CVE-2021-41215

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for DeserializeSparse can trigger a null pointer dereference. This is because the shape inference function assumes that the serialize_sparse tensor is a tensor with positive rank (and having 3 as the last dimension). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for DeserializeSparse can trigger a null pointer dereference. This is because the shape inference function assumes that the serialize_sparse tensor is a tensor with positive rank (and having 3 as the last dimension). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Impact

The shape inference code for DeserializeSparse can trigger a null pointer dereference:

import tensorflow as tf

dataset = tf.data.Dataset.range(3)
  
@tf.function                 
def test():                  
  y = tf.raw_ops.DeserializeSparse(
    serialized_sparse=tf.data.experimental.to_variant(dataset),
    dtype=tf.int32)

test()

This is because the shape inference function assumes that the serialize_sparse tensor is a tensor with positive rank (and having 3 as the last dimension). However, in the example above, the argument is a scalar (i.e., rank 0).

Patches

We have patched the issue in GitHub commit d3738dd70f1c9ceb547258cbb82d853da8771850.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for DeserializeSparse can trigger a null pointer dereference. This is because the shape inference function assumes that the serialize_sparse tensor is a tensor with positive rank (and having 3 as the last dimension). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for DeserializeSparse can trigger a null pointer dereference. This is because the shape inference function assumes that the serialize_sparse tensor is a tensor with positive rank (and having 3 as the last dimension). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Impact

The shape inference code for DeserializeSparse can trigger a null pointer dereference:

import tensorflow as tf

dataset = tf.data.Dataset.range(3)
  
@tf.function                 
def test():                  
  y = tf.raw_ops.DeserializeSparse(
    serialized_sparse=tf.data.experimental.to_variant(dataset),
    dtype=tf.int32)

test()

This is because the shape inference function assumes that the serialize_sparse tensor is a tensor with positive rank (and having 3 as the last dimension). However, in the example above, the argument is a scalar (i.e., rank 0).

Patches

We have patched the issue in GitHub commit d3738dd70f1c9ceb547258cbb82d853da8771850.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.