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CVE-2021-37682

Use of unitialized value in TensorFlow Lite

4.4 MediumFix available

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Attack profile

How easy it is to exploit

Attack vector
Local
Complexity
Low
Privileges needed
Low
User interaction
None

What an attacker can affect

Confidentiality
None
Integrity
Low
Availability
Low

Exploit likelihood

0.18%Down 0.9 points since Aug 13, 2021
90%50%10%1%

What each source reports

CVE List Score used 4.4

Use of unitialized value in TensorFlow Lite

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
CVE-2021-37682
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Weakness
CWE-908
Updated
2026-11-02
EUVDEUVD-2021-0408 4.4

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
EUVD-2021-0408
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Updated
2026-11-03
GitHub AdvisoriesGHSA-4c4g-crqm-xrxw 4.8

Use of unitialized value in TFLite

Impact

All TFLite operations that use quantization can be made to use unitialized values. For example:

    const auto* affine_quantization =
        reinterpret_cast<TfLiteAffineQuantization*>(
            filter->quantization.params);

The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code.

Patches

We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538.

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Record
GHSA-4c4g-crqm-xrxw
CVSS 4.0
4.8 MediumCVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Weakness
CWE-908
Updated
2026-11-02
NVD 4.4

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
CVE-2021-37682
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
CVSS 2.0
3.6 LowAV:L/AC:L/Au:N/C:N/I:P/A:P
Weakness
CWE-908
Updated
2026-11-02
OSV 7.1

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
CVE-2021-37682
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
Updated
2026-11-02
OSVGHSA-4c4g-crqm-xrxw 4.8

Use of unitialized value in TFLite

Impact

All TFLite operations that use quantization can be made to use unitialized values. For example:

    const auto* affine_quantization =
        reinterpret_cast<TfLiteAffineQuantization*>(
            filter->quantization.params);

The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code.

Patches

We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538.

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Record
GHSA-4c4g-crqm-xrxw
CVSS 4.0
4.8 MediumCVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Weakness
CWE-908
Updated
2026-11-02
Show 6 moreShow less
OSVBIT-tensorflow-2021-37682 No score

Use of unitialized value in TensorFlow Lite

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
BIT-tensorflow-2021-37682
Updated
2026-11-02
OSVopenSUSE-SU-2022:10014-1 No score

Security update for tensorflow2

This update for tensorflow fixes the following issues:

Update to TF2 2.6.0 which fixes multiple CVEs (boo#1189423).

  • Introduction of bazel6.3 and basel-skylib1.0.3 as build dependencies. The latter has been adapted to all a version in its package name (if %set_ver_suffix is set to 1). This allows multiple versions to exist for one product (not installed). NOTE: basel-skylib1.0.3 does not exist in oS:Factory: basel-skylib in oS:Factory - the base version - is 1.0.3.
Record
openSUSE-SU-2022:10014-1
Updated
2026-11-02
OSVopenSUSE-SU-2024:12116-1 No score

tensorflow-lite-2.9.1-1.1 on GA media

These are all security issues fixed in the tensorflow-lite-2.9.1-1.1 package on the GA media of openSUSE Tumbleweed.

Record
openSUSE-SU-2024:12116-1
Updated
2026-11-02
OSVPYSEC-2021-304 No score

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
PYSEC-2021-304
Updated
2026-11-02
OSVPYSEC-2021-595 No score

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
PYSEC-2021-595
Updated
2026-11-02
OSVPYSEC-2021-793 No score

TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. For example. The issue stems from the fact that quantization.params is only valid if quantization.type is different that kTfLiteNoQuantization. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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.

Record
PYSEC-2021-793
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.3.4
2.3.4
tensorflow
PyPI
 2.4.0 up to 2.4.3
2.4.3
tensorflow
PyPI
 2.5.0 to 2.5.0
2.5.1
tensorflow-cpu
PyPI
 before 2.3.4
2.3.4
tensorflow-cpu
PyPI
 2.4.0 up to 2.4.3
2.4.3
tensorflow-cpu
PyPI
 2.5.0 to 2.5.0
2.5.1
tensorflow-gpu
PyPI
 before 2.3.4
2.3.4
tensorflow-gpu
PyPI
 2.4.0 up to 2.4.3
2.4.3
tensorflow-gpu
PyPI
 2.5.0 to 2.5.0
2.5.1
tensorflow
Bitnami
 2.3.0 up to 2.3.4, 2.4.0 up to 2.4.3, 2.5.0 up to 2.5.1
2.3.42.4.32.5.1
Show 24 moreShow less
Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.3.4, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 44 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.3
2.3.4
tensorflow
PyPI
 2.4.0 up to 2.4.3, plus listed versions
2.4.12.4.2
2.4.3
tensorflow
PyPI
 2.5.0 up to 2.5.1
2.5.1
tensorflow-cpu
PyPI
 before 2.3.4, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 9 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.3
2.3.4
tensorflow-cpu
PyPI
 2.4.0 up to 2.4.3, plus listed versions
2.4.12.4.2
2.4.3
tensorflow-cpu
PyPI
 2.5.0 up to 2.5.1
2.5.1
tensorflow-gpu
PyPI
 before 2.3.4, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 44 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.3
2.3.4
tensorflow-gpu
PyPI
 2.4.0 up to 2.4.3, plus listed versions
2.4.12.4.2
2.4.3
tensorflow-gpu
PyPI
 2.5.0 up to 2.5.1
2.5.1
tensorflow
PyPI
 2.3.0 up to 2.3.4, 2.4.0 up to 2.4.3, plus listed versions
2.3.12.3.22.3.32.4.12.4.2
2.3.42.4.3
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.4, 2.4.0 up to 2.4.3, plus listed versions
2.3.12.3.22.3.32.4.12.4.2
2.3.42.4.3
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.4, 2.4.0 up to 2.4.3, plus listed versions
2.3.12.3.22.3.32.4.12.4.2
2.3.42.4.3
bazel-skylib1.0.3
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 1.0.3-bp153.2.1
1.0.3-bp153.2.1
bazel3.7
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 3.7.2-bp153.4.1
3.7.2-bp153.4.1
tensorflow2
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2-lite
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-hpc
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-openmpi2-hpc
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
bazel-skylib1.0.3
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 1.0.3-bp153.2.1
1.0.3-bp153.2.1
bazel3.7
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 3.7.2-bp153.4.1
3.7.2-bp153.4.1
tensorflow2
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2-lite
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-hpc
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-openmpi2-hpc
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1

History

History
RecordedChangedSources
affected, facts
aliasesEUVD
first indexedCVE List, GitHub Advisories, NVD, OSV

Dates are when Every CVE recorded each change. Upstream dates may differ.

In short

CVE-2021-37682 is a medium vulnerability (CVSS 4.4) in Google Tensorflow, published August 12, 2021. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.18%, the 7th percentile. Fixed in tensorflow 2.3.4.

References

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