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OpenAdvisories
Medium · 5.5 CVE-2021-37660 BIT-tensorflow-2021-37660EUVD-2021-0386GHSA-cm5x-837x-jf3cPYSEC-2021-282PYSEC-2021-573PYSEC-2021-771

Division by 0 in inplace operations 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 an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. 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-cm5x-837x-jf3c
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-cm5x-837x-jf3c
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-cm5x-837x-jf3c
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-282
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-cm5x-837x-jf3c
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-cm5x-837x-jf3c
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-cm5x-837x-jf3c
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-573
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-cm5x-837x-jf3c
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-cm5x-837x-jf3c
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-cm5x-837x-jf3c
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-771

Advisories 1

References