pysec - pysec-2021-700

pysec-2021-700

Vulnerability from pysec

Published

2021-05-14 20:15

Modified

2021-12-09 06:35

Details

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPool3DGradGrad exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. 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-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
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      "ranges": [
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          "type": "GIT"
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        "1.3.0",
        "1.4.0",
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        "1.5.0",
        "1.5.1",
        "1.6.0",
        "1.7.0",
        "1.7.1",
        "1.8.0",
        "1.9.0",
        "2.0.0",
        "2.0.1",
        "2.0.2",
        "2.0.3",
        "2.0.4",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.1.3",
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        "2.2.1",
        "2.2.2",
        "2.3.0",
        "2.3.1",
        "2.3.2",
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        "2.4.1"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29574",
    "GHSA-828x-qc2p-wprq"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. 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-2021-700",
  "modified": "2021-12-09T06:35:27.544548Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
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      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq"
    },
    {
      "type": "FIX",
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    }
  ]
}

ghsa-828x-qc2p-wprq

Vulnerability from github

Published

2021-05-21 14:26

Modified

2024-10-31 20:56

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

Undefined behavior in `MaxPool3DGradGrad`

Details

Impact

The implementation of tf.raw_ops.MaxPool3DGradGrad exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors:

```python import tensorflow as tf

orig_input = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32) orig_output = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32) grad = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME"

tf.raw_ops.MaxPool3DGradGrad( orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize, strides=strides, padding=padding) ```

The implementation fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer.

Patches

We have patched the issue in GitHub commit a3d9f9be9ac2296615644061b40cefcee341dcc4.

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": [
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  "aliases": [
    "CVE-2021-29574"
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    "cwe_ids": [
      "CWE-476"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T18:34:21Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nThe implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors:\n\n```python\nimport tensorflow as tf\n\norig_input = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)\norig_output = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)\ngrad = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)\nksize = [1, 1, 1, 1, 1]\nstrides = [1, 1, 1, 1, 1]\npadding = \"SAME\"\n\ntf.raw_ops.MaxPool3DGradGrad(\n    orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,\n    strides=strides, padding=padding)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer.\n\n### Patches\nWe have patched the issue in GitHub commit [a3d9f9be9ac2296615644061b40cefcee341dcc4](https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4).\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-828x-qc2p-wprq",
  "modified": "2024-10-31T20:56:37Z",
  "published": "2021-05-21T14:26:10Z",
  "references": [
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      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq"
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      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29574"
    },
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      "url": "https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4"
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      "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",
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  ],
  "summary": "Undefined behavior in `MaxPool3DGradGrad`"
}

cve-2021-29574

Vulnerability from cvelistv5

Published

2021-05-14 19:16

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.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. 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-828x-qc2p-wprq	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4	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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            {
              "name": "https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-828x-qc2p-wprq",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-29574",
    "datePublished": "2021-05-14T19:16:13",
    "dateReserved": "2021-03-30T00:00:00",
    "dateUpdated": "2024-08-03T22:11:06.261Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

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-2021-700

Vulnerability from pysec

ghsa-828x-qc2p-wprq

Vulnerability from github

Impact

Patches

For more information

Attribution

cve-2021-29574

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature