CVE-2022-29200 : What You Need to Know

TensorFlow is an open-source platform for machine learning. This CVE impacts versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 due to missing validation in the implementation of

tf.raw_ops.LSTMBlockCell

, which can lead to a denial of service attack. Let's dive deeper into the details of CVE-2022-29200.

Understanding CVE-2022-29200

This section will cover what CVE-2022-29200 entails and its potential impact.

What is CVE-2022-29200?

In TensorFlow versions before 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the lack of proper validation in

tf.raw_ops.LSTMBlockCell

can result in a denial of service attack. The issue arises from the failure to validate input argument ranks, leading to

CHECK

-failures.

The Impact of CVE-2022-29200

The vulnerability's impact is rated with a CVSS base score of 5.5, indicating a medium severity issue. The attack complexity is low, the attack vector is local, and the availability impact is high. Although it requires low privileges, it does not impact confidentiality or integrity.

Technical Details of CVE-2022-29200

Explore the technical aspects of CVE-2022-29200 below.

Vulnerability Description

The vulnerability stems from inadequate validation within the

tf.raw_ops.LSTMBlockCell

implementation, allowing malicious actors to trigger a denial of service through

CHECK

-failures.

Affected Systems and Versions

Versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 of TensorFlow are susceptible to this vulnerability, making it crucial for users to update to patched versions.

Exploitation Mechanism

The absence of proper input argument validation in

tf.raw_ops.LSTMBlockCell

enables attackers to exploit the vulnerability, potentially causing a denial of service.

Mitigation and Prevention

Learn how to mitigate the risks associated with CVE-2022-29200.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain patches addressing this vulnerability.

Long-Term Security Practices

Implementing robust input validation mechanisms and staying informed about security updates can help prevent similar vulnerabilities in the future.

Patching and Updates

Regularly check for updates from TensorFlow's official sources and apply patches promptly to protect your systems from potential threats.