ghsa-r26c-679w-mrjm
Vulnerability from github
Impact
When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.
python
import tensorflow as tf
import numpy as np
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_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
{
"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"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"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-2022-36005"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T21:28:06Z",
"nvd_published_at": "2022-09-16T23:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nWhen `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack.\n```python\nimport tensorflow as tf\nimport numpy as np \narg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)\narg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)\narg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)\narg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)\narg_4=8\narg_5=False\narg_6=\u0027\u0027\ntf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,\nmin=arg_2, max=arg_3, num_bits=arg_4, 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-r26c-679w-mrjm",
"modified": "2022-09-19T19:03:43Z",
"published": "2022-09-16T21:28:06Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r26c-679w-mrjm"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-36005"
},
{
"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 `FakeQuantWithMinMaxVarsGradient`"
}
Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.