Vulnerability-Lookup

VAR-202106-0618

Vulnerability from variot - Updated: 2024-08-14 12:51

There is a deserialization vulnerability in Huawei AnyOffice V200R006C10. An attacker can construct a specific request to exploit this vulnerability. Successfully exploiting this vulnerability, the attacker can execute remote malicious code injection and to control the device. Pillow is a Python-based image processing library. There is currently no information about this vulnerability, please feel free to follow CNNVD or manufacturer announcements. Huawei AnyOffice is an application software of China Huawei (Huawei). A mobile office solution

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CVE-2021-22439 (GCVE-0-2021-22439)

Vulnerability from cvelistv5 – Published: 2021-06-29 18:38 – Updated: 2024-08-03 18:44

Summary

Severity

No CVSS data available.

CWE

Deserialization

Assigner

huawei CNA under the mitre root

CNA scorecard D 57/100 over 60 records in the last 180 days details

References

1 reference

URL	Tags
https://www.huawei.com/en/psirt/security-advisori…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
n/a	AnyOffice	Affected: V200R006C10

Show details on NVD website

JSON

To clipboard

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              "value": "There is a deserialization vulnerability in Huawei AnyOffice V200R006C10. An attacker can construct a specific request to exploit this vulnerability. Successfully exploiting this vulnerability, the attacker can execute remote malicious code injection and to control the device."
            }
          ]
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "Deserialization"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://www.huawei.com/en/psirt/security-advisories/huawei-sa-20210619-01-injection-en",
              "refsource": "MISC",
              "url": "https://www.huawei.com/en/psirt/security-advisories/huawei-sa-20210619-01-injection-en"
            }
          ]
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "25ac1063-e409-4190-8079-24548c77ea2e",
    "assignerShortName": "huawei",
    "cveId": "CVE-2021-22439",
    "datePublished": "2021-06-29T18:38:09.000Z",
    "dateReserved": "2021-01-05T00:00:00.000Z",
    "dateUpdated": "2024-08-03T18:44:12.951Z",
    "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.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

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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.

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