- CVSS
- HIGH · 8.3 v3.1 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:H/A:H
- Published
- 2025-10-06
- Weakness
- CWE-434
- Source
- nvd.nist.gov/vuln/detail/CVE-2025-61687
Description
Flowise is a drag & drop user interface to build a customized large language model flow. A file upload vulnerability in version 3.0.7 of FlowiseAI allows authenticated users to upload arbitrary files without proper validation. This enables attackers to persistently store malicious Node.js web shells on the server, potentially leading to Remote Code Execution (RCE). The system fails to validate file extensions, MIME types, or file content during uploads. As a result, malicious scripts such as Node.js-based web shells can be uploaded and stored persistently on the server. These shells expose HTTP endpoints capable of executing arbitrary commands if triggered. The uploaded shell does not automatically execute, but its presence allows future exploitation via administrator error or chained vulnerabilities. This presents a high-severity threat to system integrity and confidentiality. As of time of publication, no known patched versions are available.
References
- https://github.com/FlowiseAI/Flowise/blob/d29db16bfcf9a4be8febc3d19d52263e8c3d0055/packages/components/src/storageUtils.ts#L1104-L1111
- https://github.com/FlowiseAI/Flowise/blob/d29db16bfcf9a4be8febc3d19d52263e8c3d0055/packages/components/src/storageUtils.ts#L170-L175
- https://github.com/FlowiseAI/Flowise/blob/d29db16bfcf9a4be8febc3d19d52263e8c3d0055/packages/components/src/storageUtils.ts#L533-L541
- https://github.com/FlowiseAI/Flowise/blob/d29db16bfcf9a4be8febc3d19d52263e8c3d0055/packages/server/src/controllers/attachments/index.ts#L4-L11
- https://github.com/FlowiseAI/Flowise/blob/d29db16bfcf9a4be8febc3d19d52263e8c3d0055/packages/server/src/routes/attachments/index.ts#L8
- https://github.com/FlowiseAI/Flowise/blob/d29db16bfcf9a4be8febc3d19d52263e8c3d0055/packages/server/src/services/attachments/index.ts#L7-L16
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2025-61687 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.