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CVE-2022-35997 : Vulnerability Insights and Analysis

Discover the impact of CVE-2022-35997, a vulnerability in TensorFlow's `tf.sparse.cross` function, allowing for denial of service attacks. Learn how to mitigate this issue and secure your machine learning systems.

TensorFlow, an open-source machine learning platform, is affected by a vulnerability in the

tf.sparse.cross
function. An attacker can induce a denial of service attack by exploiting a
CHECK
fail when an invalid input
separator
is passed to the function. The issue has been patched in the TensorFlow codebase and will be addressed in versions 2.10.0 and backported to 2.9.1, 2.8.1, and 2.7.2.

Understanding CVE-2022-35997

This section delves deeper into the impact and technical details of the vulnerability.

What is CVE-2022-35997?

The vulnerability in the

tf.sparse.cross
function of TensorFlow allows attackers to trigger a denial of service attack by exploiting a
CHECK
fail condition when a non-scalar input
separator
is provided.

The Impact of CVE-2022-35997

The CVSS score for this vulnerability is 5.9, categorizing it as a medium severity issue. The attack complexity is high, and it can lead to a significant impact on the availability of affected systems.

Technical Details of CVE-2022-35997

Let's explore the technical aspects of the vulnerability in more detail.

Vulnerability Description

The vulnerability arises due to improper handling of input separators in the

tf.sparse.cross
function, allowing for a denial of service attack to be triggered.

Affected Systems and Versions

TensorFlow versions prior to 2.7.2, 2.8.1, and 2.9.1 are affected by this vulnerability. Users using these versions are advised to update to the patched versions.

Exploitation Mechanism

Attackers can exploit this vulnerability by providing a non-scalar input

separator
to the
tf.sparse.cross
function, resulting in a
CHECK
fail condition and triggering a denial of service attack.

Mitigation and Prevention

Discover how to address and prevent CVE-2022-35997 from impacting your systems.

Immediate Steps to Take

Users are strongly recommended to update their TensorFlow installations to version 2.10.0 or apply the provided patches to versions 2.9.1, 2.8.1, and 2.7.2 to mitigate the vulnerability.

Long-Term Security Practices

Incorporating secure coding practices and regularly updating software can help prevent similar vulnerabilities in the future.

Patching and Updates

Stay informed about security patches and updates from the TensorFlow project to ensure the ongoing security of your machine learning environments.

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