CVE-2021-29538 : Security Advisory and Response

TensorFlow is an open-source platform for machine learning. The CVE-2021-29538 arises due to a division by zero issue in

Conv2DBackpropFilter

, potentially leading to a denial of service attack. The vulnerability affects TensorFlow versions < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2.

Understanding CVE-2021-29538

This section provides insights into the nature and impact of the TensorFlow vulnerability.

What is CVE-2021-29538?

The CVE-2021-29538 is a CWE-369 (Divide By Zero) vulnerability in TensorFlow, allowing an attacker to trigger a division by zero in

Conv2DBackpropFilter

, resulting in a potential denial of service attack.

The Impact of CVE-2021-29538

The impact of this vulnerability is considered low, with a CVSS base score of 2.5. Although the attack complexity is high, the availability impact is low, and no privileges are required for exploitation.

Technical Details of CVE-2021-29538

In this section, the technical aspects of CVE-2021-29538 are discussed in detail.

Vulnerability Description

The vulnerability stems from a lack of checking for empty tensor shapes before performing a division operation in

Conv2DBackpropFilter

, leading to a runtime exception.

Affected Systems and Versions

The affected versions include TensorFlow < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2.

Exploitation Mechanism

An attacker can exploit this vulnerability by providing specific input data that results in empty tensor shapes, triggering a division by zero.

Mitigation and Prevention

This section outlines the steps to mitigate the risks associated with CVE-2021-29538.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.5.0 or apply the necessary patches available for TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4 to address the vulnerability.

Long-Term Security Practices

Practicing secure coding standards, conducting regular security audits, and staying informed about security advisories are essential for maintaining the integrity of machine learning applications.

Patching and Updates

Regularly monitor and apply security patches provided by TensorFlow to address known vulnerabilities and enhance the security posture of your machine learning environment.