| 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< 2.2.0rc0 | 2.2.32.3.32.4.22.1.42.2.0rc0 |
| tensorflow-cpu | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4< 2.2.0rc0 | 2.2.32.3.32.4.22.1.42.2.0rc0 |
| tensorflow-gpu | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4< 2.2.0rc0 | 2.2.32.3.32.4.22.1.42.2.0rc0 |
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2≤ 2.1.4 | 2.2.32.3.32.4.2 |
The implementation of tf.raw_ops.ReverseSequence allows for stack overflow and/or CHECK-fail based denial of service.
import tensorflow as tf
input = tf.zeros([1, 1, 1], dtype=tf.int32)
seq_lengths = tf.constant([0], shape=[1], dtype=tf.int32)
tf.raw_ops.ReverseSequence(
input=input, seq_lengths=seq_lengths, seq_dim=-2, batch_dim=0)
The implementation fails to validate that seq_dim and batch_dim arguments are valid.
Negative values for seq_dim can result in stack overflow or CHECK-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of batch_dim.
We have patched the issue in GitHub commit ecf768cbe50cedc0a45ce1ee223146a3d3d26d23.
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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