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CVE-2023-25661: Denial of Service in TensorFlow

TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the Convolution3DTranspose function. 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. An attacker must have privilege to provide input to a Convolution3DTranspose call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.

CVE
#vulnerability#mac#dos#git

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.

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
  • GitHub 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 for more information regarding the security model and how to contact us with issues and questions.

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CVE-2023-22062: Oracle Critical Patch Update Advisory - July 2023

Vulnerability in the Oracle Hyperion Financial Reporting product of Oracle Hyperion (component: Repository). The supported version that is affected is 11.2.13.0.000. Easily exploitable vulnerability allows low privileged attacker with network access via HTTP to compromise Oracle Hyperion Financial Reporting. While the vulnerability is in Oracle Hyperion Financial Reporting, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in unauthorized access to critical data or complete access to all Oracle Hyperion Financial Reporting accessible data and unauthorized ability to cause a partial denial of service (partial DOS) of Oracle Hyperion Financial Reporting. CVSS 3.1 Base Score 8.5 (Confidentiality and Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:L).

GHSA-fxgc-95xx-grvq: TensorFlow Denial of Service vulnerability

### 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) #...

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