CVE-2022-41900 (GCVE-0-2022-41900)

Vulnerability from cvelistv5 – Published: 2022-11-18 00:00 – Updated: 2025-04-22 16:03
VLAI
Title
FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow
Summary
TensorFlow is an open source platform for machine learning. The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution. We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cherry pick this commit on TensorFlow 2.10.1.
Severity
7.1 (High)
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H
SSVC
Exploitation: poc Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-22 15:40 UTC
CWE
Assigner
GitHub_M CNA under the mitre root
CNA scorecard C 72/100 over 7958 records in the last 180 days details
References
Impacted products
Vendor Product Version CPE status
tensorflow tensorflow Affected: >= 2.10.0, < 2.10.1
Affected: >= 2.9.0, < 2.9.3
Affected: < 2.8.4
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T12:56:38.345Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472"
          },
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/216525144ee7c910296f5b05d214ca1327c9ce48"
          }
        ],
        "title": "CVE Program Container"
      },
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2022-41900",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-22T15:40:35.291563Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-04-22T16:03:33.028Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003e= 2.10.0, \u003c 2.10.1"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.9.0, \u003c 2.9.3"
            },
            {
              "status": "affected",
              "version": "\u003c 2.8.4"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "TensorFlow is an open source platform for machine learning. The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution. We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cherry pick this commit on TensorFlow 2.10.1."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-787",
              "description": "CWE-787: Out-of-bounds Write",
              "lang": "en",
              "type": "CWE"
            }
          ]
        },
        {
          "descriptions": [
            {
              "cweId": "CWE-125",
              "description": "CWE-125: Out-of-bounds Read",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2022-11-18T00:00:00.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472"
        },
        {
          "url": "https://github.com/tensorflow/tensorflow/commit/216525144ee7c910296f5b05d214ca1327c9ce48"
        }
      ],
      "source": {
        "advisory": "GHSA-xvwp-h6jv-7472",
        "discovery": "UNKNOWN"
      },
      "title": "FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2022-41900",
    "datePublished": "2022-11-18T00:00:00.000Z",
    "dateReserved": "2022-09-30T00:00:00.000Z",
    "dateUpdated": "2025-04-22T16:03:33.028Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1",
  "vulnerability-lookup:meta": {
    "epss": {
      "cve": "CVE-2022-41900",
      "date": "2026-11-08",
      "epss": "0.00658",
      "percentile": "0.51745"
    },
    "nvd": {
      "cve": {
        "affected": [
          {
            "affectedData": [
              {
                "product": "tensorflow",
                "vendor": "tensorflow",
                "versions": [
                  {
                    "status": "affected",
                    "version": "\u003e= 2.10.0, \u003c 2.10.1"
                  },
                  {
                    "status": "affected",
                    "version": "\u003e= 2.9.0, \u003c 2.9.3"
                  },
                  {
                    "status": "affected",
                    "version": "\u003c 2.8.4"
                  }
                ]
              }
            ],
            "source": "security-advisories@github.com"
          }
        ],
        "configurations": [
          {
            "nodes": [
              {
                "cpeMatch": [
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
                    "matchCriteriaId": "A694EEE1-BFB9-4E6C-B275-02DC2731961C",
                    "versionEndExcluding": "2.8.4",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
                    "matchCriteriaId": "9057B403-719C-4F10-BAB6-67F84786A89E",
                    "versionEndExcluding": "2.9.3",
                    "versionStartIncluding": "2.9.0",
                    "vulnerable": true
                  },
                  {
                    "criteria": "cpe:2.3:a:google:tensorflow:2.10.0:*:*:*:*:*:*:*",
                    "matchCriteriaId": "6AE6CFC4-0232-4E1C-960D-268C87788735",
                    "vulnerable": true
                  }
                ],
                "negate": false,
                "operator": "OR"
              }
            ]
          }
        ],
        "cveTags": [],
        "descriptions": [
          {
            "lang": "en",
            "value": "TensorFlow is an open source platform for machine learning. The security vulnerability results in FractionalMax(AVG)Pool with illegal pooling_ratio. Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or remote code execution. We have patched the issue in GitHub commit 216525144ee7c910296f5b05d214ca1327c9ce48. The fix will be included in TensorFlow 2.11.0. We will also cherry pick this commit on TensorFlow 2.10.1."
          },
          {
            "lang": "es",
            "value": "TensorFlow es una plataforma de c\u00f3digo abierto para aprendizaje autom\u00e1tico. La vulnerabilidad de seguridad da como resultado un grupo FractionalMax(AVG) con una relaci\u00f3n de agrupaci\u00f3n ilegal. Los atacantes que utilizan Tensorflow pueden aprovechar la vulnerabilidad. Pueden acceder a la memoria del mont\u00f3n que no est\u00e1 bajo el control del usuario, lo que provoca un bloqueo o la ejecuci\u00f3n remota de c\u00f3digo. Hemos solucionado el problema en el commit de GitHub 216525144ee7c910296f5b05d214ca1327c9ce48. La soluci\u00f3n se incluir\u00e1 en TensorFlow 2.11.0. Tambi\u00e9n seleccionaremos este commit en TensorFlow 2.10.1."
          }
        ],
        "id": "CVE-2022-41900",
        "lastModified": "2026-06-17T05:04:01.537",
        "metrics": {
          "cvssMetricV31": [
            {
              "cvssData": {
                "attackComplexity": "HIGH",
                "attackVector": "NETWORK",
                "availabilityImpact": "HIGH",
                "baseScore": 7.1,
                "baseSeverity": "HIGH",
                "confidentialityImpact": "HIGH",
                "integrityImpact": "HIGH",
                "privilegesRequired": "LOW",
                "scope": "UNCHANGED",
                "userInteraction": "REQUIRED",
                "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H",
                "version": "3.1"
              },
              "exploitabilityScore": 1.2,
              "impactScore": 5.9,
              "source": "security-advisories@github.com",
              "type": "Secondary"
            },
            {
              "cvssData": {
                "attackComplexity": "LOW",
                "attackVector": "NETWORK",
                "availabilityImpact": "HIGH",
                "baseScore": 9.8,
                "baseSeverity": "CRITICAL",
                "confidentialityImpact": "HIGH",
                "integrityImpact": "HIGH",
                "privilegesRequired": "NONE",
                "scope": "UNCHANGED",
                "userInteraction": "NONE",
                "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
                "version": "3.1"
              },
              "exploitabilityScore": 3.9,
              "impactScore": 5.9,
              "source": "nvd@nist.gov",
              "type": "Primary"
            }
          ],
          "ssvcV203": [
            {
              "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
              "ssvcData": {
                "id": "CVE-2022-41900",
                "options": [
                  {
                    "exploitation": "poc"
                  },
                  {
                    "automatable": "no"
                  },
                  {
                    "technicalImpact": "total"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-22T15:40:35.291563Z",
                "version": "2.0.3"
              }
            }
          ]
        },
        "published": "2022-11-18T22:15:20.273",
        "references": [
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/216525144ee7c910296f5b05d214ca1327c9ce48"
          },
          {
            "source": "security-advisories@github.com",
            "tags": [
              "Exploit",
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/216525144ee7c910296f5b05d214ca1327c9ce48"
          },
          {
            "source": "af854a3a-2127-422b-91ae-364da2661108",
            "tags": [
              "Exploit",
              "Patch",
              "Third Party Advisory"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvwp-h6jv-7472"
          }
        ],
        "sourceIdentifier": "security-advisories@github.com",
        "vulnStatus": "Modified",
        "weaknesses": [
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-125"
              },
              {
                "lang": "en",
                "value": "CWE-787"
              }
            ],
            "source": "security-advisories@github.com",
            "type": "Secondary"
          }
        ]
      }
    },
    "redhat_vex": {
      "current_release_date": "2025-05-29T03:10:04+00:00",
      "cve": "CVE-2022-41900",
      "id": "CVE-2022-41900",
      "initial_release_date": "2022-01-01T00:00:00+00:00",
      "product_status:known_not_affected": "1",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "FractionalMaxPool and FractionalAVGPool heap out-of-bounds acess in Tensorflow",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-41900.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.