CVE-2021-29516: TensorFlow GPU - Null Pointer Dereference in RaggedTensorToVariant

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 GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation. It is widely used in various industries for deep learning tasks.

Vulnerability Detail

Calling tf.raw_ops.RaggedTensorToVariant with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of RaggedTensorToVariant operations does not validate that the ragged tensor argument is non-empty. This vulnerability can be exploited by an attacker to cause a null pointer dereference, leading to a crash or potential code execution. The issue has been identified in TensorFlow versions 2.1.4 and above, including TensorFlow GPU versions. The fix for this vulnerability is included in TensorFlow 2.5.0 and has been backported to TensorFlow versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4.

pip install --upgrade "tensorflow-gpu==2.12.0"

Workaround

As a workaround, users can avoid calling tf.raw_ops.RaggedTensorToVariant with invalid ragged tensor arguments. It is important to ensure that the ragged tensor being passed as an argument is non-empty and contains valid data. Here is an example of how to validate the ragged tensor before calling the vulnerable function:

import tensorflow as tf

ragged_tensor = tf.RaggedTensor.from_value_rowids(values=[1, 2, 3], value_rowids=[0, 0, 1])

if ragged_tensor.shape.num_elements() > 0:
    tf.raw_ops.RaggedTensorToVariant(ragged_tensor)

Conclusion

In conclusion, the vulnerability (CVE-2021-29516) in TensorFlow GPU allows for a null pointer dereference when calling tf.raw_ops.RaggedTensorToVariant with invalid ragged tensor arguments. This can lead to a crash or potential code execution. It is crucial to update TensorFlow to the patched versions or apply the relevant patches to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow-gpu →

FAQ