CVE-2020-15213
Denial of service in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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
- None
- Integrity
- None
- Availability
- Low
Exploit likelihood
What each source reports
CVE List Score used
Denial of service in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-15213- CVSS 3.1
- 4.0 Medium
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L - Weakness
- CWE-119, CWE-770
- Updated
- 2026-11-02
EUVDEUVD-2020-0209
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-0209- CVSS 3.1
- 4.0 Medium
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L - Updated
- 2026-11-03
GitHub AdvisoriesGHSA-hjmq-236j-8m87
Denial of service in tensorflow-lite
Impact
In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.
However, 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 discovered from a variant analysis of GHSA-p2cq-cprg-frvm.
- Record
GHSA-hjmq-236j-8m87- CVSS 4.0
- 6.3 Medium
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:L - CVSS 3.1
- 4.0 Medium
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L - Weakness
- CWE-119, CWE-770
- Updated
- 2026-11-02
NVD
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-15213- CVSS 3.1
- 4.0 Medium
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L - CVSS 2.0
- 4.3 Medium
AV:N/AC:M/Au:N/C:N/I:N/A:P - Weakness
- CWE-119, CWE-770
- Updated
- 2026-11-02
OSV
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-15213- CVSS 3.1
- 4.0 Medium
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L - Updated
- 2026-11-02
OSVGHSA-hjmq-236j-8m87
Denial of service in tensorflow-lite
Impact
In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.
However, 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 discovered from a variant analysis of GHSA-p2cq-cprg-frvm.
- Record
GHSA-hjmq-236j-8m87- CVSS 4.0
- 6.3 Medium
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:L - CVSS 3.1
- 4.0 Medium
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L - Weakness
- CWE-119, CWE-770
- Updated
- 2026-11-02
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OSVBIT-tensorflow-2020-15213 No score
Denial of service in tensorflow-lite
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-15213- Updated
- 2026-11-02
OSVPYSEC-2020-136 No score
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-136- Updated
- 2026-11-02
OSVPYSEC-2020-293 No score
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-293- Updated
- 2026-11-02
OSVPYSEC-2020-328 No score
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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-2020-328- 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-2020-15213 is a medium vulnerability (CVSS 4.0) 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 0.76%, the 55th percentile. Fixed in tensorflow 2.2.1.
References
github.com/tensorflow/tensorflow/releases/tag/v2.3.1
nvd.nist.gov/vuln/detail/CVE-2020-15213
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