CVE-2022-29195 : What You Need to Know

A denial of service vulnerability has been identified in TensorFlow that affects versions prior to 2.6.4, 2.7.2, 2.8.1, and 2.9.0. This CVE, assigned CVE-2022-29195, highlights the importance of proper input validation to prevent exploitation.

Understanding CVE-2022-29195

TensorFlow, an open-source platform for machine learning, contains a vulnerability in the

tf.raw_ops.StagePeek

implementation that lacks proper validation of input arguments. This oversight allows for the triggering of a denial of service attack.

What is CVE-2022-29195?

The vulnerability in TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 stems from the inadequate validation of the

index

parameter in the

tf.raw_ops.StagePeek

function, leading to a

CHECK

-failure that could be exploited for malicious purposes.

The Impact of CVE-2022-29195

With a CVSS base score of 5.5 (Medium severity), this vulnerability poses a significant risk to affected systems. The attack complexity is low, but the availability impact is high, making it crucial to address promptly.

Technical Details of CVE-2022-29195

The following technical aspects of the CVE shed light on the vulnerability's nature, affected systems, and exploitation vectors.

Vulnerability Description

The

tf.raw_ops.StagePeek

implementation in TensorFlow fails to validate input arguments correctly, specifically the

index

parameter, allowing for a denial of service attack.

Affected Systems and Versions

Versions of TensorFlow prior to 2.6.4, 2.7.2, 2.8.1, and 2.9.0 are affected by this vulnerability, emphasizing the need for users to update to patched versions promptly.

Exploitation Mechanism

Attackers can exploit this vulnerability by manipulating the

index

parameter within the

tf.raw_ops.StagePeek

function to trigger a denial of service condition.

Mitigation and Prevention

Addressing CVE-2022-29195 requires immediate action to secure affected systems and prevent potential exploitation.

Immediate Steps to Take

Users of TensorFlow should update to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain patches for this vulnerability. It is critical to apply these updates promptly to mitigate the risk.

Long-Term Security Practices

Developers should prioritize proper input validation in their code to prevent similar vulnerabilities from arising. Regular security assessments and updates are essential to maintain a secure environment.

Patching and Updates

Regularly checking for security advisories from TensorFlow and promptly applying patches is crucial to ensure that systems are protected from known vulnerabilities.