pysec - pysec-2021-466

pysec-2021-466

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. An attacker can cause a division by zero to occur in Conv2DBackpropFilter. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then work_unit_size is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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": [
        {
          "events": [
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              "introduced": "0"
            },
            {
              "fixed": "c570e2ecfc822941335ad48f6e10df4e21f11c96"
            }
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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        {
          "events": [
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              "fixed": "2.2.0rc0"
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            {
              "fixed": "2.3.0rc0"
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            {
              "introduced": "2.3.0"
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            {
              "fixed": "2.3.4"
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              "introduced": "2.4.0"
            },
            {
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            }
          ],
          "type": "ECOSYSTEM"
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      ],
      "versions": [
        "1.15.0",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.1.3",
        "2.1.4",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.2.3",
        "2.3.0",
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        "2.3.2",
        "2.3.3",
        "2.4.0",
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        "2.4.2"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29538",
    "GHSA-j8qc-5fqr-52fp"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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-2021-466",
  "modified": "2021-12-09T06:34:49.257791Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp"
    }
  ]
}

ghsa-j8qc-5fqr-52fp

Vulnerability from github

Published

2021-05-21 14:22

Modified

2024-10-30 23:26

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

Division by zero in `Conv2DBackpropFilter`

Details

Impact

An attacker can cause a division by zero to occur in Conv2DBackpropFilter:

```python import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], use_cudnn_on_gpu=False, padding='SAME', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ```

This is because the implementation computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):

cc const size_t size_A = output_image_size * filter_total_size; const size_t size_B = output_image_size * dims.out_depth; const size_t size_C = filter_total_size * dims.out_depth; const size_t work_unit_size = size_A + size_B + size_C; const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;

If all shapes are empty then work_unit_size is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service.

Patches

We have patched the issue in GitHub commit c570e2ecfc822941335ad48f6e10df4e21f11c96.

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 Yakun Zhang and Ying Wang of Baidu X-Team.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
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              "fixed": "2.1.4"
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        }
      ]
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    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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        "ecosystem": "PyPI",
        "name": "tensorflow"
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            {
              "introduced": "2.3.0"
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        "name": "tensorflow"
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        "name": "tensorflow-cpu"
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        "name": "tensorflow-gpu"
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              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29538"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-369"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T22:30:42Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\nfilter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)\nout_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)\n\ntf.raw_ops.Conv2DBackpropFilter(\n  input=input_tensor,\n  filter_sizes=filter_sizes,\n  out_backprop=out_backprop,\n  strides=[1, 1, 1, 1],\n  use_cudnn_on_gpu=False,\n  padding=\u0027SAME\u0027,\n  explicit_paddings=[],\n  data_format=\u0027NHWC\u0027,\n  dilations=[1, 1, 1, 1]\n)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):\n\n```cc\nconst size_t size_A = output_image_size * filter_total_size; \nconst size_t size_B = output_image_size * dims.out_depth;\nconst size_t size_C = filter_total_size * dims.out_depth;\nconst size_t work_unit_size = size_A + size_B + size_C;\nconst size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;\n```\n\nIf all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. \n\n### Patches\nWe have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).\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 Yakun Zhang and Ying Wang of Baidu X-Team.",
  "id": "GHSA-j8qc-5fqr-52fp",
  "modified": "2024-10-30T23:26:36Z",
  "published": "2021-05-21T14:22:38Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp"
    },
    {
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      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29538"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96"
    },
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      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-466.yaml"
    },
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      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-664.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-175.yaml"
    },
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      "type": "PACKAGE",
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  "severity": [
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      "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 zero in `Conv2DBackpropFilter`"
}

cve-2021-29538

Vulnerability from cvelistv5

Published

2021-05-14 19:11

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. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp	x_refsource_CONFIRM

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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          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-j8qc-5fqr-52fp",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-29538",
    "datePublished": "2021-05-14T19:11:36",
    "dateReserved": "2021-03-30T00:00:00",
    "dateUpdated": "2024-08-03T22:11:05.489Z",
    "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-2021-466

Vulnerability from pysec

ghsa-j8qc-5fqr-52fp

Vulnerability from github

Impact

Patches

For more information

Attribution

cve-2021-29538

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature