CVE-2020-15210: Segmentation fault in tensorflow-lite

Background

TensorFlow is an open-source machine learning framework used for various tasks such as image recognition, natural language processing, and more. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. It allows developers to run machine learning models on resource-constrained platforms. However, a vulnerability has been discovered in TensorFlow Lite that can lead to a segmentation fault or memory corruption.

Vulnerability Detail

The vulnerability arises when a TFLite saved model uses the same tensor as both input and output of an operator. Depending on the specific operator, this can result in a segmentation fault or memory corruption. The issue has been patched in versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 of TensorFlow. Users are advised to upgrade to one of these versions to mitigate the vulnerability. A potential workaround is to add a custom Verifier to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs, including constant or variable tensors as well as optional tensors.

pip install --upgrade "tensorflow==2.21.0"

Workaround

A potential workaround for this vulnerability is to add a custom Verifier to the model loading code. This Verifier should check that no operator reuses tensors as both inputs and outputs. Here is an example of how to implement this workaround in Python:

import tensorflow as tf

class CustomVerifier(tf.lite.Interpreter):
    def __init__(self, model_path):
        super().__init__(model_path)
        self.verify()

    def verify(self):
        # Add your verification logic here
        pass

model_path = 'path/to/model.tflite'
interpreter = CustomVerifier(model_path)
interpreter.allocate_tensors()

By implementing a custom Verifier class and calling its verify method during model loading, you can ensure that the vulnerability is mitigated.

Conclusion

In conclusion, the vulnerability in TensorFlow Lite can lead to a segmentation fault or memory corruption. It is important to upgrade to the patched versions of TensorFlow and follow the recommended steps to fix the vulnerability. Adding a custom Verifier can also serve as a workaround. For further support on vulnerability remediation, you can reach out to DevNack.com.

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