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OpenAdvisories
High · 7.8 CVE-2021-37689 BIT-tensorflow-2021-37689EUVD-2021-0415GHSA-wf5p-c75w-w3whPYSEC-2021-311PYSEC-2021-602PYSEC-2021-800

Null pointer dereference in TensorFlow Lite MLIR optimizations

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. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. 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-wf5p-c75w-w3wh
PyPI tensorflow >= 2.4.0, < 2.4.3 2.4.3 GHSA-wf5p-c75w-w3wh
PyPI tensorflow >= 2.5.0, < 2.5.1 2.5.1 GHSA-wf5p-c75w-w3wh
PyPI tensorflow >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-311
PyPI tensorflow-cpu < 2.3.4 2.3.4 GHSA-wf5p-c75w-w3wh
PyPI tensorflow-cpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-wf5p-c75w-w3wh
PyPI tensorflow-cpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-wf5p-c75w-w3wh
PyPI tensorflow-cpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-602
PyPI tensorflow-gpu < 2.3.4 2.3.4 GHSA-wf5p-c75w-w3wh
PyPI tensorflow-gpu >= 2.4.0, < 2.4.3 2.4.3 GHSA-wf5p-c75w-w3wh
PyPI tensorflow-gpu >= 2.5.0, < 2.5.1 2.5.1 GHSA-wf5p-c75w-w3wh
PyPI tensorflow-gpu >= 2.3.0, < 2.3.4 · >= 2.4.0, < 2.4.3 2.3.4, 2.4.3 PYSEC-2021-800

Advisories 1

References