CVE-2022-21741

mediumPriorität · verfolgen · 26EPSS 0.008 · 57. PerzentilEUUS
grimoire priorityP3 · Track
Exploitable?
—
no known exploitation
Likely / severe?
0.83% (57th) EPSS
CVSS 5 · medium
Is it mine?
16 products
Prüfe deine Angriffsfläche
Who says so?
4 sources
cnnvd · enisa_euvd · osv · nvd

EPSS · bewertet 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

Technischer Schweregrad, 0–10. Eine Zeile je Quelle.

6.5
010

Basiswert 0–10 · höher = schwerwiegender · Vektor überfahren zum Auflösen

v2.05mediumAV:N/AC:L/Au:N/C:N/I:N/A:Pprimary · NVD / NIST USAusnutzbarkeit 10 · Auswirkung 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 USAusnutzbarkeit 2.8 · Auswirkung 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.comAusnutzbarkeit 2.8 · Auswirkung 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

schwächen

Die CWE-Schwächeklassen, zu denen dies gehört.

CWE-369USprimärCWE-369US
03

betroffene Produkte (16)

Betroffene Produkte und Versionsbereiche.

04

Advisories (5)

Hersteller- und Ökosystem-Meldungen dazu.

  • 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

auch bekannt als (11)

Weitere Namen und Kennungen dieser Entität.

06

quellen

Welche Quellen diesen Datensatz belegen und wann.

cnnvdenisa_euvdosvnvdNVDCVE.orgMITRE
veröffentlicht 2022-02-03geändert 2026-06-17
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Referenzen (10)

Externe Belege — Patches, Meldungen, Analysen.

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