CVE-2026-41271: Flowise AI SSRF Vulnerability

CVE-2026-41271 Overview

A Server-Side Request Forgery (SSRF) vulnerability has been discovered in FlowiseAI's Flowise application, a drag & drop user interface used to build customized large language model (LLM) flows. Prior to version 3.1.0, the POST/GET API Chain components are vulnerable to SSRF attacks that allow unauthenticated attackers to force the server to make arbitrary HTTP requests to internal and external systems.

By injecting malicious prompt templates, attackers can bypass the intended API documentation constraints and redirect requests to sensitive internal services. This vulnerability poses significant risks including internal network reconnaissance and data exfiltration from otherwise protected resources.

Critical Impact

Unauthenticated attackers can exploit this SSRF vulnerability to probe internal networks, access sensitive services, and exfiltrate data by manipulating the FlowiseAI server to make arbitrary HTTP requests on their behalf.

Affected Products

• FlowiseAI Flowise versions prior to 3.1.0
• POST API Chain component
• GET API Chain component

Discovery Timeline

• 2026-04-23 - CVE CVE-2026-41271 published to NVD
• 2026-04-23 - Last updated in NVD database

Technical Details for CVE-2026-41271

Vulnerability Analysis

This SSRF vulnerability (CWE-918) exists in FlowiseAI's POST and GET API Chain components. The vulnerability stems from insufficient validation of user-controlled input within prompt templates that specify target URLs for API requests. Attackers can manipulate these templates to redirect the server's outbound requests to arbitrary destinations.

The attack requires network access and some level of authentication to interact with the vulnerable components, though the exploitation complexity is high due to the need to craft malicious prompt templates that successfully bypass existing constraints. Once exploited, the vulnerability provides high impact to both confidentiality and integrity, with limited availability impact.

In an LLM flow context, these API Chain components are designed to allow the model to interact with external APIs based on provided documentation. However, the implementation fails to properly restrict which endpoints can be targeted, allowing attackers to inject destinations pointing to internal services such as metadata endpoints, internal databases, or administrative interfaces that would otherwise be inaccessible from external networks.

Root Cause

The root cause of this vulnerability is improper input validation in the API Chain components' URL handling logic. The application does not adequately sanitize or validate user-supplied prompt templates that define API endpoints. This allows malicious actors to inject URLs pointing to internal resources or arbitrary external systems, bypassing the intended constraints that should limit API interactions to documented endpoints only.

Attack Vector

The attack vector is network-based, requiring an attacker to send specially crafted requests to the FlowiseAI application. The attacker exploits the vulnerability by:

1. Crafting a malicious prompt template containing a target URL pointing to an internal service (e.g., http://169.254.169.254/latest/meta-data/ for cloud metadata services)
2. Submitting this template to the vulnerable POST/GET API Chain components
3. The server processes the template and makes HTTP requests to the attacker-specified destination
4. Response data from internal services is returned to the attacker, enabling reconnaissance or data exfiltration

The vulnerability can be exploited to access cloud provider metadata services, internal REST APIs, databases with HTTP interfaces, or any other network-accessible resource from the server's perspective.

Detection Methods for CVE-2026-41271

Indicators of Compromise

• Unusual outbound HTTP requests from the FlowiseAI server to internal IP ranges (e.g., 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16)
• Requests to cloud metadata endpoints such as 169.254.169.254 or metadata.google.internal
• API Chain component logs showing unexpected URL patterns in prompt templates
• Network traffic analysis revealing connections to services not part of normal LLM workflow operations

Detection Strategies

• Implement network monitoring to detect outbound requests from the FlowiseAI server to internal network segments or known metadata endpoints
• Deploy web application firewall (WAF) rules to inspect and block requests containing internal IP addresses or localhost references in URL parameters
• Enable detailed logging for all API Chain component activities and monitor for anomalous patterns
• Use intrusion detection systems (IDS) to identify SSRF attack signatures in HTTP request flows

Monitoring Recommendations

• Configure alerts for any FlowiseAI server connections to RFC 1918 private address spaces that are not explicitly whitelisted
• Monitor DNS queries from the FlowiseAI host for resolution attempts of internal hostnames
• Review API Chain component usage patterns for unexpected increases in external API calls
• Implement egress filtering and log all outbound connections from the application server

How to Mitigate CVE-2026-41271

Immediate Actions Required

• Upgrade FlowiseAI Flowise to version 3.1.0 or later immediately
• If immediate upgrade is not possible, consider temporarily disabling the POST/GET API Chain components
• Implement network segmentation to limit the FlowiseAI server's access to sensitive internal resources
• Deploy egress filtering to restrict outbound connections to only necessary external endpoints

Patch Information

The vulnerability has been fixed in FlowiseAI Flowise version 3.1.0. Organizations should update to this version or later to address the SSRF vulnerability. For detailed information about the security fix, refer to the GitHub Security Advisory.

Workarounds

• Implement a URL allowlist at the network level, restricting the FlowiseAI server to only communicate with pre-approved external API endpoints
• Deploy a forward proxy for all outbound HTTP requests from the FlowiseAI server with strict URL filtering rules
• Use network segmentation to isolate the FlowiseAI deployment from sensitive internal services
• Consider implementing authentication requirements for all API Chain component interactions to reduce the attack surface

# Example network egress filtering using iptables
# Block outbound connections to internal networks from FlowiseAI server
iptables -A OUTPUT -d 10.0.0.0/8 -j DROP
iptables -A OUTPUT -d 172.16.0.0/12 -j DROP
iptables -A OUTPUT -d 192.168.0.0/16 -j DROP
iptables -A OUTPUT -d 169.254.169.254 -j DROP