| Package | Affected versions | Patched versions |
|---|---|---|
| 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 |
| tensorflow-cpu | ≥ 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 |
| tensorflow-gpu | ≥ 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 |
| 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 |
Due to lack of validation in tf.raw_ops.SparseDenseCwiseMul, an attacker can trigger denial of service via CHECK-fails or accesses to outside the bounds of heap allocated data:
import tensorflow as tf
indices = tf.constant([], shape=[10, 0], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
shape = tf.constant([0, 0], shape=[2], dtype=tf.int64)
dense = tf.constant([], shape=[0], dtype=tf.int64)
tf.raw_ops.SparseDenseCwiseMul(
sp_indices=indices, sp_values=values, sp_shape=shape, dense=dense)
Since the implementation only validates the rank of the input arguments but no constraints between dimensions, an attacker can abuse them to trigger internal CHECK assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers.
We have patched the issue in GitHub commit 7ae2af34087fb4b5c8915279efd03da3b81028bc.
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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