CVE-2021-41216

TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for Transpose is vulnerable to a heap buffer overflow. This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid. 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 function for Transpose is vulnerable to a heap buffer overflow. This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid. 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 function for Transpose is vulnerable to a heap buffer overflow:

import tensorflow as tf
@tf.function
def test():
  y = tf.raw_ops.Transpose(x=[1,2,3,4],perm=[-10])
  return y

test()

This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid:

for (int32_t i = 0; i < rank; ++i) {
  int64_t in_idx = data[i];
  if (in_idx >= rank) {
    return errors::InvalidArgument("perm dim ", in_idx,
                                   " is out of range of input rank ", rank);
  }
  dims[i] = c->Dim(input, in_idx);
}

where Dim(tensor, index) accepts either a positive index less than the rank of the tensor or the special value -1 for unknown dimensions.

Patches

We have patched the issue in GitHub commit c79ba87153ee343401dbe9d1954d7f79e521eb14.

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 function for Transpose is vulnerable to a heap buffer overflow. This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid. 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 function for Transpose is vulnerable to a heap buffer overflow. This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid. 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 function for Transpose is vulnerable to a heap buffer overflow:

import tensorflow as tf
@tf.function
def test():
  y = tf.raw_ops.Transpose(x=[1,2,3,4],perm=[-10])
  return y

test()

This occurs whenever perm contains negative elements. The shape inference function does not validate that the indices in perm are all valid:

for (int32_t i = 0; i < rank; ++i) {
  int64_t in_idx = data[i];
  if (in_idx >= rank) {
    return errors::InvalidArgument("perm dim ", in_idx,
                                   " is out of range of input rank ", rank);
  }
  dims[i] = c->Dim(input, in_idx);
}

where Dim(tensor, index) accepts either a positive index less than the rank of the tensor or the special value -1 for unknown dimensions.

Patches

We have patched the issue in GitHub commit c79ba87153ee343401dbe9d1954d7f79e521eb14.

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.