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OpenAdvisories
High · 7.7 CVE-2021-37638 BIT-tensorflow-2021-37638EUVD-2021-0364GHSA-hwr7-8gxx-fj5pPYSEC-2021-260PYSEC-2021-551PYSEC-2021-749

Null pointer dereference in `RaggedTensorToTensor` 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. Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. We have patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314. 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-hwr7-8gxx-fj5p
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-260
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-551
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-hwr7-8gxx-fj5p
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-749

Advisories 1

References