CVE-2026-41274
Flowiseai Flowise ≤ 3.1.0
Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-41274 is a critical-severity Improper Neutralization of Special Elements in Data Query Logic (CWE-943) vulnerability in Flowiseai Flowise. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 41th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-41274 is a Cypher injection vulnerability (CWE-943) in Flowise, an open-source drag-and-drop user interface for building customized large language model (LLM) flows. In versions prior to 3.1.0, the GraphCypherQAChain node forwards user-provided input directly into the Cypher query execution pipeline of the underlying Neo4j database without proper sanitization. This flaw, published on 2026-04-23 and assigned a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), enables attackers to inject and execute arbitrary Cypher commands.
The vulnerability can be exploited by any unauthenticated remote attacker who can provide malicious input to the GraphCypherQAChain node. Successful exploitation grants full control over the Neo4j database, allowing data exfiltration, modification, or deletion depending on the injected commands and database permissions.
The official Flowise security advisory (GHSA-28g4-38q8-3cwc) states that the vulnerability is fixed in version 3.1.0 through proper input sanitization in the GraphCypherQAChain node. Practitioners should upgrade to Flowise 3.1.0 or later and review Neo4j database access controls as an interim measure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-25313
Vulnerability Data
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GraphCypherQAChain node forwards user-provided input directly into the Cypher query execution pipeline without proper sanitization. An attacker can inject arbitrary…
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Cypher commands that are executed on the underlying Neo4j database, enabling data exfiltration, modification, or deletion. This vulnerability is fixed in 3.1.0.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: flowise, large language model
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly stops unneutralized special elements from reaching query logic.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require parameterized queries and input neutralization to prevent query-logic injection.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development catches injection vulnerabilities before release but does not itself implement the fix.
Secure development life cycle mandates input validation and query parameterization that directly prevent injection flaws.
Application security requirements explicitly call for controls against injection and improper query construction.
Secure architecture principles reduce the likelihood of query-logic flaws but do not prescribe the specific coding practice.
Secure coding standards require proper neutralization of special elements in all data queries.
Outsourced development agreements can require secure coding practices, indirectly mitigating the weakness.