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CVE-2020-15210

Segmentation fault in tensorflow-lite

6.5 MediumFix available

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Attack profile

How easy it is to exploit

Attack vector
Network
Complexity
High
Privileges needed
None
User interaction
None

What an attacker can affect

Confidentiality
None
Integrity
Low
Availability
High

Exploit likelihood

0.74%Down 0.1 points since Apr 14, 2021
90%50%10%1%

What each source reports

CVE List Score used 6.5

Segmentation fault in tensorflow-lite

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
CVE-2020-15210
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
Weakness
CWE-20
Updated
2026-11-02
EUVDEUVD-2020-0206 6.5

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
EUVD-2020-0206
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
Updated
2026-11-03
GitHub AdvisoriesGHSA-x9j7-x98r-r4w2 8.3

Segmentation fault in tensorflow-lite

Impact

If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.

Patches

We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Workarounds

A potential workaround would be to add a custom Verifier to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).

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 discovered from a variant analysis of GHSA-cvpc-8phh-8f45.

Record
GHSA-x9j7-x98r-r4w2
CVSS 4.0
8.3 HighCVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:L/VA:H/SC:N/SI:N/SA:N
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
Weakness
CWE-20, CWE-787
Updated
2026-11-02
NVD 6.5

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
CVE-2020-15210
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
CVSS 2.0
5.8 MediumAV:N/AC:M/Au:N/C:N/I:P/A:P
Weakness
CWE-20, CWE-787
Updated
2026-11-02
OSV 6.5

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
CVE-2020-15210
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
Updated
2026-11-02
OSVGHSA-x9j7-x98r-r4w2 8.3

Segmentation fault in tensorflow-lite

Impact

If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.

Patches

We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Workarounds

A potential workaround would be to add a custom Verifier to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).

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 discovered from a variant analysis of GHSA-cvpc-8phh-8f45.

Record
GHSA-x9j7-x98r-r4w2
CVSS 4.0
8.3 HighCVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:L/VA:H/SC:N/SI:N/SA:N
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:L/A:H
Weakness
CWE-20, CWE-787
Updated
2026-11-02
Show 5 moreShow less
OSVBIT-tensorflow-2020-15210 No score

Segmentation fault in tensorflow-lite

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
BIT-tensorflow-2020-15210
Updated
2026-11-02
OSVopenSUSE-SU-2020:1766-1 No score

Security update for tensorflow2

This update for tensorflow2 fixes the following issues:

  • updated to 2.1.2 with following fixes (boo#1177022):
    • Fixes an undefined behavior causing a segfault in tf.raw_ops.Switch (CVE-2020-15190)
    • Fixes three vulnerabilities in conversion to DLPack format (CVE-2020-15191, CVE-2020-15192, CVE-2020-15193)
    • Fixes two vulnerabilities in SparseFillEmptyRowsGrad (CVE-2020-15194, CVE-2020-15195)
    • Fixes an integer truncation vulnerability in code using the work sharder API (CVE-2020-15202)
    • Fixes a format string vulnerability in tf.strings.as_string (CVE-2020-15203)
    • Fixes segfault raised by calling session-only ops in eager mode (CVE-2020-15204)
    • Fixes data leak and potential ASLR violation from tf.raw_ops.StringNGrams (CVE-2020-15205)
    • Fixes segfaults caused by incomplete SavedModel validation (CVE-2020-15206)
    • Fixes a data corruption due to a bug in negative indexing support in TFLite (CVE-2020-15207)
    • Fixes a data corruption due to dimension mismatch in TFLite (CVE-2020-15208)
    • Fixes several vulnerabilities in TFLite saved model format (CVE-2020-15209, CVE-2020-15210, CVE-2020-15211)
Record
openSUSE-SU-2020:1766-1
Updated
2026-11-02
OSVPYSEC-2020-133 No score

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
PYSEC-2020-133
Updated
2026-11-02
OSVPYSEC-2020-290 No score

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
PYSEC-2020-290
Updated
2026-11-02
OSVPYSEC-2020-325 No score

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
PYSEC-2020-325
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 1.15.4
1.15.4
tensorflow
PyPI
 2.0.0 up to 2.0.3
2.0.3
tensorflow
PyPI
 2.1.0 up to 2.1.2
2.1.2
tensorflow
PyPI
 2.2.0 to 2.2.0
2.2.1
tensorflow
PyPI
 2.3.0 to 2.3.0
2.3.1
tensorflow-cpu
PyPI
 before 1.15.4
1.15.4
tensorflow-cpu
PyPI
 2.0.0 up to 2.0.3
2.0.3
tensorflow-cpu
PyPI
 2.1.0 up to 2.1.2
2.1.2
tensorflow-cpu
PyPI
 2.2.0 to 2.2.0
2.2.1
tensorflow-gpu
PyPI
 before 1.15.4
1.15.4
Show 27 moreShow less
Affected packages
PackageAffectedFixed
tensorflow-gpu
PyPI
 2.0.0 up to 2.0.3
2.0.3
tensorflow-gpu
PyPI
 2.1.0 up to 2.1.2
2.1.2
tensorflow-gpu
PyPI
 2.2.0 to 2.2.0
2.2.1
tensorflow-cpu
PyPI
 2.3.0 to 2.3.0
2.3.1
tensorflow-gpu
PyPI
 2.3.0 to 2.3.0
2.3.1
tensorflow
Bitnami
 before 1.15.4, 2.0.0 up to 2.0.3, 2.1.0 up to 2.1.2, 2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1
1.15.42.0.32.1.22.2.12.3.1
tensorflow
PyPI
 before 1.15.4, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 24 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.0
1.15.4
tensorflow
PyPI
 2.0.0 up to 2.0.3, plus listed versions
2.0.12.0.2
2.0.3
tensorflow
PyPI
 2.1.0 up to 2.1.2, plus listed versions
2.1.1
2.1.2
tensorflow
PyPI
 2.2.0 up to 2.2.1
2.2.1
tensorflow
PyPI
 2.3.0 up to 2.3.1
2.3.1
tensorflow-cpu
PyPI
 before 1.15.4, plus listed versions
1.15.0
1.15.4
tensorflow-cpu
PyPI
 2.1.0 up to 2.1.2, plus listed versions
2.1.1
2.1.2
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.1
2.2.1
tensorflow-gpu
PyPI
 before 1.15.4, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 24 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.0
1.15.4
tensorflow-gpu
PyPI
 2.0.0 up to 2.0.3, plus listed versions
2.0.12.0.2
2.0.3
tensorflow-gpu
PyPI
 2.1.0 up to 2.1.2, plus listed versions
2.1.1
2.1.2
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.1
2.2.1
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.1
2.3.1
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.1
2.3.1
tensorflow
PyPI
 before 1.15.4, 2.0.0 up to 2.0.3, 2.1.0 up to 2.1.2, 2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1, plus listed versions
0.12.0rc00.12.0rc10.12.00.12.11.0.0
Show 45 moreShow less
1.0.11.1.0rc01.1.0rc11.1.0rc21.1.01.2.0rc01.2.0rc11.2.0rc21.2.01.2.11.3.0rc01.3.0rc11.3.0rc21.3.01.4.0rc01.4.0rc11.4.01.4.11.5.0rc01.5.0rc11.5.01.5.11.6.0rc01.6.0rc11.6.01.7.0rc01.7.0rc11.7.01.7.11.8.0rc01.8.0rc11.8.01.9.0rc01.9.0rc11.9.0rc21.9.01.10.0rc01.10.0rc11.10.01.10.11.11.0rc01.11.0rc11.11.0rc21.11.01.12.0rc0
1.15.42.0.32.1.22.2.12.3.1
tensorflow-cpu
PyPI
 before 1.15.4, 2.0.0 up to 2.0.3, 2.1.0 up to 2.1.2, 2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1, plus listed versions
1.15.02.1.1
1.15.42.0.32.1.22.2.12.3.1
tensorflow-gpu
PyPI
 before 1.15.4, 2.0.0 up to 2.0.3, 2.1.0 up to 2.1.2, 2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 27 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.12.0.22.1.1
1.15.42.0.32.1.22.2.12.3.1
tensorflow2
openSUSE:Leap 15.2
From an advisory covering 16 CVEs
 before 2.1.2-lp152.7.3.1
2.1.2-lp152.7.3.1
tensorflow2-lite
openSUSE:Leap 15.2
From an advisory covering 16 CVEs
 before 2.1.2-lp152.7.3.1
2.1.2-lp152.7.3.1
tensorflow2_2_1_2-gnu-hpc
openSUSE:Leap 15.2
From an advisory covering 16 CVEs
 before 2.1.2-lp152.7.3.1
2.1.2-lp152.7.3.1
tensorflow2_2_1_2-gnu-openmpi2-hpc
openSUSE:Leap 15.2
From an advisory covering 16 CVEs
 before 2.1.2-lp152.7.3.1
2.1.2-lp152.7.3.1

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-2020-15210 is a medium vulnerability (CVSS 6.5) in Google Tensorflow, published September 25, 2020. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.74%, the 54th percentile. Fixed in tensorflow 1.15.4.

References

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This page doesn't list everything

Left out here: 23 versions. The JSON record has them.

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