CVE-2024-34454 (GCVE-0-2024-34454)
Vulnerability from cvelistv5 – Published: 2024-05-26 21:48 – Updated: 2025-02-13 15:53
VLAI
EPSS
VEX
Summary
Nintendo Wii U OS 5.5.5 allows man-in-the-middle attackers to forge SSL certificates as though they came from a Root CA, because there is a secondary verification mechanism that only checks whether a CA is known and ignores the CA details and signature (and because * is accepted as a Common Name).
Severity
7.4 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-06-03 14:57 UTC
CWE
- n/a
- CWE-269 - Improper Privilege Management
Assigner
References
2 references
Impacted products
{
"containers": {
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{
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"providerMetadata": {
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"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
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"title": "CISA ADP Vulnrichment"
},
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],
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}
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}
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{
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"dateReserved": "2024-05-03T00:00:00.000Z",
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"date": "2026-09-29",
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"fkie_nvd": {
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},
{
"lang": "es",
"value": "Nintendo Wii U OS 5.5.5 permite a los atacantes intermediarios falsificar certificados SSL como si vinieran de una CA ra\u00edz, porque existe un mecanismo de verificaci\u00f3n secundario que solo verifica si se conoce una CA e ignora los detalles de la CA y firma (y porque * se acepta como nombre com\u00fan)."
}
],
"id": "CVE-2024-34454",
"lastModified": "2024-11-21T09:18:42.790",
"metrics": {
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"userInteraction": "NONE",
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"references": [
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},
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"url": "https://github.com/PretendoNetwork/SSSL"
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{
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"url": "https://github.com/PretendoNetwork/SSSL-DNS"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Awaiting Analysis",
"weaknesses": [
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],
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},
"nvd": {
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},
{
"lang": "es",
"value": "Nintendo Wii U OS 5.5.5 permite a los atacantes intermediarios falsificar certificados SSL como si vinieran de una CA ra\u00edz, porque existe un mecanismo de verificaci\u00f3n secundario que solo verifica si se conoce una CA e ignora los detalles de la CA y firma (y porque * se acepta como nombre com\u00fan)."
}
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"id": "CVE-2024-34454",
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"url": "https://security.access.redhat.com/data/csaf/v2/vex/2024/cve-2024-34454.json",
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"vulnrichment": {
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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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