CVE-2022-35988 : Security Advisory and Response

A detailed overview of CVE-2022-35988, a vulnerability in TensorFlow that affects certain versions, leading to a denial of service risk.

Understanding CVE-2022-35988

This section delves into the specifics of the vulnerability present in TensorFlow and its potential impact.

What is CVE-2022-35988?

CVE-2022-35988 pertains to TensorFlow, an open-source platform for machine learning. The vulnerability arises when the function

tf.linalg.matrix_rank

is provided with an empty input, triggering a

CHECK

fail, which may be abused to execute a denial of service attack.

The Impact of CVE-2022-35988

The vulnerability's CVSS score is 5.9, indicating a medium severity issue with a high availability impact due to the potential denial of service attack. It requires no special privileges or user interaction, making it a critical concern.

Technical Details of CVE-2022-35988

Explore the technical aspects of CVE-2022-35988, including its description, affected systems, and exploitation method.

Vulnerability Description

The flaw in

tf.linalg.matrix_rank

triggers a

CHECK

fail, posing a risk for denial of service attacks, with no known workarounds available.

Affected Systems and Versions

TensorFlow versions below 2.7.2, between 2.8.0 to 2.8.1, and 2.9.0 to 2.9.1 are impacted by this vulnerability, urging users to update to TensorFlow 2.10.0 or apply relevant patches.

Exploitation Mechanism

The vulnerability can be exploited remotely as it requires a network attack vector and has high availability impact, posing a threat even without user interaction.

Mitigation and Prevention

Learn how to mitigate the risks associated with CVE-2022-35988 and prevent potential exploitation.

Immediate Steps to Take

Users are advised to update to TensorFlow 2.10.0 or implement the fix available in GitHub commit c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a. It is crucial to address this issue promptly to safeguard systems.

Long-Term Security Practices

Incorporate rigorous security practices, such as regular software updates, security monitoring, and adherence to coding best practices, to enhance overall system security and resilience.

Patching and Updates

Stay informed about security patches and updates released by TensorFlow to address vulnerabilities promptly and ensure the ongoing security of your systems.