CVE-2022-41884: Tensorflow Seg Fault Vulnerability

MODERATE
Advisory Identifier: GHSA-jq6x-99hj-q636
CVSS 4.8
CWE-670
Published
Advisory Summary

CVE-2022-41884 is a MODERATE (CVSS 4.8) seg fault vulnerability (CWE-670: Always-Incorrect Control Flow Implementation) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves seg fault in `ndarray_tensor_bridge` due to zero and large inputs. Remediation is available by upgrading affected packages to version 2.8.4, 2.9.3, 2.10.1.

CVE-2022-41884 at a Glance

CVE Identifier CVE-2022-41884
GHSA Identifier GHSA-jq6x-99hj-q636
Severity MODERATE
CVSS v3.1 Base Score 4.8
CWE Classification CWE-670: Always-Incorrect Control Flow Implementation
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 4.8

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

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

How to Fix CVE-2022-41884

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 CVE-2022-41884, 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 CWE-670: Always-Incorrect Control Flow Implementation, where the code contains a control flow path that does not reflect the algorithm that the path is intended to implement, leading to incorrect behavior any time this path is navigated. 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 If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. E.g. the following raises an error: ```python np.ones((0, 2**31, 2**31)) ``` An example of a proof of concept: ```python import numpy as np import tensorflow as tf input_val = tf.constant([1]) shape_val = np.array([i for i in range(21)]) tf.broadcast_to(input=input_val,shape=shape_val) ``` The return value of `PyArray_SimpleNewFromData`, which returns null on such shapes, is not checked. ### Patches We have patched the issue in GitHub commit [2b56169c16e375c521a3bc8ea658811cc0793784](https://github.com/tensorflow/tensorflow/commit/2b56169c16e375c521a3bc8ea658811cc0793784). 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 Rattanukul.

CVE-2022-41884 Affected Tensorflow Versions

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

References

Frequently Asked Questions

What is CVE-2022-41884?
CVE-2022-41884 is a security vulnerability in Tensorflow involving Seg fault in `ndarray_tensor_bridge` due to zero and large inputs under CWE-670: Always-Incorrect Control Flow Implementation.
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 CVE-2022-41884?
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 is the CVSS score of CVE-2022-41884?
CVE-2022-41884 has a CVSS v3.1 base score of 4.8 (MODERATE) with vector string CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H.
What CWE is associated with CVE-2022-41884?
CVE-2022-41884 is classified under CWE-670: Always-Incorrect Control Flow Implementation (The code contains a control flow path that does not reflect the algorithm that the path is intended to implement, leading to incorrect behavior any time this path is navigated.).
How can I check PyPI dependencies for CVE-2022-41884?
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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