CVE-2021-29611 Explained : Impact and Mitigation

TensorFlow, an end-to-end open source platform for machine learning, is impacted by an incomplete validation issue in

SparseReshape

leading to a denial-of-service vulnerability based on a

CHECK

-failure. This CVE requires attention due to its potential impact on affected versions of TensorFlow.

Understanding CVE-2021-29611

This section provides insights into the nature of the vulnerability and its impact.

What is CVE-2021-29611?

CVE-2021-29611 involves incomplete validation in the

SparseReshape

function within TensorFlow, which can result in a denial of service through a

CHECK

-failure. The vulnerability arises from the lack of proper validation of input arguments specifying a valid sparse tensor.

The Impact of CVE-2021-29611

The vulnerability carries a CVSS base score of 3.6, categorizing it as LOW severity. While the availability impact is considered LOW, the attack complexity is rated as HIGH. It requires low privileges to exploit and has a local attack vector.

Technical Details of CVE-2021-29611

Delve deeper into the technical aspects of the vulnerability to better understand its implications.

Vulnerability Description

Incomplete validation in the

SparseReshape

function allows for a denial-of-service attack within TensorFlow due to a

CHECK

-failure.

Affected Systems and Versions

The vulnerability affects TensorFlow versions prior to 2.3.3 and versions between 2.4.0 and 2.4.2.

Exploitation Mechanism

The vulnerability can be exploited by attackers with local access and low privileges, making it crucial for affected users to apply necessary patches and updates.

Mitigation and Prevention

Discover how to mitigate the risks posed by CVE-2021-29611 and prevent potential exploitation.

Immediate Steps to Take

Users should upgrade to TensorFlow version 2.5.0 to address the vulnerability. For versions 2.4.2 and 2.3.3, the fix is also available.

Long-Term Security Practices

Implement robust security measures, including regular software updates, to mitigate risks associated with known vulnerabilities.

Patching and Updates

Ensure timely patching of affected systems with the latest TensorFlow versions to prevent exploitation and enhance overall security measures.