CVE-2022-21725: Tensorflow Division By Zero Vulnerability

Q: What is CVE-2022-21725?

CVE-2022-21725 is a security vulnerability in Tensorflow involving Division by zero in Tensorflow under CWE-369: Divide By Zero.

Q: Which Tensorflow versions are affected?

Affected versions include releases prior to 2.5.3, 2.6.3, 2.7.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

Q: What version fixes CVE-2022-21725?

Upgrading to 2.5.3, 2.6.3, 2.7.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Q: Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 2.5.3, 2.6.3, 2.7.1. Upgrade your dependency to 2.5.3, 2.6.3, 2.7.1 or later.

Q: Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 2.5.3, 2.6.3, 2.7.1. Upgrade your dependency to 2.5.3, 2.6.3, 2.7.1 or later.

Q: What is the CVSS score of CVE-2022-21725?

CVE-2022-21725 has a CVSS v3.1 base score of 6.5 (MODERATE) with vector string CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.

Q: What CWE is associated with CVE-2022-21725?

CVE-2022-21725 is classified under CWE-369: Divide By Zero (The product divides a value by zero.).

Q: How can I check PyPI dependencies for CVE-2022-21725?

Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.

MODERATE

Advisory Identifier: GHSA-v3f7-j968-4h5f

CVSS 6.5

CWE-369

Published 2022-02-10

Advisory Summary

CVE-2022-21725 is a MODERATE (CVSS 6.5) division by zero vulnerability (CWE-369: Divide By Zero) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves division by zero in tensorflow. Remediation is available by upgrading affected packages to version 2.5.3, 2.6.3, 2.7.1.

CVE-2022-21725 at a Glance

CVE Identifier CVE-2022-21725

GHSA Identifier GHSA-v3f7-j968-4h5f

Severity MODERATE

CVSS v3.1 Base Score 6.5

CWE Classification CWE-369: Divide By Zero

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2022-02-10

Last Updated 2024-11-13

Security Impact

CVSS: 6.5

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).

Attack Vector Network (AV:N)

Attack Complexity Low (AC:L)

Privileges Required Low (PR:L)

User Interaction None (UI:N)

Scope Unchanged (S:U)

Confidentiality None (C:N)

Integrity None (I:N)

Availability High (A:H)

How to Fix CVE-2022-21725

To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

tensorflow fixed: 2.5.3

pip install tensorflow>=2.5.3

tensorflow fixed: 2.6.3

pip install tensorflow>=2.6.3

tensorflow fixed: 2.7.1

pip install tensorflow>=2.7.1

tensorflow-cpu fixed: 2.5.3

pip install tensorflow-cpu>=2.5.3

tensorflow-cpu fixed: 2.6.3

pip install tensorflow-cpu>=2.6.3

tensorflow-cpu fixed: 2.7.1

pip install tensorflow-cpu>=2.7.1

tensorflow-gpu fixed: 2.5.3

pip install tensorflow-gpu>=2.5.3

tensorflow-gpu fixed: 2.6.3

pip install tensorflow-gpu>=2.6.3

tensorflow-gpu fixed: 2.7.1

pip install tensorflow-gpu>=2.7.1

Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release:

pip show tensorflow

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2022-21725, inspect your installed packages using pip:

pip show tensorflow

Analyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.5.3), your application is affected.

Note: The pip show tensorflow command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-369: Divide By Zero, where the product divides a value by zero. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

### Impact The [estimator for the cost of some convolution operations](https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198) can be made to execute a division by 0: ```python import tensorflow as tf @tf.function def test(): y=tf.raw_ops.AvgPoolGrad( orig_input_shape=[1,1,1,1], grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]], ksize=[1,1,1,1], strides=[1,1,1,0], padding='VALID', data_format='NCHW') return y test() ``` The function fails to check that the stride argument is stricly positive: ```cc int64_t GetOutputSize(const int64_t input, const int64_t filter, const int64_t stride, const Padding& padding) { // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() // function in third_party/tensorflow/core/framework/common_shape_fns.cc. if (padding == Padding::VALID) { return (input - filter + stride) / stride; } else { // SAME. return (input + stride - 1) / stride; } } ``` Hence, the fix is to add a check for the stride argument to ensure it is valid. ### Patches We have patched the issue in GitHub commit [3218043d6d3a019756607643cf65574fbfef5d7a](https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a). 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 Yu Tian of Qihoo 360 AIVul Team.

CVE-2022-21725 Affected Tensorflow Versions

9 component(s)

Fixed Versions:

2.5.32.6.32.7.1

Product / Package	Ecosystem	Affected Versions	Fixed Version
tensorflow pypi/tensorflow	PyPI	< 2.5.3	2.5.3
tensorflow pypi/tensorflow	PyPI	≥ 2.6.0 < 2.6.3	2.6.3
tensorflow pypi/tensorflow	PyPI	≥ 2.7.0 < 2.7.1 All 1 versions 2.7.0	2.7.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	< 2.5.3	2.5.3
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.6.0 < 2.6.3	2.6.3
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.7.0 < 2.7.1 All 1 versions 2.7.0	2.7.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	< 2.5.3	2.5.3
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.6.0 < 2.6.3	2.6.3
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.7.0 < 2.7.1 All 1 versions 2.7.0	2.7.1

References

GitHub Advisory / Wiki ADVISORY

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f

NVD — CVE-2022-21725 NVD

https://nvd.nist.gov/vuln/detail/CVE-2022-21725

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-49.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-104.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198

Frequently Asked Questions

What is CVE-2022-21725?

CVE-2022-21725 is a security vulnerability in Tensorflow involving Division by zero in Tensorflow under CWE-369: Divide By Zero.

Which Tensorflow versions are affected?

Affected versions include releases prior to 2.5.3, 2.6.3, 2.7.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

What version fixes CVE-2022-21725?

Upgrading to 2.5.3, 2.6.3, 2.7.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 2.5.3, 2.6.3, 2.7.1. Upgrade your dependency to 2.5.3, 2.6.3, 2.7.1 or later.

Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 2.5.3, 2.6.3, 2.7.1. Upgrade your dependency to 2.5.3, 2.6.3, 2.7.1 or later.

What is the CVSS score of CVE-2022-21725?

CVE-2022-21725 has a CVSS v3.1 base score of 6.5 (MODERATE) with vector string CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.

What CWE is associated with CVE-2022-21725?

CVE-2022-21725 is classified under CWE-369: Divide By Zero (The product divides a value by zero.).

How can I check PyPI dependencies for CVE-2022-21725?

Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.

Machine-readable OSV JSON OSV v1.5 Schema

Normalized schema output

View Raw JSON

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CVSS V3

6.5 MODERATE

Attack Vector Network (AV:N)

Attack Complexity Low (AC:L)

Privileges Required Low (PR:L)

User Interaction None (UI:N)

Scope Unchanged (S:U)

Confidentiality None (C:N)

Integrity None (I:N)

Availability High (A:H)

Vector String

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Advisory Metadata

Source ID: GHSA-v3f7-j968-4h5f
Schema Version: 1.4.0
Published: 2022-02-10 00:15
Last Modified: 2024-11-13 22:08
Database Record File: advisories/github-reviewed/2022/02/GHSA-v3f7-j968-4h5f/GHSA-v3f7-j968-4h5f.json