CVE-2021-29556 Explained : Impact and Mitigation

A denial of service vulnerability, CVE-2021-29556, was discovered in TensorFlow, affecting versions prior to 2.1.4, between 2.2.0 and 2.2.3, between 2.3.0 and 2.3.3, and between 2.4.0 and 2.4.2. The vulnerability allows an attacker to exploit a runtime error in

tf.raw_ops.Reverse

which can lead to a division by zero error. This impacts the availability of the system.

Understanding CVE-2021-29556

This section covers details about the CVE-2021-29556 vulnerability in TensorFlow.

What is CVE-2021-29556?

TensorFlow, an open-source machine learning platform, is prone to a denial of service vulnerability due to a division by zero error in

tf.raw_ops.Reverse

.

The Impact of CVE-2021-29556

The vulnerability can be exploited by attackers to cause a denial of service on affected TensorFlow versions, affecting the availability of services supported by the platform.

Technical Details of CVE-2021-29556

Explore the technical aspects of the CVE-2021-29556 vulnerability.

Vulnerability Description

The vulnerability arises from a division by zero error in the implementation of

tf.raw_ops.Reverse

in TensorFlow, leading to a denial of service.

Affected Systems and Versions

Versions of TensorFlow prior to 2.1.4, between 2.2.0 and 2.2.3, between 2.3.0 and 2.3.3, and between 2.4.0 and 2.4.2 are vulnerable to this issue.

Exploitation Mechanism

Attackers can exploit this vulnerability by causing a FPE runtime error in

tf.raw_ops.Reverse

, impacting the availability of TensorFlow.

Mitigation and Prevention

Learn how to mitigate the risks associated with CVE-2021-29556.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.5.0 to address this vulnerability. For those on affected versions, it is recommended to apply the fix cherrypicked to versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4.

Long-Term Security Practices

Security best practices include keeping software up to date, monitoring for security advisories, and implementing security patches promptly.

Patching and Updates

Regularly check for updates from TensorFlow and apply patches as soon as they are released to prevent exploitation of known vulnerabilities.