CVE-2020-15207 : Vulnerability Insights and Analysis

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, a vulnerability exists that could lead to segfaults and data corruption due to improper handling of negative indices.

Understanding CVE-2020-15207

This CVE involves a flaw in TensorFlow Lite versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 that could result in accessing data out of bounds, leading to potential security risks.

What is CVE-2020-15207?

In TensorFlow Lite versions before the specified patches, negative indices were not properly validated, allowing for potential data corruption and segfaults due to out-of-bounds data access.

The Impact of CVE-2020-15207

The vulnerability has a CVSS base score of 8.7 (High severity) with a high impact on integrity and availability. Attack complexity is rated as high, and the attack vector is through the network.

Technical Details of CVE-2020-15207

This section provides more in-depth technical insights into the vulnerability.

Vulnerability Description

TensorFlow Lite did not adequately validate negative indices, leading to potential data corruption and segfaults when accessing data out of bounds.

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 arises from improper handling of negative indices, which are not adequately validated, allowing for the execution of code with negative indices, leading to data corruption and segfaults.

Mitigation and Prevention

It is crucial to take immediate steps to address and prevent the exploitation of this vulnerability.

Immediate Steps to Take

Update TensorFlow Lite to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 that contain the necessary patches.
Regularly monitor for security advisories and updates from TensorFlow.

Long-Term Security Practices

Implement secure coding practices to prevent similar memory-related vulnerabilities.
Conduct regular security audits and code reviews to identify and address potential vulnerabilities.

Patching and Updates

Apply the patches provided in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 to mitigate the risk of data corruption and segfaults.