CVE-2022-23583: Tensorflow `CHECK`-failures Vulnerability

MODERATE
Advisory Identifier: GHSA-gjqc-q9g6-q2j3
CVSS 6.5
CWE-617 CWE-843
Published
Advisory Summary

CVE-2022-23583 is a MODERATE (CVSS 6.5) `check`-failures vulnerability (CWE-617: Reachable Assertion) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves `check`-failures in binary ops in tensorflow. Remediation is available by upgrading affected packages to version 2.5.3, 2.6.3, 2.7.1.

CVE-2022-23583 at a Glance

CVE Identifier CVE-2022-23583
GHSA Identifier GHSA-gjqc-q9g6-q2j3
Severity MODERATE
CVSS v3.1 Base Score 6.5
CWE Classification CWE-617: Reachable Assertion
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

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-2022-23583

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-2022-23583, 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-617: Reachable Assertion, where the product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary. 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 A malicious user can cause a denial of service by altering a `SavedModel` such that [any binary op](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137) would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved: ```cc functor::BinaryFunctor<Device, Functor, 1>()( eigen_device, out->template flat<Tout>(), input_0.template flat<Tin>(), input_1.template flat<Tin>(), error_ptr); ``` If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. ### Patches We have patched the issue in GitHub commit [a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9](https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9). 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.

CVE-2022-23583 Affected Tensorflow Versions

9 component(s)
Fixed Versions:
2.5.32.6.32.7.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
PyPI
< 2.5.3
2.5.3
tensorflow
PyPI
≥ 2.6.0
< 2.6.3
2.6.3
tensorflow
PyPI
≥ 2.7.0
< 2.7.1
All 1 versions
2.7.0
2.7.1
tensorflow-cpu
PyPI
< 2.5.3
2.5.3
tensorflow-cpu
PyPI
≥ 2.6.0
< 2.6.3
2.6.3
tensorflow-cpu
PyPI
≥ 2.7.0
< 2.7.1
All 1 versions
2.7.0
2.7.1
tensorflow-gpu
PyPI
< 2.5.3
2.5.3
tensorflow-gpu
PyPI
≥ 2.6.0
< 2.6.3
2.6.3
tensorflow-gpu
PyPI
≥ 2.7.0
< 2.7.1
All 1 versions
2.7.0
2.7.1

References

Frequently Asked Questions

What is CVE-2022-23583?
CVE-2022-23583 is a security vulnerability in Tensorflow involving `CHECK`-failures in binary ops in Tensorflow under CWE-617: Reachable Assertion.
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-2022-23583?
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-2022-23583?
CVE-2022-23583 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-2022-23583?
CVE-2022-23583 is classified under CWE-617: Reachable Assertion (The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary.).
How can I check PyPI dependencies for CVE-2022-23583?
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
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  "id" : "GHSA-gjqc-q9g6-q2j3",
  "modified" : "2024-11-07T22:28:14Z",
  "published" : "2022-02-10T00:34:13Z",
  "aliases" : [ "CVE-2022-23583" ],
  "summary" : "`CHECK`-failures in binary ops in Tensorflow",
  "details" : "### Impact\nA malicious user can cause a denial of service by altering a `SavedModel` such that [any binary op](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137) would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved:\n\n```cc\nfunctor::BinaryFunctor<Device, Functor, 1>()(\n    eigen_device, out->template flat<Tout>(),\n    input_0.template flat<Tin>(), input_1.template flat<Tin>(),\n    error_ptr);\n```\nIf `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service.\n\n### Patches\nWe have patched the issue in GitHub commit [a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9](https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9).\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.",
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