GHSA-4vf2-4xcg-65cx
Vulnerability from github
Impact
An attacker can trigger a division by 0 in tf.raw_ops.Conv2D
:
```python import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
strides = [1, 1, 1, 1] padding = "SAME"
tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding) ```
This is because the implementation does a division by a quantity that is controlled by the caller:
cc
const int64 patch_depth = filter.dim_size(2);
if (in_depth % patch_depth != 0) { ... }
Patches
We have patched the issue in GitHub commit b12aa1d44352de21d1a6faaf04172d8c2508b42b.
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.
{ "affected": [ { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-cpu" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "0" }, { "fixed": "2.1.4" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.2.0" }, { "fixed": "2.2.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.3.0" }, { "fixed": "2.3.3" } ], "type": "ECOSYSTEM" } ] }, { "package": { "ecosystem": "PyPI", "name": "tensorflow-gpu" }, "ranges": [ { "events": [ { "introduced": "2.4.0" }, { "fixed": "2.4.2" } ], "type": "ECOSYSTEM" } ] } ], "aliases": [ "CVE-2021-29526" ], "database_specific": { "cwe_ids": [ "CWE-369" ], "github_reviewed": true, "github_reviewed_at": "2021-05-18T23:14:52Z", "nvd_published_at": "2021-05-14T20:15:00Z", "severity": "LOW" }, "details": "### Impact\nAn attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\nfilter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\n\nstrides = [1, 1, 1, 1]\npadding = \"SAME\"\n \ntf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding)\n``` \n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller:\n```cc\n const int64 patch_depth = filter.dim_size(2);\n if (in_depth % patch_depth != 0) { ... }\n```\n \n### Patches\nWe have patched the issue in GitHub commit [b12aa1d44352de21d1a6faaf04172d8c2508b42b](https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b).\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-4vf2-4xcg-65cx", "modified": "2024-10-30T22:08:50Z", "published": "2021-05-21T14:21:55Z", "references": [ { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vf2-4xcg-65cx" }, { "type": "ADVISORY", "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29526" }, { "type": "WEB", "url": "https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-454.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-652.yaml" }, { "type": "WEB", "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-163.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 `Conv2D`" }
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.