CVE-2021-37663 : Security Advisory and Response

TensorFlow, an open-source platform for machine learning, is impacted by CVE-2021-37663. The vulnerability lies in

tf.raw_ops.QuantizeV2

, allowing an attacker to exploit incomplete validation. This can result in triggering undefined actions or unauthorized access to data beyond allocated arrays.

Understanding CVE-2021-37663

This CVE details the incomplete validation issue in TensorFlow's

QuantizeV2

that poses a security risk.

What is CVE-2021-37663?

In TensorFlow versions listed in the advisory, a flaw in

QuantizeV2

leads to incomplete validation. Attackers can execute malicious activities by binding a pointer to a null reference or accessing data outside array bounds.

The Impact of CVE-2021-37663

The vulnerability has a CVSS base score of 7.8, categorizing it as a high-severity issue. It affects confidentiality, integrity, and system availability.

Technical Details of CVE-2021-37663

The vulnerability description, impacted systems, exploitation method, and more technical aspects are discussed below.

Vulnerability Description

The flaw arises due to incomplete validation in

QuantizeV2

, enabling attackers to trigger undefined behavior or access data outside the array bounds.

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

Attackers can exploit this issue by binding a reference to a null pointer or accessing data beyond the bounds of heap allocated arrays in TensorFlow.

Mitigation and Prevention

Here are the recommended steps to address and prevent potential exploitation of CVE-2021-37663.

Immediate Steps to Take

Update TensorFlow to version 2.6.0, where the vulnerability is patched.

Long-Term Security Practices

Regularly update TensorFlow and monitor security advisories for patches.
Employ secure coding practices and perform thorough input validation.

Patching and Updates

Ensure the fix included in TensorFlow 2.6.0 is applied. The patch will also be backported to TensorFlow 2.5.1, 2.4.3, and 2.3.4 versions.