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CVE-2020-15202

Integer truncation in Shard API usage

9.0 CriticalFix available

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 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
High
Integrity
High
Availability
High

Exploit likelihood

1.29%Unchanged since Apr 14, 2021
90%50%10%1%

What each source reports

CVE List Score used 9.0

Integer truncation in Shard API usage

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
CVE-2020-15202
CVSS 3.1
9.0 CriticalCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
Weakness
CWE-197, CWE-754
Updated
2026-11-02
EUVDEUVD-2020-0198 9.0

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
EUVD-2020-0198
CVSS 3.1
9.0 CriticalCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
Updated
2026-11-03
GitHub AdvisoriesGHSA-h6fg-mjxg-hqq4 7.1

Integer truncation in Shard API usage

Impact

The Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/work_sharder.h#L59-L60

However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L204-L205 https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L317-L318

In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.

Patches

We have patched the issue in 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575. We 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.

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-h6fg-mjxg-hqq4
CVSS 4.0
7.1 HighCVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:N/SC:H/SI:H/SA:H
CVSS 3.1
9.0 CriticalCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
Weakness
CWE-197, CWE-754
Updated
2026-11-02
NVD 9.0

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
CVE-2020-15202
CVSS 3.1
9.0 CriticalCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
CVSS 2.0
6.8 MediumAV:N/AC:M/Au:N/C:P/I:P/A:P
Weakness
CWE-197, CWE-754
Updated
2026-11-02
OSV 9.0

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
CVE-2020-15202
CVSS 3.1
9.0 CriticalCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
Updated
2026-11-02
OSVGHSA-h6fg-mjxg-hqq4 7.1

Integer truncation in Shard API usage

Impact

The Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/work_sharder.h#L59-L60

However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L204-L205 https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L317-L318

In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.

Patches

We have patched the issue in 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575. We 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.

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-h6fg-mjxg-hqq4
CVSS 4.0
7.1 HighCVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:N/SC:H/SI:H/SA:H
CVSS 3.1
9.0 CriticalCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
Weakness
CWE-197, CWE-754
Updated
2026-11-02
Show 6 moreShow less
OSVBIT-tensorflow-2020-15202 No score

Integer truncation in Shard API usage

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
BIT-tensorflow-2020-15202
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
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-2020-125 No score

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
PYSEC-2020-125
Updated
2026-11-02
OSVPYSEC-2020-282 No score

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
PYSEC-2020-282
Updated
2026-11-02
OSVPYSEC-2020-317 No score

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the Shard API in TensorFlow expects the last argument to be a function taking two int64 (i.e., long long) arguments. However, there are several places in TensorFlow where a lambda taking int or int32 arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Record
PYSEC-2020-317
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-cpu
PyPI
 2.3.0 to 2.3.0
2.3.1
Show 27 moreShow less
Affected packages
PackageAffectedFixed
tensorflow-gpu
PyPI
 before 1.15.4
1.15.4
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-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-cpu
PyPI
 2.3.0 up to 2.3.1
2.3.1
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-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-2020-15202 is a critical vulnerability (CVSS 9.0) 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 1.29%, the 70th 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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