CVE-2022-35979 : Exploit Details and Defense Strategies

TensorFlow, an open source platform for machine learning, is impacted by a vulnerability that could lead to a denial of service attack when certain inputs are provided to

QuantizedRelu

or

QuantizedRelu6

. The issue has been patched in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89 and will be addressed in TensorFlow 2.10.0. Versions < 2.7.2, >= 2.8.0 and < 2.8.1, and >= 2.9.0 and < 2.9.1 are affected. This CVE has a CVSS base score of 5.9 (Medium severity) and is classified as CWE-20: Improper Input Validation.

Understanding CVE-2022-35979

This section provides insights into the impact, technical details, and mitigation steps related to CVE-2022-35979.

What is CVE-2022-35979?

CVE-2022-35979 is a vulnerability in TensorFlow that can result in a denial of service attack due to a segfault triggered by specific inputs to

QuantizedRelu

or

QuantizedRelu6

functions.

The Impact of CVE-2022-35979

The vulnerability poses a medium-severity risk with a CVSS base score of 5.9. Attack complexity is high, and the availability impact is significant, making it crucial to address the issue promptly.

Technical Details of CVE-2022-35979

Let's delve deeper into the specifics of the vulnerability, including the description, affected systems, and the exploitation mechanism.

Vulnerability Description

The vulnerability arises when nonscalar inputs are provided for

min_features

or

max_features

in the

QuantizedRelu

or

QuantizedRelu6

functions, leading to a segfault that can be exploited for a denial of service attack.

Affected Systems and Versions

Versions < 2.7.2, >= 2.8.0 and < 2.8.1, and >= 2.9.0 and < 2.9.1 of TensorFlow are impacted by this vulnerability.

Exploitation Mechanism

The vulnerability can be exploited by providing specific inputs that trigger the segfault, resulting in a denial of service condition.

Mitigation and Prevention

To address CVE-2022-35979, immediate steps, long-term security practices, and patching recommendations are essential.

Immediate Steps to Take

Affected users should apply the patched versions provided by TensorFlow promptly. Avoid using nonscalar inputs for

QuantizedRelu

and

QuantizedRelu6

functions to mitigate the risk.

Long-Term Security Practices

Employ best practices for input validation and regularly update TensorFlow to the latest versions to prevent similar vulnerabilities in the future.

Patching and Updates

Ensure that TensorFlow is updated to version 2.10.0 or higher to mitigate the vulnerability. For versions < 2.7.2, >= 2.8.0 and < 2.8.1, and >= 2.9.0 and < 2.9.1, apply the necessary patches to address the issue.