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CVE-2021-41207 : Vulnerability Insights and Analysis

Learn about the TensorFlow `ParallelConcat` vulnerability (CVE-2021-41207) causing division by zero. Understand its impact, affected versions, and mitigation steps.

TensorFlow is an open-source platform for machine learning. In affected versions, the implementation of

ParallelConcat
misses some input validation, potentially leading to a division by 0. This CVE highlights the impact, technical details, and mitigation strategies for the vulnerability.

Understanding CVE-2021-41207

This section provides insights into the nature and implications of the CVE-2021-41207 vulnerability.

What is CVE-2021-41207?

The CVE-2021-41207 vulnerability in TensorFlow arises from inadequate input validation in the

ParallelConcat
implementation, enabling the possibility of a division by 0.

The Impact of CVE-2021-41207

The vulnerability's impact is classified as medium severity based on the CVSS v3.1 metrics. Although it requires low privileges and no user interaction, it can have a high availability impact due to potential division by 0.

Technical Details of CVE-2021-41207

This section delves into the specific technical aspects of CVE-2021-41207.

Vulnerability Description

The vulnerability stemmed from missing input validation in the

ParallelConcat
function within TensorFlow, leading to a potential division by 0.

Affected Systems and Versions

        TensorFlow versions >= 2.6.0 and < 2.6.1
        TensorFlow versions >= 2.5.0 and < 2.5.2
        TensorFlow versions < 2.4.4

Exploitation Mechanism

The vulnerability can be exploited by providing specially crafted inputs to trigger the

ParallelConcat
function, resulting in a division by 0.

Mitigation and Prevention

This section outlines steps to mitigate and prevent exploitation of the CVE-2021-41207 vulnerability.

Immediate Steps to Take

        Update TensorFlow to version 2.7.0 when available to mitigate the vulnerability.
        For versions still within support, apply the fix by cherrypicking the commit on TensorFlow 2.6.1, 2.5.2, and 2.4.4.

Long-Term Security Practices

        Regularly update TensorFlow to the latest versions to address security vulnerabilities promptly.

Patching and Updates

Keep track of security advisories and patches released by TensorFlow to stay informed about fixes for known vulnerabilities.

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