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CVE-2020-15210 : What You Need to Know

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, a vulnerability may lead to a segmentation fault or memory corruption. Upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 for mitigation.

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, a segmentation fault or memory corruption may occur if a TFLite saved model uses the same tensor as both input and output of an operator. Upgrading to TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 is recommended.

Understanding CVE-2020-15210

This CVE addresses a vulnerability in tensorflow-lite that could lead to a segmentation fault or memory corruption under specific conditions.

What is CVE-2020-15210?

In tensorflow-lite versions prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1, using the same tensor as both input and output of an operator may trigger a segmentation fault or memory corruption.

The Impact of CVE-2020-15210

The vulnerability has a CVSS base score of 6.5 (Medium severity) with high availability impact and no confidentiality impact. It requires no privileges and has a high attack complexity.

Technical Details of CVE-2020-15210

This section provides detailed technical information about the vulnerability.

Vulnerability Description

The issue arises when a TFLite saved model utilizes the same tensor for both input and output of an operator, potentially causing a segmentation fault or memory corruption.

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 can be exploited if a TFLite saved model uses the same tensor as both input and output of an operator, leading to a segmentation fault or memory corruption.

Mitigation and Prevention

To address CVE-2020-15210, follow these mitigation strategies:

Immediate Steps to Take

        Upgrade to TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1
        Apply patches released for versions between 1.15 and 2.3

Long-Term Security Practices

        Regularly update TensorFlow to the latest versions
        Implement proper input validation practices

Patching and Updates

Ensure timely installation of patch releases for TensorFlow versions between 1.15 and 2.3 to mitigate the vulnerability.

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