CVE-2021-29536: Heap buffer overflow in tensorflow

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Background

TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow is widely used in various domains, including computer vision, natural language processing, and reinforcement learning. However, a vulnerability has been discovered in TensorFlow that could lead to a heap buffer overflow.

Vulnerability Detail

The vulnerability, identified as CVE-2021-29536, exists in the QuantizedReshape function of TensorFlow. It can be exploited by an attacker by passing invalid thresholds for quantization. This can be achieved by providing empty tensors for the tensor, shape, input_min, and input_max parameters. The implementation of QuantizedReshape assumes that these arguments are always valid scalars and attempts to access their numeric values directly. However, if any of these tensors is empty, accessing the element at position 0 results in a heap buffer overflow. This vulnerability can potentially allow an attacker to execute arbitrary code or cause a denial-of-service condition.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a temporary workaround, users can ensure that the tensors passed to the QuantizedReshape function are not empty. This can be achieved by validating the input tensors before invoking the function. Here is an example of how to perform this validation in Python:

import tensorflow as tf

tensor = tf.constant([], dtype=tf.qint32)
shape = tf.constant([], dtype=tf.int32)
input_min = tf.constant([], dtype=tf.float32)
input_max = tf.constant([], dtype=tf.float32)

if tensor.shape.num_elements() == 0 or shape.shape.num_elements() == 0 or input_min.shape.num_elements() == 0 or input_max.shape.num_elements() == 0:
    # Handle the empty tensors
    print('Invalid input tensors')
else:
    tf.raw_ops.QuantizedReshape(tensor=tensor, shape=shape, input_min=input_min, input_max=input_max)

By performing this validation, the vulnerability can be mitigated by preventing the execution of the vulnerable code path when empty tensors are encountered.

Conclusion

The vulnerability in TensorFlow can be mitigated by updating to the patched versions or by implementing the provided workaround. It is important for users to stay vigilant and apply the necessary updates to ensure the security of their TensorFlow installations. For further support on vulnerability remediation, please contact DevNack.com.

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