CVE-2022-23563: Tensorflow Insecure Temporary File Vulnerability

HIGH
Advisory Identifier: GHSA-wc4g-r73w-x8mm
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CVSS 7.1
CWE-668 CWE-367
Published
Advisory Summary

CVE-2022-23563 is a HIGH (CVSS 7.1) insecure temporary file vulnerability (CWE-668: Exposure of Resource to Wrong Sphere) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves insecure temporary file in tensorflow. Remediation is available by upgrading affected packages to version 2.5.3, 2.6.3, 2.7.1.

CVE-2022-23563 at a Glance

CVE Identifier CVE-2022-23563
GHSA Identifier GHSA-wc4g-r73w-x8mm
Severity HIGH
CVSS v3.1 Base Score 7.1
CWE Classification CWE-668: Exposure of Resource to Wrong Sphere
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 7.1

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

Attack Vector Local (AV:L)
Attack Complexity Low (AC:L)
Privileges Required Low (PR:L)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality High (C:H)
Integrity High (I:H)
Availability None (A:N)

How to Fix CVE-2022-23563

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-2022-23563, 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-668: Exposure of Resource to Wrong Sphere, where the product exposes a resource to the wrong control sphere, providing unintended actors with inappropriate access to the resource. 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 In multiple places, TensorFlow uses `tempfile.mktemp` to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in `mktemp` and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the `mktemp` function usage. ### Patches We have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern. 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 on huntr.dev for one scenario and discovered via variant analysis on other instances.

CVE-2022-23563 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-wc4g-r73w-x8mm
NVD — CVE-2022-23563 NVD
https://nvd.nist.gov/vuln/detail/CVE-2022-23563
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-72.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-127.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2022-23563?
CVE-2022-23563 is a security vulnerability in Tensorflow involving Insecure temporary file in Tensorflow under CWE-668: Exposure of Resource to Wrong Sphere.
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-2022-23563?
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-2022-23563?
CVE-2022-23563 has a CVSS v3.1 base score of 7.1 (HIGH) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N.
What CWE is associated with CVE-2022-23563?
CVE-2022-23563 is classified under CWE-668: Exposure of Resource to Wrong Sphere (The product exposes a resource to the wrong control sphere, providing unintended actors with inappropriate access to the resource.).
How can I check PyPI dependencies for CVE-2022-23563?
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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  "id" : "GHSA-wc4g-r73w-x8mm",
  "modified" : "2024-11-13T22:39:27Z",
  "published" : "2022-02-09T23:54:51Z",
  "aliases" : [ "CVE-2022-23563" ],
  "summary" : "Insecure temporary file in Tensorflow",
  "details" : "### Impact\nIn multiple places, TensorFlow uses `tempfile.mktemp` to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in `mktemp` and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness).\n\nIn several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the `mktemp` function usage.\n\n### Patches\nWe have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern.\nThe 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.\n\n### For more information\nPlease 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.\n\n### Attribution\nThis vulnerability has been reported on huntr.dev for one scenario and discovered via variant analysis on other instances.",
  "severity" : [ {
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  } ],
  "references" : [ {
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    "url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm"
  }, {
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  }, {
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}