FKIE_CVE-2024-44069
Vulnerability from fkie_nvd - Published: 2024-08-19 02:15 - Updated: 2026-06-17 07:52
Severity
Summary
Pi-hole before 6 allows unauthenticated admin/api.php?setTempUnit= calls to change the temperature units of the web dashboard. NOTE: the supplier reportedly does "not consider the bug a security issue" but the specific motivation for letting arbitrary persons change the value (Celsius, Fahrenheit, or Kelvin), seen by the device owner, is unclear.
References
| URL | Tags | ||
|---|---|---|---|
| cve@mitre.org | https://github.com/pi-hole/web/pull/3077 | Issue Tracking | |
| cve@mitre.org | https://www.kiyell.com/The-Harmless-Pihole-Bug/ | Exploit, Third Party Advisory |
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"status": "affected",
"version": "n/a"
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"source": "cve@mitre.org"
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"value": "Pi-hole before 6 allows unauthenticated admin/api.php?setTempUnit= calls to change the temperature units of the web dashboard. NOTE: the supplier reportedly does \"not consider the bug a security issue\" but the specific motivation for letting arbitrary persons change the value (Celsius, Fahrenheit, or Kelvin), seen by the device owner, is unclear."
},
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"lang": "es",
"value": "Pi-hole anterior a 6 permite llamadas admin/api.php?setTempUnit= no autenticadas para cambiar las unidades de temperatura del panel web. NOTA: seg\u00fan se informa, el proveedor \"no considera el error como un problema de seguridad\", pero la motivaci\u00f3n espec\u00edfica para permitir que personas arbitrarias cambien el valor (Celsius, Fahrenheit o Kelvin), visto por el propietario del dispositivo, no est\u00e1 clara."
}
],
"id": "CVE-2024-44069",
"lastModified": "2026-06-17T07:52:15.243",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N",
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"timestamp": "2024-08-19T13:53:45.763800Z",
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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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.
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.
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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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