CVE-2021-37656 Explained : Impact and Mitigation

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, an attacker can cause undefined behavior via binding a reference to null pointer in

tf.raw_ops.RaggedTensorToSparse

. The implementation has an incomplete validation of the splits values, not checking that they are in increasing order. This vulnerability has a CVSS base score of 7.1 (High Severity). Immediate patching is recommended.

Understanding CVE-2021-37656

This CVE impacts TensorFlow users, allowing attackers to exploit a reference binding vulnerability in

RaggedTensorToSparse

.

What is CVE-2021-37656?

CVE-2021-37656 is a security vulnerability in TensorFlow that allows an attacker to trigger undefined behavior by binding a reference to a null pointer in the

RaggedTensorToSparse

function.

The Impact of CVE-2021-37656

The vulnerability has a high severity base score of 7.1, posing a risk of integrity impact in affected versions of TensorFlow.

Technical Details of CVE-2021-37656

The vulnerability arises from incomplete validation in the splits values in TensorFlow. Attackers with local access can exploit this issue in affected versions.

Vulnerability Description

The issue stems from inadequate validation of splits values, enabling attackers to cause undefined behavior via the

tf.raw_ops.RaggedTensorToSparse

function.

Affected Systems and Versions

TensorFlow versions >= 2.5.0 and < 2.5.1, >= 2.4.0 and < 2.4.3, and < 2.3.4 are impacted by this vulnerability.

Exploitation Mechanism

Attackers can exploit this vulnerability by manipulating the splits values in

RaggedTensorToSparse

, leading to a null pointer reference.

Mitigation and Prevention

It is crucial to take immediate steps to address CVE-2021-37656 and implement long-term security practices to safeguard against similar vulnerabilities.

Immediate Steps to Take

Ensure to update TensorFlow to version 2.6.0 or apply the patch provided in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece.

Long-Term Security Practices

Regularly update TensorFlow to the latest versions, follow security best practices, and monitor for future security advisories.

Patching and Updates

The issue has been patched in TensorFlow 2.6.0, and the fix has also been backported to versions 2.5.1, 2.4.3, and 2.3.4 to address the vulnerability.