CVE-2020-15205 : What You Need to Know

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, a vulnerability exists in the

data_splits

argument of

tf.raw_ops.StringNGrams

that lacks validation. This flaw can lead to heap overflow errors and memory content leaks, potentially compromising system security.

Understanding CVE-2020-15205

This CVE involves a data leak vulnerability in Tensorflow that could allow an attacker to exploit memory contents, potentially leading to security breaches.

What is CVE-2020-15205?

In Tensorflow versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, improper validation in the

data_splits

argument of

tf.raw_ops.StringNGrams

can result in heap overflow errors and memory content leaks.

The Impact of CVE-2020-15205

The vulnerability's impact is rated as critical with a CVSS base score of 9. It can lead to high confidentiality and integrity impacts, potentially allowing attackers to bypass ASLR protections.

Technical Details of CVE-2020-15205

This section provides detailed technical information about the CVE.

Vulnerability Description

The vulnerability arises from the lack of validation in the

data_splits

argument of

tf.raw_ops.StringNGrams

, enabling heap overflow errors and memory content leaks.

Affected Systems and Versions

Tensorflow versions < 1.15.4
Tensorflow versions >= 2.0.0, < 2.0.3
Tensorflow versions >= 2.1.0, < 2.1.2
Tensorflow versions >= 2.2.0, < 2.2.1
Tensorflow versions >= 2.3.0, < 2.3.1

Exploitation Mechanism

The issue allows users to pass values that can trigger heap overflow errors and leak memory contents, potentially compromising system security.

Mitigation and Prevention

Steps to address and prevent the CVE.

Immediate Steps to Take

Update Tensorflow to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 that contain the patch.
Monitor for any unusual activities that could indicate exploitation of the vulnerability.

Long-Term Security Practices

Regularly update software and libraries to the latest versions to mitigate known vulnerabilities.
Implement secure coding practices to prevent memory-related vulnerabilities.

Patching and Updates

Apply patches provided by Tensorflow promptly to address the vulnerability and enhance system security.