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CVE-2020-26266

Uninitialized memory access in Eigen types in TensorFlow

4.4 MediumFix available

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

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

0.26%Down 0.2 points since Apr 14, 2021
90%50%10%1%

What each source reports

CVE List Score used 4.4

Uninitialized memory access in Eigen types in TensorFlow

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
CVE-2020-26266
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-2020-0213 4.4

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
EUVD-2020-0213
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-qhxx-j73r-qpm2 4.8

Uninitialized memory access in TensorFlow

Impact

Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:

struct QUInt8 {
  QUInt8() {}
  // ...
  uint8_t value;
};

struct QInt16 {
  QInt16() {}
  // ...
  int16_t value;
};

struct QUInt16 {
  QUInt16() {}
  // ...
  uint16_t value;
};

struct QInt32 {
  QInt32() {}
  // ...
  int32_t value;
};

Patches

We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

For more information

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

Record
GHSA-qhxx-j73r-qpm2
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

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
CVE-2020-26266
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
5.3 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L
CVSS 2.0
4.6 MediumAV:L/AC:L/Au:N/C:P/I:P/A:P
Weakness
CWE-908
Updated
2026-11-02
OSV 5.3

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
CVE-2020-26266
CVSS 3.1
5.3 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L
Updated
2026-11-02
OSVGHSA-qhxx-j73r-qpm2 4.8

Uninitialized memory access in TensorFlow

Impact

Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:

struct QUInt8 {
  QUInt8() {}
  // ...
  uint8_t value;
};

struct QInt16 {
  QInt16() {}
  // ...
  int16_t value;
};

struct QUInt16 {
  QUInt16() {}
  // ...
  uint16_t value;
};

struct QInt32 {
  QInt32() {}
  // ...
  int32_t value;
};

Patches

We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

For more information

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

Record
GHSA-qhxx-j73r-qpm2
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-2020-26266 No score

Uninitialized memory access in Eigen types in TensorFlow

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
BIT-tensorflow-2020-26266
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-2020-254 No score

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
PYSEC-2020-254
Updated
2026-11-02
OSVPYSEC-2020-297 No score

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
PYSEC-2020-297
Updated
2026-11-02
OSVPYSEC-2020-332 No score

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
PYSEC-2020-332
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 1.15.5
1.15.5
tensorflow
PyPI
 2.0.0 up to 2.0.4
2.0.4
tensorflow
PyPI
 2.1.0 up to 2.1.3
2.1.3
tensorflow
PyPI
 2.2.0 up to 2.2.2
2.2.2
tensorflow
PyPI
 2.3.0 up to 2.3.2
2.3.2
tensorflow-cpu
PyPI
 before 1.15.5
1.15.5
tensorflow-cpu
PyPI
 2.0.0 up to 2.0.4
2.0.4
tensorflow-cpu
PyPI
 2.1.0 up to 2.1.3
2.1.3
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.2
2.2.2
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.2
2.3.2
Show 35 moreShow less
Affected packages
PackageAffectedFixed
tensorflow-gpu
PyPI
 before 1.15.5
1.15.5
tensorflow-gpu
PyPI
 2.0.0 up to 2.0.4
2.0.4
tensorflow-gpu
PyPI
 2.1.0 up to 2.1.3
2.1.3
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.2
2.2.2
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.2
2.3.2
tensorflow
Bitnami
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2
1.15.52.0.42.1.32.2.22.3.2
tensorflow
PyPI
 before 1.15.5, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 25 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.0
1.15.5
tensorflow
PyPI
 2.0.0 up to 2.0.4, plus listed versions
2.0.12.0.22.0.3
2.0.4
tensorflow
PyPI
 2.1.0 up to 2.1.3, plus listed versions
2.1.12.1.2
2.1.3
tensorflow
PyPI
 2.2.0 up to 2.2.2, plus listed versions
2.2.1
2.2.2
tensorflow
PyPI
 2.3.0 up to 2.3.2, plus listed versions
2.3.1
2.3.2
tensorflow-cpu
PyPI
 before 1.15.5, plus listed versions
1.15.0
1.15.5
tensorflow-cpu
PyPI
 2.1.0 up to 2.1.3, plus listed versions
2.1.12.1.2
2.1.3
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.2, plus listed versions
2.2.1
2.2.2
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.2, plus listed versions
2.3.1
2.3.2
tensorflow-gpu
PyPI
 before 1.15.5, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 25 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.0
1.15.5
tensorflow-gpu
PyPI
 2.0.0 up to 2.0.4, plus listed versions
2.0.12.0.22.0.3
2.0.4
tensorflow-gpu
PyPI
 2.1.0 up to 2.1.3, plus listed versions
2.1.12.1.2
2.1.3
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.2, plus listed versions
2.2.1
2.2.2
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.2, plus listed versions
2.3.1
2.3.2
tensorflow
PyPI
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2, plus listed versions
0.12.00.12.0rc00.12.0rc10.12.11.0.0
Show 45 moreShow less
1.0.11.1.01.1.0rc01.1.0rc11.1.0rc21.10.01.10.0rc01.10.0rc11.10.11.11.01.11.0rc01.11.0rc11.11.0rc21.12.01.12.0rc01.12.0rc11.12.0rc21.12.21.12.31.13.0rc01.13.0rc11.13.0rc21.13.11.13.21.14.01.14.0rc01.14.0rc11.15.01.15.0rc01.15.0rc11.15.0rc21.15.0rc31.15.21.15.31.15.41.2.01.2.0rc01.2.0rc11.2.0rc21.2.11.3.01.3.0rc01.3.0rc11.3.0rc21.4.0
1.15.52.0.42.1.32.2.22.3.2
tensorflow-cpu
PyPI
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2, plus listed versions
1.15.02.1.12.1.22.2.12.3.1
1.15.52.0.42.1.32.2.22.3.2
tensorflow-gpu
PyPI
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 32 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.12.0.22.0.32.1.12.1.22.2.12.3.1
1.15.52.0.42.1.32.2.22.3.2
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-2020-26266 is a medium vulnerability (CVSS 4.4) in Google Tensorflow, published December 10, 2020. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.26%, the 18th percentile. Fixed in tensorflow 1.15.5.

References

Show 177 moreShow less
This page doesn't list everything

Left out here: 28 versions. The JSON record has them.

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