CVE-2021-29619 : Exploit Details and Defense Strategies

TensorFlow is an end-to-end open-source platform for machine learning. An issue in

tf.raw_ops.SparseCountSparseOutput

allows attackers to cause a segmentation fault by passing invalid arguments. This vulnerability has been assigned CVE-2021-29619. Below are the details of this vulnerability:

Understanding CVE-2021-29619

This section provides insights into the impact, technical details, and mitigation strategies for the CVE-2021-29619 vulnerability.

What is CVE-2021-29619?

TensorFlow versions < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, >= 2.4.0 and < 2.4.2 are affected by a vulnerability where passing invalid arguments to

tf.raw_ops.SparseCountSparseOutput

leads to a segfault. It has a CVSS base score of 2.5 (Low severity).

The Impact of CVE-2021-29619

The vulnerability has a low base score of 2.5 and attack complexity is high with a local attack vector. However, the impact on confidentiality and integrity is none with low privileges required.

Technical Details of CVE-2021-29619

Here are the technical details associated with this vulnerability:

Vulnerability Description

The vulnerability arises due to improper handling of exceptional conditions in TensorFlow, particularly in the

tf.raw_ops.SparseCountSparseOutput

function.

Affected Systems and Versions

TensorFlow versions < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, >= 2.4.0 and < 2.4.2 are impacted by this vulnerability.

Exploitation Mechanism

Attackers can exploit this vulnerability by passing malicious inputs (e.g., discovered via fuzzing) to the

tf.raw_ops.SparseCountSparseOutput

function, leading to a segfault.

Mitigation and Prevention

It is crucial to take immediate steps to address this vulnerability and implement long-term security practices to prevent such issues in the future:

Immediate Steps to Take

Update TensorFlow to version 2.5.0, which includes a fix for this vulnerability.
For versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4, ensure the cherrypicked commit is applied to mitigate the issue.

Long-Term Security Practices

Regularly update TensorFlow and other dependencies to the latest secure versions.
Implement secure coding practices and conduct regular security audits.

Patching and Updates

Keep track of security advisories from TensorFlow and apply patches promptly to secure your systems.