pysec - pysec-2021-820

pysec-2021-820

Vulnerability from pysec

Published

2021-11-05 23:15

Modified

2021-12-09 06:35

Details

TensorFlow is an open source platform for machine learning. In affected versions the code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive. This occurs due to using a non-reentrant Lock Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario. 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-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
      },
      "ranges": [
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          "events": [
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            },
            {
              "fixed": "afac8158d43691661ad083f6dd9e56f327c1dcb7"
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "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",
        "2.2.1",
        "2.2.2",
        "2.2.3",
        "2.3.0",
        "2.3.1",
        "2.3.2",
        "2.3.3",
        "2.3.4",
        "2.4.0",
        "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-2021-41213",
    "GHSA-h67m-xg8f-fxcf"
  ],
  "details": "TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. 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-2021-820",
  "modified": "2021-12-09T06:35:43.109542Z",
  "published": "2021-11-05T23:15:00Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf"
    }
  ]
}

ghsa-h67m-xg8f-fxcf

Vulnerability from github

Published

2021-11-10 18:59

Modified

2024-11-07 22:17

Severity ?

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

Summary

Deadlock in mutually recursive `tf.function` objects

Details

Impact

The code behind tf.function API can be made to deadlock when two tf.function decorated Python functions are mutually recursive:

```python import tensorflow as tf

@tf.function()
def fun1(num): if num == 1: return print(num) fun2(num-1)

@tf.function() def fun2(num): if num == 0: return print(num) fun1(num-1)

fun1(9) ```

This occurs due to using a non-reentrant Lock Python object.

Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive tf.function, although this is not a frequent scenario.

Patches

We have patched the issue in GitHub commit afac8158d43691661ad083f6dd9e56f327c1dcb7.

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"
        }
      ]
    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-41213"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-662",
      "CWE-667"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-11-08T22:25:35Z",
    "nvd_published_at": "2021-11-05T23:15:00Z",
    "severity": "MODERATE"
  },
  "details": "### Impact\nThe [code behind `tf.function` API](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/python/eager/def_function.py#L542) can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive:\n\n```python \nimport tensorflow as tf\n\n@tf.function()               \ndef fun1(num):\n    if num == 1:\n        return\n    print(num)\n    fun2(num-1)\n\n@tf.function()\ndef fun2(num):\n    if num == 0:\n        return\n    print(num)\n    fun1(num-1)\n\nfun1(9)\n```\n\nThis occurs due to using a non-reentrant `Lock` Python object.\n\nLoading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario.\n\n### Patches\nWe have patched the issue in GitHub commit [afac8158d43691661ad083f6dd9e56f327c1dcb7](https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7).\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-h67m-xg8f-fxcf",
  "modified": "2024-11-07T22:17:51Z",
  "published": "2021-11-10T18:59:32Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-41213"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-622.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-820.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-405.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:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Deadlock in mutually recursive `tf.function` objects"
}

cve-2021-41213

Vulnerability from cvelistv5

Published

2021-11-05 22:10

Modified

2024-08-04 03:08

Severity ?

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

Summary

TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. 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-h67m-xg8f-fxcf	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7	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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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-2021-820

Vulnerability from pysec

ghsa-h67m-xg8f-fxcf

Vulnerability from github

Impact

Patches

For more information

Attribution

cve-2021-41213

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature