CVE-2020-15202 : Vulnerability Insights and Analysis

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, the

Shard

API in TensorFlow has a vulnerability that can lead to various issues like segfaults, stack overflows, or data corruption due to integer truncation.

Understanding CVE-2020-15202

This CVE involves a vulnerability in the

Shard

API of TensorFlow that can result in critical issues.

What is CVE-2020-15202?

In Tensorflow versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, improper usage of the

Shard

API can lead to integer truncation, potentially causing severe consequences like data corruption and stack overflows.

The Impact of CVE-2020-15202

The vulnerability has a CVSS base score of 9, indicating a critical severity level. It can result in high impacts on confidentiality, integrity, and availability of affected systems.

Technical Details of CVE-2020-15202

This section provides detailed technical information about the CVE.

Vulnerability Description

The

Shard

API in TensorFlow expects a specific function argument format, leading to integer truncation when lambda functions with incorrect argument types are used, potentially causing various issues.

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 occurs due to the incorrect usage of lambda functions with incompatible argument types in the

Shard

API, leading to integer truncation and subsequent critical system issues.

Mitigation and Prevention

Protecting systems from the CVE and preventing exploitation is crucial.

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 patches for the issue.
Monitor system behavior for any signs of data corruption or unusual system activities.

Long-Term Security Practices

Regularly update software and libraries to the latest secure versions.
Conduct thorough code reviews to identify and rectify potential vulnerabilities.

Patching and Updates

Apply patches provided by TensorFlow to address the integer truncation vulnerability in the

Shard

API.