FKIE_CVE-2026-41264
Vulnerability from fkie_nvd - Published: 2026-04-23 20:16 - Updated: 2026-10-26 21:10
Severity
Summary
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the CSV_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the Flowise server. This vulnerability is fixed in 3.1.0.
References
| URL | Tags | ||
|---|---|---|---|
| security-advisories@github.com | https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-3hjv-c53m-58jj | Exploit, Vendor Advisory | |
| 134c704f-9b21-4f2e-91b3-4a467353bcc0 | https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-3hjv-c53m-58jj | Exploit, Vendor Advisory |
{
"affected": [
{
"affectedData": [
{
"product": "Flowise",
"vendor": "FlowiseAI",
"versions": [
{
"status": "affected",
"version": "\u003c 3.1.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
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"versionEndExcluding": "3.1.0",
"vulnerable": true
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "Flowise is a drag \u0026 drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the CSV_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the Flowise server. This vulnerability is fixed in 3.1.0."
},
{
"lang": "es",
"value": "Flowise es una interfaz de usuario de arrastrar y soltar para construir un flujo de modelo de lenguaje grande personalizado. Antes de la versi\u00f3n 3.1.0, la falla espec\u00edfica existe dentro del m\u00e9todo run de la clase CSV_Agents. El problema resulta de la falta de un sandboxing adecuado al evaluar un script de Python generado por LLM. Un atacante puede aprovechar esta vulnerabilidad para ejecutar c\u00f3digo en el contexto del usuario que ejecuta el servidor. Utilizando t\u00e9cnicas de inyecci\u00f3n de prompts, un atacante no autenticado con la capacidad de enviar prompts a un chatflow usando el nodo CSV Agent puede convencer a un LLM de responder con un script de Python malicioso que ejecuta comandos controlados por el atacante en el servidor de Flowise. Esta vulnerabilidad se corrige en la versi\u00f3n 3.1.0."
}
],
"id": "CVE-2026-41264",
"lastModified": "2026-10-26T21:10:00.170",
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"confidentialityImpact": "HIGH",
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"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
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"source": "nvd@nist.gov",
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"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "NOT_DEFINED",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
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"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "NONE",
"providerUrgency": "NOT_DEFINED",
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"subConfidentialityImpact": "NONE",
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"valueDensity": "NOT_DEFINED",
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"ssvcV203": [
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"options": [
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{
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"role": "CISA Coordinator",
"timestamp": "2026-04-24T13:39:06.171550Z",
"version": "2.0.3"
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"published": "2026-04-23T20:16:14.750",
"references": [
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"value": "CWE-184"
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"type": "Secondary"
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]
}
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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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