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CVE-2022-48321 (GCVE-0-2022-48321)

Vulnerability from cvelistv5 – Published: 2023-02-20 16:54 – Updated: 2024-08-03 15:10
VLAI
Title
SSRF in agent-receiver API
Summary
Limited Server-Side Request Forgery (SSRF) in agent-receiver in Tribe29's Checkmk <= 2.1.0p11 allows an attacker to communicate with local network restricted endpoints by use of the host registration API.
CWE
  • CWE-20 - Improper Input Validation
Assigner
Impacted products
Vendor Product Version CPE status
Tribe29 Checkmk Affected: 2.1.0 , ≤ 2.1.0p11 (semver)
guessed Create a notification for this product.
Show details on NVD website

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      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "SSRF in agent-receiver API",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-48321.json",
      "version": "3"
    }
  }
}



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Tags
Taxonomy of the tags.


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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.

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Detection rules are retrieved from Rulezet.

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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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