Improper Input Validation Affecting tensorflow-gpu package, versions [2.5.0,2.5.1) [2.4.0,2.4.3) [,2.3.4)


0.0
high

Snyk CVSS

    Attack Complexity Low
    Confidentiality High
    Integrity High
    Availability High

    Threat Intelligence

    EPSS 0.04% (11th percentile)
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NVD
7.8 high

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  • Snyk ID SNYK-PYTHON-TENSORFLOWGPU-1540688
  • published 13 Aug 2021
  • disclosed 13 Aug 2021
  • credit Unknown

How to fix?

Upgrade tensorflow-gpu to version 2.5.1, 2.4.3, 2.3.4 or higher.

Overview

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Improper Input Validation as, due to incomplete validation in tf.raw_ops.QuantizeV2, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The implementation has some validation but does not check that min_range and max_range both have the same non-zero number of elements. If axis is provided (i.e., not -1), then validation should check that it is a value in range for the rank of input tensor and then the lengths of min_range and max_range inputs match the axis dimension of the input tensor.

References