GHSA-xf83-q765-xm6m: Tensorflow `CHECK` Fail Vulnerability

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Advisory Summary

GHSA-xf83-q765-xm6m is a LOW `check` fail vulnerability (General Security Weakness) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves `check` fail in `tensorlistscatter` and `tensorlistscatterv2` in eager mode. Remediation is available by upgrading affected packages to version 2.8.4, 2.9.3, 2.10.1.

GHSA-xf83-q765-xm6m at a Glance

CVE Identifier N/A
GHSA Identifier GHSA-xf83-q765-xm6m
Severity LOW
CVSS v3.1 Base Score N/A
CWE Classification General Security Weakness
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

How to Fix GHSA-xf83-q765-xm6m

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: 2.8.4 
pip install tensorflow>=2.8.4
tensorflow fixed: 2.9.3 
pip install tensorflow>=2.9.3
tensorflow fixed: 2.10.1 
pip install tensorflow>=2.10.1
tensorflow-cpu fixed: 2.8.4 
pip install tensorflow-cpu>=2.8.4
tensorflow-gpu fixed: 2.8.4 
pip install tensorflow-gpu>=2.8.4
tensorflow-cpu fixed: 2.9.3 
pip install tensorflow-cpu>=2.9.3
tensorflow-gpu fixed: 2.9.3 
pip install tensorflow-gpu>=2.9.3
tensorflow-cpu fixed: 2.10.1 
pip install tensorflow-cpu>=2.10.1
tensorflow-gpu fixed: 2.10.1 
pip install tensorflow-gpu>=2.10.1
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 GHSA-xf83-q765-xm6m, 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 2.8.4), 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 General Security Weakness. 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 Another instance of CVE-2022-35991, where `TensorListScatter` and `TensorListScatterV2` crash via non scalar inputs in`element_shape`, was found in eager mode and fixed. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None) arg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) arg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536) arg_3='' tf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, element_shape=arg_2, name=arg_3) ``` ### Patches We have patched the issue in GitHub commit [bf9932fc907aff0e9e8cccf769e8b00d30fd81a1](https://github.com/tensorflow/tensorflow/commit/bf9932fc907aff0e9e8cccf769e8b00d30fd81a1). The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Pattarakrit Rattankul

GHSA-xf83-q765-xm6m Affected Tensorflow Versions

9 component(s)
Fixed Versions:
2.8.42.9.32.10.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
< 2.8.4
2.8.4
tensorflow
pypi/tensorflow
PyPI
≥ 2.9.0
< 2.9.3
2.9.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.10.0
< 2.10.1
2.10.1
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 2.8.4
2.8.4
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 2.8.4
2.8.4
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.9.0
< 2.9.3
2.9.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.9.0
< 2.9.3
2.9.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.10.0
< 2.10.1
2.10.1
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.10.0
< 2.10.1
2.10.1

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xf83-q765-xm6m
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is GHSA-xf83-q765-xm6m?
GHSA-xf83-q765-xm6m is a security vulnerability in Tensorflow involving `CHECK` fail in `TensorListScatter` and `TensorListScatterV2` in eager mode under General Security Weakness.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.8.4, 2.9.3, 2.10.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes GHSA-xf83-q765-xm6m?
Upgrading to 2.8.4, 2.9.3, 2.10.1 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.8.4, 2.9.3, 2.10.1. Upgrade your dependency to 2.8.4, 2.9.3, 2.10.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.8.4, 2.9.3, 2.10.1. Upgrade your dependency to 2.8.4, 2.9.3, 2.10.1 or later.
What CWE is associated with GHSA-xf83-q765-xm6m?
GHSA-xf83-q765-xm6m is classified under General Security Weakness.
How can I check PyPI dependencies for GHSA-xf83-q765-xm6m?
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-xf83-q765-xm6m",
  "modified" : "2022-11-21T23:51:30Z",
  "published" : "2022-11-21T23:51:30Z",
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  "summary" : "`CHECK` fail in `TensorListScatter` and `TensorListScatterV2` in eager mode",
  "details" : "### Impact\nAnother instance of CVE-2022-35991, where `TensorListScatter` and `TensorListScatterV2` crash via non scalar inputs in`element_shape`, was found in eager mode and fixed.\n```python\nimport tensorflow as tf\narg_0=tf.random.uniform(shape=(2, 2, 2), dtype=tf.float16, maxval=None)\narg_1=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)\narg_2=tf.random.uniform(shape=(2, 2, 2), dtype=tf.int32, maxval=65536)\narg_3=''\ntf.raw_ops.TensorListScatter(tensor=arg_0, indices=arg_1, \nelement_shape=arg_2, name=arg_3)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [bf9932fc907aff0e9e8cccf769e8b00d30fd81a1](https://github.com/tensorflow/tensorflow/commit/bf9932fc907aff0e9e8cccf769e8b00d30fd81a1).\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.\n\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\n### Attribution\nThis vulnerability has been reported by Pattarakrit Rattankul\n",
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