CVE-2022-21728 : Security Advisory and Response

TensorFlow, an open-source machine learning framework, is impacted by a vulnerability that allows an out-of-bounds read due to incomplete validation of the

batch_dim

parameter in the shape inference for

ReverseSequence

. This issue can lead to a heap out-of-bounds read. The vulnerability is rated as high severity with a CVSS base score of 8.1.

Understanding CVE-2022-21728

This section dives into the details of the vulnerability, its impact, technical aspects, and mitigation steps.

What is CVE-2022-21728?

TensorFlow's incomplete validation of the

batch_dim

parameter in the shape inference for

ReverseSequence

can result in a heap out-of-bounds read, posing a significant security risk.

The Impact of CVE-2022-21728

The vulnerability can be exploited to read data beyond the bounds of allocated memory, potentially leading to unauthorized information disclosure and other malicious activities.

Technical Details of CVE-2022-21728

Let's explore the technical aspects of this security flaw to better understand how it can be exploited and what systems are affected.

Vulnerability Description

The vulnerability arises from the lack of complete validation for negative values of the

batch_dim

parameter in TensorFlow's shape inference for

ReverseSequence

, enabling a heap out-of-bounds read.

Affected Systems and Versions

All versions of TensorFlow up to 2.5.3, 2.6.3, 2.7.1, and 2.8.0 are affected by this vulnerability.

Exploitation Mechanism

Attackers can exploit this vulnerability by providing excessively negative values for the

batch_dim

parameter, leading to unauthorized memory access.

Mitigation and Prevention

In response to this security issue, users and administrators are encouraged to take immediate steps to mitigate the risk and implement long-term security practices.

Immediate Steps to Take

Update TensorFlow to versions 2.8.0, 2.7.1, 2.6.3, or 2.5.3 to apply the necessary fixes and prevent exploitation of this vulnerability.

Long-Term Security Practices

Regularly monitor for security updates from TensorFlow and other software vendors, and apply patches promptly to address known vulnerabilities.

Patching and Updates

Stay informed about security advisories and updates released by TensorFlow to ensure the ongoing security of your machine learning framework.