Explore CVE-2021-29528 revealing a TensorFlow vulnerability allowing Division by 0 in `QuantizedMul`. Learn impacted versions, risks, and mitigation steps.
TensorFlow is an end-to-end open-source platform for machine learning. The vulnerability in this CVE, labeled as 'Division by 0 in
QuantizedMul
', allows an attacker to trigger a division by 0 in tf.raw_ops.QuantizedMul
. The issue arises from a division operation influenced by the caller, impacting versions ranging from < 2.1.4 to < 2.4.2. This vulnerability has a CVSS base score of 2.5 (Low severity).
Understanding CVE-2021-29528
This section will delve into what CVE-2021-29528 entails and its potential impacts.
What is CVE-2021-29528?
The CVE identifies a vulnerability in TensorFlow that enables an attacker to induce a divide by 0 scenario in
tf.raw_ops.QuantizedMul
. This arises due to a division operation impacted by external factors.
The Impact of CVE-2021-29528
The vulnerability exposes affected versions of TensorFlow (< 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2) to potential exploitation. The exploitation could lead to disruptions in operations and possible security breaches.
Technical Details of CVE-2021-29528
Let's explore the technical aspects of the CVE in more detail.
Vulnerability Description
The vulnerability stems from the
QuantizedMul
functionality within TensorFlow, where an attacker can manipulate the division operation by influencing a specific quantity, causing a division by 0.
Affected Systems and Versions
TensorFlow versions prior to 2.1.4 and between 2.2.0 to 2.4.2 are susceptible to this vulnerability, presenting a risk to systems leveraging these versions.
Exploitation Mechanism
Exploiting this vulnerability involves triggering a division by 0 scenario in
tf.raw_ops.QuantizedMul
, which can be initiated by influencing the controlled quantity.
Mitigation and Prevention
Learn how to address and prevent the CVE-2021-29528 vulnerability effectively.
Immediate Steps to Take
Update to TensorFlow version 2.5.0 to mitigate the vulnerability. For versions still within support, patches are available for TensorFlow 2.1.4, 2.2.3, 2.3.3, and 2.4.2 to address the issue.
Long-Term Security Practices
Incorporate secure coding practices, regular security assessments, and timely updates to ensure overall system security and resilience.
Patching and Updates
Stay informed about security advisories from TensorFlow and promptly apply patches and updates to safeguard against known vulnerabilities.