CVE-2022-35969: TensorFlow Conv2DBackpropInput CHECK Fail Vulnerability

Background

TensorFlow is an open-source machine learning library widely used for building and training deep learning models. It provides a flexible ecosystem of tools, libraries, and community resources that allows researchers and developers to create and deploy machine learning applications. The library includes various operations, including convolutional layers, which are essential for image processing tasks. However, a vulnerability has been identified in the Conv2DBackpropInput operation, which can lead to denial of service attacks if not addressed.

Vulnerability Detail

The vulnerability arises from the Conv2DBackpropInput implementation, which requires the input_sizes parameter to be a 4-dimensional tensor. If this requirement is not met, it results in a CHECK failure. This failure can be exploited by attackers to trigger a denial of service condition, causing the application to crash or become unresponsive. The vulnerability is particularly concerning as it can be easily triggered with minimal input, making it a potential target for malicious actors.

pip install --upgrade "tensorflow-cpu==2.21.0"

Workaround

As a temporary workaround, ensure that the input_sizes parameter passed to the Conv2DBackpropInput operation is always a 4-dimensional tensor. For example, you can modify your code as follows:

import tensorflow as tf

strides = [1, 1, 1, 1] padding = 'SAME' use_cudnn_on_gpu = True explicit_paddings = [] dilations = [1, 1, 1, 1] input_sizes = tf.constant([1, 64, 64, 2], shape=[4], dtype=tf.int32) # Ensure 4D tensor filter = tf.constant(0.159749106, shape=[3, 3, 2, 2], dtype=tf.float32) out_backprop = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.Conv2DBackpropInput(input_sizes=input_sizes, filter=filter, out_backprop=out_backprop, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, explicit_paddings=explicit_paddings, data_format='NHWC', dilations=dilations)

Conclusion

In summary, CVE-2022-35969 is a significant vulnerability in TensorFlow that can lead to denial of service attacks. It is crucial for users to update to the patched versions to mitigate the risk. Additionally, using tools like Vulert can help monitor and alert for such vulnerabilities in real-time.

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