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CVE-2021-29513 : Security Advisory and Response

Learn about CVE-2021-29513 impacting TensorFlow versions below 2.1.4 and between 2.2.0 to 2.4.2. Understand the vulnerability, its impact, and essential mitigation measures.

TensorFlow, an open-source platform for machine learning, is susceptible to a type confusion vulnerability leading to null pointer dereferences when calling TF operations with non-numeric tensors. The issue affects versions prior to 2.1.4, as well as versions between 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2. This vulnerability is assigned CVE-2021-29513 with a CVSS base score of 2.5.

Understanding CVE-2021-29513

This section provides insights into the nature of the CVE-2021-29513 vulnerability.

What is CVE-2021-29513?

TensorFlow is exposed to a type confusion flaw, triggered by using non-numeric tensors that cause null pointer dereferences during TF operations.

The Impact of CVE-2021-29513

The vulnerability enables attackers to exploit type confusion issues, potentially leading to null pointer dereferences in TensorFlow operations.

Technical Details of CVE-2021-29513

Here, we delve into the technical specifics of CVE-2021-29513.

Vulnerability Description

The vulnerability in TensorFlow arises due to a type confusion during tensor casts, causing null pointer dereferences when non-numeric tensors are utilized.

Affected Systems and Versions

Versions prior to 2.1.4, and versions 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2 of TensorFlow are impacted by this vulnerability.

Exploitation Mechanism

Attackers can exploit this vulnerability by leveraging type confusion issues to manipulate tensor casts and trigger null pointer dereferences in TensorFlow operations.

Mitigation and Prevention

This section outlines the necessary steps to mitigate and prevent exploitation of CVE-2021-29513.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to versions beyond 2.4.2 or apply the fix included in TensorFlow 2.5.0 to address this vulnerability.

Long-Term Security Practices

Implementing secure coding practices and regularly updating TensorFlow to the latest versions can help prevent similar vulnerabilities in the future.

Patching and Updates

Stay informed about security advisories and commit updates from TensorFlow to promptly address any emerging vulnerabilities.

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