github - GHSA-hpv4-7p9c-mvfr

GHSA-hpv4-7p9c-mvfr

Vulnerability from github

Published

2021-08-25 14:43

Modified

2024-11-13 17:26

Severity ?

7.1 (High) - CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
8.4 (High) - CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N

Summary

Heap buffer overflow in `FractionalAvgPoolGrad`

Details

Impact

The implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers:

```python import tensorflow as tf

tf.raw_ops.FractionalAvgPoolGrad( orig_input_tensor_shape=[0,1,2,3], out_backprop = np.array([[[[541],[541]],[[541],[541]]]]), row_pooling_sequence=[0, 0, 0, 0, 0], col_pooling_sequence=[-2, 0, 0, 2, 0], overlapping=True) ```

The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area.

Patches

We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.3.4"
            }
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          "type": "ECOSYSTEM"
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        "name": "tensorflow"
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        "name": "tensorflow"
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              "fixed": "2.5.1"
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      "package": {
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        "name": "tensorflow-cpu"
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            {
              "fixed": "2.4.3"
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        "name": "tensorflow-cpu"
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              "introduced": "2.5.0"
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        "2.5.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-37651"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125",
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-08-23T23:20:28Z",
    "nvd_published_at": "2021-08-12T21:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact\nThe implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.FractionalAvgPoolGrad(\n  orig_input_tensor_shape=[0,1,2,3],\n  out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),\n  row_pooling_sequence=[0, 0, 0, 0, 0],\n  col_pooling_sequence=[-2, 0, 0, 2, 0],\n  overlapping=True)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area.\n\n### Patches\nWe have patched the issue in GitHub commit [0f931751fb20f565c4e94aa6df58d54a003cdb30](https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30).\n\nThe fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.",
  "id": "GHSA-hpv4-7p9c-mvfr",
  "modified": "2024-11-13T17:26:29Z",
  "published": "2021-08-25T14:43:21Z",
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      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr"
    },
    {
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      "type": "CVSS_V3"
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      "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Heap buffer overflow in `FractionalAvgPoolGrad`"
}

pysec-2021-564

Vulnerability from pysec

Published

2021-08-12 21:15

Modified

2021-12-09 06:35

Details

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for tf.raw_ops.FractionalAvgPoolGrad can be tricked into accessing data outside of bounds of heap allocated buffers. The implementation does not validate that the input tensor is non-empty. Thus, code constructs an empty EigenDoubleMatrixMap and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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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        "2.3.1",
        "2.3.2",
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      ]
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  ],
  "aliases": [
    "CVE-2021-37651",
    "GHSA-hpv4-7p9c-mvfr"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
  "id": "PYSEC-2021-564",
  "modified": "2021-12-09T06:35:03.344534Z",
  "published": "2021-08-12T21:15:00Z",
  "references": [
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      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30"
    }
  ]
}

pysec-2021-273

Vulnerability from pysec

Published

2021-08-12 21:15

Modified

2021-08-27 03:22

Details

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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            {
              "fixed": "2.3.4"
            },
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
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        "2.3.0",
        "2.3.1",
        "2.3.2",
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        "2.4.0",
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      ]
    }
  ],
  "aliases": [
    "CVE-2021-37651",
    "GHSA-hpv4-7p9c-mvfr"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
  "id": "PYSEC-2021-273",
  "modified": "2021-08-27T03:22:44.051773Z",
  "published": "2021-08-12T21:15:00Z",
  "references": [
    {
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      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr"
    },
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    }
  ]
}

pysec-2021-762

Vulnerability from pysec

Published

2021-08-12 21:15

Modified

2021-12-09 06:35

Details

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
      },
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      ]
    }
  ],
  "aliases": [
    "CVE-2021-37651",
    "GHSA-hpv4-7p9c-mvfr"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
  "id": "PYSEC-2021-762",
  "modified": "2021-12-09T06:35:36.737111Z",
  "published": "2021-08-12T21:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30"
    }
  ]
}

cve-2021-37651

Vulnerability from cvelistv5

Published

2021-08-12 21:00

Modified

2024-08-04 01:23

Severity ?

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

Summary

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30	x_refsource_MISC

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: >= 2.5.0, < 2.5.1
Version: >= 2.4.0, < 2.4.3
Version: < 2.3.4

Show details on NVD website

JSON

To clipboard

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              {
                "product": {
                  "product_data": [
                    {
                      "product_name": "tensorflow",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "\u003e= 2.5.0, \u003c 2.5.1"
                          },
                          {
                            "version_value": "\u003e= 2.4.0, \u003c 2.4.3"
                          },
                          {
                            "version_value": "\u003c 2.3.4"
                          }
                        ]
                      }
                    }
                  ]
                },
                "vendor_name": "tensorflow"
              }
            ]
          }
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
          "description_data": [
            {
              "lang": "eng",
              "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "NONE",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-125: Out-of-bounds Read"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-hpv4-7p9c-mvfr",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-37651",
    "datePublished": "2021-08-12T21:00:19",
    "dateReserved": "2021-07-29T00:00:00",
    "dateUpdated": "2024-08-04T01:23:01.436Z",
    "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

GHSA-hpv4-7p9c-mvfr

Vulnerability from github

Impact

Patches

For more information

Attribution

pysec-2021-564

Vulnerability from pysec

pysec-2021-273

Vulnerability from pysec

pysec-2021-762

Vulnerability from pysec

cve-2021-37651

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature