FKIE_CVE-2026-28675
Vulnerability from fkie_nvd - Published: 2026-03-06 05:16 - Updated: 2026-06-17 10:28
Severity
Summary
OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Prior to version 1.6.3-alpha, some endpoints returned raw exception strings to clients. Additionally, login token material was exposed in UI/rendered responses and token rotation output. This issue has been patched in version 1.6.3-alpha.
References
{
"affected": [
{
"affectedData": [
{
"product": "OpenSift",
"vendor": "OpenSift",
"versions": [
{
"status": "affected",
"version": "\u003c 1.6.3-alpha"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:opensift:opensift:*:*:*:*:*:python:*:*",
"matchCriteriaId": "D1037E2F-1527-4C09-8D8D-78E56E6DDC78",
"versionEndExcluding": "1.6.3",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Prior to version 1.6.3-alpha, some endpoints returned raw exception strings to clients. Additionally, login token material was exposed in UI/rendered responses and token rotation output. This issue has been patched in version 1.6.3-alpha."
},
{
"lang": "es",
"value": "OpenSift es una herramienta de estudio de IA que tamiza grandes conjuntos de datos utilizando b\u00fasqueda sem\u00e1ntica e IA generativa. Antes de la versi\u00f3n 1.6.3-alpha, algunos puntos finales devolv\u00edan cadenas de excepci\u00f3n en bruto a los clientes. Adem\u00e1s, el material del token de inicio de sesi\u00f3n qued\u00f3 expuesto en las respuestas de la interfaz de usuario/renderizadas y en la salida de rotaci\u00f3n de tokens. Este problema ha sido parcheado en la versi\u00f3n 1.6.3-alpha."
}
],
"id": "CVE-2026-28675",
"lastModified": "2026-06-17T10:28:52.667",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-28675",
"options": [
{
"exploitation": "none"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-03-09T19:49:13.118951Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-03-06T05:16:35.900",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/OpenSift/OpenSift/commit/1126e0a503876056a68a434e19f64158a5a4840b"
},
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/OpenSift/OpenSift/commit/de99b9c"
},
{
"source": "security-advisories@github.com",
"tags": [
"Issue Tracking",
"Patch"
],
"url": "https://github.com/OpenSift/OpenSift/pull/67"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/OpenSift/OpenSift/releases/tag/v1.6.3-alpha"
},
{
"source": "security-advisories@github.com",
"tags": [
"Patch",
"Vendor Advisory"
],
"url": "https://github.com/OpenSift/OpenSift/security/advisories/GHSA-667g-rvcj-w976"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-200"
},
{
"lang": "en",
"value": "CWE-209"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…