CVE-2022-35969 : Exploit Details and Defense Strategies

This article provides an overview of CVE-2022-35969, a vulnerability affecting TensorFlow versions prior to 2.7.2, as well as versions 2.8.0 to 2.8.1, and 2.9.0 to 2.9.1.

Understanding CVE-2022-35969

In this section, we will delve into the details of the vulnerability found in TensorFlow.

What is CVE-2022-35969?

TensorFlow, an open-source machine learning platform, contains a vulnerability in the

Conv2DBackpropInput

implementation. This vulnerability requires the

input_sizes

parameter to be 4-dimensional. Failure to meet this requirement can result in a

CHECK

failure, which could potentially lead to a denial of service attack.

The Impact of CVE-2022-35969

The impact of this vulnerability is rated as medium severity with a CVSS base score of 5.9. It has a high availability impact but does not affect confidentiality or integrity. The attack complexity is considered high, and it can be exploited over a network without requiring user interaction.

Technical Details of CVE-2022-35969

This section will cover the technical aspects of CVE-2022-35969.

Vulnerability Description

The vulnerability stems from the requirement of the

input_sizes

parameter to be 4-dimensional in the

Conv2DBackpropInput

implementation.

Affected Systems and Versions

The vulnerability affects TensorFlow versions prior to 2.7.2, as well as versions 2.8.0 to 2.8.1, and 2.9.0 to 2.9.1.

Exploitation Mechanism

The vulnerability can be exploited to trigger a denial of service attack.

Mitigation and Prevention

In this section, we will outline steps to mitigate and prevent exploitation of CVE-2022-35969.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.10.0 or apply the patch available in GitHub commit 50156d547b9a1da0144d7babe665cf690305b33c.

Long-Term Security Practices

It is recommended to regularly update TensorFlow to the latest version to mitigate potential vulnerabilities.

Patching and Updates

The issue has been patched in TensorFlow 2.10.0, and the fix will be included in TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2.