CVE-2026-41264: Flowise CSV Agent RCE Vulnerability

CVE-2026-41264 Overview

Flowise, a popular drag-and-drop user interface for building customized large language model (LLM) flows, contains a critical remote code execution vulnerability in versions prior to 3.1.0. The vulnerability exists within the run method of the CSV_Agents class due to insufficient sandboxing when evaluating LLM-generated Python scripts. This flaw enables attackers to leverage prompt injection techniques to manipulate the LLM into generating malicious Python code that executes arbitrary commands on the Flowise server.

Critical Impact

An unauthenticated remote attacker can achieve arbitrary code execution on the Flowise server by sending crafted prompts to a chatflow utilizing the CSV Agent node, potentially leading to complete system compromise.

Affected Products

• Flowise versions prior to 3.1.0
• Flowise chatflows utilizing the CSV Agent node
• Deployments with unauthenticated access to chatflow endpoints

Discovery Timeline

• April 23, 2026 - CVE-2026-41264 published to NVD
• April 23, 2026 - Last updated in NVD database

Technical Details for CVE-2026-41264

Vulnerability Analysis

This vulnerability represents a dangerous intersection of AI/LLM technology and traditional security weaknesses. The root issue lies in the CSV Agent's handling of dynamically generated Python code from LLM responses. When a user interacts with a chatflow that incorporates the CSV Agent node, the LLM may generate Python scripts to process and analyze CSV data. The vulnerability arises because these generated scripts are executed without proper sandboxing or validation, creating an opportunity for code injection attacks.

The attack methodology involves prompt injection—a technique where malicious input manipulates the LLM's behavior to produce unintended outputs. In this case, an attacker crafts prompts designed to convince the LLM to embed malicious Python code within its response. Since the Flowise server executes this code with the privileges of the running user, successful exploitation grants the attacker the ability to execute arbitrary commands, potentially leading to data exfiltration, lateral movement, or complete server takeover.

Root Cause

The vulnerability stems from CWE-184: Incomplete List of Disallowed Inputs. The run method in the CSV_Agents class lacks comprehensive input validation and sandboxing mechanisms for LLM-generated Python scripts. The code evaluation process does not implement adequate restrictions on dangerous Python functions, modules, or operations, allowing malicious code to bypass any partial security controls that may exist.

Attack Vector

The attack leverages network-accessible chatflow endpoints and requires no prior authentication. An attacker can interact with a publicly exposed Flowise instance that utilizes the CSV Agent node. Through carefully crafted prompts, the attacker manipulates the LLM into generating Python code containing malicious payloads. Common exploitation approaches include:

The attacker sends prompt injections designed to override the LLM's intended behavior, instructing it to include system commands within the generated Python code. Since the CSV Agent executes this code to process data, the malicious commands run in the context of the Flowise server process. This could include reverse shells, file system manipulation, credential theft, or the deployment of additional malware.

The network-based attack vector combined with no required authentication or user interaction makes this vulnerability particularly dangerous in production environments where Flowise instances may be exposed to untrusted networks or the internet.

Detection Methods for CVE-2026-41264

Indicators of Compromise

• Unusual outbound network connections from the Flowise server process, particularly reverse shell patterns
• Unexpected Python subprocess executions or system command invocations logged in server activity
• Anomalous chatflow prompts containing code-like syntax, system command references, or prompt injection patterns
• New or modified files in unexpected locations on the Flowise server

Detection Strategies

• Monitor chatflow input logs for suspicious prompt patterns including keywords like exec, eval, subprocess, os.system, or import os
• Implement network-level detection for outbound connections initiated by the Flowise process to unexpected destinations
• Deploy endpoint detection and response (EDR) solutions to identify anomalous process spawning from the Flowise application
• Analyze LLM response logs for generated code containing potentially dangerous function calls

Monitoring Recommendations

• Enable comprehensive logging for all chatflow interactions and CSV Agent executions
• Implement real-time alerting on process creation events originating from the Flowise server process
• Deploy network segmentation to limit the blast radius of potential compromises
• Utilize SentinelOne's AI-powered behavioral analysis to detect code execution anomalies in runtime

How to Mitigate CVE-2026-41264

Immediate Actions Required

• Upgrade Flowise to version 3.1.0 or later immediately
• If upgrade is not immediately possible, disable or remove chatflows utilizing the CSV Agent node
• Implement authentication requirements for all chatflow endpoints to reduce the attack surface
• Restrict network access to Flowise instances using firewall rules or network segmentation

Patch Information

The Flowise development team has addressed this vulnerability in version 3.1.0. Organizations should upgrade to this version or later to remediate the security issue. Detailed information about the fix is available in the GitHub Security Advisory.

Workarounds

• Disable CSV Agent nodes in production environments until the patch can be applied
• Implement a web application firewall (WAF) with rules to detect and block prompt injection patterns
• Run Flowise in a containerized environment with minimal privileges and restricted system call access
• Deploy input validation at the network edge to filter suspicious prompts before they reach the application

# Configuration example - Restrict Flowise network access via iptables
# Allow only trusted IP ranges to access Flowise port
iptables -A INPUT -p tcp --dport 3000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 3000 -j DROP

# Run Flowise with reduced privileges in Docker
docker run -d \
  --name flowise \
  --user 1000:1000 \
  --read-only \
  --cap-drop ALL \
  --security-opt no-new-privileges:true \
  -p 127.0.0.1:3000:3000 \
  flowiseai/flowise:3.1.0