CVE-2023-25670 : What You Need to Know

This CVE-2023-25670 affects TensorFlow and involves a Null Pointer Error in QuantizedMatMulWithBiasAndDequantize. It was published on March 24, 2023, and has a high severity base score of 7.5.

Understanding CVE-2023-25670

This section provides insights into the nature of CVE-2023-25670 and its implications.

What is CVE-2023-25670?

CVE-2023-25670 is a vulnerability found in TensorFlow, an open-source platform used in machine learning. Specifically, versions prior to 2.12.0 and 2.11.1 are impacted by a null pointer error in QuantizedMatMulWithBiasAndDequantize when MKL is enabled. The vulnerability has been addressed in TensorFlow version 2.12.0 and version 2.11.1.

The Impact of CVE-2023-25670

The impact of this vulnerability is classified as high, with a base severity score of 7.5. It can lead to a NULL Pointer Dereference issue, potentially causing availability impact in systems utilizing affected versions of TensorFlow.

Technical Details of CVE-2023-25670

In this section, we delve into the technical aspects of the vulnerability.

Vulnerability Description

The vulnerability in TensorFlow involves a null pointer error in the operation QuantizedMatMulWithBiasAndDequantize when utilizing MKL. This error can be exploited by an attacker to potentially disrupt the availability of the system.

Affected Systems and Versions

The impacted systems are those running TensorFlow versions prior to 2.12.0 and 2.11.1. Systems using these versions with MKL enabled are at risk of experiencing the null pointer error.

Exploitation Mechanism

The vulnerability can be exploited by an attacker sending specially crafted input to trigger the null pointer error in QuantizedMatMulWithBiasAndDequantize, which can lead to a denial of service condition.

Mitigation and Prevention

To address CVE-2023-25670 and enhance system security, certain steps need to be taken.

Immediate Steps to Take

Organizations should update their TensorFlow installations to version 2.12.0 or 2.11.1 to mitigate the vulnerability.
Monitor for any unusual behavior that may indicate exploitation of the null pointer error.

Long-Term Security Practices

Regularly monitor and apply security patches provided by TensorFlow to stay protected against such vulnerabilities.
Conduct regular security audits and assessments to identify and remediate any potential weaknesses in the system.

Patching and Updates

Ensure that all software, including TensorFlow, is kept up to date with the latest patches and updates to prevent exploitation of known vulnerabilities.
Follow best practices in securing machine learning platforms to reduce the risk of similar vulnerabilities in the future.