Vulnerability-Lookup

VAR-202009-0096

Vulnerability from variot - Updated: 2023-12-18 13:56

An improper neutralization of input vulnerability in FortiAnalyzer before 6.4.1 and 6.2.5 may allow a remote authenticated attacker to inject script related HTML tags via Name parameter of Storage Connectors. Fortinet FortiAnalyzer is a centralized network security reporting solution from Fortinet. This product is mainly used to collect network log data, and analyze, report, and archive the security events, network traffic, and Web content in the logs through the report suite. Fortinet FortiAnalyzer has a security vulnerability, which stems from the lack of correct validation of client data in WEB applications. An attacker could exploit this vulnerability to trigger cross-site scripting through the storage connector to run JavaScript code in the context of the website

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CVE-2020-12817 (GCVE-0-2020-12817)

Vulnerability from cvelistv5 – Published: 2020-09-24 13:31 – Updated: 2024-10-25 14:24

Summary

Severity

No CVSS data available.

SSVC

Exploitation: none Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2024-10-24 20:03 UTC

CWE

Execute unauthorized code or commands

Assigner

fortinet mitre root

References

1 reference

URL	Tags
https://fortiguard.com/advisory/FG-IR-20-054	x_refsource_CONFIRM

Impacted products

1 product

Vendor	Product	Version
Fortinet	Fortinet FortiAnalyzer	Affected: FortiAnalyzer before 6.4.1; before 6.2.5

Show details on NVD website

JSON

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              "lang": "eng",
              "value": "An improper neutralization of input vulnerability in FortiAnalyzer before 6.4.1 and 6.2.5 may allow a remote authenticated attacker to inject script related HTML tags via Name parameter of Storage Connectors."
            }
          ]
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "Execute unauthorized code or commands"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://fortiguard.com/advisory/FG-IR-20-054",
              "refsource": "CONFIRM",
              "url": "https://fortiguard.com/advisory/FG-IR-20-054"
            }
          ]
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "6abe59d8-c742-4dff-8ce8-9b0ca1073da8",
    "assignerShortName": "fortinet",
    "cveId": "CVE-2020-12817",
    "datePublished": "2020-09-24T13:31:29.000Z",
    "dateReserved": "2020-05-12T00:00:00.000Z",
    "dateUpdated": "2024-10-25T14:24:23.911Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

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.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.