CVE-2022-23567 : Vulnerability Insights and Analysis

Tensorflow is an Open Source Machine Learning Framework. The implementations of

Sparse*Cwise*

ops are vulnerable to integer overflows, which can lead to denial of service attacks. The issue is addressed in TensorFlow 2.8.0, with patches also available for TensorFlow 2.7.1, 2.6.3, and 2.5.3.

Understanding CVE-2022-23567

This CVE highlights the risks associated with integer overflows in Tensorflow, potentially exposing systems to denial of service vulnerabilities.

What is CVE-2022-23567?

Tensorflow's

Sparse*Cwise*

ops implementation is susceptible to integer overflows, enabling attackers to trigger denial of service attacks by causing large allocations or assert failures.

The Impact of CVE-2022-23567

The vulnerability poses a medium severity risk with high availability impact, albeit with low privileges required for exploitation.

Technical Details of CVE-2022-23567

The following are essential technical details related to CVE-2022-23567:

Vulnerability Description

Missing validations on input tensors' shapes and the construction of large

TensorShape

objects using user-provided dimensions are the primary factors contributing to this vulnerability.

Affected Systems and Versions

The vulnerability affects various versions of TensorFlow, including 2.8.0, 2.7.1, 2.6.3, and 2.5.3 that fall within the supported range.

Exploitation Mechanism

By exploiting integer overflows in the

Sparse*Cwise*

ops, attackers can trigger denial of service by causing OOM-based allocations or assert failures.

Mitigation and Prevention

To address CVE-2022-23567, it is crucial to implement the following mitigation strategies:

Immediate Steps to Take

Update to TensorFlow 2.8.0 or apply the patches for versions 2.7.1, 2.6.3, and 2.5.3 to mitigate the vulnerability.

Long-Term Security Practices

Regularly monitor for security advisories and apply patches promptly to prevent potential risks.

Patching and Updates

Stay vigilant for TensorFlow updates and security advisories to address known vulnerabilities and ensure a secure machine learning environment.