CVE-2022-41910 : What You Need to Know

A vulnerability has been discovered in TensorFlow, a popular open-source machine learning platform, that could lead to a heap out-of-bounds read in the

QuantizeAndDequantizeV2

function. This CVE was published on December 6, 2022, and identified as CVE-2022-41910, with GitHub_M as the assigner.

Understanding CVE-2022-41910

This section will delve into the details of the vulnerability, its impact, technical aspects, and mitigation steps.

What is CVE-2022-41910?

CVE-2022-41910 affects TensorFlow's function MakeGrapplerFunctionItem, where providing input sizes greater than or equal to output sizes can trigger an out-of-bounds memory read or crash scenario.

The Impact of CVE-2022-41910

The vulnerability poses a medium severity risk with a CVSS v3.1 base score of 4.8. An attacker could exploit this issue to cause a denial of service (DoS) attack, impacting the availability of TensorFlow services.

Technical Details of CVE-2022-41910

Let's dive into the specifics of this vulnerability.

Vulnerability Description

The issue arises in TensorFlow versions prior to 2.10.1, 2.9.3, and 2.8.4, where improper input validation can lead to heap out-of-bounds reads.

Affected Systems and Versions

The vulnerability affects TensorFlow versions >= 2.10.0 and < 2.10.1, >= 2.9.0 and < 2.9.3, and < 2.8.4.

Exploitation Mechanism

By supplying input sizes that match or exceed output sizes, an attacker can trigger the memory read vulnerability in the

QuantizeAndDequantizeV2

function.

Mitigation and Prevention

Protect your systems against CVE-2022-41910 with these security measures.

Immediate Steps to Take

Update TensorFlow to version 2.11.0, which includes the fix. Additionally, cherrypick the commit a65411a1d69edfb16b25907ffb8f73556ce36bb7 on TensorFlow 2.8.4, 2.9.3, and 2.10.1.

Long-Term Security Practices

Regularly update TensorFlow to the latest versions and follow secure coding practices to prevent memory-related vulnerabilities.

Patching and Updates

Stay informed about security advisories from TensorFlow and apply patches promptly to mitigate potential risks.