CVE-2021-29520 : What You Need to Know

Heap buffer overflow in

Conv3DBackprop*

Understanding CVE-2021-20657

TensorFlow is an end-to-end open source platform for machine learning. The vulnerability arises due to missing validation between arguments to

tf.raw_ops.Conv3DBackprop*

operations, leading to heap buffer overflows.

What is CVE-2021-20657?

The vulnerability in TensorFlow is caused by assumptions in the implementation related to the shape of tensors, allowing heap buffer overflows. An attacker can exploit this to trigger buffer overflows and potentially execute arbitrary code.

The Impact of CVE-2021-20657

The impact of this vulnerability is considered low, with a CVSS base score of 2.5. Attack complexity is rated as high, but the privileges required for exploitation are low. The vulnerability allows for local vector attacks that could result in heap buffer overflows.

Technical Details of CVE-2021-20657

The vulnerability involves missing validation between arguments to certain operations, resulting in heap buffer overflows.

Vulnerability Description

The issue lies in the assumption that specific tensors have the same shape, leading to heap buffer overflows when accessed in parallel.

Affected Systems and Versions

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

Exploitation Mechanism

Attackers can exploit this vulnerability by crafting malicious input to trigger heap buffer overflows, potentially leading to the execution of arbitrary code.

Mitigation and Prevention

To mitigate the risks associated with CVE-2021-20657, immediate steps should be taken followed by long-term security practices.

Immediate Steps to Take

Apply patches provided by TensorFlow to address the vulnerability.
Monitor TensorFlow security advisories for updates and apply relevant fixes promptly.

Long-Term Security Practices

Regularly update TensorFlow to the latest versions to ensure any known vulnerabilities are patched.
Conduct regular security assessments to identify and remediate any potential weaknesses in the system.

Patching and Updates

Ensure that all systems running vulnerable versions of TensorFlow are patched with the appropriate fixes to prevent exploitation of this vulnerability.