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OpenAdvisories
High · 7.8 CVE-2021-37676 BIT-tensorflow-2021-37676EUVD-2021-0402GHSA-v768-w7m9-2vmmPYSEC-2021-298PYSEC-2021-589PYSEC-2021-787

Reference binding to nullptr in shape inference 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 undefined behavior via binding a reference to null pointer in `tf.raw_ops.SparseFillEmptyRows`. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. We have patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed. 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-v768-w7m9-2vmm
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-v768-w7m9-2vmm
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-v768-w7m9-2vmm
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-298
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-v768-w7m9-2vmm
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-v768-w7m9-2vmm
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-v768-w7m9-2vmm
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-589
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-v768-w7m9-2vmm
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-v768-w7m9-2vmm
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-v768-w7m9-2vmm
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-787

Advisories 1

References