CVE-2021-29557: Denial of Service in TensorFlow

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various industries and research domains for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service (DoS) attack.

Vulnerability Detail

The vulnerability (CVE-2021-29557) in TensorFlow is caused by a division by zero error in the tf.raw_ops.SparseMatMul function. This error occurs when the b tensor is empty, triggering a runtime error in the Eigen code used by TensorFlow. An attacker can exploit this vulnerability to cause a DoS condition, potentially disrupting the availability of TensorFlow-based applications. The issue has been identified and fixed in TensorFlow 2.5.0, and patches have been backported to earlier versions (2.4.2, 2.3.3, 2.2.3, and 2.1.4) that are still within the supported range.

pip install --upgrade "tensorflow==2.21.0"

Workaround

If updating TensorFlow is not immediately feasible, a temporary workaround is to avoid passing an empty tensor (b) to the tf.raw_ops.SparseMatMul function. Developers can add a check to ensure that the b tensor is not empty before invoking the function. Here is an example of how this check can be implemented in Python:

import tensorflow as tf

# Check if `b` tensor is empty
if b.shape.num_elements() == 0:
    # Handle the empty tensor case
    print('Error: Tensor `b` is empty')
else:
    # Invoke the `tf.raw_ops.SparseMatMul` function
    result = tf.raw_ops.SparseMatMul(a, b)

Conclusion

The vulnerability (CVE-2021-29557) in TensorFlow can lead to a denial of service (DoS) condition. It is crucial to update TensorFlow to version 2.5.0 or apply the relevant patches for earlier versions (2.4.2, 2.3.3, 2.2.3, and 2.1.4) to mitigate the risk. Keeping TensorFlow installations up to date is essential for maintaining the security and stability of machine learning applications. For further support on vulnerability remediation, contact DevNack.com.

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