pysec - pysec-2021-413

pysec-2021-413

Vulnerability from pysec

Published

2021-11-05 23:15

Modified

2021-11-13 06:52

Details

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the Cudnn* operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the input, input_h and input_c parameters are not validated, but code assumes they have certain values. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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": "af5fcebb37c8b5d71c237f4e59c6477015c78ce6"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            },
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            },
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            },
            {
              "introduced": "2.7.0rc0"
            },
            {
              "fixed": "2.7.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.12.0",
        "0.12.1",
        "1.0.0",
        "1.0.1",
        "1.1.0",
        "1.10.0",
        "1.10.1",
        "1.11.0",
        "1.12.0",
        "1.12.2",
        "1.12.3",
        "1.13.1",
        "1.13.2",
        "1.14.0",
        "1.15.0",
        "1.15.2",
        "1.15.3",
        "1.15.4",
        "1.15.5",
        "1.2.0",
        "1.2.1",
        "1.3.0",
        "1.4.0",
        "1.4.1",
        "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",
        "2.1.4",
        "2.2.0",
        "2.2.0rc0",
        "2.2.0rc1",
        "2.2.0rc2",
        "2.2.0rc3",
        "2.2.0rc4",
        "2.2.1",
        "2.2.2",
        "2.2.3",
        "2.3.0",
        "2.3.0rc0",
        "2.3.0rc1",
        "2.3.0rc2",
        "2.3.1",
        "2.3.2",
        "2.3.3",
        "2.3.4",
        "2.4.0",
        "2.4.0rc0",
        "2.4.0rc1",
        "2.4.0rc2",
        "2.4.0rc3",
        "2.4.0rc4",
        "2.4.1",
        "2.4.2",
        "2.4.3",
        "2.5.0",
        "2.5.1",
        "2.6.0",
        "2.7.0rc0",
        "2.7.0rc1"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-41221",
    "GHSA-cqv6-3phm-hcwx"
  ],
  "details": "TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.",
  "id": "PYSEC-2021-413",
  "modified": "2021-11-13T06:52:45.325083Z",
  "published": "2021-11-05T23:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6"
    }
  ]
}

cve-2021-41221

Vulnerability from cvelistv5

Published

2021-11-05 22:15

Modified

2024-08-04 03:08

Severity ?

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

Summary

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6	x_refsource_MISC

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: >= 2.6.0, < 2.6.1
Version: >= 2.5.0, < 2.5.2
Version: < 2.4.4

Show details on NVD website

JSON

To clipboard

{
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            "url": "https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6"
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        }
      ],
      "metrics": [
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ghsa-cqv6-3phm-hcwx

Vulnerability from github

Published

2021-11-10 18:50

Modified

2024-11-07 22:10

Severity ?

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

Summary

Access to invalid memory during shape inference in `Cudnn*` ops

Details

Impact

The shape inference code for the Cudnn* operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow:

```python import tensorflow as tf

@tf.function def func(): return tf.raw_ops.CudnnRNNV3( input=[0.1, 0.1], input_h=[0.5], input_c=[0.1, 0.1, 0.1], params=[0.5, 0.5], sequence_lengths=[-1, 0, 1])

func() ```

This occurs because the ranks of the input, input_h and input_c parameters are not validated, but code assumes they have certain values:

cc auto input_shape = c->input(0); auto input_h_shape = c->input(1); auto seq_length = c->Dim(input_shape, 0); auto batch_size = c->Dim(input_shape, 1); // assumes rank >= 2 auto num_units = c->Dim(input_h_shape, 2); // assumes rank >= 3

Patches

We have patched the issue in GitHub commit af5fcebb37c8b5d71c237f4e59c6477015c78ce6.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-41221"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-120",
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-11-08T22:02:26Z",
    "nvd_published_at": "2021-11-05T23:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact\nThe [shape inference code](https://github.com/tensorflow/tensorflow/blob/9ff27787893f76d6971dcd1552eb5270d254f31b/tensorflow/core/ops/cudnn_rnn_ops.cc) for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow:\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef func():\n  return tf.raw_ops.CudnnRNNV3(\n    input=[0.1, 0.1],\n    input_h=[0.5],\n    input_c=[0.1, 0.1, 0.1], \n    params=[0.5, 0.5],\n    sequence_lengths=[-1, 0, 1])\n  \nfunc() \n```\n  \nThis occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values:\n\n```cc\nauto input_shape = c-\u003einput(0);\nauto input_h_shape = c-\u003einput(1);\nauto seq_length = c-\u003eDim(input_shape, 0);\nauto batch_size = c-\u003eDim(input_shape, 1);  // assumes rank \u003e= 2\nauto num_units = c-\u003eDim(input_h_shape, 2); // assumes rank \u003e= 3\n``` \n\n### Patches\nWe have patched the issue in GitHub commit [af5fcebb37c8b5d71c237f4e59c6477015c78ce6](https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6).\n\nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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-cqv6-3phm-hcwx",
  "modified": "2024-11-07T22:10:37Z",
  "published": "2021-11-10T18:50:17Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-41221"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-630.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-828.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-413.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Access to invalid memory during shape inference in `Cudnn*` ops"
}

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-2021-413

Vulnerability from pysec

cve-2021-41221

Vulnerability from cvelistv5

ghsa-cqv6-3phm-hcwx

Vulnerability from github

Impact

Patches

For more information

Attribution

Tags

Sightings

Nomenclature