CVE-2020-15214
Out of bounds write in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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
- Low
- Integrity
- Low
- Availability
- High
Exploit likelihood
What each source reports
CVE List Score used
Out of bounds write in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
CVE-2020-15214- CVSS 3.1
- 8.1 High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H - Weakness
- CWE-787
- Updated
- 2026-11-02
EUVDEUVD-2020-0210
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
EUVD-2020-0210- CVSS 3.1
- 8.1 High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H - Updated
- 2026-11-03
GitHub AdvisoriesGHSA-p2cq-cprg-frvm
Out of bounds write in tensorflow-lite
Impact
In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631
This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits.
Patches
We have patched the issue in 204945b and will release patch releases for all affected versions.
We recommend users to upgrade to TensorFlow 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 the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model.
A similar validation could be done if the segment ids are generated at runtime between inference steps.
If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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-p2cq-cprg-frvm- CVSS 4.0
- 9.1 Critical
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:L/VA:H/SC:L/SI:L/SA:H - CVSS 3.1
- 8.1 High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H - Weakness
- CWE-787
- Updated
- 2026-11-02
NVD
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
CVE-2020-15214- CVSS 3.1
- 8.1 High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H - CVSS 2.0
- 6.8 Medium
AV:N/AC:M/Au:N/C:P/I:P/A:P - Weakness
- CWE-787
- Updated
- 2026-11-02
OSV
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
CVE-2020-15214- CVSS 3.1
- 8.1 High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H - Updated
- 2026-11-02
OSVGHSA-p2cq-cprg-frvm
Out of bounds write in tensorflow-lite
Impact
In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631
This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits.
Patches
We have patched the issue in 204945b and will release patch releases for all affected versions.
We recommend users to upgrade to TensorFlow 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 the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model.
A similar validation could be done if the segment ids are generated at runtime between inference steps.
If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
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-p2cq-cprg-frvm- CVSS 4.0
- 9.1 Critical
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:L/VA:H/SC:L/SI:L/SA:H - CVSS 3.1
- 8.1 High
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H - Weakness
- CWE-787
- Updated
- 2026-11-02
Show 4 moreShow less
OSVBIT-tensorflow-2020-15214 No score
Out of bounds write in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
BIT-tensorflow-2020-15214- Updated
- 2026-11-02
OSVPYSEC-2020-137 No score
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
PYSEC-2020-137- Updated
- 2026-11-02
OSVPYSEC-2020-294 No score
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
PYSEC-2020-294- Updated
- 2026-11-02
OSVPYSEC-2020-329 No score
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
- Record
PYSEC-2020-329- Updated
- 2026-11-02
Affected products
Affected packages
| Package | Affected | Fixed |
|---|---|---|
tensorflowPyPI |
2.2.0 to 2.2.0 | 2.2.1 |
tensorflowPyPI |
2.3.0 to 2.3.0 | 2.3.1 |
tensorflow-cpuPyPI |
2.2.0 to 2.2.0 | 2.2.1 |
tensorflow-cpuPyPI |
2.3.0 to 2.3.0 | 2.3.1 |
tensorflow-gpuPyPI |
2.2.0 to 2.2.0 | 2.2.1 |
tensorflow-gpuPyPI |
2.3.0 to 2.3.0 | 2.3.1 |
tensorflowBitnami |
2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1 | 2.2.12.3.1 |
tensorflowPyPI |
2.2.0 up to 2.2.1 | 2.2.1 |
tensorflowPyPI |
2.3.0 up to 2.3.1 | 2.3.1 |
tensorflow-cpuPyPI |
2.2.0 up to 2.2.1 | 2.2.1 |
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| Package | Affected | Fixed |
|---|---|---|
tensorflow-cpuPyPI |
2.3.0 up to 2.3.1 | 2.3.1 |
tensorflow-gpuPyPI |
2.2.0 up to 2.2.1 | 2.2.1 |
tensorflow-gpuPyPI |
2.3.0 up to 2.3.1 | 2.3.1 |
tensorflowPyPI |
2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1 | 2.2.12.3.1 |
tensorflow-cpuPyPI |
2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1 | 2.2.12.3.1 |
tensorflow-gpuPyPI |
2.2.0 up to 2.2.1, 2.3.0 up to 2.3.1 | 2.2.12.3.1 |
History
| Recorded | Changed | Sources |
|---|---|---|
| aliases | EUVD | |
| first indexed | CVE List, GitHub Advisories, NVD, OSV |
Dates are when Every CVE recorded each change. Upstream dates may differ.
In short
CVE-2020-15214 is a high vulnerability (CVSS 8.1) 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 0.67%, the 52nd percentile. Fixed in tensorflow 2.2.1.
References
github.com/tensorflow/tensorflow/releases/tag/v2.3.1
nvd.nist.gov/vuln/detail/CVE-2020-15214
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