CVE-2022-35970 : What You Need to Know

This article provides detailed information about CVE-2022-35970, a vulnerability in TensorFlow that could result in a denial of service attack.

Understanding CVE-2022-35970

This CVE refers to a vulnerability in TensorFlow related to a specific function,

QuantizedInstanceNorm

, which could lead to a denial of service attack when triggered.

What is CVE-2022-35970?

CVE-2022-35970 is a security flaw in TensorFlow, an open-source machine learning platform. The vulnerability arises when certain conditions related to the

QuantizedInstanceNorm

function are met, potentially resulting in a denial of service attack.

The Impact of CVE-2022-35970

The impact of this vulnerability is rated as MEDIUM with a CVSS base score of 5.9. It has a high availability impact but does not affect confidentiality or integrity. The attack complexity is rated as HIGH and does not require special privileges from the user.

Technical Details of CVE-2022-35970

This section dives into the technical aspects of the vulnerability, including its description, affected systems, and the exploitation mechanism.

Vulnerability Description

The vulnerability occurs in the

QuantizedInstanceNorm

function of TensorFlow, leading to a segfault that could be exploited to trigger a denial of service attack.

Affected Systems and Versions

TensorFlow versions < 2.7.2
TensorFlow versions >= 2.8.0, < 2.8.1
TensorFlow versions >= 2.9.0, < 2.9.1

Exploitation Mechanism

The vulnerability can be exploited by providing

QuantizedInstanceNorm

with

x_min

or

x_max

tensors of a nonzero rank, resulting in a segfault.

Mitigation and Prevention

In this section, we discuss the steps to mitigate and prevent the exploitation of CVE-2022-35970 in TensorFlow.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.10.0 to address the vulnerability. Alternatively, patches are available for TensorFlow versions 2.9.1, 2.8.1, and 2.7.2.

Long-Term Security Practices

Adopting secure coding practices, regular software updates, and vulnerability monitoring are essential for long-term security.

Patching and Updates

Ensure timely application of patches and updates released by TensorFlow to mitigate known vulnerabilities and improve overall security.