CVE-2022-41899 : Exploit Details and Defense Strategies

A vulnerability has been identified in TensorFlow that triggers a

CHECK

fail in

SdcaOptimizer

due to improper input validation. This CVE has been assigned the ID CVE-2022-41899 and was published on November 18, 2022.

Understanding CVE-2022-41899

TensorFlow, as an open-source machine learning platform, faces a security issue related to the

SdcaOptimizer

where inputs not of rank 2 lead to a

CHECK

fail. The vulnerability has been addressed through patches and version updates.

What is CVE-2022-41899?

The vulnerability in TensorFlow arises from improper input validation in the

SdcaOptimizer

, impacting versions 2.8.4, 2.9.3, and 2.10.1. Attackers can exploit this flaw to trigger a

CHECK

fail under specific conditions.

The Impact of CVE-2022-41899

With a CVSS v3.1 base score of 4.8, this medium severity vulnerability can be exploited remotely with low privileges required. While confidentiality and integrity impacts are none, the availability impact is high, affecting the stability of TensorFlow.

Technical Details of CVE-2022-41899

The vulnerability stems from improper input validation, allowing attackers to manipulate

dense_features

or

example_state_data

in a way that leads to a

CHECK

fail in

SdcaOptimizer

within TensorFlow.

Vulnerability Description

The vulnerability is classified under CWE-20 (Improper Input Validation) and can be exploited by providing inputs not of rank 2, triggering the

CHECK

fail in the optimizer.

Affected Systems and Versions

TensorFlow versions 2.8.4, 2.9.3, and 2.10.1 are affected by this vulnerability if inputs

dense_features

or

example_state_data

are not of rank 2.

Exploitation Mechanism

Attackers can exploit this vulnerability by providing manipulated inputs that do not conform to the expected rank 2, thus initiating the

CHECK

fail in the

SdcaOptimizer

component.

Mitigation and Prevention

To address CVE-2022-41899, users of affected TensorFlow versions are advised to take immediate steps, implement long-term security practices, and apply relevant patches and updates as described below.

Immediate Steps to Take

Users are recommended to update their TensorFlow installations to the patched versions (2.10.1, 2.9.3), ensuring that inputs

dense_features

and

example_state_data

adhere to the required rank of 2 to avoid triggering the

CHECK

fail.

Long-Term Security Practices

Incorporating proper input validation mechanisms in machine learning algorithms and consistently monitoring for security advisories can help mitigate similar vulnerabilities in the future.

Patching and Updates

The vulnerability has been patched in TensorFlow commit 80ff197d03db2a70c6a111f97dcdacad1b0babfa and will be included in TensorFlow 2.11. Additionally, the fix has been backported to TensorFlow 2.10.1, 2.9.3, and 2.8.4 to ensure users on these versions are protected.