CVE-2021-37677 (GCVE-0-2021-37677)

Vulnerability from cvelistv5 – Published: 2021-08-12 22:35 – Updated: 2024-08-04 01:23
VLAI
Title
Missing validation in shape inference for `Dequantize` in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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.
Severity
5.5 (Medium)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CWE
  • CWE-20 - Improper Input Validation
Assigner
GitHub_M CNA under the mitre root
CNA scorecard C 72/100 over 7763 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
tensorflow tensorflow Affected: >= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3
Affected: < 2.3.4
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-04T01:23:01.450Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_refsource_CONFIRM",
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26"
          },
          {
            "tags": [
              "x_refsource_MISC",
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003e= 2.5.0, \u003c 2.5.1"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.4.0, \u003c 2.4.3"
            },
            {
              "status": "affected",
              "version": "\u003c 2.3.4"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "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"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-20",
              "description": "CWE-20: Improper Input Validation",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2021-08-12T22:35:10.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26"
        },
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764"
        }
      ],
      "source": {
        "advisory": "GHSA-qfpc-5pjr-mh26",
        "discovery": "UNKNOWN"
      },
      "title": "Missing validation in shape inference for `Dequantize` in TensorFlow",
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2021-37677",
          "STATE": "PUBLIC",
          "TITLE": "Missing validation in shape inference for `Dequantize` 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"
                          }
                        ]
                      }
                    }
                  ]
                },
                "vendor_name": "tensorflow"
              }
            ]
          }
        },
        "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 the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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": 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-20: Improper Input Validation"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-qfpc-5pjr-mh26",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-37677",
    "datePublished": "2021-08-12T22:35:10.000Z",
    "dateReserved": "2021-07-29T00:00:00.000Z",
    "dateUpdated": "2024-08-04T01:23:01.450Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1",
  "vulnerability-lookup:meta": {
    "epss": {
      "cve": "CVE-2021-37677",
      "date": "2026-11-11",
      "epss": "0.00148",
      "percentile": "0.04033"
    },
    "nvd": {
      "cve": {
        "configurations": [
          {
            "nodes": [
              {
                "cpeMatch": [
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
                    "matchCriteriaId": "0F83C081-51CC-415F-A8C0-0A44C75E2CD6",
                    "versionEndExcluding": "2.3.4",
                    "versionStartIncluding": "2.3.0",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
                    "matchCriteriaId": "BD3F2BF8-EBA9-42BF-8F9B-D918B880B15A",
                    "versionEndExcluding": "2.4.3",
                    "versionStartIncluding": "2.4.0",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:2.5.0:*:*:*:*:*:*:*",
                    "matchCriteriaId": "D03E99A7-4E3D-427D-A156-C0713E9FB02A",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:2.6.0:rc0:*:*:*:*:*:*",
                    "matchCriteriaId": "70FA6E48-6C57-40CA-809F-4E3D07CBF348",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:2.6.0:rc1:*:*:*:*:*:*",
                    "matchCriteriaId": "42187561-E491-434D-828C-F36701446634",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:2.6.0:rc2:*:*:*:*:*:*",
                    "matchCriteriaId": "C66B61C8-450A-4C5E-9174-F970D6DEE778",
                    "vulnerable": true
                  }
                ],
                "negate": false,
                "operator": "OR"
              }
            ]
          }
        ],
        "cveTags": [],
        "descriptions": [
          {
            "lang": "en",
            "value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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."
          },
          {
            "lang": "es",
            "value": "TensorFlow es una plataforma de c\u00f3digo abierto de extremo a extremo para el aprendizaje autom\u00e1tico.\u0026#xa0;En las versiones afectadas, el c\u00f3digo de inferencia de forma para \"tf.raw_ops.Dequantize\" presenta una vulnerabilidad que podr\u00eda desencadenar una denegaci\u00f3n de servicio por medio de un error de seguridad si un atacante proporciona argumentos no v\u00e1lidos.\u0026#xa0;La inferencia de forma [implementaci\u00f3n] (https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) usa \"axis\" para seleccionar entre dos valores diferentes para \"minmax_rank \"que luego se usa para recuperar las dimensiones del tensor.\u0026#xa0;Sin embargo, el c\u00f3digo asume que el \"axis\" puede ser \"-1\" o un valor mayor que \"-1\", sin comprobaci\u00f3n para los otros valores.\u0026#xa0;Hemos solucionado el problema en GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764.\u0026#xa0;La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.6.0.\u0026#xa0;Tambi\u00e9n seleccionaremos este commit en TensorFlow versi\u00f3n 2.5."
          }
        ],
        "id": "CVE-2021-37677",
        "lastModified": "2024-11-21T06:15:40.763",
        "metrics": {
          "cvssMetricV2": [
            {
              "acInsufInfo": false,
              "baseSeverity": "LOW",
              "cvssData": {
                "accessComplexity": "LOW",
                "accessVector": "LOCAL",
                "authentication": "NONE",
                "availabilityImpact": "PARTIAL",
                "baseScore": 2.1,
                "confidentialityImpact": "NONE",
                "integrityImpact": "NONE",
                "vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
                "version": "2.0"
              },
              "exploitabilityScore": 3.9,
              "impactScore": 2.9,
              "obtainAllPrivilege": false,
              "obtainOtherPrivilege": false,
              "obtainUserPrivilege": false,
              "source": "nvd@nist.gov",
              "type": "Primary",
              "userInteractionRequired": false
            }
          ],
          "cvssMetricV31": [
            {
              "cvssData": {
                "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"
              },
              "exploitabilityScore": 1.8,
              "impactScore": 3.6,
              "source": "security-advisories@github.com",
              "type": "Secondary"
            },
            {
              "cvssData": {
                "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"
              },
              "exploitabilityScore": 1.8,
              "impactScore": 3.6,
              "source": "nvd@nist.gov",
              "type": "Primary"
            }
          ]
        },
        "published": "2021-08-12T23:15:08.090",
        "references": [
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Patch"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764"
          },
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Vendor Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Patch"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Vendor Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26"
          }
        ],
        "sourceIdentifier": "security-advisories@github.com",
        "vulnStatus": "Modified",
        "weaknesses": [
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-20"
              }
            ],
            "source": "security-advisories@github.com",
            "type": "Secondary"
          },
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-1284"
              }
            ],
            "source": "nvd@nist.gov",
            "type": "Primary"
          }
        ]
      }
    },
    "redhat_vex": {
      "current_release_date": "2026-03-27T13:44:53+00:00",
      "cve": "CVE-2021-37677",
      "id": "CVE-2021-37677",
      "initial_release_date": "2021-01-01T00:00:00+00:00",
      "product_status:known_not_affected": "1",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "Missing validation in shape inference for `Dequantize` in TensorFlow",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-37677.json",
      "version": "3"
    }
  }
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
How durations are calculated

Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.

Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.

Loading timeline…




    Related by attack behaviour

    Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.