CVE-2021-29564 : Exploit Details and Defense Strategies

TensorFlow is an open-source platform for machine learning. A vulnerability in the

EditDistance

module could allow an attacker to trigger a null pointer dereference due to incomplete input parameter validation.

Understanding CVE-2021-29564

This CVE identifies a specific vulnerability in TensorFlow that could lead to a null pointer dereference when certain input parameters are not properly validated.

What is CVE-2021-29564?

The vulnerability in

EditDistance

arises from incomplete validation of input parameters, potentially enabling an attacker to trigger a null pointer dereference within TensorFlow.

The Impact of CVE-2021-29564

With a CVSS base score of 2.5 (low severity), the impact of this vulnerability is generally low. However, it could be exploited locally with high attack complexity.

Technical Details of CVE-2021-29564

This section delves into the technical aspects of the vulnerability, including the description, affected systems, and the exploitation mechanism.

Vulnerability Description

The vulnerability allows for a null pointer dereference in the

EditDistance

implementation, opening doors for potential exploitation by malicious actors.

Affected Systems and Versions

Multiple versions of TensorFlow are affected, including versions prior to 2.1.4 and those between 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2.

Exploitation Mechanism

Exploiting this vulnerability requires an attacker to manipulate the input parameters of

EditDistance

to trigger a null pointer dereference.

Mitigation and Prevention

Discover the steps to mitigate the risks associated with CVE-2021-29564 and prevent potential security threats.

Immediate Steps to Take

To address this vulnerability, users should update TensorFlow to version 2.5.0 or apply the necessary patches backported to versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2.

Long-Term Security Practices

Incorporate robust input validation practices and keep your TensorFlow installation updated to mitigate potential vulnerabilities.

Patching and Updates

Stay informed about security advisories for TensorFlow and promptly apply patches and updates to secure your machine learning environment.