CVE-2021-29541

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in tf.raw_ops.StringNGrams. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the data_splits argument. This would result in ngrams_data(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in tf.raw_ops.StringNGrams. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the data_splits argument. This would result in ngrams_data(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Impact

An attacker can trigger a dereference of a null pointer in tf.raw_ops.StringNGrams:

import tensorflow as tf

data=tf.constant([''] * 11, shape=[11], dtype=tf.string)

splits = [0]*115
splits.append(3)
data_splits=tf.constant(splits, shape=[116], dtype=tf.int64)

tf.raw_ops.StringNGrams(data=data, data_splits=data_splits, separator=b'Ss',
                        ngram_widths=[7,6,11],
                        left_pad='ABCDE', right_pad=b'ZYXWVU',
                        pad_width=50, preserve_short_sequences=True)

This is because the implementation does not fully validate the data_splits argument. This would result in ngrams_data to be a null pointer when the output would be computed to have 0 or negative size.

Later writes to the output tensor would then cause a null pointer dereference.

Patches

We have patched the issue in GitHub commit ba424dd8f16f7110eea526a8086f1a155f14f22b.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in tf.raw_ops.StringNGrams. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the data_splits argument. This would result in ngrams_data(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in tf.raw_ops.StringNGrams. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the data_splits argument. This would result in ngrams_data(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Impact

An attacker can trigger a dereference of a null pointer in tf.raw_ops.StringNGrams:

import tensorflow as tf

data=tf.constant([''] * 11, shape=[11], dtype=tf.string)

splits = [0]*115
splits.append(3)
data_splits=tf.constant(splits, shape=[116], dtype=tf.int64)

tf.raw_ops.StringNGrams(data=data, data_splits=data_splits, separator=b'Ss',
                        ngram_widths=[7,6,11],
                        left_pad='ABCDE', right_pad=b'ZYXWVU',
                        pad_width=50, preserve_short_sequences=True)

This is because the implementation does not fully validate the data_splits argument. This would result in ngrams_data to be a null pointer when the output would be computed to have 0 or negative size.

Later writes to the output tensor would then cause a null pointer dereference.

Patches

We have patched the issue in GitHub commit ba424dd8f16f7110eea526a8086f1a155f14f22b.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.