CVE-2022-35974 : Exploit Details and Defense Strategies

TensorFlow, an open-source platform for machine learning, is affected by a vulnerability in the

QuantizeDownAndShrinkRange

function, allowing a denial-of-service attack through a segfault. Here's what you need to know about CVE-2022-35974.

Understanding CVE-2022-35974

This section provides detailed insights into the CVE-2022-35974 vulnerability affecting TensorFlow.

What is CVE-2022-35974?

TensorFlow is impacted by a vulnerability where providing nonscalar inputs for

input_min

or

input_max

to the

QuantizeDownAndShrinkRange

function can lead to a segfault, which can be exploited for a denial-of-service attack.

The Impact of CVE-2022-35974

The vulnerability has a CVSS base score of 5.9, with a medium severity. It has a high impact on availability but does not affect confidentiality or integrity. The attack complexity is high, and the attack vector is through the network.

Technical Details of CVE-2022-35974

Let's delve into the technical aspects of CVE-2022-35974 to understand the vulnerability better.

Vulnerability Description

When

QuantizeDownAndShrinkRange

receives nonscalar inputs for

input_min

or

input_max

, it triggers a segfault, enabling a denial-of-service attack.

Affected Systems and Versions

The vulnerability affects TensorFlow versions below 2.7.2, versions between 2.8.0 and 2.8.1, and versions between 2.9.0 and 2.9.1.

Exploitation Mechanism

The exploit can be triggered by providing inappropriate inputs to the

QuantizeDownAndShrinkRange

function, resulting in a segfault and potential denial-of-service attack.

Mitigation and Prevention

Here are the steps to mitigate and prevent exploitation of CVE-2022-35974.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.10.0, where the vulnerability has been patched. If unable to upgrade immediately, consider implementing additional security measures.

Long-Term Security Practices

Maintain regular updates of TensorFlow to stay protected from known vulnerabilities. Implement secure coding practices and follow TensorFlow's security advisories closely.

Patching and Updates

Ensure timely application of security patches provided by TensorFlow. Monitor official channels for updates and security alerts to protect your systems effectively.