CVE-2021-29585 : What You Need to Know

A vulnerability has been discovered in TensorFlow that allows attackers to trigger a divide by zero error in the TFLite computation, potentially leading to denial of service or other security impacts.

Understanding CVE-2021-29585

This CVE highlights a flaw in TensorFlow's TFLite computation that arises when the

stride

argument is set to 0 without proper validation, allowing malicious actors to exploit this behavior.

What is CVE-2021-29585?

TensorFlow's TFLite computation does not validate the

stride

argument before performing a division operation, enabling threat actors to create specially crafted models to trigger a divide by zero error.

The Impact of CVE-2021-29585

The vulnerability poses a low-severity risk with high attack complexity and a local attack vector. While the confidentiality and integrity impacts are none, the availability of the affected systems can be compromised.

Technical Details of CVE-2021-29585

The vulnerability affects various versions of TensorFlow, including those below 2.1.4 and between 2.2.0 and 2.4.2. The issue is related to the

ComputeOutSize

function in TensorFlow's TFLite module, which lacks proper validation of the

stride

argument.

Vulnerability Description

The flaw allows attackers to trigger a divide by zero error by setting the

stride

argument to 0 in certain models, exploiting the absence of pre-division validation.

Affected Systems and Versions

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 are impacted by this vulnerability.

Exploitation Mechanism

Threat actors can exploit this vulnerability by crafting models that invoke the

ComputeOutSize

function with a

stride

value of 0, bypassing the lack of validation and inducing a divide by zero error.

Mitigation and Prevention

To address CVE-2021-29585, users are advised to take immediate actions to secure their TensorFlow installations and implement long-term security practices.

Immediate Steps to Take

Ensure that all TensorFlow deployments are updated to version 2.5.0 or apply the specific fix available for versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 to remediate the vulnerability.

Long-Term Security Practices

Maintain regular updates of TensorFlow to stay protected against known vulnerabilities and actively monitor security advisories from TensorFlow for any emerging threats.

Patching and Updates

Regularly check for security patches and updates released by TensorFlow to address vulnerabilities like CVE-2021-29585 and strengthen the overall security posture of your machine learning environments.