FKIE_CVE-2024-5071

Vulnerability from fkie_nvd - Published: 2024-06-26 06:15 - Updated: 2026-06-17 08:15
Summary
The Bookster WordPress plugin through 1.1.0 allows adding sensitive parameters when validating appointments allowing attackers to manipulate the data sent when booking an appointment (the request body) to change its status from pending to approved.
Impacted products
Vendor Product Version
wpbookster bookster *

{
  "affected": [
    {
      "affectedData": [
        {
          "defaultStatus": "affected",
          "product": "Bookster ",
          "vendor": "Unknown",
          "versions": [
            {
              "lessThanOrEqual": "1.1.0",
              "status": "affected",
              "version": "0",
              "versionType": "semver"
            }
          ]
        }
      ],
      "source": "contact@wpscan.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:wpbookster:bookster:*:*:*:*:*:wordpress:*:*",
              "matchCriteriaId": "21656F0E-A0E2-4AF7-BAC4-233017F41089",
              "versionEndIncluding": "1.1.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "The Bookster  WordPress plugin through 1.1.0 allows adding sensitive parameters when validating appointments allowing attackers to manipulate the data sent when booking an appointment (the request body) to change its status from pending to approved."
    },
    {
      "lang": "es",
      "value": "El complemento Bookster WordPress hasta la versi\u00f3n 1.1.0 permite agregar par\u00e1metros confidenciales al validar citas, lo que permite a los atacantes manipular los datos enviados al reservar una cita (el cuerpo de la solicitud) para cambiar su estado de pendiente a aprobado."
    }
  ],
  "id": "CVE-2024-5071",
  "lastModified": "2026-06-17T08:15:06.870",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 6.5,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 3.6,
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2024-5071",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2024-06-26T23:12:00.693838Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2024-06-26T06:15:16.463",
  "references": [
    {
      "source": "contact@wpscan.com",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://wpscan.com/vulnerability/07b293cf-5174-45de-8606-a782a96a35b3/"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://wpscan.com/vulnerability/07b293cf-5174-45de-8606-a782a96a35b3/"
    }
  ],
  "sourceIdentifier": "contact@wpscan.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-863"
        }
      ],
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "type": "Secondary"
    }
  ]
}



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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

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