CVE-2021-29580 : What You Need to Know

TensorFlow is an open-source platform for machine learning. The vulnerability in

tf.raw_ops.FractionalMaxPoolGrad

can lead to undefined behavior if input tensors are empty, potentially enabling a denial of service attack due to a false

CHECK

condition. The issue affects versions prior to 2.1.4 and between 2.2.0 to 2.4.2.

Understanding CVE-2021-29580

This vulnerability in TensorFlow's implementation of

tf.raw_ops.FractionalMaxPoolGrad

can have serious consequences, including undefined behavior and a denial of service risk.

What is CVE-2021-29580?

The vulnerability arises from unchecked assumptions in the code that fail to validate input tensors, leading to potential denial of service due to false

CHECK

conditions.

The Impact of CVE-2021-29580

The vulnerability can result in undefined behavior and trigger a denial of service attack due to an erroneous

CHECK

condition, potentially allowing malicious exploitation.

Technical Details of CVE-2021-29580

The vulnerability description explains the specific issues within TensorFlow's implementation, affected systems, and details on how the exploitation can occur.

Vulnerability Description

The flaw is triggered when one of the input tensors is empty, causing a

CHECK

condition to be false and potentially leading to a denial of service situation.

Affected Systems and Versions

Versions prior to 2.1.4, and versions between 2.2.0 to 2.4.2 of TensorFlow are vulnerable to this issue.

Exploitation Mechanism

By exploiting the unchecked assumptions in TensorFlow's code related to input tensor validation, attackers can potentially trigger denial of service attacks.

Mitigation and Prevention

Taking immediate action to address this vulnerability and adopting long-term security practices is crucial to mitigate risks and prevent exploitation.

Immediate Steps to Take

Update TensorFlow to version 2.5.0, or apply the fix included in versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. Validating input tensors and checking for empty tensors can prevent exploitation.

Long-Term Security Practices

Regularly update TensorFlow to the latest versions, adhere to secure coding practices, and monitor for security advisories to stay informed.

Patching and Updates

Regular patching and updating of TensorFlow to the latest versions will help address known vulnerabilities, including CVE-2021-29580.