CVE-2021-29559: TensorFlow - Out-of-bounds Heap Access in tf.raw_ops.UnicodeEncode

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for building 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 allows an attacker to access data outside the bounds of a heap allocated array in the tf.raw_ops.UnicodeEncode function.

Vulnerability Detail

The vulnerability arises from an incorrect assumption made in the implementation of tf.raw_ops.UnicodeEncode. The function assumes that the input_value/input_splits pair specifies a valid sparse tensor. However, an attacker can manipulate the input in such a way that it goes beyond the expected bounds, leading to out-of-bounds heap access. This can result in memory corruption, data leakage, or even remote code execution, depending on how the attacker exploits the vulnerability. The issue has been assigned the identifier CVE-2021-29559.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, if upgrading to a fixed version is not immediately feasible, you can consider implementing input validation and sanitization in your code. By validating and sanitizing the input values before passing them to the tf.raw_ops.UnicodeEncode function, you can mitigate the risk of triggering the vulnerability. Here is an example of input validation in Python:

def validate_input(input_value, input_splits):
    # Perform input validation and sanitization
    # ... your code here ...
    return input_value, input_splits

input_value, input_splits = validate_input(input_value, input_splits)

Conclusion

In conclusion, TensorFlow is vulnerable to an out-of-bounds heap access issue in the tf.raw_ops.UnicodeEncode function. This vulnerability can have serious consequences, including memory corruption, data leakage, or remote code execution. It is crucial to update to one of the fixed versions of TensorFlow to mitigate the risk. If upgrading is not immediately possible, implementing input validation and sanitization can provide temporary protection. For further support on vulnerability remediation, please contact DevNack.com.

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