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CVE-2021-37651

Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow

7.1 HighFix available

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 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.

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
High
Integrity
High
Availability
None

Exploit likelihood

0.17%Down 0.4 points since Aug 13, 2021
90%50%10%1%

What each source reports

CVE List Score used 7.1

Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow

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 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.

Record
CVE-2021-37651
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
Weakness
CWE-125
Updated
2026-11-02
EUVDEUVD-2021-0377 7.1

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 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.

Record
EUVD-2021-0377
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
Updated
2026-11-03
GitHub AdvisoriesGHSA-hpv4-7p9c-mvfr 8.4

Heap buffer overflow in `FractionalAvgPoolGrad`

Impact

The implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers:

import tensorflow as tf

tf.raw_ops.FractionalAvgPoolGrad(
  orig_input_tensor_shape=[0,1,2,3],
  out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),
  row_pooling_sequence=[0, 0, 0, 0, 0],
  col_pooling_sequence=[-2, 0, 0, 2, 0],
  overlapping=True)

The implementation 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.

Patches

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.

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-hpv4-7p9c-mvfr
CVSS 4.0
8.4 HighCVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
Weakness
CWE-125, CWE-787
Updated
2026-11-02
NVD 7.1

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 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.

Record
CVE-2021-37651
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
CVSS 3.1
7.8 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
CVSS 2.0
4.6 MediumAV:L/AC:L/Au:N/C:P/I:P/A:P
Weakness
CWE-125, CWE-787
Updated
2026-11-02
OSV 7.8

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 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.

Record
CVE-2021-37651
CVSS 3.1
7.8 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Updated
2026-11-02
OSVGHSA-hpv4-7p9c-mvfr 8.4

Heap buffer overflow in `FractionalAvgPoolGrad`

Impact

The implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers:

import tensorflow as tf

tf.raw_ops.FractionalAvgPoolGrad(
  orig_input_tensor_shape=[0,1,2,3],
  out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),
  row_pooling_sequence=[0, 0, 0, 0, 0],
  col_pooling_sequence=[-2, 0, 0, 2, 0],
  overlapping=True)

The implementation 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.

Patches

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.

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-hpv4-7p9c-mvfr
CVSS 4.0
8.4 HighCVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N
CVSS 3.1
7.1 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
Weakness
CWE-125, CWE-787
Updated
2026-11-02
Show 6 moreShow less
OSVBIT-tensorflow-2021-37651 No score

Heap buffer overflow in `FractionalAvgPoolGrad` in TensorFlow

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 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.

Record
BIT-tensorflow-2021-37651
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-2021-273 No score

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 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.

Record
PYSEC-2021-273
Updated
2026-11-02
OSVPYSEC-2021-564 No score

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 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.

Record
PYSEC-2021-564
Updated
2026-11-02
OSVPYSEC-2021-762 No score

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 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.

Record
PYSEC-2021-762
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-2021-37651 is a high vulnerability (CVSS 7.1) 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 0.17%, the 6th percentile. Fixed in tensorflow 2.3.4.

References

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