ghsa-7jvm-xxmr-v5cw
Vulnerability from github
Impact
TFversion 2.11.0 //tensorflow/core/ops/array_ops.cc:1067 const Tensor* hypothesis_shape_t = c->input_tensor(2); std::vector
if hypothesis_shape_t is empty, hypothesis_shape_t->NumElements() - 1 will be integer overflow, and the it will deadlock
python
import tensorflow as tf
para={
'hypothesis_indices': [[]],
'hypothesis_values': ['tmp/'],
'hypothesis_shape': [],
'truth_indices': [[]],
'truth_values': [''],
'truth_shape': [],
'normalize': False
}
tf.raw_ops.EditDistance(**para)
Patches
We have patched the issue in GitHub commit 08b8e18643d6dcde00890733b270ff8d9960c56c.
The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1
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 r3pwnx
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.11.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.11.1"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.11.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2023-25662"
],
"database_specific": {
"cwe_ids": [
"CWE-190"
],
"github_reviewed": true,
"github_reviewed_at": "2023-03-24T21:58:31Z",
"nvd_published_at": "2023-03-25T00:15:00Z",
"severity": "HIGH"
},
"details": "### Impact\nTFversion 2.11.0 //tensorflow/core/ops/array_ops.cc:1067 const Tensor* hypothesis_shape_t = c-\u003einput_tensor(2); std::vector\u003cDimensionHandle\u003e dims(hypothesis_shape_t-\u003eNumElements() - 1); for (int i = 0; i \u003c dims.size(); ++i) { dims[i] = c-\u003eMakeDim(std::max(h_values(i), t_values(i))); }\n\nif hypothesis_shape_t is empty, hypothesis_shape_t-\u003eNumElements() - 1 will be integer overflow, and the it will deadlock\n```python\nimport tensorflow as tf\npara={\n \u0027hypothesis_indices\u0027: [[]],\n \u0027hypothesis_values\u0027: [\u0027tmp/\u0027],\n \u0027hypothesis_shape\u0027: [],\n \u0027truth_indices\u0027: [[]],\n \u0027truth_values\u0027: [\u0027\u0027],\n \u0027truth_shape\u0027: [],\n \u0027normalize\u0027: False\n }\ntf.raw_ops.EditDistance(**para)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [08b8e18643d6dcde00890733b270ff8d9960c56c](https://github.com/tensorflow/tensorflow/commit/08b8e18643d6dcde00890733b270ff8d9960c56c).\n\nThe fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1\n\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\n### Attribution\nThis vulnerability has been reported by r3pwnx",
"id": "GHSA-7jvm-xxmr-v5cw",
"modified": "2023-03-27T21:58:27Z",
"published": "2023-03-24T21:58:31Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7jvm-xxmr-v5cw"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-25662"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/08b8e18643d6dcde00890733b270ff8d9960c56c"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "TensorFlow vulnerable to integer overflow in EditDistance"
}
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.