ghsa-x83m-p7pv-ch8v
Vulnerability from github
Impact
An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedAdd:
```python import tensorflow as tf
x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8) y = tf.constant([], shape=[2, 0], dtype=tf.quint8)
min_x = tf.constant(10.723421015884028) max_x = tf.constant(15.19578006631113) min_y = tf.constant(-5.539003866682977) max_y = tf.constant(42.18819949559947)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y) ```
This is because the implementation computes a modulo operation without validating that the divisor is not zero.
cc
void VectorTensorAddition(const T* vector_data, float min_vector,
float max_vector, int64 vector_num_elements,
const T* tensor_data, float min_tensor,
float max_tensor, int64 tensor_num_elements,
float output_min, float output_max, Toutput* output) {
for (int i = 0; i < tensor_num_elements; ++i) {
const int64 vector_i = i % vector_num_elements;
...
}
}
Since vector_num_elements is determined based on input shapes, a user can trigger scenarios where this quantity is 0.
Patches
We have patched the issue in GitHub commit 744009c9e5cc5d0447f0dc39d055f917e1fd9e16.
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 Yakun Zhang and Ying Wang of Baidu X-Team.
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],
"aliases": [
"CVE-2021-29549"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T21:28:55Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedAdd`:\n\n```python\nimport tensorflow as tf\n\nx = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8)\ny = tf.constant([], shape=[2, 0], dtype=tf.quint8)\n\nmin_x = tf.constant(10.723421015884028)\nmax_x = tf.constant(15.19578006631113)\nmin_y = tf.constant(-5.539003866682977)\nmax_y = tf.constant(42.18819949559947)\n\ntf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero.\n\n```cc\nvoid VectorTensorAddition(const T* vector_data, float min_vector,\n float max_vector, int64 vector_num_elements,\n const T* tensor_data, float min_tensor,\n float max_tensor, int64 tensor_num_elements,\n float output_min, float output_max, Toutput* output) {\n for (int i = 0; i \u003c tensor_num_elements; ++i) {\n const int64 vector_i = i % vector_num_elements;\n ...\n }\n}\n```\n\nSince `vector_num_elements` is [determined based on input shapes](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0.\n\n### Patches\nWe have patched the issue in GitHub commit [744009c9e5cc5d0447f0dc39d055f917e1fd9e16](https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16).\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 Yakun Zhang and Ying Wang of Baidu X-Team.",
"id": "GHSA-x83m-p7pv-ch8v",
"modified": "2024-10-31T20:47:00Z",
"published": "2021-05-21T14:23:38Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29549"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-477.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-675.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-186.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 `QuantizedAdd`"
}
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.