Langflow — Massive Exploit Campaign Targets Critical Authenticated RCE Vulnerability in MCP Protocol (CVE-2026-105740)

Publication date: October 15, 2026
Category: Vulnerability / AI Threat Intelligence

Introduction

The security posture of artificial intelligence workflow platforms has come under severe scrutiny following an intense wave of automated attacks. According to telemetry reports from Previdian, security sensors have logged 1,423 exploitation attempts targeting a critical remote code execution (RCE) vulnerability in Langflow, tracked as CVE-2026-105740. The affected component involves the Model Context Protocol (MCP) management features, enabling bad actors to execute arbitrary operating system commands by abusing input sanitization shortcomings.

What is Langflow and the MCP Attack Vector? (General Analysis)

Langflow is an open-source platform utilized extensively by developers to build and deploy applications powered by large language models (LLMs) and agentic workflows. In February 2025, DataStax (Langflow’s parent company) was acquired by IBM. Within its architecture, Langflow integrates communication features via the Model Context Protocol (MCP), designed to link external tools and data sources.

However, versions prior to 1.9.0 suffered from a severe validation control flaw in the configuration of the MCP server’s Stdio transport. The vulnerability is formally classified under CWE-78 (Improper Neutralization of Special Elements used in an OS Command). Official National Vulnerability Database (NVD) data confirms a CVSS v3.1 score of 9.9 (CRITICAL) with the exact vector string: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H. This indicates that an attacker with minimal user privileges can achieve systemic escalation due to insufficient container or host isolation.

How Does It Work? (Technical Analysis)

The exploitation mechanism relies on the complete absence of allowlists, syntactic validation, or sandboxing when Langflow processes configuration parameters for an MCP server.

  • Initial Access and Authentication Flow: Attackers gain initial entry by exploiting weak credentials or combining it with preceding authentication flaws (such as abusing permissive auto-login settings in misconfigured deployments). Once inside the platform with a standard user role, the attacker dispatches a crafted HTTP POST request to the endpoint /api/v2/mcp/servers/{server_name}.
  • Command and Environment Injection: The request body includes a command field that is passed directly by the platform to the underlying shell via the internal execution call bash -c "exec {command}". Additionally, the env field permits the arbitrary injection of critical system variables (such as PATH overrides or LD_PRELOAD injections).
  • Trigger and Persistence: The malicious command does not require complex user interaction to execute; it fires immediately the moment the system processes or fetches the server list via an HTTP GET request (/api/v2/mcp/servers?action_count=true). This causes the configuration to persist within project settings, silently re-executing every time tools are loaded or flows are initialized.

Affected Systems / Environments

The vulnerability directly impacts multiple versions of the AI workflow platform. The related flaw landscape is summarized below:

CVE Category (CWE) Impact CVSS Vector (summarized)
CVE-2026-105740 CWE-78 (Command Injection) Critical 9.9 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H
CVE-2026-105697 CWE-78 (Command Injection) Critical 9.9 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H
CVE-2026-9198 CWE-94 (Code Injection) Critical 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CVE-2026-55255 CWE-639 (IDOR) High 8.4 CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:L
CVE-2026-0768 CWE-94 (Code Injection) Critical 9.8 CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
  • Vulnerable Langflow Versions: All versions prior to 1.9.0 (and subsequent branches lacking hardening controls prior to version 1.10.3, where fixes were finalized alongside langflow-base and lfx).
  • Risk Environments: Instances directly exposed to untrusted networks with the default LANGFLOW_AUTO_LOGIN=true directive enabled, allowing unauthenticated threat actors to harvest superuser tokens.

Mitigation and Detection

Remediation

Infrastructure administrators and engineering teams must implement the following mitigation steps immediately:

  1. Software Update: Upgrade Langflow to version 1.9.0 or higher (recommending version 1.10.3 or latest releases), which introduce strict path validation and argument sanitization controls for the Stdio transport.
  2. Configuration Hardening: Explicitly disable automatic login by setting LANGFLOW_AUTO_LOGIN=false in environment configuration files.
  3. Access Control: Avoid exposing Langflow administrative interfaces directly to the public internet and enforce strong, unique passwords across all authenticated user accounts.

Detection

Blue Teams should inspect access logs for suspicious requests directed at the /api/v2/mcp/servers/ endpoint.

Defensive Intelligence Note: Honeypot telemetry indicates that attackers prioritize the mass harvesting of critical environment variables (such as LANGFLOW_SECRET_KEY and LANGFLOW_SUPERUSER_PASSWORD), alongside reconnaissance probes utilizing basic system commands (id, whoami, and reading /etc/passwd).

Suggested heuristic pattern for HTTP log analysis:

regex 
POST /api/v2/mcp/servers/.* HTTP/.*
Host:.*
Content-Type: application/json
.*"command":\s*"(bash|sh|python|nc|curl|wget|cat\s+/etc/passwd).*"

Wrapping Up

The mass exploitation campaign targeting Langflow demonstrates how emerging integrations within AI agent architectures —specifically the Model Context Protocol— can turn into critical attack vectors when rigorous input sanitization controls are absent. With over 1,400 attempts recorded by global honeypot sensors, upgrading to patched versions and disabling development-only features (such as auto-login) in production environments remain imperative to prevent the full compromise of underlying infrastructure.

References