GHSA-FQXC-PVF8-2W9V
Vulnerability from github – Published: 2022-09-16 22:09 – Updated: 2022-09-21 19:33
VLAI?
Summary
TensorFlow vulnerable to null dereference on MLIR on empty function attributes
Details
Impact
Eig can be fed an incorrect Tout input, resulting in a CHECK fail that can trigger a denial of service attack.
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.complex128
arg_2=True
arg_3=''
tf.raw_ops.Eig(input=arg_0, Tout=arg_1, compute_v=arg_2, name=arg_3)
Patches
We have patched the issue in GitHub commit aed36912609fc07229b4d0a7b44f3f48efc00fd0.
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.
Severity ?
5.9 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
}
],
"type": "ECOSYSTEM"
}
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{
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"name": "tensorflow-gpu"
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"events": [
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"introduced": "2.9.0"
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{
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],
"type": "ECOSYSTEM"
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}
],
"aliases": [
"CVE-2022-36000"
],
"database_specific": {
"cwe_ids": [
"CWE-476"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:09:36Z",
"nvd_published_at": "2022-09-16T23:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\n`Eig` can be fed an incorrect `Tout` input, resulting in 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.complex128\narg_2=True\narg_3=\u0027\u0027\ntf.raw_ops.Eig(input=arg_0, Tout=arg_1, compute_v=arg_2, name=arg_3)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [aed36912609fc07229b4d0a7b44f3f48efc00fd0](https://github.com/tensorflow/tensorflow/commit/aed36912609fc07229b4d0a7b44f3f48efc00fd0).\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 \u5218\u529b\u6e90, Information System \u0026 Security and Countermeasures Experiments Center, Beijing Institute of Technology.\n",
"id": "GHSA-fqxc-pvf8-2w9v",
"modified": "2022-09-21T19:33:21Z",
"published": "2022-09-16T22:09:36Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fqxc-pvf8-2w9v"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-36000"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/aed36912609fc07229b4d0a7b44f3f48efc00fd0"
},
{
"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 null dereference on MLIR on empty function attributes"
}
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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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