CVE-2021-37661 Explained : Impact and Mitigation

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, an attacker can cause a denial of service by exploiting an issue in

boosted_trees_create_quantile_stream_resource

due to improper validation of input. The vulnerability allows a negative argument to trigger an integer conversion error leading to a crash. The issue has been patched by TensorFlow.

Understanding CVE-2021-37661

This section will cover what CVE-2021-37661 entails, its impact, technical details, and mitigation strategies.

What is CVE-2021-37661?

CVE-2021-37661 describes a vulnerability in TensorFlow where an attacker can exploit the improper validation of input, leading to a denial of service. TensorFlow has addressed this issue in the affected versions.

The Impact of CVE-2021-37661

The impact of CVE-2021-37661 is considered medium with a base severity score of 5.5. Attack complexity is low, requiring local access, while the availability impact is high.

Technical Details of CVE-2021-37661

The technical details of CVE-2021-37661 include the vulnerability description, affected systems and versions, and the exploitation mechanism.

Vulnerability Description

The vulnerability arises due to the improper validation of input allowing negative arguments to lead to a crash caused by an integer conversion error.

Affected Systems and Versions

Affected versions include TensorFlow >= 2.5.0 and < 2.5.1, >= 2.4.0 and < 2.4.3, and < 2.3.4.

Exploitation Mechanism

By using negative arguments in

boosted_trees_create_quantile_stream_resource

, an attacker can trigger a denial of service by causing an integer conversion error.

Mitigation and Prevention

This section covers the immediate steps to take following the discovery of CVE-2021-37661, long-term security practices, and the importance of timely patching and updates.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.6.0 to mitigate the vulnerability. Alternatively, patches are available for TensorFlow 2.5.1, 2.4.3, and 2.3.4.

Long-Term Security Practices

Incorporating secure coding practices, conducting regular security audits, and staying informed about software vulnerabilities are essential for maintaining a secure environment.

Patching and Updates

Regularly updating software components and applying security patches promptly is crucial for addressing known vulnerabilities and enhancing system security.