Heap-based Buffer Overflow Affecting tensorflow package, versions [2.3.0, 2.3.1)


0.0
high

Snyk CVSS

    Attack Complexity High
    Scope Changed
    Confidentiality High
    Integrity High
    Availability High

    Threat Intelligence

    EPSS 0.25% (64th percentile)
Expand this section
NVD
9.9 critical

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-1013609
  • published 29 Sep 2020
  • disclosed 28 Sep 2020
  • credit Unknown

How to fix?

Upgrade tensorflow to version 2.3.1 or higher.

Overview

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow. The SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. The check exists for DenseCountSparseOutput, where both tensors are fully specified.In the sparse and ragged count weights are still accessed in parallel with the data.But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights.