Learn about CVE-2021-29525, a vulnerability in TensorFlow that allows an attacker to trigger a division by 0. Understand the impact, affected systems, and mitigation strategies.
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in
tf.raw_ops.Conv2DBackpropInput
. The affected versions include TensorFlow < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2. The CVSS v3.1 base score for this vulnerability is 2.5 (Low severity).
Understanding CVE-2021-29525
This section provides insight into the impact, technical details, and mitigation strategies related to the Division by 0 vulnerability in TensorFlow.
What is CVE-2021-29525?
TensorFlow allows an attacker to exploit a division by 0 in
tf.raw_ops.Conv2DBackpropInput
, impacting the integrity of the system. The vulnerability has been addressed in TensorFlow version 2.5.0.
The Impact of CVE-2021-29525
The vulnerability poses a low severity risk with an attack complexity of HIGH. While the availability impact is LOW, confidentiality and integrity remain unaffected. No user interaction is required for exploitation.
Technical Details of CVE-2021-29525
This section outlines the vulnerability description, affected systems, and the exploitation mechanism.
Vulnerability Description
The vulnerability allows an attacker to initiate a division by 0 in Conv2DBackpropInput, potentially leading to system compromise.
Affected Systems and Versions
TensorFlow versions less than 2.1.4 and those between 2.2.0 to 2.4.2 are susceptible to this vulnerability.
Exploitation Mechanism
By exploiting the tf.raw_ops.Conv2DBackpropInput function, an attacker can cause a division by 0, compromising the system.
Mitigation and Prevention
To address CVE-2021-29525, immediate steps should be taken along with long-term security practices and regular patching and updates.
Immediate Steps to Take
Users are advised to update TensorFlow to version 2.5.0 to mitigate the risk of exploitation.
Long-Term Security Practices
Implement secure coding practices, conduct regular security audits, and stay informed about TensorFlow security advisories.
Patching and Updates
Ensure timely application of security patches released by TensorFlow to address known vulnerabilities.