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AI agent vulnerability · Flowise

CVE-2026-31829: Flowise affected by Server-Side Request Forgery (SSRF) in HTTP Node Leading to Internal Network Access

highCVSS 3.0 7.1GHSA-fvcw-9w9r-pxc7

Description: Flowise exposes an HTTP Node in AgentFlow and Chatflow that performs server-side HTTP requests using user-controlled URLs. By default, there are no restrictions on target hosts, including private/internal IP ranges (RFC 1918), localhost, or cloud metadata endpoints.

Published Mar 11, 2026 · Updated Apr 10, 2026

Affected packages

flowise

Ecosystem
npm
Affected
<= 3.0.12
Fixed in
3.0.13
Check
npm ls flowise

flowise-components

Ecosystem
npm
Affected
<= 3.0.12
Fixed in
3.0.13
Check
npm ls flowise-components

Advisory details

From the GitHub Advisory Database (CC BY 4.0).

Description: Flowise exposes an HTTP Node in AgentFlow and Chatflow that performs server-side HTTP requests using user-controlled URLs. By default, there are no restrictions on target hosts, including private/internal IP ranges (RFC 1918), localhost, or cloud metadata endpoints. This enables Server-Side Request Forgery (SSRF), allowing any user interacting with a publicly exposed chatflow to force the Flowise server to make requests to internal network resources that are inaccessible from the public internet.

Impact includes:

  • Access to internal admin panels (e.g., internal company dashboards, Jenkins, Kubernetes API, etc.).
  • Retrieval of cloud provider metadata (e.g., AWS IMDSv1 at [http://169.254.169.254], GCP, Azure).
  • Port scanning and enumeration of internal services.
  • Potential lateral movement or privilege escalation in compromised environments.

This vulnerability is particularly severe because:

  • Flowise instances are often deployed publicly without authentication (FLOWISE_USERNAME/PASSWORD not set by default).
  • The HTTP Node is easily accessible in simple flows with minimal configuration.

Proof of Concept (PoC): A minimal flow consisting of three nodes demonstrates successful internal network access: Flow Structure:

HTTP Node Configuration: The HTTP Node is configured to perform a GET request to an internal address on localhost: URL: http://127.0.0.1:8000 (or any internal service)

Successful Response from Internal Service: When the flow is triggered via chat input, the Flowise server successfully retrieves and returns content from the internal mock server running on port 8000 within the same container/network:

Impact This is a Server-Side Request Forgery (SSRF) vulnerability with both read and write capabilities. The HTTP Request node supports all standard HTTP methods (GET, POST, PUT, PATCH, DELETE), allowing attackers to not only retrieve sensitive information but also modify, create, or delete data on internal services if those services expose mutable endpoints:

  • Read access: Retrieval of sensitive internal data, cloud provider metadata (e.g., AWS IAM credentials at http://169.254.169.254/latest/meta-data/iam/security-credentials/), secrets, configuration files, or database contents.
  • Write access: Modification or deletion of internal resources via POST/PUT/PATCH/DELETE methods (e.g., creating malicious users/configurations, overwriting files, deleting data, triggering destructive actions on internal admin panels, CI/CD systems like Jenkins, Kubernetes APIs, or cloud management interfaces).

Amplification: Retrieved cloud credentials can be used for further privilege escalation or lateral movement outside the n8n instance.

Suggested Long-term Fix (for Flowise):

  • Add optional security controls to HTTP Node:
  • Toggle: "Block private IP ranges and localhost" (enabled by default).
  • Field: "Allowed domains" (whitelist).
  • Display prominent warning when URL field uses template variables (e.g., {{ }}).
  • Update documentation with explicit SSRF risks and best practices.

Credited to nlgbao1340.

References

How this page is made: the data panel comes from the GitHub Advisory Database, CISA’s KEV catalog, the NVD and FIRST EPSS, and updates twice a day. No analysis has been written for this entry yet. Methodology

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