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OpenAdvisories
High · 7.1 CVE-2021-37651 BIT-tensorflow-2021-37651EUVD-2021-0377GHSA-hpv4-7p9c-mvfrPYSEC-2021-273PYSEC-2021-564PYSEC-2021-762

Heap buffer overflow in `FractionalAvgPoolGrad` 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 the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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-hpv4-7p9c-mvfr
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-273
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-564
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-hpv4-7p9c-mvfr
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-762

Advisories 1

References