GHSA-H2WQ-PRV9-2F56
Vulnerability from github – Published: 2022-05-24 22:06 – Updated: 2022-05-24 22:06
VLAI?
Summary
Missing validation crashes `QuantizeAndDequantizeV4Grad`
Details
Impact
The implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:
import tensorflow as tf
tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=tf.constant(1, shape=[2,2], dtype=tf.float64),
input=tf.constant(1, shape=[2,2], dtype=tf.float64),
input_min=tf.constant([], shape=[0], dtype=tf.float64),
input_max=tf.constant(-10, shape=[], dtype=tf.float64),
axis=-1)
The code assumes input_min and input_max are scalars but there is no validation for this.
Patches
We have patched the issue in GitHub commit 098e7762d909bac47ce1dbabe6dfd06294cb9d58.
The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 from Secure Systems Lab at Brown University.
Severity ?
5.5 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
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"fixed": "2.6.4"
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"name": "tensorflow"
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"package": {
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"name": "tensorflow-cpu"
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{
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"name": "tensorflow-gpu"
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-29192"
],
"database_specific": {
"cwe_ids": [
"CWE-20"
],
"github_reviewed": true,
"github_reviewed_at": "2022-05-24T22:06:26Z",
"nvd_published_at": "2022-05-20T21:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nThe implementation of [`tf.raw_ops.QuantizeAndDequantizeV4Grad`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.QuantizeAndDequantizeV4Grad(\n gradients=tf.constant(1, shape=[2,2], dtype=tf.float64),\n input=tf.constant(1, shape=[2,2], dtype=tf.float64),\n input_min=tf.constant([], shape=[0], dtype=tf.float64),\n input_max=tf.constant(-10, shape=[], dtype=tf.float64),\n axis=-1)\n```\n\nThe code assumes `input_min` and `input_max` are scalars but there is no validation for this.\n\n### Patches\nWe have patched the issue in GitHub commit [098e7762d909bac47ce1dbabe6dfd06294cb9d58](https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58).\n \nThe fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by Neophytos Christou from Secure Systems Lab at Brown University.",
"id": "GHSA-h2wq-prv9-2f56",
"modified": "2022-05-24T22:06:26Z",
"published": "2022-05-24T22:06:26Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h2wq-prv9-2f56"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-29192"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/098e7762d909bac47ce1dbabe6dfd06294cb9d58"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L148-L226"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "Missing validation crashes `QuantizeAndDequantizeV4Grad`"
}
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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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