CVE-2022-41895 : What You Need to Know

A heap Out-of-Bounds read vulnerability was discovered in TensorFlow, an open-source platform for machine learning. This CVE, known as

MirrorPadGrad

, can lead to a heap out-of-bounds error when encountering outsize input

paddings

in TensorFlow. The issue has been patched in TensorFlow 2.11, with backports to versions 2.10.1, 2.9.3, and 2.8.4.

Understanding CVE-2022-41895

This section delves into the details of the vulnerability, its impact, and the technical aspects of the CVE.

What is CVE-2022-41895?

The vulnerability

MirrorPadGrad

in TensorFlow triggers a heap out-of-bounds read error due to improperly handled input

paddings

, potentially leading to security risks.

The Impact of CVE-2022-41895

The impact of this CVE could result in a scenario where an attacker could exploit the out-of-bounds read to execute arbitrary code, leading to a denial of service (DoS) attack or sensitive information exposure.

Technical Details of CVE-2022-41895

Let's explore the technical aspects associated with CVE-2022-41895.

Vulnerability Description

The vulnerability arises when TensorFlow encounters outsize input

paddings

, causing a heap out-of-bounds read error (

MirrorPadGrad

).

Affected Systems and Versions

The vulnerability affects TensorFlow versions 2.10.0 to 2.10.1, versions 2.9.0 to 2.9.3, and versions below 2.8.4, necessitating immediate attention from users of these versions.

Exploitation Mechanism

An attacker can potentially exploit this vulnerability by manipulating input

paddings

to trigger a heap out-of-bounds read in TensorFlow, highlighting the importance of prompt mitigation.

Mitigation and Prevention

Learn how to respond to and protect your systems from CVE-2022-41895.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to the patched version 2.11 to mitigate the risk of exploitation due to the

MirrorPadGrad

vulnerability.

Long-Term Security Practices

Adopting secure coding practices and maintaining up-to-date software can help prevent such vulnerabilities from affecting your systems in the future.

Patching and Updates

Regularly check for security updates and apply patches promptly to ensure the security of your TensorFlow deployment against known vulnerabilities.