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
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
Long-Term Security Practices
Patching and Updates
Ensure timely installation of patch releases for TensorFlow versions between 1.15 and 2.3 to mitigate the vulnerability.