CVE-2022-41887 : Vulnerability Insights and Analysis

A vulnerability has been discovered in TensorFlow that could result in a crash due to a size mismatch during broadcast assignment in

tf.keras.losses.poisson

when certain conditions are met.

Understanding CVE-2022-41887

This CVE affects TensorFlow versions 2.10.0 to 2.10.1 and versions below 2.9.3, potentially leading to denial of service.

What is CVE-2022-41887?

The issue arises when the dimensions overflow an

int32

, resulting in a crash due to a size mismatch during broadcast assignment in TensorFlow's

tf.keras.losses.poisson

function.

The Impact of CVE-2022-41887

The impact of this vulnerability is rated as MEDIUM, with a CVSS base score of 4.8. A successful exploit could result in a denial of service.

Technical Details of CVE-2022-41887

The vulnerability is related to an overflow issue in

tf.keras.losses.poisson

in TensorFlow, affecting specific versions and functionality.

Vulnerability Description

When certain input conditions are met, overflow in specific dimensions can lead to a crash during broadcast assignment, causing a denial of service.

Affected Systems and Versions

TensorFlow versions 2.10.0 to 2.10.1 and versions below 2.9.3 are affected by this vulnerability, impacting systems that utilize the

tf.keras.losses.poisson

function.

Exploitation Mechanism

Attackers could exploit this vulnerability by providing inputs that trigger the overflow of dimensions, leading to a crash in affected TensorFlow versions.

Mitigation and Prevention

To mitigate the risks associated with CVE-2022-41887, immediate steps and long-term security practices are recommended.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.11, which includes a patch for this vulnerability. For versions 2.10.1 and 2.9.3, the patch is available through GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c.

Long-Term Security Practices

In the long term, organizations should stay vigilant for security updates and apply patches promptly to prevent exploitation of known vulnerabilities.

Patching and Updates

TensorFlow users are encouraged to update their installations to the latest patched versions to address the overflow issue and enhance system security.