pysec - pysec-2021-505

pysec-2021-505

Vulnerability from pysec

Published

2021-05-14 20:15

Modified

2021-12-09 06:34

Details

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.AvgPool3DGrad is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the orig_input_shape and grad tensors have similar first and last dimensions but does not check that this assumption is validated. 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.

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
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          "events": [
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              "introduced": "0"
            },
            {
              "fixed": "6fc9141f42f6a72180ecd24021c3e6b36165fe0d"
            }
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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        {
          "events": [
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            {
              "fixed": "2.1.4"
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            {
              "fixed": "2.2.3"
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            {
              "introduced": "2.3.0"
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            {
              "fixed": "2.3.3"
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            {
              "fixed": "2.4.2"
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      ],
      "versions": [
        "1.15.0",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.1.3",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.3.0",
        "2.3.1",
        "2.3.2",
        "2.4.0",
        "2.4.1"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29577",
    "GHSA-v6r6-84gr-92rm"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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.",
  "id": "PYSEC-2021-505",
  "modified": "2021-12-09T06:34:55.310052Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d"
    }
  ]
}

cve-2021-29577

Vulnerability from cvelistv5

Published

2021-05-14 19:15

Modified

2024-08-03 22:11

Severity ?

2.5 (Low) - CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Summary

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d	x_refsource_MISC

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: < 2.1.4
Version: >= 2.2.0, < 2.2.3
Version: >= 2.3.0, < 2.3.3
Version: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

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ghsa-v6r6-84gr-92rm

Vulnerability from github

Published

2021-05-21 14:26

Modified

2024-11-01 17:11

Severity ?

2.5 (Low) - CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
2.0 (Low) - 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

Summary

Heap buffer overflow in `AvgPool3DGrad`

Details

Impact

The implementation of tf.raw_ops.AvgPool3DGrad is vulnerable to a heap buffer overflow:

```python import tensorflow as tf

orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32) grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME"

tf.raw_ops.AvgPool3DGrad( orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding) ```

The implementation assumes that the orig_input_shape and grad tensors have similar first and last dimensions but does not check that this assumption is validated.

Patches

We have patched the issue in GitHub commit 6fc9141f42f6a72180ecd24021c3e6b36165fe0d.

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.

Show details on source website

JSON

To clipboard

{
  "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"
        }
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    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
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          ],
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    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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        "name": "tensorflow-cpu"
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    },
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        "name": "tensorflow-cpu"
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      ]
    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
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            },
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            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29577"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-119",
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T18:12:31Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nThe implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow:\n\n```python\nimport tensorflow as tf\n\norig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32)\ngrad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32)\nksize = [1, 1, 1, 1, 1]\nstrides = [1, 1, 1, 1, 1]\npadding = \"SAME\"\n\ntf.raw_ops.AvgPool3DGrad(\n  orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides,\n  padding=padding)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated.\n\n### Patches\nWe have patched the issue in GitHub commit [6fc9141f42f6a72180ecd24021c3e6b36165fe0d](https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d).\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-v6r6-84gr-92rm",
  "modified": "2024-11-01T17:11:43Z",
  "published": "2021-05-21T14:26:18Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm"
    },
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      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29577"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d"
    },
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      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-505.yaml"
    },
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      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-703.yaml"
    },
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      "type": "WEB",
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    },
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      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
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  "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": "Heap buffer overflow in `AvgPool3DGrad`"
}

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.

Action not permitted

pysec-2021-505

Vulnerability from pysec

cve-2021-29577

Vulnerability from cvelistv5

ghsa-v6r6-84gr-92rm

Vulnerability from github

Impact

Patches

For more information

Attribution

Tags

Sightings

Nomenclature