CVE-2022-21727 : Vulnerability Insights and Analysis

Tensorflow is an Open Source Machine Learning Framework with a vulnerability in the shape inference for

Dequantize

leading to an integer overflow weakness. The issue arises due to the unchecked upper bound when the code computes

axis + 1

, potentially allowing an attacker to trigger an integer overflow. The fix for this vulnerability will be included in TensorFlow 2.8.0 and also in versions 2.7.1, 2.6.3, and 2.5.3.

Understanding CVE-2022-21727

This section dives deeper into the details of the CVE-2022-21727.

What is CVE-2022-21727?

Tensorflow's vulnerability allows an attacker to exploit an integer overflow weakness in the shape inference for

Dequantize

due to an unchecked upper bound when computing

axis + 1

.

The Impact of CVE-2022-21727

The impact of this vulnerability is rated as high severity with a CVSS base score of 7.6. It has low impacts on confidentiality, integrity, and privileges required but high availability impact.

Technical Details of CVE-2022-21727

Let's delve into the technical aspects of the CVE-2022-21727.

Vulnerability Description

The vulnerability arises from the shape inference for

Dequantize

in Tensorflow, where an attacker can trigger an integer overflow by manipulating the

axis

argument.

Affected Systems and Versions

The vulnerability affects TensorFlow versions 2.5.3, 2.6.3, 2.7.1, and will be fixed in version 2.8.0.

Exploitation Mechanism

By setting a positive value beyond the number of dimensions of the input for the

axis

argument in

Dequantize

, an attacker can trigger an integer overflow due to the lack of bounds checking.

Mitigation and Prevention

To address CVE-2022-21727, follow the mitigation strategies below.

Immediate Steps to Take

Update to TensorFlow version 2.8.0 once the fix is released to mitigate the vulnerability. For versions 2.5.3, 2.6.3, and 2.7.1, apply the cherrypicked commits as temporary solutions.

Long-Term Security Practices

Regularly update Tensorflow to the latest version and stay informed about security advisories to protect against potential vulnerabilities.

Patching and Updates

Stay vigilant for security updates from the Tensorflow team and apply patches promptly to secure your systems against known vulnerabilities.