GHSA-FXQH-CFJM-FP93
Vulnerability from github – Published: 2021-05-21 14:24 – Updated: 2024-10-31 20:54Impact
An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.Reverse:
import tensorflow as tf
tensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32)
dims = tf.constant([False, True, False], shape=[3], dtype=tf.bool)
tf.raw_ops.Reverse(tensor=tensor_input, dims=dims)
This is because the implementation performs a division based on the first dimension of the tensor argument:
const int64 N = input.dim_size(0);
const int64 cost_per_unit = input.NumElements() / N;
Since this is controlled by the user, an attacker can trigger a denial of service.
Patches
We have patched the issue in GitHub commit 4071d8e2f6c45c1955a811fee757ca2adbe462c1.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang of Baidu X-Team.
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],
"aliases": [
"CVE-2021-29556"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T20:48:50Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`:\n\n```python\nimport tensorflow as tf\n\ntensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32)\ndims = tf.constant([False, True, False], shape=[3], dtype=tf.bool)\n\ntf.raw_ops.Reverse(tensor=tensor_input, dims=dims)\n``` \n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument:\n \n```cc\nconst int64 N = input.dim_size(0);\nconst int64 cost_per_unit = input.NumElements() / N;\n```\n\nSince this is controlled by the user, an attacker can trigger a denial of service.\n\n### Patches\nWe have patched the issue in GitHub commit [4071d8e2f6c45c1955a811fee757ca2adbe462c1](https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang of Baidu X-Team.",
"id": "GHSA-fxqh-cfjm-fp93",
"modified": "2024-10-31T20:54:11Z",
"published": "2021-05-21T14:24:39Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxqh-cfjm-fp93"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29556"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-484.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-682.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-193.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Division by 0 in `Reverse`"
}
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.