GHSA-FXGC-95XX-GRVQ
Vulnerability from github – Published: 2023-03-27 21:05 – Updated: 2023-09-01 15:23
VLAI?
Summary
TensorFlow Denial of Service vulnerability
Details
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.
Severity ?
6.5 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.11.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.11.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2023-25661"
],
"database_specific": {
"cwe_ids": [
"CWE-20"
],
"github_reviewed": true,
"github_reviewed_at": "2023-03-27T21:05:10Z",
"nvd_published_at": "2023-03-27T20:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nA malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack.\nTo 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. \n```python\nimport tensorflow as tf\n\nclass MyModel(tf.keras.Model):\n def __init__(self):\n super().__init__()\n self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding=\"same\")\n \n def call(self, input):\n return self.conv(input)\nmodel = MyModel() # Defines a valid model.\n\nx = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.\noutput = model.predict(x)\nprint(output.shape) # (1, 32, 32, 32, 2)\n\nx = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.\noutput = model(x) # crash\n```\nThis 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.\n\n### Patches\nWe have patched the issue in\n- GitHub commit [948fe6369a5711d4b4568ea9bbf6015c6dfb77e2](https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2)\n- GitHub commit [85db5d07db54b853484bfd358c3894d948c36baf](https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\n\n### For more information\nPlease 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.\n\n\n",
"id": "GHSA-fxgc-95xx-grvq",
"modified": "2023-09-01T15:23:50Z",
"published": "2023-03-27T21:05:10Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxgc-95xx-grvq"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-25661"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/commit/85db5d07db54b853484bfd358c3894d948c36baf"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/948fe6369a5711d4b4568ea9bbf6015c6dfb77e2"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "TensorFlow Denial of Service vulnerability"
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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