CVE-2026-41264
Flowiseai Flowise ≤ 3.1.0
Raw vector
CVSS:4.0/AV:N/AC:H/AT:P/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-2026-41264 is a critical-severity Incomplete List of Disallowed Inputs (CWE-184) vulnerability in Flowiseai Flowise. Its CVSS base score is 9.2 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 29% of CVEs by exploit likelihood; 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 LLM/Generative AI Risks 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-2026-41264 is a code execution vulnerability in Flowise, an open-source drag-and-drop user interface for building customized large language model (LLM) flows. The flaw resides in the run method of the CSV_Agents class in versions prior to 3.1.0, stemming from insufficient sandboxing when evaluating Python scripts generated by an LLM. This allows arbitrary code execution in the context of the user running the Flowise server.
An unauthenticated attacker who can send prompts to a chatflow utilizing the CSV Agent node can exploit this vulnerability through prompt injection techniques. By crafting a malicious prompt, the attacker can trick the LLM into generating and executing a Python script that runs attacker-controlled commands on the Flowise server, potentially leading to full compromise including high-impact confidentiality, integrity, and availability violations as indicated by the CVSS v3.1 score of 9.8.
The official GitHub security advisory for Flowise (GHSA-3hjv-c53m-58jj) confirms the issue and states that it is fully addressed in version 3.1.0, recommending immediate upgrades to mitigate the risk.
This vulnerability highlights risks in AI/ML workflows, particularly prompt injection leading to insecure code evaluation in LLM-based agents. No public evidence of real-world exploitation is available at the time of publication.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-25293
Vulnerability Data
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…
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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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1426 — LLM-generated Python script reaches code-execution sink without validation/sandboxing.
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- Matched keywords: flowise, large language model, llm, prompt injection
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 2 hardening rules · 1 OS baseline
V3.5.2V4.4.2V16.2.5
Mitigating Controls (NIST 800-53 r5) AI
SI-10 requires validity checks on inputs, which structurally replaces incomplete deny-lists with complete allow-list or sanitization 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 complete, positive input validation instead of incomplete denylists.
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 can discover missing input checks, but does not prevent the weakness during development.
Application security requirements can mandate complete input validation rules, but the control itself does not prescribe how to build those rules.
Secure architecture principles include robust input validation design, yet the control is broader than this single weakness.
Secure coding standards directly require exhaustive allow-lists or complete deny-lists for inputs, addressing the root cause of incomplete disallowed-input lists.