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OpenAdvisories
High · 7.1 CVE-2021-37656 BIT-tensorflow-2021-37656EUVD-2021-0382GHSA-4xfp-4pfp-89wgPYSEC-2021-278PYSEC-2021-569PYSEC-2021-767

Reference binding to nullptr in `RaggedTensorToSparse` 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.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. 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-4xfp-4pfp-89wg
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-4xfp-4pfp-89wg
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-4xfp-4pfp-89wg
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-278
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-4xfp-4pfp-89wg
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-4xfp-4pfp-89wg
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-4xfp-4pfp-89wg
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-569
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-4xfp-4pfp-89wg
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-4xfp-4pfp-89wg
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-4xfp-4pfp-89wg
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-767

Advisories 1

References