GHSA-f78g-q7r4-9wcv
Vulnerability from github
Impact
An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.FractionalAvgPool:
```python import tensorflow as tf
value = tf.constant([60], shape=[1, 1, 1, 1], dtype=tf.int32) pooling_ratio = [1.0, 1.0000014345305555, 1.0, 1.0] pseudo_random = False overlapping = False deterministic = False seed = 0 seed2 = 0
tf.raw_ops.FractionalAvgPool( value=value, pooling_ratio=pooling_ratio, pseudo_random=pseudo_random, overlapping=overlapping, deterministic=deterministic, seed=seed, seed2=seed2) ```
This is because the implementation computes a divisor quantity by dividing two user controlled values:
cc
for (int i = 0; i < tensor_in_and_out_dims; ++i) {
output_size[i] = static_cast<int>(std::floor(input_size[i] / pooling_ratio_[i]));
DCHECK_GT(output_size[i], 0);
}
The user controls the values of input_size[i] and pooling_ratio_[i] (via the value.shape() and pooling_ratio arguments). If the value in input_size[i] is smaller than the pooling_ratio_[i], then the floor operation results in output_size[i] being 0. The DCHECK_GT line is a no-op outside of debug mode, so in released versions of TF this does not trigger.
Later, these computed values are used as arguments to GeneratePoolingSequence. There, the first computation is a division in a modulo operation:
cc
std::vector<int64> GeneratePoolingSequence(int input_length, int output_length,
GuardedPhiloxRandom* generator,
bool pseudo_random) {
...
if (input_length % output_length == 0) {
diff = std::vector<int64>(output_length, input_length / output_length);
}
...
}
Since output_length can be 0, this results in runtime crashing.
Patches
We have patched the issue in GitHub commit 548b5eaf23685d86f722233d8fbc21d0a4aecb96.
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-2021-29550"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T21:21:40Z",
"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.FractionalAvgPool`:\n\n```python\nimport tensorflow as tf\n\nvalue = tf.constant([60], shape=[1, 1, 1, 1], dtype=tf.int32)\npooling_ratio = [1.0, 1.0000014345305555, 1.0, 1.0]\npseudo_random = False\noverlapping = False\ndeterministic = False\nseed = 0\nseed2 = 0\n\ntf.raw_ops.FractionalAvgPool(\n value=value, pooling_ratio=pooling_ratio, pseudo_random=pseudo_random,\n overlapping=overlapping, deterministic=deterministic, seed=seed, seed2=seed2)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values:\n\n```cc \nfor (int i = 0; i \u003c tensor_in_and_out_dims; ++i) {\n output_size[i] = static_cast\u003cint\u003e(std::floor(input_size[i] / pooling_ratio_[i]));\n DCHECK_GT(output_size[i], 0); \n} \n``` \n \nThe user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger.\n\nLater, these computed values [are used as arguments](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to [`GeneratePoolingSequence`](https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation:\n\n```cc\nstd::vector\u003cint64\u003e GeneratePoolingSequence(int input_length, int output_length,\n GuardedPhiloxRandom* generator,\n bool pseudo_random) {\n ...\n if (input_length % output_length == 0) {\n diff = std::vector\u003cint64\u003e(output_length, input_length / output_length);\n }\n ...\n}\n```\n\nSince `output_length` can be 0, this results in runtime crashing.\n\n### Patches\nWe have patched the issue in GitHub commit [548b5eaf23685d86f722233d8fbc21d0a4aecb96](https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96).\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-f78g-q7r4-9wcv",
"modified": "2024-10-30T23:16:10Z",
"published": "2021-05-21T14:23:41Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29550"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-478.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-676.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-187.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 `FractionalAvgPool`"
}
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.