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OpenAdvisories
High · 7.8 CVE-2021-37688 BIT-tensorflow-2021-37688EUVD-2021-0414GHSA-vcjj-9vg7-vf68PYSEC-2021-310PYSEC-2021-601PYSEC-2021-799

Null pointer dereference in TensorFlow Lite

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 craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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-vcjj-9vg7-vf68
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-vcjj-9vg7-vf68
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-vcjj-9vg7-vf68
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-310
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-vcjj-9vg7-vf68
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-vcjj-9vg7-vf68
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-vcjj-9vg7-vf68
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-601
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-vcjj-9vg7-vf68
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-vcjj-9vg7-vf68
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-vcjj-9vg7-vf68
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-799

Advisories 1

References