CVE-2021-37658 : Security Advisory and Response

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type

tf.raw_ops.MatrixSetDiagV*

. The implementation has incomplete validation for the value of

k

, allowing access to the first element of an empty tensor. The issue has been patched in TensorFlow 2.6.0, with fixes also applied to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4. This vulnerability has a CVSS base score of 7.1, marking it as high severity.

Understanding CVE-2021-37658

This section provides an in-depth understanding of the vulnerability.

What is CVE-2021-37658?

The vulnerability in TensorFlow allows attackers to manipulate operations that can lead to undefined behavior by binding a reference to a null pointer in specific operations. This can result in potentially harmful consequences.

The Impact of CVE-2021-37658

The vulnerability can be exploited to cause high integrity impact, leading to unexpected behaviors and potential security breaches, especially in environments using TensorFlow versions mentioned.

Technical Details of CVE-2021-37658

Below are the technical specifics of the vulnerability.

Vulnerability Description

Incomplete validation of tensor values in TensorFlow's

tf.raw_ops.MatrixSetDiagV*

operations allows for null pointer manipulation, leading to undefined behavior and potential security risks.

Affected Systems and Versions

TensorFlow versions >= 2.5.0 and < 2.5.1, >= 2.4.0 and < 2.4.3, and < 2.3.4 are impacted by this vulnerability.

Exploitation Mechanism

The vulnerability can be exploited by binding a reference to a null pointer in specific operations, enabling attackers to cause undefined behavior and potential security breaches.

Mitigation and Prevention

This section outlines steps to mitigate and prevent exploitation of CVE-2021-37658.

Immediate Steps to Take

Users are advised to update TensorFlow to versions 2.6.0, 2.5.1, 2.4.3, or 2.3.4 containing the patched fixes to safeguard against this vulnerability.

Long-Term Security Practices

Implement secure coding practices, including thorough validation of input and error handling mechanisms to prevent similar vulnerabilities in the future.

Patching and Updates

Regularly check for security updates and patches from TensorFlow to address potential vulnerabilities and ensure a secure development environment.