CVE-2021-33430

5.3

MEDIUMCVSS v3

Published

December 17, 2021

CVSS v2

3.5 LOW

Affected

7 projects

Assigned by

MITRE

Severity scale

010

Description

A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. NOTE: The vendor does not agree this is a vulneraility; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user

numpy

Python Package IndexFundamental package for array computing in Python

>= 1.9.0, <= 1.9.3NVD

numpy/numpy

GitHubThe fundamental package for scientific computing with Python.

>= 1.9.0, <= 1.9.3NVD

numpy

Crates.io

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NVD↗GitHub Advisory↗

PyO3-based Rust bindings of the NumPy C-API

>= 1.9.0, <= 1.9.3NVD

numpy

NPMnumpy

>= 1.9.0, <= 1.9.3NVD

Numpy

NuGet GalleryC# bindings for NumPy - a fundamental library for scientific computing, machine learning and AI. Does not require a local Python installation!

>= 1.9.0, <= 1.9.3NVD

NumPy

ChocolateyNumPy is the fundamental package for scientific computing with Python. It contains among other things: - a powerful N-dimensional array object - sophisticated (broadcasting) functions - tools for integrating C/C++ and Fortran code - useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. NumPy is licensed under the [BSD license](https://www.numpy.org/license.html), enabling reuse with few restrictions. ## Getting Started To install NumPy, we strongly recommend using a ***scientific Python distribution***. See [Installing the SciPy Stack](https://www.scipy.org/install.html) for details. Many high quality online tutorials, courses, and books are available to get started with NumPy. For a quick introduction to NumPy we provide the [NumPy Tutorial](https://www.numpy.org/devdocs/user/quickstart.html). We also recommend the [SciPy Lecture Notes](https://scipy-lectures.org) for a broader introduction to the scientific Python ecosystem. For more information on the SciPy Stack (for which NumPy provides the fundamental array data structure), see [scipy.org](https://www.scipy.org). ## Documentation The most up-to-date NumPy documentation can be found at [Latest (development) version](https://www.numpy.org/devdocs). It includes a user guide, full reference documentation, a developer guide, meta information, and “NumPy Enhancement Proposals” (which include the NumPy Roadmap and detailed plans for major new features). A complete archive of documentation for all NumPy releases (minor versions; bug fix releases don’t contain significant documentation changes) since 2009 can be found at https://docs.scipy.org.

>= 1.9.0, <= 1.9.3NVD

numpy

RubyGemsNumpy wrapper for Ruby

>= 1.9.0, <= 1.9.3NVD

Security Advisories

CVE-2021-33430

Description

More on this CVE