CVE-2020-15204 : Exploit Details and Defense Strategies

In eager mode, TensorFlow versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are affected by a vulnerability that leads to a null pointer dereference, resulting in a segmentation fault when certain functions are called. This CVE has a CVSS base score of 5.3.

Understanding CVE-2020-15204

This CVE affects TensorFlow versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, potentially leading to a denial of service.

What is CVE-2020-15204?

In TensorFlow versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, a null pointer dereference occurs in eager mode, causing a segmentation fault when specific functions are invoked.

The Impact of CVE-2020-15204

The vulnerability allows attackers to trigger a segmentation fault, potentially leading to a denial of service condition. The issue is rated as having a medium severity base score of 5.3.

Technical Details of CVE-2020-15204

This section provides detailed technical information about the CVE.

Vulnerability Description

The issue arises in TensorFlow versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 due to a failure to set the session state, resulting in a null pointer dereference when certain functions are called.

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 vulnerability can be exploited by calling specific functions like

tf.raw_ops.GetSessionHandle

or

tf.raw_ops.GetSessionHandleV2

in eager mode, leading to a null pointer dereference and subsequent segmentation fault.

Mitigation and Prevention

Protect your systems from CVE-2020-15204 with the following measures:

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 activity that could indicate exploitation of the vulnerability.

Long-Term Security Practices

Regularly update TensorFlow and other software components to the latest versions to mitigate known vulnerabilities.
Implement secure coding practices to prevent null pointer dereference issues.

Patching and Updates

Apply patches provided by TensorFlow to address the vulnerability.