CVE-2020-15205: TensorFlow CPU - Heap Overflow and Memory Leak in StringNGrams

Background

TensorFlow is an open-source machine learning framework widely used for various tasks such as deep learning, neural networks, and natural language processing. It provides a comprehensive set of tools and libraries for building and deploying machine learning models. TensorFlow CPU is a specific package that focuses on running TensorFlow on CPUs rather than GPUs. This vulnerability affects versions of TensorFlow CPU before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.

Vulnerability Detail

The vulnerability in TensorFlow CPU lies in the data_splits argument of the tf.raw_ops.StringNGrams function. This argument lacks proper validation, allowing a user to pass values that can trigger heap overflow errors and potentially leak the contents of memory. An attacker could exploit this vulnerability to retrieve sensitive information from the memory stack, including return addresses, which can be used to bypass ASLR (Address Space Layout Randomization). The issue has been patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80 and is addressed in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.

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

Workaround

As a temporary workaround, users can avoid using the tf.raw_ops.StringNGrams function or ensure that the data_splits argument is properly validated before passing any values. By implementing strict input validation, the risk of triggering heap overflow errors and memory leaks can be mitigated. Here is an example of how input validation can be performed:

import tensorflow as tf

def validate_data_splits(data_splits):
    if isinstance(data_splits, str):
        # Perform necessary validation checks
        return True
    else:
        return False

# Usage example
if validate_data_splits(data_splits):
    tf.raw_ops.StringNGrams(data_splits=data_splits)

Conclusion

The vulnerability in TensorFlow CPU versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 allows for heap overflow errors and memory leaks through the data_splits argument of the tf.raw_ops.StringNGrams function. This can potentially lead to the leakage of sensitive information and bypassing of ASLR. It is crucial to update TensorFlow CPU to one of the fixed versions or implement proper input validation to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.

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