GHSA-Q5JV-M6QW-5G37
Vulnerability from github – Published: 2022-09-16 22:11 – Updated: 2022-09-19 19:10
VLAI?
Summary
TensorFlow vulnerable to floating point exception in `Conv2D`
Details
Impact
If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack.
import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
input = np.ones([1, 0, 2, 1])
filter = np.ones([1, 1, 1, 1])
strides = ([1, 1, 1, 1])
padding = "EXPLICIT"
explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
data_format = "NHWC"
res = tf.raw_ops.Conv2D(
input=input,
filter=filter,
strides=strides,
padding=padding,
explicit_paddings=explicit_paddings,
data_format=data_format,
)
Patches
We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Jingyi Shi.
Severity ?
5.9 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
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}
],
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}
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},
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"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
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"ranges": [
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{
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"package": {
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"name": "tensorflow-cpu"
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{
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
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"ranges": [
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"introduced": "2.8.0"
},
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"fixed": "2.8.1"
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{
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"name": "tensorflow-cpu"
},
"ranges": [
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"events": [
{
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{
"fixed": "2.9.1"
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],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
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]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-35996"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:11:10Z",
"nvd_published_at": "2022-09-16T23:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nIf `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\nimport numpy as np\nwith tf.device(\"CPU\"): # also can be triggerred on GPU\n input = np.ones([1, 0, 2, 1])\n filter = np.ones([1, 1, 1, 1])\n strides = ([1, 1, 1, 1])\n padding = \"EXPLICIT\"\n explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]\n data_format = \"NHWC\"\n res = tf.raw_ops.Conv2D(\n input=input,\n filter=filter,\n strides=strides,\n padding=padding,\n explicit_paddings=explicit_paddings,\n data_format=data_format,\n )\n```\n\n### Patches\nWe have patched the issue in GitHub commit [611d80db29dd7b0cfb755772c69d60ae5bca05f9](https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9).\n\nThe fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by Jingyi Shi.\n",
"id": "GHSA-q5jv-m6qw-5g37",
"modified": "2022-09-19T19:10:43Z",
"published": "2022-09-16T22:11:10Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35996"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "TensorFlow vulnerable to floating point exception in `Conv2D`"
}
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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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