CVE-2021-29549 : Exploit Details and Defense Strategies

TensorFlow, an open-source machine learning platform, is vulnerable to a Division by 0 issue in

tf.raw_ops.QuantizedBatchNormWithGlobalNormalization

. An attacker can trigger a runtime division by zero error, leading to denial of service. The vulnerability affects TensorFlow versions < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, >= 2.4.0 and < 2.4.2.

Understanding CVE-2021-29549

This section will delve into the details surrounding the TensorFlow vulnerability and its potential impact.

What is CVE-2021-29549?

TensorFlow is prone to a specific flaw where inadequate validation of divisor leads to a runtime division by zero error in

tf.raw_ops.QuantizedBatchNormWithGlobalNormalization

, potentially resulting in denial of service.

The Impact of CVE-2021-29549

Exploiting this vulnerability can cause a denial of service condition, affecting the availability of TensorFlow services, particularly in scenarios involving

QuantizedBatchNormWithGlobalNormalization

.

Technical Details of CVE-2021-29549

Let's explore the technical aspects of this vulnerability in TensorFlow.

Vulnerability Description

The vulnerability arises due to the modulus operation executed without verifying the divisor isn't zero, allowing scenarios where

vector_num_elements

can be 0.

Affected Systems and Versions

The vulnerability impacts TensorFlow versions < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, >= 2.4.0 and < 2.4.2.

Exploitation Mechanism

An attacker can trigger the runtime division by zero error by manipulating specific inputs that result in zero

vector_num_elements

.

Mitigation and Prevention

Learn how to mitigate and prevent the exploitation of CVE-2021-29549.

Immediate Steps to Take

Users should update TensorFlow to version 2.5.0 or apply the fix included in TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4, as these versions remain within the supported range.

Long-Term Security Practices

Implement secure coding practices and error-handling mechanisms to prevent similar vulnerabilities in the future.

Patching and Updates

Regularly update TensorFlow installations and monitor security advisories for any new patches to address known vulnerabilities.