CVE-2021-41224: TensorFlow Heap Out-of-Bounds Access

Sponsored Habilume Habit tracker with a companion that grows with you Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent. App Store Google Play

Background

TensorFlow is an open-source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow supports various operations and algorithms for efficient computation on both CPUs and GPUs. However, a vulnerability has been identified in TensorFlow that can lead to a heap out-of-bounds access.

Vulnerability Detail

The vulnerability exists in the implementation of the SparseFillEmptyRows function in TensorFlow. When the size of the indices parameter does not match the size of the values parameter, a heap out-of-bounds access can occur. This can be exploited by an attacker to read or modify memory beyond the allocated boundaries, potentially leading to information disclosure or arbitrary code execution. The vulnerability affects multiple versions of TensorFlow, including 2.4.4, 2.5.2, 2.6.1, and 2.7.0rc0.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can ensure that the size of the indices parameter matches the size of the values parameter when calling the SparseFillEmptyRows function. This can be done by validating the input data and resizing the arrays if necessary. Here is an example of how to perform this validation and resizing in Python:

import tensorflow as tf

# Validate and resize indices and values
if len(indices) != len(values):
    indices = indices[:len(values)]

# Call SparseFillEmptyRows with validated data
result = tf.sparse.SparseFillEmptyRows(indices, values)

Conclusion

In conclusion, the identified vulnerability in TensorFlow can lead to a heap out-of-bounds access, potentially resulting in information disclosure or arbitrary code execution. Users are advised to update their TensorFlow installation to the latest version or apply the provided workaround. For further assistance or support in vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ