FKIE_CVE-2026-63231

Vulnerability from fkie_nvd - Published: 2026-07-29 07:16 - Updated: 2026-07-30 16:54
Summary
A post-authentication SQL injection vulnerability in Koollab LMS allowed an authenticated attacker to use an error-based SQL oracle via the face-to-face runs update endpoint to read the entire application database and obtain valid JWT tokens for account takeover.
Impacted products
Vendor Product Version

{
  "affected": [
    {
      "affectedData": [
        {
          "defaultStatus": "unaffected",
          "product": "Koollab LMS",
          "vendor": "Three Learning",
          "versions": [
            {
              "status": "affected",
              "version": "5.3.2"
            }
          ]
        }
      ],
      "source": "5f57b9bf-260d-4433-bf07-b6a79e9bb7d4"
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "A post-authentication SQL injection\nvulnerability in Koollab LMS allowed an authenticated attacker to use an error-based SQL oracle via\nthe face-to-face runs update endpoint to read the entire application database\nand obtain valid JWT tokens for account takeover."
    }
  ],
  "id": "CVE-2026-63231",
  "lastModified": "2026-07-30T16:54:05.457",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 8.1,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.2,
        "source": "5f57b9bf-260d-4433-bf07-b6a79e9bb7d4",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-63231",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-07-29T15:11:57.922807Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-07-29T07:16:42.490",
  "references": [
    {
      "source": "5f57b9bf-260d-4433-bf07-b6a79e9bb7d4",
      "url": "https://www.csa.gov.sg/alerts-and-advisories/alerts/al-2026-094/"
    }
  ],
  "sourceIdentifier": "5f57b9bf-260d-4433-bf07-b6a79e9bb7d4",
  "vulnStatus": "Deferred",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-89"
        }
      ],
      "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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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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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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