GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

CVE-2022-35996 (GCVE-0-2022-35996)

Vulnerability from cvelistv5 – Published: 2022-09-16 22:55 – Updated: 2025-04-23 16:59
VLAI
Title
Floating point exception in `Conv2D` in TensorFlow
Summary
TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Severity
5.9 (Medium)
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-23 13:57 UTC
CWE
Assigner
References
Impacted products
Vendor Product Version CPE status
tensorflow tensorflow Affected: < 2.7.2
Affected: >= 2.8.0, < 2.8.1
Affected: >= 2.9.0, < 2.9.1
guessed Create a notification for this product.
Show details on NVD website
To clipboard 

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T09:51:59.680Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_refsource_CONFIRM",
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37"
          },
          {
            "tags": [
              "x_refsource_MISC",
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9"
          }
        ],
        "title": "CVE Program Container"
      },
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2022-35996",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-23T13:57:45.697455Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-04-23T16:59:06.532Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 2.7.2"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.8.0, \u003c 2.8.1"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.9.0, \u003c 2.9.1"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 5.9,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-369",
              "description": "CWE-369: Divide By Zero",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2022-09-16T22:55:09.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37"
        },
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9"
        }
      ],
      "source": {
        "advisory": "GHSA-q5jv-m6qw-5g37",
        "discovery": "UNKNOWN"
      },
      "title": "Floating point exception in `Conv2D` in TensorFlow",
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2022-35996",
          "STATE": "PUBLIC",
          "TITLE": "Floating point exception in `Conv2D` in TensorFlow"
        },
        "affects": {
          "vendor": {
            "vendor_data": [
              {
                "product": {
                  "product_data": [
                    {
                      "product_name": "tensorflow",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "\u003c 2.7.2"
                          },
                          {
                            "version_value": "\u003e= 2.8.0, \u003c 2.8.1"
                          },
                          {
                            "version_value": "\u003e= 2.9.0, \u003c 2.9.1"
                          }
                        ]
                      }
                    }
                  ]
                },
                "vendor_name": "tensorflow"
              }
            ]
          }
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
          "description_data": [
            {
              "lang": "eng",
              "value": "TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 5.9,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-369: Divide By Zero"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-q5jv-m6qw-5g37",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2022-35996",
    "datePublished": "2022-09-16T22:55:10.000Z",
    "dateReserved": "2022-07-15T00:00:00.000Z",
    "dateUpdated": "2025-04-23T16:59:06.532Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1",
  "vulnerability-lookup:meta": {
    "epss": {
      "cve": "CVE-2022-35996",
      "date": "2026-09-16",
      "epss": "0.00478",
      "percentile": "0.4013"
    },
    "fkie_nvd": {
      "configurations": "[{\"nodes\": [{\"operator\": \"OR\", \"negate\": false, \"cpeMatch\": [{\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\", \"versionEndExcluding\": \"2.7.2\", \"matchCriteriaId\": \"C6622D95-1C86-45C5-AB55-E6EEEA0996DF\"}, {\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\", \"versionStartIncluding\": \"2.8.0\", \"versionEndExcluding\": \"2.8.1\", \"matchCriteriaId\": \"0F9D273D-02DC-441E-AA91-EAC8DEAA4B44\"}, {\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\", \"versionStartIncluding\": \"2.9.0\", \"versionEndExcluding\": \"2.9.1\", \"matchCriteriaId\": \"FE4F8A81-6CC2-4F7F-9602-C170FDD926E7\"}, {\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:2.10:rc0:*:*:*:*:*:*\", \"matchCriteriaId\": \"1DBFBCE2-0A01-4575-BE45-6775ABFB8B28\"}, {\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:2.10:rc1:*:*:*:*:*:*\", \"matchCriteriaId\": \"89806CF9-E423-4CA6-A01A-8175C260CB24\"}, {\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:2.10:rc2:*:*:*:*:*:*\", \"matchCriteriaId\": \"F2B80690-A257-4E16-BD27-9AE045BC56ED\"}, {\"vulnerable\": true, \"criteria\": \"cpe:2.3:a:google:tensorflow:2.10:rc3:*:*:*:*:*:*\", \"matchCriteriaId\": \"F335F9A4-5AB8-4E53-BC18-E01F7C653E5E\"}]}]}]",
      "descriptions": "[{\"lang\": \"en\", \"value\": \"TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.\"}, {\"lang\": \"es\", \"value\": \"TensorFlow es una plataforma de c\\u00f3digo abierto para el aprendizaje autom\\u00e1tico. Si \\\"Conv2D\\\" recibe una \\\"entrada\\\" vac\\u00eda y los tama\\u00f1os de \\\"filtro\\\" y \\\"acolchado\\\" son v\\u00e1lidos, la salida es todo ceros. Esto causa excepciones de divisi\\u00f3n por cero en coma flotante que pueden ser usadas para desencadenar un ataque de denegaci\\u00f3n de servicio. Hemos parcheado el problema en el commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9 de GitHub. La correcci\\u00f3n ser\\u00e1 incluida en TensorFlow versi\\u00f3n 2.10.0. Tambi\\u00e9n seleccionaremos este compromiso en TensorFlow versi\\u00f3n 2.9.1, TensorFlow versi\\u00f3n 2.8.1, y TensorFlow versi\\u00f3n 2.7.2, ya que estos tambi\\u00e9n est\\u00e1n afectados y todav\\u00eda est\\u00e1n en el rango admitido. No se presentan mitigaciones conocidas para este problema\"}]",
      "id": "CVE-2022-35996",
      "lastModified": "2024-11-21T07:12:08.593",
      "metrics": "{\"cvssMetricV31\": [{\"source\": \"security-advisories@github.com\", \"type\": \"Secondary\", \"cvssData\": {\"version\": \"3.1\", \"vectorString\": \"CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H\", \"baseScore\": 5.9, \"baseSeverity\": \"MEDIUM\", \"attackVector\": \"NETWORK\", \"attackComplexity\": \"HIGH\", \"privilegesRequired\": \"NONE\", \"userInteraction\": \"NONE\", \"scope\": \"UNCHANGED\", \"confidentialityImpact\": \"NONE\", \"integrityImpact\": \"NONE\", \"availabilityImpact\": \"HIGH\"}, \"exploitabilityScore\": 2.2, \"impactScore\": 3.6}, {\"source\": \"nvd@nist.gov\", \"type\": \"Primary\", \"cvssData\": {\"version\": \"3.1\", \"vectorString\": \"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H\", \"baseScore\": 7.5, \"baseSeverity\": \"HIGH\", \"attackVector\": \"NETWORK\", \"attackComplexity\": \"LOW\", \"privilegesRequired\": \"NONE\", \"userInteraction\": \"NONE\", \"scope\": \"UNCHANGED\", \"confidentialityImpact\": \"NONE\", \"integrityImpact\": \"NONE\", \"availabilityImpact\": \"HIGH\"}, \"exploitabilityScore\": 3.9, \"impactScore\": 3.6}]}",
      "published": "2022-09-16T23:15:10.407",
      "references": "[{\"url\": \"https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9\", \"source\": \"security-advisories@github.com\", \"tags\": [\"Patch\", \"Third Party Advisory\"]}, {\"url\": \"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37\", \"source\": \"security-advisories@github.com\", \"tags\": [\"Third Party Advisory\"]}, {\"url\": \"https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9\", \"source\": \"af854a3a-2127-422b-91ae-364da2661108\", \"tags\": [\"Patch\", \"Third Party Advisory\"]}, {\"url\": \"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37\", \"source\": \"af854a3a-2127-422b-91ae-364da2661108\", \"tags\": [\"Third Party Advisory\"]}]",
      "sourceIdentifier": "security-advisories@github.com",
      "vulnStatus": "Modified",
      "weaknesses": "[{\"source\": \"security-advisories@github.com\", \"type\": \"Primary\", \"description\": [{\"lang\": \"en\", \"value\": \"CWE-369\"}]}]"
    },
    "nvd": "{\"cve\":{\"id\":\"CVE-2022-35996\",\"sourceIdentifier\":\"security-advisories@github.com\",\"published\":\"2022-09-16T23:15:10.407\",\"lastModified\":\"2026-06-17T04:52:42.243\",\"vulnStatus\":\"Modified\",\"cveTags\":[],\"descriptions\":[{\"lang\":\"en\",\"value\":\"TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.\"},{\"lang\":\"es\",\"value\":\"TensorFlow es una plataforma de c\u00f3digo abierto para el aprendizaje autom\u00e1tico. Si \\\"Conv2D\\\" recibe una \\\"entrada\\\" vac\u00eda y los tama\u00f1os de \\\"filtro\\\" y \\\"acolchado\\\" son v\u00e1lidos, la salida es todo ceros. Esto causa excepciones de divisi\u00f3n por cero en coma flotante que pueden ser usadas para desencadenar un ataque de denegaci\u00f3n de servicio. Hemos parcheado el problema en el commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9 de GitHub. La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.10.0. Tambi\u00e9n seleccionaremos este compromiso en TensorFlow versi\u00f3n 2.9.1, TensorFlow versi\u00f3n 2.8.1, y TensorFlow versi\u00f3n 2.7.2, ya que estos tambi\u00e9n est\u00e1n afectados y todav\u00eda est\u00e1n en el rango admitido. No se presentan mitigaciones conocidas para este problema\"}],\"affected\":[{\"source\":\"security-advisories@github.com\",\"affectedData\":[{\"vendor\":\"tensorflow\",\"product\":\"tensorflow\",\"versions\":[{\"version\":\"\u003c 2.7.2\",\"status\":\"affected\"},{\"version\":\"\u003e= 2.8.0, \u003c 2.8.1\",\"status\":\"affected\"},{\"version\":\"\u003e= 2.9.0, \u003c 2.9.1\",\"status\":\"affected\"}]}]}],\"metrics\":{\"cvssMetricV31\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H\",\"baseScore\":5.9,\"baseSeverity\":\"MEDIUM\",\"attackVector\":\"NETWORK\",\"attackComplexity\":\"HIGH\",\"privilegesRequired\":\"NONE\",\"userInteraction\":\"NONE\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"NONE\",\"integrityImpact\":\"NONE\",\"availabilityImpact\":\"HIGH\"},\"exploitabilityScore\":2.2,\"impactScore\":3.6},{\"source\":\"nvd@nist.gov\",\"type\":\"Primary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H\",\"baseScore\":7.5,\"baseSeverity\":\"HIGH\",\"attackVector\":\"NETWORK\",\"attackComplexity\":\"LOW\",\"privilegesRequired\":\"NONE\",\"userInteraction\":\"NONE\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"NONE\",\"integrityImpact\":\"NONE\",\"availabilityImpact\":\"HIGH\"},\"exploitabilityScore\":3.9,\"impactScore\":3.6}],\"ssvcV203\":[{\"source\":\"134c704f-9b21-4f2e-91b3-4a467353bcc0\",\"ssvcData\":{\"timestamp\":\"2025-04-23T13:57:45.697455Z\",\"id\":\"CVE-2022-35996\",\"options\":[{\"exploitation\":\"none\"},{\"automatable\":\"no\"},{\"technicalImpact\":\"partial\"}],\"role\":\"CISA Coordinator\",\"version\":\"2.0.3\"}}]},\"weaknesses\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Secondary\",\"description\":[{\"lang\":\"en\",\"value\":\"CWE-369\"}]}],\"configurations\":[{\"nodes\":[{\"operator\":\"OR\",\"negate\":false,\"cpeMatch\":[{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionEndExcluding\":\"2.7.2\",\"matchCriteriaId\":\"C6622D95-1C86-45C5-AB55-E6EEEA0996DF\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"2.8.0\",\"versionEndExcluding\":\"2.8.1\",\"matchCriteriaId\":\"0F9D273D-02DC-441E-AA91-EAC8DEAA4B44\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*\",\"versionStartIncluding\":\"2.9.0\",\"versionEndExcluding\":\"2.9.1\",\"matchCriteriaId\":\"FE4F8A81-6CC2-4F7F-9602-C170FDD926E7\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:2.10:rc0:*:*:*:*:*:*\",\"matchCriteriaId\":\"1DBFBCE2-0A01-4575-BE45-6775ABFB8B28\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:2.10:rc1:*:*:*:*:*:*\",\"matchCriteriaId\":\"89806CF9-E423-4CA6-A01A-8175C260CB24\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:2.10:rc2:*:*:*:*:*:*\",\"matchCriteriaId\":\"F2B80690-A257-4E16-BD27-9AE045BC56ED\"},{\"vulnerable\":true,\"criteria\":\"cpe:2.3:a:google:tensorflow:2.10:rc3:*:*:*:*:*:*\",\"matchCriteriaId\":\"F335F9A4-5AB8-4E53-BC18-E01F7C653E5E\"}]}]}],\"references\":[{\"url\":\"https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9\",\"source\":\"security-advisories@github.com\",\"tags\":[\"Patch\",\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37\",\"source\":\"security-advisories@github.com\",\"tags\":[\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/commit/611d80db29dd7b0cfb755772c69d60ae5bca05f9\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Patch\",\"Third Party Advisory\"]},{\"url\":\"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q5jv-m6qw-5g37\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Third Party Advisory\"]}]}}",
    "suse_vex": {
      "aggregate_severity": "critical",
      "current_release_date": "2025-03-15T07:27:18Z",
      "cve": "CVE-2022-35996",
      "id": "CVE-2022-35996",
      "initial_release_date": "2023-02-15T03:24:34Z",
      "product_status:recommended": "2",
      "source": "SUSE CSAF VEX",
      "status": "interim",
      "title": "SUSE CVE CVE-2022-35996",
      "url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2022-35996.json",
      "version": "6"
    }
  }
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





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.


Detection rules are retrieved from Rulezet.


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.