ghsa-37jf-mjv6-xfqw
Vulnerability from github
Impact
When Conv2DBackpropInput
receives empty out_backprop
inputs (e.g. [3, 1, 0, 1]
), the current CPU/GPU kernels CHECK
fail (one with dnnl, the other with cudnn). This can be used to trigger a denial of service attack.
```python
import tensorflow as tf
import numpy as np
input_sizes = [3, 1, 1, 2]
filter = np.ones([1, 3, 2, 3])
out_backprop = np.ones([3, 1, 0, 3])
strides = [1, 1, 2, 1]
padding = 'VALID'
tf.raw_ops.Conv2DBackpropInput( input_sizes = input_sizes, filter = filter, out_backprop = out_backprop, strides = strides, padding = padding ) ```
Patches
We have patched the issue in GitHub commit 27a65a43cf763897fecfa5cdb5cc653fc5dd0346.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Jingyi Shi.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.7.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.8.0" }, { "fixed": "2.8.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.9.0" }, { "fixed": "2.9.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.7.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.8.0" }, { "fixed": "2.8.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.9.0" }, { "fixed": "2.9.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.7.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.8.0" }, { "fixed": "2.8.1" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.9.0" }, { "fixed": "2.9.1" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2022-35999" ], "database_specific": { "cwe_ids": [ "CWE-617" ], "github_reviewed": true, "github_reviewed_at": "2022-09-16T19:24:49Z", "nvd_published_at": "2022-09-16T23:15:00Z", "severity": "MODERATE" }, "details": "### Impact\nWhen `Conv2DBackpropInput` receives empty `out_backprop` inputs (e.g. `[3, 1, 0, 1]`), the current CPU/GPU kernels `CHECK` fail (one with dnnl, the other with cudnn). This can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\nimport numpy as np\ninput_sizes = [3, 1, 1, 2]\nfilter = np.ones([1, 3, 2, 3])\nout_backprop = np.ones([3, 1, 0, 3])\nstrides = [1, 1, 2, 1]\npadding = \u0027VALID\u0027\n\ntf.raw_ops.Conv2DBackpropInput(\n input_sizes = input_sizes,\n filter = filter,\n out_backprop = out_backprop,\n strides = strides,\n padding = padding\n)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [27a65a43cf763897fecfa5cdb5cc653fc5dd0346](https://github.com/tensorflow/tensorflow/commit/27a65a43cf763897fecfa5cdb5cc653fc5dd0346).\n\nThe fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.\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 Jingyi Shi.\n", "id": "GHSA-37jf-mjv6-xfqw", "modified": "2022-09-19T19:09:33Z", "published": "2022-09-16T19:24:49Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-37jf-mjv6-xfqw" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35999" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/27a65a43cf763897fecfa5cdb5cc653fc5dd0346" }, { "type": "PACKAGE", "url": "https://github.com/tensorflow/tensorflow" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H", "type": "CVSS_V3" } ], "summary": "TensorFlow vulnerable to `CHECK` fail in `Conv2DBackpropInput`" }
Sightings
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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.