CVE-2022-41890 : What You Need to Know

A vulnerability has been identified in TensorFlow, an open-source platform for machine learning, that allows for a

CHECK

fail in

BCast

overflow. This vulnerability can lead to a crash when input larger than an

int32

is provided to

BCast::ToShape

, despite it being designed to handle up to an

int64

.

Understanding CVE-2022-41890

This section will cover the details of the CVE-2022-41890 vulnerability in TensorFlow.

What is CVE-2022-41890?

The vulnerability in TensorFlow arises from an overflow in the

BCast::ToShape

function, causing a crash when input exceeding

int32

is supplied, contrary to the expected behavior.

The Impact of CVE-2022-41890

The impact of this vulnerability is that it can lead to a denial of service condition, affecting the availability of the TensorFlow platform for machine learning applications.

Technical Details of CVE-2022-41890

Let's delve into the technical aspects of the CVE-2022-41890 vulnerability in TensorFlow.

Vulnerability Description

The vulnerability originates from the

BCast::ToShape

component in TensorFlow, where input larger than an

int32

results in a system crash, despite the intended support for up to an

int64

.

Affected Systems and Versions

The versions affected by this vulnerability include TensorFlow versions 2.10.0 to 2.10.1, versions 2.9.0 to 2.9.3, and all versions below 2.8.4.

Exploitation Mechanism

The exploit involves passing large input to the

b

input in

tf.experimental.numpy.outer

, triggering the overflow in

BCast::ToShape

and leading to a crash.

Mitigation and Prevention

Understanding how to mitigate and prevent the CVE-2022-41890 vulnerability in TensorFlow is crucial.

Immediate Steps to Take

It is recommended to update TensorFlow to versions 2.10.1, 2.9.3, or apply the patch provided in GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5.

Long-Term Security Practices

Implement secure coding practices and regularly update TensorFlow to the latest versions to avoid exposure to known vulnerabilities.

Patching and Updates

Ensure that the fix included in TensorFlow 2.11 is applied, and if using versions 2.10.0, 2.9.0, or 2.8.4, cherrypick the commit from GitHub to address the vulnerability.