pysec - pysec-2021-217

pysec-2021-217

Vulnerability from pysec

Published

2021-05-14 20:15

Modified

2021-08-27 03:22

Details

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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              "introduced": "0"
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            {
              "fixed": "2.1.4"
            },
            {
              "introduced": "2.2.0"
            },
            {
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            },
            {
              "introduced": "2.3.0"
            },
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            },
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              "introduced": "2.4.0"
            },
            {
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            }
          ],
          "type": "ECOSYSTEM"
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      "versions": [
        "0.12.0",
        "0.12.0rc0",
        "0.12.0rc1",
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        "1.11.0rc1",
        "1.11.0rc2",
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        "1.12.0rc1",
        "1.12.0rc2",
        "1.12.2",
        "1.12.3",
        "1.13.0rc0",
        "1.13.0rc1",
        "1.13.0rc2",
        "1.13.1",
        "1.13.2",
        "1.14.0",
        "1.14.0rc0",
        "1.14.0rc1",
        "1.15.0",
        "1.15.0rc0",
        "1.15.0rc1",
        "1.15.0rc2",
        "1.15.0rc3",
        "1.15.2",
        "1.15.3",
        "1.15.4",
        "1.15.5",
        "1.2.0",
        "1.2.0rc0",
        "1.2.0rc1",
        "1.2.0rc2",
        "1.2.1",
        "1.3.0",
        "1.3.0rc0",
        "1.3.0rc1",
        "1.3.0rc2",
        "1.4.0",
        "1.4.0rc0",
        "1.4.0rc1",
        "1.4.1",
        "1.5.0",
        "1.5.0rc0",
        "1.5.0rc1",
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        "1.6.0rc0",
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        "1.7.0rc1",
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        "2.3.0",
        "2.3.1",
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        "2.4.1"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29580",
    "GHSA-x8h6-xgqx-jqgp"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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-2021-217",
  "modified": "2021-08-27T03:22:35.567916Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
    }
  ]
}

ghsa-x8h6-xgqx-jqgp

Vulnerability from github

Published

2021-05-21 14:26

Modified

2024-11-01 17:13

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 and `CHECK`-fail in `FractionalMaxPoolGrad`

Details

Impact

The implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty:

```python import tensorflow as tf

orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64) orig_output = tf.constant([], dtype=tf.int64) out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64) row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)

tf.raw_ops.FractionalMaxPoolGrad( orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False) ```

The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process

```python import tensorflow as tf

orig_input = tf.constant([1], shape=[1], dtype=tf.int64) orig_output = tf.constant([1], shape=[1], dtype=tf.int64) out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64) row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)

The implementation fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.

Patches

We have patched the issue in GitHub commit 32fdcbff9d06d010d908fcc4bd4b36eb3ce15925.

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"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
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          "type": "ECOSYSTEM"
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    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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            },
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              "fixed": "2.4.2"
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          "type": "ECOSYSTEM"
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
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          "type": "ECOSYSTEM"
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    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
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        {
          "events": [
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              "introduced": "2.2.0"
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            {
              "fixed": "2.2.3"
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        "name": "tensorflow-cpu"
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            },
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        "name": "tensorflow-cpu"
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        "name": "tensorflow-gpu"
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              "introduced": "0"
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
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          "events": [
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              "introduced": "2.2.0"
            },
            {
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          ],
          "type": "ECOSYSTEM"
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    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
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          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29580"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-908"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T17:53:08Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nThe implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty:\n\n```python\nimport tensorflow as tf\n\norig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64)\norig_output = tf.constant([], dtype=tf.int64) \nout_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64)\nrow_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)\ncol_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)\n\ntf.raw_ops.FractionalMaxPoolGrad(\n  orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,\n  row_pooling_sequence=row_pooling_sequence,\n  col_pooling_sequence=col_pooling_sequence, overlapping=False)\n```\n\nThe code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process\n\n```python\nimport tensorflow as tf\n\norig_input = tf.constant([1], shape=[1], dtype=tf.int64)\norig_output = tf.constant([1], shape=[1], dtype=tf.int64)\nout_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64)\nrow_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) \ncol_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)\n\ntf.raw_ops.FractionalMaxPoolGrad(\n  orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,\n  row_pooling_sequence=row_pooling_sequence,\n  col_pooling_sequence=col_pooling_sequence, overlapping=False)\n``` \n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.\n\n### Patches\nWe have patched the issue in GitHub commit [32fdcbff9d06d010d908fcc4bd4b36eb3ce15925](https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925).\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-x8h6-xgqx-jqgp",
  "modified": "2024-11-01T17:13:23Z",
  "published": "2021-05-21T14:26:26Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29580"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-508.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-706.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-217.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": "Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`"
}

cve-2021-29580

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.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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-x8h6-xgqx-jqgp	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925	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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        }
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      "metrics": [
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            "availabilityImpact": "LOW",
            "baseScore": 2.5,
            "baseSeverity": "LOW",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
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        "discovery": "UNKNOWN"
      },
      "title": "Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`",
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2021-29580",
          "STATE": "PUBLIC",
          "TITLE": "Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`"
        },
        "affects": {
          "vendor": {
            "vendor_data": [
              {
                "product": {
                  "product_data": [
                    {
                      "product_name": "tensorflow",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "\u003c 2.1.4"
                          },
                          {
                            "version_value": "\u003e= 2.2.0, \u003c 2.2.3"
                          },
                          {
                            "version_value": "\u003e= 2.3.0, \u003c 2.3.3"
                          },
                          {
                            "version_value": "\u003e= 2.4.0, \u003c 2.4.2"
                          }
                        ]
                      }
                    }
                  ]
                },
                "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. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "HIGH",
            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
            "baseScore": 2.5,
            "baseSeverity": "LOW",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-908: Use of Uninitialized Resource"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-x8h6-xgqx-jqgp",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-29580",
    "datePublished": "2021-05-14T19:15:43",
    "dateReserved": "2021-03-30T00:00:00",
    "dateUpdated": "2024-08-03T22:11:06.079Z",
    "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-2021-217

Vulnerability from pysec

ghsa-x8h6-xgqx-jqgp

Vulnerability from github

Impact

Patches

For more information

Attribution

cve-2021-29580

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature