CVE-2021-41218 : Security Advisory and Response

TensorFlow 2.6.0 and prior versions are affected by a vulnerability in the

AllToAll

operation that allows the execution of division by 0. This CVE details the impact, technical details, and mitigation steps for the vulnerability.

Understanding CVE-2021-41218

TensorFlow's vulnerability in the

AllToAll

operation involving division by 0 has certain implications and technical specifics to consider.

What is CVE-2021-41218?

The CVE-2021-41218 vulnerability affects TensorFlow due to a flaw in the shape inference code of the

AllToAll

operation, allowing a division by 0 when the

split_count

argument is 0.

The Impact of CVE-2021-41218

The vulnerability's CVSS score of 5.5 (Medium) highlights its implications:

Attack Complexity: Low
Attack Vector: Local
Availability Impact: High
Confidentiality Impact: None
Integrity Impact: None
Privileges Required: Low
Scope: Unchanged
User Interaction: None The affected versions of TensorFlow include 2.6.0, 2.5.0, and 2.4.4, with fixes available from version 2.7.0.

Technical Details of CVE-2021-41218

The technical aspects of CVE-2021-41218 shed light on the vulnerability's nature, affected systems, versions, and exploitation mechanisms.

Vulnerability Description

The vulnerability lies in the

AllToAll

operation's shape inference code in TensorFlow.
It allows a division by 0 when the

split_count

argument equals 0.

Affected Systems and Versions

TensorFlow versions affected include:
= 2.6.0, < 2.6.1

= 2.5.0, < 2.5.2

< 2.4.4

Exploitation Mechanism

The vulnerability exploits the improper validation of user-supplied input, leading to a division by 0 in the

AllToAll

operation.

Mitigation and Prevention

Understanding the mitigation steps and preventive measures is crucial to address and prevent the CVE-2021-41218 vulnerability.

Immediate Steps to Take

Upgrade to TensorFlow 2.7.0 or later to patch the vulnerability.
Apply security updates provided by TensorFlow.
Avoid using

AllToAll

with a

split_count

argument of 0.

Long-Term Security Practices

Regularly update TensorFlow to the latest versions to address security issues promptly.
Follow TensorFlow's security advisories and best practices for secure machine learning practices.

Patching and Updates

TensorFlow has released fixes in version 2.7.0 and recommends updating to this version or applying specific commits for 2.6.1, 2.5.2, and 2.4.4 to address the vulnerability.