gsd-2021-37690
Vulnerability from gsd
Modified
2023-12-13 01:23
Details
TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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
Aliases
CVE-2021-37690


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{
  "GSD": {
    "alias": "CVE-2021-37690",
    "description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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": "GSD-2021-37690",
    "references": [
      "https://www.suse.com/security/cve/CVE-2021-37690.html",
      "https://security.archlinux.org/CVE-2021-37690"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2021-37690"
      ],
      "details": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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": "GSD-2021-37690",
      "modified": "2023-12-13T01:23:10.155900Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2021-37690",
        "STATE": "PUBLIC",
        "TITLE": "Use after free and segfault in shape inference functions in TensorFlow"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "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"
                        }
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                    }
                  }
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              "vendor_name": "tensorflow"
            }
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      "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 when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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": "HIGH",
          "baseScore": 6.6,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
          "version": "3.1"
        }
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "lang": "eng",
                "value": "CWE-416: Use After Free"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg",
            "refsource": "CONFIRM",
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1"
          }
        ]
      },
      "source": {
        "advisory": "GHSA-3hxh-8cp2-g4hg",
        "discovery": "UNKNOWN"
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003c2.3.4||\u003e=2.4.0,\u003c2.4.3||==2.5.0",
          "affected_versions": "All versions before 2.3.4, all versions starting from 2.4.0 before 2.4.3, version 2.5.0",
          "cvss_v2": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-416",
            "CWE-937"
          ],
          "date": "2021-08-25",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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.",
          "fixed_versions": [
            "2.3.4",
            "2.4.3",
            "2.5.1"
          ],
          "identifier": "CVE-2021-37690",
          "identifiers": [
            "GHSA-3hxh-8cp2-g4hg",
            "CVE-2021-37690"
          ],
          "not_impacted": "All versions starting from 2.3.4 before 2.4.0, all versions starting from 2.4.3 before 2.5.0, all versions after 2.5.0",
          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2021-08-25",
          "solution": "Upgrade to versions 2.3.4, 2.4.3, 2.5.1 or above.",
          "title": "Use After Free",
          "urls": [
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg",
            "https://nvd.nist.gov/vuln/detail/CVE-2021-37690",
            "https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1",
            "https://github.com/advisories/GHSA-3hxh-8cp2-g4hg"
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          "uuid": "8dc9de7a-2563-4422-a01f-e1c6d16059c8"
        },
        {
          "affected_range": "\u003c2.3.4||\u003e=2.4.0,\u003c2.4.3||==2.5.0",
          "affected_versions": "All versions before 2.3.4, all versions starting from 2.4.0 before 2.4.3, version 2.5.0",
          "cvss_v2": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-416",
            "CWE-937"
          ],
          "date": "2021-08-25",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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.",
          "fixed_versions": [
            "2.3.4",
            "2.4.3",
            "2.5.1"
          ],
          "identifier": "CVE-2021-37690",
          "identifiers": [
            "GHSA-3hxh-8cp2-g4hg",
            "CVE-2021-37690"
          ],
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          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2021-08-25",
          "solution": "Upgrade to versions 2.3.4, 2.4.3, 2.5.1 or above.",
          "title": "Use After Free",
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            "https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1",
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          "uuid": "3b916fb4-f9d4-422a-8ea8-944bc39215f7"
        },
        {
          "affected_range": "\u003e=2.3.0,\u003c2.3.4||\u003e=2.4.0,\u003c2.4.3||\u003e=2.5.0,\u003c=2.6.0",
          "affected_versions": "All versions starting from 2.3.0 before 2.3.4, all versions starting from 2.4.0 before 2.4.3, all versions starting from 2.5.0 up to 2.6.0",
          "cvss_v2": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
          "cwe_ids": [
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            "CWE-937"
          ],
          "date": "2021-08-19",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct.",
          "fixed_versions": [
            "2.3.4",
            "2.4.3"
          ],
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            "GHSA-3hxh-8cp2-g4hg"
          ],
          "not_impacted": "All versions before 2.3.0, all versions starting from 2.3.4 before 2.4.0, all versions starting from 2.4.3 before 2.5.0, all versions after 2.6.0",
          "package_slug": "pypi/tensorflow",
          "pubdate": "2021-08-13",
          "solution": "Upgrade to versions 2.3.4, 2.4.3 or above.",
          "title": "Use After Free",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2021-37690"
          ],
          "uuid": "c4465baa-bfb5-4c58-bb5d-e51d2daa47f8"
        }
      ]
    },
    "nvd.nist.gov": {
      "configurations": {
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        "nodes": [
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            "children": [],
            "cpe_match": [
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                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
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                "versionEndExcluding": "2.3.4",
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          "ID": "CVE-2021-37690"
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        "description": {
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              "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. 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."
            }
          ]
        },
        "problemtype": {
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        "references": {
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                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
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              "url": "https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1"
            }
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      },
      "impact": {
        "baseMetricV2": {
          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "LOW",
            "accessVector": "LOCAL",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
            "baseScore": 4.6,
            "confidentialityImpact": "PARTIAL",
            "integrityImpact": "PARTIAL",
            "vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
            "version": "2.0"
          },
          "exploitabilityScore": 3.9,
          "impactScore": 6.4,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "MEDIUM",
          "userInteractionRequired": false
        },
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 6.6,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "LOW",
            "integrityImpact": "LOW",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
            "version": "3.1"
          },
          "exploitabilityScore": 1.8,
          "impactScore": 4.7
        }
      },
      "lastModifiedDate": "2021-08-19T14:53Z",
      "publishedDate": "2021-08-13T00:15Z"
    }
  }
}


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