GHSA-W62H-8XJM-FV49
Vulnerability from github – Published: 2022-09-16 21:19 – Updated: 2022-09-19 19:38
VLAI?
Summary
TensorFlow vulnerable to `CHECK` fail in `DenseBincount`
Details
Impact
DenseBincount assumes its input tensor weights to either have the same shape as its input tensor input or to be length-0. A different weights shape will trigger a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
binary_output = True
input = tf.random.uniform(shape=[0, 0], minval=-10000, maxval=10000, dtype=tf.int32, seed=-2460)
size = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)
weights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000)
tf.raw_ops.DenseBincount(input=input, size=size, weights=weights, binary_output=binary_output)
Patches
We have patched the issue in GitHub commit bf4c14353c2328636a18bfad1e151052c81d5f43.
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 Di Jin, 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": [
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"events": [
{
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{
"fixed": "2.9.1"
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{
"package": {
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"name": "tensorflow-cpu"
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"ranges": [
{
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"introduced": "0"
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{
"package": {
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"name": "tensorflow-cpu"
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{
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"introduced": "2.9.0"
},
{
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],
"type": "ECOSYSTEM"
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
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"type": "ECOSYSTEM"
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
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"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-2022-35987"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T21:19:15Z",
"nvd_published_at": "2022-09-16T22:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\n`DenseBincount` assumes its input tensor `weights` to either have the same shape as its input tensor `input` or to be length-0. A different `weights` shape will trigger a `CHECK` fail that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\nbinary_output = True\ninput = tf.random.uniform(shape=[0, 0], minval=-10000, maxval=10000, dtype=tf.int32, seed=-2460)\nsize = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.int32, seed=-10000)\nweights = tf.random.uniform(shape=[], minval=-10000, maxval=10000, dtype=tf.float32, seed=-10000)\ntf.raw_ops.DenseBincount(input=input, size=size, weights=weights, binary_output=binary_output)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [bf4c14353c2328636a18bfad1e151052c81d5f43](https://github.com/tensorflow/tensorflow/commit/bf4c14353c2328636a18bfad1e151052c81d5f43).\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 Di Jin, Secure Systems Labs, Brown University\n",
"id": "GHSA-w62h-8xjm-fv49",
"modified": "2022-09-19T19:38:33Z",
"published": "2022-09-16T21:19:15Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w62h-8xjm-fv49"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35987"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/bf4c14353c2328636a18bfad1e151052c81d5f43"
},
{
"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 `DenseBincount`"
}
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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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