CVE-2022-21741

mediumpriority · track · 26EPSS 0.008 · 57 %ileEUUS
grimoire priorityP3 · Track
Exploitable?
—
no known exploitation
Likely / severe?
0.83% (57th) EPSS
CVSS 5 · medium
Is it mine?
16 products
Check your exposure
Who says so?
4 sources
cnnvd · enisa_euvd · osv · nvd

EPSS · scored 2026-10-26

Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

01

cvss

Technical severity, 0–10. One row per scoring source.

6.5
010

base score 0–10 · higher = more severe · hover a vector to decode

v2.05mediumAV:N/AC:L/Au:N/C:N/I:N/A:Pprimary · NVD / NIST USexploitability 10 · impact 2.9
v3.16.5mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:Hprimary · NVD / NIST USexploitability 2.8 · impact 3.6
v3.16.5mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:Hsecondary · ENISA EU
v3.16.5mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:Hsecondary · security-advisories@github.comexploitability 2.8 · impact 3.6
v3.16.5mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:Hsecondary · OSV
v3.16mediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H/E:Usecondary · CVSS-BT (t0sche)
02

weaknesses

The CWE weakness classes this flaw belongs to.

CWE-369USprimaryCWE-369US
03

affected products (16)

Products and version ranges known to be affected.

04

advisories (5)

Vendor and ecosystem advisories describing this.

  • GHSA-428x-9xc2-m8mjDivision by zero in TFLite2022-02-09ghsa · osvUS

    ### Impact An attacker can craft a TFLite model that would trigger a division by zero in [the implementation of depthwise convolutions](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96). The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is stricly positive. ### Patches We have patched the issue in GitHub commit [e5b0eec199c2d03de54fd6a7fd9275692218e2bc](https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team.

  • BIT-tensorflow-2022-21741Division by zero in TFLite2024-03-06vendor · osvUS

    Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

  • PYSEC-2022-1202022-02-03vendor · osvUS

    Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

  • PYSEC-2022-652022-02-03vendor · osvUS

    Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

  • PYSEC-2026-3095Division by zero in TFLite2026-07-09vendor · osvUS

    ### Impact An attacker can craft a TFLite model that would trigger a division by zero in [the implementation of depthwise convolutions](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96). The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is stricly positive. ### Patches We have patched the issue in GitHub commit [e5b0eec199c2d03de54fd6a7fd9275692218e2bc](https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team.

05

also known as (11)

Other names and identifiers for this entity.

06

sources

Which sources assert this record, and when.

cnnvdenisa_euvdosvnvdNVDCVE.orgMITRE
published 2022-02-03modified 2026-06-17
08

references (10)

External citations — patches, advisories, analysis.

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