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.