Out-of-Bounds Affecting tensorflow package, versions [2.4.0,2.4.2) [2.3.0,2.3.3) [2.2.0,2.2.3) [,2.1.4)


0.0
high

Snyk CVSS

    Attack Complexity Low
    Confidentiality High
    Integrity High
    Availability High

    Threat Intelligence

    EPSS 0.09% (39th percentile)
Expand this section
NVD
7.8 high

Do your applications use this vulnerable package?

In a few clicks we can analyze your entire application and see what components are vulnerable in your application, and suggest you quick fixes.

Test your applications
  • Snyk ID SNYK-PYTHON-TENSORFLOW-1315682
  • published 1 Jul 2021
  • disclosed 1 Jul 2021
  • credit Unknown

How to fix?

Upgrade tensorflow to version 2.4.2, 2.3.3, 2.2.3, 2.1.4 or higher.

Overview

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. Incomplete validation in SparseAdd results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of *_indices matches the size of corresponding *_shape. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation.