CVE-2022-29198: Denial of Service Vulnerability 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 resources for building and deploying machine learning models. TensorFlow supports various operations, including sparse tensor operations, which allow efficient handling of large and sparse datasets. However, a vulnerability has been identified in the SparseTensorToCSRSparseMatrix function of TensorFlow, which can lead to a denial of service attack.

Vulnerability Detail

The SparseTensorToCSRSparseMatrix function in TensorFlow does not properly validate input arguments, leading to a CHECK failure. An attacker can exploit this vulnerability by providing malicious input, causing the function to fail and resulting in a denial of service condition. The vulnerability arises from the lack of validation for the dense_shape and indices parameters, which are assumed to have specific dimensions but are not checked for compliance. This vulnerability has a CVSS score of 5.5, indicating a moderate severity level.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can add manual validation checks for the dense_shape and indices parameters before calling the SparseTensorToCSRSparseMatrix function. Here is an example of how to perform the validation in Python:

import tensorflow as tf

indices = tf.constant(53, shape=[3], dtype=tf.int64)
values = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)
dense_shape = tf.constant(53, shape=[3], dtype=tf.int64)

if len(dense_shape.shape) != 1 or len(indices.shape) != 2:
    raise ValueError("Invalid dimensions for dense_shape or indices")

# Call the function with validated inputs
result = tf.raw_ops.SparseTensorToCSRSparseMatrix(
  indices=indices,
  values=values,
  dense_shape=dense_shape)

Conclusion

The vulnerability in TensorFlow's SparseTensorToCSRSparseMatrix function can be exploited to cause a denial of service condition. It is recommended to update to the patched versions of TensorFlow (2.6.4, 2.7.2, or 2.8.1) to mitigate this vulnerability. Users can also implement a manual validation check as a workaround. For further support on vulnerability remediation, please contact DevNack.com.

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