CVE-2021-29517 : Vulnerability Insights and Analysis

TensorFlow is an open-source platform for machine learning where a division by 0 could be triggered in the

Conv3D

implementation. Malicious inputs can cause a division by 0 or a program crash due to an Eigen assertion. The issue affects 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 will be patched in TensorFlow 2.5.0.

Understanding CVE-2021-29517

This section delves into the details of the TensorFlow vulnerability.

What is CVE-2021-29517?

CVE-2021-29517 refers to a division by zero vulnerability in the

Conv3D

implementation of TensorFlow, leading to potential program crashes.

The Impact of CVE-2021-29517

The vulnerability allows a malicious user to exploit the

Conv3D

implementation in TensorFlow, resulting in a division by 0 or program crashes. The impact is rated as LOW based on the CVSS v3.1 scoring.

Technical Details of CVE-2021-29517

This section provides insights into the technical aspects of the TensorFlow vulnerability.

Vulnerability Description

The issue arises from the

Conv3D

implementation in TensorFlow, where a division by zero can occur under specific conditions, potentially leading to a program crash.

Affected Systems and Versions

Versions of TensorFlow affected by this vulnerability include:

Versions less than 2.1.4
Versions between 2.2.0 and 2.2.3
Versions between 2.3.0 and 2.3.3
Versions between 2.4.0 and 2.4.2

Exploitation Mechanism

A malicious user can manipulate inputs in such a way that triggers a division by zero in the

Conv3D

implementation, potentially crashing the program.

Mitigation and Prevention

This section outlines the steps to mitigate and prevent exploitation of CVE-2021-29517.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.5.0 to mitigate the vulnerability. Additionally, applying the necessary patches on versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 is crucial.

Long-Term Security Practices

Developers should implement input validation and sanitize user-controlled inputs to prevent such vulnerabilities in the future. Regularly updating TensorFlow to the latest version is recommended.

Patching and Updates

Ensuring that all TensorFlow installations are regularly updated with the latest security patches and fixes is essential to prevent exploitation of known vulnerabilities.