nov 26

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

1 pkg affected
🐍numpy

Real-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

The numpy.pad function in Numpy 1.13.1 and older versions is missing input validation. An empty list or ndarray will stick into an infinite loop, which can allow attackers to cause a DoS attack.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPInumpyall versions1.13.3pip install --upgrade 'numpy==1.13.3'

Affected Products

1 product · 1 configurations
Application
numpynumpy
≤ 1.13.1
range
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

The numpy.pad function in Numpy 1.13.1 and older versions is missing inp…

github.comNVDOct 2019

Detection & mitigation playbook

Open-source dependency

  1. Detect

    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.

  2. Fix

    Update numpy to 1.13.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-frgw-fgh6-9g52 is resolved across your whole dependency graph.

  3. Workarounds

    Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.

Frequently Asked Questions

The numpy.pad function in Numpy 1.13.1 and older versions is missing input validation. An empty list or ndarray will stick into an infinite loop, which can allow attackers to cause a DoS attack.
O3 Security · Impact-Aware SCA

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Numpy missing input validation (High 7.5)