First published: Mon Mar 27 2023(Updated: )
### Impact A malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. To minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes. ```python import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super().__init__() self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding="same") def call(self, input): return self.conv(input) model = MyModel() # Defines a valid model. x = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input. output = model.predict(x) print(output.shape) # (1, 32, 32, 32, 2) x = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input. output = model(x) # crash ``` This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. ### Patches We have patched the issue in - GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2) - GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf). The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1 ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
Credit: security-advisories@github.com security-advisories@github.com
Affected Software | Affected Version | How to fix |
---|---|---|
Google TensorFlow | <2.11.1 |
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The vulnerability ID for this TensorFlow issue is CVE-2023-25661.
This vulnerability allows a malicious invalid input to crash a TensorFlow model and can be used to trigger a denial of service attack.
This vulnerability can be exploited by providing a malicious invalid input to a TensorFlow model.
The severity of CVE-2023-25661 is medium with a CVSS score of 6.5.
To fix this vulnerability, update your TensorFlow installation to version 2.11.1 or later.