CVE-2021-29546: TensorFlow Integer Division by Zero Vulnerability

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 domains, including research, industry, and academia. The vulnerability described in this report affects the 'tensorflow-cpu' package, which is a Python package for running TensorFlow on CPUs. This package is commonly used when GPU acceleration is not available or required.

Vulnerability Detail

The vulnerability (CVE-2021-29546) in TensorFlow arises from an integer division by zero undefined behavior in the 'tf.raw_ops.QuantizedBiasAdd' function. The issue stems from the implementation of the Eigen kernel, specifically in the 'quantization_utils.h' file. The kernel performs a division by the number of elements of the smaller input, based on shape, without checking if the divisor is zero. This can lead to a division by zero error, resulting in unexpected behavior or crashes. An attacker can exploit this vulnerability to cause a denial-of-service (DoS) condition or potentially execute arbitrary code.

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

Workaround

As a workaround, if updating to the fixed versions is not immediately feasible, you can consider avoiding the usage of the 'tf.raw_ops.QuantizedBiasAdd' function in your code. This can be achieved by using alternative functions or methods provided by TensorFlow for similar functionality. Here is an example of a code snippet that demonstrates the workaround:

import tensorflow as tf

# Instead of using tf.raw_ops.QuantizedBiasAdd
# Use an alternative function or method

# Example:
# bias_added = tf.add(quantized_input, quantized_bias)

Conclusion

In conclusion, the CVE-2021-29546 vulnerability in TensorFlow poses a risk of integer division by zero undefined behavior in the 'tf.raw_ops.QuantizedBiasAdd' function. This can lead to a denial-of-service (DoS) condition or potential code execution. It is crucial to update to the fixed versions of TensorFlow to mitigate this vulnerability. If immediate updates are not possible, consider the provided workaround and avoid using the affected function. For further support on vulnerability remediation, you can reach out to DevNack.com.

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