CVE-2021-37645 : What You Need to Know

Integer overflow due to conversion to unsigned in TensorFlow.

Understanding CVE-2021-37645

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, the implementation of

tf.raw_ops.QuantizeAndDequantizeV4Grad

is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one.

What is CVE-2021-37645?

The vulnerability in TensorFlow allows an attacker to trigger an integer overflow, leading to a memory allocation issue due to incorrect conversion between numeric types. This could potentially be exploited by an attacker in a local context.

The Impact of CVE-2021-37645

The impact of this vulnerability is rated as MEDIUM. It has a CVSS base score of 5.5 with a HIGH availability impact. Although there is no impact on confidentiality or integrity, immediate mitigation is recommended to prevent exploitation.

Technical Details of CVE-2021-37645

In the affected versions of TensorFlow, the issue lies in the

tf.raw_ops.QuantizeAndDequantizeV4Grad

implementation. An integer overflow occurs when converting a signed integer to an unsigned integer, leading to memory allocation based on this value.

Vulnerability Description

The vulnerability arises from the incorrect conversion between numeric types, specifically from a signed to an unsigned integer, triggering an integer overflow. The issue is related to the allocation of memory based on the converted value.

Affected Systems and Versions

Versions >= 2.5.0 and < 2.5.1 of TensorFlow are affected, as well as version < 2.4.3. Users on these versions are at risk of exploitation and should take immediate action.

Exploitation Mechanism

An attacker can exploit this vulnerability in a local context to trigger an integer overflow, potentially leading to a denial of service or other impact.

Mitigation and Prevention

It is crucial for users to take immediate steps to mitigate the risk posed by CVE-2021-37645 in TensorFlow.

Immediate Steps to Take

Users should update to the patched versions of TensorFlow (2.6.0) or applicable fixes in TensorFlow 2.5.1 and 2.4.3 to address the vulnerability. Additionally, monitoring for any suspicious activities is recommended.

Long-Term Security Practices

Practicing secure coding habits, regularly updating software, and staying informed about security patches are essential for maintaining a secure environment.

Patching and Updates

Ensure that all systems running TensorFlow are regularly updated with the latest patches and security fixes to prevent any potential exploitation of this vulnerability.