CVE-2022-21733 : Security Advisory and Response

Tensorflow, an Open Source Machine Learning Framework, is susceptible to a memory exhaustion vulnerability that can lead to a denial of service attack. The issue arises due to missing validation on

pad_width

, resulting in a negative value for

ngram_width

, causing an out of memory condition after an integer overflow. The vulnerability will be addressed in TensorFlow 2.8.0, with patches also available for TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.

Understanding CVE-2022-21733

This section provides insights into the nature of the vulnerability, its impact, technical details, and mitigation strategies.

What is CVE-2022-21733?

CVE-2022-21733 refers to a memory exhaustion vulnerability in Tensorflow due to the absence of proper validation checks, resulting in an integer overflow situation that triggers a denial of service attack.

The Impact of CVE-2022-21733

The vulnerability allows malicious actors to exploit the

StringNGrams

implementation to cause an out of memory condition, potentially disrupting the availability of services utilizing Tensorflow.

Technical Details of CVE-2022-21733

This section delves into the specifics of the vulnerability, including the description, affected systems, and exploitation mechanism.

Vulnerability Description

The issue stems from inadequate validation on

pad_width

, leading to a negative value for

ngram_width

and subsequent memory exhaustion after an integer overflow, enabling a denial of service attack.

Affected Systems and Versions

Tensorflow versions up to 2.8.0 are impacted by this vulnerability, necessitating immediate attention to prevent potential exploitation.

Exploitation Mechanism

Malicious actors can leverage the flawed

StringNGrams

implementation to allocate parts of the output incorrectly, causing memory exhaustion and service disruption.

Mitigation and Prevention

This section outlines steps to mitigate the CVE-2022-21733 vulnerability and prevent future occurrences.

Immediate Steps to Take

Users are advised to update their Tensorflow installations to version 2.8.0 or apply the available patches for versions 2.7.1, 2.6.3, and 2.5.3 to safeguard against potential memory exhaustion attacks.

Long-Term Security Practices

Implementing secure coding practices, conducting regular security audits, and staying informed about software vulnerabilities can enhance overall system resilience.

Patching and Updates

Regularly checking for security updates and promptly applying patches released by the Tensorflow team is crucial to maintain a secure environment.