CVE-2021-29572 : Vulnerability Insights and Analysis

A vulnerability has been identified in TensorFlow, tracked as CVE-2021-29572, that allows users to trigger undefined behavior by dereferencing a null pointer in the

SdcaOptimizer

. This vulnerability affects versions of TensorFlow before 2.1.4 and between 2.2.0 to 2.4.2. The impact of this CVE is rated as LOW with a CVSS base score of 2.5.

Understanding CVE-2021-29572

This section provides insights into the nature of the vulnerability, its impact, affected systems, and mitigation strategies.

What is CVE-2021-29572?

TensorFlow, an open-source machine learning platform, contains a vulnerability in the

SdcaOptimizer

implementation that allows a null pointer dereference, leading to undefined behavior. The bug arises from inadequate validation of user-supplied arguments against expected constraints.

The Impact of CVE-2021-29572

The impact of this vulnerability is deemed LOW, with no confidentiality or integrity impact. However, it can be exploited locally with low privileges.

Technical Details of CVE-2021-29572

This section delves into the specifics of the vulnerability encompassing its description, affected systems, and exploitation mechanisms.

Vulnerability Description

The flaw arises from referencing a nullptr in the

SdcaOptimizer

implementation, triggering undefined behavior due to unchecked user inputs.

Affected Systems and Versions

The vulnerability affects versions of TensorFlow before 2.1.4 and between 2.2.0 to 2.4.2, making a range of systems vulnerable to this issue.

Exploitation Mechanism

Attackers can locally exploit this vulnerability with low privileges by providing crafted inputs that trigger the null pointer dereference.

Mitigation and Prevention

This section outlines immediate steps to take and long-term security practices to prevent and mitigate the impact of CVE-2021-29572.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.5.0, as the fix is included in this version. Cherrypicking the commit on versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 is recommended for unsupported but affected systems.

Long-Term Security Practices

Implementing input validation mechanisms and staying updated with patches and security releases can help prevent similar vulnerabilities in the future.

Patching and Updates

Regularly checking for TensorFlow updates, applying patches promptly, and monitoring security advisories are essential for maintaining a secure machine learning environment.