CVE-2026-91936 (GCVE-0-2026-91936)
Vulnerability from cvelistv5 – Published: 2026-09-15 15:17 – Updated: 2026-09-15 15:17
VLAI
EPSS
VEX
Title
Flowise before 3.1.4 Script Injection via Docker Workflows
Summary
Flowise versions before 3.1.4 contain a script injection vulnerability in Docker image build workflows where workflow_dispatch inputs are directly interpolated into shell run blocks. Attackers with repository write access can inject shell metacharacters through inputs like tag_version and node_version to execute arbitrary commands and steal AWS credentials and Docker Hub tokens.
Severity
CWE
- CWE-78 - Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/FlowiseAI/Flowise/security/adv… | vendor-advisory |
| https://www.vulncheck.com/advisories/flowise-befo… | third-party-advisory |
Impacted products
Date Public
2026-08-31 00:00
{
"containers": {
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"packageURL": "pkg:npm/flowise",
"product": "Flowise",
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"versions": [
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"version": "0",
"versionType": "semver"
},
{
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"version": "3.1.4",
"versionType": "semver"
}
]
}
],
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}
],
"metrics": [
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"attackVector": "NETWORK",
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"baseScore": 6.8,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
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"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-78",
"description": "Improper Neutralization of Special Elements used in an OS Command (\u0027OS Command Injection\u0027)",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
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"orgId": "83251b91-4cc7-4094-a5c7-464a1b83ea10",
"shortName": "VulnCheck"
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"name": "GitHub Security Advisory (GHSA-jrcq-qjw5-xx5q)",
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],
"url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-jrcq-qjw5-xx5q"
},
{
"name": "VulnCheck Advisory: Flowise before 3.1.4 Script Injection via Docker Workflows",
"tags": [
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],
"url": "https://www.vulncheck.com/advisories/flowise-before-3.1.4-script-injection-via-docker-workflows"
}
],
"title": "Flowise before 3.1.4 Script Injection via Docker Workflows",
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}
}
},
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"datePublished": "2026-09-15T15:17:59.008Z",
"dateReserved": "2026-09-15T11:06:02.263Z",
"dateUpdated": "2026-09-15T15:17:59.008Z",
"state": "PUBLISHED"
},
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"vulnerability-lookup:meta": {
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"date": "2026-09-16",
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"percentile": "0.28299"
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}
}
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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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