gsd-2021-41213
Vulnerability from gsd
Modified
2023-12-13 01:23
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
Aliases
CVE-2021-41213


To clipboard 

{
  "GSD": {
    "alias": "CVE-2021-41213",
    "description": "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": "GSD-2021-41213",
    "references": [
      "https://www.suse.com/security/cve/CVE-2021-41213.html",
      "https://security.archlinux.org/CVE-2021-41213"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2021-41213"
      ],
      "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": "GSD-2021-41213",
      "modified": "2023-12-13T01:23:27.177798Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2021-41213",
        "STATE": "PUBLIC",
        "TITLE": "Deadlock in mutually recursive `tf.function` objects"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
                  {
                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
                        {
                          "version_value": "\u003e= 2.6.0, \u003c 2.6.1"
                        },
                        {
                          "version_value": "\u003e= 2.5.0, \u003c 2.5.2"
                        },
                        {
                          "version_value": "\u003c 2.4.4"
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      "description": {
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          {
            "lang": "eng",
            "value": "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."
          }
        ]
      },
      "impact": {
        "cvss": {
          "attackComplexity": "LOW",
          "attackVector": "LOCAL",
          "availabilityImpact": "HIGH",
          "baseScore": 5.5,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
          "version": "3.1"
        }
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "lang": "eng",
                "value": "CWE-667: Improper Locking"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf",
            "refsource": "CONFIRM",
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7"
          }
        ]
      },
      "source": {
        "advisory": "GHSA-h67m-xg8f-fxcf",
        "discovery": "UNKNOWN"
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003e=2.6.0,\u003c2.6.1||\u003e=2.5.0,\u003c2.5.2||\u003c2.4.4",
          "affected_versions": "All versions starting from 2.6.0 before 2.6.1, all versions starting from 2.5.0 before 2.5.2, all versions before 2.4.4",
          "cvss_v2": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-667",
            "CWE-937"
          ],
          "date": "2021-11-10",
          "description": "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.",
          "fixed_versions": [
            "2.6.1",
            "2.4.4",
            "2.4.4"
          ],
          "identifier": "CVE-2021-41213",
          "identifiers": [
            "GHSA-h67m-xg8f-fxcf",
            "CVE-2021-41213"
          ],
          "not_impacted": "All versions before 2.6.0, all versions starting from 2.6.1, all versions before 2.5.0, all versions starting from 2.4.4 before 2.5.2",
          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2021-11-10",
          "solution": "Upgrade to versions 2.6.1, 2.4.4, 2.4.4 or above.",
          "title": "Improper Locking",
          "urls": [
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf",
            "https://nvd.nist.gov/vuln/detail/CVE-2021-41213",
            "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7",
            "https://github.com/advisories/GHSA-h67m-xg8f-fxcf"
          ],
          "uuid": "3e98a983-f90d-4e2f-8d40-2813321a6023"
        },
        {
          "affected_range": "\u003e=2.6.0,\u003c2.6.1||\u003e=2.5.0,\u003c2.5.2||\u003c2.4.4",
          "affected_versions": "All versions starting from 2.6.0 before 2.6.1, all versions starting from 2.5.0 before 2.5.2, all versions before 2.4.4",
          "cvss_v2": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-667",
            "CWE-937"
          ],
          "date": "2021-11-10",
          "description": "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.",
          "fixed_versions": [
            "2.6.1",
            "2.4.4",
            "2.4.4"
          ],
          "identifier": "CVE-2021-41213",
          "identifiers": [
            "GHSA-h67m-xg8f-fxcf",
            "CVE-2021-41213"
          ],
          "not_impacted": "All versions before 2.6.0, all versions starting from 2.6.1, all versions before 2.5.0, all versions starting from 2.4.4 before 2.5.2",
          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2021-11-10",
          "solution": "Upgrade to versions 2.6.1, 2.4.4, 2.4.4 or above.",
          "title": "Improper Locking",
          "urls": [
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf",
            "https://nvd.nist.gov/vuln/detail/CVE-2021-41213",
            "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7",
            "https://github.com/advisories/GHSA-h67m-xg8f-fxcf"
          ],
          "uuid": "e865b916-25cd-432e-967a-da4bd3140f55"
        },
        {
          "affected_range": "\u003e=2.4.0,\u003c2.4.4||\u003e=2.5.0,\u003c2.5.2||\u003e=2.6.0,\u003c2.6.1||==2.7.0",
          "affected_versions": "All versions starting from 2.4.0 before 2.4.4, all versions starting from 2.5.0 before 2.5.2, all versions starting from 2.6.0 before 2.6.1, version 2.7.0",
          "cvss_v2": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-662",
            "CWE-937"
          ],
          "date": "2022-10-20",
          "description": "TensorFlow is an open source platform for machine learning.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.",
          "fixed_versions": [
            "2.4.4",
            "2.5.2",
            "2.6.1",
            "2.7.1"
          ],
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          "identifiers": [
            "CVE-2021-41213",
            "GHSA-h67m-xg8f-fxcf"
          ],
          "not_impacted": "All versions before 2.4.0, all versions starting from 2.4.4 before 2.5.0, all versions starting from 2.5.2 before 2.6.0, all versions starting from 2.6.1 before 2.7.0, all versions after 2.7.0",
          "package_slug": "pypi/tensorflow",
          "pubdate": "2021-11-05",
          "solution": "Upgrade to versions 2.4.4, 2.5.2, 2.6.1, 2.7.1 or above.",
          "title": "Improper Locking",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2021-41213",
            "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7",
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf"
          ],
          "uuid": "dc7e5755-91a1-4040-b0ab-3edae47df67f"
        }
      ]
    },
    "nvd.nist.gov": {
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        "nodes": [
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            "children": [],
            "cpe_match": [
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
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        "description": {
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              "value": "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."
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        "references": {
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            {
              "name": "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7"
            },
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              ],
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf"
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      },
      "impact": {
        "baseMetricV2": {
          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "MEDIUM",
            "accessVector": "NETWORK",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
            "baseScore": 4.3,
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "vectorString": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
            "version": "2.0"
          },
          "exploitabilityScore": 8.6,
          "impactScore": 2.9,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "MEDIUM",
          "userInteractionRequired": true
        },
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 5.5,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
            "version": "3.1"
          },
          "exploitabilityScore": 1.8,
          "impactScore": 3.6
        }
      },
      "lastModifiedDate": "2022-10-20T21:25Z",
      "publishedDate": "2021-11-05T23:15Z"
    }
  }
}


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