CVE-2022-29202 : Vulnerability Insights and Analysis

TensorFlow, an open-source platform for machine learning, prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, is vulnerable to a denial of service attack due to inadequate validation in

tf.ragged.constant

. Find out the impact, technical details, and mitigation strategies below.

Understanding CVE-2022-29202

This section provides insights into the critical vulnerability identified as CVE-2022-29202 in TensorFlow.

What is CVE-2022-29202?

CVE-2022-29202 highlights a denial of service risk in TensorFlow versions 2.6.4, 2.7.2, 2.8.1, and 2.9.0, arising from insufficient validation within

tf.ragged.constant

.

The Impact of CVE-2022-29202

The vulnerability poses a medium-severity threat with a CVSS base score of 5.5, targeting local attack vectors and resulting in high availability impact without affecting confidentiality or integrity.

Technical Details of CVE-2022-29202

Explore the specific technical aspects of the CVE-2022-29202 vulnerability in TensorFlow.

Vulnerability Description

The flaw in

tf.ragged.constant

allows an attacker to trigger a denial of service condition by depleting system memory, affecting versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

Affected Systems and Versions

The issue impacts TensorFlow versions < 2.6.4, >= 2.7.0rc0 and < 2.7.2, >= 2.8.0rc0 and < 2.8.1, and >= 2.9.0rc0 and < 2.9.0, urging users to update to patched versions.

Exploitation Mechanism

Attackers can exploit the lack of input validation in

tf.ragged.constant

to exhaust all available memory, executing a denial of service attack.

Mitigation and Prevention

Discover the necessary steps to mitigate the risks associated with CVE-2022-29202 and prevent similar vulnerabilities in the future.

Immediate Steps to Take

Users are advised to update TensorFlow to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain patches addressing the memory consumption issue.

Long-Term Security Practices

Implement robust input validation mechanisms and consistently monitor security advisories to safeguard against potential threats like uncontrolled resource consumption.

Patching and Updates

Regularly apply security patches released by TensorFlow to close known vulnerabilities and enhance the platform's resilience against attacks.