CVE-2021-37659: TensorFlow - Undefined Behavior via Null Pointer Reference in Binary Cwise Operations

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. It offers extensive support for deep learning algorithms and enables efficient computation on both CPUs and GPUs. However, a vulnerability has been discovered in TensorFlow that can lead to undefined behavior and potential security risks.

Vulnerability Detail

The vulnerability (CVE-2021-37659) in TensorFlow arises from a flaw in the implementation of binary cwise operations. These operations involve element-wise computations between two input tensors. The vulnerability specifically affects binary cwise operations that do not require broadcasting, such as gradients of binary cwise operations. The implementation assumes that the two input tensors have the same number of elements but fails to check this condition. As a result, when the eigen functor executes, it triggers heap out-of-bounds reads and undefined behavior by binding to a null pointer. This vulnerability can be exploited by an attacker to cause a denial-of-service (DoS) condition or potentially execute arbitrary code.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can consider implementing input validation checks in their code to ensure that the two input tensors provided to binary cwise operations have the same number of elements. By explicitly validating the input tensors, the risk of triggering the vulnerability can be mitigated. Here is an example of how input validation can be performed in Python:

import tensorflow as tf

def binary_cwise_op(tensor1, tensor2):
    if tensor1.shape != tensor2.shape:
        raise ValueError('Input tensors must have the same shape')
    # Perform binary cwise operation
    ...

Conclusion

The vulnerability (CVE-2021-37659) in TensorFlow poses a risk of undefined behavior and potential security issues due to a flaw in the implementation of binary cwise operations. Users are advised to update their TensorFlow installations to the latest patched versions and consider implementing input validation checks in their code. Vulnerability monitoring tools like Vulert can also be utilized to receive real-time alerts and ensure the security of TensorFlow-based applications. For further support and assistance in vulnerability remediation, please contact DevNack.com.

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