FKIE_CVE-2025-59527
Vulnerability from fkie_nvd - Published: 2025-09-22 20:15 - Updated: 2026-09-30 17:10
Severity
Summary
Flowise is a drag & drop user interface to build a customized large language model flow. In version 3.0.5, a Server-Side Request Forgery (SSRF) vulnerability was discovered in the /api/v1/fetch-links endpoint of the Flowise application. This vulnerability allows an attacker to use the Flowise server as a proxy to access internal network web services and explore their link structures. This issue has been patched in version 3.0.6.
References
{
"affected": [
{
"affectedData": [
{
"product": "Flowise",
"vendor": "FlowiseAI",
"versions": [
{
"status": "affected",
"version": "= 3.0.5"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:flowiseai:flowise:3.0.5:*:*:*:*:*:*:*",
"matchCriteriaId": "D5D151AD-7484-4BE3-B42F-7D0279B5E886",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Flowise is a drag \u0026 drop user interface to build a customized large language model flow. In version 3.0.5, a Server-Side Request Forgery (SSRF) vulnerability was discovered in the /api/v1/fetch-links endpoint of the Flowise application. This vulnerability allows an attacker to use the Flowise server as a proxy to access internal network web services and explore their link structures. This issue has been patched in version 3.0.6."
},
{
"lang": "es",
"value": "Flowise es una interfaz de usuario de arrastrar y soltar para construir un flujo de modelo de lenguaje grande personalizado. En la versi\u00f3n 3.0.5, fue descubierta una vulnerabilidad de falsificaci\u00f3n de petici\u00f3n del lado del servidor (SSRF) en el endpoint /api/v1/fetch-links de la aplicaci\u00f3n Flowise. Esta vulnerabilidad permite a un atacante utilizar el servidor Flowise como un proxy para acceder a servicios web de la red interna y explorar sus estructuras de enlaces. Este problema ha sido parcheado en la versi\u00f3n 3.0.6."
}
],
"id": "CVE-2025-59527",
"lastModified": "2026-09-30T17:10:00.187",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-59527",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-09-22T20:25:46.315263Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-09-22T20:15:39.387",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Product"
],
"url": "https://github.com/FlowiseAI/Flowise/blob/5930f1119c655bcf8d2200ae827a1f5b9fec81d0/packages/components/src/utils.ts#L474-L478"
},
{
"source": "security-advisories@github.com",
"tags": [
"Product"
],
"url": "https://github.com/FlowiseAI/Flowise/blob/5930f1119c655bcf8d2200ae827a1f5b9fec81d0/packages/server/src/controllers/fetch-links/index.ts#L6-L24"
},
{
"source": "security-advisories@github.com",
"tags": [
"Product"
],
"url": "https://github.com/FlowiseAI/Flowise/blob/5930f1119c655bcf8d2200ae827a1f5b9fec81d0/packages/server/src/services/fetch-links/index.ts#L8-L18"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/FlowiseAI/Flowise/releases/tag/flowise%403.0.6"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Vendor Advisory"
],
"url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-hr92-4q35-4j3m"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-918"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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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