GHSA-27J5-4P9V-PP67
Vulnerability from github – Published: 2021-08-25 14:43 – Updated: 2024-11-13 16:13Impact
Providing a negative element to num_elements list argument of tf.raw_ops.TensorListReserve causes the runtime to abort the process due to reallocating a std::vector to have a negative number of elements:
import tensorflow as tf
tf.raw_ops.TensorListReserve(
element_shape = tf.constant([1]),
num_elements=tf.constant([-1]),
element_dtype = tf.int32)
The implementation calls std::vector.resize() with the new size controlled by input given by the user, without checking that this input is valid.
Patches
We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.
{
"affected": [
{
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"ecosystem": "PyPI",
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],
"aliases": [
"CVE-2021-37644"
],
"database_specific": {
"cwe_ids": [
"CWE-617"
],
"github_reviewed": true,
"github_reviewed_at": "2021-08-23T19:16:53Z",
"nvd_published_at": "2021-08-12T21:15:00Z",
"severity": "MODERATE"
},
"details": "### Impact\nProviding a negative element to `num_elements` list argument of `tf.raw_ops.TensorListReserve` causes the runtime to abort the process due to reallocating a `std::vector` to have a negative number of elements:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.TensorListReserve(\n element_shape = tf.constant([1]),\n num_elements=tf.constant([-1]),\n element_dtype = tf.int32)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls `std::vector.resize()` with the new size controlled by input given by the user, without checking that this input is valid.\n\n### Patches\nWe have patched the issue in GitHub commit [8a6e874437670045e6c7dc6154c7412b4a2135e2](https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2).\n\nThe fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.",
"id": "GHSA-27j5-4p9v-pp67",
"modified": "2024-11-13T16:13:05Z",
"published": "2021-08-25T14:43:40Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27j5-4p9v-pp67"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-37644"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-557.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-755.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-266.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"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"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "`std::abort` raised from `TensorListReserve`"
}
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.