CVE-2022-21726 Explained : Impact and Mitigation

Tensorflow has been identified with a vulnerability that could lead to an out-of-bounds read due to inadequate validation of the

axis

value in the

Dequantize

implementation. This issue can result in heap Out-Of-Bound accesses, impacting the confidentiality of data within the affected systems. The vulnerability is assigned a CVSS base score of 8.1 (High Severity).

Understanding CVE-2022-21726

In this section, we will delve into the details of the CVE-2022-21726 vulnerability in Tensorflow.

What is CVE-2022-21726?

Tensorflow, an open-source machine learning framework, is affected by an issue where the

axis

parameter in the

Dequantize

function is not adequately validated. This can lead to heap OOB accesses due to unchecked upper bounds.

The Impact of CVE-2022-21726

The vulnerability poses a high severity risk with a CVSS base score of 8.1, affecting the confidentiality of data in the impacted systems. Attackers could exploit this flaw to read past the end of the array containing the dimensions of the input tensor.

Technical Details of CVE-2022-21726

Let's explore the technical aspects of the CVE-2022-21726 vulnerability.

Vulnerability Description

The issue arises from the lack of proper validation for the

axis

argument in the

Dequantize

function, allowing for potential heap OOB accesses.

Affected Systems and Versions

All versions of Tensorflow up to 2.8.0 are affected by this vulnerability. The issue has been addressed in Tensorflow 2.8.0, and patches are also available for versions 2.7.1, 2.6.3, and 2.5.3.

Exploitation Mechanism

By manipulating the

axis

argument, threat actors can trigger heap OOB accesses, compromising the integrity and availability of data.

Mitigation and Prevention

To protect your systems from the CVE-2022-21726 vulnerability, consider the following measures.

Immediate Steps to Take

Update Tensorflow to version 2.8.0 or apply the specific patches for versions 2.7.1, 2.6.3, and 2.5.3.
Monitor for any unusual activities that might indicate exploitation of this vulnerability.

Long-Term Security Practices

Regularly update software components to stay protected against known vulnerabilities.
Conduct security assessments to identify and remediate potential risks in your machine learning frameworks.

Patching and Updates

Stay informed about security advisories from Tensorflow and apply recommended patches promptly to mitigate emerging threats.