gsd-2020-15211
Vulnerability from gsd
Modified
2023-12-13 01:21
Details
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.
Aliases
Aliases
CVE-2020-15211


To clipboard 

{
  "GSD": {
    "alias": "CVE-2020-15211",
    "description": "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.",
    "id": "GSD-2020-15211",
    "references": [
      "https://www.suse.com/security/cve/CVE-2020-15211.html"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2020-15211"
      ],
      "details": "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.",
      "id": "GSD-2020-15211",
      "modified": "2023-12-13T01:21:43.584282Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2020-15211",
        "STATE": "PUBLIC",
        "TITLE": "Out of bounds access in tensorflow-lite"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
                  {
                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
                        {
                          "version_value": "\u003c 1.15.4"
                        },
                        {
                          "version_value": "\u003e= 2.0.0, \u003c 2.0.3"
                        },
                        {
                          "version_value": "\u003e= 2.1.0, \u003c 2.1.2"
                        },
                        {
                          "version_value": "\u003e= 2.2.0, \u003c 2.2.1"
                        },
                        {
                          "version_value": "\u003e= 2.3.0, \u003c 2.3.1"
                        }
                      ]
                    }
                  }
                ]
              },
              "vendor_name": "tensorflow"
            }
          ]
        }
      },
      "data_format": "MITRE",
      "data_type": "CVE",
      "data_version": "4.0",
      "description": {
        "description_data": [
          {
            "lang": "eng",
            "value": "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."
          }
        ]
      },
      "impact": {
        "cvss": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 4.8,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
          "version": "3.1"
        }
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "lang": "eng",
                "value": "{\"CWE-125\":\"Out-of-bounds Read\"}"
              }
            ]
          },
          {
            "description": [
              {
                "lang": "eng",
                "value": "{\"CWE-787\":\"Out-of-bounds Write\"}"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45",
            "refsource": "CONFIRM",
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859"
          },
          {
            "name": "openSUSE-SU-2020:1766",
            "refsource": "SUSE",
            "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
          }
        ]
      },
      "source": {
        "advisory": "GHSA-cvpc-8phh-8f45",
        "discovery": "UNKNOWN"
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003c1.15.4||\u003e=2.0.0,\u003c2.0.3||\u003e=2.1.0,\u003c2.1.2||\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions before 1.15.4, all versions starting from 2.0.0 before 2.0.3, all versions starting from 2.1.0 before 2.1.2, all versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:M/Au:N/C:P/I:P/A:N",
          "cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
          "cwe_ids": [
            "CWE-1035",
            "CWE-125",
            "CWE-787",
            "CWE-937"
          ],
          "date": "2020-10-29",
          "description": "In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope.",
          "fixed_versions": [
            "2.1.2",
            "2.2.1",
            "2.3.1"
          ],
          "identifier": "CVE-2020-15211",
          "identifiers": [
            "CVE-2020-15211",
            "GHSA-cvpc-8phh-8f45"
          ],
          "not_impacted": "All versions starting from 1.15.4 before 2.0.0, all versions starting from 2.0.3 before 2.1.0, all versions starting from 2.1.2 before 2.2.0, all versions starting from 2.2.1 before 2.3.0, all versions starting from 2.3.1",
          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.1.2, 2.2.1, 2.3.1 or above.",
          "title": "Out-of-bounds Write",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15211"
          ],
          "uuid": "01eb50d7-88cf-42fa-8e44-2cb0222ac4cb"
        },
        {
          "affected_range": "\u003c1.15.4||\u003e=2.0.0,\u003c2.0.3||\u003e=2.1.0,\u003c2.1.2||\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions before 1.15.4, all versions starting from 2.0.0 before 2.0.3, all versions starting from 2.1.0 before 2.1.2, all versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:M/Au:N/C:P/I:P/A:N",
          "cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
          "cwe_ids": [
            "CWE-1035",
            "CWE-125",
            "CWE-787",
            "CWE-937"
          ],
          "date": "2020-10-29",
          "description": "In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of `input/output` tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope.",
          "fixed_versions": [
            "1.15.4",
            "2.0.3",
            "2.1.2",
            "2.2.1",
            "2.3.1"
          ],
          "identifier": "CVE-2020-15211",
          "identifiers": [
            "CVE-2020-15211",
            "GHSA-cvpc-8phh-8f45"
          ],
          "not_impacted": "All versions starting from 1.15.4 before 2.0.0, all versions starting from 2.0.3 before 2.1.0, all versions starting from 2.1.2 before 2.2.0, all versions starting from 2.2.1 before 2.3.0, all versions starting from 2.3.1",
          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or above.",
          "title": "Out-of-bounds Write",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15211"
          ],
          "uuid": "059fbfa1-83f7-4b4a-9fb9-385cc3a17bcd"
        },
        {
          "affected_range": "\u003c1.15.4||\u003e=2.0.0,\u003c2.0.3||\u003e=2.1.0,\u003c2.1.2||\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions before 1.15.4, all versions starting from 2.0.0 before 2.0.3, all versions starting from 2.1.0 before 2.1.2, all versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:M/Au:N/C:P/I:P/A:N",
          "cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
          "cwe_ids": [
            "CWE-1035",
            "CWE-125",
            "CWE-787",
            "CWE-937"
          ],
          "date": "2021-09-16",
          "description": "In TensorFlow Lite, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope.",
          "fixed_versions": [
            "1.15.4",
            "2.0.3",
            "2.1.2",
            "2.2.1",
            "2.3.1"
          ],
          "identifier": "CVE-2020-15211",
          "identifiers": [
            "CVE-2020-15211",
            "GHSA-cvpc-8phh-8f45"
          ],
          "not_impacted": "All versions starting from 1.15.4 before 2.0.0, all versions starting from 2.0.3 before 2.1.0, all versions starting from 2.1.2 before 2.2.0, all versions starting from 2.2.1 before 2.3.0, all versions starting from 2.3.1",
          "package_slug": "pypi/tensorflow",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or above.",
          "title": "Out-of-bounds Write",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15211"
          ],
          "uuid": "27ef4580-ba7a-4a52-89d7-f8caacb038db"
        }
      ]
    },
    "nvd.nist.gov": {
      "configurations": {
        "CVE_data_version": "4.0",
        "nodes": [
          {
            "children": [],
            "cpe_match": [
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "1.15.4",
                "vulnerable": true
              },
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "2.0.3",
                "versionStartIncluding": "2.0.0",
                "vulnerable": true
              },
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "2.1.2",
                "versionStartIncluding": "2.1.0",
                "vulnerable": true
              },
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "2.2.1",
                "versionStartIncluding": "2.2.0",
                "vulnerable": true
              },
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "2.3.1",
                "versionStartIncluding": "2.3.0",
                "vulnerable": true
              }
            ],
            "operator": "OR"
          },
          {
            "children": [],
            "cpe_match": [
              {
                "cpe23Uri": "cpe:2.3:o:opensuse:leap:15.2:*:*:*:*:*:*:*",
                "cpe_name": [],
                "vulnerable": true
              }
            ],
            "operator": "OR"
          }
        ]
      },
      "cve": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2020-15211"
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
          "description_data": [
            {
              "lang": "en",
              "value": "In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don\u0027t expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code."
            }
          ]
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "en",
                  "value": "CWE-125"
                },
                {
                  "lang": "en",
                  "value": "CWE-787"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45",
              "refsource": "CONFIRM",
              "tags": [
                "Exploit",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1",
              "refsource": "MISC",
              "tags": [
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35",
              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35"
            },
            {
              "name": "openSUSE-SU-2020:1766",
              "refsource": "SUSE",
              "tags": [
                "Mailing List",
                "Third Party Advisory"
              ],
              "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
            }
          ]
        }
      },
      "impact": {
        "baseMetricV2": {
          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "MEDIUM",
            "accessVector": "NETWORK",
            "authentication": "NONE",
            "availabilityImpact": "NONE",
            "baseScore": 5.8,
            "confidentialityImpact": "PARTIAL",
            "integrityImpact": "PARTIAL",
            "vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:N",
            "version": "2.0"
          },
          "exploitabilityScore": 8.6,
          "impactScore": 4.9,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "MEDIUM",
          "userInteractionRequired": false
        },
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "NONE",
            "baseScore": 4.8,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "LOW",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
            "version": "3.1"
          },
          "exploitabilityScore": 2.2,
          "impactScore": 2.5
        }
      },
      "lastModifiedDate": "2021-09-16T15:45Z",
      "publishedDate": "2020-09-25T19:15Z"
    }
  }
}


Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




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.