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OpenAdvisories
Medium · 5.5 CVE-2021-37675 BIT-tensorflow-2021-37675EUVD-2021-0401GHSA-9c8h-2mv3-49wwPYSEC-2021-297PYSEC-2021-588PYSEC-2021-786

Division by 0 in most convolution operators in TensorFlow

Published Aug 12, 2021 · Updated Aug 4, 2024 · Assigned by GitHub_M

Description

TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Affected products

VendorProductAffectedFixed
tensorflow tensorflow >= 2.5.0, < 2.5.1, >= 2.4.0, < 2.4.3, < 2.3.4 —

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
SUSE Package Hub 15 SP3 bazel-skylib1.0.3 1.0.3-bp153.2.1 openSUSE-SU-2022:10014-1
SUSE Package Hub 15 SP3 bazel3.7 3.7.2-bp153.4.1 openSUSE-SU-2022:10014-1
SUSE Package Hub 15 SP3 tensorflow2 +3 2.6.0-bp153.2.3.1 openSUSE-SU-2022:10014-1
ReleasePackageFixed inAdvisory
openSUSE Leap 15.3 bazel-skylib1.0.3 1.0.3-bp153.2.1 openSUSE-SU-2022:10014-1
openSUSE Leap 15.3 bazel3.7 3.7.2-bp153.4.1 openSUSE-SU-2022:10014-1
openSUSE Leap 15.3 tensorflow2 +3 2.6.0-bp153.2.3.1 openSUSE-SU-2022:10014-1

Affected packages

EcosystemPackageAffectedFixedRecord
PyPI tensorflow < 2.3.4 2.3.4 GHSA-9c8h-2mv3-49ww
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-9c8h-2mv3-49ww
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-9c8h-2mv3-49ww
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-297
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-9c8h-2mv3-49ww
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-9c8h-2mv3-49ww
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-9c8h-2mv3-49ww
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-588
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-9c8h-2mv3-49ww
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-9c8h-2mv3-49ww
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-9c8h-2mv3-49ww
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-786

Advisories 1

References