CVE-2022-32320 (GCVE-0-2022-32320)
Vulnerability from cvelistv5 – Published: 2022-07-17 16:24 – Updated: 2024-08-03 07:39
VLAI
EPSS
VEX
Summary
A Cross-Site Request Forgery (CSRF) in Ferdi through 5.8.1 and Ferdium through 6.0.0-nightly.98 allows attackers to read files via an uploaded file such as a settings/preferences file.
Severity
8.8 (High)
CWE
- n/a
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://getferdi.com/ | x_refsource_MISC |
| https://github.com/getferdi/ferdi | x_refsource_MISC |
| https://gist.github.com/omriinbar-cyesec/c1179fe9… | x_refsource_MISC |
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],
"negate": false,
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]
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "A Cross-Site Request Forgery (CSRF) in Ferdi through 5.8.1 and Ferdium through 6.0.0-nightly.98 allows attackers to read files via an uploaded file such as a settings/preferences file."
},
{
"lang": "es",
"value": "Una vulnerabilidad de tipo Cross-Site Request Forgery (CSRF) en Ferdi versiones hasta 5.8.1 y Ferdium versiones hasta 6.0.0-nightly.98, permite a atacantes leer archivos por medio de un archivo cargado, como un archivo de configuraci\u00f3n/preferencias"
}
],
"id": "CVE-2022-32320",
"lastModified": "2026-06-17T04:47:06.667",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.9,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2022-07-17T17:15:08.180",
"references": [
{
"source": "cve@mitre.org",
"tags": [
"Product"
],
"url": "https://getferdi.com/"
},
{
"source": "cve@mitre.org",
"tags": [
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],
"url": "https://gist.github.com/omriinbar-cyesec/c1179fe99725d2b828b6573c0d110c9c"
},
{
"source": "cve@mitre.org",
"tags": [
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],
"url": "https://github.com/getferdi/ferdi"
},
{
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"tags": [
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],
"url": "https://getferdi.com/"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
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],
"url": "https://gist.github.com/omriinbar-cyesec/c1179fe99725d2b828b6573c0d110c9c"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
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],
"url": "https://github.com/getferdi/ferdi"
}
],
"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-352"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T12:19:52+00:00",
"cve": "CVE-2022-32320",
"id": "CVE-2022-32320",
"initial_release_date": "2022-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "CVE-2022-32320",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-32320.json",
"version": "3"
}
}
}
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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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