Executive brief
Flowise is an open-source tool used to build customized AI workflows and LLM applications. A security flaw in the 'Execute Flow' component allows users with low-level access to force the server to make unauthorized requests to internal systems. This could allow an attacker to scan your private network, access sensitive cloud metadata, or interact with internal services that are not supposed to be exposed to the internet.
Technical details
A Server-Side Request Forgery (SSRF) vulnerability exists in Flowise versions prior to 3.1.0 within the 'Execute Flow' node. The vulnerability stems from a failure to invoke 'secureFetch' verification in the 'httpSecurity.ts' component when processing the base URL field. An authenticated attacker with low privileges can provide intranet or loopback addresses to bypass intended security denylists. This allows the attacker to initiate HTTP requests from the server to internal network addresses, potentially leading to the enumeration of internal services, access to cloud instance metadata (IMDS), or unauthorized interaction with internal APIs. The issue is resolved in version 3.1.0 by implementing proper validation via secureFetch.
Affected products
- FlowiseAI Flowise < 3.1.0
- FlowiseAI flowise-components < 3.1.0
Timeline
- 2026-04-15: advisory: GitHub Security Advisory published
- 2026-06-23: disclosed: NVD publication date