CVE-2020-15212 : Vulnerability Insights and Analysis

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. This vulnerability can lead to memory corruption and potentially advanced exploits.

Understanding CVE-2020-15212

This CVE involves an out-of-bounds write vulnerability in TensorFlow Lite.

What is CVE-2020-15212?

The vulnerability allows users to alter data in a way that can lead to memory corruption and potentially create more advanced exploits.

The Impact of CVE-2020-15212

The vulnerability has a CVSS base score of 8.1 (High severity) and can result in a segmentation fault, memory corruption, and potential exploitation.

Technical Details of CVE-2020-15212

This section provides detailed technical information about the vulnerability.

Vulnerability Description

The issue arises from models using segment sum in TensorFlow Lite, allowing users to write outside of allocated buffers by manipulating segment ids data.

Affected Systems and Versions

Product: TensorFlow
Versions Affected: >= 2.2.0, < 2.2.1 and >= 2.3.0, < 2.3.1

Exploitation Mechanism

Users with access to segment_ids_data can alter output_index and write outside of output_data buffer, potentially leading to memory corruption.

Mitigation and Prevention

Protect systems from CVE-2020-15212 with the following measures.

Immediate Steps to Take

Upgrade to patched TensorFlow versions 2.2.1 or 2.3.1
Implement a custom Verifier to ensure segment ids are positive

Long-Term Security Practices

Regularly update TensorFlow to the latest versions
Conduct security audits to identify and address vulnerabilities

Patching and Updates

Ensure timely application of patches and updates to address known vulnerabilities.