Langflow AI Untrusted Control Sphere Remote Code Execution

Langflow AI Untrusted Control Sphere
RCE (CVE-2026-0770)

OVERVIEW

The SonicWall Capture Labs threat research team became aware of an unauthenticated remote code execution vulnerability in Langflow AI, assessed its impact and developed mitigation measures. Langflow AI is a Python-based web application that provides a visual interface to build AI-driven agents and workflows.

The issue, tracked as CVE-2026-0770, affects all versions up to and including 1.7.3 and has been added to the CISA-KEV catalog. This flaw, categorized under CWE-829 (Inclusion of Functionality from Untrusted Control Sphere), allows an unauthenticated remote attacker to achieve remote code execution. The flaw carries a critical CVSS score of 9.8. Langflow exposes unauthenticated REST API validation endpoints such as /api/v1/validate/code that process user-controlled JSON data and pass embedded Python code directly to the exec() function without sandboxing or structural code isolation. As Langflow continues to gain popularity and broader adoption, the risk associated with this vulnerability increases significantly. Users are strongly encouraged to apply the vendor-provided updates without delay.

TECHNICAL OVERVIEW

Langflow offers a visual interface that makes it easy to design workflows, along with built-in APIs and MCP servers that allow these workflows to be used as tools in applications across different frameworks and technologies. It supports major LLMs, vector databases, and a growing set of AI tools.

Figure 1 illustrates the overall Langflow architecture. Users can visually build workflows, which are sent to the backend API server for management and execution. The execution engine processes the flow step-by-step, invoking AI models, tools, and external services as needed, and then returns the results while storing data in databases or file storage.

Key security characteristics of Langflow include:

• Its modular, flow-based architecture.
• Support for integration with external services such as LLMs and APIs.
• The ability to execute dynamically defined workflows.
• In patched versions, Langflow enforces stricter trust boundaries by ensuring that only validated, server-side flow definitions are executed, thereby reducing the risk of code injection and unauthorized manipulation.

ROOT CAUSE

The vulnerability stems from the unsafe, server-side execution of user-supplied Python function definitions using Python’s built-in exec() function within Langflow’s code validation pipeline (specifically in validate_code() inside src/lfx/src/lfx/custom/validate.py). Rather than performing secure static syntax analysis, the validation routine on the unauthenticated /api/v1/validate/code endpoint processed user-controlled inputs and evaluated them dynamically inside the exec_globals namespace. Because Python evaluates default arguments and decorators at function-definition time rather than execution time, malicious expressions embedded in uploaded function snippets execute immediately upon parsing, granting unauthenticated remote code execution (RCE) with the privileges of the Langflow server process.

The vulnerable endpoint /api/v1/validate/code in Langflow was designed to act as a helper API for the user interface. Its purpose was to check custom Python code blocks written by users for syntax errors and import issues before saving them into an AI workflow component graph. Because this validation needed to happen dynamically as a user typed or edited a component, the architecture exposed this endpoint without strict security controls, as shown in Figure 2.

• Lack of Authentication

The endpoint was completely unauthenticated by default. Anyone with network access to the Langflow server could send a POST request to /api/v1/validate/code. They did not need a valid user account, an API key, or an active session token to trigger the backend code validation logic.

• The Request Structure

The endpoint accepted an HTTP POST request carrying a JSON payload. The payload was typically a string containing the raw Python code block to check, together with exec_globals, an object intended to represent global variables or environment contexts for the code execution environment.

• Flawed Validation Logic

Instead of using safe parsing methods like Python’s built-in ast.parse(), which analyzes code structure safely without running it, the backend application passed the user-supplied string directly into Python’s exec() function.

PATCH ANALYSIS

In the security fix introduced in Langflow version 1.10.1, as shown in Figure 3, validate_code now compiles each FunctionDef only (to surface syntax/compile errors) and never executes it. The legitimate component-execution helpers (create_function / execute_function / eval_function), which are separately gated by allow_custom_components, are untouched.

The security updates removed the unsafe execution path rather than adding request filtering. Figure 4 summarizes the origin validation and hardening measures recommended for this endpoint. The fix itself is verified by the following checks:

• Added test_validate_code_does_not_execute_default_args — a default-arg side effect (file write) no longer runs during validation.
• All existing TestValidateCode cases pass (import-error detection and syntax-error reporting are unaffected).

TRIGGERING THE VULNERABILITY

• The trigger process begins at an unauthenticated, publicly accessible validation endpoint, typically POST /api/v1/validate/code.
• It attempts to safely "register" or "inspect" the function within the runtime memory space by calling exec(code_objects, exec_globals).
• The vulnerability is independent of the LANGFLOW_AUTO_LOGIN environment variable, whether it is set to true or false, as shown in Figure 6.
• Upon receiving the network packet, the backend web server passes the request to the application’s API router layer. The router extracts the string value from the incoming JSON payload.
• The string is passed directly into the validation subsystem, where input sanitization mechanisms are omitted to avoid breaking valid developer code.
• The Python interpreter triggers definition-time evaluation (Decorator / Default Arg).
• Because the validation engine successfully evaluated the definition-time expression, control flow escapes the intended validation path and reaches the operating system command layer.
• An attacker executes system-level commands with process privileges.

EXPLOITATION

Successful exploitation enables a remote attacker to gain full control of the affected system via the API server engine. Figure 5 demonstrates a real-world proof of concept against vulnerable instances, showing a successful takeover of Langflow using a publicly available exploit. Given Langflow’s extensive system privileges, this vulnerability can result in complete compromise of the operator’s machine.

Figure 6 verifies the exploitation. Unlike CVE-2026-33017, this vulnerability is independent of the auto-login feature. Due to CVE-2026-33017, CVE-2026-5027 and CVE-2026-0770, the auto-login feature was disabled in multiple versions, which can be further verified in both Figure 6 and Figure 7. The auto-login feature was changed to be disabled by default starting in Langflow version 1.11.x for all official Docker deployments. Disabling it places the workflow UI behind an authentication wall, mitigating mass public exploitation.

SONICWALL PROTECTIONS

To ensure SonicWall customers are prepared for any exploitation that may occur due to this vulnerability, the following signatures have been released:

• IPS: 22312 Langflow Control Sphere Remote Code Execution

REMEDIATION RECOMMENDATIONS

With Langflow’s growing user base and increasing deployment footprint, organizations and individual users should upgrade to the latest patched version as outlined in the official vendor advisory.

RELEVANT LINKS

• Vendor Advisory
• CISA-KEV
• EDB
• POC
• Patch-fix
• https://github.com/langflow-ai/langflow/releases