CVE-2020-15213 : Security Advisory and Response

In TensorFlow Lite before versions 2.2.1 and 2.3.1, a vulnerability exists that can lead to a denial of service due to an out-of-memory allocation issue. Attackers can exploit this by using a large value to trigger excessive memory allocation.

Understanding CVE-2020-15213

This CVE involves a denial of service vulnerability in TensorFlow Lite versions 2.2.0 and 2.3.0.

What is CVE-2020-15213?

The vulnerability in TensorFlow Lite allows attackers to cause a denial of service by manipulating segment sum models to trigger out-of-memory allocations.

The Impact of CVE-2020-15213

The impact is rated as MEDIUM severity with a CVSS base score of 4. It has a high attack complexity and can be exploited over a network.

Technical Details of CVE-2020-15213

This section provides more technical insights into the vulnerability.

Vulnerability Description

The issue arises from the improper handling of memory allocations in the segment sum implementation, allowing attackers to exhaust memory resources.

Affected Systems and Versions

Affected versions: TensorFlow Lite 2.2.0 and 2.3.0
Patched versions: TensorFlow Lite 2.2.1 and 2.3.1

Exploitation Mechanism

Attackers exploit the vulnerability by using large values to trigger excessive memory allocations, leading to denial of service.

Mitigation and Prevention

To address and prevent the exploitation of CVE-2020-15213, the following steps are recommended:

Immediate Steps to Take

Upgrade to patched versions 2.2.1 or 2.3.1 of TensorFlow Lite
Implement a custom

Verifier

to limit the maximum value in the segment ids tensor

Long-Term Security Practices

Regularly update TensorFlow Lite to the latest versions
Conduct security assessments and audits to identify and mitigate similar vulnerabilities

Patching and Updates

Apply the patches provided by TensorFlow to fix the vulnerability