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CVE-2020-26268

Write to immutable memory region in TensorFlow

4.4 MediumFix available

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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

0.21%Unchanged since Apr 14, 2021
90%50%10%1%

What each source reports

CVE List Score used 4.4

Write to immutable memory region in TensorFlow

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-26268
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-471
Updated
2026-11-02
EUVDEUVD-2020-0215 4.4

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-0215
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-hhvc-g5hv-48c6 4.8

Write to immutable memory region in TensorFlow

Impact

The tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area:

>>> import tensorflow as tf
>>> with open('/tmp/test.txt','w') as f: f.write('a'*128)
>>> tf.raw_ops.ImmutableConst(dtype=tf.string,shape=2,
                              memory_region_name='/tmp/test.txt')

If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault.

This is because the alocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overriden.

Patches

We have patched the issue in GitHub commit c1e1fc899ad5f8c725dcbb6470069890b5060bc7 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.

Attribution

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

Record
GHSA-hhvc-g5hv-48c6
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-471
Updated
2026-11-02
NVD 4.4

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-26268
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 2.0
3.6 LowAV:L/AC:L/Au:N/C:N/I:P/A:P
Weakness
CWE-471
Updated
2026-11-02
OSV 4.4

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-26268
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
2026-11-02
OSVGHSA-hhvc-g5hv-48c6 4.8

Write to immutable memory region in TensorFlow

Impact

The tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area:

>>> import tensorflow as tf
>>> with open('/tmp/test.txt','w') as f: f.write('a'*128)
>>> tf.raw_ops.ImmutableConst(dtype=tf.string,shape=2,
                              memory_region_name='/tmp/test.txt')

If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault.

This is because the alocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overriden.

Patches

We have patched the issue in GitHub commit c1e1fc899ad5f8c725dcbb6470069890b5060bc7 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.

Attribution

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

Record
GHSA-hhvc-g5hv-48c6
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-471
Updated
2026-11-02
Show 5 moreShow less
OSVBIT-tensorflow-2020-26268 No score

Write to immutable memory region in TensorFlow

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-26268
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
OSVPYSEC-2020-255 No score

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-255
Updated
2026-11-02
OSVPYSEC-2020-299 No score

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-299
Updated
2026-11-02
OSVPYSEC-2020-334 No score

In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. 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-2020-334
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-2020-26268 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 0.21%, the 11th percentile. Fixed in tensorflow 1.15.5.

References

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

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