CVE-2021-37652 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open-source platform for machine learning. In certain versions, a vulnerability exists in the implementation of

tf.raw_ops.BoostedTreesCreateEnsemble

, allowing for a use after free error with specially crafted arguments.

Understanding CVE-2021-37652

This CVE describes a use after free vulnerability in boosted trees creation within TensorFlow.

What is CVE-2021-37652?

CVE-2021-37652 is a vulnerability in TensorFlow versions that allows an attacker to trigger a use after free error by supplying specially crafted arguments to

tf.raw_ops.BoostedTreesCreateEnsemble

function. This can lead to a double-free process and access of invalid memory, impacting system integrity, confidentiality, and availability.

The Impact of CVE-2021-37652

The impact of this CVE is rated as high, with a CVSS base score of 7.8. It requires low privileges to exploit locally, but can result in high confidentiality, integrity, and availability impacts. The vulnerability is classified as CWE-416: Use After Free.

Technical Details of CVE-2021-37652

In the affected versions, TensorFlow incorrectly handles the lifecycle of a resource object used in

tf.raw_ops.BoostedTreesCreateEnsemble

, leading to a double-free scenario. This issue arises due to a change in resource representation from a naked pointer to a smart pointer during code refactoring.

Vulnerability Description

The vulnerability stems from improper resource handling, resulting in a situation where a resource is freed multiple times, leading to memory corruption and potential exploitation by attackers.

Affected Systems and Versions

The vulnerability affects TensorFlow versions >= 2.5.0 and < 2.5.1, >= 2.4.0 and < 2.4.3, and < 2.3.4.

Exploitation Mechanism

Attackers can exploit this vulnerability by providing specially crafted arguments to the vulnerable function, triggering the use after free error and double-free process.

Mitigation and Prevention

To mitigate the impact of CVE-2021-37652, immediate steps should be taken to address the vulnerability and prevent exploitation.

Immediate Steps to Take

Users are advised to apply the patches provided by TensorFlow to address the vulnerability. It is crucial to update to TensorFlow 2.6.0 or the patched versions (2.5.1, 2.4.3, 2.3.4) to prevent exploitation.

Long-Term Security Practices

In the long term, developers should follow secure coding practices, conduct regular security audits, and stay informed about security updates for third-party libraries like TensorFlow.

Patching and Updates

TensorFlow has released patches addressing the CVE in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. These fixes are included in TensorFlow 2.6.0 and will be backported to versions 2.5.1, 2.4.3, and 2.3.4 to cover all affected and supported releases.