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OpenAdvisories
Critical · 9.0 CVE-2020-15202 BIT-tensorflow-2020-15202EUVD-2020-0198GHSA-h6fg-mjxg-hqq4PYSEC-2020-125PYSEC-2020-282PYSEC-2020-317

Integer truncation in Shard API usage

Published Sep 25, 2020 · Updated Aug 4, 2024 · Assigned by GitHub_M

Description

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Affected products

VendorProductAffectedFixed
tensorflow tensorflow < 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 —

Fixed in Linux distributions

Distributions backport fixes, so the version that fixes this vulnerability differs per release — upgrade to at least the version shown. From the distributions' security trackers and advisories.

ReleasePackageFixed inAdvisory
openSUSE Leap 15.2 tensorflow2 +3 2.1.2-lp152.7.3.1 openSUSE-SU-2020:1766-1

Affected packages

EcosystemPackageAffectedFixedRecord
PyPI tensorflow < 1.15.4 1.15.4 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow >= 2.0.0, < 2.0.3 2.0.3 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow >= 2.1.0, < 2.1.2 2.1.2 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow >= 2.2.0, < 2.2.1 2.2.1 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow >= 2.3.0, < 2.3.1 2.3.1 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow < 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 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 PYSEC-2020-125
PyPI tensorflow-cpu < 1.15.4 1.15.4 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-cpu >= 2.0.0, < 2.0.3 2.0.3 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-cpu >= 2.1.0, < 2.1.2 2.1.2 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-cpu >= 2.2.0, < 2.2.1 2.2.1 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-cpu >= 2.3.0, < 2.3.1 2.3.1 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-cpu < 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 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 PYSEC-2020-282
PyPI tensorflow-gpu < 1.15.4 1.15.4 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-gpu >= 2.0.0, < 2.0.3 2.0.3 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-gpu >= 2.1.0, < 2.1.2 2.1.2 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-gpu >= 2.2.0, < 2.2.1 2.2.1 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-gpu >= 2.3.0, < 2.3.1 2.3.1 GHSA-h6fg-mjxg-hqq4
PyPI tensorflow-gpu < 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 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 PYSEC-2020-317

Advisories 1

References