CVE-2022-21730 : What You Need to Know

Tensorflow is an Open Source Machine Learning Framework where the implementation of

FractionalAvgPoolGrad

allows an attacker to read from outside the bounds of heap due to invalid input tensors. This vulnerability is tracked as CVE-2022-21730.

Understanding CVE-2022-21730

This CVE details an out-of-bounds read vulnerability in Tensorflow due to the improper handling of input tensors, leading to a potential security risk.

What is CVE-2022-21730?

CVE-2022-21730 highlights a flaw in Tensorflow that could enable an attacker to access data beyond the designated memory bounds, potentially compromising confidentiality.

The Impact of CVE-2022-21730

The impact of this vulnerability is rated as HIGH, with a CVSS base score of 8.1. It poses a significant risk to the confidentiality of data due to the unauthorized access potential.

Technical Details of CVE-2022-21730

This section delves into the specific technical aspects of the CVE, including vulnerability description, affected systems, and exploitation details.

Vulnerability Description

The vulnerability arises from the

FractionalAvgPoolGrad

implementation in Tensorflow, which fails to account for invalid input tensors, allowing an attacker to read data outside the heap bounds.

Affected Systems and Versions

The vulnerability impacts various versions of Tensorflow, including 2.8.0, 2.7.1, 2.6.3, and 2.5.3. Users of these versions are strongly advised to take immediate action to mitigate the risk.

Exploitation Mechanism

The vulnerability can be exploited by malicious actors to gain unauthorized access to sensitive data by manipulating input tensors within the framework.

Mitigation and Prevention

To address CVE-2022-21730, users and administrators are recommended to implement the following security measures to protect their systems and data.

Immediate Steps to Take

Update Tensorflow to the patched versions (2.8.0, 2.7.1, 2.6.3, 2.5.3) to address the vulnerability.
Monitor for any unusual activity or unauthorized access attempts on the affected systems.

Long-Term Security Practices

Regularly update software and frameworks to the latest secure versions to avoid known vulnerabilities.
Conduct security assessments and audits to identify and remediate potential risks proactively.

Patching and Updates

Stay informed about security advisories and patches released by Tensorflow to address vulnerabilities promptly.