CVE-2020-15199 : Exploit Details and Defense Strategies

In Tensorflow before version 2.3.1, a vulnerability exists in the

RaggedCountSparseOutput

that can lead to a denial of service due to improper input validation.

Understanding CVE-2020-15199

This CVE describes a denial of service vulnerability in Tensorflow versions prior to 2.3.1, caused by inadequate validation of input arguments.

What is CVE-2020-15199?

The issue arises from the

RaggedCountSparseOutput

not verifying that input arguments create a valid ragged tensor, potentially resulting in a

SIGABRT

signal if certain conditions are met.

The Impact of CVE-2020-15199

The vulnerability has a CVSS base score of 5.9, indicating a medium severity issue with high availability impact but no confidentiality or integrity impact.

Technical Details of CVE-2020-15199

The technical details of the vulnerability provide insight into its nature and potential exploitation.

Vulnerability Description

The

RaggedCountSparseOutput

in Tensorflow fails to validate input arguments properly, leading to a denial of service risk.

Affected Systems and Versions

Product: Tensorflow
Vendor: Tensorflow
Versions Affected: 2.3.0

Exploitation Mechanism

Attack Complexity: High
Attack Vector: Network
Privileges Required: None
User Interaction: None
Scope: Unchanged

Mitigation and Prevention

To address CVE-2020-15199 and enhance overall security, specific steps and practices are recommended.

Immediate Steps to Take

Upgrade to Tensorflow version 2.3.1 or later to apply the necessary patch.
Monitor official security advisories for any future updates or patches.

Long-Term Security Practices

Implement secure coding practices to validate input data thoroughly.
Conduct regular security assessments and audits to identify and address vulnerabilities proactively.

Patching and Updates

Apply patches and updates promptly to mitigate known security risks and ensure system integrity.