FKIE_CVE-2026-71854
Vulnerability from fkie_nvd - Published: 2026-10-14 21:17 - Updated: 2026-10-15 16:18
Severity
Summary
OpenAEV is an open source platform allowing organizations to plan, schedule and conduct cyber adversary simulation campaign and tests. Prior to version 2.260622.0, on a default installation, the `/api/player/**` lessons endpoints can be called without authentication, and they trust a client-supplied `userId` query parameter without verifying that the caller is that user or a member of the exercise. An unauthenticated client that knows an exercise UUID can read lessons-learned questions and answers (including each answer's author identity) and create or overwrite answers attributed to an arbitrary user. Version 2.260622.0 contains a patch.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "openaev",
"vendor": "OpenAEV-Platform",
"versions": [
{
"status": "affected",
"version": "\u003c 2.260622.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "OpenAEV is an open source platform allowing organizations to plan, schedule and conduct cyber adversary simulation campaign and tests. Prior to version 2.260622.0, on a default installation, the `/api/player/**` lessons endpoints can be called without authentication, and they trust a client-supplied `userId` query parameter without verifying that the caller is that user or a member of the exercise. An unauthenticated client that knows an exercise UUID can read lessons-learned questions and answers (including each answer\u0027s author identity) and create or overwrite answers attributed to an arbitrary user. Version 2.260622.0 contains a patch."
}
],
"id": "CVE-2026-71854",
"lastModified": "2026-10-15T16:18:19.883",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 8.2,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 4.2,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-71854",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-10-15T13:55:51.211773Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-10-14T21:17:39.177",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/OpenAEV-Platform/openaev/security/advisories/GHSA-47wr-wp4h-8v3f"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-306"
},
{
"lang": "en",
"value": "CWE-639"
},
{
"lang": "en",
"value": "CWE-862"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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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.
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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.