| 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< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| 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< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.42.2.0rc0 |
| 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< 2.2.0rc0 | 2.2.32.3.0rc02.3.32.3.42.4.22.4.32.1.4 |
The validation in tf.raw_ops.QuantizeAndDequantizeV2 allows invalid values for axis argument:
import tensorflow as tf
input_tensor = tf.constant([0.0], shape=[1], dtype=float)
input_min = tf.constant(-10.0)
input_max = tf.constant(-10.0)
tf.raw_ops.QuantizeAndDequantizeV2(
input=input_tensor, input_min=input_min, input_max=input_max,
signed_input=False, num_bits=1, range_given=False, round_mode='HALF_TO_EVEN',
narrow_range=False, axis=-2)
The validation uses || to mix two different conditions:
OP_REQUIRES(ctx,
(axis_ == -1 || axis_ < input.shape().dims()),
errors::InvalidArgument(...));
If axis_ < -1 the condition in OP_REQUIRES will still be true, but this value of axis_ results in heap underflow. This allows attackers to read/write to other data on the heap.
We have patched the issue in GitHub commit c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9.
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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Scan my site free2.2.0rc0| 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 |