Out-of-Bounds Affecting tensorflow package, versions [,1.15.4) [2.0.0, 2.0.3) [2.1.0, 2.1.2) [2.2.0, 2.2.1) [2.3.0, 2.3.1)


0.0
high

Snyk CVSS

    Attack Complexity High
    Confidentiality High
    Integrity High

    Threat Intelligence

    EPSS 0.17% (53rd percentile)
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NVD
9.8 critical

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  • Snyk ID SNYK-PYTHON-TENSORFLOW-1013502
  • published 28 Sep 2020
  • disclosed 25 Sep 2020
  • credit Aivul Team from Qihoo 360

How to fix?

Upgrade tensorflow to version 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or higher.

Overview

tensorflow is a machine learning framework.

Affected versions of this package are vulnerable to Out-of-Bounds. When determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes.

Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. .